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- -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master) - -# CosmoStat Tutorials ---- - -CosmoStat is committed to the philosophy of reproducible research, endeavouring -to provide source code and data for all publications. In this spirit, we have -additionally put significant effort into providing useful educational -materials. The aim being to provide other researchers with an in-depth -understanding of the various tools we use in our work. - - -## Contents +## Sparsity --- -1. [Set Up](#Set-Up) - * [Requirements](#Requirements) - * [Running Remotely](#Running-Remotely) - * [Running Locally](#Running-Locally) -1. [Introduction to Python](./python/README.md) - * [Requirements](./python/README.md#Requirements) - * [Beginner Topics](./python/README.md#tutorial-1-beginner-topics) - * [Intermediate and Advanced Topics](./python/README.md#tutorial-2-intermediate-and-advanced-topics) -1. [Sparsity](./ada/README.md) - * [Requirements](./ada/README.md#Requirements) - * [Notebooks](./ada/README.md#Notebooks) - * [Acknowledgements](./ada/README.md#Acknowledgements) -1. [Low-Rank](./low-rank/README.md) - * [Requirements](./low-rank/README.md#Requirements) - * [Notebooks](./low-rank/README.md#Notebooks) - * [Acknowledgements](./low-rank/README.md#Acknowledgements) - -## Set Up ---- +> Author: [Samuel Farrens](http://www.cosmostat.org/people/sfarrens) +> Year: 2018 +> Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) -### Requirements +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/CosmoStat/Tutorials/ada) -Each tutorial has its own set of requirements, but all require Python 3.5 or higher. +This tutorial was originally presented at the ninth edition of the +[Astronomical Data Analysis (ADAIX)](http://ada.cosmostat.org/) summer school held in Valencia +in 2018. The objective is to provide a beginner level introduction to the concept of sparsity, in particular as a regularisation method for solving linear inverse problems. The tutorial does not provide an in-depth mathematical background nor detailed explanations for every topic. Tutees should supplement this tutorial with further reading of the various references provided in the notebooks for a more comprehensive understanding of the subject. More information about how cutting-edge sparsity techniques are being applied to astrophysical data is available on the CosmoStat website. -* Python (require >=3.5) +This tutorial is comprised of a series of Jupyter notebooks that demonstrate how the tools implemented in sparsity work as well as showing the applicability of these tools to various simple problems. +All code blocks are provided in Python (support for both 2.7 and 3.6+) and the number of external packages required to run the examples has be kept to a minimum. With the exception of plotting routines, all of the code has been clearly presented inside the notebooks at least once to avoid the use of any "black boxes" for solving the problems presented. -### Running Remotely - -All of the tutorials can be run remotely by launching the repository -[Binder](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master). No further set -up is required. +### Requirements -### Running Locally +In order to run the tutorial notebooks tutees will need to have the following installed: -In order to run the tutorials offline, please follow these steps: +* Python (2.7 or 3.6+, users are recommended to use Python 3.6) +* NumPy (recommend v1.14.1+) +* SciPy (recommend v1.0.0+) +* Matplotlib (recommend v2.1.0+) +* Jupyter (recommend v1.0.0+) -1. Download or clone the GitHub repository. +Python 2.7 users should additonally install: - +* Future (recommend v0.16.0+) - *e.g.* +All of the packages listed above can easily be installed using either `pip` or Anaconda. - ```bash - git clone https://github.com/CosmoStat/Tutorials.git - ``` +*e.g.* -2. Install the tutorial dependencies. +```bash +$ pip install numpy scipy matplotlib jupyter +``` - This can be done using `pip` as follows: +or - ```bash - pip install -r requirements.txt - ``` +```bash +$ conda install numpy scipy matplotlib jupyter +``` - or using `conda` as follows: +### Notebooks - ```bash - conda env create -f environment.yml - conda activate cstutorials - ``` +1. [Introduction to Inverse Problems](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Finverse_problems_1.ipynb) +1. [Introduction to Sparsity I](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fsparsity_1.ipynb) +1. [Introduction to Sparsity II: Compressed Sensing](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fsparsity_2_compressed_sensing.ipynb) +1. [Introduction to Sparsity III: Deconvolution](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fsparsity_3_deconvolution.ipynb) +1. [Introduction to Wavelets](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fwavelets_1.ipynb) +1. [Exercise Solutions](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fexercise_solutions.ipynb) -3. Finally, the notebooks can be launched by running: +### Acknowledgements - ```bash - jupyter notebook - ``` +S. Farrens would like to acknowledge the following people for helpful feedback and ideas for producing this tutorial: Fred Ngolè-Mboula and Jean-Luc Starck. diff --git a/ada/README.md b/ada/README.md deleted file mode 100644 index 7210041..0000000 --- a/ada/README.md +++ /dev/null @@ -1,55 +0,0 @@ -## Sparsity ---- - -> Author: [Samuel Farrens](http://www.cosmostat.org/people/sfarrens) -> Year: 2018 -> Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) - -This tutorial was originally presented at the ninth edition of the -[Astronomical Data Analysis (ADAIX)](http://ada.cosmostat.org/) summer school held in Valencia -in 2018. The objective is to provide a beginner level introduction to the concept of sparsity, in particular as a regularisation method for solving linear inverse problems. The tutorial does not provide an in-depth mathematical background nor detailed explanations for every topic. Tutees should supplement this tutorial with further reading of the various references provided in the notebooks for a more comprehensive understanding of the subject. More information about how cutting-edge sparsity techniques are being applied to astrophysical data is available on the CosmoStat website. - -This tutorial is comprised of a series of Jupyter notebooks that demonstrate how the tools implemented in sparsity work as well as showing the applicability of these tools to various simple problems. - -All code blocks are provided in Python (support for both 2.7 and 3.6+) and the number of external packages required to run the examples has be kept to a minimum. With the exception of plotting routines, all of the code has been clearly presented inside the notebooks at least once to avoid the use of any "black boxes" for solving the problems presented. - -### Requirements - -In order to run the tutorial notebooks tutees will need to have the following installed: - -* Python (2.7 or 3.6+, users are recommended to use Python 3.6) -* NumPy (recommend v1.14.1+) -* SciPy (recommend v1.0.0+) -* Matplotlib (recommend v2.1.0+) -* Jupyter (recommend v1.0.0+) - -Python 2.7 users should additonally install: - -* Future (recommend v0.16.0+) - -All of the packages listed above can easily be installed using either `pip` or Anaconda. - -*e.g.* - -```bash -$ pip install numpy scipy matplotlib jupyter -``` - -or - -```bash -$ conda install numpy scipy matplotlib jupyter -``` - -### Notebooks - -1. [Introduction to Inverse Problems](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Finverse_problems_1.ipynb) -1. [Introduction to Sparsity I](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fsparsity_1.ipynb) -1. [Introduction to Sparsity II: Compressed Sensing](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fsparsity_2_compressed_sensing.ipynb) -1. [Introduction to Sparsity III: Deconvolution](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fsparsity_3_deconvolution.ipynb) -1. [Introduction to Wavelets](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fwavelets_1.ipynb) -1. [Exercise Solutions](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fexercise_solutions.ipynb) - -### Acknowledgements - -S. Farrens would like to acknowledge the following people for helpful feedback and ideas for producing this tutorial: Fred Ngolè-Mboula and Jean-Luc Starck. diff --git a/ada/data/cs_mask.npy b/data/cs_mask.npy similarity index 100% rename from ada/data/cs_mask.npy rename to data/cs_mask.npy diff --git a/ada/data/cs_obs_data.npy b/data/cs_obs_data.npy similarity index 100% rename from ada/data/cs_obs_data.npy rename to data/cs_obs_data.npy diff --git a/ada/data/cs_true_data.npy b/data/cs_true_data.npy similarity index 100% rename from ada/data/cs_true_data.npy rename to data/cs_true_data.npy diff --git a/ada/data/deconv_obs_data.npy b/data/deconv_obs_data.npy similarity index 100% rename from ada/data/deconv_obs_data.npy rename to data/deconv_obs_data.npy diff --git a/ada/data/deconv_psf.npy b/data/deconv_psf.npy similarity index 100% rename from ada/data/deconv_psf.npy rename to data/deconv_psf.npy diff --git a/ada/data/venus.npy b/data/venus.npy similarity index 100% rename from ada/data/venus.npy rename to data/venus.npy diff --git a/ada/exercise_solutions.ipynb b/exercise_solutions.ipynb similarity index 100% rename from ada/exercise_solutions.ipynb rename to exercise_solutions.ipynb diff --git a/ada/images/cea_logo.png b/images/cea_logo.png similarity index 100% rename from ada/images/cea_logo.png rename to images/cea_logo.png diff --git a/ada/images/dwt.png b/images/dwt.png similarity index 100% rename from ada/images/dwt.png rename to images/dwt.png diff --git a/ada/images/galaxy_example.npy b/images/galaxy_example.npy similarity index 100% rename from ada/images/galaxy_example.npy rename to images/galaxy_example.npy diff --git a/ada/images/uwt.png b/images/uwt.png similarity index 100% rename from ada/images/uwt.png rename to images/uwt.png diff --git a/ada/inverse_problems_1.ipynb b/inverse_problems_1.ipynb similarity index 100% rename from ada/inverse_problems_1.ipynb rename to inverse_problems_1.ipynb diff --git a/low-rank/README.md b/low-rank/README.md deleted file mode 100644 index edaf4af..0000000 --- a/low-rank/README.md +++ /dev/null @@ -1,45 +0,0 @@ -## Low-Rank Approximation ---- - -> Author: [Samuel Farrens](http://www.cosmostat.org/people/sfarrens) -> Year: 2018 -> Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) - -The objective is to provide a beginner level introduction to the concept of low-rank approximation, in particular as a regularisation method for solving linear inverse problems. The tutorial does not provide an in-depth mathematical background nor detailed explanations for every topic. Tutees should supplement this tutorial with further reading of the various references provided in the notebooks for a more comprehensive understanding of the subject. More information about how cutting-edge sparsity techniques are being applied to astrophysical data is available on the CosmoStat website. - -This tutorial is comprised of a Jupyter notebook that demonstrate how the low-rank approximation works as well as showing the applicability of this tools to various simple problems. - -All code blocks are provided in Python (support for >=3.5) and the number of external packages required to run the examples has be kept to a minimum. All of the code has been clearly presented inside the notebook at least once to avoid the use of any "black boxes" for solving the problems presented. - -### Requirements - -In order to run the tutorial notebooks tutees will need to have the following installed: - -* Python (require >=3.5) -* NumPy (recommend >=1.16.2) -* SciPy (recommend >=1.2.1) -* Astropy (recommend >=3.1.2) -* Matplotlib (recommend >=3.0.3) -* Jupyter (recommend >=1.0.0) - -All of the packages listed above can easily be installed using either `pip` or Anaconda. - -*e.g.* - -```bash -$ pip install numpy scipy astropy matplotlib jupyter -``` - -or - -```bash -$ conda install numpy scipy astropy matplotlib jupyter -``` - -### Notebooks - -1. [Introduction to Low-Rank Approximation](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Flow-rank%2Flow-rank.ipynb) - -### Acknowledgements - -S. Farrens would like to acknowledge the following people for helpful feedback and ideas for producing this tutorial: Fred Ngolè-Mboula. diff --git a/low-rank/low-rank.ipynb b/low-rank/low-rank.ipynb deleted file mode 100644 index 446b0e2..0000000 --- a/low-rank/low-rank.ipynb +++ /dev/null @@ -1,894 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to Low-Rank Approximation\n", - "\n", - "---\n", - "\n", - "> Author: Samuel Farrens \n", - "> Year: 2017 \n", - "> Email: [samuel.farrens@gmail.com](mailto:samuel.farrens@gmail.com) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", - "\n", - "## Contents\n", - "\n", - "---\n", - " \n", - "1. [Set-Up](#Set-Up)\n", - "1. [Introduction](#Introduction)\n", - "1. [Rank of a Matrix](#Rank-of-a-Matrix)\n", - " * [Full Rank Matrix](#Full-Rank-Matrix) \n", - " * [Reduced Rank Matrix](#Reduced-Rank-Matrix) \n", - " * [Uneven Matrix](#Uneven-Matrix)\n", - "1. [Singular Value Decomposition](#Singular-Value-Decomposition) \n", - " * [Reducing the Rank](#Reducing-the-Rank)\n", - " * [Hard Thresholding](#Hard-Thresholding)\n", - "1. [Low Rank Approximation](#Low-Rank-Approximation)\n", - " * [Nuclear Norm](#Nuclear-Norm)\n", - "1. [Deconvolution Exercise](#Deconvolution-Exercise)\n", - " * [Gradient](#Gradient)\n", - " * [Rank Reduction](#Rank-Reduction)\n", - " * [Optimisation](#Optimisation)\n", - " * [Deconvolution](#Deconvolution)\n", - "1. [Hints](#Hints)\n", - "1. [Exercise Solutions](#Exercise-Solutions)\n", - " * [Deconvolution Exercise Solution](#Deconvolution-Exercise-Solution)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Set-Up\n", - "\n", - "Here we will import a couple of packages that we will need throughout the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Tell Jupyter to display plots in this notebook.\n", - "%matplotlib inline\n", - "\n", - "# Import the numpy package with the alias np.\n", - "import numpy as np \n", - "\n", - "# Import the FFT convolution function from Scipy.\n", - "from scipy.signal import fftconvolve\n", - "\n", - "# Import the Gaussian kernel function from Astropy.\n", - "from astropy.convolution import Gaussian2DKernel\n", - "\n", - "# Import the pyplot package from matplotlib with the alias plt.\n", - "import matplotlib.pyplot as plt \n", - "from matplotlib import pylab\n", - "\n", - "# Ignore warnings.\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Introduction\n", - "\n", - "The knowledge that a given matrix contains a certain amount of redundancy can be used a powerful prior for solving inverse problems. The notebook provides a brief introduction to the concepts of matrix rank and the low-rank approximation for regularisation. This notebook does not provide a comprehensive review on these topics, rather it is intended to give the user some intuitive hands-on experience." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Rank of a Matrix\n", - "\n", - "The rank of a matrix can be defined in the following ways:\n", - "\n", - "1. the maximum number of linearly independent column vectors in a given matrix\n", - "1. the maximum number of linearly independent row vectors in a given matrix\n", - "\n", - "Both of these definitions are equivalent.\n", - "\n", - "### Full Rank Matrix\n", - "\n", - "For the matrix\n", - "\n", - "$$ A = \\begin{bmatrix} 6 & 1 & 2 \\\\ 5 & 6 & 1 \\\\ 5 & 7 & 7 \\end{bmatrix} $$\n", - "\n", - "the rank is given by\n", - "\n", - "$$\\textrm{rank}(A) = 3$$\n", - "\n", - "since all three rows are linearly idependent. $A$ is then said to have **full rank**.\n", - "\n", - "This can be implemented in Python as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.array([[6, 1, 2], [5, 6, 1], [5, 7, 7]])\n", - "\n", - "print('A =')\n", - "print(A)\n", - "print('')\n", - "print('rank(A) =', np.linalg.matrix_rank(A))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reduced Rank Matrix\n", - "\n", - "For the matrix\n", - "\n", - "$$ A = \\begin{bmatrix} 1 & 0 & 1 \\\\ -2 & -3 & 1 \\\\ 3 & 3 & 0 \\end{bmatrix} $$\n", - "\n", - "the rank is given by\n", - "\n", - "$$\\textrm{rank}(A) = 2$$\n", - "\n", - "since the third row is simply the difference of the first two, which are linearly idependent. In this case $A$ is **rank deficient** or has **reduced rank**." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.array([[1, 0, 1], [-2, -3, 1], [3, 3, 0]])\n", - "\n", - "print('A =')\n", - "print(A)\n", - "print('')\n", - "print('rank(A) =', np.linalg.matrix_rank(A))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Uneven Matrix\n", - "\n", - "For a matrix $\\in\\mathbb{R}^{m\\times n}$, if $m>n$ then the maximum rank of the matrix is $n$ (*i.e.* if the matrix has more rows than columns then the maximum value of the rank is the number of columns). Conversely, if $mReducing the Rank\n", - "\n", - "By definition setting one or more singular values to zero reduces the rank of a given matrix.\n", - "\n", - "For example, for a matrix with rank 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the matrix A\n", - "A = np.array([[7, 3, -4], [-5, 1, 2], [2, 4, -2]])\n", - "\n", - "# Calculate the SVD of A\n", - "U, S, V = np.linalg.svd(A)\n", - "S = np.around(S, 2)\n", - "\n", - "print('A =')\n", - "print(A)\n", - "print('')\n", - "print('Sigma =')\n", - "print(np.diag(S))\n", - "print('')\n", - "print('rank(A) =', np.linalg.matrix_rank(A))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "the rank is reduced to 1 when the second singular value is set to zero." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Set the second singular value to 0\n", - "S[1] = 0\n", - "\n", - "# Create a new matrix with the updated singular values\n", - "B = np.dot(U, np.dot(np.diag(S), V))\n", - "\n", - "print('Sigma =')\n", - "print(np.diag(S))\n", - "print('')\n", - "print('B =')\n", - "print(B)\n", - "print('')\n", - "print('rank(B) =', np.linalg.matrix_rank(B))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hard Thresholding\n", - "\n", - "Another way of looking at this would be apply a **Hard Threshold** to the singular values of the matrix.\n", - "\n", - "$$ \n", - "HT_{\\lambda}(\\mathbf{x}) = \n", - "\\begin{cases} \n", - "\\mathbf{x} & \\text{if}\\ |\\mathbf{x}| \\geq \\lambda \\\\ \n", - "0 & \\text{otherwise} \\\\\n", - "\\end{cases}\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def hard_thresh(value, threshold):\n", - " '''Hard Threshold\n", - " \n", - " Returns hard threshold of input\n", - " \n", - " '''\n", - " \n", - " return value * (np.abs(value) >= threshold)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For example, for a full rank matrix $A$ ($\\textrm{rank}(A)=3$)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a matrix of random values with seed 1\n", - "np.random.seed(1)\n", - "A = np.random.randint(-9, 9, (3, 3))\n", - "\n", - "# Calculate the SVD of A\n", - "U, S, V = np.linalg.svd(A)\n", - "S = np.around(S, 2)\n", - "\n", - "print('A =')\n", - "print(A)\n", - "print('')\n", - "print('Sigma =')\n", - "print(np.diag(S))\n", - "print('')\n", - "print('rank(A) =', np.linalg.matrix_rank(A))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "a new matrix $B$ with a rank of 1 can be obtained by applying a hard threshold with $\\lambda=6$ to the singular values of $A$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Hard threshold values of sigma\n", - "S_ht = hard_thresh(S, 6)\n", - "\n", - "# Create a new matrix with the updated singular values\n", - "B = np.dot(U, np.dot(np.diag(S_ht), V))\n", - "\n", - "print('HT(Sigma) =')\n", - "print(np.diag(S_ht))\n", - "print('')\n", - "print('B =')\n", - "print(B)\n", - "print('')\n", - "print('rank(B) =', np.linalg.matrix_rank(B))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Therefore, with an appropriate threshold, one can reduce the rank of a given matrix." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Low Rank Approximation\n", - "\n", - "The low-rank approximation is a minimisation constraint that is subject to the condition that the matrix one wishes to obtain has reduced rank. In other words, we can use the prior knowledge that the matrix one wants to recover is of reduced rank when solving an inverse problem.\n", - "\n", - "### Nuclear Norm\n", - "\n", - "As with the $l_0$ norm in sparse regularisation, the rank of the matrix cannot be used directly for regularisation as it is a non-convex function. Instead the nuclear norm (or trace norm) is used to promote low-rank solutions. The nuclear norm is given by the sum of singular values,\n", - "\n", - "$$\\|X\\|_* = \\sum_{k=1} \\sigma_k(X)$$\n", - "\n", - "where $\\sigma_k(X)$ denotes the $k^{\\textrm{th}}$ largest singular value of $X$. This can be implemented in Python as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def nuclear_norm(data):\n", - " '''Nuclear Norm\n", - " \n", - " This function returns the nuclear norm of the input data.\n", - " \n", - " '''\n", - "\n", - " # Get SVD of the data.\n", - " u, s, v = np.linalg.svd(data)\n", - "\n", - " # Return nuclear norm.\n", - " return np.sum(s)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For example, given a full rank matrix $A$ and a rank 2 matrix $B$ one can see that the nuclear norm of $B$ is smaller than that of $A$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.array([[6, 1, 2], [5, 6, 1], [5, 7, 7]])\n", - "B = np.array([[1, 0, 1], [-2, -3, 1], [3, 3, 0]])\n", - "\n", - "print('rank(A)=', np.linalg.matrix_rank(A))\n", - "print('||A||*=', nuclear_norm(A))\n", - "print('')\n", - "print('rank(B)=', np.linalg.matrix_rank(B))\n", - "print('||B||*=', nuclear_norm(B))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Deconvolution Exercise\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This exercise deals with the concept of deconvolution.\n", - "\n", - "We begin by defining a matrix $X$ with rank 1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "#############################\n", - "# NO NEED TO EDIT THIS CELL #\n", - "# JUST EXECUTE IT #\n", - "#############################\n", - "\n", - "# Define a 11x11 matrix X\n", - "vals = np.array(list(range(6)) + list(range(5))[::-1]) \n", - "X = np.array([i * vals for i in range(6)] + [i * vals for i in range(5)[::-1]], dtype='float')\n", - "X /= X.sum()\n", - "\n", - "# Calculate the rank of X\n", - "print('rank(X)=', np.linalg.matrix_rank(X))\n", - "\n", - "# Display X\n", - "plt.imshow(X, interpolation='none', cmap='magma')\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A slightly blurred version of $X$ with noise is given by\n", - "\n", - "$$Y = MX + N$$\n", - "\n", - "where $M$ represents the convolution of $X$ with a blurring kernel and $N$ is additive Gaussian noise." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#############################\n", - "# NO NEED TO EDIT THIS CELL #\n", - "# JUST EXECUTE IT #\n", - "#############################\n", - "\n", - "def blur(data, kernel):\n", - " '''Blurring operator\n", - "\n", - " This function convolves the input data with the blurring kernel.\n", - "\n", - " '''\n", - " \n", - " return fftconvolve(data, kernel, 'same')\n", - "\n", - "def blur_transpose(data, kernel):\n", - " '''Adjoint blurring operator\n", - " \n", - " This function convolves the input data with the rotated blurring kernel.\n", - " \n", - " '''\n", - " \n", - " return fftconvolve(data, np.rot90(kernel, 2), 'same')\n", - "\n", - "# Define a 3x3 Gaussian kernel\n", - "gauss_kernel = np.array(Gaussian2DKernel(0.5, x_size=3, y_size=3))\n", - "\n", - "# Blur X and add noise\n", - "Y = blur(X, gauss_kernel) + 0.003 * np.random.randn(*X.shape)\n", - "\n", - "# Calculate the rank of Y\n", - "print('rank(Y)=', np.linalg.matrix_rank(Y))\n", - "\n", - "# Display X and Y\n", - "plt.figure(figsize=(15, 12))\n", - "plt.subplot(121)\n", - "plt.imshow(X, interpolation='none', cmap='magma')\n", - "plt.title('X', fontsize=20)\n", - "plt.subplot(122)\n", - "plt.imshow(Y, interpolation='none', cmap='magma')\n", - "plt.title('Y', fontsize=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$Y$ is clearly distorted with respect to $X$. This sort of distortion is present in virtually all images obtained with optical instruments (*e.g.* telescopes, microscopes, *etc.*).\n", - "\n", - "In order to recover $X$ we will aim to solve the problem using the following minimsation:\n", - "\n", - "$$\\begin{aligned} & \\underset{X}{\\text{argmin}} & \\frac{1}{2}\\|Y-MX\\|_2^2 + \\lambda\\|X\\|_*\\end{aligned}$$\n", - "\n", - "which imposes a low-rank solution of $X$ via the nuclear norm term.\n", - "\n", - "### Gradient\n", - "\n", - "Your first task is to define a function that calculates the gradient of the minimisation problem, which is given by\n", - "\n", - "$$\\nabla F(X) = M^T(MX-Y)$$\n", - "\n", - "> See the [Hints](#Hints) if you get stuck.\n", - "\n", - "
\n", - "EDIT THE CELL BELOW" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##############################\n", - "# YOU NEED TO EDIT THIS CELL #\n", - "##############################\n", - "\n", - "def grad(y, x_rec, kernel):\n", - " '''Gradient\n", - " \n", - " This function calculates the gradient given the observation and an approximation of the true signal.\n", - " \n", - " '''\n", - "\n", - " return None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rank Reduction\n", - "\n", - "Your next task is to define a function for imposing that the recovered matrix has a reduced rank.\n", - "\n", - "To complete this task you will need to:\n", - "1. calculate some measure of the rank of the input data\n", - "1. reduce the this rank using the input threshold\n", - "1. return the updated matrix\n", - "\n", - "> See the [Hints](#Hints) if you get stuck.\n", - "\n", - "
\n", - "EDIT THE CELL BELOW" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##############################\n", - "# YOU NEED TO EDIT THIS CELL #\n", - "##############################\n", - "\n", - "def reduce_rank(x, threshold):\n", - " '''Reduce Rank\n", - " \n", - " This function reduces the rank of the input value x using the specified threshold.\n", - " \n", - " '''\n", - " \n", - " return None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optimisation\n", - "\n", - "For this exercise we will use the forward-backward splitting algorithm with a rank reduction operation to promote low-rank solutions. It has the following form for each iteration:\n", - "\n", - "$$X_{n+1} = X_n - \\gamma * \\nabla F(X_n)$$\n", - "$$X_{n+1} = \\text{RR}_\\lambda(X_{n+1})$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#############################\n", - "# NO NEED TO EDIT THIS CELL #\n", - "# JUST EXECUTE IT #\n", - "#############################\n", - "\n", - "def cost_func(y, x_rec, threshold, kernel):\n", - " '''Cost Function\n", - " \n", - " Return the cost of the given estimate of x\n", - " \n", - " '''\n", - " \n", - " return 0.5 * np.linalg.norm(y - blur(x_rec, kernel)) ** 2 + threshold * nuclear_norm(x_rec)\n", - "\n", - "def forwardBackward(observation, first_guess, threshold, kernel):\n", - " '''Forward-Backward Algorithm\n", - " \n", - " Return the recovered signal using a rank reduction operator\n", - " \n", - " '''\n", - " \n", - " x_rec = first_guess\n", - " \n", - " n_iter = 15\n", - " gamma = 1.0\n", - " \n", - " for i in range(n_iter):\n", - " \n", - " x_temp = x_rec - gamma * grad(observation, x_rec, kernel)\n", - " x_rec = reduce_rank(x_temp, threshold)\n", - " cost = cost_func(observation, x_rec, threshold, kernel)\n", - " print('Iteration', i + 1, 'Cost =', cost)\n", - " \n", - " return x_rec" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Deconvolution\n", - "\n", - "Your final task is to perform the deconvolution.\n", - "\n", - "1. Set the low-rank threshold value ($\\lambda$).\n", - "1. Set an initial value for $X$.\n", - "1. Implement the forward-backward algorithm with the following inputs:\n", - " 1. The blurred noisy matrix.\n", - " 1. The initial value for $X$.\n", - " 1. The threshold.\n", - " 1. The blurring kernel.\n", - "\n", - "> See the [Hints](#Hints) if you get stuck.\n", - "\n", - "
\n", - "EDIT THE CELL BELOW" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##############################\n", - "# YOU NEED TO EDIT THIS CELL #\n", - "##############################\n", - "\n", - "# Set the threshold value.\n", - "threshold = None\n", - "\n", - "# Set initial guess for X.\n", - "first_guess = None\n", - "\n", - "# Recover the matrix.\n", - "X_rec = None\n", - "\n", - "# Calculate the rank of recoverd matrix.\n", - "rank_x_rec = None\n", - "\n", - "if not isinstance(rank_x_rec, type(None)):\n", - " print('')\n", - " print('rank(X_rec)=', rank_x_rec)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#############################\n", - "# NO NEED TO EDIT THIS CELL #\n", - "# JUST EXECUTE IT #\n", - "#############################\n", - "\n", - "# Display\n", - "if not isinstance(X_rec, type(None)):\n", - " plt.figure(figsize=(17, 12))\n", - " plt.subplot(131)\n", - " plt.imshow(X, interpolation='none', cmap='magma')\n", - " plt.title('$X$', fontsize=20)\n", - " plt.subplot(132)\n", - " plt.imshow(Y, interpolation='none', cmap='magma')\n", - " plt.title('$Y$', fontsize=20)\n", - " plt.subplot(133)\n", - " plt.imshow(X_rec, interpolation='none', cmap='magma')\n", - " plt.title('$\\hat{X}$', fontsize=20)\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Does your final $\\hat{X}$ look more like $X$ or $Y$? Try ajusting $\\lambda$ to see if that improves your results." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Hints\n", - "\n", - "** Gradient **\n", - "\n", - "1. The blurring operators are already defined.\n", - "\n", - "** Rank Reduction **\n", - "\n", - "1. Make sure you are thresholding something *singular*.\n", - "1. Thresholding matrices can be quite *hard*.\n", - "\n", - "** Deconvolution **\n", - "\n", - "1. Make sure your first guess of $X$ is the right shape.\n", - "1. Try adjusting your value of $lambda$\n", - "\n", - "---\n", - "\n", - "## Exercise Solutions\n", - "\n", - "**Warning!** Try your best to solve all of the exercises on your own before checking the answers.\n", - "\n", - "
\n", - "\n", - "### Deconvolution Exercise Solution\n", - "\n", - "** Gradient **\n", - "\n", - "```Python\n", - "\n", - "def grad(y, x_rec, kernel):\n", - " '''Gradient\n", - " \n", - " This function calculates the gradient given the observation and an approximation of the true signal.\n", - " \n", - " '''\n", - "\n", - " return blur_transpose(blur(x_rec, kernel) - y, kernel)\n", - " \n", - "```\n", - "\n", - "** Rank Reduction **\n", - "\n", - "```Python\n", - "\n", - "def reduce_rank(x, threshold):\n", - " '''Reduce Rank\n", - " \n", - " This function reduces the rank of the input value x using the specified threshold.\n", - " \n", - " '''\n", - " \n", - " u, s, v = np.linalg.svd(x)\n", - " \n", - " s_new = hard_thresh(s, threshold)\n", - " \n", - " return np.dot(u, np.dot(np.diag(s_new), v))\n", - "\n", - "```\n", - "\n", - "** Deconvolution **\n", - "\n", - "```Python\n", - "\n", - "# Set the threshold value.\n", - "threshold = 0.1\n", - "\n", - "# Set initial guess for X.\n", - "first_guess = np.ones(Y.shape)\n", - "\n", - "# Recover the matrix.\n", - "X_rec = forwardBackward(Y, first_guess, threshold, gauss_kernel)\n", - "\n", - "# Calculate the rank of recoverd matrix.\n", - "rank_x_rec = np.linalg.matrix_rank(X_rec)\n", - "\n", - "```" - ] - } - ], - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/python/Answers-to-exercises-2.ipynb b/python/Answers-to-exercises-2.ipynb deleted file mode 100644 index ccddda9..0000000 --- a/python/Answers-to-exercises-2.ipynb +++ /dev/null @@ -1,454 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 3:** Obtain the array `[4., 4., 4.]` from `extended_array` by using slicing and `log`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Hidden answer Puzzle 3:\n", - "#np.log(extended_array[3::4])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 4:** Change all the last two columns by zeros using slicing, np.shape and np.zeros (assuming you don't know the size of the array beforehand)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Hidden answer Puzzle 4:\n", - "#array2d[:,-2:]=np.zeros((array2d.shape[0],2))\n", - "#print(array2d)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Exercise 1:** Using broadcasting and in max. 2 lines of code, construct a multiplication table of the numbers from 1 to 10, i.e., where for each column corresponding to 1,2,3,..., the rows correspond to their integer multiples. For 1 to 3, it looks like this:\n", - "\n", - "---\n", - "\n", - "|1|2|3|\n", - "|--|--|--|\n", - "|2|4|6|\n", - "|3|6|9|\n", - "\n", - "> Then, using masking, remove all multiples of 3, in one line of code. So that the result (in this case for 3x3) looks like this:\n", - "\n", - "|1|2|0|\n", - "|--|--|--|\n", - "|2|4|0|\n", - "|0|0|0|\n", - "\n", - "> Finally, compute the sum over each column and write it into a list:\n", - "\n", - " [3,6,0]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x1 = np.arange(1,11)\n", - "mult = x1[:,np.newaxis] * x1[None,:]\n", - "print(mult)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mult[(mult %3 == 0)] = 0\n", - "mult" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#With list comprehensions\n", - "[np.sum(mult[:,ii]) for ii in range(mult.shape[1])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#With numpy functions\n", - "np.apply_along_axis(np.sum,1, mult)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Exercise 2:** \n", - " * Produce a random set of points, following a normal distribution with mean 0 and covariance matrix `cov=[[1, 3.0/5], [3/5.0, 2]]`.\n", - " * Plot the set of points using `plt.scatter(X, Y)`\n", - " * Comment: Write equation in LaTeX $$\\exp(x^2)$$\n", - " * Compute the marginal distributions for the x and y coordinates and using numpy functions, check that they follow a Gaussian distribution when compared to their histograms. (Hints: `plt.hist(points, bins, density=True)`, `np.exp`, `np.sqrt`, `np.std`, `np.mean`). Use 25 bins.\n", - " * Using masking and `np.where` remove all points whose y-coordinate is more than 2-sigma away from the mean.\n", - " * Plot the remaining points in a scatter plot with blue color, with `facecolor='none'` and size `s=200`, together with the old set of points in red color with a transparency of alpha=0.6." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean = [0, 0]\n", - "cov = [[1, 3.0/5],\n", - " [3/5.0, 2]]\n", - "X = np.random.multivariate_normal(mean, cov, 1000)\n", - "X.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "#import seaborn; seaborn.set() # for plot styling\n", - "\n", - "plt.scatter(X[:, 0], X[:, 1]);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "xx = np.linspace(-3,3,1000)\n", - "yy = np.empty((2,xx.shape[0]))\n", - "yy[0] = 1/np.sqrt(2*np.pi*(np.std(X[:,0]))**2) * np.exp(-xx**2 / (2*(np.std(X[:,0]))**2))\n", - "yy[1] = 1/np.sqrt(2*np.pi*(np.std(X[:,1]))**2) * np.exp(-xx**2 / (2*(np.std(X[:,1]))**2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.hist(X[:,0], 25, density=True)\n", - "plt.plot(xx, yy[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.hist(X[:,1], 25, density=True)\n", - "plt.plot(xx, yy[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exclude=np.where(np.sqrt(X[:,1]**2) > 2*np.std(X[:,1]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Xnew.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exclude[0].shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X.shape[0]-(exclude[0].shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Xnew = np.empty((X.shape[0]-(exclude[0].shape[0]), 2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Xnew[:,1] = np.delete(X[:,1], exclude)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Xnew[:,0] = np.delete(X[:,0], exclude)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.hist(Xnew[:,1], 25, density=True)\n", - "plt.plot(xx, yy[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.scatter(Xnew[:, 0], Xnew[:, 1], color='blue', facecolor='none', s=200);\n", - "plt.scatter(X[:, 0], X[:, 1], color='red', alpha=0.6);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pandas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cr_temp = pd.read_csv('./materials/CR_Temp.csv')\n", - "cr_temp = pd.read_csv('./materials/CR_Temp.csv', index_col=1)\n", - "cr_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Remove the 'N' column\n", - "cr_temp = cr_temp.drop(columns='N')\n", - "cr_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cr_temp.loc[:, 'ENE':'DIC'] ;" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Compute the mean along the axis 1, on a sliced subset.\n", - "my_mean = (cr_temp.loc[:, 'ENE':'DIC']).mean(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check if the two mean columns agree.\n", - "np.mean(my_mean - cr_temp.AVERAGE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "years = pd.date_range('1958', '2017', freq='Y')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "years\n", - "\n", - "print(years.size)\n", - "\n", - "cr_temp.index.size" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crr = cr_temp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crr['time'] = years" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crr = crr.set_index('time')\n", - "crr.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crr.asfreq('10Y')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crr.asfreq('10Y').plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crr.AVERAGE.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/python/Answers-to-exercises.ipynb b/python/Answers-to-exercises.ipynb deleted file mode 100644 index 86ddd32..0000000 --- a/python/Answers-to-exercises.ipynb +++ /dev/null @@ -1,496 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5 Answers to Exercises\n", - "\n", - "---\n", - "\n", - "> Author: Samuel Farrens \n", - "> Year: 2017 \n", - "> Email: [samuel.farrens@gmail.com](mailto:samuel.farrens@gmail.com) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", - "This notebook contains some example answers to the exercises.\n", - "\n", - "---\n", - "\n", - "## Contents\n", - "\n", - "1. [Native Python](#Native-Python)\n", - " * [Execise 1.1](#Exercise-1.1)\n", - " * [Execise 1.2](#Exercise-1.2)\n", - " * [Execise 1.3](#Exercise-1.3)\n", - "1. [Numpy](#Numpy)\n", - " * [Execise 2.1](#Exercise-2.1)\n", - " * [Execise 2.2](#Exercise-2.2)\n", - "1. [Matplotlib](#Matplotlib)\n", - " * [Execise 3.1](#Exercise-3.1)\n", - " * [Execise 3.2](#Exercise-3.2)\n", - "1. [Other Packages](#Other-Packages)\n", - " * [Exercise 4.1](#Exercise-4.1)\n", - " * [Exercise 4.2](#Exercise-4.2) \n", - "\n", - "---\n", - "\n", - "## Native Python\n", - "\n", - "### Exercise 1.1\n", - "\n", - "The equation $F = \\frac{Gm_1m_2}{r^2}$ can be implemented as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def force(m1, m2, r):\n", - " \"\"\"Grvaitational Force\n", - " \n", - " Function to calculate the gravitational attraction between two bodies.\n", - " \n", - " \"\"\"\n", - " \n", - " return 6.674e-11 * m1 * m2 / r ** 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and the force between the Earth and the Moon is given by" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(force(5.9742e24, 7.36e22, 384402 * 1e3), 'N')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 1.2\n", - "\n", - "The variance of the sample $x = [2, 4, 4, 4, 5, 5, 7, 9]$ can be calculated as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the list of values \n", - "x = [2, 4, 4, 4, 5, 5, 7, 9]\n", - "\n", - "# Calculate the mean value of x\n", - "mean = sum(x) / len(x)\n", - "\n", - "# Calculate the square differences between each value and the mean\n", - "square_diff = [(i - mean) **2 for i in x]\n", - "\n", - "# Calculate the variance\n", - "variance = sum(square_diff) / len(x)\n", - "\n", - "print('variance =', variance)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can check if the value of the variance is in $x$ and count how many times it occurs as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('The variance value is in x:', variance in x)\n", - "print('The variance value occurs', x.count(variance), 'times in x.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 1.3\n", - "\n", - "You can write a class to convert between degrees and radians as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class angleConverter(object):\n", - " \"\"\"Angle Converter Class\n", - " \n", - " This class converts angles between degrees and radians.\n", - " \n", - " \"\"\"\n", - " \n", - " def __init__(self):\n", - " \n", - " self.pi = 3.14159\n", - " \n", - " def deg2rad(self, angle):\n", - " \"\"\"Convert input angle from degrees to radians\n", - "\n", - " \"\"\"\n", - " \n", - " return angle * self.pi / 180.0\n", - " \n", - " def rad2deg(self, angle):\n", - " \"\"\"Convert input angle from radians to degrees\n", - "\n", - " \"\"\"\n", - " \n", - " return angle * 180.0 / self.pi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and you can implement the class on the angles as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define an instance of the class\n", - "ac = angleConverter()\n", - "\n", - "# Use the class methods\n", - "print('45 deg is', ac.deg2rad(45), 'radians')\n", - "print('30 deg is', ac.deg2rad(30), 'radians')\n", - "print('135 deg is', ac.deg2rad(135), 'radians')\n", - "print('pi radians is', ac.rad2deg(ac.pi), 'degrees')\n", - "print('2pi/3 radians is', ac.rad2deg(2 * ac.pi / 3), 'degrees')\n", - "print('5pi/6 radians is', ac.rad2deg(5 * ac.pi / 6), 'degrees')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "---\n", - "\n", - "## Numpy\n", - "\n", - "### Exercise 2.1\n", - "\n", - "You can read in the file and determine the standard deviation of the distances of the stars with spectral type M as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the numpy package\n", - "import numpy as np\n", - "\n", - "# Read the ASCII file\n", - "data = np.genfromtxt('materials/nearest_stars.txt', names=True, dtype=None, delimiter=',')\n", - "\n", - "# Calculate the standard deviation of the M type stars\n", - "print('The standard deviation is', data[data['Spectral_Type'] == 'M']['Distance'].std())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 2.2\n", - "\n", - "You can generate the array of random floats and calculate the mean $l_1$-norm for each 4x4 matrix as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the numpy package\n", - "import numpy as np\n", - "\n", - "# Set a random seed of 8\n", - "np.random.seed(8)\n", - "\n", - "# Generate an array of random floats\n", - "x = np.random.ranf((3, 4, 4))\n", - "\n", - "# Calculate the mean l1-norm of each 4x4 matrix\n", - "print('The mean l1-norms are', np.linalg.norm(x, ord=1, axis=(1, 2)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Matplotlib\n", - "\n", - "### Exercise 3.1\n", - "\n", - "You can generate an array of Gaussian distributed random numbers and plot a histogram as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# display plot in notebook\n", - "%matplotlib inline\n", - "\n", - "# import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# generate the Gaussian random numbers\n", - "x = np.random.randn(1000)\n", - "\n", - "# set the plot size\n", - "plt.figure(figsize=(8, 6))\n", - "\n", - "# plot the histogram\n", - "plt.hist(x, bins=50)\n", - "plt.title('Gaussian distributed random numbers')\n", - "\n", - "# display the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 3.2\n", - "\n", - "You can read the file and plot the earnings vs the ratings as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# display plot in notebook\n", - "%matplotlib inline\n", - "\n", - "# import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# read the data from the text file\n", - "data = np.genfromtxt('materials/marvel_movies.txt', names=True, dtype=None, delimiter=',')\n", - "\n", - "# set the plot size\n", - "plt.figure(figsize=(8, 6))\n", - "\n", - "# plot the rating as function of the earnings\n", - "plt.plot(data['Earnings'], data['Rating'], 'm^')\n", - "plt.xlabel('Earnings in $', fontsize=20)\n", - "plt.ylabel('Rating', fontsize=20)\n", - "plt.title('MCU Movies', fontsize=20)\n", - "plt.xscale('log')\n", - "\n", - "# display the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 3.3\n", - "\n", - "You can generate the chequered image as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# display plot in notebook\n", - "%matplotlib inline\n", - "\n", - "# import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# generate an 8x8 array of ones\n", - "x = np.ones((8, 8))\n", - "\n", - "# set alternate elements to 0\n", - "x[::2, ::2] = 0\n", - "x[1::2, 1::2] = 0\n", - "\n", - "# set the plot size\n", - "plt.figure(figsize=(8, 6))\n", - "\n", - "# plot the image\n", - "plt.imshow(x, interpolation='none', cmap='bone')\n", - "\n", - "# display the plot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Other Packages\n", - "\n", - "\n", - "### Exercise 4.1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import the necessary packages\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "from scipy import fftpack\n", - "from scipy.misc import ascent\n", - "from scipy.ndimage import gaussian_filter\n", - "from astropy.convolution import convolve_fft\n", - "\n", - "# set the input image\n", - "image = ascent()\n", - "\n", - "# set the Gaussian kernel\n", - "kernel = np.zeros(image.shape)\n", - "kernel[tuple(np.array(kernel.shape) // 2)] = 1\n", - "kernel = gaussian_filter(kernel, 10)\n", - "\n", - "# define the convolution function\n", - "def convolve(x, y):\n", - " \"\"\"Convolve\n", - " \n", - " This method returns the convolution of x with y.\n", - " \n", - " \"\"\"\n", - " \n", - " return np.real(fftpack.fftshift(fftpack.ifftn(fftpack.fftn(x) * fftpack.fftn(y))))\n", - "\n", - "# convolve the image with the kernel\n", - "convolved_image = convolve(image, kernel)\n", - "\n", - "# convolve the image with the kernel using astropy\n", - "convolved_image_astropy = convolve_fft(image, kernel, boundary='wrap')\n", - "\n", - "# display the images\n", - "plt.figure(figsize=(16, 6))\n", - "plt.subplot(131)\n", - "plt.imshow(image, cmap='gray')\n", - "plt.title('Original Image')\n", - "plt.subplot(132)\n", - "plt.imshow(convolved_image, cmap='gray')\n", - "plt.title('My Convolved Image')\n", - "plt.subplot(133)\n", - "plt.imshow(convolved_image_astropy, cmap='gray')\n", - "plt.title('Astropy Convolved Image')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 4.2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# define the deconvolution function\n", - "def deconvolve(x, y):\n", - " \"\"\"Deconvolve\n", - " \n", - " This method returns the deconvolution of x by y.\n", - " \n", - " \"\"\"\n", - " \n", - " return np.real(fftpack.fftshift(fftpack.ifftn(fftpack.fftn(x) / fftpack.fftn(y))))\n", - "\n", - "# deconvolve the blurry image\n", - "deconvolved_image = deconvolve(convolved_image, kernel)\n", - "\n", - "# display the images\n", - "plt.figure(figsize=(16, 6))\n", - "plt.subplot(121)\n", - "plt.title('Original Image')\n", - "plt.imshow(image, cmap='gray')\n", - "plt.subplot(122)\n", - "plt.imshow(deconvolved_image, cmap='gray')\n", - "plt.title('Deconvolved Image')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/python/Classes_I.ipynb b/python/Classes_I.ipynb deleted file mode 100644 index c3f9906..0000000 --- a/python/Classes_I.ipynb +++ /dev/null @@ -1,1684 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"CosmoStat\n", - "
\n", - "
\n", - "
\n", - "
\n", - "\n", - "# The Anatomy of a Python Class (Part I)\n", - "---\n", - "\n", - "> Author: Samuel Farrens \n", - "> Email: samuel.farrens@cea.fr \n", - "> Year: 2019 \n", - "> Version: 1.0\n", - "\n", - "---\n", - "
\n", - "\n", - "Classes are one of the fundamental building blocks of Python and are essential for object-oriented programming. In this tutorial we will explore how classes work and look at tips and tricks for getting the most out of them. By the end you should have, not only a much better understanding of what Python classes are, but also some new ideas for writing better code.\n", - "\n", - "This first notebook presents some introductory and intermidate topics. The focus being on a intimate understanding of how classes work in Python. You are expected to already have a good grasp on functions, dictionaries, decorators and ... . More advanced topics are discussed in the [second notebook](./Classes_II.ipynb). This tutorial is in no way exhaustive and you are encouraged to suppliment your understanding with further reading. To this end, some links to some additional resources are provided.\n", - "\n", - "If you are new to Jupyter notebooks note that cells are executed by pressing SHIFT+ENTER (⇧+ ⏎). See the Jupyter documentation for more details.\n", - "\n", - "
\n", - "\n", - "## Contents\n", - "---\n", - "\n", - "1. [Set-Up](#1-Set-Up)\n", - "1. [Object-Oriented Programming](#2-Object-Oriented-Programming)\n", - " 1. [A Simple Example](#A-Simple-Example)\n", - "1. [Disecting a Class](#3-Disecting-a-Class)\n", - " 1. [The Class Dictionary](#The-Class-Dictionary)\n", - " 1. [Instantiation](#Instantiation)\n", - "1. [Methods](#4-Methods)\n", - " 1. [Functions as Attributes](#Functions-as-Attributes)\n", - " 1. [Static Methdos](#Static-Methods)\n", - " 1. [Class Methods](#Class-Methods)\n", - " 1. [Instance Methods](#Instance-Methods)\n", - " 1. [Mixing Methods](#Mixing-Methods)\n", - "1. [Properties](#5-Properties)\n", - " 1. [Getters and Setters](#Getters-and-Setters)\n", - " 1. [Class Properties](#Class-Properties)\n", - "1. [Operator Overloading](#6-Operator-Overloading)\n", - " 1. [Class String](#Class-String)\n", - " 1. [Class Representation](#Class-Representation)\n", - " 1. [Mathematical Operators](#Mathematical-Operators)\n", - " 1. [Further Options](#Further-Options)\n", - "1. [Exercises](#7-Exercises)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1 Set-Up\n", - "\n", - "The following cell contains some set-up commands. Be sure to execute this cell before continuing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Notebook Set-Up Commands\n", - "\n", - "from math import pi, sqrt\n", - "\n", - "def print_error(error):\n", - " \"\"\" Print Error\n", - " \n", - " Function to print exceptions in red.\n", - " \n", - " Parameters\n", - " ----------\n", - " error : string\n", - " Error message\n", - " \n", - " \"\"\"\n", - " print('\\033[1;31m{}\\033[1;m'.format(error))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "## 2 Object-Oriented Programming\n", - "---\n", - "\n", - "Object-Oriented Programming (OOP) is based around the concept of *[objects](https://en.wikipedia.org/wiki/Object_(computer_science))*, which in turn are usually generated by *[classes](https://en.wikipedia.org/wiki/Class_(computer_programming))*. The philosophy of OOP is generally summarised by four core concepts:\n", - "\n", - "* [abstraction](https://en.wikipedia.org/wiki/Abstraction_(computer_science)) - *only showing certain features to the user*\n", - "* [encapsulation](https://en.wikipedia.org/wiki/Encapsulation_(computer_programming)) - *grouping together certain features*\n", - "* [inheritance](https://en.wikipedia.org/wiki/Inheritance_(object-oriented_programming)) - *reusing and extending features*\n", - "* [polymorphism](https://en.wikipedia.org/wiki/Polymorphism_(computer_science)) - *allow objects to take various forms*\n", - "\n", - "Some of the main benfits that OOP provides, which are directly linked to these core concepts are:\n", - "\n", - "* **reusability** - *minimising the amount of copying and pasting of code*\n", - "* **maintainability** - *minimising the amount of code that needs to be changed to fix a bug*\n", - "* **security** - *minimising the amount of misuse by a user*\n", - "\n", - "In this tutorial we will take an in-depth look at Python classes, which should provide you with all of the tools you need to take advantage of OOP. \n", - "\n", - "
\n", - "\n", - "### A Simple Example\n", - "\n", - "Before diving into what a class is and how it works let's take a moment to understand why you would ever need a class in the first place by looking at a very simple example.\n", - "\n", - "Imagine you are working on a project where you need to keep track of various properties of the planets in the Solar System. Specifically, you want to know the radius (in $\\textrm{km}$) at the equator, the mass (in $\\textrm{kg}$) and the gravitational acceleration at the surface (in $\\textrm{ms}^{-2}$). There are obviously many different ways of doing this. The simplest (in terms of coding) would be to define a variable for each parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set individual parameter values for the Earth\n", - "earth_radius = 6371\n", - "earth_mass = 5.97e24\n", - "earth_gravity = 9.8\n", - "\n", - "# Print the Earth properties\n", - "print('planet Earth')\n", - "print(' - Radius: {}km'.format(earth_radius))\n", - "print(' - Mass: {}kg'.format(earth_mass))\n", - "print(' - Gravity: {}m/s'.format(earth_gravity))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This approach would require us to define 21 more variables if we extended it for all of the other planets in the Solar System. It's pretty clear that this becomes quite messy and difficult to maintain as the number of parameters increases.\n", - "\n", - "Another approach would be to define a single variable for each planet, and a simple way to store all of the paremeters would be using a list (or a tuple)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set list values for the Earth\n", - "earth = [6371, 5.97e24, 9.8]\n", - "\n", - "# Print the Earth properties\n", - "print('planet Earth')\n", - "print(' - Radius: {}km'.format(earth[0]))\n", - "print(' - Mass: {}kg'.format(earth[1]))\n", - "print(' - Gravity: {}m/s'.format(earth[2]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This certainly makes things easier, as we only need to define 7 more variables. The problem here is that we now need to keep track of the order of the paremers in the list, which, in addition to be being less readable, also becomes complicated as the number of parameters increases.\n", - "\n", - "A third option would be to use a dictionary. This allows us to keep the simplicty of a list-like structure, with a single variable for each planet, but also allows us to add labels to the parameter values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set dictionary keys and values for the Earth\n", - "earth = {'radius': 6371, 'mass': 5.97e24, 'gravity': 9.8}\n", - "\n", - "# Print the Earth properties\n", - "print('planet Earth')\n", - "print(' - Radius: {}km'.format(earth['radius']))\n", - "print(' - Mass: {}kg'.format(earth['mass']))\n", - "print(' - Gravity: {}m/s'.format(earth['gravity']))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This solution is pretty good but we still need to manually define a dictionary for each planet.\n", - "\n", - "Let's try tackling this problem using a an object-oriented approach. For this solution we can define a generic class for handling planets. Each planet then constitutes an *object* or instance of the class.\n", - "\n", - "> For now don't worry too much about the syntax and content of this class as we will explore this in depth in the following sections." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a planet class\n", - "class Planet:\n", - " \n", - " # class variable\n", - " object_type = 'planet'\n", - " \n", - " # class initialiser\n", - " def __init__(self, name, radius, mass, gravity):\n", - " \n", - " # instance variables\n", - " self.name = name\n", - " self.radius = radius\n", - " self.mass = mass\n", - " self.gravity = gravity\n", - " \n", - " # class instance method\n", - " def show(self):\n", - " \n", - " # Print the object properties\n", - " print(self.object_type, self.name)\n", - " print(' - Radius: {}km'.format(self.radius))\n", - " print(' - Mass: {}kg'.format(self.mass))\n", - " print(' - Gravity: {}m/s'.format(self.gravity))\n", - " print()\n", - " \n", - "# create class instances\n", - "earth = Planet('Earth', 6371, 5.97e24, 9.8)\n", - "earth.show()\n", - "\n", - "jupiter = Planet('Jupiter', 69911, 1.89e27, 24.79)\n", - "jupiter.show()\n", - "\n", - "saturn = Planet('Saturn', 58232, 5.68e26, 10.45)\n", - "saturn.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This approach is a bit more complicated in terms of the amount of code required initially but once the class is defined it is very easy to generate instances for each planet. We can even define properties for all instances (*e.g.* the `object_type`).\n", - "\n", - "Hopefully this simple example has given you a sneak preview of the power of classes. In the following sections we will take a deeper look at how classes work and the various ways in which they can be used. Once you have mastered these concepts you may find that you will want to rethink the way in which you structure your codes moving towards a more object-oriented implementation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3 Disecting a Class\n", - "\n", - "---\n", - "\n", - "To get a better grip on what a class is and how it works lets have a look at what is going on \"inside\". We can start by defining a dummy class and looking at its properties.\n", - "\n", - "Classes are defined with the keyword `class`, much like the keyword `def` is used for defining functions. In the following cell we define a class without any attributes (note the use of the null `pass` statement)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a dummy class\n", - "class myClass:\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This object clearly has little use, but it is a good starting place to look at the structure of classes in general. Let's look at the `help` for this class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print help for dummy class\n", - "help(myClass)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that despite not setting attributes we have two default attributes predefined, namely `__dict__` and `__weakref__`. `__weakref__` is a special attribute that lists *weak references* to the class object (a topic outside the scope of this tutorial). Instead, we will focus on the `__dict__` attribute.\n", - "\n", - "
\n", - "\n", - "### The Class Dictionary\n", - "\n", - "Let's begin by printing the contents and type of `__dict__` for our dummy class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the class dictionary\n", - "print('myClass.__dict__ =', myClass.__dict__)\n", - "print()\n", - "print('__dict__ is of type', type(myClass.__dict__))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This attribute is a *mappingproxy*, which is a special type of dictionary that does not have a `__setattr__` method (we will come back to this). In addition to the default attributes discussed above we can see:\n", - "\n", - "* `'__doc__'` : The class docstring (defaults to `None`).\n", - "* `'__module__'` : The name of module in which the class was defined.\n", - "\n", - "Now, let's see what happens when we assign a new attribute to the class. Class attributes are accessed and set using a dot (`.`) following the class name.\n", - "\n", - "> Note that any Python object can be assigned as a class attribute. We will come back to this concept later!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Assign the attribute myattr with value True to the class\n", - "myClass.myattr = True\n", - "\n", - "# Print the class dictionary\n", - "print('myClass.__dict__ =', myClass.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the class dictionary has a new entry with the key `myattr` and corresponding value `True`. We can even demonstrate that this dictionary behaves as any other..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the class dictionary with key 'myattr'\n", - "print('myattr =', myClass.__dict__['myattr'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "...except that it does not allow assignment, given the absence of the `__setattr__` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Assign a new value to 'myattr' in the class dictionary\n", - "try:\n", - " myClass.__dict__['myattr'] = False\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's define a new class with some predefined attributes and see what changes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a new dummy class with some attributes\n", - "class myClass:\n", - " \"\"\" This is my class \n", - " \"\"\"\n", - "\n", - " mybool = True\n", - " myint = 1\n", - " myfloat = 1.0\n", - " mystring = 'string'\n", - " \n", - "# Print the class dictionary \n", - "print(myClass.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All of the attributes can be found in the class dictionary. Notice that `__doc__` is now a string. \n", - "\n", - "All of these attributes can be accessed..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the class attributes\n", - "print('mybool =', myClass.mybool)\n", - "print('myint =', myClass.myint)\n", - "print('myfloat =', myClass.myfloat)\n", - "print('mystring =', myClass.mystring)\n", - "print('mydoc =', myClass.__doc__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "...and modified." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Modify the value of the string attribute\n", - "myClass.mystring = 'my new string'\n", - "\n", - "# Print the string attribute\n", - "print('mystring =', myClass.mystring)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In summary the class `__dict__` attribute is useful for understanding how class attributes are stored, but should not really be used directly.\n", - "\n", - "
\n", - "\n", - "### Instantiation\n", - "\n", - "While accessing class attributes directly can be useful, in most applications it will be necessary to create a *class instance* (also reffered to simply as *objects*). Every time a class is isntantiated a unique Python class object is created. These objects retain the global class attributes but can also be assigned unique instance attributes.\n", - "\n", - "We can create a class instance by calling the class name followed by `()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of the class\n", - "myinst = myClass()\n", - "\n", - "# Print the instance dictionary\n", - "print('myinst.__dict__ =', myinst.__dict__)\n", - "print()\n", - "print('__dict__ is of type', type(myinst.__dict__))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we see that the instance dictionary is a true dictionary and is empty, which simply means that no *instance attributes* have been set. \n", - "\n", - "However, instances still have access the class attributes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the instance class dictionary\n", - "print(myinst.__class__.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can clearly demonstrate that istances are unique." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create two instances of the class\n", - "myinst1 = myClass()\n", - "myinst2 = myClass()\n", - "\n", - "# Check if instances are equal\n", - "print('Instances are equal?', myinst1 == myinst2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As with the class itself, attributes can be assigned to the instance object. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Assign an attribute to the instance\n", - "myinst.newbool = False\n", - "\n", - "# Print the instance and dictionaries\n", - "print('myinst.__dict__ =', myinst.__dict__)\n", - "print()\n", - "print('myClass.__dict__ =', myClass.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the instance attribute has no impact on the class attributes.\n", - "\n", - "In order to preassign instance attributes we will need to define a special initialisation (or constructor) method (`__init__`), which takes the special variable `self` as an argument." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a new dummy class with an init method\n", - "class myClass:\n", - " \n", - " # Initialisation method called when instance is created\n", - " def __init__(self):\n", - " \n", - " # Set the instance attribute\n", - " self.myfloat = 3.5\n", - " \n", - "# Create an instance of the class and print the attribute properties\n", - "myinst = myClass()\n", - "print('myinst.__dict__ =', myinst.__dict__)\n", - "print('myfloat =', myinst.myfloat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`self` represents any given class instance and attributes are assigned to it as usual with a dot. Printing the instance dictionary (which is equivalent to `self.__dict__`) reveals the attributes that have been assigned to it.\n", - "\n", - "As with class attributes, instance attribute values can be modified." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myinst.myfloat = 7.2\n", - "print('myinst.__dict__ =', myinst.__dict__)\n", - "print('myfloat =', myinst.myfloat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additional agruments can be passed to `__init__` allowing for a more instance specific initialisation of the class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a new dummy class with a more dynamic init method\n", - "class myClass:\n", - " \n", - " # Pass the argument value when creating an instance\n", - " def __init__(self, value):\n", - " \n", - " # Set the instance attribute\n", - " self.myfloat = value\n", - "\n", - "# Create instances of the class and print the attribute properties\n", - "myinst1 = myClass(3.14)\n", - "myinst2 = myClass(6.28)\n", - "print('myfloat =', myinst1.myfloat)\n", - "print('myfloat =', myinst2.myfloat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An important detail to take into account is that instance attributes will override class attributes with the same name, but only for that instance object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a new dummy class with a class attribute\n", - "class myClass:\n", - " \n", - " myfloat = 6.15\n", - " \n", - " def __init__(self):\n", - " pass\n", - "\n", - "# Create 2 instances of the class\n", - "myinst1 = myClass()\n", - "myinst2 = myClass()\n", - "print('myClass.myfloat =', myClass.myfloat)\n", - "print('myinst1.myfloat =', myinst1.myfloat)\n", - "print('myinst1.__dict__', myinst1.__dict__)\n", - "print('myinst2.myfloat =', myinst2.myfloat)\n", - "print('myinst2.__dict__', myinst2.__dict__)\n", - "print()\n", - "\n", - "# Modify the myfloat attribute of the first instance\n", - "myinst1.myfloat = 8.18\n", - "print('myClass.myfloat =', myClass.myfloat)\n", - "print('myinst1.myfloat =', myinst1.myfloat)\n", - "print('myinst1.__dict__', myinst1.__dict__)\n", - "print('myinst2.myfloat =', myinst2.myfloat)\n", - "print('myinst2.__dict__', myinst2.__dict__)\n", - "print()\n", - "\n", - "# Show the first instance class dictionary\n", - "print('myinst1.__class__.__dict__', myinst1.__class__.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By now you should have a pretty clear idea of the differences between a class and a clsss instance. When designing a class it is important to think about which attributes need to be assigned to the class and which to the class instances.\n", - "\n", - "In the following section we will focus on the various types of method attributes.\n", - "\n", - "> Further Reading \n", - "> https://codesachin.wordpress.com/2016/06/09/the-magic-behind-attribute-access-in-python/ \n", - "> https://rushter.com/blog/python-class-internals/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4 Methods\n", - "\n", - "---\n", - "\n", - "### Functions as Attributes\n", - "\n", - "As briefly mentioned in the previous section, any Python object can be allocated as class attribute. This of course includes functions. To do so we could define a class and a function and manually assign the function object to the a class attribute as follows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a new dummy class\n", - "class myClass:\n", - " pass\n", - "\n", - "# Define a simple function\n", - "def say_hello():\n", - " print('Hello!')\n", - " \n", - "# Assign the function to the class\n", - "myClass.myfunc = say_hello\n", - "# Execute the function\n", - "myClass.myfunc()\n", - "\n", - "# Print the class dictionary\n", - "print()\n", - "print(myClass.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the class is able to call the function and we can also see the object in the class dictionary.\n", - "\n", - "If we only want the function to exist as a class attribute we can simply define the function inside the class. These functions are called *class methods*.\n", - "\n", - "In the following example we define a calculator class with four methods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a calculator class\n", - "class Calculator:\n", - " \n", - " def add(x, y):\n", - " return x + y\n", - " \n", - " def substract(x, y):\n", - " return x - y\n", - "\n", - " def multiply(x, y):\n", - " return x * y\n", - " \n", - " def divide(x, y):\n", - " return x / y\n", - " \n", - "# Print the class dictionary\n", - "print(Calculator.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These methods work the same as any function..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('1 + 2 =', Calculator.add(1, 2))\n", - "print('5 - 2 =', Calculator.substract(5, 2))\n", - "print('7 * 4 =', Calculator.multiply(7, 4))\n", - "print('6 / 2 =', Calculator.divide(6, 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... but only exist inside the class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " print('1 + 2 =', add(1, 2))\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While this may be useful for grouping certain methods together we don't gain any new functionality. We could have simply defined the functions in separate module.\n", - "\n", - "
\n", - "\n", - "### Static Methods\n", - "\n", - "If we create an instance of `Calculator` we cannot use its methods, as the instance object is also passed as an argument (we will come back to this)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of the class\n", - "calc = Calculator()\n", - "\n", - "try:\n", - " print(calc.add(1, 2))\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To avoid this problem we can declare that the methods are *static*. This can be done using the `@staticmethod` decorator, which essentially tells the method not to expect the instance object as a first argument." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class staticCalculator:\n", - " \n", - " @staticmethod\n", - " def add(x, y):\n", - " return x + y\n", - " \n", - " @staticmethod\n", - " def substract(x, y):\n", - " return x - y\n", - " \n", - " @staticmethod\n", - " def multiply(x, y):\n", - " return x * y\n", - " \n", - " @staticmethod\n", - " def divide(x, y):\n", - " return x / y\n", - " \n", - "# Print the class dictionary\n", - "print(staticCalculator.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the methods are accessable direcly from the class or from an instance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an instance of the class\n", - "calc = staticCalculator()\n", - "print('1 + 2 =', staticCalculator.add(1, 2))\n", - "print('1 + 2 =', calc.add(1, 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is an improvement, but we are still not getting much from the class that we could not already get from individual functions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Class Methods\n", - "\n", - "Similarly to static methods, we can use the `@classmethod` decorator to define a class method. This tells the method to expect the class object (`cls`) as the first argument to a given method rather than an instance object. This means class methods have access to all class attributes.\n", - "\n", - "In the following example we define a class for implementing the Stefan-Boltzmann law. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class StefBoltz:\n", - " \"\"\" The Stefan–Boltzmann law\n", - " \"\"\"\n", - " \n", - " # The Stefan-Boltzmann constant\n", - " sigma = 5.670367e-8 # Wm^−2K^−4\n", - " \n", - " @classmethod\n", - " def luminosity(cls, radius, effective_temp):\n", - " \n", - " return 4 * pi * radius ** 2 * cls.sigma * effective_temp ** 4\n", - " \n", - " @classmethod\n", - " def radius(cls, luminosity, effective_temp):\n", - " \n", - " return sqrt(luminosity / (4 * pi * cls.sigma * effective_temp ** 4))\n", - " \n", - " @classmethod\n", - " def temperature(cls, radius, luminosity):\n", - " \n", - " return (luminosity / (4 * pi * radius ** 2 * cls.sigma)) ** 0.25\n", - " \n", - "# Print the class dictionary\n", - "print(StefBoltz.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that all three methods have access to the class attribute `sigma`, which means that this variable does not have be redefined or passed to each method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('The luminosity of the Sun is {:.2e}W'.format(StefBoltz.luminosity(7e8, 5800)))\n", - "print('The radius of Alpha Centauri is {:.2e}m'.format(StefBoltz.radius(4e28, 9700)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Class methods can also be called by class instances." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sb = StefBoltz()\n", - "print('The effective temperature of the Earth is {:.2f}K'\n", - " ''.format(sb.temperature(6.37e6, 1.75e+17)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, class methods can also access static methods and/or other class methods, meaning that a more complicated class structure can be designed that does not require instantitaion." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Newton:\n", - " \n", - " @staticmethod\n", - " def velocity(displacement, time):\n", - " return displacement / time\n", - " \n", - " @classmethod\n", - " def acceleration(cls, displacement, time):\n", - " return cls.velocity(displacement, time) / time\n", - " \n", - " @classmethod\n", - " def force(cls, mass, displacement, time):\n", - " return mass * cls.acceleration(displacement, time)\n", - " \n", - "print('Force = {}N'.format(Newton.force(5, 2, 1)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While this certainly adds some new functionality, we are still not using class methods to their full potential.\n", - "\n", - "
\n", - "\n", - "### Instance Methods\n", - "\n", - "In the previous section you learned how to instantiate a class using the `self` variable. You also saw that (if we don't add a special decorator) when an instance calls a method it expects an extra agrument. You might have already guessed that this argument is `self` and by defining methods with this argument we can pass instance attributes.\n", - "\n", - "This is the default and arguably most powerful way of using methods. In the following example we demonstrate a very simply class for doubling and tripling an input value.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class simpleClass:\n", - " \n", - " def __init__(self, value):\n", - " self.value = value\n", - " \n", - " def double(self):\n", - " return self.value * 2\n", - " \n", - " def triple(self):\n", - " return self.value * 3\n", - "\n", - "# Create a class instance\n", - "sc1 = simpleClass(3)\n", - "sc2 = simpleClass(7)\n", - "print('Instance 1:', sc1.value, sc1.double(), sc1.triple())\n", - "print('Instance 2:', sc2.value, sc2.double(), sc2.triple())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This may seem trivial, but this allows us to use methods that are automatically tailored to the class instance. The input value only needs to passed once, meaning the code is easier to debug and a single Python object ends up with a lot of functionality (*i.e.* encapsulation).\n", - "\n", - "
\n", - "\n", - "### Mixing Methods\n", - "\n", - "In the next example we show that various class methods can be combined to provide more complex behaviour." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class listHandler:\n", - " \"\"\" List Handler\n", - " \n", - " Class for handling list properties.\n", - " \n", - " \"\"\"\n", - " \n", - " def __init__(self, mylist):\n", - " \n", - " self._list = mylist\n", - " self.get_length()\n", - " self.get_first()\n", - " self.get_last()\n", - " \n", - " @staticmethod\n", - " def _get_length(_list):\n", - " \n", - " return len(_list)\n", - " \n", - " @staticmethod\n", - " def _get_element(_list, index):\n", - " \n", - " return _list[index]\n", - " \n", - " @classmethod\n", - " def _get_first(cls, _list):\n", - " \n", - " return cls._get_element(_list, 0)\n", - " \n", - " @classmethod\n", - " def _get_last(cls, _list):\n", - " \n", - " return cls._get_element(_list, -1)\n", - " \n", - " def get_length(self):\n", - " \n", - " self.len = self._get_length(self._list)\n", - " \n", - " def get_first(self):\n", - " \n", - " self.first = self._get_first(self._list)\n", - " \n", - " def get_last(self):\n", - " \n", - " self.last = self._get_last(self._list)\n", - "\n", - "print('listHandler.__dict__', listHandler.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Note that an underscore is the convention used to denote a \"private\" attribute. These attributes can be accessed in the usual way, but it is assumed that the user should not need to (*i.e.* abstraction)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lh = listHandler(list(range(10)))\n", - "print('Length:', lh.len)\n", - "print('First:', lh.first)\n", - "print('Last:', lh.last)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a very complicated way of doing something simple, but it demonstrates how static, class and instance methods can be used together." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5 Properties\n", - "---\n", - "\n", - "Now that you are familiar with basic class attributes and methods we can look at class properties.\n", - "\n", - "
\n", - "\n", - "### Getters and Setters\n", - "\n", - "As you have already seen, in Python attributes can direcly be assigned to classes or instances. In some cases, however, it may be useful to formally define methods for getting and setting attributes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class myClass:\n", - " \n", - " def __init__(self):\n", - " self.myattr = None\n", - " \n", - " def get_attr(self):\n", - " return self.myattr\n", - " \n", - " def set_attr(self, value):\n", - " self.myattr = value\n", - " \n", - "myinst = myClass()\n", - "print('myinst.myattr =', myinst.get_attr())\n", - "myinst.set_attr(5)\n", - "print('myinst.myattr =', myinst.get_attr())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This may seem silly, as we could have just as easily done the following to get the same result without defining two additional methods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "myinst = myClass()\n", - "print('myinst.myattr =', myinst.myattr)\n", - "myinst.myattr = 5\n", - "print('myinst.myattr =', myinst.myattr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But now let's assume that we want `myattr` to be a positive integer, but returned as a float and if you attemp to set something else the code will raise an exception." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class myClass:\n", - " \n", - " def __init__(self):\n", - " self.myattr = 1\n", - " \n", - " def get_attr(self):\n", - " return float(self.myattr)\n", - " \n", - " def set_attr(self, value):\n", - " if not isinstance(value, int) or value < 1:\n", - " raise ValueError('myattr must be a positive integer! '\n", - " 'Input value {} is of type {}.'.format(value, type(value)))\n", - " self.myattr = value\n", - " \n", - "myinst = myClass()\n", - "print('myinst.myattr =', myinst.get_attr())\n", - "myinst.set_attr(5)\n", - "print('myinst.myattr =', myinst.get_attr())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that everything works as before, but when we try setting a negative integer " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " myinst.set_attr(-4)\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "or a float the code raises an exception." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " myinst.set_attr(5.6)\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that getters and setters provide a means of adding conditions to the assignment or retrieval of an attribute.\n", - "\n", - "
\n", - "\n", - "### Class Properties\n", - "\n", - "In the previous example we saw that we could add conditions to the assignment of an attribute using getters and setters, however there is nothing to prevent a user from directly accessing the attribute and assigning an inccorect value. One way to help avoid this is by using the special `property` decorator." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class myClass:\n", - " \n", - " def __init__(self):\n", - " self._myattr = 1\n", - " \n", - " def get_attr(self):\n", - " return float(self._myattr)\n", - " \n", - " def set_attr(self, value):\n", - " if not isinstance(value, int) or value < 1:\n", - " raise ValueError('myattr must be a positive integer! '\n", - " 'Input value is of type {}.'.format(type(value)))\n", - " self._myattr = value\n", - " \n", - " myattr = property(get_attr, set_attr)\n", - " \n", - "myinst = myClass()\n", - "print('myinst.myattr =', myinst.myattr)\n", - "myinst.myattr = 5\n", - "print('myinst.myattr =', myinst.myattr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This allows us to return to the more natural syntax of simply assigning a value to the attribute via the intermediate private attribute `_myattr`.\n", - "\n", - "We can rewrite the same class in the following way for a simpler implementation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class myClass:\n", - " \n", - " def __init__(self):\n", - " self._myattr = 1\n", - " \n", - " @property\n", - " def myattr(self):\n", - " return float(self._myattr)\n", - " \n", - " @myattr.setter\n", - " def myattr(self, value):\n", - " if not isinstance(value, int) or value < 1:\n", - " raise ValueError('myattr must be a positive integer! '\n", - " 'Input value {} is of type {}.'.format(value, type(value)))\n", - " self._myattr = value\n", - " \n", - "myinst = myClass()\n", - "print('myinst.myattr =', myinst.myattr)\n", - "myinst.myattr = 5\n", - "print('myinst.myattr =', myinst.myattr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which retains all of the same functionality." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " myinst.myattr = 5.6\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6 Operator Overloading\n", - "---\n", - "\n", - "Now that you have mastered the basic properties of classes you may want to add more functionality. One way of doing so is to customise your class by defining some special methods that overload standard Python operators.\n", - "\n", - "
\n", - "\n", - "### Class String\n", - "\n", - "As you are no doubt aware you can print any Python object, however the output is not always user friendly. For example, if we were print a class instance..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class myClass:\n", - " pass\n", - "\n", - "print(myClass())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... the output tells us the name of the class but little more.\n", - "\n", - "The special `__str__` method can be used to customise this output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Wolf:\n", - " \n", - " def __init__(self, age, name):\n", - " self.age = age\n", - " self.name = name\n", - " \n", - " def __str__(self):\n", - " return 'A {} year old wolf named {}'.format(self.age, self.name)\n", - "\n", - "inst1 = Wolf(3, 'Grey Wind')\n", - "inst2 = Wolf(2, 'Nymeria')\n", - "\n", - "print('inst1 =', inst1)\n", - "print('inst2 =', inst2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This can be used to provide the user with details of how the class was instantiated.\n", - "\n", - "
\n", - "\n", - "### Class Representation\n", - "\n", - "Similar to `__str__`, `__repr__` can be provided to customise the class representation. This is intended to be more explicit and more for the benefit of developers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Wolf:\n", - " \n", - " def __init__(self, age, name):\n", - " self.age = age\n", - " self.name = name\n", - " \n", - " def __repr__(self):\n", - " return 'Wolf({}, \"{}\")'.format(self.age, self.name)\n", - "\n", - "inst1 = Wolf(3, 'Lady')\n", - "inst2 = Wolf(2, 'Ghost')\n", - "\n", - "print('inst1 =', repr(inst1))\n", - "print('inst2 =', repr(inst2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Note: The representation is what is called simply by typing the object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inst1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Mathematical Operators\n", - "\n", - "In some cases we may need to define ways in which class instances can interact with each other. For example, what happens if we add two instances or multiply them? How can we determine if a given intance is bigger or smaller than another?\n", - "\n", - "This can be occomplished using special methods such as `__add__`, `__mul__`, `__lt__` and `__gt__` (see the [Python data model](https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types) for a more comprehensive list)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Building:\n", - " \n", - " def __init__(self, name, year, height):\n", - " self.name = name\n", - " self.year = year\n", - " self.height = height\n", - " \n", - " def __add__(self, inst):\n", - " \n", - " return self.height + inst.height\n", - " \n", - " def __mul__(self, inst):\n", - " \n", - " return self.height * inst.height\n", - " \n", - " def __lt__(self, inst):\n", - " \n", - " return self.height < inst.height\n", - " \n", - " def __gt__(self, inst):\n", - " \n", - " return self.height > inst.height\n", - "\n", - "inst1 = Building('Burj Khalifa', 2010, 828)\n", - "inst2 = Building('Empire State Building', 1931, 381)\n", - "\n", - "print('inst1.height =', inst1.height)\n", - "print('inst2.height =', inst2.height)\n", - "print('inst1 + inst2 =', inst1 + inst2)\n", - "print('inst1 * inst2 =', inst1 * inst2)\n", - "print('inst1 < inst2 =', inst1 < inst2)\n", - "print('inst1 > inst2 =', inst1 > inst2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calling\n", - "\n", - "It is even possible to define a `__call__` method for a class instance. This effectively allows the instance to behave like a function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Greeting:\n", - " \n", - " def __init__(self, greet):\n", - " \n", - " self._greet = greet\n", - " \n", - " def __call__(self, name):\n", - " \n", - " return '{} {}!'.format(self._greet, name)\n", - " \n", - "inst = Greeting('Howdy')\n", - "print(inst('Arnold'))\n", - "print(inst('Sylvester'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Further Options \n", - "\n", - "This mechanism is quite extensive. You will find there may different ways in which you can customise your class and we can't possibly cover all of them. If, however, you understand the principle you will find it very easy to extrapolate this to other special methods.\n", - "\n", - "Here is an example of some things that can be done." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class BirthDates:\n", - " \n", - " def __init__(self):\n", - " self._names = []\n", - " self._years = []\n", - " \n", - " def add(self, name, year):\n", - " self._names.append(name)\n", - " self._years.append(year)\n", - " \n", - " # define the length of an instance\n", - " def __len__(self):\n", - " return len(self._names)\n", - " \n", - " # define what the instance contains\n", - " def __contains__(self, name):\n", - " return name in self._names\n", - " \n", - " # define what the indexing an instance returns\n", - " def __getitem__(self, index):\n", - " return (self._names[index], self._years[index])\n", - " \n", - "inst = BirthDates()\n", - "inst.add('Isaac Newton', 1643)\n", - "inst.add('Marie Curie', 1867)\n", - "inst.add('Albert Einstein', 1879)\n", - "\n", - "print('len(inst) =', len(inst))\n", - "print('Isaac Newton' in inst)\n", - "print('inst[1] =', inst[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7 Exercises\n", - "---\n", - "\n", - "1. Write a class to calculate the distance between two massive objects using using Newton's law of universal gravitation. \n", - " 1. Use it to determine the distance between the Earth and the Moon in kilometres. \n", - " 1. Then use it to determine the distance between the Earth and the Sun in kilometres.\n", - "\n", - "$$F=\\frac{Gm_1m_2}{r^2}$$\n", - "\n", - "\n", - "\n", - "> You may find the following values useful:\n", - "* Gravitational Constant: $G = 6.674\\times 10^{-11}~\\textrm{m}^3 \\textrm{kg}^{-1} \\textrm{s}^{-2}$\n", - "* Mass of the Earth: $M_\\oplus = 5.972\\times 10^{24}~\\textrm{kg}$ \n", - "* Mass of the Moon: $m = 7.348\\times 10^{22}~\\textrm{kg}$ \n", - "* Mass of the Sun: $M_\\odot = 1.989\\times 10^{30}~\\textrm{kg}$\n", - "* Force of attraction between the Earth and the Moon: $F = 1.986\\times 10^{20}~\\textrm{N}$\n", - "* Force of attraction between the Earth and the Sun: $F = 3.6\\times 10^{22}~\\textrm{N}$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your solution here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. Write a class that generates a numpy array of whole numbers from 0 to a given limit and has a method that returns a given metric on this array.\n", - " 1. Use it to return the median of an array of 20 values.\n", - " 1. Then use it to return the standard deviation of an array of 50 values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your solution here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. Write a class that can be used to store galaxy properties, in particular right ascension, declination and redshift. \n", - " 1. Make sure you only permit appropriate values for these parameters. \n", - " 1. Make it such that your class instances can be added and subtracted to create a new instance for which the all attributes are updated. \n", - " 1. Include an appropriate representation for your class instances. \n", - " 1. Finally, create instances to demonstrate that your class works." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your solution here" - ] - } - ], - "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": "3.7.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/python/Classes_II.ipynb b/python/Classes_II.ipynb deleted file mode 100644 index 0a74d6e..0000000 --- a/python/Classes_II.ipynb +++ /dev/null @@ -1,1038 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"CosmoStat\n", - "
\n", - "
\n", - "
\n", - "
\n", - "\n", - "# The Anatomy of a Python Class (Part II)\n", - "---\n", - "\n", - "> Author: Samuel Farrens \n", - "> Email: samuel.farrens@cea.fr \n", - "> Year: 2019 \n", - "> Version: 1.0\n", - "\n", - "---\n", - "
\n", - "\n", - "This notebook introduces some more advanced concepts in the manipulation of Python classes. Before starting you should make sure you have completed the [first notebook](./Classes_I.ipynb) or are at least familiar with the topics covered therein. As with the first part, this tutorial is in no way exhaustive and you are encouraged to suppliment your understanding with further reading.\n", - "\n", - "If you are new to Jupyter notebooks note that cells are executed by pressing SHIFT+ENTER (⇧+ ⏎). See the Jupyter documentation for more details.\n", - "\n", - "\n", - "\n", - "## Contents\n", - "\n", - "---\n", - "\n", - "1. [Set-Up](#1-Set-Up)\n", - "1. [Inheritance](#2-Inheritance)\n", - " 1. [Inherting Attributes](#Inherting-Attributes)\n", - " 1. [Overriding](#Overriding)\n", - " 1. [Handling Instantiation](#Handling-Instantiation)\n", - " 1. [Multiple Parents](#Multiple-Parents)\n", - " 1. [Method Resolution Order](#Method-Resolution-Order)\n", - "1. [Composition](#3-Composition)\n", - "1. [Abstract Classes](#4-Abstract-Classes)\n", - " 1. [Abstract Methods](#Abstract-Methods)\n", - " 1. [Abstract Properties](#Abstract-Properties)\n", - "1. [Exercises](#5-Exercises)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1 Set-Up\n", - "\n", - "The following cell contains some set-up commands. Be sure to execute this cell before continuing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Notebook Set-Up Commands\n", - "\n", - "def print_error(error):\n", - " \"\"\" Print Error\n", - " \n", - " Function to print exceptions in red.\n", - " \n", - " Parameters\n", - " ----------\n", - " error : string\n", - " Error message\n", - " \n", - " \"\"\"\n", - " print('\\033[1;31m{}\\033[1;m'.format(error))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1 Inheritance\n", - "\n", - "---\n", - "\n", - "A powerful property of classes is the ability to inherit attributes and methods from other classes. This helps avoid repetition of code, which makes it easier to develop and maintain. \n", - "\n", - "
\n", - "\n", - "### Inherting Attributes\n", - "\n", - "We will begin by defining a simple class with a single static method that will act as a *parent* class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a parent class\n", - "class Parent:\n", - " \n", - " @staticmethod\n", - " def add(a, b):\n", - " return a + b\n", - "\n", - "# Print the parent dictionary\n", - "print('Parent.__dict__ =', Parent.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that this class has no special properties, it the same as classes we saw in the previous notebook.\n", - "\n", - "Now we will define a second class, also with a single static function. This class will serve as a *child* class that will inherit from the parent class. This is done by simply passing the parent class name in `()` when defining the child class. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a child class\n", - "class Child(Parent):\n", - " \n", - " @staticmethod\n", - " def subtract(a, b):\n", - " return a - b\n", - "\n", - "print('Child.__dict__ =', Child.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the contents of the child class dictionary are only what is expected from a normal class definition. Using the special `__bases__` attribute, however, we can see a tuple of classes that the child inherits from." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Child.__bases__ =', Child.__bases__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can demonstrate that the child has indeed inherited new attributes from the parent as follows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('1 + 2 =', Child.add(1, 2))\n", - "print('3 - 2 =', Child.subtract(3, 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can clearly see that `Child` has inhertited the static `add` method from `Parent`.\n", - "\n", - "In the following example, we can see that this works for any type of class attribute." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Parent:\n", - " \n", - " x = 1\n", - " \n", - "class Child(Parent):\n", - " \n", - " @classmethod\n", - " def show(cls):\n", - " return print('x =', cls.x)\n", - "\n", - "Child.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Overriding\n", - "\n", - "We saw in the previous notebook that class intance attributes will override class attributes of the same name. The same happens with child attributes with respect to parent attributes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Parent:\n", - " \n", - " x = 1\n", - " \n", - "class Child(Parent):\n", - " \n", - " x = 2\n", - " \n", - " @classmethod\n", - " def show(cls):\n", - " return print('x =', cls.x)\n", - "\n", - "Child.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hierachy\n", - "\n", - "It is possible to define a hierchy of parent classes, the attributes of which will be inheried by a given child class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class GrandParent:\n", - " \n", - " x = 1\n", - " name = 'Grandparent'\n", - "\n", - "class Parent(GrandParent):\n", - " \n", - " y = 2\n", - " name = 'Parent'\n", - " \n", - "class Child(Parent):\n", - " \n", - " z = 3\n", - " name = 'Child'\n", - "\n", - "print(Child.name, Child.x, Child.y, Child.z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that overriding will also act hierachically, meaning that the last class in the chain will be given precedence.\n", - "\n", - "
\n", - "\n", - "### Handling Instantiation \n", - "\n", - "If the parent class contains an `__init__` method, this too can be inherited by the child." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Parent:\n", - " \n", - " def __init__(self, value):\n", - " self.myattr = value\n", - " \n", - "class Child(Parent):\n", - " \n", - " def show(self):\n", - " return print('myattr =', self.myattr)\n", - " \n", - "Child('A string').show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In fact it also works the other way around." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Parent:\n", - " \n", - " def show(self):\n", - " return print('myattr =', self.myattr)\n", - " \n", - "class Child(Parent):\n", - " \n", - " def __init__(self, value):\n", - " self.myattr = value\n", - " \n", - "Child('A string').show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is because the child will lookup any attributes not listed in its own dictionary in all the parent (or base) classes.\n", - "\n", - "If both classes have an `__init__` method only the child class will be initialised (see [Overriding](#Overriding))." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Parent:\n", - " \n", - " def __init__(self):\n", - " self.pval = 'parent value'\n", - " \n", - "class Child(Parent):\n", - " \n", - " def __init__(self):\n", - " self.cval = 'child value'\n", - " \n", - "inst = Child()\n", - "print('cval =', inst.cval)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " print('pval =', inst.pval)\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can fix this by manually instantiating the parent class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Parent:\n", - " \n", - " def __init__(self):\n", - " self.pval = 'parent value'\n", - " \n", - "class Child(Parent):\n", - " \n", - " def __init__(self):\n", - " self.cval = 'child value'\n", - " Parent.__init__(self)\n", - " \n", - "inst = Child()\n", - "print('cval =', inst.cval)\n", - "print('pval =', inst.pval)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python has a useful shortcut to make this process easier using the [`super`](https://docs.python.org/3/library/functions.html?highlight=super#super)function. This allows us to instantiate the parent without naming it, this particularly useful if the name of the parent class were to change." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Parent:\n", - " \n", - " def __init__(self):\n", - " self.pval = 'parent value'\n", - " \n", - "class Child(Parent):\n", - " \n", - " def __init__(self):\n", - " self.cval = 'child value'\n", - " super().__init__()\n", - " \n", - "inst = Child()\n", - "print('cval =', inst.cval)\n", - "print('pval =', inst.pval)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multiple Parents\n", - "\n", - "It is possible for a child to inherit attributes from multiple parents." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Mother:\n", - " \n", - " def __init__(self, value):\n", - " self.mval = value\n", - " \n", - " def show_mother(self):\n", - " print('mother value =', self.mval)\n", - " \n", - "class Father:\n", - " \n", - " def __init__(self, value):\n", - " self.fval = value\n", - " \n", - " def show_father(self):\n", - " print('father value =', self.fval)\n", - " \n", - "class Child(Mother, Father):\n", - " \n", - " def __init__(self, value1, value2, value3):\n", - " self.cval = value3\n", - " Mother.__init__(self, value1)\n", - " Father.__init__(self, value2)\n", - " \n", - " def show_child(self):\n", - " print('child value =', self.cval)\n", - " \n", - "inst = Child(1, 2, 3)\n", - "inst.show_mother()\n", - "inst.show_father()\n", - "inst.show_child()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the parent classes were explicitly instantiated in the previous example. Attempting the same thing using the `super` function will raise an error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Mother:\n", - " \n", - " def __init__(self, value):\n", - " self.mval = value\n", - " \n", - " def show_mother(self):\n", - " print('mother value =', self.mval)\n", - " \n", - "class Father:\n", - " \n", - " def __init__(self, value):\n", - " self.fval = value\n", - " \n", - " def show_father(self):\n", - " print('father value =', self.fval)\n", - " \n", - "class Child(Mother, Father):\n", - " \n", - " def __init__(self, value1, value2):\n", - " self.cval = value2\n", - " super().__init__(value1)\n", - " \n", - " def show_child(self):\n", - " print('child value =', self.cval)\n", - " \n", - "inst = Child(1, 2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " inst.show_mother()\n", - " inst.show_father()\n", - " inst.show_child()\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see the `Mother` parent class was properly instantiated, but the `Father` parent class was not. The reasons for this are explained in the following subsection.\n", - "\n", - "> In general, caution should be used when building a class archtecture that requires inheritance for multiple parent classes.\n", - "\n", - "
\n", - "\n", - "### Method Resolution Order\n", - "\n", - "The Method Resolution Order (MRO) dictates the order in which the child class will search the base classes for a given attribute. We can see the MRO of the child class from the previous example using the `__mro__` attribute as follows." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Child.__mro__ =', Child.__mro__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the `Mother` class appears before the `Father` class in the MRO, hence the `super` method in `Child` instantiates `Mother`. \n", - "\n", - "In order to make the previous example work, we would need to add another `super` in the `__init__` of the `Mother` class as follows.\n", - "\n", - "> Note: Don't do this in your code!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Mother:\n", - " \n", - " def __init__(self, value):\n", - " self.mval = value\n", - " super().__init__(value)\n", - " \n", - " def show_mother(self):\n", - " print('mother value =', self.mval)\n", - " \n", - "class Father:\n", - " \n", - " def __init__(self, value):\n", - " self.fval = value\n", - " \n", - " def show_father(self):\n", - " print('father value =', self.fval)\n", - " \n", - "class Child(Mother, Father):\n", - " \n", - " def __init__(self, value1, value2):\n", - " self.cval = value2\n", - " super().__init__(value1)\n", - " \n", - " def show_child(self):\n", - " print('child value =', self.cval)\n", - " \n", - "inst = Child(1, 2)\n", - "inst.show_mother()\n", - "inst.show_father()\n", - "inst.show_child()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This, however, is not recommended as it adds unncessary ambiguity to the code and makes debugging extremely difficult.\n", - "\n", - "
\n", - "\n", - "## 3 Composition\n", - "---\n", - "\n", - "Inhertitance is not the only way in which a class can access attributes from other classes. This comes back one of main take-away messages of this tutorial, namely that everything in Python is an object and any object can be assigned as an attribute.\n", - "\n", - "This means that a class attribute can actually be an instance of another class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Composer:\n", - " \n", - " def __init__(self):\n", - " self.myattr = 'composer value'\n", - " \n", - "class myClass:\n", - " \n", - " def __init__(self):\n", - " self.comp = Composer()\n", - "\n", - "inst = myClass()\n", - "print(\"myattr =\", inst.comp.myattr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This can particularly useful as we can pass classes or class intances to our new class without knowing anything about the other classes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Star:\n", - " \n", - " def __init__(self):\n", - " self.whoami = 'I am a star!'\n", - " \n", - "class Galaxy:\n", - " \n", - " def __init__(self):\n", - " self.whoami = 'I am a Galaxy!'\n", - " \n", - "class myClass:\n", - " \n", - " def __init__(self, composer):\n", - " self.comp = composer()\n", - "\n", - "inst1 = myClass(Star)\n", - "inst2 = myClass(Galaxy)\n", - "print(\"whoami =\", inst1.comp.whoami)\n", - "print(\"whoami =\", inst2.comp.whoami)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can even achieve inheritance-like properties if we know the name of a given attribute that the composer class should have." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Star:\n", - " \n", - " def __init__(self):\n", - " self.whoami = 'I am a star!'\n", - " \n", - "class Galaxy:\n", - " \n", - " def __init__(self):\n", - " self.whoami = 'I am a Galaxy!'\n", - " \n", - "class myClass:\n", - " \n", - " def __init__(self, composer):\n", - " self.whoami = composer().whoami\n", - "\n", - "inst1 = myClass(Star)\n", - "inst2 = myClass(Galaxy)\n", - "print(\"whoami =\", inst1.whoami)\n", - "print(\"whoami =\", inst2.whoami)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Note: This will break if the composer does not have an attribute called `whoami`.\n", - "\n", - "Composition also alows preinitialised class instances to be passed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Star:\n", - " \n", - " def __init__(self, mag):\n", - " self.mag = mag\n", - " \n", - "class Galaxy:\n", - " \n", - " def __init__(self, mag):\n", - " self.mag = mag\n", - " \n", - "class myClass:\n", - " \n", - " def __init__(self, composer):\n", - " self.comp = composer\n", - " self.mag = self.comp.mag\n", - "\n", - " \n", - "star = Star(11.05)\n", - "inst1 = myClass(star)\n", - "inst2 = myClass(Galaxy(8.2))\n", - "print(\"mag =\", inst1.mag)\n", - "print(\"mag =\", inst2.mag)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This can be useful, especially if we want to be able to change the original instance attributes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "inst1.mag = 12.3\n", - "inst1.comp.mag = 13.1\n", - "print(\"inst1 mag =\", inst1.mag)\n", - "print(\"star mag =\", star.mag)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the `mag` attribute of the instance `star` changed!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4 Abstract Classes\n", - "---\n", - "\n", - "Abstract classes are classes that contain abstract methods. So it makes sense to move right along and explain what an abstract method is.\n", - "\n", - "
\n", - "\n", - "### Abstract Methods\n", - "\n", - "An abstract method is a method that is defined but not implemented. This is useful for definining parent classes that impose some conditions on the child." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from abc import ABC, abstractmethod\n", - "\n", - "class Parent(ABC):\n", - " \n", - " @abstractmethod\n", - " def get_id(self):\n", - " pass\n", - "\n", - "print ('Parent.__dict__', Parent.__dict__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the method `get_id` has been defined but does nothing.\n", - "\n", - "We now define a child class that inherits this parent." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Child(Parent):\n", - " \n", - " def __init__(self):\n", - " self.whoami = 'I am a child!'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " inst = Child()\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This raises an error when trying to create an instance. This is because the child class needs to override the abstract method `get_id`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Child(Parent):\n", - " \n", - " def __init__(self):\n", - " self.get_id()\n", - " \n", - " def get_id(self):\n", - " self.whoami = 'I am a child!'\n", - " \n", - "inst = Child()\n", - "print(inst.whoami)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This allows the parent class, which may be written with no knowledge of the properties of the child class, to impose some conditions on what a given child class should do.\n", - "\n", - "
\n", - "\n", - "### Abstract Properties\n", - "\n", - "In the previous notebook we learned how class properties can be defined to impose conditions on the values of certain class attributes. Using Abstract methods we can combine these tools to ensure that a given class has the properties we want.\n", - "\n", - "We start by defining an abstract class with the abstract property `whoami` and its corresponding setter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class AstroObject(ABC):\n", - " \n", - " @property\n", - " @abstractmethod\n", - " def whoami(self):\n", - " pass\n", - " \n", - " @whoami.setter\n", - " @abstractmethod\n", - " def whoami(self, value):\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then define a child class that inherits this abstract class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Star(AstroObject):\n", - " \n", - " def __init__(self):\n", - " self.whoami = 'I am a star!'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " star = Star()\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that while a value for the attribute is provided the property is not explicitly defined. This way we can impose that the `Star` class must define a getter and setter for the attribute `whoami`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class Star(AstroObject):\n", - " \n", - " def __init__(self):\n", - " self.whoami = 'I am a star!'\n", - " \n", - " @property\n", - " def whoami(self):\n", - " return self._whoami\n", - " \n", - " @whoami.setter\n", - " def whoami(self, value):\n", - " if not isinstance(value, str):\n", - " raise ValueError('whoami must be a string')\n", - " self._whoami = value\n", - "\n", - "star = Star()\n", - "print(\"whoami =\", star.whoami)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This in turn could be used in conjunction with what we learned about class composition to ensure that composers have the properties needed to work with the child class." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5 Exercises\n", - "---\n", - "\n", - "1. Create a parent class and a child class that can identify its progenitor. \n", - " 1. Your parent class should have the class attribute `parent_name` with the value of your choice.\n", - " 1. Your child class should have the attribute `name` with the value of your choice.\n", - " 1. Printing an instance of your child class should contain its name and its parent's name. *e.g.* \n", - " \n", - " ```python\n", - " print(Child('Thor'))\n", - " Thor Odinson\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your solution here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. Define a class that can be initialised with composer classes that have been constrained by an abstract class.\n", - " 1. Define an abstract class called `EarthAttr` that has the abstract method `whatami`, whcih should return a string of your choice.\n", - " 1. Define at least two composer classes (*e.g.* `Moon` and `Core`) that satisfy the requirements of `EarthAttr`.\n", - " 1. Define a class called `Earth` that composes these classes to get the `whatami` attribute.\n", - " 1. Printing an instance of your `Earth` class should include the value of `whatami`. *e.g.*\n", - " \n", - " ```python\n", - " print(Earth(Moon))\n", - " The Earth has a moon!\n", - " ```\n", - " \n", - " 5. Finally, define a final composer class (*e.g.* `Lake`) and demonstrate that it will not instantiate if not correctly constrained by `EarthAttr`. You should get the following error:\n", - " \n", - " ```bash\n", - " 'Cant instantiate abstract class Lake with abstract methods whatami'\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your solution here" - ] - } - ], - "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": "3.7.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/python/Intro-2.ipynb b/python/Intro-2.ipynb deleted file mode 100644 index 4414769..0000000 --- a/python/Intro-2.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to Python \n", - "## Tutorial 2: Intermediate and Advanced Topics\n", - "\n", - "by Samuel Farrens and Santiago Casas\n", - "- 16/07/2019 first version\n", - "\n", - "![](https://www.python.org/static/community_logos/python-logo.png)\n", - "\n", - "## Contents\n", - "\n", - "1. [Pythonic Thinking](./Pythonic.ipynb)\n", - "\n", - "1. [Numpy](./Numpy-Intro.ipynb)\n", - "\n", - "1. [Pandas](./Pandas-Intro.ipynb)\n", - "\n", - "1. [Scipy-Cosmo](./Scipy-Cosmo.ipynb)\n", - "\n", - "1. [The Anatomy of a Python Class: Part I](./Classes_I.ipynb)\n", - "\n", - "1. [The Anatomy of a Python Class: Part II](./Classes_II.ipynb)\n", - "\n", - "1. [Anwers to Exercises](./Answers-to-exercises-2.ipynb)" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/python/Intro.ipynb b/python/Intro.ipynb deleted file mode 100644 index 796d04d..0000000 --- a/python/Intro.ipynb +++ /dev/null @@ -1,51 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to Python\n", - "## Tutorial 1: Beginner Topics\n", - "\n", - "by Samuel Farrens\n", - "- 2019 python 3 revision\n", - "- 2017 first version\n", - "\n", - "![](https://www.python.org/static/community_logos/python-logo.png)\n", - "\n", - "## Contents\n", - "\n", - "1. [Native Python](./Native.ipynb)\n", - "\n", - "1. [Basic Numpy](./Numpy.ipynb)\n", - "\n", - "1. [Matplotlib](./Matplotlib.ipynb)\n", - "\n", - "1. [Other Packages](./Other-Packages.ipynb)\n", - "\n", - "1. [Anwers to Exercises](./Answers-to-exercises.ipynb)" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/python/Matplotlib.ipynb b/python/Matplotlib.ipynb deleted file mode 100644 index 0b5c58e..0000000 --- a/python/Matplotlib.ipynb +++ /dev/null @@ -1,457 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 3 Matplotlib\n", - "\n", - "---\n", - "\n", - "> Author: Samuel Farrens \n", - "> Year: 2017 \n", - "> Email: [samuel.farrens@gmail.com](mailto:samuel.farrens@gmail.com) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", - "To get started with your research you will most likely want to be able to plot your results. After installing numpy, the second package you will want to install is Matplotlib.\n", - "\n", - "As with numpy, there is plenty of documentation and examples on the matplotlib website. Much more than I could put in this notebook, so these examples are just meant to get you started.\n", - "\n", - "---\n", - "\n", - "## Contents\n", - "\n", - "1. [Installation](#Installation)\n", - "1. [Importing Matplotlib](#Importing-Matplotlib)\n", - "1. [Line Plots](#Line-Plots)\n", - "1. [Sub-Plots](#Sub-Plots)\n", - "1. [Histograms](#Histograms)\n", - "1. [Matrix Images](#Matrix-Images)\n", - "1. [Exercises](#Exercises)\n", - " * [Exercise 3.1](#Exercise-3.1)\n", - " * [Exercise 3.2](#Exercise-3.2)\n", - "\n", - "---\n", - "\n", - "## Installation\n", - "\n", - "To install matplotlib simply run the following command in a terminal (providing you have already set up pip)\n", - "\n", - "```bash\n", - "\n", - "$ pip install matplotlib\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing Matplotlib\n", - "\n", - "To import numpy use the **`import`** command as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In general, you will not need to use the majority of the vast number of modules available in matplotlib. Therefore, it is convenient to directly import the modules you intend to use such as **`pyplot`** (matplotlib.pyplot)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**TIP:** When working in a Jupyter notebook like this one, it is very handy to display the plots inside the notebook. This can be done with the following \"magic\" **`inline`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " > **NOTE:** this will only work in a Jupyter (iPython) notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Line Plots\n", - "\n", - "The most basic command in **`pyplot`** is **`plot()`**, which can be used to plot lines or points from lists or arrays.\n", - "\n", - "For example, to plot $x^2$ as a function of $x$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import numpy\n", - "import numpy as np\n", - "\n", - "# create a range of points in x from 0 to 1\n", - "x = np.arange(10) * 0.1\n", - "\n", - "# calculate x^2\n", - "y = x ** 2\n", - "\n", - "# set the figure size\n", - "plt.figure(figsize=(8, 6))\n", - "\n", - "# plot x vs x^2 with a blue (b) line (-)\n", - "plt.plot(x, y, 'b-')\n", - "\n", - "# set the x axis label with a fontsize of 20\n", - "plt.xlabel('$x$', fontsize=20)\n", - "# set the y axis label with a fontsize of 20\n", - "plt.ylabel('$x^2$', fontsize=20)\n", - "\n", - "# display the plot\n", - "plt.show()\n", - "\n", - "# close the plot\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the `'b-'` option specifies that the plot shoudl display a blue line. The same data could be plotted as red circles using the option `'ro'` as follows " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# set the figure size\n", - "plt.figure(figsize=(8, 6))\n", - "\n", - "# plot x vs x^2 with a blue (b) line (-)\n", - "plt.plot(x, y, 'ro')\n", - "\n", - "# set the x axis label with a fontsize of 20\n", - "plt.xlabel('$x$', fontsize=20)\n", - "# set the y axis label with a fontsize of 20\n", - "plt.ylabel('$x^2$', fontsize=20)\n", - "\n", - "# display the plot\n", - "plt.show()\n", - "\n", - "# close the plot\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also plot multple lines in a single figure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# set the figure size\n", - "plt.figure(figsize=(8, 6))\n", - "\n", - "# plot x vs x^2 with a blue (b) line (-)\n", - "plt.plot(x, 3 * x ** 2, 'b:', label='$3x^2$')\n", - "plt.plot(x, 4 * x ** 2, 'r--', label='$4x^2$')\n", - "\n", - "# set the x axis label with a fontsize of 20\n", - "plt.xlabel('$x$', fontsize=20)\n", - "# set the y axis label with a fontsize of 20\n", - "plt.ylabel('$y$', fontsize=20)\n", - "\n", - "# display plot legend()\n", - "plt.legend(loc=0, fontsize=20)\n", - "\n", - "# display the plot\n", - "plt.show()\n", - "\n", - "# close the plot\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Sub-Plots\n", - "\n", - "The **`subplot()`** command can be used to display mutiple plots simultaneously." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# set the figure size\n", - "plt.figure(figsize=(12, 5))\n", - "\n", - "\n", - "# subplot row 1 of 1 column 1 of 2\n", - "plt.subplot(121)\n", - "\n", - "# plot x vs 2x with a blue (b) line (-)\n", - "plt.plot(x, 2 * x, 'm-')\n", - "plt.xlabel('$x$', fontsize=20)\n", - "plt.ylabel('$2x$', fontsize=20)\n", - "\n", - "# subplot row 1 of 1 column 2 of 2\n", - "plt.subplot(122)\n", - "plt.plot(x, 3 * x ** 3, 'g-')\n", - "plt.xlabel('$x$', fontsize=20)\n", - "plt.ylabel('$3x^3$', fontsize=20)\n", - "\n", - "# automatically adjust layout of subplots\n", - "plt.tight_layout()\n", - "\n", - "# display the plot\n", - "plt.show()\n", - "\n", - "# close the plot\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "# Histograms\n", - "\n", - "Histograms can be produced using the **`hist()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate some random data\n", - "x = np.random.randint(0, 9, 100)\n", - "\n", - "# set the figure size\n", - "plt.figure(figsize=(15, 6))\n", - "\n", - "# plot a histogram of the data with 10 bins (bars)\n", - "plt.subplot(121)\n", - "plt.hist(x, bins=10)\n", - "plt.title('Histogram of random values')\n", - "\n", - "# plot a histogram of the data with 10 bins (step)\n", - "plt.subplot(122)\n", - "plt.hist(x, bins=10, histtype='step')\n", - "plt.title('Histogram of random values')\n", - "\n", - "# display the plot\n", - "plt.show()\n", - "\n", - "# close the plot\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Matrix Images\n", - "\n", - "The **`imshow()`** command can be used to display the contents of a matrix as an image.\n", - "\n", - "For example, to create the Swedish flag you could do the following." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate and array of zeros\n", - "x = np.zeros((9, 15))\n", - "\n", - "# set certain values to one\n", - "x[4] = 1\n", - "x[:, 5] = 1\n", - "\n", - "# set the figure size\n", - "plt.figure(figsize=(12, 6))\n", - "\n", - "# plot the matrix image\n", - "plt.imshow(x, interpolation='none', cmap='plasma')\n", - "\n", - "# display the plot\n", - "plt.show()\n", - "\n", - "# close the plot\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the **`interpolation`** option specifies that each pixel should displayed independently and the **`cmap`** option sets the colour map." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Exercises\n", - "\n", - "### Exercise 3.1\n", - "\n", - "Generate an array of 1000 Gaussian distributed random numbers and plot a histogram of the array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 3.2\n", - "\n", - "Read in the data from the file `materials/marvel_movies.txt` and plot the film earnings against the ratings.\n", - "\n", - "> If you have not already done so, you will need to download the [Materials](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/sfarrens/notebooks/tree/master/Python/materials) directory and unzip it. \n", - "> You can use the following command in a terminal: \n", - "> ``` bash\n", - "> $ unzip materials.zip\n", - "> ``` \n", - "> **NOTE:** you need to place the `Materials` folder in the same directory as this notebook.\n", - "\n", - "- Display the points as magenta triangles\n", - "- Plot the earnings in log scale\n", - "- Make sure you label your axes and title your plot\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 3.3\n", - "\n", - "Generate a chequered image (*e.g.* like a chess board).\n", - "\n", - "* It should have $8\\times8$ patches\n", - "* half of the patches should be one colour and the other half a different colour\n", - "* Use and your favourite colour map" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find some example answers to the exercises [here](./Answers-to-exercises.ipynb).\n", - "\n", - "---\n", - "\n", - "> **Continue to [next topic](./Other-Packages.ipynb)**" - ] - } - ], - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/python/Native.ipynb b/python/Native.ipynb deleted file mode 100644 index eb3170b..0000000 --- a/python/Native.ipynb +++ /dev/null @@ -1,1578 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1 Native Python\n", - "\n", - "---\n", - "\n", - "> Author: Samuel Farrens \n", - "> Year: 2017 \n", - "> Email: [samuel.farrens@gmail.com](mailto:samuel.farrens@gmail.com) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", - "In the context of Python the term \"native\" refers to the core commands, operators and functions that come with a default installation. The following subsections will cover some of the most essential elements provided in native Python (version 3.5 or higher).\n", - "\n", - "---\n", - "\n", - "## Contents\n", - "\n", - "1. [Comments](#Comments)\n", - "1. [Operators](#Operators)\n", - " * [Mathematical Operators](#Mathematical-Operators)\n", - " * [Logic Operators](#Logic-Operators)\n", - " * [Scientific Notation](#Scientific-Notation)\n", - "1. [Built in Functions](#Built-in-Functions)\n", - " * [Print](#Print)\n", - " * [Variables](#Variables)\n", - " * [Integers and Floats](#Integers-and-Floats)\n", - " * [Strings](#Strings)\n", - " * [Type](#Type)\n", - " * [Boolean](#Boolean)\n", - " * [Absolute Value](#Absolute-Value)\n", - "1. [Arrays](#Arrays)\n", - " * [Lists](#Lists)\n", - " * [List Operations](#List-Operations)\n", - " * [Tuples](#Tuples)\n", - " * [Dictionaries](#Dictionaries)\n", - "1. [Loops](#Loops)\n", - " * [Nested Loops](#Nested-Loops)\n", - " * [List Comprehension](#List-Comprehension)\n", - "1. [Functions](#Functions)\n", - "1. [Classes](#Classes) \n", - " * [Class Type](#Class-Type)\n", - "1. [Reading and Writing Files](#Reading-and-Writing-Files)\n", - "1. [Exercises](#Exercises)\n", - " * [Execise 1.1](#Exercise-1.1)\n", - " * [Execise 1.2](#Exercise-1.2)\n", - " * [Execise 1.3](#Exercise-1.3)\n", - " \n", - "---\n", - "\n", - "## Comments\n", - "\n", - "Reading and understanding coding languages can be challenging especially when reading code written by someone else. One of the most important habits one should try to establish early on (and this goes for any language) is adding clear and detailed comments throughout the code. Python is no exception! This practive will improve the readability and faciliate the understanding of the code. \n", - "\n", - "Commenting in Python is very easy. Every comment line simply needs to begin with a hash (**`#`**)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This is an example of a comment." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively comments can be placed between single or double quotes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "''' This is another comment. '''\n", - "\"\"\" This is also a comment. \"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This latter approach is often implemented for documenting files (modules), functions (methods) and classes, but this will be discussed later." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Operators\n", - "\n", - "Native python supports various [mathematical operators](https://docs.python.org/2/reference/lexical_analysis.html#operators) (*e.g.* addition, subtraction, multiplication, division, *etc.*) and [logic operators](https://docs.python.org/2/library/stdtypes.html?highlight=boolean) (*e.g.* and, or, not, *etc.*).\n", - "\n", - "These operators do not require any declaration of variables. In fact, one can easily treat a given Python session like a sophisticated calculator.\n", - "\n", - "### Mathematical Operators\n", - "\n", - "The following table lists the basic mathematical operations in Python. These are the operations that most people will be familiar with and one simply needs to learn the appropriate symbols to perform these operations in Python.\n", - "\n", - "| Operation | Mathematical Operator | Python Operator |\n", - "|:--------------------------:|:---------------------:|:---------------:|\n", - "| Addition | $+$ | + |\n", - "| Subtraction | $-$ | - |\n", - "| Multiplication | $\\times$ | * |\n", - "| Division | $\\div$ | / |\n", - "| Exponentiation | $x^n$ | ** |\n", - "| Equality | $=$ | == |\n", - "| Inequality | $\\neq$ | != |\n", - "| Greater Than | $>$ | > |\n", - "| Less Than | $<$ | < |\n", - "| Greater Than or Equal to | $\\geq$ | >= |\n", - "| Less Than or Equal to | $\\leq$ | <= |\n", - "\n", - "Here are a few examples:\n", - "\n", - "$$1+1=2$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "1 + 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$12\\div4=3$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "12 / 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** integer division returns a float." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$3^2=9$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "3 ** 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$2<3$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "2 < 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to these standard operations, native Python includes some standard computational operations which are shown in the following table. These operations will be familiar to those with prior knowledge of virtually any programming language.\n", - "\n", - "| Operation | Python Operator |\n", - "|:--------------------------:|:---------------:|\n", - "| Modulo | % |\n", - "| Floor Division | // |\n", - "\n", - "The modulo operator returns the remainder of the division of one number by another. For example, in the following operation:\n", - "\n", - "$$5 \\div 2 = 2.5 = 2 \\text{ remainder } 1$$\n", - "\n", - "the remainder of the division of 5 by 2 is 1. So, in Python the modulo operator will return 1.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "5 % 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The floor division operator, on the other hand, returns the result of the division without the remainder. In other words, only the values before the decimal place are returned. So, for the previous example, the floor division operator will return 2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "5 // 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Logic Operators\n", - "\n", - "In Python logic or boolean operators return values that are either *True* or *False*. The following tables lists the logic operators available in native Python.\n", - "\n", - "| Python Operator |\n", - "|:---------------:|\n", - "| and |\n", - "| or |\n", - "| not |\n", - "\n", - "The **`and`** operator tests whether or not both statements are true." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "True and False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The **`or`** operator tests whether or not one of the statements is true." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "True or False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The **`not`** operator reverse the statement it preceedes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "True and not False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Scientific Notation\n", - "\n", - "Larger numbers can be inputted using scientific notation with the letter **`e`** followed by the power (n) which represents $10^n$. For example, for $n=5$\n", - "\n", - "$$200000 = 2 \\times 10^5$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "2e5 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Built-in Functions\n", - "\n", - "A default Python installation includes some standard [built-in functions](https://docs.python.org/2/library/functions.html). \n", - "\n", - "The following subsections will cover some of the most essential native Python functions.\n", - "\n", - "### Print\n", - "\n", - "The first lesson with any programming language is learing how to print output to the terminal. In python this is done with the **`print`** command. Text (strings of characters) are represented either by single (**`''`**) or double quotes (**`\"\"`**). For example, one can display the standard `\"Hello world!\"` output as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('Hello world!')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Hello world again!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Single and double quotes can be combined to display the inner set of quotes in the text." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('\"What is hell? I maintain that it is the suffering of being unable to love.\" ― Fyodor Dostoyevsky')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The print command also can be used to output the result of an operation. A comma (**`,`**) is used to separate the elements in a print statement." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('1 + 1 =', 1 + 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Variables\n", - "\n", - "In python virtually any combination of letters, numbers and undescores can be used to define a variable. The variable must, however, start with a letter. For example to set some variable $x = 1$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = 1\n", - "\n", - "print('x =', x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The variables can of course also be used in conjunction with operators." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = 1\n", - "y = 2\n", - "\n", - "print('x + y =', x + y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the simple examples above using very short variables names such as $x$ and $y$ if perfectly adequate, but it is generally good practice to use longer more expressive variable names in codes in order to avoid ambiguity and faciliate readability. For example," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "speed_of_light_c = 2.99e8 #m/s\n", - "mass_of_human_m = 79.5 #kg\n", - "\n", - "energy = mass_of_human_m * speed_of_light_c ** 2\n", - "\n", - "print('The energy of this human is', energy, 'Joules.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Apart from numbers, variables can also be used to reference other Python objects such as strings of text." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = 'Bob'\n", - "\n", - "print('Hi, my name is', x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Variables can be removed using the **`del`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = 17\n", - "print('x =', x)\n", - "\n", - "del x\n", - "print('x =', x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is important to note that variables are assigned as pointers to objects." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Integers and Floats\n", - "\n", - "The commands **`int()`** and **`float()`** can be used to convert numbers between integers (`int`) and floating point numbers (`float`, *i.e.* real numbers). Python by default sets any number with a decimal to a float and without a decimal to an int. So, the number $1$, for example, is an integer and the number $1.0$ is a float." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# x is an int\n", - "x = 1\n", - "\n", - "# y is a float\n", - "y = 1.0\n", - "\n", - "# convert x to a float and y to an int\n", - "float(x), int(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Strings\n", - "\n", - "As already discussed strings of characters are defined between single or double quotes, however numerical values can also be converted to strings using the command **`str()`**. This is useful because in Python strings can be concatenated using the **`+`** operator." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# x is an int\n", - "x = 7\n", - "\n", - "# convert x to a string and concatenate with other strings\n", - "string = 'Have you ever watched the ' + str(x) + ' Samuari?'\n", - "\n", - "# print the new string\n", - "print(string)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Type\n", - "\n", - "The **`type()`** command can be used to determine the \"type\" of object in question. For example, if a variable has been set and you wish to find out if the object it refers to is an int, a float or a string" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = 5.6\n", - "y = 'Bob'\n", - "z = 12\n", - "\n", - "print('x is a', type(x))\n", - "print('y is a', type(y))\n", - "print('z is a', type(z))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Boolean\n", - "\n", - "A boolean is a special data type that can only have one of two possible values **`True`** (1) or **`False`** (0). This data type can be very useful when performing logic operations. The function **`bool()`** can be used to convert the integers values $0$ and $1$ to boolean values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# x and y are ints\n", - "x = 1\n", - "y = 0\n", - "\n", - "# convert x and y to booleans\n", - "bool(x), bool(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The reverse process is also possible." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# x and y are booleans\n", - "x = True\n", - "y = False\n", - "\n", - "# convert x and y to ints\n", - "int(x), int(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Absolute Value\n", - "\n", - "The function **`abs()`** simply returns the absolute value of a number." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = -16.6\n", - "\n", - "print('The absolute value of', x, 'is', abs(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Arrays \n", - "\n", - "There are various ways to store multiple values in a single object. Some of the most basic types are **`list`**, **`tuple`** and **`dict`**." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### Lists\n", - "\n", - "Lists are mutable (*i.e.* can be changed) objects in Python and can be speficied using square brackets **`[]`** or converted using the **`list()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# x is a list\n", - "x = [1, 2, 3]\n", - "\n", - "print('x =', x)\n", - "print('This second element of x is', x[1])\n", - "print('')\n", - "\n", - "# change the second element of x\n", - "x[1] = 5\n", - "\n", - "print('This second element of x has been changed to', x[1])\n", - "print('x =', x)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "List (and tuple) objects also allow concatenation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = [1, 2]\n", - "b = [3, 4]\n", - "a + b" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### List Operations\n", - "\n", - "Python has many built in functions for dealing with lists." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = [9, 3, 4, 1, 2, 3, 7, 5, 4, 5, 2, 6]\n", - "\n", - "print('x =', x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can check the length of the list with the **`len()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('len(x) =', len(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find the maximum value in the list with the **`max()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('max(x) =', max(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find the minimum value in the list with the **`min()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('min(x) =', min(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can sum all of the elements in the list with the **`sum()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('sum(x) =', sum(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can check if a certain value is in the list with the **`in`** statement." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('3 in x:', 3 in x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also check if a certain value is not in the list with the **`not in`** statement." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('8 not in x:', 8 not in x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can add elements to a list using the **`append()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x.append(5)\n", - "\n", - "print('x =', x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find the index of a certain element in the list with the **`index()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('the index of value 7 is', x.index(7))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can count the number of times a certain value occurs in a list with the **`count()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('the value 5 occurs', x.count(5), 'times')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is possible to create lists of ordered numbers in a given range using the **`range()`** command. For example, to produce a list of numbers between 4 and 11 (*i.e.* the half open interval $[4, 12)$)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('integers between 4 and 11 are', list(range(4, 12)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nested Lists\n", - "\n", - "Most Python objects can be stored in lists such as strings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = ['this', 'is', 'a', 'list']\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "but also lists can be stored in lists" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = [[1, 2], [3, 4]]\n", - "\n", - "print('x =', x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To index items in a nested list you have to index the lists sequentially. For example, to retrieve the value `3`, you would take the first item (*i.e.* index 0) of the second list (*i.e.* index 1) as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('the first item of the second list is', x[1][0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tuples\n", - "\n", - "Tuples are similar to lists and are specified using brackets **`()`** or converted using the **`tuple()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = (1, 2, 3)\n", - "\n", - "print('x =', x)\n", - "print('This second element of x is', x[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Tuples, however, are immutable." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# you cannot change the elements of a tuple\n", - "x[1] = 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is possible to convert lists to tuples and vice versa." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# x is a list\n", - "x = [1, 2, 3]\n", - "\n", - "print('x is a', type(x))\n", - "print('')\n", - "\n", - "# convert x to a tuple\n", - "x = tuple(x)\n", - "\n", - "print('x is a', type(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If variable values are specified without any brackets and separated by commas then the object will automatically be a tuple" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = 1, 2, 3\n", - "\n", - "print('x =', x)\n", - "print('x is a', type(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "unless you define several variables at the same time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x, y, z = 1, 2, 3\n", - "\n", - "print('x =', x)\n", - "print('x is a', type(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dictionaries\n", - "\n", - "Dictionaries are used for storing *key : value* pairs and are specified using curly brackets **`{}`** or converted using the **`dict()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = {'day' : 15, 'month' : 'March', 'year' : 2017}\n", - "print('This cell was written on the ' + str(a['day']) + 'th of ' + a['month'] + ' ' + str(a['year']) + '.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Loops\n", - "\n", - "Loops can be implemented in a fairly familiar way (for those who have some coding experience in another language) using a **`for`** statement." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = [1, 2, 3, 4]\n", - "\n", - "for i in range(len(a)):\n", - " print('element', i, 'of a is', a[i])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here **`len`** provides the length of the list a and **`range`** produces a list of integers from 0 to 3 (*i.e.* the indices of the list). \n", - "\n", - "> **NOTE:** the indentation after the **`for`** statement line is essential. \n", - "\n", - "A more elegant way of implementing a loop would be to simply iterate through the list. Fortunately Python allows this." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = [1, 2, 3, 4]\n", - "\n", - "for value in a:\n", - " print('value =', value)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here `value` is simply a dummy variable.\n", - "\n", - "> **NOTE:** This also works for tuples.\n", - "\n", - "### Nested Loops\n", - "\n", - "All *code blocks* (begin and end statements) in Python are specified with indentation. For example, to implement a nested loop\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = [[1, 2], [3, 4]]\n", - "\n", - "for sublist in a:\n", - " print('sublist =', sublist)\n", - " for value in sublist:\n", - " print('value =', value)\n", - " print('')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the lines\n", - "\n", - "```Python\n", - "print('sublist =', sublist)\n", - "for value in sublist:\n", - "print('')\n", - "```\n", - "\n", - "all occur within the first **`for`** loop, while the line\n", - "\n", - "```Python\n", - "print('value =', value)\n", - "```\n", - "\n", - "occurs in the second **`for`** loop." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### List Comprehension\n", - "\n", - "Another elegant option provided in Python is list comprehension, which automatically creates a list object after iterating." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = [1, 2, 3, 4]\n", - "b = [value ** 2 for value in a]\n", - "print('b =', b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the operation iterates through the elements in the list `a`, stores each element in a dummy variable `value` and finally adds the square of the variable to a new list `b`.\n", - "\n", - "You can iterate through multiple lists using the **`zip()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = range(1, 5)\n", - "b = ['a', 'b', 'c', 'd']\n", - "\n", - "c = [i * j for i, j in zip(a, b)]\n", - "\n", - "print(c)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Functions\n", - "\n", - "Functions are an essential part of any programming language and should be used as often as possible to avoid repeating lines of code (which can lead to bugs).\n", - "\n", - "Functions (or methods) are defined using the **`def`** statement and results are returned with the **`return`** statement. For example, to define a function that adds two values you would do the following" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Here we define a function\n", - "def add(a, b):\n", - " \n", - " return a + b\n", - "\n", - "# Here we implement the function\n", - "print('3 plus 5 is', add(3, 5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** as with loops, the indentation after the **`def`** statement line is essential. Only lines indented after the function definition will be included in the function. \n", - "\n", - "\n", - "Here is another example function that multiplies three numbers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def multiply(a, b, c):\n", - " \n", - " \"\"\" Multiply\n", - " \n", - " This function mutliplies three input values.\n", - " \n", - " This is an example of how you can add comments to functions.\n", - " \n", - " \"\"\"\n", - " \n", - " return a * b * c\n", - "\n", - "print('The answer is', multiply(3, 7, 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Virtually all Python objects can be passed to functions including other functions. For example, we can pass the `add` function we just defined to a new function as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def operate(a, b, func):\n", - " \n", - " return func(a, b)\n", - "\n", - "print('the operation \"add\" of 6 and 4 is', operate(6, 4, add))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Classes\n", - "\n", - "Classes can be an extremely powerful tool when used correctly in your code.\n", - "\n", - "Classes are defined using the **`class`** statement. A simple example of a class (which need not be a class) is given below" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Here we define a class\n", - "class exampleClass:\n", - " \"\"\"Example Class\n", - " \n", - " This is an example of how to add comments to a class.\n", - " \n", - " \"\"\"\n", - "\n", - " def __init__(self, a):\n", - " \n", - " self.a = a\n", - "\n", - " def add(self, b):\n", - " \"\"\" Add\n", - " \n", - " Add input to self.a and return result.\n", - " \n", - " \"\"\"\n", - " \n", - " return self.a + b\n", - "\n", - " def multiply(self, b):\n", - " \"\"\" Multiply\n", - " \n", - " Multiply input by self.a and return result.\n", - " \n", - " \"\"\"\n", - " \n", - " return self.a * b\n", - "\n", - "# Here we initalise a class instance\n", - "x = exampleClass(3)\n", - "\n", - "# Here we implement the class methods\n", - "print('add 7 gives', x.add(7))\n", - "print('')\n", - "print('multiply 4 gives', x.multiply(4))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example a class called `exampleClass` is defined. Then a special **`__init__`** method is defined that takes two arguments: the class instance **`self`** and an input value `a`. This method sets the value of the instance variable `self.a`. The **`add()`** method adds the value of `self.a` to an input value b. The **`multiply()`** method multiplies the value of `self.a` by an input value b.\n", - "\n", - "`x` is a class instance with `self.a=3`. \n", - "\n", - "All instance properties are accessible." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('self.a for instance x is', x.a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** as with loops and functions, the indentation after the **`class`** statement line is essential." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Reading and Writing Files\n", - "\n", - "There many ways to read and write information to and from files using Python. Personally, I prefer some of the options offered by numpy, which will be discussed in the next section, to those available in native Python. Therefore, I will only briefly mention how to read and write files here.\n", - "\n", - "> To run the following cells you will need to download the [Materials](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/sfarrens/notebooks/tree/master/Python/materials) directory and unzip it. \n", - "> You can use the following command in a terminal: \n", - "> ``` bash\n", - "> $ unzip materials.zip\n", - "> ``` \n", - "> **NOTE:** you need to place the `Materials` folder in the same directory as this notebook.\n", - "\n", - "You can read an ASCII (plain text) file using the **`open()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# open the text file with reading permission only\n", - "data_file = open('materials/star_wars.txt', 'r')\n", - "\n", - "# print each line in the file\n", - "for line in data_file:\n", - " print(line)\n", - "\n", - "# close the file\n", - "data_file.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can write to an ASCII file using the **`write()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# open the text file with writing permission\n", - "data_file = open('materials/empty.txt', 'w')\n", - "\n", - "# write string to the file\n", - "data_file.write('This is a text string')\n", - "\n", - "# close the file\n", - "data_file.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Exercises" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 1.1\n", - "\n", - "Write a function that implements the following equation.\n", - "\n", - "$$F = \\frac{Gm_1m_2}{r^2}$$\n", - "\n", - "where $G = 6.674 \\times 10^{−11}\\,\\textrm{N (m/kg)}^2$ and $1\\,\\textrm{N} = 1\\,\\textrm{kg m/s}^2$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write your function here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now use your function to calculate the force of attraction between the Earth and the Moon using the following facts:\n", - "\n", - "- Mass of the Earth: $5.9742 \\times 10^{24}\\,\\textrm{kg}$\n", - "- Mass of the Moon: $7.36 \\times 10^{22}\\,\\textrm{kg}$\n", - "- Distance between the Earth and the Moon: $384,402\\,\\textrm{km}$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement your function here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Hint:** think about how many inputs your funciton should have and what it should return." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 1.2\n", - "\n", - "Calculate the variance ($\\sigma^2$) of the following set of values using native Python only.\n", - "\n", - "$$x = [2, 4, 4, 4, 5, 5, 7, 9]$$\n", - "\n", - "Recall that\n", - "\n", - "$$\\sigma^2 = \\frac{\\sum(x-\\bar{x})^2}{N}$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check if the value you obtained for the variance is in $x$ and if so count how many times it occurs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 1.3\n", - "\n", - "Write a class for converting angles between degrees and radians. Your class should have one method to convert from \n", - "degrees to radians and another method to convert from radians to degrees." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write your class here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now use your class to do the following conversions:\n", - "\n", - "- 45° in radians\n", - "- 30° in radians\n", - "- 135° in radians\n", - "- $\\pi$ radians in degrees\n", - "- $2\\pi/3$ radians in degrees\n", - "- $5\\pi/6$ radians in degrees" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement your class here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find some example answers to the exercises [here](./Answers-to-exercises.ipynb).\n", - "\n", - "---\n", - "\n", - "> **Continue to [next topic](./Numpy.ipynb)**" - ] - } - ], - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/python/Numpy-Intro.ipynb b/python/Numpy-Intro.ipynb deleted file mode 100644 index 074e8ef..0000000 --- a/python/Numpy-Intro.ipynb +++ /dev/null @@ -1,1931 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\"CosmoStat\n", - "
\n", - "
\n", - "
\n", - "
\n", - "\n", - "# Numpy Intro\n", - "\n", - "---\n", - "\n", - "> Author: Santiago Casas \n", - "> Email: santiago.casas@cea.fr \n", - "> Year: 2019 \n", - "> Version: 1.0\n", - "\n", - "---\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far we have seen how to use and define *lists, dictionaries, functions* and some other *pythonic* tools. However, in scientific research one often needs more than simple algorithms and one needs specialized libraries for working with arrays, math functions, databases and graphics." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Let's start by importing the necessary libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It more convenient to assign the numpy package contents to an alias to avoid having longer expressions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the **`as`** statement assigns the numpy package contents to the object `np`.\n", - "\n", - "---\n", - "\n", - "## Arrays\n", - "\n", - "### The Basics\n", - "\n", - "The most essential numpy object is the numpy array (numpy.ndarray)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# a is a list\n", - "a = [1, 2, 3, 4]\n", - "print('a is', type(a))\n", - "\n", - "# b is a numpy array\n", - "b = np.array(a)\n", - "print('b is', type(b))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Accessing and printing a single entry works exactly the same." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('first element of a is', a[0])\n", - "print('first element of b is', b[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('last element of a is', a[-1])\n", - "print('last element of b is', b[-1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, their printed forms are slightly different." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('list: ', a)\n", - "print('np array: ',b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Moreover, while lists can contain different object types" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = [1, 1.0, 'a', True]\n", - "print(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "numpy arrays are of a single type only, which is one of the reasons why they are so efficient.\n", - "\n", - "---\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For example, in this case it will convert all entries to strings (upcasting)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.array(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or in this case, all entries to floats" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.array([1, 2.5, 23, 100.0, np.pi])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One can also specify the type directly with the optional argument **dtype** and the entries will be converted to the specified type." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.array([1, 2.5, 23, 100.0, np.pi], dtype='int32')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Did you notice the $\\pi$ constant in the list above? Here you can find a list of available (constants). Another one useful in science is" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Euler's constant\n", - "np.e" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating arrays from scratch" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sometimes it is useful to create a numpy array in a fast way from scratch. Numpy offers several neat methods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Zeros and Ones" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a length-10 integer array filled with zeros\n", - "np.zeros(10, dtype=int)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a length-5 floating-point array filled with ones\n", - "np.ones(5, dtype=float)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### A range and linspace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the most useful arrays for everyday scientific work is to create arrays that contain evenly-spaced numbers within an interval. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#A range of floats up to 3.0 with default step 1.0\n", - "print(np.arange(3.0))\n", - "# A range from start to stop, with a given step\n", - "print(np.arange(5.0, 405., 50))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** Note that with **`arange`** the endpoint is not included !\n", - "\n", - "Remember to check the documentation within the Jupyter notebook running on a cell: **`?np.arange`**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If one needs to specify the number of samples and also include the endpoint, then linspace is the right tool. It even contains an optional argument `endpoint`, which defaults to `True`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Three floats evenly spaced in the interval 0. to 3. \n", - "print(np.linspace(0.,3.,3))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#With endpoint=False, we get the same behavior as `np.arange`\n", - "print(np.linspace(0.,3.,3, endpoint=False))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#default number of samples is 50\n", - "np.linspace(0,100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another important array for scientists is a **logarithmically-spaced** interval. The default logarithm is base 10, but that can be changed with the `base` optional argument. The initial and final values of the interval have to be specified in their logarithms." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# A log10-spaced interval from 10^-2 to 10^3 of size 5.\n", - "np.logspace(-2, 3, 5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Applying a $\\log_{10}$ on the whole array shows that it is indeed log-spaced." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.log10(np.logspace(-2, 3, 5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Notice** how we are using `numpy` internal functions, called uFuncs to calculate properties on the entire array. We will explain that better a bit later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#A ln-spaced interval from e^-1 to e^4 of size 4.\n", - "e_array = np.logspace(np.log(np.exp(-1)), np.log(np.exp(4)), 4, base=np.e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 1:** What is the ouput of `np.log(e_array)[-1]` ?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Answer Puzzle 1:\n", - "#Uncomment to see the answer\n", - "#print('the result is: ', np.log(e_array)[-1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multi-dimensional arrays" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Arrays can also be multi-dimensional. And their shape can be specified at creation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#2-dimensional array of size 3x5\n", - "np.ones((3,5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creating the ***identity*** matrix of size 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.identity(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`np.eye` is a generalization of the identity, with arguments `numpy.eye(N, M=None, k=0, dtype=, order='C')`. `N` is the number of rows of the array, `M` defaults to `N` and is the number of columns, while `k` shifts the diagonal by a positive or negative integer with respect to the main diagonal. The other arguments can be looked up in the documentation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#rectangular matrix\n", - "np.eye(3,4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#shifted diagonal\n", - "np.eye(5, k=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sometimes one just needs an array with garbage numbers which is to be filled later on. `np.empty` does the job:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an uninitialized array of three integers\n", - "# The values will be whatever happens to already exist at that memory location\n", - "np.empty((3,6))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Very useful in science is the creation of arrays with **random numbers** following a given distribution. Check the extensive documentation of (numpy.random) for much more information on all the available methods." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a 3x3 array of normally distributed random values\n", - "# with mean 0 and standard deviation 1\n", - "np.random.normal(0, 1, (3, 3))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a 6x6 array of uniformly distributed\n", - "# random integers between 0 and 10\n", - "rand_mat = np.random.randint(0, 10, (6, 6))\n", - "print(rand_mat[:,0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One can create a random set of points following a Gaussian distribution with a given covariance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean = [0, 0]\n", - "cov = [[1, 0.5],\n", - " [0.5, 2]]\n", - "X = np.random.multivariate_normal(mean, cov, 1000)\n", - "X.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** We will see more details about matplotlib in the next session!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "#import seaborn; seaborn.set() # for plot styling\n", - "\n", - "plt.scatter(X[:, 0], X[:, 1]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To construct multi-dimensional arrays, one can also reshape 1-dimensional arrays, using the useful method `reshape(i,j)`. The arguments indicate the rows and the columns of the new array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert 1-dim array into 2x2 matrix\n", - "e_array.reshape((2,2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Reshape a 2-dim array\n", - "rand_mat.reshape(9,4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the second argument is `-1` then the size of the second axis is inferred from the previous array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rand_mat.reshape(3,-1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 2:** The attribute `shape` returns the shape of a numpy array in the form of a tuple. What is the output of `rand_mat.reshape(2,-1).shape[1]` ?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Answer Puzzle 2:\n", - "#Uncomment to see the answer\n", - "#print('the answer is: ', rand_mat.reshape(2,-1).shape[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another useful method is `np.ravel` which is roughly the \"inverse\" of reshape in this case. It returns a flattened 1-d array from a 2-d array, equivalent in most cases to `np.flatten`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "e_array.reshape((2,2)).ravel()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "e_array.reshape((2,2)).flatten()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And for scientific purposes, the ***transpose*** is a very important attribute" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "e_array.reshape((2,2)).T" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(e_array.reshape((2,2)).T)[0,1]==(e_array.reshape((2,2)))[1,0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Other available attributes are:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Number of dimensions, ndim: \", rand_mat.ndim)\n", - "print(\"Array shape:\", rand_mat.shape)\n", - "print(\"Array size: \", rand_mat.size)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Slicing and accessing elements" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you might now, slicing works for lists, using the `:` operator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list = [1,2,3,4,5,6]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Take the first two elements of the list\n", - "list[:2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(list[2:])\n", - "#Omitting the number after the semicolon is equivalent to indicating the list size:\n", - "list[2:6] == list[2:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Take every second element\n", - "list[::2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reverse the list\n", - "list[::-1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Slicing in visual form:\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we extend our `e_array` by concatenating it with the useful method `concatenate`, three times." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "extended_array = np.concatenate([e_array, e_array, e_array])\n", - "print(extended_array)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To obtain three times the same number, we just need to get every fourth element" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "extended_array[::4]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 3:** Obtain the array `[4., 4., 4.]` from `extended_array` by using slicing and `log`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Answers can be found in the Answers.ipynb" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** Slicing performs a view of the element and not a copy !" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Slicing: View vs. Copy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Nested list comprehension\n", - "array2d = np.array([[ ii+jj for jj in range((ii-1)*4,(ii-1)*4+5)] for ii in range(1,1+4)])\n", - "print(array2d)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For multi-dimensional arrays, the different axis are accessed by separating the slices with commas." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#All rows, every other column\n", - "array2d[:,::2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Fourth row, all columns\n", - "array2d[3,:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Obtain shape\n", - "array2d.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Obtain central array\n", - "array2d_center=array2d[1:array2d.shape[0]-1, 1:array2d.shape[1]-1]\n", - "print(array2d_center)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Now if we modify this subarray, we'll see that the original array is changed! \n", - "array2d_center[0,0]=1001.5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(array2d_center)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(array2d)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** Notice not only the big array was changed, but also how numpy converted automatically the type of the variable to `int`, to match the other variable types !" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the `Python Data Science Handbook`: This default behavior is actually quite useful: it means that when we work with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, if we don't want this behavior, because it can be confusing and introduce possible bugs (believe me, it has happened to me), we can use the `copy` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sub_array = array2d[2:,:3].copy()\n", - "print(sub_array)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Modify an element of the subarray\n", - "sub_array[1,1] = 999\n", - "print(sub_array)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now the large array is not touched:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(array2d)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Useful trick: Many elements of the large array can be modified through the small array using slicing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "array2d_center[:2,:2]=[[42,42],[42,42]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(array2d)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 4:** Change all the last two columns by zeros using slicing, np.shape and np.zeros (assuming you don't know the size of the array beforehand)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Answers.ipynb" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Numpy integrated universal functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The power of **numpy** lies in its speed and efficiency in performing operations on large arrays." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Functions that operate on the entire array are called ***universal functions*** or for short, uFuncs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Arithmetic" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.arange(5)\n", - "print(\"x =\", x)\n", - "print(\"x + 5 =\", x + 5)\n", - "print(\"x - 5 =\", x - 5)\n", - "print(\"x * 2 =\", x * 2)\n", - "print(\"x / 2 =\", x / 2)\n", - "print(\"x // 2 =\", x // 2) # floor division\n", - "print(\"-x = \", -x)\n", - "print(\"x ** 2 = \", x ** 2)\n", - "print(\"x % 2 = \", x % 2) # modulo" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These operators are actually wrappers to the method form:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.add(x,5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.floor_divide(x,2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "More functions like this can be found in the documentation of the uFuncs ." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Trigonometric functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Numpy can compute mathematical functions very efficiently over a large array. They are computed to withon machine precision, therefore tiny values instead of exact zeros can appear." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# An array of 3000 elements between 0 and Pi.\n", - "theta = np.linspace(0, np.pi, 3000)\n", - "print(\"theta = \", theta[0])\n", - "print(\"sin(theta) = \", np.sin(theta)[-1])\n", - "print(\"cos(theta) = \", np.cos(theta)[0])\n", - "print(\"tan(theta) = \", np.tan(theta)[-1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Numpy also offers mathematical functions like `exp` and `log` and versions that are more accurate for tiny numbers, like **`expm1`** and **`log1p`**. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For tiny x values, log(1+x) and exp(x)-1 are very very close to x.\n", - "x = np.array([0., 1e-10, 1e-12, 1e-14])\n", - "print(\" exp(x) - 1 =\", np.expm1(x))\n", - "print(\"std: exp(x) - 1 =\", np.exp(x)-1.0)\n", - "print(\" log(1 + x) =\", np.log1p(x))\n", - "print(\"std: log(1 + x) =\", np.log(1.0+x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are tons of functionalities more, but this is just a rough overview. In the documentation and many other excellent tutorials and books, such as the \n", - " Python Data Science Handbook one can find much more on these topics." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Slowness of loops and lists vs. Numpy " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In a standard programming language, like C, we would have to define the following non-pythonic function in order to compute the reciprocal of a list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_reciprocals(values):\n", - " output = []\n", - " for i in range(len(values)):\n", - " output.append(1.0 / values[i])\n", - " return output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a_list = np.random.randint(1,10,10000000).tolist() #notice the tolist method here\n", - "print(a_list[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Computing the reciprocal will be slow, since each element of the python list is a data structure object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "b_list = compute_reciprocals(a_list)\n", - "#print(b_list[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> `%time` is one of these magic functions we will see explained in the next session." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a_array = np.random.randint(1,10,10000000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In Numpy we don't even need to define a function, we just calculate 1.0/list." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "b_array = 1.0 / a_array\n", - "#print(b_array)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> 25 times faster?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Broadcasting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the most powerful features of `numpy` is, as we just saw, the fact that one can operate directly on entire arrays, element by element, without the need of cumbersome loops." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***Broadcasting*** is a way of applying uFuncs to arrays of different sizes. In the simplest case, addition, multiplication and so on, but actually it can be done with any uFunc that receives 2 arguments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a scalar to an array\n", - "s=5\n", - "ones = np.ones((3,5))\n", - "sixes = ones + s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(sixes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add two arrays\n", - "ones + sixes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** Lists do not behave like that, when added, they concatenate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "#Convert numpy arrays to lists\n", - "ones.tolist() + sixes.tolist()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One can also add arrays of different sizes, thanks to broadcasting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.array([0,1,2])\n", - "b = np.ones((3,3))\n", - "print('a= ',a)\n", - "print('b=', b)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a+b" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('shape: ', a.shape, '| dimensions: ', a.ndim, '| size: ', a.size)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('shape: ', b.shape, '| dimensions: ', b.ndim, '| size: ', b.size)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case what `numpy` has done is to compare the shape of the arrays. \n", - " - If they differ in the number of dimensions the shape gets augmented on the left (padded) with 1s. \n", - " - Then, if the arrays do not match in all their dimensions, the array with the padded 1s, is stretched in that dimension to match the size of the other array. \n", - " - Finally the two arrays can be combined.\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the third graphical example we can create row and column vectors using slicing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.array([0,1,2])\n", - "# row vector\n", - "row = x[np.newaxis, :]\n", - "print(row)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What `np.newaxis` does is to increase the dimensions of the array by 1 on the axis where it is used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "row.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#column vector\n", - "col = x[:,np.newaxis]\n", - "print(col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "row + col" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that with simple 1-d arrays, we do get the expected scalar in the dot product:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.dot(x,x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While with the row and column vectors we get a matrix:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.dot(row,col)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.dot(col,row)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Boradcasting is much more powerful and has many more subtleties that we cannot cover here. I refer again to Python Data Science Handbook for a deeper treatment of this." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Fancy indexing and masking" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's look at a couple of important tricks and methods available for numpy arrays, which make a scientist's life much easier." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Obtaining several entries of an array at once" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fancy indexing is simply the property of accessing arrays not only with slicing, but with integers or booleans." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a100 = np.arange(100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's say we wanted to obtain entries 1, 21, 41 and 61 and put them in an array. In other languages one would do something like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[a100[1], a100[21], a100[41] , a100[61]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Option 1:** In python we could use slicing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a100[1:62:20]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Option 2:** We could use integers:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#accessing entries with integers\n", - "a100[[1,21,41,61]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Option 3:** Or we could define a list and extract only those that match using booleans (masking)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "extract = [1,21,41,61]\n", - "mask = np.array([aa in extract for aa in a100])\n", - "print(mask)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a100[mask]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fancy indexing is very powerful, because it can be used for assigning:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a100[[10,20,30]] = -1000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And it can also change the shape of the original array, if it is indexed with an array of integers:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "idx = np.array([[40,50],[60,70]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a100[idx]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Masking and comparisons" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Obtain booleans for values smaller than 10\n", - "a100 < 0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the values\n", - "a100[a100 < 0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Get all odd values\n", - "a100[a100 %2 != 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** Note how powerful this masking and comparison is compared to other languages." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Other functions like `np.sum` or `np.count_nonzero` can be used with these logical operators (True==1, False==0):" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 5:** What is the result of np.count_nonzero(a100 > 90)? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Answer Puzzle 5:\n", - "#Uncomment to see the answer:\n", - "#np.count_nonzero(a100 > 90)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Sum all entries larger than 90\n", - "np.sum(a100[a100>90])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Sum all boolean entries larger than 90, equivalent to count_nonzero\n", - "np.sum(a100>90)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One can also combine comparisons with logical operators:\n", - "\n", - "|Operator|uFunc|\n", - "|------|---------------|\n", - "|&|`np.bitwise_and`|\n", - "|^|`np.bitwise_xor`|\n", - "|||`np.bitwise_or`|\n", - "|~|`np.bitwise_not`|" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Number of integers larger than 80, which are odd:\n", - "np.sum((a100 > 80) & ~(a100%2 == 0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One can also convert an array of `1` and `0` to booleans right at creation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "identity_mask=np.identity(5, dtype=bool)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which can be neatly used as a mask" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Create a matrix out of its diagonal using np.diag and replace the zeros by 7.\n", - "\n", - "int_arr = np.diag(np.arange(1,6))\n", - "int_arr[int_arr == 0] = 7\n", - "print(int_arr)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Apply the mask and return a 1-d array.\n", - "int_arr[identity_mask]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercises" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 1:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Using broadcasting and in max. 2 lines of code, construct a multiplication table of the numbers from 1 to 10, i.e., where for each column corresponding to 1,2,3,..., the rows correspond to their integer multiples. For 1 to 3, it looks like this:\n", - "\n", - "---\n", - "\n", - "|1|2|3|\n", - "|--|--|--|\n", - "|2|4|6|\n", - "|3|6|9|\n", - "\n", - "> Then, using masking, remove all multiples of 3, in one line of code. So that the result (in this case for 3x3) looks like this:\n", - "\n", - "|1|2|0|\n", - "|--|--|--|\n", - "|2|4|0|\n", - "|0|0|0|\n", - "\n", - "> Finally, compute the sum over each column and write it into a list:\n", - "\n", - " [3,6,0]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Exercise 2:** \n", - " * Produce a random set of points, following a normal distribution with mean 0 and covariance matrix `cov=[[1, 3.0/5], [3/5.0, 2]]`.\n", - " * Plot the set of points using `plt.scatter(X, Y)`\n", - " * Compute the marginal distributions for the x and y coordinates and using numpy functions, check that they follow a Gaussian distribution when compared to their histograms. Gaussian distribution:\n", - " $$ \\mathcal{N}(x) = \\frac{1}{\\sqrt{2 \\pi \\sigma^2}} \\exp(-(x-\\mu)^2/ 2 \\sigma^2)$$\n", - " * Here $\\mu$ is the mean and $\\sigma$ the standard deviation.\n", - " * (Hints: `plt.hist(points, bins, density=True)`, `np.exp`, `np.sqrt`, `np.std`, `np.mean`). Use 25 bins for the histogram.\n", - " * Using masking and `np.where` remove all points whose y-coordinate is more than 2-sigma away from the mean.\n", - " * Plot the remaining points in a scatter plot with blue color, with `facecolor='none'` and size `s=200`, together with the old set of points in red color with a transparency of alpha=0.6." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **[Answers](./Answers.ipynb) to puzzles and exercises**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **[Next Chapter: Pandas](./Pandas-Intro.ipynb)**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/python/Numpy.ipynb b/python/Numpy.ipynb deleted file mode 100644 index fa8003f..0000000 --- a/python/Numpy.ipynb +++ /dev/null @@ -1,891 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2 Numpy\n", - "\n", - "---\n", - "\n", - "> Author: Samuel Farrens \n", - "> Year: 2017 \n", - "> Email: [samuel.farrens@gmail.com](mailto:samuel.farrens@gmail.com) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", - "Numpy is a numerical python package for scientific computing. If you only ever install one Python package this should be it.\n", - "\n", - "The website contains extensive documentation on all the modules available with examples. This notebook barely skims the surface of what you cand do with numpy, but I have provided a few examples to help get you started.\n", - "\n", - "---\n", - "\n", - "## Contents\n", - "\n", - "1. [Installation](#Installation)\n", - "1. [Importing Numpy](#Importing-Numpy)\n", - "1. [Arrays](#Arrays)\n", - " * [The Basics](#The-Basics)\n", - " * [Multidimensional Arrays](#Multidimensional-Arrays)\n", - " * [Mathematical Operations](#Mathematical-Operations)\n", - "1. [Random Numbers](#Random-Numbers)\n", - "1. [Reading and Writing Files](#Reading-and-Writing-Files)\n", - " * [ASCII Files](#ASCII-Files)\n", - " * [Binary Files](#Binary-Files)\n", - "1. [Linear Alegbra](#Linear-Algebra)\n", - "1. [Exercises](#Exercises)\n", - " * [Exercise 2.1](#Exercise-2.1)\n", - " * [Exercise 2.2](#Exercise-2.1)\n", - "\n", - "---\n", - "\n", - "## Installation\n", - "\n", - "To install numpy simply run the following command in a terminal (providing you have already set up pip)\n", - "\n", - "```bash\n", - "\n", - "$ pip install numpy\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Importing Numpy\n", - "\n", - "To import numpy use the **`import`** command as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It more convenient to assign the numpy package contents to an alias to avoid having longer expressions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the **`as`** statement creates an alias of numpy called `np`.\n", - "\n", - "---\n", - "\n", - "## Arrays\n", - "\n", - "### The Basics\n", - "\n", - "The most essential numpy object is the numpy array (numpy.ndarray)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# a is a list\n", - "a = [1, 2, 3, 4]\n", - "print('a is', type(a))\n", - "\n", - "# b is a numpy array\n", - "b = np.array(a)\n", - "print('b is', type(b))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The **`np.array()`** command converts the list `a` to a numpy array `b`.\n", - "\n", - "Numpy arrays have several fantastic properties. Some basic examples are shown below.\n", - "\n", - "For a given array:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# a is 1D numpy array\n", - "a = np.array([4, 5, 6, 8, 7, 5, 2, 1, 0, 4, 0, 6, 5, 4, 2, 4, 1, 7, 1, 4])\n", - "\n", - "# print a\n", - "print('a =', a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can print a given element of the array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print the 10th element of a\n", - "print('the 10th element of a is', a[10])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also index from the end of the array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print the last element of a\n", - "print('the last element of a is', a[-1])\n", - "\n", - "# print the second to last element of a\n", - "print('the second to last element of a is', a[-2])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can specfiy every element in an array with a colon **`:`**. This can also be used for *slicing* (*i.e.* to specify every element before or after a certain point)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print every element in a\n", - "print('a =', a[:])\n", - "\n", - "# print every element up to index 10\n", - "print('first half of elements in a are', a[:10])\n", - "\n", - "# print every element after index 10\n", - "print('second half of elements in a are', a[10:])\n", - "\n", - "# print every element between index 2 and 6\n", - "print('the 3rd through 6th elements of a are', a[2:6])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can print every $i^{th}$ element using a double set of colons **`::`**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print every 5th element\n", - "print('every fifth element of a is', a[::5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also use double colons to reverse the entire array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print a backwards\n", - "print('a backwards is', a[::-1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is possible to subample a list using a list of indices." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# extract elements 1 3 11 and 18 from a\n", - "print('elements at indices 1, 3, 11 and 18 of a are', a[[1, 3, 11, 18]])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also mask out items using booleans and hence conditions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('the elements of a greater than 3 are', a[a > 3])\n", - "print('the elements of a excluding 5 are', a[a != 5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can check if any or all elements in an array have certain properties using the **`any()`** and **`all()`** commands." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('are there any elements in a greater than 10?', np.any(a > 10))\n", - "print('are there all of elements in a less than 10?', np.all(a < 10))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There several built-in functions for calculating the properties of Numpy arrays." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('a has size', a.size)\n", - "print('a has mean', a.mean())\n", - "print('a has standard deviation', a.std())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the size of the numpy array is displayed using the **`size`** command. The mean and standard deviation of the array elements are calculated using the **`mean()`** and **`std()`** commands, respectively." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The **`where()`** command can be used to find the index of a given elements." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('a = 8 at index', np.where(a == 8)[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Numpy includes several nice ways to quickly generate arrays using the commands **`zeros()`**, **`ones()`** and **`arange()`**." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('- Generate an array of zeros.')\n", - "print(' ', np.zeros(10))\n", - "print('')\n", - "\n", - "print('- Generate an array of ones.')\n", - "print(' ', np.ones(10))\n", - "print('')\n", - "\n", - "print('- Generate a range of integers from 0 to 9.')\n", - "print(' ', np.arange(10))\n", - "print('')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multidimensional Arrays\n", - "\n", - "The **`ndim`** command can be used to display the number of dimensions of a given array. while **`shape`** command can be used to display the size of each of the dimensions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# a is a 2D numpy array\n", - "a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", - "\n", - "print('a =', a)\n", - "print('a has', a.ndim, 'dimensions')\n", - "print('the shape of a is', a.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** notice the extra set of square brackets!\n", - "\n", - "It is also possible to change the shape of an existing array using the **`reshape()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# x is a 1D array\n", - "x = np.arange(16)\n", - "print('x =', x)\n", - "print('')\n", - "\n", - "# x is now a 2D array\n", - "x = x.reshape(4, 4)\n", - "print('x =', x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All of the indexing options shown for 1D arrays are equally applicable to multidimensional arrays. For example, for a 5x5 array" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.arange(25).reshape(5, 5)\n", - "\n", - "print('x =', x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can specify whole rows or columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# first row of x\n", - "print('the first row of x is', x[0])\n", - "\n", - "# 3rd column of x\n", - "print('the third column of x is', x[:, 2])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or you can specify single elements" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 2nd element in the 4th row of x\n", - "print('the second element in the 4th row of x is', x[3, 1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Slicing also works." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print every other element in the 1st two rows of x\n", - "print('every other element in the first two rows of x are')\n", - "print(x[:2, ::2])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There several handy numpy commands for transforming multidimensional arrays. The command **`rot90()`** rotates a matrix by 90 degrees." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# rotate x by 180 degrees\n", - "print('x rotated by 180 degrees is')\n", - "print(np.rot90(x, 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The command **`transpose()`** will transpose an array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# transpose x\n", - "print('x transpose is')\n", - "print(np.transpose(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Mathematical Operations\n", - "\n", - "One of the most useful features of numpy is that it allows element-wise mathematical operations. It also has built-in mathematical operators.\n", - "\n", - "You can multiply every element in an array by a single number." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.arange(5) + 1\n", - "\n", - "print('x =', x)\n", - "print('2x =', 2 * x)\n", - "print('x^2 =', x ** 2)\n", - "print('sqrt(x) =', np.sqrt(x))\n", - "print('x * pi =', np.pi * x)\n", - "print('log(x) =', np.log(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also multiply each element in one array by each element in another of the same size." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y = np.array([7, 9, 2, 3, 2])\n", - "print('y =', y)\n", - "\n", - "# check if x and y have the same size\n", - "print('x is the same size as y:', x.size == y.size)\n", - "\n", - "# add the elements of x to those of y\n", - "print('x + y =', x + y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This also works for multidimensional arrays with the same shape." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.ones((2, 2)).astype(int) * 2\n", - "b = np.arange(4).reshape(2, 2)\n", - "print('a =', a)\n", - "print('b =', b)\n", - "print('')\n", - "\n", - "# check if a and b have the same shape\n", - "print('a has the same shape as b:', a.shape == b.shape)\n", - "print('')\n", - "\n", - "# multiply the elements of a by those of b\n", - "print('a * b =')\n", - "print(a * b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Random Numbers\n", - "\n", - "The numpy **`random`** module (numpy.random) contain various methods for generating random numbers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('- Generate an array of 10 random integers with values between 2 and 9.')\n", - "print(' ', np.random.randint(2, 9, 10))\n", - "print('')\n", - "\n", - "print('- Generate an array of 5 random floats with values between 0 and 1.')\n", - "print(' ', np.random.ranf(5))\n", - "print('')\n", - "\n", - "print('- Generate an array of 5 Gaussian distributed random floats with values between -1 and 1.')\n", - "print(' ', np.random.randn(5))\n", - "print('')\n", - "\n", - "print('- Generate an array of 5 Poisson distributed random integers.')\n", - "print(' ', np.random.poisson(1, 5))\n", - "print('')\n", - "\n", - "print('- Generate an 2D array of random numbers.')\n", - "print(' ', np.random.rand(5, 5))\n", - "print('')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Reading and Writing Files\n", - "\n", - "Numpy provides some advatages over native Python in terms of reading and writing files.\n", - "\n", - "> If you have not already done so, you will need to download the [Materials](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/sfarrens/notebooks/tree/master/Python/materials) directory and unzip it. \n", - "> You can use the following command in a terminal: \n", - "> ``` bash\n", - "> $ unzip materials.zip\n", - "> ``` \n", - "> **NOTE:** you need to place the `Materials` folder in the same directory as this notebook.\n", - "\n", - "### ASCII Files\n", - "\n", - "ASCII (plain text) files can be read using the **`genfromtxt()`** command (numpy.genfromtxt) as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read in the file\n", - "data = np.genfromtxt('materials/ninja_turtles.txt', names=True, dtype=None, encoding='ascii')\n", - "\n", - "# Print the column names\n", - "print(data.dtype.names)\n", - "print('')\n", - "\n", - "# Print the rows\n", - "for line in data:\n", - " print(line)\n", - "\n", - "print('')\n", - "\n", - "# Calculate the average age\n", - "print('The average age is', data['Age'].mean())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the example above a file called `ninja_turtles.txt` which is in the directory `materials` is read in. The first row\n", - "of this file provides column names which are stored in `dtype.names` using the **`names`** option. Setting the **`dtype`** as `None` allows the command to assign the appropriate data type to each column in the file.\n", - "\n", - "Data can be saved to an ASCII file using the **`savetxt()`** command (numpy.savetext) as follows " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create a new string\n", - "string = 'average turtle age is ' + str(data['Age'].mean())\n", - "\n", - "# save the string to a file\n", - "np.savetxt('materials/turtle_age.txt', [string], fmt='%s')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example we need to pass the string as a list (a numpy array would also work) to **`savetext()`**. Also, we need to define the format of the data using the **`fmt`** option." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Binary Files\n", - "\n", - "Numpy arrays can also be read in or saved to special numpy binary files (`.npy` extension) using the **`load()`** and **`save()`** commands (numpy.save, numpy.load), respectively." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read in the numpy binary file\n", - "data = np.load('materials/ninja_turtles.npy')\n", - "\n", - "# Print the array contents\n", - "print(data)\n", - "print('')\n", - "\n", - "# Increase ages by 1\n", - "data['Age'] += 1\n", - "print(data)\n", - "\n", - "# Save new data to a binary file\n", - "np.save('materials/ninja_turtles_new.npy', data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Numpy binaries have the fantastic property of retaining formatting and data structures. In addition, they can be read and written much faster than text files." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Linear Algebra\n", - "\n", - "Numpy also handles linear algebra (numpy.linalg). For example, you can multiply two matrices using the **`dot()`** command." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.random.randint(0, 9, (3, 3))\n", - "print('x =')\n", - "print(x)\n", - "print('')\n", - "\n", - "y = np.random.randint(0, 9, (3, 3))\n", - "print('y =')\n", - "print(y)\n", - "print('')\n", - "\n", - "print('xy =')\n", - "print(np.dot(x, y))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also calculate the inverse of a matrix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.array([[1., 2.], [3., 4.]])\n", - "print('a =')\n", - "print(a)\n", - "print('')\n", - "\n", - "print('a^-1 =')\n", - "print(np.linalg.inv(a))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "or you can calculate the $l_2$-norm of a matrix using the **`norm`** command" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.random.ranf((3, 3))\n", - "print('x =', x)\n", - "print('')\n", - "print('||x|| =', np.linalg.norm(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Exercises\n", - "\n", - "### Exercise 2.1\n", - "\n", - "Read in the file called `nearest_stars.txt` in the directory `materials` and determine the standard deviation of the distances of the stars with spectral type `M`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 2.2\n", - "\n", - "Generate a three dimensional array of random floats with shape (3, 4, 4) using a random seed of 8. For each 4 x 4 matrix in the array calculate the $l_1$-norm and then calculate the mean of these values.\n", - "\n", - "**HINT:** Make sure you check the documentation pages for commands that are not mentioned in this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find some example answers to the exercises [here](./Answers-to-exercises.ipynb).\n", - "\n", - "---\n", - "\n", - "> **Continue to [next topic](./Matplotlib.ipynb)**" - ] - } - ], - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/python/Other-Packages.ipynb b/python/Other-Packages.ipynb deleted file mode 100644 index 821c45e..0000000 --- a/python/Other-Packages.ipynb +++ /dev/null @@ -1,419 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4 Other Packages\n", - "\n", - "---\n", - "\n", - "> Author: Samuel Farrens \n", - "> Year: 2017 \n", - "> Email: [samuel.farrens@gmail.com](mailto:samuel.farrens@gmail.com) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", - "Now that you have mastered the basics of Python and the essential packages you can begin to explore some of the other packages that are available. Here I will briefly introduce a few that I use often.\n", - "\n", - "---\n", - "\n", - "## Contents\n", - "\n", - "1. [Installation](#Installation)\n", - "1. [Scipy](#Scipy)\n", - " * [Fourier Transforms](#Fourier-Transforms)\n", - " * [SVD](#SVD)\n", - "1. [Astropy](#Astropy)\n", - " * [FITS Files](#FITS-Files)\n", - " * [Convolution](#Convolution)\n", - "1. [Exercises](#Exercises)\n", - " * [Exercise 4.1](#Exercise-4.1)\n", - " * [Exercise 4.2](#Exercise-4.2)\n", - " \n", - "---\n", - "\n", - "## Installation\n", - "\n", - "To install the packages discussed in this notebook simply run the following commands in a terminal\n", - "\n", - "```bash\n", - "\n", - "$ pip install scipy\n", - "$ pip install astropy\n", - "\n", - "```\n", - "---\n", - "\n", - "## Scipy\n", - "\n", - "Scipy is an open source package for scientific computing that contains a collection of useful algorithms and tools. Scipy overlaps with some Numpy modules. Scipy, however, tends to be a bit more comprehensive in certain areas.\n", - "\n", - "### Fourier Transforms\n", - "\n", - "The **`fftpack`** module in scipy (scipy.fftpack) contains virtually all the tools you will need for working in Fourier space. \n", - "\n", - "You can import the package as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy import fftpack" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and since it is easier to understand these operations visually, we can also import matplotlib." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.image import imread" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can start with a simple sine curve time series." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# set the signal properties\n", - "sample_rate = 150.0\n", - "signal_freq = 5\n", - "\n", - "# generate a range of time values\n", - "t = np.arange(0, 1, 1 / sample_rate)\n", - "\n", - "# calculate the sin curve\n", - "y = np.sin(2 * np.pi * signal_freq * t)\n", - "\n", - "# plot the curve\n", - "plt.figure(figsize=(8, 6))\n", - "plt.plot(t, y, 'b-')\n", - "plt.ylim(-1.2, 1.2)\n", - "plt.xlabel('$t$', fontsize=20)\n", - "plt.ylabel('$y(t)$', fontsize=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use the command **`fft()`** to transform the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# define the frequency range\n", - "frq = (np.arange(y.size) * sample_rate / y.size)[:int(y.size / 2)]\n", - "\n", - "# calculate the FFT of the sine curve\n", - "abs_y = np.abs((fftpack.fft(y) / y.size)[:frq.size])\n", - "\n", - "# display the FFT data\n", - "plt.figure(figsize=(8, 6))\n", - "plt.plot(frq, abs_y, 'b-')\n", - "plt.ylim(0, 0.6)\n", - "plt.xlabel('$\\omega$', fontsize=20)\n", - "plt.ylabel('$|y(\\omega)|$', fontsize=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SVD\n", - "\n", - "Singular Value Decomposition can be performed using the **`svd()`** command in the **`linalg`** module.\n", - "\n", - "> If you have not already done so, you will need to download the [Materials](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/sfarrens/notebooks/tree/master/Python/materials) directory and unzip it. \n", - "> You can use the following command in a terminal: \n", - "> ``` bash\n", - "> $ unzip materials.zip\n", - "> ``` \n", - "> **NOTE:** you need to place the `Materials` folder in the same directory as this notebook.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import the linalg module\n", - "from scipy import linalg\n", - "\n", - "# read in an image\n", - "x = imread('materials/r2d2.jpg')[:, :, 0]\n", - "\n", - "# calculate the svd\n", - "u, s, v = linalg.svd(x)\n", - "\n", - "# keep only singular values >= 1.0\n", - "s_new = s * (np.abs(s) >= 1.0)\n", - "\n", - "# reconstruct the image with thresholded singular values\n", - "s_new = linalg.diagsvd(s_new, *x.shape)\n", - "x_new = np.dot(u, np.dot(s_new, v))\n", - "\n", - "# dsiplay the two images\n", - "plt.figure(figsize=(10, 8))\n", - "plt.subplot(121)\n", - "plt.imshow(x, interpolation='none', cmap='gray')\n", - "plt.title('Original Image')\n", - "plt.subplot(122)\n", - "plt.imshow(x_new, interpolation='none', cmap='gray')\n", - "plt.title('Reconstructed Image')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the **`diagsvd()`** command diagonalises and reshapes the array of singular values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Astropy\n", - "\n", - "Astropy is a package that contains tools designed for the analysis of astronomical data.\n", - "\n", - "### FITS Files\n", - "\n", - "One of the most useful modules in Astropy is **`io.fits`** (astropy.io.fits), which handles the reading and writing FITS binary files.\n", - "\n", - "You can import the module as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from astropy.io import fits" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and use it to read a fits image as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# read image data from FITS file\n", - "x = fits.getdata('materials/r2d2.fits')\n", - "\n", - "# Display the image\n", - "plt.figure(figsize=(6, 6))\n", - "plt.imshow(x)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can write image data to a new file as follows" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# generate an array of random numbers\n", - "x = np.random.ranf((10, 10))\n", - "\n", - "# save array to a FITS file\n", - "fits.PrimaryHDU(x).writeto('materials/random_data.fits')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convolution\n", - "\n", - "You can convolve vectors or matrices using the **`convolve_fft()`** command in the **`convolution`** module (astropy.convoluion)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# read in image from scipy.misc\n", - "from scipy.misc import face\n", - "\n", - "# import Gaussian filter function\n", - "from scipy.ndimage import gaussian_filter\n", - "\n", - "# import astropy methods\n", - "from astropy.convolution import convolve_fft\n", - "\n", - "# set the image as a 2D array\n", - "image = face()[:, :, 0]\n", - "\n", - "# create a 2D Gaussian kernel with sigma=0.5\n", - "kernel = np.zeros(image.shape)\n", - "kernel[tuple(np.array(kernel.shape) // 2)] = 1\n", - "kernel = gaussian_filter(kernel, 20)\n", - "\n", - "# convolve the image by the kernel\n", - "convolved_image = convolve_fft(image, kernel, boundary='wrap')\n", - "\n", - "# display the images\n", - "plt.figure(figsize=(16, 6))\n", - "plt.subplot(131)\n", - "plt.imshow(image, cmap='gray')\n", - "plt.subplot(132)\n", - "plt.imshow(kernel)\n", - "plt.subplot(133)\n", - "plt.imshow(convolved_image, cmap='gray')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Exercises\n", - "\n", - "### Exercise 4.1\n", - "\n", - "Write your own convolution function and compare its performance to the astroy module. Your function should:\n", - "\n", - "- Take an image and a convolution kernel as inputs\n", - "- Output a convolved image\n", - "- The convolution kernel should be a Gaussian with sufficiently large $\\sigma$ to visually distort the image\n", - "- You can use the scipy **`fftpack`** module\n", - "- You can use any input image you like\n", - "\n", - "Recall the *convolution theorem*\n", - "\n", - "$$\\mathcal{F}\\{x * y\\} = \\mathcal{F}\\{x\\} \\cdot \\mathcal{F}\\{y\\}$$\n", - "\n", - "where $\\mathcal{F}\\{x\\}$ denotes the Fourier transform of $x$, and $x * y$ denotes the convolution of $x$ and $y$.\n", - "\n", - "Display your input image next to the convolved image you obtain with your function and with Astropy. Make sure you title your images.\n", - "\n", - "**Hints:**\n", - "- Make sure the output of your function is *real*-istic. There might be a Numpy function to help with this.\n", - "- If your convolved image is blurry but some pices are in the wrong places, you made need to check the fftpack documentation for the correct way to *shift* the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 4.2\n", - "\n", - "Now write a deconvolution function (*i.e.* a function that removes the kernel distortion from the convolved image) to recover the original image. Your function should:\n", - "\n", - "- Take a distorted image and convolution kernel as inputs\n", - "- Output a deconvolved image\n", - "- You can use the scipy **`fftpack`** module\n", - "- Your input image should be the output of Exercise 4.1\n", - "\n", - "Display your deconvolved image next to the original image. Make sure you title your images.\n", - "\n", - "**Hints:**\n", - "- If your deconvolution function is not working make sure the noise level set in the previous exercise is not too high." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Implement your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can find some example answers to the exercises [here](./Answers-to-exercises.ipynb).\n", - "\n", - "---\n", - "\n", - "> **Continue to [next topic](./Answers-to-exercises.ipynb)**" - ] - } - ], - "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": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/python/Pandas-Intro.ipynb b/python/Pandas-Intro.ipynb deleted file mode 100644 index 4aa822c..0000000 --- a/python/Pandas-Intro.ipynb +++ /dev/null @@ -1,1310 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\"CosmoStat\n", - "
\n", - "
\n", - "
\n", - "
\n", - "\n", - "# Pandas Intro\n", - "\n", - "---\n", - "\n", - "> Author: Santiago Casas (based on the Python Data Science Handbook)\n", - "> Email: santiago.casas@cea.fr \n", - "> Year: 2019 \n", - "> Version: 1.0\n", - "\n", - "\n", - "\"CosmoStat\n", - "\n", - "---\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Let's start by importing the necessary libraries\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Numpy with structured arrays" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Numpy is quite powerful because it can host structured data into the array, using a dictionary structure." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Imagine we have this data about people and their age and weight." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "name = ['Alice', 'Bob', 'Cathy', 'Doug']\n", - "age = [25, 45, 37, 19]\n", - "weight = [55.0, 85.5, 68.0, 61.5]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can create a structured array using a compound data type specification" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use a compound data type for structured arrays\n", - "data = np.zeros(4, dtype={'names':('name', 'age', 'weight'),\n", - " 'formats':('U10', 'int32', 'float32')})\n", - "print(data.dtype)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have a data type that contains unicode strings, ints and floats." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now fill this structure data array using `keys` as we did for dictionaries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data['name'] = name\n", - "data['age'] = age\n", - "data['weight'] = weight\n", - "print(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This allows us to access the array with indices" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get first row of data\n", - "data[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Get name of the last row of data\n", - "data[-1]['name']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Masking with structured arrays" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the powerful masking we saw before, we can ask questions such as: What is the name of all the persons below an age of 30?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get names where age is under 30\n", - "data[data['age'] < 30]['name']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Despite the power of numpy, once we start dealing with more complicated structures, we need to resort to Pandas. A python package specialized for databases, structured data and statistical analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Enter Pandas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pandas Series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Pandas `Series` is a one-dimensional array of indexed data. It is the most basic structure. It can be created from a list or array as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.Series([0.25, 0.5, 0.75, 1.0])\n", - "data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how we now get an index column, next to the array values. This index column is by default just a numbering of the entries." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The values are simply a numpy array:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While the index is just an array-type object, special to `Pandas`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Data can be accessed like in a numpy array" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data[1:3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data[1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Pandas Series has an explicitly defined index associated with the values.\n", - "\n", - "This explicit index definition gives the Series object additional capabilities. For example, the index need not be an integer, but can consist of values of any desired type. For example, if we wish, we can use strings as an index:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n", - " index=['a', 'b', 'c', 'd'])\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now we can access the data through an index\n", - "data['b']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Series as specialized dictionaries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of specifying the data and the indices seprately, we can inmediatly create a series from a dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "population_dict = {'California': 38332521,\n", - " 'Texas': 26448193,\n", - " 'New York': 19651127,\n", - " 'Florida': 19552860,\n", - " 'Illinois': 12882135}\n", - "population = pd.Series(population_dict)\n", - "population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So we can simply ask, What is the population of California?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "population['California']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we can even perform slicing on the `'string'` indices:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "population['California':'New York']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that the indices can be non-contiguous numbers in any order and the values can be strings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "series = pd.Series({2:'a', 1:'b', 3:'c', 6:'d'})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "series[1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Which can lead to counter-intuitive slicing such as" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "series[1:6].values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Frames as generalized numpy arrays and special dictionaries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From several dictionaries one can also construct a `DataFrame` object, basically what we scientists call tables, with a row of headers and a column of indices." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,\n", - " 'Florida': 170312, 'Illinois': 149995}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Series object\n", - "area = pd.Series(area_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "area" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now use population and area as two different columns in a `\"database\"`, which corresponds to a DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states = pd.DataFrame({'population': population,\n", - " 'area': area})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** Notice the nice printing of a DataFrame in the form of a table !" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DataFrame also has an index attribute" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But now also the columns, have a generalized index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A `DataFrame` behaves like a dictionary in which the `keys` are the columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states['area']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And as opposed to a `numpy` array it does not return the rows when indexed with numbers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will raise an error, uncomment to see it\n", - "#states[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A `DataFrame` can be created from a list of dictionaries and `Pandas` will merge the indices accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list_of_dicts = [{'a': 1, 'b': 2}, {'b': 3, 'a': 4}]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(list_of_dicts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If some indices are not present in one or more of the dicts, `Pandas` will fill the columns with `naN`s." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list_of_dicts = [{'a': 1, 'b': 2, 'c':3}, {'a': 3, 'd': 4, 'b':6}]\n", - "pd.DataFrame(list_of_dicts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A useful tool for scientists working with numerical data, is to construct a `Pandas` `DataFrame` out of a numpy array, in which we label the rows and columns with strings." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "xy_data=pd.DataFrame(np.random.rand(3, 2),\n", - " columns=['X', 'Y'],\n", - " index=['a', 'b', 'c'])\n", - "xy_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the same way that dictionaries can be \"filled-up\" by providing a new \"key-value\" pair, the same applies for `DataFrames`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "xy_data['Z'] = pd.Series([1.,1.,1.], index=xy_data.index) # We pass the same index of the original DataFrame\n", - "xy_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 6:** Do you think data frames can also be transposed like numpy arrays? \n", - "What is the output of `(xy_data.T).columns[2]` ?\n", - "\n", - " * Option a): `c`\n", - " * Option b): `X`\n", - " * Option c): `0.303641`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Answer Puzzle 5:\n", - "#Uncomment to see the result\n", - "#(xy_data.T).columns[2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Manipulating Data Frames" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A very useful tool for scientists is to be able to compute derived quantities from given data.\n", - "In our `states` DataFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can create quickly a new `density` column by operating on the available data columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states['density'] = states['population']/states['area']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that now the columns are attributes of the `DataFrame`, which return `Series`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states.area" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states.density" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Masking* also works for `DataFrames`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Which states have a density larger than 100?\n", - "states[states.density > 100]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can easily see, this properties are quite powerful when one has to analyze large datasets with thousands of columns and rows." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Indexing with integers: loc and iloc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.Series(['a', 'b', 'c', 'd'], index=[1, 2, 5, 6])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that when the indices are integers, some confusions might appear." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# explicit index when indexing\n", - "data[2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This will raise an error: (uncomment to see it and comment back)\n", - "#data[3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 6:** What do you expect from the following slice: `data[2:4]` ? Which values will be returned?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Answer Puzzle 6:\n", - "#Uncomment to see the answer\n", - "#data[2:4]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The maybe surprising result, is because `Pandas` uses the explicit indices in `index` when indexing, while the implicit (standard numpy) indices when slicing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Due to this confusion, it is better to use the attributes `loc`, `iloc`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### loc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, the `loc` attribute allows indexing and slicing that always references the ***explicit*** index:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.loc[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.loc[1:3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states.loc['California':'Texas']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### iloc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `iloc` attribute allows indexing and slicing that always references the implicit Python-style index:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.iloc[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.iloc[1:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE:** Notice the difference with `loc` above !" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states.iloc[0:2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One guiding principle of Python code is that \"explicit is better than implicit.\" The explicit nature of `loc` and `iloc` make them very useful in maintaining clean and readable code; especially in the case of integer indexes, I recommend using these both to make code easier to read and understand, and to prevent subtle bugs due to the mixed indexing/slicing convention. (from the PDSH)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Subtleties of indexing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For `DataFrames`, the situation is a bit more complicated. Passing a single index to the values, as we saw above, returns a row:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states.values[0:2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Passing a column name as a key, returns a column:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states['area']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But slicing is always performed on the rows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states['California':'Texas']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states.iloc[:3, :2]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Equivalently:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "states.loc[:'New York',:'area']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Operations between DataFrames and Series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the same way as boradcasting for `numpy` where we could have operations between arrays of different dimensions, here we can operate between `DataFrames` and `Series`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is quite useful again for treatment of numerical and statistical data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a `DataFrame` of random ints of size (3,4), with given column names." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame(np.random.randint(0, 10, (3, 4)),\n", - " columns=['A', 'B', 'C', 'D'])\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Apply a complicated function on the whole `DataFrame`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.sin(df * np.pi / 4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Internally, this has been done using `uFuncs` and `broadcasting`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For numpy arrays, the operation works `row-wise` (notice the first row always being zero):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.random.randint(10, size=(3, 4))\n", - "A - A[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a `DataFrame` from `A`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame(A, columns=list('ABCD'))\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Subtract the `0` column:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df - df.iloc[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you wish to operate column-wise, use `loc` and the corresponding `uFunc` along the wanted axis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.subtract(df['A'], axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas has lots more of functionality that we can't cover in this session. But a look at the Python Data Science Handbook can reveal many more methods, attributes and tricks." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Importing CSV files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A useful property of Pandas, is that it allows us to work with spreadsheets in a natural way." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Import CSV data of average monthly Temperatures in Turrialba, Costa Rica from 1958 until 2016" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cr_temp = pd.read_csv('./materials/CR_Temp.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Look at the first 5 rows with head()\n", - "cr_temp.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to avoid having an index column which is just an integer count of the rows, we can specify it at import time." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cr_temp = pd.read_csv('./materials/CR_Temp.csv', index_col=1)\n", - "cr_temp.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Get the columns\n", - "cr_temp.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Get the index\n", - "cr_temp.index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Get the temperature of the 'ENE' month (January) in 1965\n", - "cr_temp.ENE[1965]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Exercise 1:** Compute the yearly average and check if it matches what is given in the last column of the csv file. You can drop the `N` column with pd.drop(columns=...)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Exercise 2:** Averaging over all the years, which is the hottest month in Turrialba, CR? *Hints:* np.mean, np.max, `loc` and slicing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Exercise 3:** Using the built-in plotting routine of Pandas, can you notice any effect of climate change over the years? *Hint:* plot is a built-in method of a Pandas `DataFrame`. You can plot any column or any row using the masking and slicing seen above." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **[Answers](./Answers.ipynb) to puzzles and exercises**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/python/Pythonic.ipynb b/python/Pythonic.ipynb deleted file mode 100644 index 3b213a4..0000000 --- a/python/Pythonic.ipynb +++ /dev/null @@ -1,1611 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"CosmoStat\n", - "
\n", - "
\n", - "
\n", - "
\n", - "\n", - "# Pythonic Thinking\n", - "---\n", - "\n", - "> Author: Samuel Farrens \n", - "> Email: samuel.farrens@cea.fr \n", - "> Year: 2019 \n", - "> Version: 1.0\n", - "\n", - "---\n", - "
\n", - "\n", - "Python is a high-level interepreted programming language. It provides the simplicity needed for quick development, while retaining the power needed for dedicated software development. One of the core tenets of Python is the emphasis on readability, *i.e.* the ability to implement complex functionality with simple and elegant syntax.\n", - "\n", - "In order to get the most out of using Python, it is essntial to know how to code in a Pythonic way. This notebook will introduce some useful objects and operators that are central to how Python works.\n", - "\n", - "If you are new to Jupyter notebooks note that cells are executed by pressing SHIFT+ENTER (⇧+ ⏎). See the Jupyter documentation for more details.\n", - "\n", - "
\n", - "\n", - "---\n", - "\n", - "There is plenty to cover in this tutorial but first let's take a deep breath and take in *The Zen of Python.*\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import this" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Contents\n", - "---\n", - "\n", - "1. [Set-Up](#1-Set-Up)\n", - "1. [A Quick Recap](#2-A-Quick-Recap)\n", - "1. [Unpacking](#3-Unpacking)\n", - " 1. [Basic Unpacking](#Basic-Unpacking)\n", - " 1. [Positional Expansion](#Positional-Expansion)\n", - " 1. [Unpacking with Functions](#Unpacking-with-Functions)\n", - "1. [Dictionaries](#4-Dictionaries)\n", - " 1. [First Example](#First-Example)\n", - " 1. [Dictionary Unpacking](#Dictionary-Unpacking)\n", - " 1. [Keyword Arguments](#Keyword-Arguments)\n", - " 1. [Tips and Tricks](#Tips-and-Tricks)\n", - "1. [List Comprehension](#5-List-Comprehension)\n", - " 1. [Loops](#Loops)\n", - " 1. [List Comprehension](#List-Comprehension)\n", - " 1. [Absolute Magnitude Example](#Absolute-Magnitude-Example)\n", - " 1. [Redshift Example](#Redshift-Example)\n", - " 1. [Conditions](#Conditions)\n", - " 1. [Nested Lists](#Nested-Lists)\n", - "1. [Iterators & Generators](#6-Iterators-&-Generators)\n", - " 1. [Iterators](#Iterators)\n", - " 1. [Built-In Functions](#Built-In-Functions)\n", - " 1. [Generators](#Generators)\n", - " 1. [Generator Expressions](#Generator-Expressions)\n", - "1. [Exercises](#7-Exercises)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1 Set-Up\n", - "---\n", - "\n", - "The following cell contains some set-up commands. Be sure to execute this cell before continuing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Notebook Set-Up Commands\n", - "\n", - "import math\n", - "\n", - "def print_error(error):\n", - " \"\"\" Print Error\n", - " \n", - " Function to print exceptions in red.\n", - " \n", - " Parameters\n", - " ----------\n", - " error : string\n", - " Error message\n", - " \n", - " \"\"\"\n", - " print('\\033[1;31m{}\\033[1;m'.format(error))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2 A Quick Recap\n", - "---\n", - "\n", - "Before getting into the specifics of Pythonic coding, let's take a second to refresh the basics. In order to understand the topics covered in this notebook you will need to know the following:\n", - "\n", - "### Basic Python object types " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Basic Python objects\n", - "\n", - "myint = 1\n", - "print('Object is of type:', type(myint))\n", - "\n", - "myfloat = 1.0\n", - "print('Object is of type:', type(myfloat))\n", - "\n", - "mybool = True\n", - "print('Object is of type:', type(mybool))\n", - "\n", - "mystring = 'hello'\n", - "print('Object is of type:', type(mystring))\n", - "\n", - "mylist = [1, 2, 3]\n", - "print('Object is of type:', type(mylist))\n", - "\n", - "mytuple = (1, 2, 3)\n", - "print('Object is of type:', type(mytuple))\n", - "\n", - "myset = set([1, 1, 2])\n", - "print('Object is of type:', type(myset))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Standard operators" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Standard operators\n", - "\n", - "print('Addition: 1 + 2 = ', 1 + 2)\n", - "print('Subtraction: 1 - 2 = ', 1 - 2)\n", - "print('Multiplication: 4 * 2 = ', 4 * 2)\n", - "print('Division: 4 / 2 = ', 4 / 2)\n", - "print('Floor Division: 4 // 2 = ', 4 // 2)\n", - "print('Exponentiation: 4 ** 2 = ', 4 ** 2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simple logic operations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Simple logic operations\n", - "\n", - "print('True and True = ', True and True)\n", - "print('True and False = ', True and False)\n", - "print('True or True = ', True or True)\n", - "print('True or False = ', True or False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Functions\n", - "\n", - "def say_hello():\n", - " print('Hello world!')\n", - " \n", - "def show_value(value):\n", - " print('My value is {}.'.format(value))\n", - " \n", - "say_hello()\n", - "show_value(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3 Unpacking\n", - "---\n", - "\n", - "A fundamental first step on your journey of Pythonic coding is understanding how handle array-like objects (*i.e.* lists and tuples).\n", - "\n", - "### Basic Unpacking\n", - "\n", - "As you are no doubt aware lists and tuples can be indexed by passing an element number inside `[]`..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a tuple\n", - "mytuple = (1, 2, 3)\n", - "\n", - "# Get index 1 from the tuple\n", - "print('mytuple[1] =', mytuple[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... however, it is also possible to *unpack* these objects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Unpack a tuple\n", - "val1, val2, val3 = (1, 2, 3)\n", - "\n", - "print('val1 = {} ; val2 = {} ; val3 = {}'.format(val1, val2, val3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that when three objects are assigned to the tuple each value is unpacked automatically, but if we attempt to unpack three values into two objects we raise an exception." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Try to unpack values into two objects\n", - "try:\n", - " val1, val2 = (1, 2, 3)\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Positional Expansion\n", - "\n", - "We can bypass the problem using the *splat* (`*`) operator. In addition to be used as the standard operator for multiplication, `*` also has special unpacking features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print mytuple\n", - "print('mytuple =', mytuple)\n", - "\n", - "# Print mytuple after unpacking\n", - "print('unpacked mytuple =', *mytuple)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using this operator we can now unpack our three-element tuple into just two objects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Unpack a tuple\n", - "val1, *val2 = mytuple\n", - "\n", - "print('val1 = {} ; val2 = {}'.format(val1, val2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the first value was unpacked and the remaining values were packed into a list.\n", - "\n", - "> **Puzzle 1:** Guess the value of `val2` in the following example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a list\n", - "mylist = [7, 2, 8, 2, 6]\n", - "\n", - "# Unpack the list\n", - "val1, *val2, val3 = mylist\n", - "\n", - "# Uncomment to see the answer\n", - "# print(val2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also recursively unpack array-like objects.\n", - "\n", - "> **Puzzle 2:** Guess the value of `b` in the following cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a list of tuples\n", - "list_of_tuples = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]\n", - "\n", - "# Unpack the list\n", - "*A, B = list_of_tuples\n", - "\n", - "# Unpack one of the tuples\n", - "*a, b = A[1]\n", - "\n", - "# Uncomment to see the answer\n", - "# print('b =', b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Further Reading\n", - "> - Understanding the Asterisk of Python" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unpacking with Functions\n", - "\n", - "Unpacking can be particularly useful for passing arguments to functions. *e.g.* if you want to pass a single object to a function that expects multiple arguments." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a function that takes multiple arguments\n", - "def myfunc(a, b, c):\n", - " \n", - " return a + b - c\n", - "\n", - "# Set a tuple of values\n", - "myvalues = (4, 5, 3)\n", - "\n", - "# Unpack the values into the function\n", - "print('The result of myfunc is {}.'.format(myfunc(*myvalues)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you could pass multiple values to a function that does not know how many arguments to expect." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a function that has an unknown number of arguments\n", - "def count_agrs(*args):\n", - " \n", - " return len(args)\n", - "\n", - "# Pass multiple values to the function\n", - "print('count_agrs received {} arguments.'.format(count_agrs(1, 2, 3)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4 Dictionaries\n", - "---\n", - "\n", - "Python provides many different ways of storing multiple values in a single object (*e.g.* lists, tuples, sets, *etc*). One of the most useful, and certainly one of the most important, are *dictionaries*. \n", - "\n", - "Dictionaries are designated with `{}` and are comprised of two main components: *keys* and *values*. The benefit that dictionaries provide with respect to simpler objects like lists is the ability to label values. This makes it a lot easier to store a large number of values in a single object without losing track of what they are.\n", - "\n", - "### First Example\n", - "\n", - "Let's look at a concrete example. Imagine we want to keep track of the colours associated to the Teenage Mutant Ninja Turtles.\n", - "\n", - "\n", - "\n", - "We could start by defining a unique object for each turtle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Unique objects for turtle colours\n", - "Leonardo = 'blue'\n", - "Raphael = 'red'\n", - "Donatello = 'purple'\n", - "Michelangelo = 'orange'\n", - "\n", - "print('Leonardo wears {}.'.format(Leonardo))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It might be nicer instead to define a single object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# List of turtle colours\n", - "turtles = ['blue', 'red', 'purple', 'orange']\n", - "\n", - "print('Raphael wears {}.'.format(turtles[1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This, however, assume you will remember the order and names of the turtles. A dictionary makes it possible to retain both the simplicty of a list, but the details of the single objects.\n", - "\n", - "To define a dictionary object we need to specify a series of keys followed by a colon (`:`) and then the corresponding values, all comma separated and within `{}`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Dictionary of turtle names and colours\n", - "turtles = {'Leo': 'blue', 'Raph': 'red', 'Donny': 'purple', 'Mickey': 'orange'}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can look at the pieces that make up the dictionary by looking at the `keys`, `values` and `items`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Show dictionary components\n", - "print('dict:', turtles)\n", - "print('keys:', turtles.keys())\n", - "print('values:', turtles.values())\n", - "print('items:', turtles.items())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use the keys (*i.e.* the turtles' names) to look up the values (*i.e.* the corresponding colours). *e.g.* To look up what colour Donatello wears we simply need to pass the key `'Donny'` to the dictionary `turtles` as follows.\n", - "\n", - "```python\n", - "turtles['Donny']\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Look up dictionary value|\n", - "print('Donatello wears {}.'.format(turtles['Donny']))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Note that dictionary look up in Python is highly optimised, but it only works one way (*i.e.* keys → values).\n", - "\n", - "It is possible to add entries to a dictionary by specifying a new key and assigning a corresponding value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add dictionary entry\n", - "turtles['Splinter'] = 'wine'\n", - "print(turtles)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is also possible to modify existing entry values by takinging an existing key and assigning a new value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Modify dictionary value\n", - "turtles['Splinter'] = 'yellow'\n", - "print(turtles)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will raise an exception if you try to index a dictionary or look up a key for a entry that doesn't exist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " turtles['Rocksteady']\n", - "except Exception as error:\n", - " print_error('KeyError: {}'.format(error))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dictionary Unpacking\n", - "\n", - "Similarly to array-like objects, dictionaries can be unpacked." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a dictionary\n", - "mydict = {'a' : 1, 'b' : 2, 'c' : 3}\n", - "\n", - "# Unpack the dictionary\n", - "val1, val2, val3 = mydict\n", - "\n", - "print('val1 = {} ; val2 = {} ; val3 = {}'.format(val1, val2, val3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that by default unpacking is performed over the keys. To unpack the values you have to be more explicit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Unpack the dictionary values\n", - "val1, val2, val3 = mydict.values()\n", - "\n", - "print('val1 = {} ; val2 = {} ; val3 = {}'.format(val1, val2, val3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 3:** What values will you get when you unpack the dictionary items?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Unpack the dictionary values\n", - "val1, val2, val3 = mydict.items()\n", - "\n", - "# Uncomment to see the answer\n", - "# print('val1 = {} ; val2 = {} ; val3 = {}'.format(val1, val2, val3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Keyword Arguments\n", - "\n", - "In addition to standard position arguments, Python functions accept *keyword arguments* (or simply *kwagrs*), *i.e.* a dictionary of keys and values. \n", - "\n", - "kwargs can be used to define default values in functions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a function with defaults\n", - "def myfunc(a=1, b=2, c=2):\n", - " \n", - " return a + b * 3\n", - "\n", - "print('myfunc =', myfunc())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "They can also be used to pass arguments in any given order." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('myfunc =', myfunc(c=5, a=3, b=7))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is also a special keyword expansion operator `**` thay behaves similarly to the splat operator `*` for use in functions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create dictionary of values\n", - "mydict = {'a': 3, 'b': 4, 'c': 5}\n", - "\n", - "# Unpack the dictionary kwargs into the function\n", - "print('myfunc =', myfunc(**mydict))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, you can define functions for which an idefinite number of kwargs can be passed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a function with optional kwargs\n", - "def subtract(a, b, **kwargs):\n", - " \n", - " res = a - b\n", - " \n", - " if 'return_abs' in kwargs and kwargs['return_abs']:\n", - " res = abs(res)\n", - " \n", - " if 'add_value' in kwargs:\n", - " res += kwargs['add_value']\n", - "\n", - " return res\n", - " \n", - "print('1 - 2 = {}'.format(subtract(1, 2)))\n", - "print('|1 - 2| = {}'.format(subtract(1, 2, return_abs=True)))\n", - "print('1 - 2 + 3 = {}'.format(subtract(1, 2, add_value=3, dummy_arg=None)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tips and Tricks\n", - "\n", - "There are plenty of useful tricks that make dictionary handling a lot easier.\n", - "\n", - "For example, you can convert a list of tuples to a dictionay." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Tuples of film names and release dates\n", - "film1 = ('alien', '1979')\n", - "film2 = ('the shining', '1980')\n", - "film3 = ('the evil dead', '1981')\n", - "film4 = ('blade runner', '1982')\n", - "\n", - "# List of films\n", - "films = [film1, film2, film3, film4]\n", - "print('List: ', films)\n", - "print()\n", - "\n", - "# Dictionary of films\n", - "films = dict(films)\n", - "print('Dict: ', films)\n", - "print()\n", - "print('Alien came out in {}.'.format(films['alien']))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "You can concatinate two dictionaries in a single line." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Make another dictionary\n", - "more_films = dict([('WarGames', '1983'), ('Amadeus', '1984')])\n", - "\n", - "# Concatinate the dictionaries\n", - "films_80s = {**films, **more_films}\n", - "print('Concatination: ', films_80s)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can clear the contents of an existing dictionary using the `clear` method." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Clear the films dictionary\n", - "films.clear()\n", - "print('Clear: ', films)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, you can add, look up and remove dicttionary entries using the `update`, `get` and `pop` methods respectively." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add a value to the dictionary\n", - "films.update({'Back to the Future': '1985'})\n", - "print('Update:', films)\n", - "print()\n", - "\n", - "# Get the value for the key\n", - "print('Get: Back to the Future came out in {}.'.format(films.get('Back to the Future')))\n", - "print()\n", - "\n", - "# Pop the entry out of the dictionary\n", - "films.pop('Back to the Future')\n", - "print('Pop: ', films)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Note that the `get` method will not raise an error if the key is not found, instead it will simply return `None`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Further Reading \n", - "> - Python dicts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5 List Comprehension\n", - "---\n", - "\n", - "This section introduces the concept of *list comprehension*, a tool that you will likely have seen implemented but perhaps not understood. The most sensible way to begin understanding this concept is by looking at basic loops.\n", - "\n", - "### Loops\n", - "\n", - "Loops are a core component of any programming language and Python is certainly no different." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Simple loop\n", - "for i in (1, 2, 3):\n", - " print('i =', i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we can see the use of a basic `for` statement using the variable `i`, which iterates through the values in a tuple.\n", - "\n", - "That being said, Python does offer an extremely powerful way to generate lists from loops using a process called list comprehension. In lower level lanaguages (*e.g.* C, Fortran, *etc.*) arrays are usually built by declaring an empty object of a given size and type and then iteratively filling this array. It is possible to do something similar in Python, in fact, this is often done by people who come to Python from one of these languages." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a list of size 3\n", - "my_int_list = [None, None, None]\n", - "\n", - "# Run a loop\n", - "for i in (1, 2, 3):\n", - " # Fill the list values\n", - " my_int_list[i - 1] = i\n", - " \n", - "print('my_int_list =', my_int_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While this works, it goes against most of what Python aims to achieve. Using Python list properties we can improve a little bit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an empty list\n", - "my_int_list = []\n", - "\n", - "# Run a loop\n", - "for i in range(1, 4):\n", - " # Add list values\n", - " my_int_list.append(i)\n", - " \n", - "print('my_int_list =', my_int_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Note that `range` is a built-in function that produces a list-like object of values within a given range.\n", - "\n", - "This approach may be appropriate in certain situations, particularly if we plan to *break* the loop when a condition is met. For this particular example, however, there is a much easier way to generate the desired object.\n", - "\n", - "### List Comprehension\n", - "\n", - "To perform list comprehension we need the following structure: `[final_expression for_loop_and_conditions]`. For example, to generate the same list object as the previous cell we can do this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an object through list comprehension\n", - "my_int_list = [i for i in range(1, 4)]\n", - "\n", - "print('my_int_list =', my_int_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example the first `i` is the final expression we want (*i.e.* simply the value), this is followed by a for loop that is identical to that in the loop example.\n", - "\n", - "Using list comprehension it is possible to create a list object in one line! This may seem trivial, but it is actually a very useful and highly optimised tool in Python.\n", - "\n", - "### Absolute Magnitude Example\n", - "\n", - "Imagine you need to calculate the absolute magnitude \n", - "\n", - "$$M = m - 5\\log_{10}(d_{\\textrm{pc}}) + 5$$\n", - "\n", - "of various stars:\n", - "\n", - "|Star|$m_v$|$d_{\\textrm{pc}}$|\n", - "|----|-----|-----------------|\n", - "|Alpha Centauri|-0.3|1.3|\n", - "|Canopus|-0.72|30.1|\n", - "|Rigel|0.14|276.1|\n", - "|Deneb|1.26|490.8|\n", - "\n", - "\n", - "\n", - "One way would be be to store the information about the stars in a dictionary (as we just learned about them)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Dictionary of star properties\n", - "stars = dict([('alpha centauri', {'mv': -0.3, 'dpc': 1.3}), \n", - " ('canopus', {'mv': -0.72, 'dpc': 30.1}), \n", - " ('rigel', {'mv': 0.14, 'dpc': 276.1}), \n", - " ('deneb', {'mv': 1.26, 'dpc': 490.8})])\n", - "\n", - "print('The apparent magnitude of Rigel is {}.'.format(stars['rigel']['mv']))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and to loop through the stars." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create an empty list\n", - "abs_mags = []\n", - "\n", - "# Loop through the stars\n", - "for star in stars.values():\n", - " # Calculate absolute magnitude\n", - " mag = round(star['mv'] - 5 * math.log10(star['dpc']) + 5, 2)\n", - " # Add value to list\n", - " abs_mags.append(mag)\n", - " \n", - "print('Absolute magnitude values =', abs_mags)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A more Pythonic approach, however, would be to define a reusable function and generate the final object via list comprehension." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a function to calculate absolute magnitude\n", - "def abs_mag(mv, dpc):\n", - " \n", - " return round(mv - 5 * math.log10(dpc) + 5, 2)\n", - "\n", - "# Produce a list of absolute magnitude values\n", - "abs_mags = [abs_mag(star['mv'], star['dpc']) for star in stars.values()]\n", - "\n", - "print('Absolute magnitude values =', abs_mags)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This way, we not only have a function that can be used for future calculations, but we also generate our object in one line.\n", - "\n", - "### Redshift Example\n", - "\n", - "Let's look at another example. Imagine you want to calculate the redshift of an object given the emitted and observed wavelenths.\n", - "\n", - "$$z = \\frac{\\lambda_{\\textrm{obs}}-\\lambda_{\\textrm{emit}}}{\\lambda_{\\textrm{emit}}}$$\n", - "\n", - "|$\\lambda_{\\textrm{emit}}$|$\\lambda_{\\textrm{obs}}$|\n", - "|-------------------------|------------------------|\n", - "|450|679|\n", - "|348|956|\n", - "|579|264|\n", - "\n", - "We can approach this in a very similar way to the previous example, except in this case let's try without using a dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create lists of wavelength values\n", - "lambda_emit = [450., 348., 579.]\n", - "lambda_obs = [679., 956., 264.]\n", - "\n", - "# Define a function to calculate redshift\n", - "def redshift(emit, obs):\n", - " \n", - " return round((obs - emit) / emit, 3)\n", - "\n", - "# Generate redshift values via list comprehension\n", - "z = [redshift(e, o) for e, o in zip(lambda_emit, lambda_obs)]\n", - "\n", - "print('Redshifts =', z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 4:** Note the use of the `zip` function. Any guess for what it does?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment to see the answer\n", - "# print('The result of zip is:', list(zip(lambda_emit, lambda_obs)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conditions\n", - "\n", - "It is possible to embed a complex series of conditions (*e.g.* `if` statements) into list comprehension." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# A list of names\n", - "names = ['paul', 'jessica', 'leto', 'gunrey', 'duncan', 'piter']\n", - "\n", - "# A new list of names satisfying various conditions\n", - "new_names = [name for name in names if 'e' in name and name[0] < 'k']\n", - "\n", - "print('Names containing the letter \"e\" and starting with a letter lower than \"k\":', new_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nested Lists\n", - "\n", - "It is also possible to flatten nested lists using unpacking and conditions.\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Nested list\n", - "cats = [['lion-o', 'cheetara', 'tygra', 'panthro'], ['wilykit', 'wilycat'], ['snarf'], ['mumm-ra']]\n", - "\n", - "# Flatten list with condition\n", - "cats = [cat for group in cats for cat in group if cat!='mumm-ra']\n", - "\n", - "print('Thundercats:', cats)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Further Reading \n", - "> - List Comprehension in Python" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6 Iterators & Generators\n", - "---\n", - "\n", - "Some very useful tools in Python, particularly when it comes to memory management, are iterators and generators.\n", - "\n", - "### Iterators\n", - "\n", - "The `iter` function can be used to convert objects to iterators, such as lists..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create list iterator\n", - "myiter = iter([1, 2, 3, 4])\n", - "\n", - "print('myiter =', myiter)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... or tuples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create tuple iterator\n", - "myiter = iter((1, 2, 3, 4))\n", - "\n", - "print('myiter =', myiter)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle 5:** How would you go about converting an iterator into a list?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment and edit the following lines appropriately\n", - "# Convert iterator into a list\n", - "# mylist = myiter\n", - "# \n", - "# print('mylist =', mylist)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is not possible to index an iterator." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Try to index iterator\n", - "try:\n", - " myiter[0]\n", - "except Exception as error:\n", - " print_error(error)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead, iterator values are accessed one at a time using the `next` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('next(myiter) =', next(myiter))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Try re-running the previous cell multiple times to see what happens.\n", - "\n", - "### Built-In Functions\n", - "\n", - "Most built-in functions that work on lists (*e.g.* `sum`, `min`, `max`, *etc.*) will also work directly on iterators." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a list\n", - "mylist = [1, 2, 3, 4, 5]\n", - "\n", - "# Create an iterator\n", - "myiter = iter(mylist)\n", - "\n", - "print('sum(mylist) =', sum(mylist))\n", - "print('sum(myiter) =', sum(myiter))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But note that, unlike a list, once an interator has been fully run it remains empty." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('sum(mylist) =', sum(mylist))\n", - "print('sum(myiter) =', sum(myiter))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addtion, many built-in function return iterator-like objects by default (*e.g.* `zip`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create zip object\n", - "myzip = zip([1, 2], [3, 4])\n", - "\n", - "print('Next:', next(myzip))\n", - "print('Next:', next(myzip))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generators\n", - "\n", - "Generators can be thought of as functions that behave like iterators. \n", - "\n", - "Imagine you want a function that can provide the square of a list of values. One approach would be to write a function that takes all of the inputs and returns a list of the corresponding squared values. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to calculate square of input values\n", - "def square(values):\n", - " \n", - " return [value ** 2 for value in values]\n", - "\n", - "print('Squared values =', square([1, 2, 3, 4, 5]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, while this certainly works, we are storing all of the outputs in memory. In some situations we may only wish to calculate the output value when it's needed. To do so we can define a *generator* using a `yield` statement. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a generator function to calculate square of input values\n", - "def square(values):\n", - " \n", - " for value in values:\n", - " \n", - " yield value ** 2\n", - " \n", - "# Create a generator object\n", - "mygen = square([1, 2, 3, 4, 5])\n", - "\n", - "# Run the generator\n", - "print('next(mygen) =', next(mygen))\n", - "print('next(mygen) =', next(mygen))\n", - "print('next(mygen) =', next(mygen))\n", - "print('next(mygen) =', next(mygen))\n", - "print('next(mygen) =', next(mygen))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case the function is only evaluated when the `next` function is called.\n", - "\n", - "An important feature of generators is that mutiple `yield` statements can be included in a single function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a generator function with multiple yields\n", - "def peasant():\n", - " \n", - " yield \"Ready to work.\"\n", - " yield \"Job's done.\"\n", - " yield \"Alright.\"\n", - " yield \"You're the king? Well, I didn't vote for you.\"\n", - "\n", - "# Create a generator object\n", - "p = peasant()\n", - "\n", - "# Run the generator\n", - "print(next(p))\n", - "print(next(p))\n", - "print(next(p))\n", - "print(next(p))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generator Expressions\n", - "\n", - "Similarly to list comprehension, it is possible to produce generator objects in one line using `()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define a generator expression to square values from 1 to 5\n", - "mygen = (value ** 2 for value in range(1, 6))\n", - "\n", - "print('next(mygen) =', next(mygen))\n", - "print('next(mygen) =', next(mygen))\n", - "print('next(mygen) =', next(mygen))\n", - "print('next(mygen) =', next(mygen))\n", - "print('next(mygen) =', next(mygen))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate the sum of cubes using a generator expression\n", - "soc = sum((value ** 3 for value in range(10)))\n", - "\n", - "print('The sum of cubes from 0 to 9 is {}.'.format(soc))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Further Reading\n", - "> - Python Practice Book: Iterators" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7 Exercises\n", - "---\n", - "\n", - "1. Create a list of your favourite superheros and another list of their secret identities.\n", - " 1. Convert your two lists into a dictionary. (Can you do it in one line?)\n", - " 1. Remove one of your heroes and add a villain to your dictionary.\n", - " 1. Add a character that has multiple identities to your dictionary. (What kind of object should this be?)\n", - " 1. Demonstrate that you can look up one of your character's identities.\n", - " \n", - "\n", - "| Superhero | Identity |\n", - "|:-----------:|:---------------------------:|\n", - "| Iron Man | Tony Stark |\n", - "| The Thing | Ben Grimm |\n", - "| Storm | Ororo Munroe |\n", - "| Spider-Man | Peter Parker, Miles Morales |\n", - "\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your solution here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. Use Hubble's law ($v=H_{0}\\,D$) to calculate the distance (in Mpc) of the galaxies in the following table.\n", - "\n", - "|Galaxy|Velocity (km/s)|\n", - "|------|---------------|\n", - "|NGC 123|1320|\n", - "|NGC 2342|5690|\n", - "|NGC 4442|8200|\n", - "\n", - "Remember that $H_0 \\approx 70$ km/s/Mpc." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your solution here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. Flatten the following list using list comprehension.\n", - "\n", - "```python\n", - "mylist = [[[1, 2], [3, 4, 5]], [[6], [7, 8]]]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your solution here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4. Write a generator function that can be used to calculate the Fibonacci sequence.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add your solution here" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/python/README.md b/python/README.md deleted file mode 100644 index cf34aeb..0000000 --- a/python/README.md +++ /dev/null @@ -1,58 +0,0 @@ - - -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master) - -## Introduction to Python ---- - -> Authors: [Samuel Farrens](http://www.cosmostat.org/people/sfarrens), [Santiago Casas](http://www.cosmostat.org/people/santiago-casas) -> Year: 2019 -> Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr), [santiago.casas@cea.fr](mailto:santiago.casas@cea.fr) - - - -This tutorial is comprised of a series of Jupyter notebooks with simple demonstrations and exercises. - -All code blocks are provided in Python (support for >=3.5) and the number of external packages required to run the examples has be kept to a minimum. All of the code has been clearly presented inside the notebook at least once to avoid the use of any "black boxes" for solving the problems presented. - -### Requirements - -In order to run the tutorial notebooks tutees will need to have the following installed: - -* Python (require >=3.5) -* Astropy (recommend >=3.1.2) -* Jupyter (recommend >=1.0.0) -* Matplotlib (recommend >=3.0.3) -* NumPy (recommend >=1.16.2) -* Pandas (recommend >= 0.24.0) -* scikit-learn (recommend >= 0.21.0) -* SciPy (recommend >=1.2.1) -* Seaborn (recommend >= 0.9.0) - -### Notebooks - -#### Tutorial 1: Beginner Topics - -The objective of this tutorial is to provide a first look at Python for beginners. The level is aimed at individuals with little or no experience whatsoever with Python. Experienced users are unlikely to benefit from this tutorial. - -1. [Table of Contents](./Intro.ipynb) -1. [Native Python](./Native.ipynb) -1. [Basic Numpy](./Numpy.ipynb) -1. [Matplotlib](./Matplotlib.ipynb) -1. [Other Packages](./Other-Packages.ipynb) -1. [Answers to Exercises](./Answers-to-exercises.ipynb) - -#### Tutorial 2: Intermediate and Advanced Topics - -The objective of this tutorial is to provide a more in-depth look at object-oriented -and pythonic coding. The level is aimed at individuals with some experience with -Python and good knowledge of basic object types. This tutorial will likely benefit all except the most advanced users. - -1. [Table of Contents](./Intro-2.ipynb) -1. [Pythonic Thinking](./Pythonic.ipynb) -1. [Numpy](./Numpy-Intro.ipynb) -1. [Pandas](./Pandas-Intro.ipynb) -1. [Scipy-Cosmo](./Scipy-Cosmo.ipynb) -1. [The Anatomy of a Python Class: Part I](./Classes_I.ipynb) -1. [The Anatomy of a Python Class: Part II](./Classes_II.ipynb) -1. [Answers to Exercises](./Answers-to-exercises-2.ipynb) diff --git a/python/Scipy-Cosmo.ipynb b/python/Scipy-Cosmo.ipynb deleted file mode 100644 index baea7f2..0000000 --- a/python/Scipy-Cosmo.ipynb +++ /dev/null @@ -1,1270 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"CosmoStat\n", - "
\n", - "
\n", - "
\n", - "
\n", - "\n", - "# Scipy, Matplotlib and useful tricks applied to Cosmology\n", - "\n", - "---\n", - "\n", - "> Author: Santiago Casas\n", - "\n", - "> Email: santiago.casas@cea.fr \n", - "> Year: 2019 \n", - "> Version: 1.0\n", - "\n", - "\n", - "---\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Let's import some useful libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Tell Jupyter to display plots in this notebook.\n", - "%matplotlib inline\n", - "##\n", - "import numpy as np #numpy for arrays and numerics\n", - "import pandas as pd #pandas for working with ordered datasets\n", - "import scipy as sci # for scientific functions\n", - "from scipy import interpolate #for interpolation\n", - "from scipy import integrate #for integration\n", - "import matplotlib.pyplot as plt # for plotting\n", - "# Import interaction module.\n", - "from ipywidgets.widgets import *\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> In this tutorial we will do some basic cosmology use functions, dictionaries, numpy arrays, matplotlib and scipy, to illustrate some of the common tools used by researchers in their everyday life." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Cosmological redshift: easy plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The cosmological redshift we observe in galaxies (their spectral lines move towards larger wavelengths) $z$ is related to the relative size of the observable Universe $a$, by $$a = \\frac{1}{1+z}$$ \n", - "\n", - "This can be obtained from the fact that light travels in null-geodesics, since it is described as a relativistic particle." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Let's code this in a simple function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def a_of_z(z):\n", - " a=1/(1+z)\n", - " return a " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z_arr = np.linspace(0.001,3, 100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a_arr = a_of_z(z_arr)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a_arr.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot this using matplotlib's function `plot`, which takes an `x` and a `y` array as arguments, a format argument `fmt = '[marker][line][color]'` and many other optional keyword arguments, like `color`, linestyle, opacity and many more. Available markers, such as `o` for circles, `-.` for dot-dashed linestyles and `'r'` for colors can be checked in the documentation ." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(z_arr, a_arr)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For plots in log-log space, we can use `plt.loglog`. And it is always better to start the plot with a figure creation, since it simplifies exporting and adjustments later on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "fig = plt.figure()\n", - "#Let's add some color and linestyle, r for red and a label for the curve.\n", - "plt.loglog(z_arr, a_arr, '-.r', label='$a(z)$') #notice how you can use LaTeX here\n", - "#Let's add a plot title\n", - "plt.title(\"Redshift\")\n", - "#Let's add a legend\n", - "plt.legend()\n", - "#Let's add an x-label, with some font-size\n", - "plt.xlabel(\"z\", fontsize=15)\n", - "# and a y-label, rotated by 90 degrees (which is the default in this case anyway)\n", - "plt.ylabel(\"a\", rotation=90, fontsize=15) \n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# The Hubble function: working with dictionaries and optional parameters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Hubble function $H(z)$ can be found solving Einstein's field equations of General Relativity under the Friedmann-Lemaître-Robertson-Walker metric, i.e. under the assumption of homogeneity and isotropy. It describes the expansion rate (speed) of an observable Universe of size a, $H(a) = \\dot{a}/a$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Cosmic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we know the energy and matter components of the Universe today:\n", - " * Cold Dark Matter density $\\Omega_c$ \n", - " * Baryonic (normal) matter density $\\Omega_b$\n", - " * Radiation density $\\Omega_r$\n", - "we can write down the Hubble function as:\n", - "$$ H(z) = H_0 \\sqrt{(\\Omega_c(1+z)^3 + \\Omega_b(1+z)^3) + \\Omega_r(1+z)^4 + \\Omega_{\\Lambda}}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "where this mysterious component $\\Omega_{\\Lambda}$, is what we think is causing the accelerated expansion of the Universe and we think it comes from a *Cosmological constant*. In the general case where we are looking for alternatives to the cosmological constant, we call it also \"Dark Energy\", for the lack of a better name." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the Universe is flat, then $\\Omega_{\\Lambda}$ is defined by whatever we can't observe and it's simply given by: $$ \\Omega_{\\Lambda} = 1 - \\Omega_c - \\Omega_b - \\Omega_r $$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Let's code first this constraint:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def Omega_L(Omega_c, Omega_b, Omega_r):\n", - " oL = 1 - Omega_c - Omega_b - Omega_r\n", - " return oL" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Now let's write a function for $H(z)$. If you still don't know or haven't asked, $H_0$ is the rate of expansion of the universe today and it is measured in km/s/Mpc. Its value can be inferred in many ways, from the Cosmic Microwave Background, from Gravitational Lensing or by measuring the distance to Supernovae. Currently it is a hot topic in cosmology, since there are strong tensions in measurements from different probes.\n", - "A value of $70$ km/s/Mpc, means that a galaxy at a distance of $1$Mpc from us, moves away from us at a speed of $70$km/s." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> To be fancy and practice some dictionary techniques, let's avoid defining so many arguments in the function and let's just pass a dictionary instead, that contains the cosmological parameters." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> This function will return default parameters when nothing is passed and can return either all the cosmological parameters or a single one." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def cosmological_parameters(cosmo_pars=dict(), which='all'):\n", - " H0 = cosmo_pars.get('H0', 67.32) # Planck 2018 cosmological parameters as default\n", - " Oc = cosmo_pars.get('Omega_c', 0.26641)\n", - " Ob = cosmo_pars.get('Omega_b', 0.04939)\n", - " Or = cosmo_pars.get('Omega_r', 0.0000930479)\n", - " Om = Ob+Oc\n", - " OL = Omega_L(Oc, Ob, Or)\n", - " default_dict = {'Omega_b': Ob, \n", - " 'Omega_c':Oc,\n", - " 'Omega_m': Om,\n", - " 'Omega_r':Or,\n", - " 'Omega_L':OL,\n", - " 'H0':H0}\n", - " if which=='all':\n", - " return H0, Oc, Ob, Or, Om, OL\n", - " else: # this code is prone to bugs if which is not a string or not a parameter\n", - " return default_dict[which]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cosmological_parameters()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cosmological_parameters(which='Omega_m')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Here we have used `dict.get()` instead of `dict['key']`, since `get('key', default)` allows us to define default values in case the keys are not present in the dictionary. And we have created a keyword argument for cosmo_pars, so we can evaluate $H(z)$ with a single redshift argument:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def Hubble(z, cosmo_pars=dict()):\n", - " H0, Oc, Ob, Or, Om, OL = cosmological_parameters(cosmo_pars)\n", - " H = H0 * np.sqrt(Om*(1+z)**3 + Or*(1+z)**4 + OL)\n", - " return H" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Notice how we recover the default H_0 when we evaluate at z=0.\n", - "Hubble(0.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Or how we recover another H0 that we passed as dictionary \n", - "Hubble(0., {'H0':73})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define a dictionary of cosmological parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mycosmo = {'H0': 72, \n", - " 'Omega_c':0.32,\n", - " 'Omega_b':0.05}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now we get the H_0 we actually provided\n", - "Hubble(0., mycosmo)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Exercise 1**: Plot the Hubble function $H(z)$, for a newly created cosmology of your choice, next to `mycosmo` and the default Planck cosmology, using the colors `red`, `green` and `blue`, respectively. Change also the line styles (`'--', or '-' or ':'`) (You can use the `fmt` keywords or the `color` optional argument). Use a range in redshift from 0. to 10. Use linear scales for the plot `plot`. Hint: Copy as much as possible from the plotting code above." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Matter, Radiation and Dark Energy densities as a function of time: Complicated subplots, annotations and labeling." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The density as a function of time of any of those components can be obtained with the Hubble function as follows: \n", - "$$ \\Omega_i(z) = \\frac{H_0^2 \\Omega_i (1+z)^p}{H^2(z)}$$ \n", - "where $i$ is the component, and $p$ is the exponent corresponding to the specific component.\n", - "For matter, $p=3$, for radiation, $p=4$, for the cosmological constant $p=0$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's code this one up, by first creating a dictionary of exponents:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cosmo_pexp = {'Omega_b': 3, \n", - " 'Omega_c':3,\n", - " 'Omega_m':3,\n", - " 'Omega_r':4,\n", - " 'Omega_L':0}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def Omega_of_z(z, component='Omega_c', cosmo_pars=dict({None:0}) ):\n", - " O_generic = cosmological_parameters(cosmo_pars, which=component)\n", - " H0 = cosmological_parameters(cosmo_pars, which='H0')\n", - " p = cosmo_pexp[component]\n", - " O_of_z = (H0**2 * O_generic*(1+z)**p)/np.power(Hubble(z, cosmo_pars),2)\n", - " return O_of_z" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check that the function works for a given array" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z_arr = np.logspace(-3,2.5, 100)\n", - "Omega_of_z(z_arr)[1:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crazycosmo = {'H0': 75, \n", - " 'Omega_c':0.58,\n", - " 'Omega_b':0.06, \n", - " 'Omega_r':0.005}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Now let's plot the evolution of matter and radiation species as a function of redshift for two different cosmologies. For this we will use the subplot functionality. There are many ways of doing this, but the most common one is `plt.subplot(Rows, Cols, *kwargs) `, where some of the optional keyword arguments are for sharing axes, for setting the figure size or for adjusting interplot spaces. It will return a figure instance and an array of axis instances, which can be indexed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z_arr = np.logspace(-3,2.5, 100)\n", - "#return a figure object instance and an axis object instance\n", - "fig, ax = plt.subplots(2, 2, sharey='row', sharex='col', figsize=(15,8)) #all plots in the same row, share the y-axis.\n", - "\n", - "# once you specify an axis, it is in this instance where plots are performed\n", - "ax[0,0].semilogx(z_arr, Omega_of_z(z_arr, 'Omega_m'), '-', label='$\\Omega_m$', color='orange') \n", - "ax[0,0].semilogx(z_arr, Omega_of_z(z_arr, 'Omega_L'), '-.', label='$\\Omega_{\\Lambda}$', color='violet')\n", - "ax[0,0].legend()\n", - "#Let's add a yellow shade for the matter domination era\n", - "ax[0,0].axvspan(0.279, 300, alpha=0.3, color='yellow')\n", - "# and let's annotate it using text()\n", - "ax[0,0].text(5,0.8, \"matter domination era\", size=10, color='darkorange')\n", - "ax[1,1].set_xlabel('redshift $z$', fontsize=15) #notice the change from plt.xlabel() -> ax.set_xlabel()\n", - "ax[1,0].set_xlabel('redshift $z$', fontsize=15)\n", - "ax[0,1].semilogx(z_arr, Omega_of_z(z_arr, 'Omega_m', crazycosmo), '-', label='$\\Omega_m$', color='orange') \n", - "ax[0,1].semilogx(z_arr, Omega_of_z(z_arr, 'Omega_L', crazycosmo), '-.', label='$\\Omega_{\\Lambda}$', color='violet')\n", - "# Let's add an annotation with an arrow. Check the documentation for more about the settings.\n", - "ax[0,1].annotate('no dark energy domination', xy=(0.01,0.66), xytext=(3,0.4), \n", - " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle3, angleA=0, angleB=90\"))\n", - "ax[0,1].legend()\n", - "ax[1,0].semilogx(z_arr, Omega_of_z(z_arr, 'Omega_c'), '-', label='$\\Omega_c$', color='darkorange') \n", - "ax[1,0].semilogx(z_arr, Omega_of_z(z_arr, 'Omega_r'), ':', label='$\\Omega_{r}$', color='darkblue')\n", - "ax[1,0].legend(loc='upper left') #notice the different location compared to the default\n", - "ax[1,1].set_xlabel('redshift $z$', fontsize=15) #notice the change from plt.xlabel() -> ax.set_xlabel()\n", - "ax[1,0].set_xlabel('redshift $z$', fontsize=15)\n", - "ax[1,1].semilogx(z_arr, Omega_of_z(z_arr, 'Omega_c', crazycosmo), '-', label='$\\Omega_c$', color='darkorange') \n", - "ax[1,1].semilogx(z_arr, Omega_of_z(z_arr, 'Omega_r', crazycosmo), ':', label='$\\Omega_{r}$', color='darkblue')\n", - "ax[1,1].legend()\n", - "# Let's add an annotation with an arrow. Check the documentation for more about the settings.\n", - "ax[1,1].annotate('radiation dominated era', xy=(250,0.66), xytext=(0.001,0.4), \n", - " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle3, angleA=0, angleB=90\"))\n", - "ax[1,1].set_xlabel('redshift $z$', fontsize=15) #notice the change from plt.xlabel() -> ax.set_xlabel()\n", - "ax[1,0].set_xlabel('redshift $z$', fontsize=15) #notice that we only label the bottom 2 plots\n", - "ax[0,0].set_ylabel('Fractional energy density $\\Omega_i$', fontsize=15) #notice the change from plt.xlabel() -> ax.set_xlabel()\n", - "ax[1,0].set_ylabel('Fractional energy density $\\Omega_i$', fontsize=15) #notice that we only label the bottom 2 plots\n", - "#let's save the figure\n", - "fig.savefig('./materials/cosmological_energy_densities.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE**: This plotting code seems a bit clumsy and extended, another way of doing this, without so many lines of code, is through a proper loop and some zip() and list comprehensions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hubble function: Working with external data, scipy interpolation and fit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One very useful function in numpy is `np.loadtxt` which reads very efficiently data tables and automatically ignores headers (which start usually with `#`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_h_ofz=np.loadtxt('./materials/alldataEz-H0planck-real-H0planck.txt')\n", - "data_h_ofz[1:5]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This data contains measurements of the dimensionless Hubble function $E(z) = H(z)/H_0$ at different redshifts and with given error bars. This data was taken using supernovae, cepheids or cosmic chronometers (measurements of the age of very massive galaxies)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot the data using the function `plt.errorbar`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure()\n", - "plt.errorbar(data_h_ofz[:,0], data_h_ofz[:,1], data_h_ofz[:,2], fmt='o', color='black', ecolor='black', capsize=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Exercise 2:** Overplot the dimensionless Hubble function $E(z)$, using the default cosmological parameters." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scipy Interpolation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Scipy is a package that includes many useful tools for scientific research, such as interpolation, fitting and integration." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's compute an interpolation function through the given points." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's use interp1d as the simplest one first\n", - "interp1_h_ofz = interpolate.interp1d(data_h_ofz[:,0], data_h_ofz[:,1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The domain of the interpolation is\n", - "data_range=[data_h_ofz[0,0],data_h_ofz[-1,0]]\n", - "data_range" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now we have a function of E(z)\n", - "interp1_h_ofz(0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Let's plot it together with the data\n", - "fig = plt.figure()\n", - "z_arr = np.linspace(data_range[0], data_range[1], 100)\n", - "plt.errorbar(data_h_ofz[:,0], data_h_ofz[:,1], data_h_ofz[:,2], fmt='o', color='black', ecolor='black', capsize=2)\n", - "plt.plot(z_arr, interp1_h_ofz(z_arr), '-', color='tomato', label='Interpolation')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Now let's try a cubic and a UnivariateSpline interpolation\n", - "interp2_h_ofz = interpolate.interp1d(data_h_ofz[:,0], data_h_ofz[:,1], kind='cubic')\n", - "interp3_h_ofz = interpolate.UnivariateSpline(data_h_ofz[:,0], data_h_ofz[:,1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Let's plot it together with the data\n", - "fig = plt.figure(figsize=(8,8))\n", - "z_arr = np.linspace(data_range[0], data_range[1], 100)\n", - "plt.errorbar(data_h_ofz[:,0], data_h_ofz[:,1], data_h_ofz[:,2], fmt='o', color='black', ecolor='black', capsize=2, \n", - " markersize=2)\n", - "plt.plot(z_arr, interp1_h_ofz(z_arr), '-', color='tomato', label='linear')\n", - "plt.plot(z_arr, interp2_h_ofz(z_arr), ':', color='indigo', label='cubic')\n", - "plt.plot(z_arr, interp3_h_ofz(z_arr), ':', color='forestgreen', label='spline')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One can see that the result of the interpolation might differ a lot between different methods" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `UniVariateSpline` method has a `weight` optional argument `w[i]`, which sets a smoothing factor `s`. If this smoothing factor is set to `0`, the interpolation will pass through all points. By default it will be set to `s=len(w)` if `1/w[i]` is a good estimate of the standard deviation of the `y[i]` data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Let's use a neat trick of IPython notebooks, **`interact`**, which allows us to play with sliders on the notebook itself, to control some of the parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z_arr = np.linspace(data_range[0], data_range[1], 100)\n", - "# Let's define a wrapper function for the above plot with the smoothing factor as an interactive parameter\n", - "def plot_interpol_data(smoothing):\n", - " interp4_h_ofz = interpolate.UnivariateSpline(data_h_ofz[:,0], data_h_ofz[:,1], s=0)\n", - " interp5_h_ofz = interpolate.UnivariateSpline(data_h_ofz[:,0], data_h_ofz[:,1], w=(1/data_h_ofz[:,2]) )\n", - " interp6_h_ofz = interpolate.UnivariateSpline(data_h_ofz[:,0], data_h_ofz[:,1], s=smoothing)\n", - " fig = plt.figure(figsize=(8,8))\n", - " plt.errorbar(data_h_ofz[:,0], data_h_ofz[:,1], data_h_ofz[:,2], fmt='o', color='black', ecolor='black', capsize=2, \n", - " markersize=2)\n", - " plt.plot(z_arr, interp3_h_ofz(z_arr), '-', color='orange', lw=3,label='auto spline')\n", - " plt.plot(z_arr, interp4_h_ofz(z_arr), '-.', color='firebrick', lw=3,label='s=0 spline')\n", - " plt.plot(z_arr, interp5_h_ofz(z_arr), ':', color='violet', lw=3,label='weigthed spline')\n", - " plt.plot(z_arr, interp6_h_ofz(z_arr), '-.', color='teal', lw=3, label='smoothed spline')\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Simply passing the function and the argument, together with a tuple, specifying (min, max, step), \n", - "#already produces nice interactive plots.\n", - "interact(plot_interpol_data, smoothing=(0.0,1.0,0.05));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE**: Interpolating through data points with error bars is an active area of research and a not completely solved problem. The best is to test several methods and find the most appropriate one for the given case." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Numpy polynomial fit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A very useful tool for data is to perform a polynomial fit by minimizing the equation:\n", - "$$ E = \\sum_{j=0}^k |p(x_j)-y_j|^2 $$\n", - "where $p$ is a polynomial of degree $d$ for a dataset of length $k$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# numpy.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False)\n", - "# The weights should be w=1/sigma\n", - "# deg is the degree of the polynomial\n", - "coeffs = np.polyfit(data_h_ofz[:,0], data_h_ofz[:,1], 3, w=1/(data_h_ofz[:,2]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The coefficients are returned from larger to smaller degree." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(coeffs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One can use `poly1d` to deal with polynomials easily:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "poly = np.poly1d(coeffs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "poly(np.arange(0.0,3,0.1))[0:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig=plt.figure()\n", - "plt.plot(np.arange(0.0,3,0.1), poly(np.arange(0.0,3,0.1)), color='red', label='polyfit')\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Exercise 3:** Create an interactive plot, that shows the data with errorbars, the smoothing spline (with the same steps as before) and the polyfit with degrees 1-8, with steps of 1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Gaussian Processes with Scikit-Learn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "from sklearn.gaussian_process import GaussianProcessRegressor\n", - "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n", - "\n", - "x = np.atleast_2d(np.linspace(data_range[0], data_range[1], 1000)).T\n", - "\n", - "X = data_h_ofz[:,0]\n", - "X = np.atleast_2d(X).T\n", - "\n", - "# Observations and noise\n", - "y = data_h_ofz[:,1]\n", - "dy = data_h_ofz[:,2]\n", - "\n", - "kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))\n", - "# Instantiate a Gaussian Process model\n", - "gp = GaussianProcessRegressor(kernel=kernel, alpha=dy ** 2,\n", - " n_restarts_optimizer=10)\n", - "\n", - "# Fit to data using Maximum Likelihood Estimation of the parameters\n", - "gp.fit(X, y)\n", - "\n", - "# Make the prediction on the meshed x-axis (ask for MSE as well)\n", - "y_pred, sigma = gp.predict(x, return_std=True)\n", - "\n", - "# Plot the function, the prediction and the 95% confidence interval based on\n", - "# the MSE\n", - "plt.figure(figsize=(9,6))\n", - "plt.plot(x, poly(x), 'r:', lw=3, label=r'$E(z) polyfit$')\n", - "plt.errorbar(X.ravel(), y, dy, fmt='r.', markersize=10, label='Observations')\n", - "plt.plot(x, y_pred, 'b-', label='Prediction')\n", - "plt.fill(np.concatenate([x, x[::-1]]),\n", - " np.concatenate([y_pred - 1.9600 * sigma,\n", - " (y_pred + 1.9600 * sigma)[::-1]]),\n", - " alpha=.5, fc='b', ec='None', label='95% confidence interval')\n", - "plt.xlabel('$z$')\n", - "plt.ylabel('$E(z)$')\n", - "plt.ylim()\n", - "plt.legend(loc='upper left')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Comoving and angular diameter distance: Scipy integration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The comoving distance [in Mpc] to a galaxy far away from us, is defined as\n", - "$$ d_{\\rm com}(z) = \\int_0^z \\frac{c \\,\\, \\rm{d}\\tilde{z}}{H(\\tilde{z})}$$\n", - "where $c$ is the speed of light and $H(z)$ the Hubble function in km/s/Mpc. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# speed of light\n", - "import scipy.constants as sciconst\n", - "clight = sciconst.speed_of_light/1000 #in km/s\n", - "clight" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's code up the integral:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def drdz(zp, cosmo_pars=dict()):\n", - " return (clight)/Hubble(zp, cosmo_pars)\n", - "\n", - "def d_comoving(zc, cosmo_pars=dict()):\n", - " dd = lambda zc : drdz(zc, cosmo_pars=cosmo_pars) \n", - " #notice the use of a lambda function to pass the optional arguments\n", - " int = integrate.romberg(dd,0.,zc) #notice the use of a romberg integration\n", - " return int\n", - "\n", - "#notice the use of np.vectorize to allow for array arguments\n", - "vect_dcom = np.vectorize(d_comoving)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The distance to a galaxy at redshift $z=0.5$ is almost 2 Gpc!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(vect_dcom(0.5))\n", - "print(d_comoving(0.5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While the angular diameter distance $d_A$, is defined as the distance to an object of size x, that is viewed under an angle $\\theta$. Doing the math, one gets simply:\n", - "$$ d_{\\rm A}(z) = \\frac{1}{1+z} \\int_0^z \\frac{c \\,\\, \\rm{d}\\tilde{z}}{H(\\tilde{z})}$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def d_angular(zc, cosmo_pars=dict()):\n", - " da=(1/(1+zc)) * vect_dcom(zc, cosmo_pars=cosmo_pars)\n", - " return da" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d_angular(0.5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Notice how this distance is smaller than the comoving one." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot both distances together, for a new cosmology!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "newcosmo = {'H0': 72, \n", - " 'Omega_c':0.32,\n", - " 'Omega_b':0.05,\n", - " 'Omega_r':0.00001}\n", - "z_arr = np.linspace(0.,10., 100)\n", - "fig = plt.figure()\n", - "#Let's add some color and linestyle, r for red and a label for the curve.\n", - "plt.plot(z_arr, d_angular(z_arr, newcosmo), '-r', label='$d_A$') #notice how you can use LaTeX here\n", - "plt.plot(z_arr, vect_dcom(z_arr, newcosmo), '--g', label='$d_{com}$') #notice how you can use LaTeX here\n", - "plt.plot(z_arr, vect_dcom(z_arr), '-.b', label='$d_{com}$ Planck cosmo') #notice how you can use LaTeX here\n", - "\n", - "#Let's add a plot title\n", - "plt.title(\"cosmological distances\")\n", - "#Let's add a legend\n", - "plt.legend()\n", - "#Let's add an x-label, with some font-size\n", - "plt.xlabel(\"z\", fontsize=15)\n", - "# and a y-label, rotated by 90 degrees (which is the default in this case anyway)\n", - "plt.ylabel(\"d(z)\", rotation=90, fontsize=15) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Integration of arrays" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we saw before, obtaining an interpolating function from sparse and noisy data is a bit tricky." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this reason, sometimes one is forced to integrate directly the given array. Let's try this with the data we imported before. The tool for this is called `integrate.simps`. (See the documentation)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def data_drdz(zc, e_data, cosmo_pars=dict()): #accepting the {z, E(z)} data\n", - " xx = e_data[:,0]\n", - " maxi = np.where(xx <= zc)[0][-1] + 1\n", - " #print(maxi)\n", - " xx = xx[:maxi]\n", - " h0 = cosmological_parameters(cosmo_pars, which='H0')\n", - " yy = (clight/h0)*(1/e_data[:,1])\n", - " yy = yy[:maxi]\n", - " return xx, yy\n", - "\n", - "def data_comoving_distance(zc, e_data, cosmo_pars=dict()): #remember the data we have is in E(z)\n", - " xx, yy = data_drdz(zc, e_data, cosmo_pars)\n", - " dc = integrate.simps(yy, xx)\n", - " return dc \n", - "\n", - "#this function can't be easily vectorized, because of np.where" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_comoving_distance(0.5,data_h_ofz)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z_range = np.linspace(0.,2.5,10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d_com_data = np.array([data_comoving_distance(zz, data_h_ofz) for zz in z_range])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "newcosmo = {'H0': 72,\n", - " 'Omega_r':0.00001}\n", - "z_arr = np.linspace(0.,5., 100)\n", - "fig = plt.figure()\n", - "#Let's add some color and linestyle, r for red and a label for the curve.\n", - "plt.plot(z_range, d_com_data, 'o-r', label='$d_{com}$ from data') #notice how you can use LaTeX here\n", - "plt.plot(z_arr, vect_dcom(z_arr, newcosmo), '--g', label='$d_{com} \\,\\,H_0=72$km/s/Mpc') #notice how you can use LaTeX here\n", - "plt.plot(z_arr, vect_dcom(z_arr), '-.b', label='$d_{com}$ Planck cosmo') #notice how you can use LaTeX here\n", - "\n", - "#Let's add a plot title\n", - "plt.title(\"cosmological distances\")\n", - "#Let's add a legend\n", - "plt.legend()\n", - "#Let's add an x-label, with some font-size\n", - "plt.xlabel(\"z\", fontsize=15)\n", - "# and a y-label, rotated by 90 degrees (which is the default in this case anyway)\n", - "plt.ylabel(\"d(z)\", rotation=90, fontsize=15) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **NOTE**: Notice that despite the data being evaluated with $H_0$ from Planck (the default one), the distance at higher redshifts seems to indicate a higher value of $H_0 \\approx 72$km/s/Mpc. Does this indicate some sort of tension with Planck? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **Puzzle:** Can you propagate the errors from $E(z)$ onto $d_{com}(z)$ and add the error bars to the red dots above?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Homework Exercise 4:** \n", - "* In the materials folder you can find a file called `Fs8dataclean`.It contains `z`, $f\\sigma_8 (z)$ data and its error bars.\n", - "* The function $f\\sigma_8 (z)$ is an indication of how fast structures have grown in the universe.\n", - "* For the $\\Lambda$CDM model, this function can be written as:\n", - "$$ f\\sigma_8(z) = f(z)D(z)\\sigma_{8,0}$$\n", - "* Where the growth rate is:\n", - "$$f(z) = \\Omega_m (z)^{\\gamma}$$ with $\\gamma=0.55$\n", - "* And the growth function is:\n", - "$$D(z) = \\exp \\big(- \\int_0^z \\frac{f(\\tilde{z})}{(1+\\tilde{z})} \\big) $$\n", - "* Finally, the free parameter is $\\sigma_{8,0} = 0.83$\n", - "\n", - " 1. Import the data and plot it with its error bars.\n", - " 2. Plot the data together with a couple of interpolation functions of your choice and a polynomial fit of order 3.\n", - " 3. Compute the theoretical curve, according to the points above, using integration. Does the theoretical model pass through the data points? try out different cosmological values.\n", - " 4. Compute the integral of the data alone, to obtain $D(z)$. Is it a good approximation or is the data too noisy?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> **[Answers](./Answers.ipynb) to puzzles and exercises**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bonus Chapter on Seaborn plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " > Notice that despite matplotlib being a really nice library, the standard settings\n", - "produce a very rough plot and it needs a lot of tweaking to get publication-ready plots. A way to improve this quickly, is to use `seaborn`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "sns.set()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Consider the example we had before in the `Numpy` lesson:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n", - "data = pd.DataFrame(data, columns=['x', 'y'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#matplotlib with seaborn style\n", - "for col in 'xy':\n", - " plt.hist(data[col], density=True, alpha=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pure seaborn\n", - "sns.distplot(data['x'])\n", - "sns.distplot(data['y']);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " sns.kdeplot(data['x'], data['y']);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with sns.axes_style('white'):\n", - " sns.jointplot(\"x\", \"y\", data, kind='kde');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - 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-5,1962,20.8,20.7,21.9,22.0,23.0,23.3,22.8,22.8,23.2,23.0,21.9,20.9,22.2 -6,1963,20.0,20.8,21.5,22.0,22.6,23.1,22.6,22.9,23.1,22.6,22.0,21.2,22.0 -7,1964,20.6,21.6,22.3,22.5,22.9,22.7,22.8,23.0,22.7,22.3,22.1,20.8,22.2 -8,1965,20.1,20.5,21.5,20.9,21.8,22.0,21.8,21.4,21.5,21.5,21.5,20.3,21.2 -9,1966,21.2,20.8,21.0,22.1,22.3,22.8,22.5,22.4,22.3,22.1,20.8,20.5,21.7 -10,1967,20.6,20.6,19.9,21.1,21.7,21.9,21.4,21.7,21.7,21.7,21.1,20.6,21.2 -11,1968,19.8,19.6,19.9,20.9,21.7,21.7,21.4,21.5,22.0,21.9,21.4,20.1,21.0 -12,1969,20.8,20.7,22.1,22.7,23.4,22.8,21.9,22.0,22.0,22.0,21.2,20.9,21.9 -13,1970,20.9,20.1,21.6,22.1,21.9,22.4,21.9,21.7,21.8,21.9,20.5,20.3,21.4 -14,1971,20.1,20.0,20.2,21.5,21.8,21.4,21.1,21.4,21.5,21.6,21.2,19.9,21.0 -15,1972,20.0,20.3,21.0,21.5,22.3,22.1,22.5,21.6,22.0,21.6,21.8,21.0,21.5 -16,1973,21.0,20.6,22.1,22.4,22.2,22.2,21.6,21.9,22.0,22.0,21.3,19.5,21.6 -17,1974,19.7,19.6,20.8,21.2,22.1,21.9,21.0,21.6,21.9,21.2,20.8,20.3,21.0 -18,1975,19.8,19.9,21.4,21.3,22.2,21.4,20.9,21.1,21.5,21.5,21.4,19.4,21.0 -19,1976,19.2,19.3,20.4,21.5,21.8,21.8,21.5,21.4,21.8,22.1,21.4,20.6,21.1 -20,1977,19.8,20.9,21.1,21.5,22.9,22.3,21.7,21.9,21.8,22.0,21.7,21.1,21.6 -21,1978,20.0,20.8,21.2,22.0,22.7,22.0,21.8,22.0,21.9,22.1,21.9,21.1,21.6 -22,1979,20.5,20.6,21.6,22.0,22.6,22.5,22.5,22.2,22.6,22.7,21.6,21.0,21.9 -23,1980,21.1,20.3,21.6,22.2,23.0,22.7,22.5,22.2,22.1,22.1,21.6,20.2,21.8 -24,1981,20.0,20.6,21.3,21.4,22.9,22.3,21.6,21.9,21.8,22.2,21.2,21.0,21.5 -25,1982,21.1,21.0,21.2,22.1,22.9,22.8,21.8,22.0,22.2,21.4,21.2,20.9,21.7 -26,1983,21.4,22.2,22.6,23.1,22.9,23.3,22.8,22.8,22.7,21.8,21.9,21.0,22.4 -27,1984,19.9,20.7,21.5,22.9,21.9,21.8,22.0,22.1,22.0,21.8,20.8,19.8,21.4 -28,1985,20.0,20.2,20.8,21.6,22.8,22.3,21.8,21.5,22.2,21.8,21.2,20.4,21.4 -29,1986,19.7,20.8,20.6,21.5,22.5,22.2,21.9,22.1,22.1,22.0,21.8,21.2,21.5 -30,1987,20.7,21.2,22.7,21.6,22.6,23.0,22.7,22.3,22.7,22.1,22.1,21.8,22.1 -31,1988,21.0,21.0,21.0,22.7,22.8,22.7,22.0,22.2,22.5,21.9,21.6,20.4,21.8 -32,1989,20.3,19.6,20.6,21.7,22.1,22.0,21.8,22.2,22.1,21.8,22.0,20.6,21.4 -33,1990,20.5,20.5,20.9,22.4,22.4,22.4,22.1,22.2,22.4,22.3,22.0,20.7,21.7 -34,1991,20.5,20.3,22.2,22.0,22.4,22.8,22.2,21.8,22.5,21.9,21.1,20.3,21.7 -35,1992,20.6,21.3,21.6,22.5,22.4,23.0,21.9,21.5,22.0,21.5,22.0,20.9,21.8 -36,1993,20.7,21.0,21.6,23.0,23.4,22.9,22.7,23.6,23.3,23.9,23.1,22.5,22.6 -37,1994,21.8,21.2,22.2,22.2,23.0,22.7,21.9,22.1,22.8,22.5,21.8,21.3,22.1 -38,1995,21.4,22.7,22.7,24.2,23.5,22.5,22.6,22.9,22.9,22.4,22.2,22.4,22.7 -39,1996,21.2,20.9,21.6,22.4,22.8,22.8,22.5,22.2,22.7,22.4,21.4,20.4,21.9 -40,1997,20.7,21.2,21.4,22.7,22.1,22.4,22.7,23.0,22.9,22.7,22.6,21.6,22.2 -41,1998,21.7,22.8,22.6,22.7,23.3,23.0,22.3,22.4,23.2,22.8,22.1,21.0,22.5 -42,1999,21.0,20.7,21.0,22.0,22.6,22.3,21.7,22.0,22.1,21.8,21.3,19.7,21.5 -43,2000,19.6,20.0,21.1,21.6,22.3,22.5,22.0,22.3,22.6,22.2,22.0,20.9,21.6 -44,2001,20.0,20.6,21.7,21.6,23.0,22.1,22.1,22.7,22.7,23.0,21.6,21.6,21.9 -45,2002,21.0,21.2,21.5,21.6,22.5,23.1,22.4,22.2,23.0,22.4,21.8,21.5,22.0 -46,2003,21.0,21.7,22.5,22.9,22.8,22.6,22.7,22.5,23.0,23.0,22.3,21.0,22.3 -47,2004,21.0,21.2,21.2,22.6,22.2,22.6,22.8,22.3,22.3,23.0,21.6,20.6,22.0 -48,2005,19.9,20.5,23.1,22.8,23.6,23.5,23.0,22.6,22.8,22.7,21.4,21.3,22.3 -49,2006,20.9,20.7,21.0,22.1,22.8,22.7,22.5,23.0,22.5,22.9,21.7,21.7,22.0 -50,2007,20.8,21.4,21.9,22.8,23.5,23.4,22.4,22.9,22.6,22.4,21.5,21.0,22.2 -51,2008,21.0,21.0,21.4,22.3,22.8,22.9,22.6,22.9,22.9,22.2,21.1,20.8,22.0 -52,2009,20.4,20.0,20.7,22.1,22.6,23.0,22.5,22.5,23.1,22.7,21.6,21.7,21.9 -53,2010,20.9,22.0,22.2,23.1,23.3,22.9,22.9,22.9,22.5,22.5,21.2,20.0,22.2 -54,2011,20.6,21.1,21.2,22.3,23.0,23.2,22.5,22.8,23.0,22.0,21.5,20.6,22.0 -55,2012,20.3,20.8,20.8,22.1,23.1,23.1,22.2,22.8,22.6,22.4,21.1,21.4,21.9 -56,2013,21.2,21.0,21.4,22.7,22.6,22.7,22.7,22.9,23.3,23.0,22.7,21.7,22.3 -57,2014,21.2,21.4,22.1,22.9,23.4,23.8,23.8,23.7,22.8,24.0,23.7,23.3,23.0 -58,2015,22.7,22.6,22.6,23.7,22.4,23.1,22.3,22.8,22.7,23.2,22.7,23.0,22.8 -59,2016,22.1,21.5,23.0,23.7,24.1,23.2,22.6,23.2,23.2,23.2,22.5,21.9,22.9 diff --git a/python/materials/Fs8covariance.txt b/python/materials/Fs8covariance.txt deleted file mode 100644 index 6768492..0000000 --- a/python/materials/Fs8covariance.txt +++ /dev/null @@ -1,20 +0,0 @@ -0.003025 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0.0225 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0.0036 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0.00339889 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0.004489000000000001 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0.009409 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0.00142884 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0.002025 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0.0046240000000000005 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0064 0. 0. 0. 0.0038441958326807446 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.001444 0. 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.001444 0. 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0036 0. 0. 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0038441958326807446 0. 0. 0. 0.0088775 0. 0.003933091290066886 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0011560000000000001 0. 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.003933091290066886 0. 0.005556500000000001 0. 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0064 0. 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0121 0. 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.00905 0. -0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.013456000000000001 \ No newline at end of file diff --git a/python/materials/Fs8dataclean.txt b/python/materials/Fs8dataclean.txt deleted file mode 100644 index 2c89591..0000000 --- a/python/materials/Fs8dataclean.txt +++ /dev/null @@ -1,20 +0,0 @@ -0.067 0.423 0.055 -0.15 0.48999999999999994 0.15 -0.17 0.5099999999999999 0.06 -0.25 0.3512 0.0583 -0.3 0.366 0.067 -0.35 0.445 0.097 -0.37 0.46019999999999994 0.0378 -0.38 0.497 0.045 -0.4 0.394 0.068 -0.44 0.41600000000000004 0.08 -0.51 0.458 0.038 -0.55 0.44400000000000006 0.038 -0.57 0.48799999999999993 0.06 -0.6 0.4295160397021415 0.09422048609511628 -0.61 0.43600000000000005 0.034 -0.7284999999999999 0.3712262215423378 0.07454193450669228 -0.8 0.47000000000000003 0.08 -0.85 0.45000000000000007 0.11 -0.86 0.468950276243094 0.09513148795220225 -1.36 0.48200000000000004 0.116 \ No newline at end of file diff --git a/python/materials/alldataEz-H0planck-real-H0planck.txt b/python/materials/alldataEz-H0planck-real-H0planck.txt deleted file mode 100644 index 67c1d89..0000000 --- a/python/materials/alldataEz-H0planck-real-H0planck.txt +++ /dev/null @@ -1,33 +0,0 @@ -0. 1. 0.008796960890725113 -0.07 0.997 0.023 -0.09 1.0207100591715974 0.17593921781450225 -0.12 1.0147928994082838 0.3841339588949966 -0.17 1.2278106508875737 0.11729281187633485 -0.199 1.1094674556213013 0.07330800742270928 -0.27 1.1390532544378695 0.20526242078358597 -0.28 1.3136094674556211 0.5366146143342319 -0.35 1.128 0.037 -0.38 1.2056213017751478 0.027857042820629525 -0.4 1.405325443786982 0.24924722523721154 -0.4247 1.288461538461538 0.16420993662686875 -0.44 1.22189349112426 0.11436049157942647 -0.4783 1.1967455621301775 0.1319544133608767 -0.51 1.3372781065088757 0.027857042820629525 -0.55 1.364 0.063 -0.593 1.538461538461538 0.1906008192990441 -0.61 1.439349112426035 0.030789363117537896 -0.68 1.3609467455621298 0.11729281187633485 -0.73 1.4393491124260351 0.10263121039179299 -0.781 1.5532544378698225 0.17593921781450225 -0.875 1.8491124260355027 0.24924722523721154 -0.9 1.52 0.12 -1.037 2.278106508875739 0.2932320296908371 -1.3 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deleted file mode 100644 index 1b79ce0..0000000 --- a/python/materials/marvel_movies.txt +++ /dev/null @@ -1,15 +0,0 @@ -Title,Earnings,Rating -Iron Man,585174222,94 -The Incredible Hulk,263427551,67 -Iron Man 2,623933331,72 -Thor,449326618,77 -Captain America: The First Avenger,370569774,80 -Marvel's The Avengers,1518812988,92 -Iron Man 3,1214811252,79 -Thor: The Dark World,644571402,66 -Captain America: The Winter Soldier,714264267,89 -Guardians of the Galaxy,773328629,91 -Avengers: Age of Ultron,1405403694,75 -Ant-Man,519311965,81 -Captain America: Civil War,1153304495,90 -Doctor Strange,676867562,90 \ No newline at end of file diff --git a/python/materials/nearest_stars.txt b/python/materials/nearest_stars.txt deleted file mode 100644 index d370b13..0000000 --- a/python/materials/nearest_stars.txt +++ /dev/null @@ -1,11 +0,0 @@ -Order,Name,Spectral Type,Distance -1,Proxima Centauri,M,4.2421 -2,Alpha Centauri a,G,4.3650 -3,Alpha Centauri b,K,4.3650 -4,Barnard’s Star,M,5.9630 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Calculate Y.\n", - "y = np.dot(H, a)\n", + "y = H @ a \n", "\n", "# Plot results. \n", - "plot.regression_plot(x, y, np.arange(14), y_func(np.arange(14), a[1], a[0]))" + "plot.regression_plot(data=(x, y), model=(np.arange(14), y_func(np.arange(14), a[1], a[0])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As our data has been produced using the model the fit is perfect." + "As our data has been produced using the model, the fit is perfect." ] }, { @@ -265,13 +261,13 @@ "source": [ "#### Inverse Problem\n", "\n", - "For the inverse problem we will assume we already know $y$ and use this to work out $a$, so in other words we already have a set of data points and we want to work out the model parameters of the best fitting line.\n", + "For the inverse problem we will assume we already know $y$ and use this to work out $a$. So, in other words, we already have a set of data points, $(x, y)$, and we want to work out the model parameters, $a$, of the best fitting line.\n", "\n", - "$$x = \\begin{bmatrix} 8 & 2 & 11 & 6 & 5 & 4 & 12 & 9 & 6 & 11 \\end{bmatrix}$$\n", + "$$x = \\begin{bmatrix} 8 & 2 & 11 & 6 & 5 & 4 & 12 & 9 & 6 & 1 \\end{bmatrix}$$\n", "\n", "$$y = \\begin{bmatrix} 3 & 10 & 3 & 6 & 8 & 12 & 1 & 4 & 9 & 14 \\end{bmatrix}$$\n", "\n", - "and again\n", + "The matrix $H$ remains unchanged\n", "\n", "$$H = \\begin{bmatrix} \n", "1 & 8 \\\\\n", @@ -283,14 +279,14 @@ "1 & 12 \\\\\n", "1 & 9 \\\\\n", "1 & 6 \\\\\n", - "1 & 11 \n", + "1 & 1 \n", "\\end{bmatrix}$$\n", "\n", - "to solve this problem we need to invert $H$ *i.e.*\n", + "but, to solve this problem, we need to invert $H$ *i.e.*\n", "\n", "$$a = H^{-1}y$$\n", "\n", - "however, $H$ is not a square matrix which means it cannot be inverted directly. So, first we need to multiply by $H^{T}$.\n", + "however, $H$ is not a square matrix, which means it cannot be inverted directly. So, first we need to multiply by $H^{T}$.\n", "\n", "> Because $H \\in \\mathbb{R}^{m \\times n}$ and $H^{T} \\in \\mathbb{R}^{n \\times m}$ so $H^{T}H \\in \\mathbb{R}^{n \\times n}$\n", "\n", @@ -303,14 +299,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# This function implements the normal equation\n", "def normal_eq(H, y):\n", " \n", - " return np.dot(np.dot(np.linalg.inv(np.dot(H.T, H)), H.T), y)" + " return np.linalg.inv(H.T @ H) @ H.T @ y" ] }, { @@ -322,9 +318,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a = [14.08108108 -1.10641892]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Set points in y-axis.\n", "y = np.array([3, 10, 3, 6, 8, 12, 1, 4, 9, 14])\n", @@ -335,7 +351,7 @@ "print('a =', a)\n", "\n", "# Plot results.\n", - "plot.regression_plot(x, y, np.arange(14), y_func(np.arange(14), a[1], a[0]))" + "plot.regression_plot(data=(x, y), model=(np.arange(14), y_func(np.arange(14), a[1], a[0])))" ] }, { @@ -364,15 +380,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Define the new function y(x) for any kth degree polynomial.\n", - "# This defines a function called y_func2 with input variables x and a, and returns the values of a0 + a1x + a2x^2 + ...\n", + "# This defines a function called y_func2 with input variables x and a, and returns the values \n", + "# of a0 + a1x + a2x^2 + ...\n", "def y_func2(x, a):\n", " \n", - " return sum([(a_i * x ** n) for a_i, n in zip(a, range(a.size))])" + " return sum([(a_i * x ** i) for i, a_i in enumerate(a)])" ] }, { @@ -410,14 +427,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# The values for x and y.\n", "x_k = np.linspace(0.0, 1.0, 21)\n", - "y_k = np.array([0.486, 0.866, 0.944, 1.144, 1.103, 1.202, 1.166, 1.191, 1.124, 1.095, 1.122, 1.102, 1.099, 1.017, \n", - " 1.111, 1.117, 1.152, 1.265, 1.380, 1.575, 1.857])" + "y_k = np.array([\n", + " 0.486, 0.866, 0.944, 1.144, 1.103, 1.202, 1.166, \n", + " 1.191, 1.124, 1.095, 1.122, 1.102, 1.099, 1.017, \n", + " 1.111, 1.117, 1.152, 1.265, 1.380, 1.575, 1.857\n", + "])" ] }, { @@ -426,13 +446,14 @@ "source": [ "Your job is to define the matrix operator $H$ and find the model parameters $a$ by solving the inverse problem $y=Ha$.\n", "\n", - "
\n", - "EXERCISE CELL " + "### EXERCISE \n", + "\n", + "Modify the cell below." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -456,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "scrolled": false }, @@ -464,7 +485,7 @@ "source": [ "# Display the plot.\n", "if a_k is not None:\n", - " plot.regression_plot(x_k, y_k, x_k, y_func2(x_k, a_k))" + " plot.regression_plot(data=(x_k, y_k), model=(x_k, y_func2(x_k, a_k)))" ] }, { @@ -499,33 +520,48 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "85d21a63f24a4742bc5263218ebc8f77", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=20, description='point'), Output()), _dom_classes=('widget-interact',))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", "######################################\n", "\n", - "@interact(point=(0, 99, 1))\n", + "@interact(point=(0, 100, 1))\n", "def show_grad(point=20):\n", " \n", - " x = np.linspace(-1, 1, 100)\n", - " y1 = np.array([np.linalg.norm(xi) for xi in x])\n", - " y2 = np.array([np.linalg.norm(xi) ** 2 for xi in x])\n", - " dy = x[point] ** 2 + 2 * x[point] * (x - x[point])\n", + " x = np.linspace(-1, 1, 101)\n", + " x_norm = np.array([np.linalg.norm(x_i) for x_i in x])\n", + " x_norm2 = np.array([np.linalg.norm(x_i) ** 2 for x_i in x])\n", + " grad = x[point] ** 2 + 2 * x[point] * (x - x[point])\n", "\n", - " print('Gradient =', 2 * x[point])\n", + " print('Gradient =', grad[point])\n", "\n", " # Display\n", - " plot.grad_plot(x, y1, y2, dy, point)" + " plot.grad_plot(x, x_norm, x_norm2, grad, point)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The plot clearly shows that both $\\|x\\|_2$ and $\\|x\\|_2^2$ have a clearly defined global minimum. The plot also displays the gradient of $\\|x\\|_2^2$ at the point $x_{20}$, try ajusting the position of this point and see what happens to the gradient.\n", + "The plot shows that both $\\|x\\|_2$ and $\\|x\\|_2^2$ have a clearly defined global minimum. The plot also displays the gradient of $\\|x\\|_2^2$ at the point $x_{20}$, try ajusting the position of this point and see what happens to the gradient.\n", "\n", "Unsurprisingly, as the point apporachs the minimum the gradient tends to zero. Also, depending on which side of the minimum the point is, the gradient will be either positive or negative. Gradient descent captialises on this by iteratively updating solutions in the following way\n", "\n", @@ -551,14 +587,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Cost function for linear regression problem\n", "def cost_func(y, a, H):\n", " \n", - " return 0.5 * np.linalg.norm(y - np.dot(H, a)) ** 2" + " return 0.5 * np.linalg.norm(y - H @ a) ** 2" ] }, { @@ -574,14 +610,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Gradient of linear regression problem\n", "def grad(y, a, H):\n", " \n", - " return np.dot(H.T, np.dot(H, a) - y)" + " return H.T @ (H @ a - y)" ] }, { @@ -597,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -607,7 +643,7 @@ " a_rec = a_guess\n", " cost = []\n", " \n", - " for i in range(n_iter):\n", + " for _ in range(n_iter):\n", " \n", " a_rec = a_rec - gamma * grad(y, a_rec, H)\n", " cost.append(cost_func(y, a_rec, H))\n", @@ -627,24 +663,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fc5f24196c4641198821e50132ffe68f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=1, description='n_iter', max=601, min=1, step=50), Output()), _dom_class…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", "######################################\n", "\n", + "@interact(n_iter=IntSlider(value=1, min=1, max=601, step=50))\n", "def test_gradient_descent(n_iter):\n", "\n", " a_new, cost = grad_desc(y, [0, 0], H, grad, cost_func, gamma=0.003, n_iter=n_iter)\n", - " plot.regression_plot(x, y, np.arange(14), y_func(np.arange(14), a_new[1], a_new[0]))\n", - " plot.cost_plot(cost)\n", - "\n", - "iter_slider = IntSlider(value=1, min=1, max=601, step=50)\n", - "interact(test_gradient_descent, n_iter=iter_slider)" + " \n", + " plot.regression_plot(data=(x, y), model=(np.arange(14), y_func(np.arange(14), a_new[1], a_new[0])))\n", + " plot.cost_plot(cost)" ] }, { @@ -674,15 +724,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Solutions\n", + "### EXERCISE\n", "\n", - "EXERCISE: provide your solutions in the cell(s) below." + "Provide your solutions in the cell(s) below." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [] }, @@ -694,12 +746,12 @@ "\n", "## Hints\n", "\n", - "** Linear Regression Exercise **\n", + "**Linear Regression Exercise**\n", "\n", "1. You do not need to redefine the function for the normal equation. \n", "1. The polynomial should have as many degrees as there are good Indiana Jones films. \n", "\n", - "** Gradient Descent Exercise **\n", + "**Gradient Descent Exercise**\n", "\n", "1. Make sure your first guess for $a$ has the right size.\n", "1. If your cost function is increasing you may need to decrease the value of $\\gamma$.\n", @@ -717,7 +769,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -731,7 +783,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.9.11" } }, "nbformat": 4, diff --git a/sparsity_tutorial/plot.py b/sparsity_tutorial/plot.py index aec8e74..2ce68a5 100644 --- a/sparsity_tutorial/plot.py +++ b/sparsity_tutorial/plot.py @@ -1,11 +1,29 @@ import numpy as np import matplotlib.pyplot as plt +from matplotlib import pylab -def regression_plot(x1, y1, x2, y2): +# Set the default size of figures. +pylab.rcParams['figure.figsize'] = (10.0, 8.0) - plt.plot(x1, y1, 'o', color="#19C3F5", label='Data') - plt.plot(x2, y2, '--', color='#FF4F5B', label='Model') + +def regression_plot(data, model): + """Regression Plot. + + This function plots the data points (in blue) and a the corresponding + model line (in red). + + Parameters + ---------- + data : tuple + Data points in (x, y) + model : tuple + Model points in (x, y) + + """ + + plt.plot(*data, 'o', color="#19C3F5", label='Data') + plt.plot(*model, '--', color='#FF4F5B', label='Model') plt.title('Best Fit Line', fontsize=20) plt.xlabel('x', fontsize=18) plt.ylabel('y', fontsize=18) @@ -13,12 +31,32 @@ def regression_plot(x1, y1, x2, y2): plt.show() -def grad_plot(x, y1, y2, dy, point): - - plt.plot(x, y1, 'b-', label='$||x||_2$') - plt.plot(x, y2, 'g-', label='$||x||_2^2$') - plt.plot(x[point], y2[point], 'ro') - plt.plot(x, dy, 'r--', label='Grad $||x_i||_2^2$') +def grad_plot(x, x_norm, x_norm2, grad, point): + """Gradient Plot. + + This function plots the values of x against the corresponding l2-norm + (in blue) and squared l2-norm (in green). The gradient of the squared + l2-norm is shown at a specified point (in red). + + Parameters + ---------- + x : numpy.ndarray + Array of data values in the x-axis + x_norm : numpy.ndarray + Array of l2-norm values + x_norm2 : numpy.ndarray + Array of squared l2-norm values + grad : float + Gradient at the specified point + point : int + Position to show the gradient + + """ + + plt.plot(x, x_norm, 'b-', label='$||x||_2$') + plt.plot(x, x_norm2, 'g-', label='$||x||_2^2$') + plt.plot(x[point], x_norm2[point], 'ro') + plt.plot(x, grad, 'r--', label='Grad $||x_i||_2^2$') plt.ylim(-0.1, 1.0) plt.title('Convex L2-Norm') plt.xlabel('$x$', fontsize=24) @@ -77,9 +115,19 @@ def line_plot(data, title=None, ylim=None, xlab=None): plt.show() -def cost_plot(data): +def cost_plot(cost): + """Cost Plot. + + This function plots the cost for a set of iterations. + + Parameters + ---------- + cost : list + List of cost values + + """ - plt.plot(data, linestyle='-', color='#FF4F5B') + plt.plot(cost, linestyle='-', color='#FF4F5B') plt.xlabel('Iteration', fontsize=18) plt.ylabel('Cost', fontsize=18) plt.title('Cost Function', fontsize=20) @@ -149,9 +197,9 @@ def filter_plot(n, Fn, g, Fg, Fg_fft): ax[1].plot(n, g, '-', color='#0764DB') ax[1].set_title('$g[n-m]$', fontsize=24) ax[2].plot(n, Fg, '-', color='#0764DB') - ax[2].set_title('$F \cdot g$', fontsize=24) + ax[2].set_title(r'$F \cdot g$', fontsize=24) ax[3].plot(n, Fg_fft, '-', color='#0764DB') - ax[3].set_title('$\mathcal{F}(F \cdot g)$', fontsize=24) + ax[3].set_title(r'$\mathcal{F}(F \cdot g)$', fontsize=24) for ax_i in ax: ax_i.set_xticks([]) ax_i.set_yticks([]) From cf623ec9a6d7b22ea49609b69031c30904b6269b Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Wed, 4 Jan 2023 17:51:01 +0100 Subject: [PATCH 04/13] added slide information to inverse problems notebook --- inverse_problems_1.ipynb | 630 ++++++++++++++++++++++++++------------ sparsity_tutorial/plot.py | 82 +++-- 2 files changed, 486 insertions(+), 226 deletions(-) diff --git a/inverse_problems_1.ipynb b/inverse_problems_1.ipynb index b6dfd9d..4e555b7 100644 --- a/inverse_problems_1.ipynb +++ b/inverse_problems_1.ipynb @@ -2,19 +2,30 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "# Introduction to Inverse Problems\n", "\n", "---\n", "\n", "> Author: Samuel Farrens \n", - "> Year: 2018 \n", + "> Year: 2018 (updated in 2023) \n", "> Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", + "> Website: https://sfarrens.github.io" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "## Contents\n", " \n", "1. [Set-Up](#Set-Up)\n", @@ -35,10 +46,12 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Set-Up\n", "\n", "Here we will import a couple of packages that will be needed throughout the notebook. \n", @@ -48,15 +61,19 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Import the numpy package with the alias np.\n", "import numpy as np \n", "\n", "# Import interactive widgets.\n", - "from ipywidgets.widgets import interact, IntSlider\n", + "from ipywidgets.widgets import interact, IntSlider, FloatSlider\n", "\n", "# Import tutorial plotting functions.\n", "from sparsity_tutorial import plot" @@ -64,28 +81,45 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ - "> Note that the plotting routines have been abstracted to keep the code cells as simple as possible. You can run\n", - "> ```python\n", - "> help(plot)\n", - "> ```\n", - "> to see the expected inputs for these routines." + "Note that the plotting routines have been abstracted to keep the code cells as simple as possible. You can run\n", + "\n", + "```python\n", + "help(plot)\n", + "```\n", + "\n", + "to see the expected inputs for these routines." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Introduction\n", "\n", - "### Objective\n", + "### Objective\n", "\n", - "The objective of this notebook is to introduce the concept of an inverse problem. The focus will be on linear inverse problems, which are those that can be modelled using linear algebra.\n", - "\n", - "### Inverse Problem Definition\n", + "The objective of this notebook is to introduce the concept of an inverse problem. The focus will be on linear inverse problems, which are those that can be modelled using linear algebra." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Inverse Problem Definition\n", "\n", "A standard \"forward\" problem is one in which data are obtained from model parameters, *i.e.*\n", "\n", @@ -101,22 +135,33 @@ "\n", "See the following links for more information about inverse problems.\n", "* [Wikipedia](https://en.wikipedia.org/wiki/Inverse_problem)\n", - "* [Scatting Ideas](https://cmontalto.wordpress.com/2013/03/08/what-are-inverse-problems/)\n" + "* [Scatting Ideas](https://cmontalto.wordpress.com/2013/03/08/what-are-inverse-problems/)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Linear Regression\n", "\n", - "One of the simplest and most intuitive applications of an inverse problem is fitting a line to a set of data points. \n", - "\n", - "### Mathematical Representation\n", + "One of the simplest and most intuitive applications of an inverse problem is fitting a line to a set of data points. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "### Mathematical Representation\n", "\n", - "We will start by defining some variables. We will use $y \\in \\mathbb{R}^{m}$ to represent data measurements/observations, $a \\in \\mathbb{R}^{n}$ to represent a given model and $H \\in \\mathbb{R}^{m \\times n}$ to represent a matrix of equations that relate the model parameters to the measured data. Now we can pose a linear inverse problem of the following form\n", + "We will start by defining some variables. We will use $y \\in \\mathbb{R}^{m}$ to represent data measurements/observations, $a \\in \\mathbb{R}^{n}$ to represent a given model, and $H \\in \\mathbb{R}^{m \\times n}$ to represent a matrix of equations that relate the model parameters to the measured data. Now we can pose a linear inverse problem of the following form\n", "\n", "$$y = Ha$$\n", "\n", @@ -127,13 +172,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Straight Line Example \n", - "\n", - "The best place to start is with something that should be intimately familiar to anyone with even the most basic background in mathematics, a straight line.\n", - "\n", + "### Straight Line Example \n", "\n", + "The best place to start is with something that should be intimately familiar to anyone with even the most basic background in mathematics, a straight line." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "#### Equation of a Straight Line\n", "\n", "A simple straight line can be represented with the following well known equation\n", @@ -145,8 +202,12 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# This defines a function called y_func with input variables x, m and b, and returns the values of mx + b.\n", @@ -157,7 +218,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "#### Forward Problem\n", "\n", @@ -171,7 +236,18 @@ "\n", "$$b = 14,$$\n", "\n", - "to simply plug this information into the equation above to get the corresponding data on the y-axis. To think of the of this in terms of a linear forward problem, however, we need to know that the slope and intercept correspond to our model parameters and the data on the x-axis need to be converted into a matrix operator. So, a better way of representing our line equation would be:\n", + "to simply plug this information into the equation above to get the corresponding data on the y-axis. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "To think of the of this in terms of a linear forward problem, however, we need to know that the slope and intercept correspond to our model parameters and the data on the x-axis need to be converted into a matrix operator. So, a better way of representing our line equation would be:\n", "\n", "$$y = a_0x^0 + a_1x^1$$\n", "\n", @@ -192,15 +268,28 @@ "1 & 9 \\\\\n", "1 & 6 \\\\\n", "1 & 1 \n", - "\\end{bmatrix}$$\n", - "\n", - "We can implement this in python as follows. First we define $a$ and $H$ " + "\\end{bmatrix}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "We can implement this in Python as follows. First we define $a$ and $H$ " ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Set points in x-axis.\n", @@ -215,31 +304,35 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "then we can use these values to determine $y$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ - "then we can use these values to determine $y$.\n", - "\n", "> Note that the `@` operator in the following cell is equivalent to [`np.matmul`](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html) or [`np.dot`](https://numpy.org/doc/stable/reference/generated/numpy.dot.html)." ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], "source": [ "# Calculate Y.\n", "y = H @ a \n", @@ -250,14 +343,22 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "As our data has been produced using the model, the fit is perfect." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "#### Inverse Problem\n", "\n", @@ -284,8 +385,17 @@ "\n", "but, to solve this problem, we need to invert $H$ *i.e.*\n", "\n", - "$$a = H^{-1}y$$\n", - "\n", + "$$a = H^{-1}y$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "however, $H$ is not a square matrix, which means it cannot be inverted directly. So, first we need to multiply by $H^{T}$.\n", "\n", "> Because $H \\in \\mathbb{R}^{m \\times n}$ and $H^{T} \\in \\mathbb{R}^{n \\times m}$ so $H^{T}H \\in \\mathbb{R}^{n \\times n}$\n", @@ -299,8 +409,12 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# This function implements the normal equation\n", @@ -311,36 +425,24 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "With this function we can attempt to fit a line to our data." ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a = [14.08108108 -1.10641892]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], "source": [ "# Set points in y-axis.\n", "y = np.array([3, 10, 3, 6, 8, 12, 1, 4, 9, 14])\n", @@ -356,20 +458,28 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "As you can see we can easily recover a good fit to the data points with only a few lines of code." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "## Linear Regression Exercise\n", "\n", "For this exercise you should apply the techniques learned for fitting a straight line to a set of data points to a new set of data that requires a polynomial fit.\n", "\n", - "### Equation of a Polynomial Line \n", + "### Equation of a Polynomial Line \n", "\n", "The expression for a straight line (*i.e.* $k = 1$) generalises to the following form for a $k^{\\textrm{th}}$ degree polynomial:\n", "\n", @@ -380,8 +490,12 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Define the new function y(x) for any kth degree polynomial.\n", @@ -394,7 +508,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "The data for this exercise are the following:\n", "\n", @@ -427,8 +545,12 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "outputs": [], "source": [ "# The values for x and y.\n", @@ -442,19 +564,36 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ - "Your job is to define the matrix operator $H$ and find the model parameters $a$ by solving the inverse problem $y=Ha$.\n", - "\n", - "### EXERCISE \n", + "Your job is to define the matrix operator $H$ and find the model parameters $a$ by solving the inverse problem $y=Ha$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### EXERCISE \n", "\n", "Modify the cell below." ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "##############################\n", @@ -470,16 +609,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Now you can test how well your model line fits the data." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [], "source": [ @@ -490,7 +636,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "## Gradient Descent\n", "\n", @@ -504,79 +654,109 @@ "\n", "The corresponding gradient is\n", "\n", - "$$\\nabla F(x) = H^{T}(Hx-y)$$\n", - "\n", - "### L2 Norm \n", + "$$\\nabla F(x) = H^{T}(Hx-y)$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### L2 Norm \n", "\n", "The l2 or Euclidian norm is calculated as follows:\n", "\n", "$$\\|x\\|_2 = \\Big(\\sum_{i=1}^n|x_i|^2\\Big)^{\\frac{1}{2}}$$\n", "\n", - "We can show that this function is convex as follows. \n", + "We can show that this function is convex as follows. First we define a range of $x$ values, the we calculate $\\|x\\|_2$ and $\\|x\\|_2^2$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# Set a range of x values\n", + "x_range = np.around(np.linspace(-1, 1, 101), 2)\n", + "# Calculate the l2-norm of x\n", + "x_norm = np.array([np.linalg.norm(x_i) for x_i in x_range])\n", + "# Calculate the squared l2-norm of x\n", + "x_norm2 = x_norm ** 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Now let's plot these functions.\n", "\n", - "
\n", - "INTERACTIVE CELL" + "#### INTERACTIVE CELL" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "85d21a63f24a4742bc5263218ebc8f77", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=20, description='point'), Output()), _dom_classes=('widget-interact',))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", "######################################\n", - "\n", - "@interact(point=(0, 100, 1))\n", - "def show_grad(point=20):\n", - " \n", - " x = np.linspace(-1, 1, 101)\n", - " x_norm = np.array([np.linalg.norm(x_i) for x_i in x])\n", - " x_norm2 = np.array([np.linalg.norm(x_i) ** 2 for x_i in x])\n", - " grad = x[point] ** 2 + 2 * x[point] * (x - x[point])\n", - "\n", - " print('Gradient =', grad[point])\n", - "\n", + "@interact(x_val=FloatSlider(value=-0.8, min=-1.0, max=1.0, step=0.2))\n", + "def show_grad(x_val):\n", + " \n", + " # Calculate the gradient of the squared l2-norm \n", + " grad = 2 * x_val\n", + " # Calculate the tangent line at this point\n", + " tangent = x_val ** 2 + 2 * x_val * (x_range - x_val)\n", " # Display\n", - " plot.grad_plot(x, x_norm, x_norm2, grad, point)" + " plot.grad_plot(x_range, x_norm, x_norm2, grad, tangent, index=int(np.where(x_range == x_val)[0]))" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "The plot shows that both $\\|x\\|_2$ and $\\|x\\|_2^2$ have a clearly defined global minimum. The plot also displays the gradient of $\\|x\\|_2^2$ at the point $x_{20}$, try ajusting the position of this point and see what happens to the gradient.\n", + "The plot shows that both $\\|x\\|_2$ and $\\|x\\|_2^2$ have a clearly defined global minimum. The plot also displays the gradient of $\\|x\\|_2^2$ at the point $x=-0.8$, try ajusting the position of this point and see what happens to the gradient.\n", "\n", - "Unsurprisingly, as the point apporachs the minimum the gradient tends to zero. Also, depending on which side of the minimum the point is, the gradient will be either positive or negative. Gradient descent captialises on this by iteratively updating solutions in the following way\n", + "Unsurprisingly, as the point apporachs the minimum, the gradient tends to zero. Also, depending on which side of the minimum the point is, the gradient will be either positive or negative. Gradient descent captialises on this by iteratively updating solutions in the following way\n", "\n", "$$x_{n+1} = x_n - \\gamma \\nabla F(x_n)$$\n", "\n", - "where $x_n$ corresponds to the solution at given iteration. So, as the gradient approaches zero and consequenly as $x$ approaches the global minimum the solution will converge. \n", + "where $x_n$ corresponds to the solution at given iteration. So, as the gradient approaches zero and consequenly as $x$ approaches the global minimum, the solution will converge. \n", "\n", "Now, we can try this apporach to out linear regression problem. " ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Linear Regression Application \n", + "### Linear Regression Application \n", "\n", "The cost function for our problem is\n", "\n", @@ -587,19 +767,27 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Cost function for linear regression problem\n", "def cost_func(y, a, H):\n", " \n", - " return 0.5 * np.linalg.norm(y - H @ a) ** 2" + " return 0.5 * np.linalg.norm(H @ a - y) ** 2" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "The gradient of this problem is given by\n", "\n", @@ -610,8 +798,12 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Gradient of linear regression problem\n", @@ -622,7 +814,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "The gradient descent algorithm is simply\n", "\n", @@ -633,8 +829,12 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Function that performs gradient descent.\n", @@ -653,60 +853,71 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ - "We can test this approach on our first straight line example.\n", - "\n", - "
\n", - "INTERACTIVE CELL" + "We can test this approach on our first straight line example." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### INTERACTIVE CELL" ] }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fc5f24196c4641198821e50132ffe68f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=1, description='n_iter', max=601, min=1, step=50), Output()), _dom_class…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", "######################################\n", "\n", - "@interact(n_iter=IntSlider(value=1, min=1, max=601, step=50))\n", + "@interact(n_iter=IntSlider(value=1, min=1, max=1001, step=100))\n", "def test_gradient_descent(n_iter):\n", "\n", " a_new, cost = grad_desc(y, [0, 0], H, grad, cost_func, gamma=0.003, n_iter=n_iter)\n", " \n", - " plot.regression_plot(data=(x, y), model=(np.arange(14), y_func(np.arange(14), a_new[1], a_new[0])))\n", - " plot.cost_plot(cost)" + " plot.regression_plot_cost(\n", + " data=(x, y), \n", + " model=(np.arange(14), y_func(np.arange(14), a_new[1], a_new[0])), \n", + " cost=cost,\n", + " )" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "You can clearly see how the model parameters are improved after each iteration and as the cost function converges we are left with a good fit to the data points." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "## Gradient Descent Exercise\n", "\n", @@ -722,9 +933,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### EXERCISE\n", + "### EXERCISE\n", "\n", "Provide your solutions in the cell(s) below." ] @@ -733,18 +948,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [], "source": [] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "---\n", "\n", - "## Hints\n", + "## Hints\n", "\n", "**Linear Regression Exercise**\n", "\n", @@ -761,7 +983,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Open next notebook ->](./sparsity_1.ipynb)" ] @@ -783,7 +1009,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.11" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/sparsity_tutorial/plot.py b/sparsity_tutorial/plot.py index 2ce68a5..6bfaeaf 100644 --- a/sparsity_tutorial/plot.py +++ b/sparsity_tutorial/plot.py @@ -31,7 +31,7 @@ def regression_plot(data, model): plt.show() -def grad_plot(x, x_norm, x_norm2, grad, point): +def grad_plot(x, x_norm, x_norm2, grad, tangent, index): """Gradient Plot. This function plots the values of x against the corresponding l2-norm @@ -48,15 +48,17 @@ def grad_plot(x, x_norm, x_norm2, grad, point): Array of squared l2-norm values grad : float Gradient at the specified point - point : int - Position to show the gradient + tangent : numpy.ndarray + Array of tangent line y-axis values + index : int + Index of currently selected point """ plt.plot(x, x_norm, 'b-', label='$||x||_2$') plt.plot(x, x_norm2, 'g-', label='$||x||_2^2$') - plt.plot(x[point], x_norm2[point], 'ro') - plt.plot(x, grad, 'r--', label='Grad $||x_i||_2^2$') + plt.plot(x[index], x_norm2[index], 'ro') + plt.plot(x, tangent, 'r--', label=rf'$\nabla ||x_i||_2^2={grad}$') plt.ylim(-0.1, 1.0) plt.title('Convex L2-Norm') plt.xlabel('$x$', fontsize=24) @@ -64,6 +66,57 @@ def grad_plot(x, x_norm, x_norm2, grad, point): plt.show() +def regression_plot_cost(data, model, cost): + """Regression Plot. + + This function plots the data points (in blue) and a the corresponding + model line (in red). + + Parameters + ---------- + data : tuple + Data points in (x, y) + model : tuple + Model points in (x, y) + + """ + + _, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6)) + + ax[0].plot(*data, 'o', color="#19C3F5", label='Data') + ax[0].plot(*model, '--', color='#FF4F5B', label='Model') + ax[0].set_title('Best Fit Line', fontsize=20) + ax[0].set_xlabel('x', fontsize=18) + ax[0].set_ylabel('y', fontsize=18) + ax[0].legend() + + ax[1].plot(cost, linestyle='-', color='#FF4F5B') + ax[1].set_xlabel('Iteration', fontsize=18) + ax[1].set_ylabel('Cost', fontsize=18) + ax[1].set_title('Cost Function', fontsize=20) + + plt.show() + + +def cost_plot(cost): + """Cost Plot. + + This function plots the cost for a set of iterations. + + Parameters + ---------- + cost : list + List of cost values + + """ + + plt.plot(cost, linestyle='-', color='#FF4F5B') + plt.xlabel('Iteration', fontsize=18) + plt.ylabel('Cost', fontsize=18) + plt.title('Cost Function', fontsize=20) + plt.show() + + def stem_plot(data, x_vals=None, title=None, imag=True, ylim=None, xlab=None, ylab=None, line=False, f=None): @@ -115,25 +168,6 @@ def line_plot(data, title=None, ylim=None, xlab=None): plt.show() -def cost_plot(cost): - """Cost Plot. - - This function plots the cost for a set of iterations. - - Parameters - ---------- - cost : list - List of cost values - - """ - - plt.plot(cost, linestyle='-', color='#FF4F5B') - plt.xlabel('Iteration', fontsize=18) - plt.ylabel('Cost', fontsize=18) - plt.title('Cost Function', fontsize=20) - plt.show() - - def display(data, title='example', shape=None, cmap='gist_stern', vmax=None, vmin=None): From 2f9d60c327dfce134e5e3c30cd9b49f37c9dbf0f Mon Sep 17 00:00:00 2001 From: Samuel Farrens Date: Thu, 5 Jan 2023 18:15:30 +0100 Subject: [PATCH 05/13] updated sparsity notebook --- inverse_problems_1.ipynb | 3 +- sparsity_1.ipynb | 574 +++++++++++++++++++++++++++++---------- 2 files changed, 429 insertions(+), 148 deletions(-) diff --git a/inverse_problems_1.ipynb b/inverse_problems_1.ipynb index 4e555b7..39739c4 100644 --- a/inverse_problems_1.ipynb +++ b/inverse_problems_1.ipynb @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "slideshow": { "slide_type": "fragment" @@ -994,6 +994,7 @@ } ], "metadata": { + "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", diff --git a/sparsity_1.ipynb b/sparsity_1.ipynb index b34c116..3fe3595 100644 --- a/sparsity_1.ipynb +++ b/sparsity_1.ipynb @@ -2,20 +2,30 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "# Introduction to Sparsity I\n", "\n", "---\n", "\n", "> Author: Samuel Farrens \n", - "> Year: 2018 \n", + "> Year: 2018 (updated in 2023) \n", "> Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", - "\n", + "> Website: https://sfarrens.github.io" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "## Contents\n", "\n", "1. [Set-Up](#Set-Up)\n", @@ -41,10 +51,12 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Set-Up\n", "\n", "Here we will import a couple of packages that will be needed throughout the notebook. \n", @@ -54,85 +66,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "scrolled": true + "scrolled": true, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [], "source": [ - "# Tell Jupyter to display plots in this notebook.\n", - "%matplotlib inline\n", - "\n", "# Import the numpy package with the alias np.\n", "import numpy as np\n", "\n", - "# Import function to set size of figures.\n", - "from matplotlib.pylab import rcParams\n", - "\n", - "# Import interaction module.\n", - "from ipywidgets.widgets import *\n", + "# Import interactive widgets.\n", + "from ipywidgets.widgets import interact, IntSlider, FloatSlider, Select\n", "\n", "# Import tutorial plotting functions.\n", "from sparsity_tutorial import plot" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For Python 2 users only\n", - "# Uncomment the following commands\n", - "\n", - "# Import Python 3 behaviour\n", - "# from __future__ import print_function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set size of figures.\n", - "rcParams['figure.figsize'] = (14.0, 8.0)" - ] - }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", "\n", "## Introduction\n", "\n", - "### Objective\n", + "### Objective\n", "\n", "The objective of this notebook is to introduce the concept of sparsity, particularly in the context of solving inverse problems. To that end, the following examples focus on providing some basic practical applications of the tools and techniques used in sparse analysis. This tutorial does not provide all of mathematical background and readers are encouraged to refer to the following resources for more in-depth explanations.\n", "\n", - "* Sparse Image and Signal Processing (2015)\n", - "\n", - "### Sparsity Definition\n", + "* Sparse Image and Signal Processing (2015)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Sparsity Definition\n", "\n", "A sparse signal is one that is comprised mostly of zeros when expressed in the appropriate basis, in other words the only a few coefficients are required to represent the entire signal. This is an inherent propery of the signal that can be expolited to great effect.\n", "\n", "A signal $x\\in\\mathbb{R}^N$, *i.e.* a discrete signal with $N$ coefficients $x_i$, is exactly $k$-sparse if only $k$ of its coefficients are non-zero. In practice, however, one generally deals with approximately sparse signals in which the majority of the coefficients are very small with respect to those that carry the majority of the information. Additionally, some signals that are not naturally sparse can be \"sparsified\" with an appropriate transform basis.\n", "\n", - "### Notes\n", - "\n", - "By default the term signal will be used to refer to discrete signals." + "> Note that by default the term signal will be used to refer to discrete signals." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", "\n", "## Time-Frequency Example\n", "\n", - "### Signal\n", + "### Signal\n", "\n", "Let's start with a simple sine wave example. We have a time series signal where\n", "\n", @@ -143,11 +142,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "subslide" + } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Set the signal properties.\n", "sampling_rate = 30.0\n", @@ -165,11 +178,24 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ - "We can see that out of the 30 coefficients only 2 are equal to zero and therefore this signal is clearly not sparse according the definition provided.\n", - "\n", - "### Discrete Fourier Transform\n", + "We can see that out of the 30 coefficients only 2 are equal to zero and therefore this signal is clearly not sparse according the definition provided." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Discrete Fourier Transform\n", "\n", "We can transform our signal using the Discrete Fourier Transform (DFT) in order to examine its frequency components. \n", "\n", @@ -182,8 +208,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Calculate the DFT of a given signal\n", @@ -194,23 +224,41 @@ " n = np.arange(N)\n", " k = n.reshape((N, 1))\n", " \n", - " return np.dot(np.exp(-2j * np.pi * k * n / N), xn)" + " return np.exp(-2j * np.pi * k * n / N) @ xn" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "Using this function we can compute the normalised DFT of the time series signal." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "subslide" + } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Get the frequency range.\n", "w = np.fft.fftfreq(t.size, 1 / sampling_rate)\n", @@ -219,13 +267,24 @@ "y_fft = np.abs(dft(y)) / y.size \n", "\n", "# Plot the signal\n", - "plot.stem_plot(y_fft, x_vals=w, title='Fourier Transform of Sine', imag=False, ylim=(-0.1, 0.6),\n", - " xlab='$\\omega$', ylab=r'$\\frac{|y(\\omega)|}{n}$')" + "plot.stem_plot(\n", + " y_fft, \n", + " x_vals=w, \n", + " title='Fourier Transform of Sine', \n", + " imag=False, \n", + " ylim=(-0.1, 0.6),\n", + " xlab='$\\omega$', \n", + " ylab=r'$\\frac{|y(\\omega)|}{n}$',\n", + ")" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Now, we have only two coefficients which are non-zero (in fact we only need one as the signal is symmetric in Fourier space). So, although in its natural domain our signal does not appear to be sparse, we were able to find a sparse representation in a different basis.\n", "\n", @@ -234,17 +293,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Fast Fourier Transform\n", + "### Fast Fourier Transform\n", "\n", "Note that we can more easily convert signals back and forth between the direct and Fourier space using the Fast Fourier Transform (FFT)." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# FFT\n", @@ -260,9 +327,12 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", "\n", "## Sparsity\n", "\n", @@ -272,9 +342,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Sparse Representation\n", + "### Sparse Representation\n", "\n", "A signal $x$ can be respresented as\n", "\n", @@ -285,9 +359,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Measures of Sparsity\n", + "### Measures of Sparsity\n", "\n", "The true sparsity of a signal can be measured with the $l_0$ pseudo-norm.\n", "\n", @@ -304,11 +382,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { - "scrolled": true + "scrolled": true, + "slideshow": { + "slide_type": "fragment" + } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The l1 norm of the time signal: 19.028728908445174\n", + "The l1 norm of the frequency signal: 1.000000000000012\n" + ] + } + ], "source": [ "# Function to measure the l1 norm.\n", "def l1_norm(signal):\n", @@ -322,16 +412,24 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "As expected the frequency signal is significantly more sparse." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Promoting Sparsity\n", + "### Promoting Sparsity\n", "\n", "In order to actually produce solutions that are more sparse one typically performs a thresholding operation in the sparse domain.\n", "\n", @@ -350,8 +448,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Function for performing hard thresholding.\n", @@ -367,7 +469,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "In the following cell you can see how these functions work on the following matrix for different values of the threshold, $\\lambda$.\n", "\n", @@ -379,15 +485,43 @@ "21 & 22 & 23 & 24 & 25 \n", "\\end{bmatrix}$$\n", "\n", - "
\n", - "INTERACTIVE CELL" + "#### INTERACTIVE CELL" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "57e57040525741d79728dc2ac9560234", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(Select(description='Threshold Type', options=('Hard', 'Soft'), value='Hard'), IntSlider(…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", @@ -416,9 +550,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Median Absolute Deviation\n", + "### Median Absolute Deviation\n", "\n", "The threshold value, $\\lambda$, will vary depending on the type of problem. In the case of denoising $\\lambda=n\\sigma$, where $\\sigma$ an estimate of the noise standard deviation in the sparse domain and $n$ is multiplicative factor (usually around 3 or 4).\n", "\n", @@ -431,8 +569,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Function to calculate the noise standard deviation using the median absolute deviation.\n", @@ -443,19 +585,51 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "The following cell demonstrates how well this function recovers the value of $\\sigma$.\n", "\n", - "
\n", - "INTERACTIVE CELL" + "#### INTERACTIVE CELL" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eb95bd6630c44cb0932a92d888eaf2ac", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.2, description='sigma', max=2.0, min=0.2, step=0.2), Output()), _dom…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", @@ -477,9 +651,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Sparse Regularisation\n", + "### Sparse Regularisation\n", "\n", "For solving an inverse problem of the form\n", "\n", @@ -502,10 +680,12 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Denoising Example\n", "\n", "This example provides a very simple demonstration of how sparse representations of signals can be extremely useful. The objective is to recover the original signal from a noisy observation by denoising it in a appropriate sparse domain.\n", @@ -515,11 +695,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { - "scrolled": false + "slideshow": { + "slide_type": "subslide" + } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Define a range of values\n", "t = np.linspace(0, 22 * np.pi, 1000, endpoint=False)\n", @@ -530,30 +723,61 @@ "# Define the signal.\n", "x = np.cos(t + 11 * np.pi / 1000)\n", "\n", + "plot.line_plot(x, title='True Signal', ylim=(-1.1, 1.1))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Add noise to the signal.\n", "np.random.seed(1)\n", "y = x + sigma * np.random.randn(x.size)\n", "\n", - "plot.line_plot(x, title='True Signal', ylim=(-1.1, 1.1))\n", "plot.line_plot(y, title='Noisy Observation')" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Choose Sparse Representation\n", + "### Choose Sparse Representation\n", "\n", "Your first task is to identify an appropriate sparse domain for the signals.\n", "\n", - "
\n", - "EXERCISE CELL " + "### EXERCISE " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "##############################\n", @@ -567,15 +791,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Now we can have a look at the siganls in the sparse domain you chose." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Plot the signals\n", @@ -587,24 +819,40 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "Can you identify and similarities between the original and noisy signals in the sparse domain?\n", "\n", - "If not, you may want to rethink your choice.\n", - "\n", - "### Denoising\n", + "If not, you may want to rethink your choice." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Denoising\n", "\n", "Your next task is to determine the appropriate threshold value, $\\lambda$, and use this to remove the noise from the observed signal. \n", "\n", - "
\n", - "EXERCISE CELL " + "### EXERCISE" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "##############################\n", @@ -620,15 +868,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Now let's look at the signal you have recovered." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Plot the denoised signal\n", @@ -638,16 +894,24 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "Did it work?" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Residual\n", + "### Residual\n", "\n", "Finally, we can examine the residual of your recovered signal to get a sense of how close to the original signal it is.\n", "\n", @@ -656,8 +920,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Plot the residual\n", @@ -667,7 +935,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "Which threshold produces the best residual? Why do we have a residual? Try decreasesing the noise added to the original signal and see what happens." ] @@ -675,14 +947,17 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } }, "source": [ "---\n", "\n", - "## Hints\n", + "## Hints\n", "\n", - "** Denoising Exercise Hints **\n", + "**Denoising Exercise Hints**\n", "\n", "1. For your sparse representation you may want to use soemthing *fast and f(o)urious*.\n", "1. Try not to get *mad* when choosing $\\lambda$.\n", @@ -693,15 +968,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Open next notebook ->](./sparsity_2_compressed_sensing.ipynb)" ] } ], "metadata": { + "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -715,7 +995,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.10.8" } }, "nbformat": 4, From f685141b720764d3fc6ebf9c6dbc60dcb4099517 Mon Sep 17 00:00:00 2001 From: sfarrens Date: Sat, 7 Jan 2023 18:14:06 +0100 Subject: [PATCH 06/13] updated notebooks --- inverse_problems_1.ipynb | 324 +++++++++++--- sparsity_1.ipynb | 309 +++++++------ sparsity_2_compressed_sensing.ipynb | 644 +++++++++++++++++++-------- sparsity_3_deconvolution.ipynb | 636 +++++++++++++++++++++------ sparsity_tutorial/plot.py | 6 +- wavelets_1.ipynb | 649 +++++++++++++++++++++------- 6 files changed, 1883 insertions(+), 685 deletions(-) diff --git a/inverse_problems_1.ipynb b/inverse_problems_1.ipynb index 39739c4..2d28ae5 100644 --- a/inverse_problems_1.ipynb +++ b/inverse_problems_1.ipynb @@ -22,7 +22,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "skip" } }, "source": [ @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -83,7 +83,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "source": [ @@ -123,16 +123,52 @@ "\n", "A standard \"forward\" problem is one in which data are obtained from model parameters, *i.e.*\n", "\n", - "$$\\text{Model}\\rightarrow\\text{Data,}$$\n", - "\n", + "$$\\text{Model}\\rightarrow\\text{Data,}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "while an inverse problem is one in which the model parameters are determined from the data, *i.e.*\n", "\n", - "$$\\text{Data}\\rightarrow\\text{Model.}$$\n", - "\n", - "In other words, with an inverse problem one attempts to obtain information about a physical system from observed measurements. This can be very useful as some model parameters can not be measured directly.\n", - "\n", - "Inverse problems have been applied to various topics such as oceanography, weather prediction, astrophysics, medical imaging and geophysics.\n", - "\n", + "$$\\text{Data}\\rightarrow\\text{Model.}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "In other words, with an inverse problem one attempts to obtain information about a physical system from observed measurements. This can be very useful as some model parameters can not be measured directly." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Inverse problems have been applied to various topics such as oceanography, weather prediction, astrophysics, medical imaging and geophysics." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "See the following links for more information about inverse problems.\n", "* [Wikipedia](https://en.wikipedia.org/wiki/Inverse_problem)\n", "* [Scatting Ideas](https://cmontalto.wordpress.com/2013/03/08/what-are-inverse-problems/)" @@ -155,19 +191,48 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ "### Mathematical Representation\n", "\n", - "We will start by defining some variables. We will use $y \\in \\mathbb{R}^{m}$ to represent data measurements/observations, $a \\in \\mathbb{R}^{n}$ to represent a given model, and $H \\in \\mathbb{R}^{m \\times n}$ to represent a matrix of equations that relate the model parameters to the measured data. Now we can pose a linear inverse problem of the following form\n", - "\n", - "$$y = Ha$$\n", - "\n", - "When dealing with forward problems $a$ and $H$ are known and can be used to obtain $y$. For inverse problems $y$ and $H$ are known and can be used to obtain $a$.\n", + "We will start by defining some variables. We will use $y \\in \\mathbb{R}^{m}$ to represent data measurements/observations, $a \\in \\mathbb{R}^{n}$ to represent a given model, and $H \\in \\mathbb{R}^{m \\times n}$ to represent a matrix of equations that relate the model parameters to the measured data. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Now we can pose a linear inverse problem of the following form\n", "\n", - "> Note: Here we use $a$ for our model vector to avoid confusion with data points on the $x$-axis. Commonly (and in the other tutorials) the model vector will be represented by the variable $x$." + "$$y = Ha$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "When dealing with forward problems $a$ and $H$ are known and can be used to obtain $y$. For inverse problems $y$ and $H$ are known and can be used to obtain $a$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "Note that here we use $a$ for our model vector to avoid confusion with data points on the $x$-axis. Commonly (and in the other tutorials) the model vector will be represented by the variable $x$." ] }, { @@ -187,7 +252,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ @@ -228,7 +293,7 @@ "\n", "A straightforward way to approach this problem is, when we have some data on the x-axis\n", "\n", - "$$x = \\begin{bmatrix} 8 & 2 & 11 & 6 & 5 & 4 & 12 & 9 & 6 & 11 \\end{bmatrix}$$\n", + "$$x = \\begin{bmatrix} 8 & 2 & 11 & 6 & 5 & 4 & 12 & 9 & 6 & 1 \\end{bmatrix}$$\n", "\n", "and we know the slope and intercept of the line,\n", "\n", @@ -249,8 +314,17 @@ "source": [ "To think of the of this in terms of a linear forward problem, however, we need to know that the slope and intercept correspond to our model parameters and the data on the x-axis need to be converted into a matrix operator. So, a better way of representing our line equation would be:\n", "\n", - "$$y = a_0x^0 + a_1x^1$$\n", - "\n", + "$$y = a_0x^0 + a_1x^1$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "Then, in terms of our forward problem formulation ($y=Ha$), \n", "\n", "$$a = \\begin{bmatrix} a_0 & a_1 \\end{bmatrix} = \\begin{bmatrix} 14 & -1.1 \\end{bmatrix}$$\n", @@ -306,7 +380,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "fragment" } }, "source": [ @@ -329,7 +403,7 @@ "execution_count": null, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "outputs": [], @@ -366,8 +440,17 @@ "\n", "$$x = \\begin{bmatrix} 8 & 2 & 11 & 6 & 5 & 4 & 12 & 9 & 6 & 1 \\end{bmatrix}$$\n", "\n", - "$$y = \\begin{bmatrix} 3 & 10 & 3 & 6 & 8 & 12 & 1 & 4 & 9 & 14 \\end{bmatrix}$$\n", - "\n", + "$$y = \\begin{bmatrix} 3 & 10 & 3 & 6 & 8 & 12 & 1 & 4 & 9 & 14 \\end{bmatrix}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "The matrix $H$ remains unchanged\n", "\n", "$$H = \\begin{bmatrix} \n", @@ -381,8 +464,17 @@ "1 & 9 \\\\\n", "1 & 6 \\\\\n", "1 & 1 \n", - "\\end{bmatrix}$$\n", - "\n", + "\\end{bmatrix}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "but, to solve this problem, we need to invert $H$ *i.e.*\n", "\n", "$$a = H^{-1}y$$" @@ -392,19 +484,39 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "fragment" } }, "source": [ "however, $H$ is not a square matrix, which means it cannot be inverted directly. So, first we need to multiply by $H^{T}$.\n", "\n", - "> Because $H \\in \\mathbb{R}^{m \\times n}$ and $H^{T} \\in \\mathbb{R}^{n \\times m}$ so $H^{T}H \\in \\mathbb{R}^{n \\times n}$\n", - "\n", + "> Because $H \\in \\mathbb{R}^{m \\times n}$ and $H^{T} \\in \\mathbb{R}^{n \\times m}$ so $H^{T}H \\in \\mathbb{R}^{n \\times n}$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "So, the solution is\n", "\n", "$$a = (H^TH)^{-1}H^{T}y$$\n", "\n", - "which is often called the Normal Equation. We can implement this in python as follows." + "which is often called the Normal Equation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "We can implement this in python as follows." ] }, { @@ -477,15 +589,35 @@ "source": [ "## Linear Regression Exercise\n", "\n", - "For this exercise you should apply the techniques learned for fitting a straight line to a set of data points to a new set of data that requires a polynomial fit.\n", - "\n", + "For this exercise you should apply the techniques learned for fitting a straight line to a set of data points to a new set of data that requires a polynomial fit." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "### Equation of a Polynomial Line \n", "\n", "The expression for a straight line (*i.e.* $k = 1$) generalises to the following form for a $k^{\\textrm{th}}$ degree polynomial:\n", "\n", "$$y = a_0 + a_1x + a_2x^2 + ... + a_kx^k$$\n", "\n", - "where $a_i$ are the polynomial coefficients. We can represent this in Python with the following function:" + "where $a_i$ are the polynomial coefficients." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "We can represent this in Python with the following function:" ] }, { @@ -538,8 +670,17 @@ "| 0.85 | 1.265 |\n", "| 0.90 | 1.380 |\n", "| 0.95 | 1.575 |\n", - "| 1.00 | 1.857 |\n", - "\n", + "| 1.00 | 1.857 |" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "These values have already been defined for you." ] }, @@ -548,7 +689,7 @@ "execution_count": null, "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "fragment" } }, "outputs": [], @@ -644,14 +785,41 @@ "source": [ "## Gradient Descent\n", "\n", - "In the previous examples we used an analytical approach to solve the inverse problem, however for many problems this is not possible (*e.g.* if the matrix $H$ is not invertable).\n", - "\n", - "An alternative is to try an iterative method such as gradient descent, which searches for the local optimum of a function from a given starting position.\n", - "\n", + "In the previous examples we used an analytical approach to solve the inverse problem, however for many problems this is not possible (*e.g.* if the matrix $H$ is not invertable)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "An alternative is to try an iterative method such as gradient descent, which searches for the local optimum of a function from a given starting position." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "For this approach we want to define a convex function that measures the accuracy of a given reconstruction. One way to test the accuracy is to measure the residual, $y-Hx$, for a given estimate of $x$. With a convex function, like the l2-norm, we will be searching for the global minimum of the residual\n", "\n", - "$$F(x) = \\frac{1}{2}\\|Hx-y\\|_2^2$$\n", - "\n", + "$$F(x) = \\frac{1}{2}\\|Hx-y\\|_2^2$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "The corresponding gradient is\n", "\n", "$$\\nabla F(x) = H^{T}(Hx-y)$$" @@ -671,7 +839,7 @@ "\n", "$$\\|x\\|_2 = \\Big(\\sum_{i=1}^n|x_i|^2\\Big)^{\\frac{1}{2}}$$\n", "\n", - "We can show that this function is convex as follows. First we define a range of $x$ values, the we calculate $\\|x\\|_2$ and $\\|x\\|_2^2$." + "We can show that this function is convex as follows. First we define a range of values, then we calculate $\\|x\\|_2$ and $\\|x\\|_2^2$." ] }, { @@ -685,11 +853,22 @@ "outputs": [], "source": [ "# Set a range of x values\n", - "x_range = np.around(np.linspace(-1, 1, 101), 2)\n", + "values = np.around(np.linspace(-1, 1, 101), 2)\n", "# Calculate the l2-norm of x\n", - "x_norm = np.array([np.linalg.norm(x_i) for x_i in x_range])\n", + "norm = np.array([np.linalg.norm(value) for value in values])\n", "# Calculate the squared l2-norm of x\n", - "x_norm2 = x_norm ** 2" + "norm2 = norm ** 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Now let's plot these functions." ] }, { @@ -700,8 +879,6 @@ } }, "source": [ - "Now let's plot these functions.\n", - "\n", "#### INTERACTIVE CELL" ] }, @@ -720,13 +897,24 @@ "######################################\n", "@interact(x_val=FloatSlider(value=-0.8, min=-1.0, max=1.0, step=0.2))\n", "def show_grad(x_val):\n", - " \n", + " x_val = np.around(x_val, 2)\n", " # Calculate the gradient of the squared l2-norm \n", " grad = 2 * x_val\n", " # Calculate the tangent line at this point\n", - " tangent = x_val ** 2 + 2 * x_val * (x_range - x_val)\n", + " tangent = x_val ** 2 + 2 * x_val * (values - x_val)\n", " # Display\n", - " plot.grad_plot(x_range, x_norm, x_norm2, grad, tangent, index=int(np.where(x_range == x_val)[0]))" + " plot.grad_plot(values, norm, norm2, grad, tangent, index=int(np.where(values == x_val)[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "The plot shows that both $\\|x\\|_2$ and $\\|x\\|_2^2$ have a clearly defined global minimum. The plot also displays the gradient of $\\|x\\|_2^2$ at the point $x=-0.8$, try ajusting the position of this point and see what happens to the gradient." ] }, { @@ -737,14 +925,32 @@ } }, "source": [ - "The plot shows that both $\\|x\\|_2$ and $\\|x\\|_2^2$ have a clearly defined global minimum. The plot also displays the gradient of $\\|x\\|_2^2$ at the point $x=-0.8$, try ajusting the position of this point and see what happens to the gradient.\n", - "\n", - "Unsurprisingly, as the point apporachs the minimum, the gradient tends to zero. Also, depending on which side of the minimum the point is, the gradient will be either positive or negative. Gradient descent captialises on this by iteratively updating solutions in the following way\n", + "Unsurprisingly, as the point apporachs the minimum, the gradient tends to zero. Also, depending on which side of the minimum the point is, the gradient will be either positive or negative." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Gradient descent captialises on this by iteratively updating solutions in the following way\n", "\n", "$$x_{n+1} = x_n - \\gamma \\nabla F(x_n)$$\n", "\n", - "where $x_n$ corresponds to the solution at given iteration. So, as the gradient approaches zero and consequenly as $x$ approaches the global minimum, the solution will converge. \n", - "\n", + "where $x_n$ corresponds to the solution at given iteration. So, as the gradient approaches zero and consequenly as $x$ approaches the global minimum, the solution will converge. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "Now, we can try this apporach to out linear regression problem. " ] }, @@ -785,7 +991,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ diff --git a/sparsity_1.ipynb b/sparsity_1.ipynb index 3fe3595..deccc34 100644 --- a/sparsity_1.ipynb +++ b/sparsity_1.ipynb @@ -22,7 +22,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "skip" } }, "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "scrolled": true, "slideshow": { @@ -142,25 +142,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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EhFQ6/hEjRpjLli1zP//iiy+qfO/PP/+8wjwq+3uVN3/+fDMyMrLazzQkJMT8/PPPfZonADQmnE4JAKgXEREROnDggL788ks9/fTTGjp0qOLj49WsWTPZbDZFRkYqISFBI0aM0HvvvactW7aoZ8+eAR2zzWbTs88+q++++05//vOf1b17d51//vmy2Ww677zzdNlll+n222/X22+/rezsbK8X9KiJ8qdCVvZ7uFLlj7p5O5Wy1D333KNVq1bpt7/9rS644AKFhYUpNjZW1157rRYsWKD58+dXeRStvAEDBqhdu3bu5506dfL57zVixAjt3r1b//d//6errrpKrVu3VlhYmJo2bar4+Hhdd911mjp1qnbv3l3l6ZkA0FgZpsnloQAAAADAKjgSBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwkNBAD6AxKy4u1sGDB3XeeefJMIxADwcAAABAgJimqRMnTqhNmzYKCan6WBshLoAOHjyouLi4QA8DAAAAQJDYt2+f2rVrV2UfQlwAnXfeeZJK/lCRkZEBHg0AAACAQMnLy1NcXJw7I1SFEBdApadQRkZGEuIAAAAA+PQzKy5sAgAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGNIsStXLlS1113ndq0aSPDMPThhx9WO82KFSvUs2dPRUREKD4+Xm+88UaFPh988IE6d+6s8PBwde7cWYsXL/bD6NEYFJmm1hwv0uIjhVpzvEhFphnoIQEAKkHNBhBojSLEnTp1St26ddMrr7ziU/9du3YpJSVF/fr1U1ZWlh577DE98MAD+uCDD9x91q5dqxEjRig1NVWbN29Wamqqhg8frszMTH8tBhqo9NxC9f76rG7akq/7trt005Z89f76rNJzCwM9NABAOdRsAMHAMM3GtfvIMAwtXrxYv/3tbyvt88gjj+jjjz/W999/724bPXq0Nm/erLVr10qSRowYoby8PH366afuPtdee63OP/98zZs3z6ex5OXlyeFwyOl0KjIysnYLBEtLzy1U2jaXyv8jNP7ff2ck2JUSFVrfwwIAeEHNBuBPNckGjeJIXE2tXbtWgwcP9mgbMmSI1q9fr4KCgir7rFmzptL55ufnKy8vz+OBxqvINDVxZ0GFjQFJ7rYndxZwmg4ABAFqNoBgQojzIicnR9HR0R5t0dHRKiwsVG5ubpV9cnJyKp3vlClT5HA43I+4uLi6HzwsI9NZrGxX5V/2pqSDLlOZzuL6GxQAwCtqNoBgQoirhGEYHs9Lzzot2+6tT/m2siZMmCCn0+l+7Nu3rw5HDKs5VODb3lpf+wEA/IeaDSCYcOK2FzExMRWOqB0+fFihoaFq1apVlX3KH50rKzw8XOHh4XU/YFhSdFjlgb82/QAA/kPNBhBMOBLnRVJSkjIyMjzali5dql69eiksLKzKPsnJyfU2TlhboiNEsXZDlX3dG5La2A0lOvhnCgCBRs0GEEwaRaU5efKkNm3apE2bNkkquYXApk2btHfvXkklpzneeeed7v6jR4/Wnj17NG7cOH3//feaNWuWZs6cqfHjx7v7PPjgg1q6dKmeffZZbdu2Tc8++6yWLVumsWPH1ueiwcJshqFn4kt2CpTfKCh9Pjk+TLYqTtEFANQPajaAYNIoQtz69evVvXt3de/eXZI0btw4de/eXU8++aQkKTs72x3oJKljx45KT0/X8uXLdcUVV+iZZ57RSy+9pBtvvNHdJzk5WfPnz9dbb72lyy+/XLNnz9aCBQuUmJhYvwsHS0uJCtWMBLti7J5f+rF2g0tVA0CQoWYDCBaN7j5xwYT7xKFUkVlyRbPFK77UDQP6KtERwt5cAAhS1GwA/lCTbMAuIyAI2AxDyS1s2nP6gJJb2AI9HABAFajZAAKNEAf4oHSv66ECU9FhBntdASBIUa8BNAaEOKAa6bmFmrizwOMmr7H2kh+48/sHAAge1GsAjUWjuLAJUFvpuYVK2+by2CCQpByXqbRtLqXnFgZoZACAsqjXABoTQhxQiSLT1MSdBfJ25Z/Stid3FqiIawMBQEBRrwE0NoQ4oBKZzuIKe3TLMiUddJX89gIAEDjUawCNDSEOqMShAt/22PraDwDgH9RrAI0NIQ6oRHSYb1cz87UfAMA/qNcAGhtCHFCJREeIYu2GKvvKNyS1sZdcvhoAEDjUawCNDdUMqITNKLkstaQKGwalzyfHh3H/IQAIMOo1gMaGEAdUISUqVDMS7Iqxe37xx9oNzUiwc98hAAgS1GsAjQkVDahGSlSohrSyKdNZrMUrvtQNA/oq0RHCHl0ACDLUawCNBSEO8IHNMJTcwqY9pw8ouYUt0MMBAFSCeg2gMeB0SgAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWEhroAQCoH0WmqUxnsQ4VmIoOM5ToCJHNMAI9LABAOdRrANUhxAGNQHpuoSbuLFC2y3S3xdoNPRMfppQoygAABAvqNQBfcDol0MCl5xYqbZvLY4NAknJcptK2uZSeWxigkQEAyqJeA/AVIQ5owIpMUxN3Fsj08lpp25M7C1RkeusBAKgv1GsANUGIAxqwTGdxhT26ZZmSDrpKfnsBAAgc6jWAmiDEAQ3YoQLf9tj62g8A4B/UawA1QYgDGrDoMN+uZuZrPwCAf1CvAdQEIQ5owBIdIYq1G6rsK9+Q1MZecvlqAEDgUK8B1ASVAGjAbEbJZaklVdgwKH0+OT6M+w8BQIBRrwHUBCEOaOBSokI1I8GuGLvnF3+s3dCMBDv3HQKAIEG9BuArqgHQCKREhWpIK5syncVavOJL3TCgrxIdIezRBYAgQ70G4AtCHNBI2AxDyS1s2nP6gJJb2AI9HABAJajXAKrD6ZQAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAtpNCHutddeU8eOHRUREaGePXtq1apVlfa9++67ZRhGhcdll13m7jN79myvfc6ePVsfiwMAAACgkWoUIW7BggUaO3asHn/8cWVlZalfv34aOnSo9u7d67X/iy++qOzsbPdj3759atmypW6++WaPfpGRkR79srOzFRERUR+LhHKKTFNrjhdp8ZFCrTlepCLTDPSQAABeUK8B4NyFBnoA9WHq1KkaOXKkRo0aJUmaNm2aPvvsM73++uuaMmVKhf4Oh0MOh8P9/MMPP9SxY8d0zz33ePQzDEMxMTH+HTyqlZ5bqIk7C5Tt+t+GQKzd0DPxYUqJahSrOABYAvUaAOpGgz8S53K5tGHDBg0ePNijffDgwVqzZo1P85g5c6YGDhyo9u3be7SfPHlS7du3V7t27TRs2DBlZWVVOZ/8/Hzl5eV5PHBu0nMLlbbN5bFBIEk5LlNp21xKzy0M0MgAAGVRrwGg7jT4EJebm6uioiJFR0d7tEdHRysnJ6fa6bOzs/Xpp5+6j+KVSkhI0OzZs/Xxxx9r3rx5ioiIUN++fbVjx45K5zVlyhT3UT6Hw6G4uLjaLRQklZySM3FngbydiFPa9uTOAk7VAYAAo14DQN1q8CGulGEYHs9N06zQ5s3s2bPVokUL/fa3v/Vo79Onj+644w5169ZN/fr107/+9S916tRJL7/8cqXzmjBhgpxOp/uxb9++Wi0LSmQ6iyvs0S3LlHTQZSrTWVx/gwIAVEC9BoC61eBPQI+KipLNZqtw1O3w4cMVjs6VZ5qmZs2apdTUVNnt9ir7hoSE6Je//GWVR+LCw8MVHh7u++BRpUMFvu2x9bUfAMA/qNcAULca/JE4u92unj17KiMjw6M9IyNDycnJVU67YsUK/fjjjxo5cmS172OapjZt2qTY2NhzGi98Fx1W/ZHUmvQDAPgH9RoA6laDPxInSePGjVNqaqp69eqlpKQkTZ8+XXv37tXo0aMllZzmeODAAc2ZM8djupkzZyoxMVFdunSpMM+nn35affr00cUXX6y8vDy99NJL2rRpk1599dV6WSZIiY4QxdoN5bhMr7+zMFRy1bNER4PfVwEAQY16DQB1q1GEuBEjRujo0aOaPHmysrOz1aVLF6Wnp7uvNpmdnV3hnnFOp1MffPCBXnzxRa/zPH78uO69917l5OTI4XCoe/fuWrlypXr37u335UEJm1FyWeq0bS4ZkseGQem+3MnxYbL58NtHAID/UK8BoG41ihAnSWPGjNGYMWO8vjZ79uwKbQ6HQ6dPn650fi+88IJeeOGFuhoeaiklKlQzEuT1vkOTue8QAAQN6jUA1B0qJiwvJSpUQ1rZlOks1uIVX+qGAX2V6Ahhjy4ABBnqNQDUDUIcGgSbYSi5hU17Th9QcgtboIcDAKgE9RoAzh2/IAYAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAsJDTQAwDQMBSZpjKdxTpUYCo6zFCiI0Q2wwj0sAAA5VCvAesjxAE4Z+m5hZq4s0DZLtPdFms39Ex8mFKiKDMAECyo10DDwOmUAM5Jem6h0ra5PDYIJCnHZSptm0vpuYUBGhkAoCzqNdBwEOIA1FqRaWrizgKZXl4rbXtyZ4GKTG89AAD1hXoNNCyEOAC1luksrrBHtyxT0kFXyW8vAACBQ70GGhZCHIBaO1Tg2x5bX/sBAPyDeg00LIQ4ALUWHebb1cx87QcA8A/qNdCwEOIA1FqiI0SxdkOVfeUbktrYSy5fDQAIHOo10LDwLxVArdmMkstSS6qwYVD6fHJ8GPcfAoAAo14DDQshDsA5SYkK1YwEu2Lsnl/8sXZDMxLs3HcIAIIE9RpoOPjXCuCcpUSFakgrmzKdxVq84kvdMKCvEh0h7NEFgCBDvQYaBkIcgDphMwwlt7Bpz+kDSm5hC/RwAACVoF4D1sfplAAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAAC2k0Ie61115Tx44dFRERoZ49e2rVqlWV9l2+fLkMw6jw2LZtm0e/Dz74QJ07d1Z4eLg6d+6sxYsX+3sxAAAAADRyjSLELViwQGPHjtXjjz+urKws9evXT0OHDtXevXurnG779u3Kzs52Py6++GL3a2vXrtWIESOUmpqqzZs3KzU1VcOHD1dmZqa/FwcAAABAI9YoQtzUqVM1cuRIjRo1SpdeeqmmTZumuLg4vf7661VO17p1a8XExLgfNpvN/dq0adM0aNAgTZgwQQkJCZowYYKuueYaTZs2zc9LAwAAAKAxa/AhzuVyacOGDRo8eLBH++DBg7VmzZoqp+3evbtiY2N1zTXX6IsvvvB4be3atRXmOWTIkCrnmZ+fr7y8PI8HAAAAANREgw9xubm5KioqUnR0tEd7dHS0cnJyvE4TGxur6dOn64MPPtCiRYt0ySWX6JprrtHKlSvdfXJycmo0T0maMmWKHA6H+xEXF3cOSwYAAACgMQoN9ADqi2EYHs9N06zQVuqSSy7RJZdc4n6elJSkffv26fnnn1f//v1rNU9JmjBhgsaNG+d+npeXR5ADAAAAUCMN/khcVFSUbDZbhSNkhw8frnAkrSp9+vTRjh073M9jYmJqPM/w8HBFRkZ6PAAAAACgJhp8iLPb7erZs6cyMjI82jMyMpScnOzzfLKyshQbG+t+npSUVGGeS5curdE8AQAAAKCmGsXplOPGjVNqaqp69eqlpKQkTZ8+XXv37tXo0aMllZzmeODAAc2ZM0dSyZUnO3TooMsuu0wul0vvvvuuPvjgA33wwQfueT744IPq37+/nn32Wf3mN7/RRx99pGXLlmn16tUBWUYAAAAAjUOjCHEjRozQ0aNHNXnyZGVnZ6tLly5KT09X+/btJUnZ2dke94xzuVwaP368Dhw4oCZNmuiyyy7Tf/7zH6WkpLj7JCcna/78+XriiSc0ceJE/eIXv9CCBQuUmJhY78sHAAAAoPFoFCFOksaMGaMxY8Z4fW327Nkezx9++GE9/PDD1c7zpptu0k033VQXwwMAAAAAnzT438QBAAAAQENCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALCQ0EAPAA1fkWkq01msQwWmosMMJTpCZDOMQA8LAOAFNRsAgh8hDn6VnluoiTsLlO0y3W2xdkPPxIcpJYrVDwCCCTUbAKyB0ynhN+m5hUrb5vLYGJCkHJeptG0upecWBmhkAIDyqNkAYB2EOPhFkWlq4s4CmV5eK217cmeBikxvPQAA9YmaDQDWQoiDX2Q6iyvszS3LlHTQVfK7CwBAYFGzAcBaCHHwi0MFvu2t9bUfAMB/qNkAYC2EOPhFdJhvVzLztR8AwH+o2QBgLYQ4+EWiI0SxdkOVfd0bktrYSy5dDQAILGo2AFgL1Rh+YTNKLkktqcJGQenzyfFh3HsIAIIANRsArIUQB79JiQrVjAS7YuyeX/qxdkMzEuzccwgAggg1GwCsg4oMv0qJCtWQVjZlOou1eMWXumFAXyU6QtibCwBBiJoNANZAiIPf2QxDyS1s2nP6gJJb2AI9HABAFajZABD8OJ0SAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyk0YS41157TR07dlRERIR69uypVatWVdp30aJFGjRokC644AJFRkYqKSlJn332mUef2bNnyzCMCo+zZ8/6e1EAAAAANGKNIsQtWLBAY8eO1eOPP66srCz169dPQ4cO1d69e732X7lypQYNGqT09HRt2LBBv/rVr3TdddcpKyvLo19kZKSys7M9HhEREfWxSAAAAAAaqdBAD6A+TJ06VSNHjtSoUaMkSdOmTdNnn32m119/XVOmTKnQf9q0aR7P//a3v+mjjz7SJ598ou7du7vbDcNQTEyMX8cOAAAAAGU1+CNxLpdLGzZs0ODBgz3aBw8erDVr1vg0j+LiYp04cUItW7b0aD958qTat2+vdu3aadiwYRWO1JWXn5+vvLw8jwcAAAAA1ESDD3G5ubkqKipSdHS0R3t0dLRycnJ8msc//vEPnTp1SsOHD3e3JSQkaPbs2fr44481b948RUREqG/fvtqxY0el85kyZYocDof7ERcXV7uFAgAAANBoNfgQV8owDI/npmlWaPNm3rx5mjRpkhYsWKDWrVu72/v06aM77rhD3bp1U79+/fSvf/1LnTp10ssvv1zpvCZMmCCn0+l+7Nu3r/YLBAAAAKBRavC/iYuKipLNZqtw1O3w4cMVjs6Vt2DBAo0cOVLvv/++Bg4cWGXfkJAQ/fKXv6zySFx4eLjCw8N9HzwAAAAAlNPgj8TZ7Xb17NlTGRkZHu0ZGRlKTk6udLp58+bp7rvv1nvvvadf//rX1b6PaZratGmTYmNjz3nMAAAAAFCZBn8kTpLGjRun1NRU9erVS0lJSZo+fbr27t2r0aNHSyo5zfHAgQOaM2eOpJIAd+edd+rFF19Unz593EfxmjRpIofDIUl6+umn1adPH1188cXKy8vTSy+9pE2bNunVV18NzEICAAAAaBQaRYgbMWKEjh49qsmTJys7O1tdunRRenq62rdvL0nKzs72uGfcm2++qcLCQt13332677773O133XWXZs+eLUk6fvy47r33XuXk5MjhcKh79+5auXKlevfuXa/LBgAAAKBxaRQhTpLGjBmjMWPGeH2tNJiVWr58ebXze+GFF/TCCy/UwcgAAAAAwHeNJsQBsLYi01Sms1iHCkxFhxlKdITI5sMVZgEA9Y+aDfgXIQ5A0EvPLdTEnQXKdpnutli7oWfiw5QSRRkDgGBCzQb8r8FfnRKAtaXnFiptm8tjY0CSclym0ra5lJ5bGKCRAQDKo2YD9YMQByBoFZmmJu4skOnltdK2J3cWqMj01gMAUJ+o2UD9IcQBCFqZzuIKe3PLMiUddJX87gIAEFjUbKD+EOIABK1DBb7trfW1HwDAf6jZQP0hxAEIWtFhvl3JzNd+AAD/oWYD9YcQByBoJTpCFGs3VNnXvSGpjb3k0tUAgMCiZgP1h39FAIKWzSi5JLWkChsFpc8nx4dx7yEACALUbKD+EOIABLWUqFDNSLArxu75pR9rNzQjwc49hwAgiFCzgfrBvyQAQS8lKlRDWtmU6SzW4hVf6oYBfZXoCGFvLgAEIWo24H+EOACWYDMMJbewac/pA0puYQv0cAAAVaBmA/7F6ZQAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCQutqRj/88IPWrl2rgwcP6siRIzp79qxatWqlCy64QJdeeqn69u2rpk2b1tXbAQAAAECjdE4hbu3atZo+fbqWLFmiw4cPV/1GoaHq0aOHbr/9dqWmpsrhcJzLWwMAAABAo1SrEPfuu+/queee09atW2Wapru9efPmatWqlVq2bKkmTZro559/1s8//6zc3FwVFBQoMzNT69at06OPPqpbb71VTz75pOLi4upsYQAAAACgoatRiFu+fLnGjx+vrKwsmaapli1b6sYbb1T//v2VmJioiy66yOt0J0+e1Pr165WZmamPP/5Ya9eu1cyZMzV37lw9+OCDeuyxx3TeeefVyQIBAAAAQENWoxB39dVXS5KGDBmi0aNHKyUlRWFhYdVO17x5c1111VW66qqr9Mgjj2j37t2aM2eOXn75ZT333HNq2rSpJk6cWLslAAAAAIBGpEZXpxwyZIjWrl2rTz/9VL/5zW98CnDedOjQQU8++aT27NmjKVOmqGXLlrWaDwAAAAA0NjU6Evfpp59WaPvb3/6mXr16afDgwTV+86ZNm+rhhx+u8XQAAAAA0Fid8y0GnnjiCcXGxurAgQN1MR4AAAAAQBXq5GbfZa9QWZ2lS5dq//79dfG2AAAAANDo1NnNvn1111136ciRIyosLKzvtwYAAAAAy6txiJs1a5a++uor9enTR7/85S9r9aY1OXIHAAAAAPifGoe4ffv26Z///Kdmzpzpbjt27Jjuvvtu9ejRQ927d9cVV1zh9b5vpmnK6XQqNLTeDwACAAAAQINQ4zR1zTXX6KefflJmZqZ27NghwzCUn5+vOXPm6J133pEkGYah+Ph4de/e3R3q2rZtq4ULF+rs2bPq2LFjnS8IAAAAADQGNQ5xV155pa688kpJJUfgWrVqpebNm+vmm29WVlaWtm7dqoKCAv3444/68ccftXDhQo/pDcPQDTfcUDejBwAAAIBG5pzOazz//PMlSc2bN3efXllQUKAtW7YoKytLWVlZ2rhxo7755hudOnVKTZo00fDhw/XMM8+c+8hRZ4pMU5nOYh0qMBUdZijRESKbYQR6WAAAL6jZAIBzvsXADz/84PH7uLCwMHXv3l2///3v9fLLL+vLL7/UiRMndPToUZ08eVJvvfWWmjRpcq5vW2OvvfaaOnbsqIiICPXs2VOrVq2qsv+KFSvUs2dPRUREKD4+Xm+88UaFPh988IE6d+6s8PBwde7cWYsXL/bX8P0mPbdQvb8+q5u25Ou+7S7dtCVfvb8+q/Rcrh4KAMGGmg0AkOogxF100UUaOnRotf3OP/98GQHaU7hgwQKNHTtWjz/+uLKystSvXz8NHTpUe/fu9dp/165dSklJUb9+/ZSVlaXHHntMDzzwgD744AN3n7Vr12rEiBFKTU3V5s2blZqaquHDhyszM7O+FuucpecWKm2bS9kuz6uF5rhMpW1zsVEAAEGEmg0AKFUnN/sOdlOnTtXIkSM1atQoXXrppZo2bZri4uL0+uuve+3/xhtv6MILL9S0adN06aWXatSoUfr973+v559/3t1n2rRpGjRokCZMmKCEhARNmDBB11xzjaZNm1ZPS3VuikxTE3cWyNvNHkrbntxZoCJuBwEAAUfNBgCUZZgN/KZtLpdLTZs21fvvv+9xQZUHH3xQmzZt0ooVKypM079/f3Xv3l0vvviiu23x4sUaPny4Tp8+rbCwMF144YV66KGH9NBDD7n7vPDCC5o2bZr27NnjdSz5+fnKz893P8/Ly1NcXJycTqciIyPrYnF9tuZ4kW7aku/R1nXv17p873qPtlZhhuzlDqDGxsbq1ltv9WibN2+esrOzq3zPM2fO6Oqrr1ZSUpK7LT8/X6+++qpPY77lllvUpk0b9/MffvhB//73vyu8R/nTde12u+6//36PtqVLl2rLli3VvufFF1+s6667zv183rx5OnHihE6ePFnttIMGDVLXrl3dz3NzczVnzpwqpykdf1pamsdtOjZs2OB1XS2vVatWuuuuuzzaFi1apN27d1d4j/J69Oihq666yqNt6tSp1b6nJP3ud79Thw4dJJV8RklJSVq0aJFP044bN87j+fLly7Vx48ZK+5eOv0OHDvrd737n8drbb7+to0ePVvueAwYMUM+ePd3PT5w4oRkzZvg03jvvvFNRUVHu599++60yMjKqna558+a69957Pdo++eQT7dixo9ppu3TposGDB3u0vfLKK3K5XNVOO2zYMHXq1Mn9/ODBg5o/f36100nSfffdp/DwcPfztWvXau3atdVOV9saIUlJSUl+rRHe1GWNkKTp06fXeY1wmdLRgv99XS/+ZapOhzd3P7/0wGb12LXGa80uy5caUap8rairGiFJu3fv1ty5c336eUVNa0RV09Z1jfBWT+uyRkybNk3FxcXVTlvbGnHmzBndfPPNfq0R3j4jakTd14jy/LkdUZm6rhH+2o4o5W07ItDy8vLkcDh8ygY1urDJ888/r/vuu69Of9P29ddfKzc316dTMmsjNzdXRUVFio6O9miPjo5WTk6O12lycnK89i8sLFRubq5iY2Mr7VPZPCVpypQpevrpp2u5JHXrUEHF7G4vdKl5vmdRyc+X8sv1czgcFaY9ffq0Tpw4Ue37lg2xpXyZTpKKioo8nhcUFHidtnyb3W6v0Ofs2bM+ve/Zs2c9nnfp0kUZGRk+TVtQUODxvLi42KfpTpw4ofL7Vlwul0/TRkREVGjz9rfxNq9z+dsUFv7vNK4uXbqosLDQ52m9jaO6aU+cOKHTp09XaD916pRP71t+w8Y0TZ/HW36DqrL1sCrffvutunbtWuv1UCr5DHwJceXXw6KiIr/+baS6qRGln5FU9zWivLqsEZJ08uRJv9SI5mX+3yhXI0KLSuq3t5pdlq81olTZ9rqqEaXPa1snfF0PW7duXaHNHzWifHtd1IhSzZs314EDB6rtF+w1onxbXW5HbN26lRrhRdntiG+//bbOtyO8qesa4e/vqrLbEWW/c6yiRiHu4Ycf1j/+8Q89/PDDuueee9SiRYtav/Hq1av1f//3f/r000/11FNP+S3ElSr/ezzTNKv8jZ63/uXbazrPCRMmeOwtKD0SFwjRYRXH6Qq162R4c482b3t1mzZtWmHapk2ber3Be3ll99iV8mU6SbLZbB7Pw8LCfJrWW/GNiIjwadryxaxr164+HYkoHV9ZISEhPi9r+fXIbrf7NG2zZs0qtNXH3yY09H+lpGvXrtq9e7fP03obhy/TelsPmzVr5vULs7zy64RhGO73rOxIZamQEM+z0H1dD5s3/9+/rS1btqhr1661Xg+lkr9NZRtoZZeh/Hpos9nO+W9T3WdUFzWi9DOS/FMjyi5DXdYIqeRvXd1nVDq+sqqqEeWPxJnlakShraR+V3ckLhhqROlzf9eI8jtapbqpEdWpixpRKjo6Wnl5edVOW9MaUX58ZdVFjahOXW5HfP/9937djjhz5ozXPudaI8rOv7JaUVfbEVu2bFGnTp38th1Rugz+qhG+1NOyarMelv3OsQyzBh5//HGzadOmZkhIiBkREWHeeOON5sKFC81Dhw5VO63L5TLXrVtnPvHEE2Z8fLwZEhJiGoZhJiYmmps3b67JMGokPz/ftNls5qJFizzaH3jgAbN///5ep+nXr5/5wAMPeLQtWrTIDA0NNV0ul2maphkXF2dOnTrVo8/UqVPNCy+80OexOZ1OU5LpdDp9nqauFBYXmz0yT5ttVp0yY7082qw6ZfbMPG0WFhfX+9iAQHrvvfcs/x5Wn399vIfV5k/NBryz2r/l+p5/fbyH1edfX+/hi5pkgxodifvrX/+qP/7xj3rsscf03nvvadGiRe7L6sfFxalbt2664IIL1LJlS4WHh+vYsWP6+eeftXPnTm3evNm9R8g0Tf3iF7/QM888o1tuuaWuc6kHu92unj17KiMjw+M3cRkZGfrNb37jdZqkpCR98sknHm1Lly5Vr1693HtFkpKSlJGR4fGbuKVLlyo5OdkPS1H3bIahZ+LDlLbNJUPy+LF86b6byfFh3HsIAIIANRsAUFaNb/bdtm1bvf3225oyZYqmT5+uWbNmaf/+/dq7d6/27t3r9XRC8/+dihgaGqpf//rX+sMf/qAhQ4bU2y0Hxo0bp9TUVPXq1UtJSUmaPn269u7dq9GjR0sqOc3xwIED7h+Ljh49Wq+88orGjRuntLQ0rV27VjNnztS8efPc83zwwQfVv39/Pfvss/rNb36jjz76SMuWLdPq1avrZZnqQkpUqGYkSBN3FnhcsjrWbmhyfJhSos7pXvAAgDpEzQYAlKp1xW/Tpo0mTZqkSZMmacuWLVq5cqUyMzN18OBBHTlyRGfPnlWrVq10wQUXqHPnzurfv7/69u1b6/Osz8WIESN09OhRTZ48WdnZ2erSpYvS09PVvn17SVJ2drbHPeM6duyo9PR0PfTQQ3r11VfVpk0bvfTSS7rxxhvdfZKTkzV//nw98cQTmjhxon7xi19owYIFSkxMrPflOxcpUaEa0sqmTGexDhWYig4zlOgIYW8uAAQhajYAQDqHEFdWly5d1KVLF40ZM6YuZucXY8aMqXR8s2fPrtA2YMCAai9PetNNN+mmm26qi+EFlM0wlNzCVn1HAEDAUbMBAI3iZt8AAAAA0FCcc4i78sortWnTpjoYCgAAAACgOucc4tasWaNf/vKXGjNmjH7++ee6GBMAAAAAoBLnHOJKf2f25ptvqlOnTnrjjTc87hIPAAAAAKg75xziXnnlFa1fv159+/bVzz//rPvuu089e/bUl19+WRfjAwAAAACUUScXNunWrZtWrlypOXPmKCYmRps2bVL//v115513Kjs7uy7eAgAAAACgOr465R133KHt27dr3LhxCg0N1dy5c5WQkKDnn39ehYWFdflWAAAAANAo1fktBpo3b67nn39emzdv1tVXX60TJ07okUceUdeuXbV06dK6fjsAAAAAaFT8dp+4hIQEZWRk6P3339eFF16o7du3a+jQobrhhhu0a9cuf70tAAAAADRofr/Z9/XXX69Zs2apQ4cOMk1TH3/8sS677DJNmjRJZ8+e9ffbAwAAAECDUuchbufOnZo3b54efPBB9enTR5GRkRo4cKD27NkjSTJNU2fPntUzzzyjLl266PPPP6/rIQAAAABAgxV6rjPIyMhQZmam+3H06FH3a6X3i7PZbOratav69u2r5ORkNWnSRE899ZS+/fZbDR48WBMnTtRTTz11rkMBAAAAgAbvnEPckCFDZBiGxw2+HQ6HEhMT3aGtT58+atasmcd0119/vV588UU9+uijmjx5stq3b6+77777XIcDAAAAAA3aOYc4SerYsaM7sPXt21eXXXaZDMOocpqQkBA99NBDiomJ0e23365XX32VEAcAAAAA1TjnEJeTk6PWrVvXevpbb71Vf/zjH7Vt27ZzHQoAAAAANHjnfGGTcwlwpVq0aKHTp0+f83wAAAAAoKGrk9Mpz9Vrr72mL7/8MtDDAAAAAICgFxQhLiUlRSkpKYEeBgAAAAAEPb/f7BsAAAAAUHcIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAkFZmm1hwv0sambbXmeJGKTDPQQwIAVIKajcYuNNADAIBAS88t1MSdBcp2mVKrXpq7JV+xdkPPxIcpJYoyCQDBhJoNcCQOQCOXnluotG2uko2BMnJcptK2uZSeWxigkQEAyqNmAyUIcQAarSLT1MSdBfJ2Ek5p25M7CzhNBwCCADUb+B9CHIBGK9NZXGFvblmmpIMuU5nO4vobFADAK2o28D+EOACN1qEC3/bW+toPAOA/1GzgfwhxABqt6DCjTvsBAPyHmg38DyEOQKOV6AhRrN1QZV/3hqQ2dkOJDkolAAQaNRv4H9ZyAI2WzSi5JLWkChsFpc8nx4fJZrBXFwACjZoN/A8hDkCjlhIVqhkJdsXYPb/0Y+2GZiTYuecQAAQRajZQgjUdQKOXEhWqIa1synQW61CBqeiwktNx2JsLAMGHmg0Q4gBAUslpOsktbIEeBgDAB9RsNHacTgkAAAAAFtLgQ9yxY8eUmpoqh8Mhh8Oh1NRUHT9+vNL+BQUFeuSRR9S1a1c1a9ZMbdq00Z133qmDBw969LvqqqtkGIbH45ZbbvHz0gAAAABo7Bp8iLvtttu0adMmLVmyREuWLNGmTZuUmppaaf/Tp09r48aNmjhxojZu3KhFixbphx9+0PXXX1+hb1pamrKzs92PN99805+LAgAAAAAN+zdx33//vZYsWaKvvvpKiYmJkqQZM2YoKSlJ27dv1yWXXFJhGofDoYyMDI+2l19+Wb1799bevXt14YUXutubNm2qmJgY/y4EAAAAAJTRoI/ErV27Vg6Hwx3gJKlPnz5yOBxas2aNz/NxOp0yDEMtWrTwaJ87d66ioqJ02WWXafz48Tpx4kSV88nPz1deXp7HAwAAAABqokEficvJyVHr1q0rtLdu3Vo5OTk+zePs2bN69NFHddtttykyMtLdfvvtt6tjx46KiYnRli1bNGHCBG3evLnCUbyypkyZoqeffrrmCwIAAAAA/48lj8RNmjSpwkVFyj/Wr18vSTK83DPENE2v7eUVFBTolltuUXFxsV577TWP19LS0jRw4EB16dJFt9xyixYuXKhly5Zp48aNlc5vwoQJcjqd7se+fftquOQAAAAAGjtLHom7//77q70SZIcOHfTNN9/o0KFDFV47cuSIoqOjq5y+oKBAw4cP165du/T55597HIXzpkePHgoLC9OOHTvUo0cPr33Cw8MVHh5e5XwAAAAAoCqWDHFRUVGKioqqtl9SUpKcTqfWrVun3r17S5IyMzPldDqVnJxc6XSlAW7Hjh364osv1KpVq2rfa+vWrSooKFBsbKzvCwIAAAAANWTJ0yl9demll+raa69VWlqavvrqK3311VdKS0vTsGHDPK5MmZCQoMWLF0uSCgsLddNNN2n9+vWaO3euioqKlJOTo5ycHLlcLknSTz/9pMmTJ2v9+vXavXu30tPTdfPNN6t79+7q27dvQJYVAAAAQOPQoEOcVHIFya5du2rw4MEaPHiwLr/8cr3zzjsefbZv3y6n0ylJ2r9/vz7++GPt379fV1xxhWJjY92P0ita2u12/fe//9WQIUN0ySWX6IEHHtDgwYO1bNky2Wy2el9GAAAAAI2HJU+nrImWLVvq3XffrbKPaZru/+/QoYPHc2/i4uK0YsWKOhkfAAAAANREgz8SBwAAAAANCSEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAdaTINLXmeJE2Nm2rNceLVGSagR4SAKASVq7ZoYEeAAAADUF6bqEm7ixQtsuUWvXS3C35irUbeiY+TClRfN0CQDCxes3mSBwAAOcoPbdQadtcJRsDZeS4TKVtcyk9tzBAIwMAlNcQajYhDgCAc1Bkmpq4s0DeTsIpbXtyZ4GlTtMBgIaqodRsQhwAAOcg01lcYW9uWaakgy5Tmc7i+hsUAMCrhlKzCXEAAJyDQwW+7a31tR8AwH8aSs0mxAEAcA6iw4w67QcA8J+GUrMJcQAAnINER4hi7YYq+7o3JLWxG0p08JULAIHWUGp2cI8OAIAgZzNKLkktqcJGQenzyfFhshnBvVcXABqDhlKzCXEAAJyjlKhQzUiwK8bu+aUfazc0I8FuiXsOAUBj0RBqdvCPEAAAC0iJCtWQVjZlOot1qMBUdFjJ6TjBvjcXABojq9dsQhwAAHXEZhhKbmEL9DAAAD6wcs3mdEoAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQhp8iDt27JhSU1PlcDjkcDiUmpqq48ePVznN3XffLcMwPB59+vTx6JOfn68//elPioqKUrNmzXT99ddr//79flwSAAAAAGgEIe62227Tpk2btGTJEi1ZskSbNm1SampqtdNde+21ys7Odj/S09M9Xh87dqwWL16s+fPna/Xq1Tp58qSGDRumoqIify0KAAAAACg00APwp++//15LlizRV199pcTEREnSjBkzlJSUpO3bt+uSSy6pdNrw8HDFxMR4fc3pdGrmzJl65513NHDgQEnSu+++q7i4OC1btkxDhgyp+4UBAAAAADXwI3Fr166Vw+FwBzhJ6tOnjxwOh9asWVPltMuXL1fr1q3VqVMnpaWl6fDhw+7XNmzYoIKCAg0ePNjd1qZNG3Xp0qXK+ebn5ysvL8/jAQAAAAA10aBDXE5Ojlq3bl2hvXXr1srJyal0uqFDh2ru3Ln6/PPP9Y9//ENff/21rr76auXn57vna7fbdf7553tMFx0dXeV8p0yZ4v5tnsPhUFxcXC2XDAAAAEBjZckQN2nSpAoXHin/WL9+vSTJMIwK05um6bW91IgRI/TrX/9aXbp00XXXXadPP/1UP/zwg/7zn/9UOa7q5jthwgQ5nU73Y9++fT4uMQAAAACUsORv4u6//37dcsstVfbp0KGDvvnmGx06dKjCa0eOHFF0dLTP7xcbG6v27dtrx44dkqSYmBi5XC4dO3bM42jc4cOHlZycXOl8wsPDFR4e7vP7AgAAAEB5lgxxUVFRioqKqrZfUlKSnE6n1q1bp969e0uSMjMz5XQ6qwxb5R09elT79u1TbGysJKlnz54KCwtTRkaGhg8fLknKzs7Wli1b9Nxzz9ViiQAAAADAN5Y8ndJXl156qa699lqlpaXpq6++0ldffaW0tDQNGzbM48qUCQkJWrx4sSTp5MmTGj9+vNauXavdu3dr+fLluu666xQVFaUbbrhBkuRwODRy5Ej9+c9/1n//+19lZWXpjjvuUNeuXd1XqwQAAAAAf7DkkbiamDt3rh544AH3lSSvv/56vfLKKx59tm/fLqfTKUmy2Wz69ttvNWfOHB0/flyxsbH61a9+pQULFui8885zT/PCCy8oNDRUw4cP15kzZ3TNNddo9uzZstls9bdwAAAAABqdBh/iWrZsqXfffbfKPqZpuv+/SZMm+uyzz6qdb0REhF5++WW9/PLL5zxGAAAAAPBVgz6dEgAAAAAaGkIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiAAAAAMBCCHEAAAAAYCGEOAAAAACwEEIcAAAAAFgIIQ4AAAAALIQQBwAAAAAWQogDgHpSZJpac7xIG5u21ZrjRSoyzUAPCQDgBfUawS400AMAgMYgPbdQE3cWKNtlSq16ae6WfMXaDT0TH6aUKEoxAAQL6jWsgCNxAOBn6bmFStvmKtkgKCPHZSptm0vpuYUBGhkAoCzqNayCEAcAflRkmpq4s0DeTsQpbXtyZwGn6gBAgFGvYSWEOADwo0xncYU9umWZkg66TGU6i+tvUACACqjXsBJCHAD40aEC3/bY+toPAOAf1GtYCSEOAPwoOsyo034AAP+gXsNKCHEA4EeJjhDF2g1V9pVvSGpjN5TooBwDQCBRr2ElrIUA4Ec2o+Sy1JIqbBiUPp8cHyabwZ5dAAgk6jWshBAHAH6WEhWqGQl2xdg9v/hj7YZmJNi57xAABAnqNayCNREA6kFKVKiGtLIp01msQwWmosNKTslhjy4ABBfqNayAEAcA9cRmGEpuYQv0MAAA1aBeI9hxOiUAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAoNEoMk2tOV6kjU3bas3xIhWZZqCHBADwgnpdtdBADwAAgPqQnluoiTsLlO0ypVa9NHdLvmLthp6JD1NKFF+HABAsqNfV40gcAKDBS88tVNo2V8kGQRk5LlNp21xKzy0M0MgAAGVRr33T4EPcsWPHlJqaKofDIYfDodTUVB0/frzKaQzD8Pr4+9//7u5z1VVXVXj9lltu8fPSAABqqsg0NXFngbydiFPa9uTOAk7VAYAAo177rsGHuNtuu02bNm3SkiVLtGTJEm3atEmpqalVTpOdne3xmDVrlgzD0I033ujRLy0tzaPfm2++6c9FAQDUQqazuMIe3bJMSQddpjKdxfU3KABABdRr3zXok0q///57LVmyRF999ZUSExMlSTNmzFBSUpK2b9+uSy65xOt0MTExHs8/+ugj/epXv1J8fLxHe9OmTSv0BQAEl0MFvu2x9bUfAMA/qNe+a9BH4tauXSuHw+EOcJLUp08fORwOrVmzxqd5HDp0SP/5z380cuTICq/NnTtXUVFRuuyyyzR+/HidOHGiynnl5+crLy/P4wEA8K/oMKNO+wEA/IN67bsGfSQuJydHrVu3rtDeunVr5eTk+DSPt99+W+edd55+97vfebTffvvt6tixo2JiYrRlyxZNmDBBmzdvVkZGRqXzmjJlip5++umaLQQA4JwkOkIUazeU4zK9/s7CkBRrN5ToaND7NQEg6FGvfWfJT2DSpEmVXnyk9LF+/XpJJRcpKc80Ta/t3syaNUu33367IiIiPNrT0tI0cOBAdenSRbfccosWLlyoZcuWaePGjZXOa8KECXI6ne7Hvn37arDUAIDasBkll6WWSjYAyip9Pjk+TDYfvxcAAP5BvfadJY/E3X///dVeCbJDhw765ptvdOjQoQqvHTlyRNHR0dW+z6pVq7R9+3YtWLCg2r49evRQWFiYduzYoR49enjtEx4ervDw8GrnBQCoWylRoZqRoP/dd+j/ibUbmsx9hwAgaFCvfWPJTyEqKkpRUVHV9ktKSpLT6dS6devUu3dvSVJmZqacTqeSk5OrnX7mzJnq2bOnunXrVm3frVu3qqCgQLGxsdUvAACg3qVEhWpIK5syncU6VGAqOqzklBz26AJAcKFeV8+SIc5Xl156qa699lqlpaW5L/9/7733atiwYR5XpkxISNCUKVN0ww03uNvy8vL0/vvv6x//+EeF+f7000+aO3euUlJSFBUVpe+++05//vOf1b17d/Xt29f/CwYAqBWbYSi5hS3QwwAAVIN6XTVL/iauJubOnauuXbtq8ODBGjx4sC6//HK98847Hn22b98up9Pp0TZ//nyZpqlbb721wjztdrv++9//asiQIbrkkkv0wAMPaPDgwVq2bJlsNlY2AAAAAP7ToI/ESVLLli317rvvVtnH9HLX93vvvVf33nuv1/5xcXFasWJFnYwPAAAAAGqiwR+JAwAAAICGhBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAAAAAwEIIcQAAAABgIYQ4AAAAALAQQhwAAAAAWAghDgAAAAAshBAHAAAAABZCiAMAAAAACyHEAQAAAICFEOIAoIEoMk2tOV6kjU3bas3xIhWZZqCHBADwgnqNcxUa6AEAAM5dem6hJu4sULbLlFr10twt+Yq1G3omPkwpUZR6AAgW1GvUBY7EAYDFpecWKm2bq2SDoIwcl6m0bS6l5xYGaGQAgLKo16grhDgAsLAi09TEnQXydiJOaduTOws4VQcAAox6jbpEiAMAC8t0FlfYo1uWKemgy1Sms7j+BgUAqIB6jbpEiAMACztU4NseW1/7AQD8g3qNukSIAwALiw4z6rQfAMA/qNeoS4Q4ALCwREeIYu2GKvvKNyS1sRtKdFDuASCQqNeoS6wlAGBhNqPkstSSKmwYlD6fHB8mm8GeXQAIJOo16hIhDgAsLiUqVDMS7Iqxe37xx9oNzUiwc98hAAgS1GvUFdYUAGgAUqJCNaSVTZnOYh0qMBUdVnJKDnt0ASC4UK9RFwhxANBA2AxDyS1sgR4GAKAa1GucK06nBAAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAQNAoMk2tOV6kjU3bas3xIhWZZqCHBADwgnodWKGBHgAAAJKUnluoiTsLlO0ypVa9NHdLvmLthp6JD1NKFF9XABAsqNeBx5E4AEDApecWKm2bq2SDoIwcl6m0bS6l5xYGaGQAgLKo18GBEAcACKgi09TEnQXydiJOaduTOws4VQcAAox6HTwIcQCAgMp0FlfYo1uWKemgy1Sms7j+BgUAqIB6HTwIcQCAgDpU4NseW1/7AQD8g3odPAhxAICAig4z6rQfAMA/qNfBgxAHAAioREeIYu2GKvvKNyS1sRtKdPCVBQCBRL0OHnzCAICAshkll6WWVGHDoPT55Pgw2Qz27AJAIFGvgwchDgAQcClRoZqRYFeM3fOLP9ZuaEaCnfsOAUCQoF4HBz5lAEBQSIkK1ZBWNmU6i3WowFR0WMkpOezRBYDgQr0OPEIcACBo2AxDyS1sgR4GAKAa1OvA4nRKAIBPikxTa44XaWPTtlpzvIibuQJAEKNmN2yGafIXDZS8vDw5HA45nU5FRkYGejgAUKn03EJN3FngcZPXWHvJD9z5/QMABBdqtjXVJBtwJA4AUKX03EKlbXN5bAxIUo7LVNo2l9JzCwM0MgBAedTsxqHBh7j/7//7/5ScnKymTZuqRYsWPk1jmqYmTZqkNm3aqEmTJrrqqqu0detWjz75+fn605/+pKioKDVr1kzXX3+99u/f74clAIDAKTJNTdxZIG+nbJS2PbmzgNN0ACAIULMbjwYf4lwul26++Wb98Y9/9Hma5557TlOnTtUrr7yir7/+WjExMRo0aJBOnDjh7jN27FgtXrxY8+fP1+rVq3Xy5EkNGzZMRUVF/lgMAAiITGdxhb25ZZmSDrpMZTqL629QAACvqNmNR4M/Kfbpp5+WJM2ePdun/qZpatq0aXr88cf1u9/9TpL09ttvKzo6Wu+9957+8Ic/yOl0aubMmXrnnXc0cOBASdK7776ruLg4LVu2TEOGDPHLsgBAfTtU4NveWl/7AQD8h5rdeDT4I3E1tWvXLuXk5Gjw4MHutvDwcA0YMEBr1qyRJG3YsEEFBQUefdq0aaMuXbq4+3iTn5+vvLw8jwcABLPoMN/u+eNrPwCA/1CzGw9CXDk5OTmSpOjoaI/26Oho92s5OTmy2+06//zzK+3jzZQpU+RwONyPuLi4Oh49ANStREeIYu2GKvu6NyS1sZfc5BUAEFjU7MbDkn/BSZMmyTCMKh/r168/p/cwyt1x3jTNCm3lVddnwoQJcjqd7se+ffvOaYwA4G82o+SS1JIqbBSUPp8cHyZbNfURAOB/1OzGw5K/ibv//vt1yy23VNmnQ4cOtZp3TEyMpJKjbbGxse72w4cPu4/OxcTEyOVy6dixYx5H4w4fPqzk5ORK5x0eHq7w8PBajQsAAiUlKlQzEuT1nkOTuecQAAQVanbjYMm/YlRUlKKiovwy744dOyomJkYZGRnq3r27pJIrXK5YsULPPvusJKlnz54KCwtTRkaGhg8fLknKzs7Wli1b9Nxzz/llXAAQSClRoRrSyqZMZ7EOFZiKDis5HYe9uQAQfKjZDZ8lQ1xN7N27Vz///LP27t2roqIibdq0SZJ00UUXqXnz5pKkhIQETZkyRTfccIMMw9DYsWP1t7/9TRdffLEuvvhi/e1vf1PTpk112223SZIcDodGjhypP//5z2rVqpVatmyp8ePHq2vXru6rVQJAQ2MzDCW3sAV6GAAAH1CzG7YGH+KefPJJvf322+7npUfXvvjiC1111VWSpO3bt8vpdLr7PPzwwzpz5ozGjBmjY8eOKTExUUuXLtV5553n7vPCCy8oNDRUw4cP15kzZ3TNNddo9uzZstn4xwIAAADAfwzT5JbtgZKXlyeHwyGn06nIyMhADwcAAABAgNQkG1jy6pQAAAAA0FgR4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsJDQQA+gMTNNU5KUl5cX4JEAAAAACKTSTFCaEapCiAugEydOSJLi4uICPBIAAAAAweDEiRNyOBxV9jFMX6Ie/KK4uFgHDx7UeeedJ8MwAjqWvLw8xcXFad++fYqMjAzoWGBdrEeoC6xHOFesQ6gLrEeoCzVZj0zT1IkTJ9SmTRuFhFT9qzeOxAVQSEiI2rVrF+hheIiMjKRQ4ZyxHqEusB7hXLEOoS6wHqEu+LoeVXcErhQXNgEAAAAACyHEAQAAAICFEOIgSQoPD9dTTz2l8PDwQA8FFsZ6hLrAeoRzxTqEusB6hLrgr/WIC5sAAAAAgIVwJA4AAAAALIQQBwAAAAAWQogDAAAAAAshxAEAAACAhRDiGpHXXntNHTt2VEREhHr27KlVq1ZV2X/FihXq2bOnIiIiFB8frzfeeKOeRopgVpP1aNGiRRo0aJAuuOACRUZGKikpSZ999lk9jhbBqKa1qNSXX36p0NBQXXHFFf4dICyhputRfn6+Hn/8cbVv317h4eH6xS9+oVmzZtXTaBGsaroezZ07V926dVPTpk0VGxure+65R0ePHq2n0SLYrFy5Utddd53atGkjwzD04YcfVjtNXW1fE+IaiQULFmjs2LF6/PHHlZWVpX79+mno0KHau3ev1/67du1SSkqK+vXrp6ysLD322GN64IEH9MEHH9TzyBFMaroerVy5UoMGDVJ6ero2bNigX/3qV7ruuuuUlZVVzyNHsKjpOlTK6XTqzjvv1DXXXFNPI0Uwq816NHz4cP33v//VzJkztX37ds2bN08JCQn1OGoEm5quR6tXr9add96pkSNHauvWrXr//ff19ddfa9SoUfU8cgSLU6dOqVu3bnrllVd86l+n29cmGoXevXubo0eP9mhLSEgwH330Ua/9H374YTMhIcGj7Q9/+IPZp08fv40Rwa+m65E3nTt3Np9++um6Hhosorbr0IgRI8wnnnjCfOqpp8xu3br5cYSwgpquR59++qnpcDjMo0eP1sfwYBE1XY/+/ve/m/Hx8R5tL730ktmuXTu/jRHWIclcvHhxlX3qcvuaI3GNgMvl0oYNGzR48GCP9sGDB2vNmjVep1m7dm2F/kOGDNH69etVUFDgt7EieNVmPSqvuLhYJ06cUMuWLf0xRAS52q5Db731ln766Sc99dRT/h4iLKA269HHH3+sXr166bnnnlPbtm3VqVMnjR8/XmfOnKmPISMI1WY9Sk5O1v79+5Weni7TNHXo0CEtXLhQv/71r+tjyGgA6nL7OrQuB4bglJubq6KiIkVHR3u0R0dHKycnx+s0OTk5XvsXFhYqNzdXsbGxfhsvglNt1qPy/vGPf+jUqVMaPny4P4aIIFebdWjHjh169NFHtWrVKoWG8pWF2q1HO3fu1OrVqxUREaHFixcrNzdXY8aM0c8//8zv4hqp2qxHycnJmjt3rkaMGKGzZ8+qsLBQ119/vV5++eX6GDIagLrcvuZIXCNiGIbHc9M0K7RV199bOxqXmq5HpebNm6dJkyZpwYIFat26tb+GBwvwdR0qKirSbbfdpqefflqdOnWqr+HBImpSi4qLi2UYhubOnavevXsrJSVFU6dO1ezZszka18jVZD367rvv9MADD+jJJ5/Uhg0btGTJEu3atUujR4+uj6Gigair7Wt2azYCUVFRstlsFfYsHT58uMLegFIxMTFe+4eGhqpVq1Z+GyuCV23Wo1ILFizQyJEj9f7772vgwIH+HCaCWE3XoRMnTmj9+vXKysrS/fffL6lkY9w0TYWGhmrp0qW6+uqr62XsCB61qUWxsbFq27atHA6Hu+3SSy+VaZrav3+/Lr74Yr+OGcGnNuvRlClT1LdvX/3lL3+RJF1++eVq1qyZ+vXrp7/+9a+cpYRq1eX2NUfiGgG73a6ePXsqIyPDoz0jI0PJyclep0lKSqrQf+nSperVq5fCwsL8NlYEr9qsR1LJEbi7775b7733Hr8baORqug5FRkbq22+/1aZNm9yP0aNH65JLLtGmTZuUmJhYX0NHEKlNLerbt68OHjyokydPutt++OEHhYSEqF27dn4dL4JTbdaj06dPKyTEc9PZZrNJ+t/RFKAqdbp9XeNLocCS5s+fb4aFhZkzZ840v/vuO3Ps2LFms2bNzN27d5umaZqPPvqomZqa6u6/c+dOs2nTpuZDDz1kfvfdd+bMmTPNsLAwc+HChYFaBASBmq5H7733nhkaGmq++uqrZnZ2tvtx/PjxQC0CAqym61B5XJ0Splnz9ejEiRNmu3btzJtuusncunWruWLFCvPiiy82R40aFahFQBCo6Xr01ltvmaGhoeZrr71m/vTTT+bq1avNXr16mb179w7UIiDATpw4YWZlZZlZWVmmJHPq1KlmVlaWuWfPHtM0/bt9TYhrRF599VWzffv2pt1uN3v06GGuWLHC/dpdd91lDhgwwKP/8uXLze7du5t2u93s0KGD+frrr9fziBGMarIeDRgwwJRU4XHXXXfV/8ARNGpai8oixKFUTdej77//3hw4cKDZpEkTs127dua4cePM06dP1/OoEWxquh699NJLZufOnc0mTZqYsbGx5u23327u37+/nkeNYPHFF19UuZ3jz+1rwzQ5/gsAAAAAVsFv4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQkIDPQAAABqzVatW6b///a/i4uI0cuTIQA8HAGABHIkDACCApk+frqefflpr164N9FAAABZBiAMAIIAyMzMlSYmJiQEeCQDAKghxAAAEyLFjx7Rjxw5JUu/evQM8GgCAVRDiAACoZzt27JBhGGrZsqW77YorrpBhGO7HX//61wCOEAAQzLiwCQAA9WzHjh2Kjo7WyZMnderUKdntdp1//vkefXr16hWg0QEAgp1hmqYZ6EEAANAY/f73v9dbb72l1NRUzZkzJ9DDAQBYBKdTAgAQIFlZWZKkHj16BHgkAAArIcQBABAALpdLW7dulSR17949wKMBAFgJIQ4AgADYsmWLCgoKZBiGrrjiikAPBwBgIYQ4AAACoPRUyvj4eDkcjgCPBgBgJYQ4AAACoDTEcSolAKCmCHEAAATAxo0bJRHiAAA1R4gDAKCemaapb775RpL4PRwAoMYIcQAA1LNjx47p1KlTkqS2bdsGeDQAAKshxAEAUM9atGihJk2aSJIWLlyo4uLiAI8IAGAlhDgAAOpZSEiIhg8fLkn661//qmbNmikmJkYxMTFasmRJgEcHAAh2oYEeAAAAjdGrr76qli1bavHixdq/f78OHTokSercuXOARwYACHaGaZpmoAcBAAAAAPANp1MCAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYCCEOAAAAACyEEAcAAAAAFkKIAwAAAAALIcQBAAAAgIUQ4gAAAADAQghxAAAAAGAhhDgAAAAAsBBCHAAAAABYyP8PTRVoJalppFYAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Set the signal properties.\n", "sampling_rate = 30.0\n", @@ -203,15 +192,15 @@ "\n", "$$X_k = \\sum_{n=0}^{N-1}x_n e^{-i2\\pi kn/N}$$\n", "\n", - "which can be implemented as follows." + "which can be implemented as follows:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "outputs": [], @@ -240,25 +229,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Get the frequency range.\n", "w = np.fft.fftfreq(t.size, 1 / sampling_rate)\n", @@ -306,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -371,10 +349,30 @@ "\n", "$$\\|x\\|_0 = \\text{Card}(\\text{Supp}(x))$$\n", "\n", - "*i.e.* the number of non-zero elements in the signal. However, this function is non-convex and is computationally hard to solve. Therefore, in practice the convex $l_1$ norm is used instead.\n", - "\n", - "$$\\|x\\|_1 = \\sum_{i=1}^n |x_i|$$\n", + "*i.e.* the number of non-zero elements in the signal. However, this function is non-convex and is computationally hard to solve." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Therefore, in practice the convex $l_1$ norm is used instead.\n", "\n", + "$$\\|x\\|_1 = \\sum_{i=1}^n |x_i|$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "The $l_1$ norm is the closest convex norm to $l_0$ and Donoho and Huo (1999) have shown that for sufficiently sparse signals the two norms are equivalent.\n", "\n", "We can use this to measure the sparsity of our time-frequency example." @@ -382,23 +380,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The l1 norm of the time signal: 19.028728908445174\n", - "The l1 norm of the frequency signal: 1.000000000000012\n" - ] - } - ], + "outputs": [], "source": [ "# Function to measure the l1 norm.\n", "def l1_norm(signal):\n", @@ -433,22 +422,49 @@ "\n", "In order to actually produce solutions that are more sparse one typically performs a thresholding operation in the sparse domain.\n", "\n", - "Here we introduce the soft thresholding operator and compare it with a more commonly known hard thresholding operator.\n", - "\n", + "Here we introduce the soft thresholding operator and compare it with a more commonly known hard thresholding operator." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "**Hard Thresholding**\n", "\n", - "$$HT_{\\lambda}(x_i) = \\begin{cases} x_i & \\text{if}\\ |x_i| \\geq \\lambda \\\\ 0 & \\text{otherwise} \\\\ \\end{cases}$$\n", - "\n", + "$$HT_{\\lambda}(x_i) = \\begin{cases} x_i & \\text{if}\\ |x_i| \\geq \\lambda \\\\ 0 & \\text{otherwise} \\\\ \\end{cases}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "**Soft Thresholding**\n", "\n", - "$$ST_{\\lambda}(x_i) = \\begin{cases} x_i - \\lambda\\text{sign}(x_i) & \\text{if}\\ |x_i| \\geq \\lambda \\\\ 0 & \\text{otherwise} \\\\ \\end{cases}$$\n", - "\n", + "$$ST_{\\lambda}(x_i) = \\begin{cases} x_i - \\lambda\\text{sign}(x_i) & \\text{if}\\ |x_i| \\geq \\lambda \\\\ 0 & \\text{otherwise} \\\\ \\end{cases}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "These operations can be implemented with the following functions." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -483,53 +499,23 @@ "11 & 12 & 13 & 14 & 15 \\\\\n", "6 & 17 & 18 & 19 & 20 \\\\\n", "21 & 22 & 23 & 24 & 25 \n", - "\\end{bmatrix}$$\n", - "\n", - "#### INTERACTIVE CELL" + "\\end{bmatrix}$$" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "57e57040525741d79728dc2ac9560234", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(Select(description='Threshold Type', options=('Hard', 'Soft'), value='Hard'), IntSlider(…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "######################################\n", - "# YOU SHOULD INTERACT WITH THIS CELL #\n", - "######################################\n", + "# Define the matrix M\n", + "matrix = np.arange(25).reshape(5, 5) + 1\n", "\n", "def test_thresh(thresh_type=None, lambda_val=None):\n", - "\n", - " matrix = np.arange(25).reshape(5, 5) + 1\n", " \n", " if thresh_type == 'Hard':\n", " thresh_matrix = hard_thresh(matrix, lambda_val)\n", @@ -543,8 +529,34 @@ " print('The l1 norm of the matrix:', l1_norm(thresh_matrix))\n", " \n", "lambda_slider = IntSlider(value=0, min=0, max=26, step=1)\n", - "thresh_option = Select(options=['Hard', 'Soft'], value='Hard', description='Threshold Type', disabled=False)\n", - " \n", + "thresh_option = Select(options=['Hard', 'Soft'], value='Hard', description='Threshold Type', disabled=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### INTERACTIVE CELL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "######################################\n", + "# YOU SHOULD INTERACT WITH THIS CELL #\n", + "######################################\n", + "\n", "interact(test_thresh, thresh_type=thresh_option, lambda_val=lambda_slider)" ] }, @@ -569,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -598,43 +610,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "eb95bd6630c44cb0932a92d888eaf2ac", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=0.2, description='sigma', max=2.0, min=0.2, step=0.2), Output()), _dom…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", "######################################\n", "\n", + "@interact(sigma=FloatSlider(value=0.2, min=0.2, max=2, step=0.2))\n", "def test_sigma_mad(sigma=None):\n", "\n", " # Generate random signal\n", @@ -642,11 +630,7 @@ " random_signal = np.random.randn(1000) * sigma\n", "\n", " # Calcualte sigma from MAD\n", - " print('sigma estimate from MAD =', sigma_mad(random_signal))\n", - " \n", - "sigma_slider = FloatSlider(value=0.2, min=0.2, max=2, step=0.2)\n", - "\n", - "interact(test_sigma_mad, sigma=sigma_slider)" + " print('sigma estimate from MAD =', sigma_mad(random_signal))" ] }, { @@ -665,17 +649,48 @@ "\n", "we aim to minimise a function of the following form\n", "\n", - "$$\\begin{aligned} & \\underset{x}{\\text{argmin}} & \\frac{1}{2}\\|y-Hx\\|_2^2\\end{aligned}$$\n", - "\n", + "$$\\begin{aligned} & \\underset{x}{\\text{argmin}} & \\frac{1}{2}\\|y-Hx\\|_2^2\\end{aligned}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "Now, if we know that $x$ is sparse in the domain $x = \\phi\\alpha$, we can update our minimisation problem as follows\n", "\n", "$$\\begin{aligned} & \\underset{\\alpha}{\\text{argmin}} & \\frac{1}{2}\\|y-H\\phi\\alpha\\|_2^2 + \\lambda\\|\\alpha\\|_1\\end{aligned}$$\n", "\n", - "where we now seek a value of $\\alpha$ that both minimises the data fidelity term and requires that $\\alpha$ be sparse. This formulation is generally referred to as a *synthesis prior*. This problem can alternatively be formulated as follows\n", + "where we now seek a value of $\\alpha$ that both minimises the data fidelity term and requires that $\\alpha$ be sparse. This formulation is generally referred to as a *synthesis prior*. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "This problem can alternatively be formulated as follows\n", "\n", "$$\\begin{aligned} & \\underset{x}{\\text{argmin}} & \\frac{1}{2}\\|y-Hx\\|_2^2 + \\lambda\\|\\phi^{T}x\\|_1\\end{aligned}$$\n", "\n", - "which is referred to as an *analysis prior*. When $\\phi$ is orthogonal these two formulations are equivalent, when $\\phi$ is not orthogonal this is no longer the case. Each approach has certain benefits when applied to different problems." + "which is referred to as an *analysis prior*. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "When $\\phi$ is orthogonal these two formulations are equivalent, when $\\phi$ is not orthogonal this is no longer the case. Each approach has certain benefits when applied to different problems." ] }, { @@ -695,24 +710,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Define a range of values\n", "t = np.linspace(0, 22 * np.pi, 1000, endpoint=False)\n", @@ -728,25 +732,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Add noise to the signal.\n", "np.random.seed(1)\n", @@ -772,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -802,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -847,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -879,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -920,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" diff --git a/sparsity_2_compressed_sensing.ipynb b/sparsity_2_compressed_sensing.ipynb index 5ebd368..b60d898 100644 --- a/sparsity_2_compressed_sensing.ipynb +++ b/sparsity_2_compressed_sensing.ipynb @@ -9,12 +9,19 @@ "---\n", "\n", "> Author: Samuel Farrens \n", - "> Year: 2018 \n", + "> Year: 2018 (updated in 2023) \n", "> Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", + "> Website: https://sfarrens.github.io" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ "## Contents\n", "\n", "1. [Set-Up](#Set-Up)\n", @@ -40,10 +47,12 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Set-Up\n", "\n", "Here we will import a couple of packages that will be needed throughout the notebook. \n", @@ -53,130 +62,178 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ - "# Tell Jupyter to display plots in this notebook.\n", - "%matplotlib inline\n", - "\n", "# Import the numpy package with the alias np.\n", "import numpy as np\n", "\n", - "# Import function to set size of figures.\n", - "from matplotlib.pylab import rcParams\n", - "\n", "# Import interaction module.\n", - "from ipywidgets.widgets import *\n", + "from ipywidgets.widgets import interact, IntSlider\n", "\n", "# Import tutorial functions.\n", "from sparsity_tutorial import plot\n", "from sparsity_tutorial.functions import l1_norm, sigma_mad" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For Python 2 users only\n", - "# Uncomment the following commands\n", - "\n", - "# Import Python 3 behaviour\n", - "# from __future__ import print_function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set size of figures.\n", - "rcParams['figure.figsize'] = (14.0, 8.0)" - ] - }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Introduction\n", "\n", - "### Objective\n", + "### Objective\n", "\n", "This notebook aims to give a brief introduction to how Compressed Sensing (CS) works in practice without getting into too much mathematical detail. For more comprehensive details please see *e.g.*\n", "\n", "* Candès et al. (2006b)\n", "* Candès and Tao (2006)\n", - "* Donoho (2006a)\n", - "\n", - "### Nyquist-Shannon Sampling Theorem\n", + "* Donoho (2006a)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Nyquist-Shannon Sampling Theorem\n", "\n", "The Nyquist-Shannon Sampling Theorem dictates that a bandlimited singal can perfectly be recovered if the sample frequency, $f_s$, (*i.e.* number of sample taken per unit time/space) is at least twice the highest frequency contained in the signal, $f_c$.\n", "\n", - "$$f_s \\geq 2f_c$$\n", - "\n", - "In the following cell you can see a sine wave (green dotted line) that we wish to recover. Initially this signal is sampled well below the Nyquist rate (blue dots). The resulting effect, known as aliasing, creates the illusion of a signal with a lower frequency (red line). Try increasing the sampling rate and/or frequency to see what happens.\n", - "\n", - "
\n", - "INTERACTIVE CELL" + "$$f_s \\geq 2f_c$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "In the following cell you can see a sine wave (green dotted line) that we wish to recover. Initially this signal is sampled well below the Nyquist rate (blue dots). The resulting effect, known as aliasing, creates the illusion of a signal with a lower frequency (red line). Try increasing the sampling rate and/or frequency to see what happens." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### INTERACTIVE CELL" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ca345eb582874b3ea665f2a24d200e56", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=9, description='sampling_rate', max=42, min=2), IntSlider(value=10, desc…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", "######################################\n", "\n", "# function to plot signal of given frequency with a given sampling rate\n", + "@interact(sampling_rate=IntSlider(value=9, min=2, max=42, step=1), frequency=IntSlider(value=10, min=1, max=10, step=1))\n", "def sampling(sampling_rate, frequency):\n", "\n", " t = np.arange(0, 1, 1.0 / sampling_rate)\n", " y = np.sin(2 * np.pi * frequency * t)\n", - " plot.stem_plot(y, x_vals=t, title='Aliasing', imag=False, ylim=(-1.1, 1.1), line=True, f=frequency)\n", - " \n", - "sampling_slider = IntSlider(value=9, min=2, max=42, step=1)\n", - "frequency_slider = IntSlider(value=10, min=1, max=10, step=1)\n", - " \n", - "interact(sampling, sampling_rate=sampling_slider, frequency=frequency_slider)" + " plot.stem_plot(y, x_vals=t, title='Aliasing', imag=False, ylim=(-1.1, 1.1), line=True, f=frequency)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Compressed Sensing\n", + "### Compressed Sensing\n", "\n", "Compressed (or compressive) sensing is a paradigm that allows one to sample certain singals at a rate lower than the Nyquist rate by exploiting the sparsity of the signal in a given domain." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "---\n", - "\n", "## 1D Example\n", "\n", - "To get a more intuitive understanding of how the principles of CS work we begin with an example of a very simple 1D signal. This example will demonstrate how to recover the signal from a masked observation.\n", - "\n", - "### Sparse Signal\n", + "To get a more intuitive understanding of how the principles of CS work we begin with an example of a very simple 1D signal. This example will demonstrate how to recover the signal from a masked observation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "### Sparse Signal\n", "\n", "First we define a sparse signal, $\\alpha$, with 128 coefficients of which only 5 have non-zero values (*i.e.* a $k$-sparse singal with $k=5$)." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# full sample size\n", "n_samples = 128\n", @@ -196,9 +253,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Fourier Transform\n", + "### Fourier Transform\n", "\n", "Next we want to generate a new signal $x$ which is the Fourier transform of $\\alpha$. *i.e.*\n", "\n", @@ -217,8 +278,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "outputs": [], "source": [ "# normalised FFT\n", @@ -234,18 +299,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Use the functions above to generate the singal $x$.\n", "\n", - "
\n", - "EXERCISE CELL " + "### EXERCISE" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "##############################\n", @@ -262,17 +334,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Observation\n", + "### Observation\n", "\n", "To simulate a CS problem we need to randomly subsample $x$ in order to generate an observation (or acquisition) $y$. We can do this using a masking operator $H$ that we define in the cell below." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Function to mask a signal.\n", @@ -283,20 +363,27 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "For this example we will keep only a quarter of the coefficients in $x$.\n", "\n", "Define a mask that can be used with above function that will keep only $25\\%$ of the coefficients.\n", "\n", - "
\n", - "EXERCISE CELL " + "### EXERCISE " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "##############################\n", @@ -312,7 +399,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Now, we can model $y$ as \n", "\n", @@ -320,15 +411,17 @@ "\n", "Use the mask you defined to generate the observation $y$.\n", "\n", - "
\n", - "EXERCISE CELL " + "### EXERCISE" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [], "source": [ @@ -346,7 +439,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Notice that the signal $y$ is indeed shorter than $x$, however in order to properly visualse this masked signal we need to upsample.\n", "\n", @@ -355,8 +452,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 11, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Function to upsample a signal.\n", @@ -369,15 +470,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Now we can display the upsampled version of $y$." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# plot real and imaginary components of y\n", @@ -387,9 +496,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Inverse Problem\n", + "### Inverse Problem\n", "\n", "\n", "The problem we aim to solve is to recover the original signal $\\alpha$ from $y$ given that we know both $\\phi$ and $H$. This corresponds to the following optimisation problem\n", @@ -401,8 +514,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "outputs": [], "source": [ "# Function for performing soft thresholding with complex coefficients.\n", @@ -414,15 +531,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "For a given estimate of $\\alpha$ the optimisation problem provides the cost of the solution. To solve this problem we aim to minimise this cost, which we will cacluate with the following function." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Function to calculate the cost.\n", @@ -433,9 +558,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Optimisation Algorithm\n", + "### Optimisation Algorithm\n", " \n", "For this example we will use the forward-backward splitting algorithm with a soft thresholding operation to promote sparse solutions. It has the following form for each iteration\n", "\n", @@ -447,8 +576,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "outputs": [], "source": [ "# Function that performs simple forward backward splitting.\n", @@ -471,9 +604,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Gradient\n", + "### Gradient\n", "\n", "The gradient of the above problem is given by\n", "\n", @@ -481,15 +618,17 @@ "\n", "write a funciton that implements the gradient calculation.\n", "\n", - "
\n", - "EXERCISE CELL " + "### EXERCISE" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [], "source": [ @@ -506,58 +645,102 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Signal Recovery\n", - "\n", - "Now we can use the forward-backward algorithm to recover $\\alpha$. Notice how the solution improves after a given number of iterations until it converges on a very good reconstruction of $\\alpha$.\n", + "### Signal Recovery\n", "\n", - "
\n", - "INTERACTIVE CELL" + "Now we can use the forward-backward algorithm to recover $\\alpha$. Notice how the solution improves after a given number of iterations until it converges on a very good reconstruction of $\\alpha$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### INTERACTIVE CELL" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "af50446bb5e44b7fa908e8b5691e1fcb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(Output(),), _dom_classes=('widget-interact',))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", "######################################\n", "\n", "# Interactive function to plot the recovered alpha after a given number of iterations\n", - "def solve(n_iter):\n", - " \n", - " alpha_rec, cost = forwardBackward(observation=y, first_guess=np.ones(n_samples), mask=mask, grad=grad,\n", - " lambda_val=0.01, n_iter=n_iter, gamma=1.0, return_cost=True)\n", + "@interact(n_iter=IntSlider(value=1, min=1, max=361, step=40))\n", + "def solve():\n", " \n", - " plot.stem_plot(alpha_rec, title=r'$\\hat{\\alpha}$')\n", - " plot.cost_plot(cost)\n", - " \n", - "slider = IntSlider(value=1, min=1, max=361, step=40)\n", - "if grad is not None:\n", - " interact(solve, n_iter=slider)" + " if grad is not None:\n", + " alpha_rec, cost = forwardBackward(observation=y, first_guess=np.ones(n_samples), mask=mask, grad=grad,\n", + " lambda_val=0.01, n_iter=n_iter, gamma=1.0, return_cost=True)\n", + "\n", + " plot.stem_plot(alpha_rec, title=r'$\\hat{\\alpha}$')\n", + " plot.cost_plot(cost)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ - "Notice that the solution converges after roughly 200 iterations.\n", - "\n", - "### Performance\n", + "Notice that the solution converges after roughly 200 iterations." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Performance\n", "\n", "Finally, we can test the quality of our solution by comparing our recovered $\\hat{\\alpha}$ with the true $\\alpha$. Notice that the difference between the two signals is extrmeley small even for the imaginary components." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [], "source": [ @@ -572,7 +755,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "The Normalised Mean Squared Error (NMSE) provides a useful measure of the quality of a given signal reconstruction.\n", "\n", @@ -583,8 +770,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "#normalised mean squared error\n", @@ -595,20 +786,27 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "We can use this function to test solutions to our minimisation problem.\n", "\n", "Calculate the NMSE of $\\hat{\\alpha}$ after 10 and 200 iterations.\n", "\n", - "
\n", - "EXERCISE CELL " + "### EXERCISE" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "##############################\n", @@ -628,17 +826,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "Can you see an improvement in the NMSE after an appropriate number of iterations?" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## 2D Exercise\n", "\n", "Attempt to solve the following problem using the techniques you learned from the first exercise." @@ -646,9 +850,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Problem\n", + "### Problem\n", "\n", "An observer with limited time on her detection instrument has been forced to take only few noisy samples of a singal of interest. Fortunately she has been celever and has chosen the samples randomly. She now provides you with her samples and the mask she used to collect her data and asks for help in recoevering the original signal.\n", "\n", @@ -656,8 +864,17 @@ "\n", "The observation can be modelled as\n", "\n", - "$$y = Hx + n$$\n", - "\n", + "$$y = Hx + n$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "**Questions and tasks**\n", "\n", "1. What percentage of the coefficients have been sampled?\n", @@ -673,17 +890,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Data\n", + "### Data\n", "\n", "For this exercise you are given a subsampled noisy observation $y$ and a mask $H$. Your task is to recoever the original image $x$. " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 25, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Shape of the true image\n", @@ -693,47 +918,105 @@ "mask2d = np.load('data/cs_mask.npy')\n", "\n", "# Load the observation\n", - "y2d = np.load('data/cs_obs_data.npy')\n", - "\n", + "y2d = np.load('data/cs_obs_data.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Display the mask H\n", - "plot.display(mask2d, 'Mask', shape=image_shape, cmap='bone')\n", - "\n", + "plot.display(mask2d, 'Mask', shape=image_shape, cmap='bone')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Display the observation y\n", "plot.display(y2d, r'$y$', shape=(25, 20))" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Solutions\n", + "### Solutions\n", "\n", - "EXERCISE: provide your solutions in the cell(s) below.\n" + "### EXERCISE\n", + "\n", + "Provide your solutions in the cell(s) below." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "---\n", "\n", "## Hints\n", "\n", - "** 1D Example Hints**\n", + "**1D Example Hints**\n", "\n", "1. The mask should only contain zeros and ones.\n", "1. The mask should have the same number of coefficients as the original signal.\n", "1. To define $y$ note that $x$ has already been defined. \n", "1. To define the gradient make sure you use ALL your operators in the right order.\n", "\n", - "** 2D Exercise Hints **\n", + "**2D Exercise Hints**\n", "\n", "1. You can work out the percentage of coefficients retained using only the mask.\n", "1. Note that, although the signal is a 2D image, data have been provided in the form of 1D vectors. Think about how this compares to the 1D example.\n", @@ -743,15 +1026,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Open next notebook ->](./sparsity_3_deconvolution.ipynb)" ] } ], "metadata": { + "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -765,7 +1053,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/sparsity_3_deconvolution.ipynb b/sparsity_3_deconvolution.ipynb index 1193b20..d0c1a98 100644 --- a/sparsity_3_deconvolution.ipynb +++ b/sparsity_3_deconvolution.ipynb @@ -2,19 +2,30 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "# Introduction to Sparsity III: Deconvolution\n", "\n", "---\n", "\n", "> Author: Samuel Farrens \n", - "> Year: 2018 \n", + "> Year: 2018 (updated in 2023) \n", "> Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", + "> Website: https://sfarrens.github.io" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ "## Contents\n", "\n", "1. [Set-Up](#Set-Up)\n", @@ -34,10 +45,12 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Set-Up\n", "\n", "Here we will import a couple of packages that will be needed throughout the notebook. \n", @@ -47,25 +60,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ - "# Tell Jupyter to display plots in this notebook.\n", - "%matplotlib inline\n", - "\n", "# Import the numpy package with the alias np.\n", "import numpy as np\n", "\n", "# Import tools from Scipy\n", "from scipy.ndimage import gaussian_filter\n", - "from scipy.misc import face\n", - "\n", - "# Import function to set size of figures.\n", - "from matplotlib.pylab import rcParams\n", - "\n", - "# Import interaction module.\n", - "from ipywidgets.widgets import *\n", + "from scipy.datasets import face\n", "\n", "# Import tutorial functions.\n", "from sparsity_tutorial import plot\n", @@ -73,56 +81,68 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "# For Python 2 users only\n", - "# Uncomment the following commands\n", + "## Introduction\n", + "\n", + "### Objective\n", + "\n", + "The objective of this notebook is to demonstrate how sparsity can be used for deconvolution. A very simple image is used to simpify the implementation. More realistic applications to astrophysical images are provided in\n", "\n", - "# Import Python 3 behaviour\n", - "# from __future__ import print_function" + "* Farrens et al. (2017)" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "# Set size of figures.\n", - "rcParams['figure.figsize'] = (14.0, 8.0)" + "### Point Spread Function\n", + "\n", + "All optical systems are subject to imperfections that distort the images obtained. The sum of these aberrations is commonly referred to as the point spread function (PSF) or instrumental response. Removing the effects of the PSF from images of interest if a fundamental aspect of image processing, particularly in fields such as astrophysics where accurate measurments of the shapes and flux of objects has a significant impact on the science.\n", + "\n", + "The blurring introduced by the PSF can be modelled as a convolution of the PSF with the image. Consequently, the reverse process, deconvolution, removes this effect." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "---\n", - "\n", - "## Introduction\n", - "\n", - "### Objective\n", - "\n", - "The objective of this notebook is to demonstrate how sparsity can be used for deconvolution. A very simple image is used to simpify the implementation. More realistic applications to astrophysical images are provided in\n", - "\n", - "* Farrens et al. (2017)\n", - "\n", - "### Point Spread Function\n", - "\n", - "All optical systems are subject to imperfections that distort the images obtained. The sum of these aberrations is commonly referred to as the point spread function (PSF) or instrumental response. Removing the effects of the PSF from images of interest if a fundamental aspect of image processing, particularly in fields such as astrophysics where accurate measurments of the shapes and flux of objects has a significant impact on the science.\n", - "\n", - "The blurring introduced by the PSF can be modelled as a convolution of the PSF with the image. Consequently, the reverse process, deconvolution, removes this effect.\n", - "\n", "In the following cell we define a function for generating a Gaussian PSF." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Function to generate a Gaussian PSF.\n", "def make_psf(shape, sigma):\n", @@ -137,11 +157,15 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "## Deconvolution Example\n", "\n", - "### Convolution\n", + "### Convolution\n", "\n", "The convolution theorem shows that a signal $a$ can be convolved with a signal $b$ (under certain conditions) as follows\n", "\n", @@ -152,8 +176,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# This function convolves an image with a kernel using FFT.\n", @@ -167,15 +195,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "The following cell demonstrates what happens to an image when it is convolved with the PSF we defined." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Load example image\n", @@ -185,20 +221,68 @@ "psf_1 = make_psf(image.shape, 10)\n", "\n", "# Convolve image with PSF\n", - "image_conv = fftconvolve(image, psf_1)\n", - "\n", + "image_conv = fftconvolve(image, psf_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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ziUncJPH93//9+MhHPoJoNDoZH/M1GpN7YBKT+NqICQM2iUm8SqLdbssImq/7uq+7wUcziRsRk3tgEpP42okJAJvEJF6h2N7efkH7geFwiB/+4R+WGYLs0pzEV1dM7oFJTGISjEkJchKTeIXiPe95D5588kk88MADuOuuu+D3+9Fut3Hx4kX8/u//Pp555hkAwJve9CY8/PDDN6QLchIvb0zugUlMYhKMG9oF+du//dv4tV/7NaTTaZw6dQof/OAH8brXve5GHtIkJvGyxtWrV7+od9Pdd9+NT33qU5ON96s4JvfAJCYxCeAGMmCf+tSn8OCDD+K3f/u3cffdd+N3f/d38Qd/8AdYW1vD1NTUjTikSUziZY319XX82Z/9GR5++GHs7+8jn8+j3+/D4/Hg9ttvx7ve9S488MADL6sp7CRubEzugUlMYhKMGwbA7rrrLtx666348Ic/LP924sQJfMu3fAt++Zd/+UYc0iQmMYlJTGISk5jEKxI3pATZ6/Xw9NNP4wMf+MCxf7///vvx6KOPfsnfH41GSKVSsNlsE5p+EpOYxCQmMYkvEePxGPV6HeFw+IYwrJ1OB71e72V5b71ef0OMq7/SuCEArFAoYDgcIhAIHPv3QCCATCbznNd3u110u135ezKZxMmTJ1/245zEJCYxiUlM4qspEokEotHoK/qZnU4HMzMzz7u/vxQRDAaxu7t704GwGyrCfzZ7NR6Pn5fR+uVf/mX8p//0n57z7x/4wAfg8/lgMpmgUqmg0+kwGAzQ7/eh0+kwHo/RbrfRbDbRarXQarXQ7XalDXwwGGA4HEKv10On00Gj0WAwGECj0cBms8FsNsNoNMJsNmM0GmE8HsNkMmE4HCKVSqHZbCIYDOLMmTOw2+2oVqvIZrNIpVKoVqtot9tot9sYDAawWq0IhUJQqVTyPXu9HprNJjqdDtRqNer1OpLJJDKZDBYXFzE9PY3xeIxGo4HxeIxer4fhcAibzQan0wm1Wi3/5vF44Ha7MRwO0W63AUCOdTgcotPpQKVSIRqNwuFwoNFoYHNzE4eHh+h0OtDr9fB4PLBYLNBoNDAYDFCr1Wi1WhgOhzCbzbDb7RgMBmi322i1WjCbzTCZTNBqtRiPx7BardBoNMjn8+j1evD5fHC5XDAYDPL5DocDw+EQ2WwW1WoVFosFw+EQ6XQauVwOzWYTPp8PNpsNOp0O4XAYXq9XjqvT6WB7exsXLlzAYDDA9PQ04vE4er0eMpkMzGYzYrEYut0url27Bq/Xi+npaXS7XVy6dAkAsLi4CLvdjnq9jna7jX6/D7fbDZfLhW63i8ceewy7u7swmUyYm5uDyWRCJpNBo9GAwWDAzMwMPB6P3HPAEatbLBaxvb2N7e1t1Ot1OBwOrK6uYnFxEf1+H4eHh2g2m2g0Gmg0GgAAi8UCq9WKpaUleDwe7O3t4YknnkCz2UQ8HofP58NoNEKz2USv10O/38doNIJOp4PJZILZbJZzlcvlcPnyZVQqFRgMBrmGAOByuRAKheBwOKDX6+XfDQYD9Ho9BoMBRqORuLCbzWaYzWY0m02Uy2VYLBbodDoUCgVks1mUy2UYjUY4nU6oVCoMBgPodDpUKhXs7e1Br9fD5XJhNBqh1+vB4XDA5XJBpVLJ/TYYDFCtVlGpVBCNRmE2m5HP51EqlaDVamGz2aBWq+Xeq1Qq6PV6cLvdaDabyGazGI1GcLlcUKvVGI1Gck76/b48G7xuHo8Ho9EIiUQCmUwGCwsLuPXWW5FKpXDx4kV5TnnsNpsNg8EABoMBWq0Wg8EAjUZDnuVqtYrd3V04HA5MTU2h1WohnU6jWq3CaDTC7/fLeQ4Gg7BYLDh37hwSiQTi8ThOnDgBm82GXq+HbrcLjUaDfr+PYrEo2jCNRiP3qdlsxszMDJaXl6FSqZDL5dDpdNDv91Eul1EoFGAwGGC32xGLxWCz2bC3t4d2uw2Hw4F+v49Wq4VAIICpqSk0m01cvHgROp0Oy8vLcLvdAI4S5Hw+D5fLhWAwiGw2i93dXbjdblnnRqMRZmdnYTKZZC2p1WrQ6/UwGAzQ6XQIBAJyrdLpNHw+H5xOp7y/0+kEABSLRdRqNUQiETgcDrm2NpsNPp8PtVoNlUoFbrcb4XAYg8EAiUQC7XYbo9EIrVYLGo0GDocDarUaOp0OpVIJxWIRer0e4XAYsVgMo9EIw+EQ/X4fW1tbqFQq0Gg0UKvVMBgMMJlM0Ov1MJvNsFqt6Ha7stbV63XU63VoNBq5RxwOB2w2G7rdLorFIiqVCkajEVQqFbRaLXw+HywWC3q9Hsbj8bF9p1wuY2trS97DYDDAarXK/jAcDuWeHo1Gsu6ORiNotVqYzWYAgEajgU6nQ7/fR61WQ7PZlM8YDAYYj8fQ6/Xw+/1QqVT4mZ/5Gdhsti9vo34JgutzIpF4yUdj1Wo1xGIx9Hq9CQB7MeH1eqHRaJ6DhnO53HNYMQD4qZ/6Kbz3ve+Vv/OEk3bU6/VotVrIZrMCetRqtWyqVqsVRqMRkUhEQNZoNJIHjRtPv9+Xm1/5IDmdTlnQOp0O9vb2kEwm0e12UavVcHBwgHA4DLvdjunpaYRCIXS7XdTrdfR6PZTLZfnZeDxGsVhEuVxGp9NBJBKB3W4X4DI9PS3H0W63ZcPx+/3QaDTo9XoYDAbQao8unVqthkqlgtfrhcfjwXA4RC6XQyaTQblcFhDpcrmg1WqhUqlgs9ngdrvh9XqRyWSQTCbl3KlUKrjdbgQCAdn0ms2mHBPHnXQ6HYxGI9jtdrmefOg7nQ6q1Sp6vZ6ALofDgWKxiGq1Cp/Ph6mpKaytrWE4HMJisSAej8PlcqHT6WAwGMDpdKJUKuHSpUsIBAJYWlpCOByGXq/H7OwsSqUS9vb2sLOzg3q9jjNnziAUCqHf7wMA/H4/BoMBdnd3UalUcPLkSZw4cQLFYhEHBwdwuVzw+XwoFosoFAoYjUaIRCKw2WyYnp7G2toaLl68iEKhgOXlZZhMJlgsFpjNZpRKJYzHY8zMzECr1UKv10Oj0SAQCMDpdMJqtaJUKslCnUql4HQ6sbCwgFarhUqlglarJZsFF1GCRwL5TqcjG+9wOES9XofJZIJOp4PRaIROp0O320Wr1cLMzAxOnTqF4XCIzc1N9Pt9GI1GjMdjdLtdZLNZFAoFeDweeL1e2O12AW9+vx/j8RjZbBa9Xk82/NFohHK5LMCWYFGlUglQMBqNCAaDaLVaaLfbsNvtmJ+fl3OkBPIsP2g0GqhUKuj1egSDQSwsLKDX60Gr1cLj8aBarWJvbw/NZhNms1lABgD5PILqRCKBQqGATqeD8XgMnU4Hg8Eg90Imk0Gn05HzS6CoVqvRbrelJFOr1bCzsyNgjc+VSqVCu91GMBhEJBJBPp+HwWDA/Pw8tFot/H4/KpUKHA4HotEojEYjUqkUMpkMisUizpw5c+yaW61WDAYDbG9vQ6vV4sSJE3KNxuOxJIUajUYSPovFIuCBiYhKpZLE0Gq1wmKxwGazCYgheLDb7QICjUajvLfVaoXJZILP50O/34dKpYJarYbRaMTy8jIikQjq9TpGoxFsNhui0aiA6fF4LOujzWaTe2lnZweFQgEOh0OOkQmu3W7HaDSC0WiExWLB1atX0e/3cfLkSahUKlkr+/0+tFotRqMRNBoN/H4/3G43dnZ2ZL0zmUyw2WzQaDQYjUaoVqtoNBqy1nEj9vv9sNvtmJqaEqBbrVYxGAygUqnQbDah1WrhcDhkH2Ey2ev15Fnj/UvQ5fV6YTQaoVarodVqUavV0Gg0MBwOYbfbJXHg7xqNRmi1WrnGdrsd4/EYbrdbPoPXIBgMQqfToV6vo9lsotvtCijjmm+322GxWFAsFtHtdmEwGKDRaKDVao/tb/xsrkOdTgfA8w94f6XCbrdPZpMq4oYAML1ej9tuuw0PP/ww3vGOd8i/P/zww3j729/+nNczm3922Gw22Gw2eL1eAQeHh4colUqoVqu4du2aAJh4PI6pqSnZjMgqcZHlDavMiHw+HwKBAILBIMbjMXK5HLrdLsxmM1ZXV9FqtSQD29vbQzgcht/vh8VikZJpr9cTFm5nZwdarVayFWa5Pp8PXq8XJpMJd9xxhzADe3t7shHv7OwIu8f/d7tdaLVa6HQ6+P1+hMNhOJ1OAZvlclmy442NDeRyOYTDYdx+++3weDzQ6XSyQDudTmHjhsMhSqWSMAr9fl8AD1kfAjaPx4Pp6WnMzMxApVIhm81iMBjA4/HA4XBAq9XK4sjMlIuew+FAtVpFtVpFp9NBrVYTnUIikYDFYpEMe21tDcBRmdrhcOD222+H3+/H3t6enINgMIjRaCTMgN/vR6PREFaCwNNqtQqo9Pl8ksGr1WqcPHkSsVgMr3nNa+D1emEwGAQAFYtFWYC1Wi3K5bKcH6PRKItyKBTC9PQ0HA4H2u22gB+eX25szH6NRuOxjYxAZWtrC81mE1NTU4hEItjf35cNl/cWQdZwOES1WpXngowKnx1uPvl8HuVyWe57g8GAhYUFLCwsoFAoIJVKCQjodDpoNBpyPM1mE81mEw6HAx6PB61WC81mE+PxGF6vF6VSCf1+X5iufD4vbB3ZKAY3nUqlAqPRiFAoBIPBALfbDZ1Oh4ODA2SzWUxPTwuTy2fp3Llz2NrawunTp7GwsIDxeIyLFy+i2+0iFovB6XQKi2oymeQ9zWazsHmhUAij0QiXL1+GxWJBq9WCyWTCaDRCt9tFOp1Gt9tFMBgUhpZsHYHe6uoqYrGYANJarQa1Wi3sKNlDo9Eoa4DP58PMzAzS6bQkUzqdToAeAQIZ/PF4DIvFAr1ej0qlgkajIQlKp9ORc+xyueByueDxeGAwGFCtVtFsNgVscYMmyOh0OjCZTJiamsLm5iYajQa8Xi/q9ToajQacTqecL61Wi0gkgna7jVKphMFggGazicPDQ5jNZgQCAblvybqoVCphz/V6PdxuNzQaDYbDIaxWKxwOB2q1GrLZLFqtllQenE4ner0eRqMR2u22VCOsViuq1SpKpZJcJ61WK+w8nz0mNkye+Qwwer2e3NPPBiJ8Jvr9vjB5ZJ5YVeC9zHPLc8n7nPcpj43JMisR4/EYzWZTWEkCJgDQarXChA0GAznXrJaQOeYeRQBKpqzf76PdbsuzbzabodVq5bsqn79JvDrihpUg3/ve9+LBBx/E7bffjte85jX4vd/7PRwcHOCHfuiHXvR77OzsIJfLIR6PY25uDjMzM1I6WF9fR7lcRj6fR61WE/bI6XTKQtVut9HpdCRLMJvN0Ov1Um5kplMoFFAqldBoNIRFUqlUkqWS9anVaiiXy7BarfB4PPIZlUoF6XRaSlAEjVqtFs1mU8pbbrcbbrdbyics7xFIMePif9y8u90ums0m9vf3USqVpETJhX08HqPT6WBjYwOj0Qhzc3NotVrHFnNuVszWa7UatFqtHJNer0exWESxWEQoFILH40EikUAikYDBYIDNZpNzr9x0CW57vR70ej0CgYAwjPF4HMViEblcTs55qVQCAMmyrVYr7HY7ms0m9vb2ZCG32WyYnZ2VjZwMQLvdRq1Ww8bGBk6dOiVlEoLa8XgsmxWzbG7y2WxW7gWPxyNlMma5mUwG165dw5kzZzA1NSUbYr1ex2AwQCQSEQZmPB5jZ2cH2WwWyWQS/X5fkgCPxyOsB0FNt9sVwEcWmCWsaDQKu90Oj8eDq1evCgvS7/fhdDphsViktOl2u4VlHQwGsNlsiMfj0Gq1KBQKAqa4KZDtstlsOHv2LPR6Pc6dOweNRgO73S5MIY/XaDTCZDJJZp7L5WA2m3HmzBno9XpkMhlhsorFopSeyY7yOjudTng8HpTLZQGWBOJerxdOpxPb29tyP7tcLnnuWYI6d+4cTpw4gdnZWXg8Hly5cgXZbBalUgmhUEiYrWw2C6vVCp1OB4vFgmAwCKvVilqthlKpBJVKhVAoJADo2aUgr9cLtVqNbrcLm82GTCaDK1euCIuhfM4oWZiZmUGr1QJwxPi5XC70ej1UKhWYzWZ4vV4Mh0Pk83kpITNp6XQ66Ha7KJVKqFQqGA6HCAaDsNvtUjI3GAxwuVxS9rTb7QiHw1CpVKhWq0in02g0GrDb7fD5fALoyK4Ui0X4/X65toPBQBiTnZ0dec5ZsrNYLBiPx6hUKiiXywJ28vk8LBYLnE4nvF4vut0uOp2OPKeNRgOpVAperxd+vx+Hh4dQqVSYmZlBpVIBAEkMDAYDgsEg1Go1LBYLBoOBlP0ofyDIY2m63W5Lma9arYo0guXUVquFarUqJbhut4vBYCBlayaxfD+DwSCsP8vOGo0GRqNR2H6yvUr5iFqtlnWW4MxutwuYYuI6HA5l3VCuFXwee70edDqdyGkIJJl06/V66PV6ADj2M5axWXKktEAJMimXuJGhLMW+lO95s8YNA2Dvete7UCwW8Qu/8AtIp9NYWVnB3/7t3yIej7/o92B9vtlsYjgc4uTJk4hEItBqtZIJzc3NodlsolqtolgswmQyIRgMStamVqvRaDQENACQG/rg4ACHh4dQq9WIx+OIxWJot9soFotSYmE2Z7VaRX/VaDTQarUQi8UwHo8RiUTkQUilUqhUKpJdnzhxQhaLcrmMRx99FKlUCmq1WsqKSi0JaW1mcuVyWWr/3PgAiHaAWWOz2RQqvNfrQa1Wy4bHMqler4fFYhF2zGq1YjweCxg7deoUer2eaGPsdjvy+TyGwyESiQSazSbC4bAwNKlUCr1eTzQ8TqcTdrtdFlGz2SyapNFohGKxCABSMmJZrNVqSfbe6/VgsVgAHDGpPp8P6XQa5XJZtA1utxuVSkWut9frRbVaRa1Wk82HwFKlUsFisUiJFzjSP1WrVezv72M4HCIcDouGilR+qVSC2+2GWq2WrFSpI2w2mzg4OEC73cb09PQxhlKpUer1eqJ9y2azcDqdAiKVGW0kEkEikcDOzg6Gw6HcU/l8XjRLzPh5fVheNZvNCAaDcLlcyGazsvlSn9dut5HJZDA9PY2FhQXs7+9jd3cXpVIJTqcTer0eVqtV3oOAkWXinZ0duN1u0UIeHByg2+2KXmg4HEqJi6W0RqMBvV4vOp9Go4Fms4lisQin04nxeAyPx4NOp4NCoSAbWSaTgVqthsvlEkbV4/FgYWEB8XgcW1tb2NvbQ6PREJnAxsYGAGB5eRlTU1NwOBwArmtOeQ9QQ9lqtY6xJgaDQe7d0WgEg8Egzx01Q2QjCDqoFXO73VI64md1u100Gg0Ui0UMh0O4XC5Jtnw+H+bn54WhLxaLWF9fx8HBgXwOn3M+V/v7+1hfX8dwOMTc3BzcbreAXwbL5wQyvLdjsRgcDgcKhYIknQCQyWRQKpXk/goEArBYLAiHw3J+IpEILBYLyuWynCOyZAQXBBIsu/F+D4VCx0p8o9FIrhnZHeoay+WyJBej0Ug0jlzDCDrq9TqGwyGMRiMcDodofrn2slqhZLO41nNtZGmfbC7L/CaTCSaTCePxWHSY4/FYWD2CdWo7jUajXG/e/yyt9no9YbEI/Lj/UCPJdZPrBMvwvM/IcpH5IviiVIR7Gb87j3MSR5ry//N//g+uXbsGk8mE1772tfiv//W/YmlpSV7znve8B3/0R3907PfuuusuPP744/L3breL97///fjkJz+JdruNN73pTfjt3/7tL6vB4YaK8H/kR34EP/IjP/Kv/n0KMlkyMJvNWFxcRCAQwL333isPbrFYFFE0Rc2dTkd0GtVqVTJF6lY6nQ7K5TLG4zFsNpuUFck6aLVaJJNJHB4eisbH6XRK2Q0A0um0lJlmZ2flZ3yo6vU6crmcbD67u7uo1WrCgnBDVeoc+PAqGwQ6nQ4qlYqUugqFAvx+v7BsJpMJnU4HNpsNer1eMq1OpyMMBRcGZpcej0fYGn5OKBQ6lo2x1NVqtXB4eIhLly6hUqngrrvukgVwa2tLNltubmwsUDZAUNfE3yPNv7+/j0qlgna7jWQyiWazidXVVTidTphMJhgMBqhUKhFWEwwvLi6iVqvJRsRz12q1kMlksL29jbNnz2Jubk6AFzcWh8MhOoX9/X20Wi34fD7ce++9KJVKSKVSyOVy0Gq1mJ+fBwDs7e1Jg0S9XodarcbU1BTUajWCwSBMJhPa7TYuXbokmziZq3w+L00YynJUoVDA/v4+7HY7brvtNlSrVWFLdTqdnANuHgBQqVQkOaC2JpVKiRA3FouJ2NhisSCXy6Hf70s2r9VqMTs7i8FggHw+j1arBYvFIhm6Wq3GzMwM5ufnBYhevHgRlUoFTz31lDSPVCoVhMNh5HI5bGxsCBDT6/XCdA6HQymvctMhM0uGh+WdZrMJu90OnU6HRCKBjY0NYQXZYOPz+bC8vIyFhQWUy2WUSiXs7+8LeGOjBM97p9OB3W6XxMlqtSIajQqA9Pv9wk6xJORwOGAymVAqlaRUxg2PoGE8HiOZTGJ/fx9ut1tKkhS/B4NBZDIZHB4eiiZLyeJyU3U6nYhGo3LsLI+RoWYyQM2gstnH5XKhVquJXqjRaMhm7HA4pDyfy+Vkc87n8zCZTPD7/VK25rll4hWJRGSdYjkzm80KU+tyuQSIbG1todvtIhAICEsei8WQyWRQKBQwOzsr8odgMAgAsv6SAWs2mxgMBsL69Pt9uX/J3hqNRgH2TBRYEqXujCXNfr8vgIkgqN/vi8aQ4vtqtQqtVot2uy1yCIvFImxup9ORRLDRaKDb7UqZnQkLS5g877VaTRg4gqXBYCDgezQayfPa7XYFRBE4kUVj0xm/Bz+X4Iuv1Wq1cu/z/ScB/OM//iN+9Ed/VOQ+P/3TP437778fa2trck0B4G1vexseeugh+TvvQcZ/+A//AX/913+NP/mTP4HH48H73vc+fOM3fiOefvpp2VO+VNxQAPaVRrvdhtvtxtTUlCwGFD+zjMEyCzcaduCl02np2HK5XLjrrrugVquxubmJXC4Hr9eLmZkZnDx5EoPBADs7O9jZ2REReCAQwIkTJ0SXwg2qVquh1+sJe8UGAAImZlPUICk3OgrA9Xq9LKo7OzsieCWtrHzgrFYrYrEYwuEw6vW6aHyy2SwAHBO0ut1u6Z5pNpuo1Wpot9tCgVPAmk6nRbNkt9thMpkQDocRDocxHA5F32YymSSztdls0umn0Whw5swZuN1unD59WhY3gi9mviyHEXAws2TZlcyWx+NBMBiUc6LRaHDq1ClpwvB6vce6VPm9WHrkZ3BD0ev1yOVy+MIXvgCr1YqZmRnpNuN9ZDAYsLy8LNoTbmQ8f2Ts2EjhcDgwHo8RjUYFrLMcxI4t4OghDoVC8Hq9aDQaUKvVcDgcotvJ5/PQarVS8mEZCzhqXlleXka5XJaONHa/9vt9pFKpY12EZOfUajWGw6Fo73Q6HZrNppxXsm0UbrNkT6aIG0ir1UKxWJSSGkuJb3jDG1CpVJDJZIQJILA+deoUHA4HnnzySSQSCZjNZinBNptNKWGyrE5mQ1lqqdfrSKfTsFgsOHHiBGKxGDY2NnDt2jVsbGzgzJkzAkojkQgikQj8fr9cE26oLOFzHRiNRmKJQ92j0+mERqORki7F3hRVs/zndDrRarWQSCRE48TrSHZ9fX1dOg/VarVoeqjb8Xg8aDQa0ilNdotMIFkwdhISqFF/ZbFYRNQeDAaF/eLmTTE+AQFwlLWzxM7ElIlMqVRCIpGQkis7CJXMi8PhwMLCAhKJhDBs1IiS8aI2t9fr4ZlnnsH6+jpOnjyJW2+9FU6nE/F4HNlsVtYd3qNMAAmkCJooXNdoNFKqt9lsUsrj+VSr1bBarfKe1LXyuPjcNpvNY00MAI6V3FkGJBjm641Go1wLdpyyQxG4XuLT6/UwmUwAIABrPB5LswibIZjsMoninpbP5+X5YyWDx8iOSwDHmjbIlrPExwSMejiemxsdr4YS5Kc//eljf3/ooYfg9/vx9NNP495775V/Z0n8+aJareIjH/kIPvaxj+HNb34zAODjH/84YrEY/u7v/g5vfetbX9Sx3NQAjA9KqVRCJpNBu92G3+/HwsKCbDTtdhvpdBp7e3uii1GyXdPT01hZWcHs7Cx2d3elI46lN6vVCqfTicXFRbjdbmGMSNnzP71eL4wT9WHlchnb29uS5fn9fszPz+Ps2bMi2mX3C7seKazlTcXsG4B06LAdvlAoSCdWIBBAKBSCz+cTcAVcz54I3pjFUatAwajBYBD6fm5uDuPxWDQS1BcUCgWoVCoRNvOB1mg00Ov1iMViaDQauHLlCtrtNl7/+teLdocaIG6CRqNR9E92u102HnZdud1uZDIZ7Ozs4OLFi5idnYXf75cOzEwmg16vJ12uzNgZvAbsXDIajajX69ja2kIulxPN11NPPSUMBcEFu2N5zgngCRLIdFFX5HK5RNtGcFkul5FKpeDz+QQcExSR2YxGowIQVCoVpqamBGyzXKJSqZBMJtHr9YSVHAwGyGazSCQS8Pl8mJ6exvT0NObn52Gz2bC7u4u9vT20Wi3JipV2E9S6HB4eSudssViEWq1GOBzG7u4ustks7HY7DAYDarWabCTpdBobGxu455574Pf7kc/nsbe3B6fTiZWVFcm8VSoVdnd3cfHiRbjdbnzLt3wL2u226BmZjLA0TJaScoLBYACv14tIJIJGo4Ht7W2kUilhaKg7unLlipSa+P77+/uIxWJYWlqC3W4XUMwyDjswlWJxnvNKpSIAmNIFJlEsl83MzCAWi8m6Q0DX7/fR6XTQarVE9M/yuc/nAwDR/XW7XSlN6fV6tNttHBwcYDQaIRgMShmd7CWBFXVAbG4h01ooFKRr2mg0QqPRyHdoNpsIBAIol8uy/rFxSZnV88+UM7DTlawNdaeLi4vwer3Y2toSINJut7G1tSWlY5b4yUaOx2Osr6/D4/FIAwQ/h1olWhVQkqBWq1EsFoX1osaKzxD3AKWNBLWdvCZKeQbXdXa2O51OZDIZNJtN+UyyW9R4sYmBiTavF+8bXhOyn0ajUcrXzWZT/u3ZYJHPuZLZo80Orx07JwmGec74bzwHZNR4f3Ed4jrfbrePWc98tQb3PMYLNe89O9i8RCkQ43Of+xz8fj+cTifuu+8+/OIv/iL8fj8A4Omnn0a/38f9998vrw+Hw1hZWcGjjz76tQHAOp0ODg4OkEwmodVqceedd+LMmTMwmUwoFApIJpPSiUMWCID4ePV6PaysrGB1dRWZTAYXLlyAXq/H4uKidN9xY7JarfD7/SL2JI3ORdDpdKLRaMjiydImGa96vS6t3yw7sNxE4TvLNABEp8ZFg5k031PZbUNNCrUR9Mthea5UKqFcLh8TfXKBIphjCYTgiFYUzKIPDg4k8zIYDGIRwCzcZrNhYWEBLpcLTz75JPb29vD4449jaWlJhMr8zsqHIpfLoVariXaMGz1LPalUCmtra8JgsFxiMBhEAO/z+eTfqcGg4JXXj0L2mZkZaT6wWq1ot9vY3d0V7WC9XkelUhEAx8y/WCzKIkdmk4xeuVyWrJz3JEts1JGk02kcHBxIGXg0GsHj8WBxcRE2m00YFzKd3CBe+9rXYm1tTUpNh4eHyOVyssHrdDrk83kAQDQaRSgUkgxd2Qav0WhQKpUkSWFnXiaTgclkQrlcFgAai8WQTqdFoE4mjuLezc1N/O3f/i2+9Vu/FXa7HZ1OB0899RS2trbEdy0SiYhZMp/Pubk5SYjYXRePx0XATJF6LpcT1pAlUYPBgGKxKI03oVBIZsZubW2hVqvB7XaLlQFLzfRw4gZHQTVL+bQr4f3Pss5gMEA6nUYymRQtF5nDRqMhQnm2+FNbSf0XWQ4maGQoqHtkOZSAWK1WI5PJoFarIRQKyXGRzSQI7PV68pnVahWFQkEsC7heUJPIBITsGtcesnhkrXlOKGegrECj0QiAoRUGf877l/pSCuBHoxEsFot0ebOcTEBeKBSwsrKCaDSKZDKJdDotLDTXAp4jrk+89w0Gg1ioUHrCRig+O7QaITsMHJUImQiOx2PpZDaZTPIsWa1WkQGwK5NdrTz+druNXC4n9h5kL3kv8T4g806wSLkHy4dMBJjMKe0huKYDkNIo708yYQStTByYLCurCmS+WB5X2lB8tUYsFjv295/7uZ/Dz//8z3/R3xmPx3jve9+Le+65BysrK/Lv/+bf/Bt8+7d/O+LxOHZ3d/GzP/uzeOMb34inn34aBoNBEgVlcxDwwmbyLxQ3NQCr1+sAgNXVVbz+9a+H3W5HoVDA008/ja2tLbENoC4FgLA59XodZrMZp06dEmHs6dOnZSOm9oMdMSwhsWRks9mwvLws78kHrVKp4OrVq6hUKvB6vVhdXZUFN5vN4sKFC2JiyGPKZDLY29tDrVYTnQcAAWi0YyDbwiCopHZAo9GgWCyi2WyKDYLL5RLNGTdsZdcTASABRiQSQSAQEOE4RcVsViDwOjw8BHAEFGOxmNgP+Hw+vPWtb0UikUC1WpUsT7mIEIhRL0KdCRk3/o7H48Hy8rKcfzZdcHgxs91qtSqeOsz8gKOMiJsU9VperxderxdXr16VjPb8+fMCaPL5PA4ODsRskjocvh8XRDZ4ENzXajUMh0Ox12A5hdk1tSm8vhTSUuNEI00es0qlwtmzZ3HixAncc889KBQKAsRmZmawuroKg8GAa9euYW1tDRsbG4jFYrjttttw4sQJzMzMYH19XXRG7NJtt9ti3UEjVfolHR4e4sKFCwiHw7BYLEilUgAg3W3c/N1uN86dOwe32427774bfr9fdHUXLlxAoVDA1NQU7r77bpw+fVo0U9wkarUatre3pZuOZdkTJ06IfolMXi6Xg8fjwa233iqeZLxGVqsVgUBAdDq8t5gwsW2fujGW4ZWCeWppeC/ws2ndsLa2hr29PYxGI/j9ftGJGY1GYZHJvhH0ENRQTzQajZBOpwWIEaiXy2XU63V4vV6YzWZh9gj+I5GI3N8sXVosFin107rE5XIJCC8UCsLisURPJo72DWR2RqMRrl69CqfTieXlZUnOyEJqNBrx+FMmr9St8j5hswl94gg0LBaLgEOyizRUXVpags1mk/uCzxjZHeD6eBlat9hsNoTDYWg0Guzv70tSq9Pp5By1Wi1JONgUUK1W4fV6BfjQA5Emu5QuEDSxMYSAhbpUal2p4SRjRpaZejKlTxqvX7fbFQkD1xAAAobJjgPXgRjXCCbcAI55fSn94gAcA4FKE2aWMW/mbsEXE882eX0x7NeP/diP4eLFi/jnf/7nY//+rne9S/68srKC22+/HfF4HH/zN3+Db/3Wb33B9yOr/mLjpgZgq6urWFhYENPAz372syiXywAgWh2LxSJAhLMjlW3B3PRnZ2dRrVaxsbEhGpd6vS4lEpqOUsvldDpF60EqWqfTIRaLYWFhQWhgLpYHBwfI5/PSkdZsNrG+vg7g6IFzu93C6lB0SzqaoEPpN8OHliaMZKLMZrMYAzJD8vl8iEajoh/L5XICwoDr5UmTyYRQKATgSHxO/QRZRpYoKWrlMSo79nhOYrGYlDFGo5FcD7VaLQsfM1qWHhjMUi0WC2655RYMh0NcunRJ/N24WHJBYUmSthzKdmuz2Sw+X3SWn52dFcf6fD6P3d1dAMDrX/96YZAoxqQdiN/vl9KG2+2WRZ7aoVQqhXa7jYWFBYTDYYzHY+zu7iIYDGJ1dRXBYFAYVb5Hp9PB1taWNHF4PB5EIhF4vV7UajUkk0mYzWZMTU3Bbrfj9OnT4mCuLNspAUMulxPWi2UmGo5yISYw7PV6wnLZ7XZheMlGsazF5gR2Wno8HsRiMWxtbYlJsM/nk+y93W7jmWeeQSqVwvnz57G8vCz2BuPxGPPz8/imb/om7Ozs4Omnn5Yy5u7urvjJ0dqDuj/qDql1KhQK2N7eFk2N3W6XzkyaPfJeB4D5+XnZIJXlT25M7KYmMHM6nTh16hTsdrswyPT66/f7CIVC8mySZaV1CTdY3os8tkKhgHK5LMkGxeWNRgOlUgkWiwUGg0GSEKPRCJ/PJ2yMUkStnBAwGAwEwJC94TEtLS0hFovBYDCg1WodA70OhwNOp1PKzbRRYQkMgDzDZP7Y5MJ7WavVwm63Sxl1PD7y8aOflclkks7l4XAozQfhcFi0T7w/uSbTB5ClPmoqCXhZMSBTxJLicDgUJ3g2Z7BSQYBDwESAyq7e0WgkrDV1r2QwyZCRkeOaw3uS5UCW+djgQSCtZJ4IyJU+fMq1RClr4bNHoMXgM0zw9WxxPUEaAKlwKJsBbmS8nBqwL9fk9cd//MfxV3/1V/inf/qnL9m5GAqFEI/Hsbm5CeBoHaJ7gJIUyeVyeO1rX/uij+GmBmBTU1OiUTk8PMRgMMD8/Dw8Ho/cgEqbANLFBAQsVZDG397ehkp15EbMdnbe3PTioTFro9HAxsaGgCKXy4XZ2Vl5qJVdLhx+GolE4PF4YLfbhTGgUazT6UQkEhF3fS7UBBoEWwwlo0EvG2ZmJ06cQDweh9lsRr1eF9f9aDQqHZy00iD7wOybDyxw9KAnk0lcvHgR1WpVXPvJPBAI1et1EdWOx2MEg0HJPNnwMDs7K6wLnZtJjbNDjwtkvV5HJpMR36WzZ8+iWCxic3MThUJBxsCQTaCPU71eP+anRVbL5XKJJisQCCAajeL06dOiR6KIuN/vY2ZmRgDiYDDA1tYWdDqddNwmk0nk83nxZBoOhwLm2HVLXQitTOhUz3E7LMtwoVSOrCLzyjb9ZrOJzc1NAf4sIdGTLZfLCetJoDQ1NSX+Z61WSzr2lB15LDNxUybY5r1OXdvGxgYuXLggycXs7CxisRimp6fx9NNP44knnsDq6qp4Lw2HQxGyDwYD6ZgjsGQpUa1WIxAI4JZbbhEHdWr0uBHTgqLX62F6eho2m038ojgiippN2pFQ70Y9EK9POp2Gx+OREjCBBB3YeV1ov0Bgy2kSLBt3Oh0kEgnk83n4/X65LkpTVN4DLJHROy0QCKBSqUhpit5UtFMol8vo9XrSmUmtEMX/LIdxw1cmm2TLlVY62WwWV65cgc1mkw5mBsuhPp8PqVQK165dk+eT5TyW9tmNyuMAIMkime+pqSlks1lsbW1Bq9XK/TI9PY29vT0xsSYDSXsU6loLhQIACNtPEM6SGs9/JpMRBosjjajppa8bwQt9IA8PD2G32+WzySixgYH6U+V4OY64IwB6PnNWyjgInMi20oqIZU0AkkSzi5xaSVqusIxJFo7NLNzHWELmdTYajfL8AtftifhzNjCw257M2CSOANuP//iP48///M/xuc99DjMzM1/yd4rFIhKJhBAUt912G3Q6HR5++GG8853vBHDkenD58mX86q/+6os+lpv6itDLiKJZmqb2ej1pIWeGSS1Is9k81uHD2n6xWEQ8HofFYhGNisPhED2CxWKREpvRaJSusV6vh1AoJJ5f1J0xG6LXlkqlks211zuak0gfKS5C1A+wa40ZVz6fF4qfCxFLqUrTPrJY1JT0+30kk0kkEgnJbEnLU7TPBY9WFuzOpH6KWo6dnR2kUinRZnEmnbJbb29vD/V6XTRA0WhU9GmlUkl0EyxPKDcNpWBSozkaU8WOR5Z7qSUCgFKpJCUFCo8XFhZEAM73ZzfS/v6+uO47nU6cPHkS7XYbOzs78pq1tTWMx9dHDFFE7nA48IY3vAHLy8vweDx4/PHHUa/XMTU1JWVaam+GwyEKhYKUFnq9Hj7/+c/jySefxP3334+ZmRm5vg6HA6dOnUIwGBRmjBso71cyUuzSonaImzgZSXbGMoun9QcXbrVaLX5NtNngnwkYmTTQyoDdcpVKRRzS2cBA9oezEIPBoDAUBBIs5fd6PWxtbSGbzSIWi0lpjaVcl8slIIbz+aiB6/V6ODw8RDAYxMrKCrrdLs6dO4d6vY6ZmRk4HA4pHdJCgs+5yWTC/Pw8fD4fEomEdAcfHh4K28tnimJogiZan9AIlRowshJqtRqVSgU+nw9Wq1UsWTgDlmwpS2/0heI90e12xf+LjDrXKgavBzfRer0u+kNeU5ZOKfynd5/H48FgMMDm5iYef/xxKb9z9BDBm9vtxvLyspSbeZyFQkE8zrgW8R5j8xPLd4eHh0in08L4kaHy+XwCGqlfJWNPkTvlDWRzKLgnS6lkmXjeWBIla0npgUajES2asmw7HA6fA6SVzUnK55FrMBMsJStKUER5Ae9hCut1Op1oAfk+ZAZZ1WCw6sBzS1YMuN6wxOSf7202m58jYaAkRFmVUFpS8FiUpcqv9fjRH/1RfOITn8Bf/uVfirEyANnbGo0Gfv7nfx7/9t/+W4RCIezt7eH/+//+P3i9Xpnc43A48H3f93143/veJ00n73//+7G6uipdkS8mbmoARmd6LlDK+iuHnRJUGAwGYXlYUqKrsVK3sLm5CZVKhdtvv13MB1myKZfLSCaT4vpsMpkEcF2+fBmtVksyPQBSRqHvDrMU4HrWSt8j6jIIRGjwShDI7i3guscLNw6aH7KLkuLdTCaDdDotHkc0fgQgrd5WqxX5fF7EpQSD3IBoMaGcKUcQubu7K6UWn893rKFAq9WK3oqu2dScURPGhYdaD+C66SWZIeCIUj958iRMJhOefPJJ7O/vo9/vyzWg/QKvKc8jM2wyV7QymJ+fh9vtxm233SbAOZ1O4/z580gmk8KU0BSTC5rRaEQ4HIbH48ETTzyBSqUizAzBJhdNemSxCSOVSuHv//7vMTs7K9d+ZmYGwWDwGHhpNpuo1+tigkn7CC7u7Nas1+vCZvL/fF/e42QZbTYbtFqtlLSVrt0A5N+YgdNewWw2Y2Vl5VjZstVqwePxiKHs4eGhjP8pFosyYoXsMV9PNoCt+KFQCIPBQDLKRCIhlgnsNo1Go7DZbNjc3ESlUsHly5cxMzODmZkZlEol0Q+RJWCHHa8/AaPdbsfi4iJUKhXK5TI2Nzexs7OD/f19RCIRTE9PIxAIwGg0olqtynnTaI6mZ6RSKTnP1GBRw7W/vw+bzSbeUyqVClarVQxEmWQBEJ0QN0N2xpIFm5mZEf0ffb+Uth5KyQDXlFKphFwuJ5MsAIglg8fjkdLl1atXodFocPvttyMajUoDCp9dlj35mTSaZZLEeaEEFQCE7SPAIeBneZvTL/r9vszKpTkwLTv4/XhulJYSZBxZYieYpVkvQTHHENHdngkKZRPxeFyOjSVyaoCZmNDSiBIKVhtsNps0G7FUzGvDxIvWOXyOaOtBJk9pBaHsgCT7xnuDawAZMIZSp8jrS4aUSRgZXO4TPDZ22pKBmwTw4Q9/GMCR5EQZDz30EN7znvdAo9Hg0qVL+OhHP4pKpYJQKIQ3vOEN+NSnPnVskPl//+//HVqtFu985zvFiPUP//APX7QHGHCTA7DBYCAO0D6fD85/mQFHLQBZKraB+3w+MWOlUFqlUiEej8vNe++990pZixkY50tydIZarYbNZkMqlcKFCxcAHIGS0WgkWiCbzYbxeCzMXL1elwWN425YJpmamjqWxSjF6mx/ZobLTJnCapYyDw4OoFKpEA6HodPpRKycTCbRaDREA6PVamVkkcvlgtfrRTqdRjqdlrZxsi7sxBqPxyJ4p1aEZVSWxxYWFjAzM4Pp6Wlpt+Zmq/Q2IqDlokWWj1oQdpZRF0E9C0s4HN6rUqnEZoD6oqeeekpG0FCMy3Ke2WzGlStXsL29jdFohPn5ecl0ufkvLCyg3+9jd3cX8XhcwPDly5ext7eHM2fOYGlpCSdOnBCDVm5A7KijJqbX62F7exvFYhEejwdnz56VzU2r1aJSqeCRRx6RDSIQCIj7fqVSQa/XE+d+GqgqJyLk83np1lSKeqk7pJEkAAHlFKf7fD6Z80eLCbJONptN5mcSnFC3wvPpcDgQDocRjUaldOv3+2WsVjqdRjAYxNramhiEkvUlM0wT5FwuJzYiBLo0o+TnU4dIwEVWWqkD5AgijhFiQsOZjg6HQ0r9p0+fhtVqlcaXw8NDSVI4RYCWLTzWdDp9rFuO4IFmxmSAAoGAsE/JZBKFQgGtVksSMAIasijtdltc/fl8UkRNfdB4PJaZixS68z5gswzButPpPGaDQKaYjOru7q5oVsgSWSyWY+OWlJpHGuTy+Wy1WnA6naIbtFgsqFarSCaT4pdHZq5arR4zWSZjmUgkMBgM4Ha75fmu1WrCWit9D7m2+nw+JJNJ6UBk1zFBFXBdAM3yrFInxTWDjCeZe4IbfkcAx+wmIpEIDAaDGOPq9XpJapXdmExclcf9bKCk1+uPlZfJxlHHRR0tJRDUK9MahyCOnZ6UnRB4c99Qar24xrJac6Pj1eAD9qVebzKZ8JnPfOZLvo/RaMSHPvQhfOhDH/qyPl8ZNzUAW1xcFKqVzAC9V7xeLwKBgAwL5oR6MkfMBvjwknmhaDSdTuPChQtIJpOiw+h0OvD5fMIQcHPkpkKGgf5ULpcLwWBQSkTUlfB3SBkPBgMZ8ttqtUREzw2m1WohGAxiaWkJ3W4X6+vr2N3dFS8wzoKbm5tDtVqVEUpcDO644w7ceuut0Gg0uHbtGra2tlAul8WbiBoseg9x/BJnwbEckc/nBehy0bLZbGi1Wtjc3BSfKnpsFYtFbG9vA4AIlLvdrrCE1FSQTmfWRr0JNyUuIBqNBh6PB/F4XMpiPp8P8Xhc5tKx8QA4KjtSe2ez2XDrrbdKKTKTySAYDApjRKE0OwfJDPBaE4BXq1W85jWvwdvf/nYkEgkUCgVkMhmMRiO43W4ZdcJNh5o/p9MpYmaWgp566ilhUNvttswepUYkGo2KluPg4AB+vx9LS0vo9XpIJBLI5XLSaUaNEMfVUNtGjQiZpampKekqo43KeDyWe5Tgg2wFj6VQKMhGQ/PjxcVF0XRR3M3yVCAQgM1mk67ORqMhw+f5nCm9jVhSVXYU5nI5VCoVAR30i+OmFAgEZJwUGZhQKIRKpYL19fVj5bx8Po9CoQDnv8yC7fWO5sLSj257e1tGM/H9p6amxKVd2fLPsjl1QM1mU6ZgcDM9e/YsZmZmcO3aNQFnMzMz4v/H7thWqyWl9FKpJA0NSnE2nwkyO3wuyNqEw2EYjUasr69ja2sLsVhM1h2ec5fLJf5vZLrpUUbGlGycsotvd3dXNIo0seYEAdrFaLVa5PN5sXfgVI9SqSRzL2kgyk5aPtNk0wmSXS6XjFijhok2L71eT5pByPyzWkBdKydeUB+q1WrFbJfJKZsX1Gq1TB4huOIQczKVBHD0QnO73dLVSO9HJjvKcj8ZQDJ6vLeY0GQyGQGFZrNZrDRY2qWOi+wZrXf43LCbneeOSTOBnJKR4zPz1e4DdjPGTQ3AqLMh06XT6USAz84hOqKTjiVb0Ov1sLm5KYaeZrMZ09PTsNvt0u7OLiIOH6aQneOMWG7Q6XSyEbMUxaGypOPZls7ummw2i2w2i0qlIpkLN0LSxsFgUL5Pv9/HlStXsL6+fgz8kZ0KhUJQqVTCzExPT8scyWg0ikajgXPnzuHy5cuyoNO4bnZ2FvF4HA6HA9lsFsPhEGazGTabTUoFXq9XdBSsm9N7jJvS+vo6DAYDbrnlFng8HtTrdaytrWF9fR1ms1mE7OVyGYlEQpgUZrH87pxrxo2GYn/qG6xWK1KpFJ555hl0u10sLi7i9OnT2N7eFq0SjWIvXbqERCKBEydOwG63C4VMhlOv1yMajQpDR+1cq9WC2+2W8seFCxdw8eJFZLNZjEYj3HvvvVhYWJCBwCw3MyvN5/PY399HNpuFy+WSMiFZTqvViunpaTEEJlMaj8dFvE/WjwwiLVCsViuuXbuGw8NDGAwGeDyeY5k0AGl0UHoBcVOnlohz/Oixxi41ltip/2P5K5VKSddhIpFANpsV8MtuysFgIA0UBJ6hUEg2MTYPMBHiBs4yCoE9r0Mmk4HL5cLCwoK4UlerVengJLPGe0SlUh1jaDnKRjmJgAJvsldsnCFzphRMs+QcDAZl2HsqlZKStNVqlXWI+i4axtLSxuv1CjOjLCdxo2YCxX9nckhpRafTEd0ef4/vxzI+rR0uXryInZ0dYf2dTqeUNT0eD0wmk5x/ShXUajXsdru8J1kiivG73S729vak45ujs5i8UrBPvRTL/xTJD4dDhEIhSQr4jNdqNQG4NLRlF3c2m5Wkot/v4+DgALVa7djYrUajIZpB6liBIx1fNpsVT0RWN6gBI9POfeHZnY1k4bLZrLBmrDgwwaEXG58brvHslK9UKsjn85IEU0fo8/lQKpWE2WSiSfuRZDIpmmGloF4pGSDDpmwIYPLE5IHJEj+HxzmJV1fc1ACsWCyKVoMaCCXdTvqcxpQ0DB2Px9ja2sLVq1cBQCwgCLL8fj9CoZAwCmq1Gvl8Ht3u0QDt4XAorAM/k+7hzMorlQoSiYR0THHBph2G0oCP3YCk4Anq7HY7Wq0WDg4OsLOzI+Uiao64YVFHxWyT7ewsdVy5ckW68dhGT28tCr/1er24+1NbMR6PBUgwC2V3JZsX2PFJ12YK2VdWVmSmHLv2OMDa4/GIpoblFG6QDG4yACTjY4cQ/YnK5TLOnTuHfr+PpaUlBAIB9Pt9YU0ASHnJYDCICWuj0ZDyiTJbpcCcQ6DZUXnrrbfKIODNzU088sgjsNvtOHnyJBwOB2KxmDRbsLssHo+j0+kgl8sd24QcDgdSqZToWFhO4Hw/n88nJfC/+7u/E4E7RducynDHHXcIO8LsmRk3/YAIxljC0Ov1KBaLqFQqsFqtiMfjovcjEKG5JDUzfH5sNhumpqbEfFar1Urpic8aWcNwOIx8Pi/drDynLpdL3KaV7O1gMJAGF7/fLxMBUqmUAICdnR3pUKMrOjefXC4nr+NGx3ubZSUAsrkSnG9vb0ujB78rGZJyuYxyuQy73S4Jnlp9NCnAbDYLGCVzQvG6kg3P5XLo9XrSMUy/PgI3Xidlxy6nKJDpoIxAOXJLWQ6mvQKnR6ysrGBjY0NmPJpMJgSDQWHx+ayxDEN5xeLiopTUuKYYDAasrq4ilUrJdWbZnSVy3hvKLlGtVivD4kulkiQk1GrxWaflBH+fIIP3F599/kxp06E0UyYwJCPMasdgMBANpE6ng9/vl+YiSgNYomU1gDo35WBvg8EgFjhkqymyJ5PI70OgTHNa6sE4VYXPJ/cXzrPk88P9SakXI+AjM0zmlIm60gfMYrGIpQXXByYTrwYN2KuhBPlqipsagHHhoicSW3P5d7JUzPb4IHNodSAQkLlxzn+Z7QYcGW6mUinJwkg/u1wueDweEX1Tm8CsjSMNgCM/kFwuJ4szhXnUF5AOpk6D70/NAR3aWa6jvQAzbafTKdQz28OpS2FXJ0WrzB6Z5XPxt9vtmJ2dxdTUFBqNBh577DExBA0EAuLHpbRIcLvd0Gq1AiYACOVPTRoF3CdOnJDuxGaziUuXLiEcDosmi5mwzWZDNps9dj5Jn9MWgyU9l8uFTCaDqakpWK1W/NM//RPOnz8vmTtZO7rLu1wutFotpFIpRKNRaf2nroxdeqPRSMZXZTIZsawgy2AymXDXXXchHA7jySefxMMPPyydrdQSERTT2sHr9WJlZQWFQgGDwUA6+/b397G3tyfAhDouslN2ux1LS0twOBzY3NxEOp2WDWNtbQ0OhwPxeBztdhtXr16VEh/tD9iVw/uCmTaNSLnRsjTD0guZOXqAURNGMMXNTtkyT880MjnAdV0SWeFSqYS1tTUMh0MsLi5iZmZGZify/mdZhl5TZEYoCD88PEQmk5GJFBS522y2Y9MdbDYbnE6nMBC0YGApicPqKayORCKyYRGwkr2jnmtvb080Trw+BPDUkSr1aEofKCYoZFeVVg+0w5mZmcFoNBLZAJM0dqoyudHpdFIKpxCfJU0mNhwTlkgkkEqljrGjHDtFkAVc98nLZDIwGo0CpDhlwul0YmpqCqFQCOl0GoeHh+LLxqkg1KyR8Uyn0+J153Q6Ua/XpUxN8KhWqwXs07KBgIzrDsEOnz8mcxzVxsYqs9ksHc7tdluaRJhYmUwm8UHMZrPy+2wO4fdgCZXd5AQz1Kzy/Cg1kXytcroA7y2us2R0KTEgcKIvI8ExRfuUZBCc2+12kauwFE9pCoPCe34mO/WfPZJuEq+uuKkB2N7eHvL5PNLpNJrNpojIWWenDsHhcCASiUinVjKZxGAwQDAYxOzsLIxGo8y4y2azkumQbSHgAY7sD9h2zTZrAJLdGwyGY+NiKDbnokZWTmkMSF+b6elpdDodbGxsiJmhz+eThZj0PY0naWBKB3VqAgjwmD0S1IzHY9FLjEYj2Zyq1aqwARRqh8Nh6W5jw8FwOMTGxoZQ/xaLRQwPldlZrVaTcqzP50MkEhEhMR3Lubkya2RJVKVSIRAISPmMTJiy5EN/LLPZjEgkIrYHVqtVmi+0Wi0CgQDe8pa34LHHHsPVq1exubmJU6dOweVyiQi3XC5LCTQQCACALNQ8Z1zsDAYDzpw5A7vdji984QvY39/H3NycmDgaDAbMz8/D4XDIbFIa+FHbxJbnYDCIZDKJSqUiIILXwev1iv5odXVVbA3cbrfo8ljGffzxx6HT6TA3N4dwOIxkMim6RXamEkCT/fF4PMIMNRoNmRbQbDZlNBI3HVo1ZLNZuSfJTLJzjjYZNpsNuVxOni0AMtCYbOr6+jqKxaL4MgWDwWOaILIbLJk9u7zK+yUWi6Hb7Yq9CVkUbnwOhwN6vV6eV/47TYvNZrN0nPJ45+bmpNSaSqVgsVgQCASk47Hb7UrHMEECN1GWodmJx7Kuw+E4NrVBr9fLXEaOByPr3el0kM1m5b1YmorH41K6JNPG+1xpf0Jdmdfrld8nwCBrRaaQ6wOTRxovE1RSjsFz5/P5EAwGpcuRLCSrAZ1OBy6XSxjDZDKJSCQCk8mESqUibBEBPwDpuCWQVs7vZbB03Wq1RP7AtZDPudFohN/vR6PREHBIqxMmqGQq+ZmlUkmahsxmMy5fvoxyuSzd1mSPCOLIUNJQmmsd9XPs/KaNjNVqxdTUlCQCbAKh7ouv5fgsJkUbGxsiwud9Chx5TNEkliD52TYYPCabzSbehxwPxXLuJF5dcVMDsKefflp0ATTV40galiMIYug9o9PpcOutt4rQlZok0sXBYFA6JJUiTL53o9GAz+dDIBAQzQfLS263G/v7+8jn89IMwO4YAhtaALC0YzKZMDc3h1AohEKhgIsXL2IwGCAQCEi2oyyX8WGi7or1fnofMdMZj8diUEihLu0sKF5ndsqxTdScUTyu1Wqxs7OD9fV1YR/oScPvwc+ijoLlEADY3t5GpVJBIBBAIBCQUufu7q6AQXbxuN1u0ZB0u13JkulrxPKH0WhEJBLB3t6ejBghACAwOzw8lFKq1WrF6uoqms2mlIW50JPGN5vN4p9ms9kQi8VEwMyNttPpYGdnR1iT17zmNVCpVMIIVioVZLNZ0d9ptVqUSiW0Wi0ZXUM/NIPBgGg0Co/HI2CcJT/af5CxpXkpF9hOp4P19XVhks6ePSuaJJVKhZMnTx4rAVIXMh6PRc/EaxgMBkWcWyqV8OSTT+LKlStYWloS5obAls7nrVZLNDFKFkypOWO5OxqNyqbLphiWvzQajdyzZCzowUN9EAAZ55PP5+FyuY5plmZmZpBOp0VMzjJ6t9vFmTNnMD8/LyOx6KdVq9Wg0WhEx8kpFuxS9vv9OHnypHTdDYdDMZotFArSpJPJZIQhYrMGx3nRmiSZTGJ/f1++Pzs0h8MhpqamZIIHS47xeBxOp1NKeWxs4HrGNY2sLhsYqO+jBk6v1yMWi8k9zRIY7R4cDoeU1JRWBdQRkXGdmpo6pss0m81wOp2YnZ0VQEq5B9ehwWAgQIXsk8FgODbwmskYE59MJoNkMgmXy4VwOHxMW8b1hDqu+fl5+P1+ZLNZHBwcIJvNSgMEy/5cbym0Z3OHSqWC1+uVGZG8N2n/oDQiprid54zAnQm63W4Xlp7JuclkQrVahcvlkjWD9ytLldR28bNoeWG1WrGxsYGNjQ0xNeYUBA4zByCdjFar9ZjsgV3Yer1eEg2lno0gfRKvrripAZhOp0M0GpU2fofDIYJ1m82GEydOyIJeKBSOtS9z01xfX0e5XBZNBell3ujUD3m9XgBH3VT0puGYInoapVIpEaoDR2aazKC44HCMDDsz5+fnEQqFcO3aNTz55JPCfkQiEVl8AYjLOt3blS3xBA6s81MMzgWPr1dqQijwVY5RYYmJTI3Sm4qbK7uxuGEyWyUTwoWCWhEKoWOxGCwWixiREiCMx2OUSiUBftS+EFCwLZ+bADcYn88ngK5SqWBvb09AF8Xk09PTkumfPn1a7BkODw/FxFA5uYAdk+zkogCdHZLUT7HbDjhaxKenp6Xzk+eS5ZF0Oo29vT00m03Mz88jHo9jOBxCp7s+C5M6sdFohJmZGQGkSs1Kq9VCNpvFxsaGsEOxWAxTU1MolUqipyJLFo/HodFoZIyO3W6Hw+GQAdYsUcXjcdxzzz2IRqMIBALY2tqSTZIbFzViZOl4TdiZytI+AQA3Tm4yZG7JApCtYgs/wSVLMEo/JNqzcHoAtX3U7bjdbqRSKSmZFYtF7O/vw2q1Yn5+XlhMpa6Nom4yI1arFbVaDefPnxdjUrI7BIiRSEQ2MLI//X5fdGuj0UjsCQCI7c0TTzyBZ555BsvLy6Lh4ibJ54QAkMa1/AxusByvQqd3MldkyABIQsRSKp9Fgl12G9O01Gq1Ip1Oo16viw5SOcA6l8thd3dXDKjJQlO7SS0gm4rI2qnVakxNTQkLXKvVxC5COeuWHeE04eUweCbTyntHrVZLAs31lIAxm82KpIGyCuXYJoJQAAKa2ZFNr0GuAxSqs1mLz6lSS0XzV34eWTCHwyH3HwEm/SCVnlzUdOl0OmkQoXSGNiqsGhgMBpEv6PV6mTtK09/hcAibzSaD0mmrw+YK+mTyXn01ALCJBux43NQA7MyZM1hYWDjmTM8xPryxSfXSaXt/f1/mu62trWFra0ta0sl2kSJXdiPq9XocHBygXC5Dq9XK4nXnnXdibm4Om5ub2N7ehlarFcNXPtxs8e92uzIeY2pqCmfOnIHP58MjjzyCxx57DKdOncIdd9whTEE+nxfLiFqtJmwbR4jQWoAbGRdEagCU3TMs5XGhSKfTx7QUpM6ZtTr/ZTYhtSzPzng5CN3v98u5UjKF3W5XNkaKcpkZWiwWXL16VYAp3d2VY4ooTOfGQIBHPRjtECjqfvjhh1Gr1bCysiLGp9euXcNtt90m14ugQqVSCcNWrVbFzX00OhqqPT09LZMSuFGSceG5ZFmSQ5GVFgZkLtkYQl8ipTaL3UqDwdG8w3Q6jUQigVOnTmF1dVU2PXZyUc9BJpE+S4FAAKdOnRJ9UrvdFtH2a17zGkSjUVy9elU6Gq1WK2655RYMBgNcuXIF+Xwep0+fFt0WBdwse3IzYclwY2MD5XJZSvpkngmc6vW6sLVKvWG1WpXzA0A0NtT1Kf2ayADQl4ssWKvVkvO6vr6ObDYrQ+1ZZuM92263xT4lk8lgZ2dHmirYTJDP58Uug6C52WziypUrsFgsKBQK2NjYQCKRwNzcnGiNaI5LPRpZUM6P5VgbnU6H5eVlYW+oEaIWjfcSNURMHCmWpv0HO/HY2c1zDkCuDZ8/lqaoOaVWi1orbtKj0UjsHMiKA0cl48XFRZnAQfBNIEK9HJlVVgpY2qT1hMfjgU6nE583l8slYnNaT3BaA60lCGKZfBDkAEdAjOC50WhIMkdTYlqU0OOMzzpBe7lcxsHBAUajkXSqE2gxSSOzz3NIPRaTWqU1ENkvAAKoAIgdCr0MKXVggsLORjKaLLHSMJwsoMViES9HXk/637HMy0SICQ3d9Znk8zkj+FOOspvEqyNuagA2NTWF4XCI/f39Y35a9Hxh6Sefz+PKlSuSYXS7XTFJ5ALGB4IlGW4gdrtdxKEsU1SrVZjNZnzd130dbrnlFhwcHIiBJgEBDTl9Pt+x+YA6nQ6rq6u488474fF48LnPfQ4HBwd4/etfj7Nnz0KtVmNnZ0fakVk64SJM4Tf1VSwlkKYmK8SFggsKAClD0NTV4XAIIKS+hB1EzCrIUBBIsERI2wYAUpJiaZTMhMViEb3c008/jZWVFTidTrFUYLs6M/B8Po98Po9wOAyXyyVZGzNgZRcks30KiqempgTEkE3rdDoi4GVrOkuWytKfUiTLjSEUCgkYpF0BvbMSiQS2t7el9EBWkq7knU5HxNulUgnxeBzT09NIJpPY3d2VTlX6A83NzYmmj6O12NXFTllq/diBOhqNZBOu1Wqo1WrQarUIBoOySVUqFUxNTSEajWJjYwOVSgXPPPMMdDodpqen4Xa7j5n4UoTMeySTyaBSqSAajcLhcMh5rlarco9Rh6JkEGgoyddxA+O14vgZNgnQ+4o6KGrqCBgp7uZ9yRI5GQHe3wRwo9FIvJr4/OVyOWxtbUnzCEfdVCoV0WLRET6RSIhgOhwOH7Od4XQKggHabpAxZwckwRQ1YATc1Hwpu9K0Wq3ojJ7d9Wc2m4WRZ0csk7DxeCw6QiUgI5tPCwcyH2R8stmslD1dLhesVivcbrckOOx4ZamVrDFZSoIXAl0AxzRZ1LuyKYbNSzSs5XcvFArC+HBSA1kjJftEgEm9FZsX9Hq9HAPlCUqX+mebtLJ7kIkq7UbUarUkf7yHKQNg6ZH7i7KRgu+rBKS8B8iOkQmjHIFNVGTzmFRQMkKQxUkmJA/YqEB5jNLri93RKpVKgBdwvbte2ak5iVdX3NQA7OrVq2JFwVEgCwsLImw8PDyUQdS93tHMRgpU2S1Dbx7qKfigMfNX1vJpQuj1enHHHXdgdXUV2WwWuVxOBP0sGQLXBaSj0UgGHp88eRJ33XUXisUiPve5zwEA3vzmN0Or1eLq1avi6A1ANmPSyKTHAUjHCzdk6iSUo4gI+Ag0SqUStre3JRsiQKSPGS0UAoEA4vG4iP+VTBnpbPrpNJvNY87L9AZjxx81LdRI8fio/2KXKEsOBNOk1ylep96OHaPA0TwuArmTJ0+K8W6320UoFEIoFJImjNXV1WMDtAlMWdIkm0HtlnIWHstq7Kykzoj3BzN3WqAQoB8eHuLixYs4ODjA4uKilEtTqZQYSIbDYSwuLiIWiyGTyWB/f//YSCil8JcsGO8PlpO4mLNczPcm08AO2larha2tLVy4cAF7e3viKUWDT61WKwPNuYnu7+8jk8lgZWUFXq9XSo/sBLZYLDLJgSwAWWieV2bg3W5XStfcsCicZmav9ETiRkNGmswccNTEQBE9kwc+Dy6XC9lsFnq9Xsq+FMKvra1hZ2cHU1NTsknzHqD1C+8t2rqEw2EBO9TbtFotbGxsYDgcwu/3S1myWCwim83KWCSuA0r/Kj7bSmaEZTAeBxMI/pzX0Wg0SolNpVKhWq3KhA4+h0rNIJ8VMiUEiASJNKulDo/gl5s9QQNBDcvDTGKo5RyPx2Jtw6SQxqcE3rw+BFd8BtnpyfOj1WqlaUDJ6FCrySYEJpzsYiQLy2SVcgaef7/fj0QiIQ1bKpVKfAwJ3vL5vFjDAJD1haw5pwbwOJVTKMxms8yGZVLGUGq/DAaDJHRk0agLNZlMKJVKuHLlCoAjs/HRaCQldJ4b+u6xk582OtScMSFSAkP+fxKvnripAdj58+cRDAaxurqKmZkZYWLoP1QsFqFSqTA9PQ2Xy4XBYCClQuqJWE6gmSlpXG4E9IBJpVJSlpufn4fFYsH+/r5smMzOlQOU2Z5OGv/kyZO488470Wg0cPHiRdFLZDIZrK2tycLHBZTlQJYTyQApPbLIZClLke12W8qWmUxGFj96ZGUyGVmgOEh8cXFRvL8oAB4MjsapbG5uYjAYIB6Pi2EmH2ZujspuTzrN63RH8w6XlpZE20KNBDVrBHQcAF0oFAQgWywWAVQWiwW1Wg2FQkFA9/LysrBhg8FARvXQgoMt4HwvnU6HRCIBu90uxpAEjBQ8m81mKSNRL8jzS0Nb2oPQB4m6EwI2es4Fg0EcHBwcO39kSAkyS6USNjY2oFarkU6npcRFAb5Wq5Uu3qmpKRwcHAgztba2JswCgQgF3CwfErS1Wi24XC6cPXsWu7u7UrKiPQAtOdjRarFYpDnkqaeewrVr1/C6170Ofr9ftI0UCHOT5mZCS4BoNAq32w29Xo/NzU00m00xsOS1p/aQ7DM96mjdMhxen8QQDocBHI0don8YN1w2F4RCITlXLNcx0aD9RaFQkMHkFGYTIFJbB0CAJkui7CyjZjQSiYg2jYkMky4yhmReyMYowRFBjdIWgj/js00ZBDdSrnEEHNPT0+LeTsYDgDDFSj8tgh5a2KjVRzNaKStg9zFHplGuwFI4wRvZFLJ+/B70OSMLRVDN8iY1d5QlsGuc4J3rsVJSweSY55Kvy+Vysg8wCfR6vXC5XDLBgaa7vKfG4yN/sVqtJgauBEGc7kGGn9cfuG5qTAClTBDYHETpBeeAMvklS+x0OoXJY2ckWXdqJs1ms3hKkhmmRIPJjM1mk2P2eDwiSaD1Er3peK07nY7oI18NAGyiATseNzUAm5mZwRve8AZx2s7n88hms2g2myKIpaeQVqvFo48+iscffxz1el08rFhupAknSwak9OnXMz09LWUwivdpzsqFjg8sO0+ovzAajbjllltw+vRp5HI5fOELXxDfsvPnzyOVSgE4yuqZDXJhoriV7MHCwgKc/zKIluJLauAotmVXTaVSEcE7GThm0gRgPp8PJ06cQDgcRrFYxKVLl3D16lXRmbHFmf5qXATUarUIpZ/d4cRzxMVJrVbjxIkTmJ+fl0Wv3W6jUqnIQslOKvrhUK9CUXC/35eSxvb2NsrlMgAgFAqJHoeDfrmplMtlTE1NIRAIQKVSCaNjtVoFPFCbRxNegkgAAgio5bLb7bBarXK+9vb2kEql0Gw2EY/Hpe38ySefxN7eHiKRCObm5uRahUIhBINB1Ot1uXdoOMqWdIrnqecBIJsrB4lHIhFUKhWxQyCYzWazSCaTaDab8kxQJE0/Lp/Ph1gsdkwLyM2fmzc1KoeHh4hEInjjG9+Ira0tPP300zh9+jRWVlZEAH14eChNAmQyqZUim8qOrWq1KmVFlmBZsuZzw9IWO8Y4nsbj8cDhcAirwVIxNzqyZXzuWELkZ5pMJiwtLeHd7343Dg4OcP78eekk5MZUKpUE3JPtHY1GuHTpkmjEdDqdMOJ+vx9+v1/uHZfLhZmZGXl+qUeiQFrpb6cskynLhQyWupRGm9TH8TxTp0edndLHjDYVXBsAiMCdrP5gMIDH4xGftXw+L+dZp7s+VaRer6NSqQhwYkmU7BBLzmz+IehnlyiZPaUMg6BBmehSN9toNCTx4nOoUqkkGe10OtIMojSFJuvKxIPMIYEVJSW8XmyE4Sgygj8261SrVfE+BCDM98zMjDC7yq5tJg20GKIha7lcFr0jAAG8BF9MZnQ6nQBrdnuS8SLTzBI5y9FMdkkosIO72WxKiZKj0vh8T+LVEzc1APuGb/gG2O127Ozs4Omnn0axWJQ2d7q++/1+XLt2TdggZnLUMTD7pGCYDA8dlwmIOBZlY2MDu7u7cgxkzLiZUKBJfYnBYMCdd96JM2fOoNVq4dKlS6jX6+K/MxqNjnXGDYdDKd0NBgPkcrljc+PK5bKUW7koEUSyBEQvJWa/XFBJozN7p5CagKndbgsNzg2Czt1kk+j+r9RhsfRFIDAYDOD1erG0tCSbFJkpGhUSEHGTo94lGo3KYk8zSgI2n88nLfD5fB4HBwfyO1x4aUvg8/nQ7/exv78vQ8fJKFWr1WOO7Cz1KUtxFFYrR3xQa8WB7oFAQNrhled1cXFRBPJ0BKcGiFk+R94QdAGQOZ7UrhGURSIRADjmPg4clbG40J49exaj0Uh+n9k+cJ39KBQKwkrS7JNsAjdP3kuDwQC7u7u4evUqVldXZW7mwcGB3L+cgUhWrNPpwGq1YnFxUY5T6WlEQEFQ5HK55F6iRQObZ1iSZmmPHYHs+KV+iawzcLSx7e7u4sqVK7jtttsQCoWkW5j+WqdPn8bCwgL0ej0uXrwobJ7X64XD4ZCNjMlYtVoV6wEAAt5p4UAj3VqtJmwe/a84G5SsNXVbwHXWCIBonSh9YPcvdWa8lkyGyE7xXHLNIgDlOSSbNhwOkU6npSzKsn+v15OxQ+xC5PVQqVRi78BZpGSalDpEglJlAsjydaVSkS5RNqI0m00p73LmLS1wbDabACx24PKeIUgmQFeWManRopksqwhMBsko+3w+GI1G7O/vYzAYHNM8slrC68CmJ9pNEMSx2qG0dWAVBTiShhDgkm2r1+uiQ2V1hJ3dvBdoiULQTKDP78+pGaVSSQgEpdUSR8Zxn2Biy1KzUiIwiVdP3NQArFwu4x//8R/x+OOPo1KpyAw4m80mwuadnR1sbm5K7d5ut4tWih1YtKdghk2hKW0jEokEksmkWEF0u13JOrlQPDt71Wg0CAaDOHnyJM6cOYP9/X088cQTknnx87mYcbGgMFulUiGfz4vmZDwe4/DwUMADMyan04l2u4319XUpk66srEgrPbNhAkyd7mg0R7FYRCKRwMHBgWg2DAYD4vG4nLvhcIjt7W3s7e0da5UnKKULND2huHnNzs7KtRgMjhzXr1y5IqOL5ubmhJXiKBmdTifmo8xeCdYI/Lj4sxSXzWZl7p1arZa5nxRwT09Py3k1Go1YWlpCp9PBM888g1KpJCajFM+zlEatCU0vDYajeYtkMCgk5/mni/nVq1cxPz+PEydOYHd3V64zpyKwrMt7RCkEJ9MJQDYcapzo22Y2m2UkDABhXXnfnDhxAnNzcygWi1hbW5PuNpVKhXg8LkCOzFEsFoPH4xHjVrKiZAoMBgOSySQuXboEr9croHR/fx/A9fI3s312+Nntdtxzzz2SHJDhaLfb0nV4cHAg7DATInalUihNNoKNKBSqs9QDHDGjdrsd8/PziEaj8Hq9skmxy492H51OB5ubmwgGg+KJxlE03Bjtdrtcd05UoASArK/BYMDCwoIItjkjUtkAwvuGwIiSAZabgOvlGDbLEGSRrVF2AHLT5+upV6PGk6CQrJ6SSacBNZ3nCfII/IxGI+bm5nDt2jVhkarVqoBXgkl2EtMMlfchj53HS4kCJ4qQ1eb35Ngr2n8cHh4KQ8ZrzmPn+WLpmiVyPu985pk08B5RGrpSr8l7jAyvRnM0uqdSqSCZTIrFBgERTYvJUjFhYWd9o9EQbRoNeFmeZUMDPepot8PEn80q3EM4SYTNBsCR5pHPht1uR6fTEe0lm6LYvMJ7kXpjEgG8hmSJb3RMSpDH46YGYLu7u2i321hYWEAoFJLaNxdNzpLjwOdQKIRwOCxz9wyGo6HZsVhMym3cEBOJhIga2VGl0+lEF8RFlg89N0FS6hR122w2XLhwAc888ww2NzfFa4ddMtQoEMBxNApHjig9hzgehZsZ3cvJTnHhJk3NrkcuoHxQr127hkceeQTb29uYm5sTT5p6vY7d3V34/X7EYjGh6zn0m7qRdrst35/l1uHwaAYfx5bQV+nw8PCY/4zSxoGLqs1mE+E2By3TF4cZNS0paA2xtLQkmTuZGC5AwPVBxmzZZuelx+NBOBxGq9US8MjskqUigiR+NsFTKBSSzZwaM41GIzonut9Tz5ZKpeTnHBvTbrePjcrq9/viL0ZBNDeL8XiMXC6HRCIhrensgCNzoSz9sjOz0+mgUCigVCphYWEBBoNBmBiWhTmQOB6PIx6Py3w8MgNm89Fw+lgsJmwWy0cshfCz0+m0bFAEXPRa4v3JIdCcDkD3cKVAmPcbGQsyucrmGCY3Wq1WmkDIHJtMJkxNTUGtVsu5X1paknJ1MBhEu93G3t6ejLOKRqMAIKCd5UeOvAkGg+Jcz0SDmhwOl6f2i93FSkNPCurJfALXNwxuxMpyJIEn1xulDxXBA3+fmzfvRbJiZGT4PgDEGoYAhZYbPJdOpxMul0u8Bnl9l5aWpPRvMBhQqVREa0bQo/RN5PWnXIPMpxL88flXat/I5Dz7fJDtVdq3KJkjrktkd8gYUovKRJpmvuPxkRFtPp8XD0Syv2xeYsJnsVjk/VqtljC1ZKvy+TxKpRKWl5fl3KTTaWEQySzT4ojNP2TPeO9z31Fq0Cgt8Xq9Ati5Pmq1Wng8HkxNTR1jrXk/cW8j88095Nnzdidx4+OmBmAUSPNm401OtsZms+G1r32tiH6ZEbTbbaTTadHQMDtstVqirWEJz2AwiHiXOhAuhFx4R6ORLJbsqGE2SHaDmVS5XJZuQ26KrM37fD5Eo1HxC2PmRoNSZqvUtuzv78Pr9WJ+fh4LCwsIh8PI5XI4d+4cDAYDQqGQMCfNZhMbGxsoFou4du0aPv/5z0Or1eKWW245NrOMWgp+D2oHZmZmRLtAXRfLBTSLnJ6eFnfvZDKJWq0mbKKya6pYLEpXFjd7lni58NAMloss3e3JaITDYdGfsUVfqZmo1Wqy+VCgTibsxIkTUj7lcdClnnoYMkLcBPhzjjICrouQNZqjMTy0CKAo2+12Y3NzU+Y40jGd43moWdNoNIjFYmg2m9jZ2cHa2pr4/ni9XoRCIWFrtra24HA4sLS0hOnpaYRCIfGdWltbQyKRwMLCAmZmZjAYDI7pHev1ungvjUYj7O/vC4NFwTQNXVkGtNvtiEQiUmJhOZYLeqVSgcPhEBaN3bZkgJXlKaPRKN1y7FTj5l0sFqXkpDShpa6TDSkEKRTqUxNWr9fFhVypURwOh9KoMBqNBERnMhkZ4s0ZrNTskNmhfQBnSZIhTSQSWFtbQ6vVkgSGGx11WQQgtDtRgiUCKJa0eM+T4RoOh6JdUvpNKUeYKUEYG2AoM2CCRHDCEhTLWSwbDgaDYyPEpqamsLOzIwbEGo1GfMjIuik7EgHImsdjIUBiJYEsIhNNJoo0oOX78BwwgeRn8vjIehFQcZ3n/UNHea7N9FHk7zDJox+bspyv9H0kKNLpdDJDlmw4/53nlPcxfe4uX74sz7dGo5HPZ0LF78H7l0a+AGQfYRMVqwvcR9iFDxzNWqX5M6d5kEkeDAaivVSr1TJqjcn+JF5dcVMDMGqsaA1QKpVEvKnsXOJDznZzuhdzfhhFmewW5INMDRLp4Xq9LloSZkQs7VHDQk+dbreL7e3tY2MiOB+PImNqZLgwzs3NwWQy4eDgQIbDki0j20YAw82AA4OTySQuXLiAVquFqakpYT7K5TK2t7dlYC/LaXfccYd0SlG70esdDWFW+tyQRaPRITNsgh6aB8ZiMQFA9KXhxkEKn8xMMpmUEgA3GY5yIZNiMplkcacZKfVELNXwmvE8er1e+Hw+AUuFQgHA0fSCVqslcxrZsu10OkU/UiwWpbuQTREsbfB8DAYDrK+vy3Xh4kkTRZ5PjpThwkfgyjKEXq+X2Y0qlQqHh4cIBoM4deoUTCaTlKkJ2tmhS5EuS+dqtVq+Izcd6tG8Xq9YNTQaDRnCfXBwcMw3igwOkwi/33/M9JIJA5MXdqNxjqjf7xfhuk6nQzgclgSnWCxibm4OHo9Hykt8Bsle0biYwmdm6eVyWZpnyMCSbWHzAEtVLCOn02l53tXqo/FKFy5cwMLCAqxWqwB8r9eLSCQi3mBOpxPxeFyAaiqVOqY5YhcbyzsESslkUuZF8tkgCCK7yWeAzA6AY00evIe50fLPZHT4eyzFMZEBIK8hmOL5JVhWJogErvyPjStklilEpwaUwI/3KwEEtUsc68a/s6ypVqulE5JgjAmbEvTxuxMsttttHB4eyjFrtVoxqO12uzJOiqw5zx/BENkflhR5zqi1ZEmf3Y88V0qrBp5r5fpHHRabBri/sPTHrs5cLod0Oi3d6wCOsVJKRpgNQBxrxQoH10jgCJCxk52MGfcjlnjJJhKM8vpMTU3BbreLFQaZfN6Lk3j1xE0NwKjd4AZI9oFDmTmbj50sZGOYodhsNtnAuSEos81qtYpkMolMJiPt9mRVKHhlCZOi3Hg8jnq9jkQiIVoZzlCjrxdBDDUD1WpVuqdSqRQSiQQAHAMhBIksm7F1ut1uY3NzU8pJDocDg8EAa2trYqLYbDbFlJYZaCQSkbEW7FQiqKS2iItCuVxGoVA4tvj7/X6xgiA44+t4jlnOo8cN7RoovuW5JKhkOclms8nvN5tNZDIZofI5BoQgaH9/H7lcTjSAHHxNs8lkMikdW8yilUPW2ehQKBRkc+ZoELIaysyRwI8bDgEiz9VgMEA2m5XyEztbC4UC1tfXxcOL8+Q4GYDDk9/0pjchHo8jn8/L+9GLit8PgLSz09eMx+H3+1GpVHDhwgXMzMxIkwWBd61WEz+tQCAgUw/IBvH80EqAGyUXeN6zBoMBq6urmJubEyDOTc1sNosHW61Wk019MDjy/ZqdnYXFYkGj0ZByEmf0UQtH/ZqyZMXNisdCMMGNi8BnMBjAZDKhXq9jZ2dHGlcqlQo2NzcRi8UwPT0tz3Eul5NnkOzq+vr6se7YmZkZsZbg+eQILGVnsBIo8VkHIAwQN0ulFonrD+0NCGZVKpWU7MmiKRuHCECVDUDKkhQAKXnxNbzPKMpW+o6Nx2OZ4kHwTUaGrApL6ARRfGaV3ZYEPyzlsZzJUiDvV55bPksErPyuTBjJjvIZY6mVf1eWhglI+H35WVzfyQArtVcEqLwmXP9VKpUkhuxiZVJWr9elLMrXEJQqz6dWqz3GYHNwOb0N2dGrvN78rgTxZLg8Hs8xn0meO7JfrBAQGBPE8j5+NWjAJnE8bmoAls1mpWvE5/NJOYK1eXoxxeNx+Xd6gCnbv7nIVCoV0WMpywDKG50izcFgIPoQBhkRjklRenp1u11sbW2JFoIsXavVgtlsRjgcxnA4FLE/7RVYNuLCxeyMomSHw4GZmRkBfuvr69ja2pKSC12/Keyv1WrIZrNwu93HxNMU3tKzjHqy8XgsCyHNJDl/jB5ibBjI5/PiY5XJZCQ71uv1CAaDcDqd4pFFN3+yFel0Gna7XRZH+tcQINFIkcCD3XcOhwOpVAqXLl0SqwG/3y+jT8iycIEiUKDVBfVUvBeGwyEWFxfhdDrFAZxl1kwmA7X6aF5kv9/H7u6udARyzqFy/BQ7qAhQAYiPD4W/HFFEl/rbb78dkUhERqfw98fjsWjVyCKwlEnrCXYGcpMks2a324Vx5WaUSCTED4pdf89mHJRlb5YSgSPmgV1xZNdoU0LbDiYdHNdE+4PLly+j3W5jenoaVqsV7XYbqVQK+/v7MBqNmJ2dxalTp0SMTWNPsr4AROvFTjA2GhBUEBCxTb/RaAgbCUCMk8nacu4pNTtutxvRaFTmxFIDurS0JBY1er0eV69elc5k2jcoGSeW1JSdj2SEWIpsNBrCYnJCgnI6g7LR59nlQ95nvLcIZFhuUuoU+Tre4wSzXN+UzB1fp7RWYPmTjvkEkvToo60Ij4PaKT7LBFdcW7mG8jNZTuRzqARwBKosN1JSQvNhvp7nh8CM4F2ZSBAUUWNINpIlP6WmlyCRiSK1VGSyUqmUGPcCENNkXj+WA6kHq1QqAoSU+wwBNa8F9wbqWpWJB8GaMrkFIOeO51IJyLmm87WTePXETQ3Ams0mfD4fXC6XZCFkqmZnZ7G6ugqtVoudnR2ZqcibmGwIs1TW8pXZIwARrDKjoMjU6/XKg0iGh2UQuiuzLEUhLctzyg7A8XiMcDgMp9OJvb09tFotnD17FrFYDMlkUmZOApCFmENt/X6/lBqvXr2Kzc1N5HI5cVumAJY6BJ1OJ40FSjqdQLFer2Nvbw8q1ZEPTSAQkMYGLlpc0DlbkA0B29vbODw8lPIsxbPUK1EjceHCBRHeLyws4OTJk4hEIsJsKdvCaQ/BMgIZQGbgJpMJgUAA6XRazAYJ9hYXF8WLjCCF7CgXXY6W4vXnotZsNhEOh6Uk1Ww2MRqNZOgyOztZPuLYHS6UFPQDR6Cc+hmv1ysZPb2wGo2G3Hv5fB4XLlzA3Nwc/H4/Dg8P5V5iCYijsKjTs9lswmT4/X7ZGDQaDaanp6Wrb2dnRzZVThjgddrc3MRodDQI3GAwCDglmKAWjLoqNgesra2h1+vhxIkTwhhzhBHZuOFwiN3dXUxNTUln7u7uLvb398UKhLrMcrl8bKwPjVFPnTqF8fi4z9+z9UCNRgPpdFo0gr1eT3SbZA24EReLRezt7cmzSJaQGz2bPTQajZTF+v0+zp07h+npaQSDQRnVs7m5iY2NDXS7XZw8eVI2ZDJUBDwsn/JzlGJ5brQU/pNB5nWlzoubKBkgniN22ZGZ5HrDsia/O9kZJnRc69hgQPDAkhXNkwkguPErG2/I0rACQcaJLBSPnYCGpWS+L9dapQUHkwSuUTxPBCi8h3kMBGsEHrRfoBUDzw+fI4I3smF8b74f7wEeE9fe0WgkTvM8PgLEQqEgpUaXyyWTMagpbLfbssaQ8ex2u7h27ZokVtSa0tCZOkJeRzYc0b6FCTXvLY/HI6CT4JkdzQRiNzomXZDH46YGYBSWZrNZWYApLF9ZWcH29jauXLmCWq0mpQqKH/lA0cyPYyaUZQSCGC4i3GBJL/NhdDgcMrSXXUJknagX4fuxnk/BPw0+8/k8+v0+Zmdn4XA4ZBAryzFcOABIhyU78q5du4ZLly4JE8j2bWZ9nFnIhYxTAarVKnK5HLLZrHSgsbU5Ho/D7/ejVCphc3MT1WpVsv9arYaDgwP0+30ZUswNgJ1tTqdTypss0QKQcUGpVArr6+vY3d3FfffdJ+Nc8vm8jHxxu90IBoMwGo0ydJeaC+C6547P55MuwmKxCJfLhWg0ir29PdRqNRF708+JAnAycMoZdVqtFslkEuPxWMYHsVuKQ9nJLo1GI2HwuHgTINH4lEJxlkkCgQCi0SiSySTW19eFMeRiycV2cXERLpcLjzzyCJLJJFqtFmZmZuQYms2mNCbQvy4Wix0zplSr1dje3pZr22w2xQZCr78+y5GJAt/bZrPJHEfecwRiLNHabDZkMhmEw2HxaqMmhhYEAEQTViwWxUJEq9WiUChIlxrF2kxIOp2OPLNMDKxWq+jmOKBYWbIDINrO0WgkExVCoZAYGvNZBY60jWtra5ifnxcrGrLiBBUcaZbJZGC32+U8Up8ZjUah1WrFq4/PvlKMToBIFpV6TiXo5zUjmCcAoP8g9TscHURmhawVN2mloJ3JJHV7XNMI3JQ6KqUPHwERdYDU63HsFHWJPCaW8dm0xO/Kf1d2iRJgKcuyAI4xZzwHlErw5+yQ5LNPsMd7lIlpr9eDx+M5Vv5kM5WygYNJHAfB8z3JgLHEquxqVXapssGFJqw8p3q9XrosuUex5MjjYWWlVqthe3tbSvL1el3Mkrm/0PtOpVLJXsASLV/DOZH0BWRjGnDEqtJ3b3t7+yvZbifxMsRND8Dobp5OpxGJRHD//ffDYrHgySefxD//8z9DpVKJPgOAzKIjGGKmQKGlslauzMZIfXOjrNVqwiyl02lsbm7Kxm6z2WQ0CjMUZlzK9+cCTcHx8vKylBETiQTUarVknNTKUJCu0+nEOqBer4tYn99LKbgdDAayObBbsNfrwWq1ypBYZmHT09Pix7Wzs4ONjQ1sbW1hOBzilltukaYE4MhPigtIOBxGIBCQLJQbGcEAKXebzSaAYX9/H7u7u7BYLFhZWRHfrlarhWQyKfo1liHZkcdOO2Z3XGT29/fFTmQwGMj8Q27gymyZZZ/V1VXZLMrlstiEHB4eiv/WwcEBEokE5ufnEQwGZcg2DTefzawqO6sODg4wPz8vHYacQzc9PY18Po+9vT0xu/V6vdJpBhxNerDb7Xj00UeF3eF0AGbWKpVKOhY7nQ7C4TBsNhueeOIJ7O7uwuVyyRQHMhVWqxWxWEwAFQA5H4uLiyiXy0in0wAgHkIctxIOh4UJm52dFbE+NySKgNkQMxqNsLy8LF5oZrNZRPlkNJWGu2x2UYruAUiXo1Icz+tFrzi1Wi3mrBcuXBB7DK/XK2U4n88nz1ylUhG7FzbgkBGjdorfcXt7GxaLBcFgUJI5i8WCWCwmmlOWTGl8zA5afh+CJ5YJCc6oyaK3E7VABADccFlmIsihVxtZZ3YzM5TlNbKZAGT9U9ogcKQSh0cTYNH6RNmJaLfbUSwWxfFeWf6iMJ7AmCUxituV34WsEz+LTCXXC+U4KuC6dx6fVVYwWM5VdnJz7SbQ4dBxtVotJWU+NyyP056F78fmE0pUlJYfysRK2fygZDzZeEKwRFCn1KnxepLdpWSm0Wjg8PAQdrtdxmfR2244HOLg4ED2G+A4085/YyKxv7+Pzc1NEfhP4tUTNzUAazabODg4wM7ODl73utfh7rvvxmAwwCOPPIKLFy/KgkntFo0L+RDwIaF+RWmoqsxk+XdmMsqHjbPhLBYLHA6HDCZuNpsyE4wLNo0DaUDpcDiwuLgowvFMJoOtrS10u12Z8aUUvlNgSbaPY4W42ZO6ZrZMHRIAYd0ajYYInrmQUnvldrvFbJSaLq1Wi7m5ORkcTcEndREsodHzjIsyvdaMRuOxzZhlD+rKOMyc8ymtViumpqZgtVplUDOF+GQklM7f/HylL5SynGAymaQ05na7RchP/QQ1N1yoOp0ObrvtNmi1WhwcHMDn8+G1r32tlGatViuCwaDcO9VqVTJvrVYLu90uC+BwOMTVq1cBHDF/nDmZyWQQjUZx6623IhQKiXs/m0h6vR6SyaTMJp2fnxfWlc0QlUpFGgVsNhvy+TxyuRymp6cRjUaFzeIGaLPZcPr0abnX2bnXaDSQSCSwtbUFt9uNWCwmDBkAKXWORiPk83kUCgW43W40m02xm6B4vtFowOfzwev1wmw249y5c7h48SKGw6H8O0veLpdLWGXq+XgNqbuifovAjSUeMnTUUwaDQfj9/mMC6rvuukuaZzqdjrjUD4dHfnXValVKQ2xGSCQS2NnZwfz8vIybIQgYDAYy99XtdqNUKomzPA11CQK4EfJY2GmoBGPKbmsyXUoDWgrmCUyY+BGEkylUPv8A5BiUbNezDV4BCBNHcM0GI3bhcrMmOxuJRKRRhjrQZ8+bJGDmusW1T1l2UnZN0oaDLCjZPLJcZJRYMuSaw+ebpUUCOmW3KDur6S7PpIglWq4NSkkJjWZ1Op3Y7XBtZFOIVqsVFtVgMEipnsekXLOp3yLwJUBUsrb0YiuXy1CpVNJBzTWThth8JlgW5ufxPfg5Xq9XLCiq1Sp2d3exsbGBRqMh9jk3MiYlyONxUwOwRCKB8XiMd7zjHfi6r/s6FAoF/OVf/iU6nQ7OnDkjCysHUVPwSh0LNTBcKPjwKhddlg+p96EInhkNs0O32y3arv39fVQqFdFNEdRRHKvRaBCNRnHXXXchFAphb28PX/jCF1CtVuH1enHy5Ekp5VB8TJ8y6qHYfs/jZmmo3W4fc/ZmRsTOHWZSnU5H9HOctwccPcz5fB61Wg0ajUbAF0XaBHUs+1F/lc1mBcCy7ZzsClkoAJI9UkxK4DcajbC+vo7x+MiNnAsuzzuvdzgclrIXyzWj0Uhc92lyarPZxOLDbDbLUHJmlASbPCY2I5D5OX36NHw+HwqFAkKhEG677TYpAeh0OlQqFezs7MgiaDAY5FySEWKHbSaTkdIz9XLb29s4efKkaIo44Js2DLVaDel0GrFYDI1GA5ubm8hmszLaRSlu9ng8smgnEgkBsRaLBevr69jZ2UEwGITP50M4HBa/Ks45ZWPJhQsX4HA4pDOTmzSbBmhWGovFYDKZZL4dy+b0h1KpjsZMsTMukUigWCxK0kF2gswXQQCB0srKioiOed+wbM0ZemQQ0+k0CoUCIpEI3G43VKqj8Ty0mclkMkgmkwAgyYvVasXp06dlth8bPIxGo1wHo9GIcDgsJaXFxUUx/OTILYJZAFIO4/1OJorPIu8RzjFUlhn53JJ5YYcfALlHuS51u0dG0fTNGo1GAl6UG72yM1L5vsoSIDcullZ5z1Irqlxj6JFWqVRgt9tlPeD7EHAwUSWLy8oDEyIlqwlAyrBcJ5nwMthUweoBgRaPlywS/69selCycTQwJUDms0jWkeyU3+8XmcF4PJafExwzuaLgn5o04HrXOkez8fh4L/CZpYyFx0tGk+PQrl27hmKxKN587BxnwsK1mdYYrMbodDrMz88DOEqcOKuW98pEhP/qi5sagLEsdsstt+DatWs4f/68ZPoEFOl0WlzhmW0oTQmVfjtcJAAc076whEgwQyGlsn3cYrGg0+lIJhMIBKRTke/B9zEYDFK+On/+vGisaL2QTqdFY8SHTmmzwdIaNWQ0W6VvDBcgZsm03KBHEkFKNpuV88HyA3Dkg8TskaUTglOn04n5+XmEw+FjmhTgqExEQS+dpZXnlACUAldle7vb7cb29jZyuRzm5uYks+fsPTKEiURCyrBc2EajkWTKZJmog8hkMigUClI+oCh/MBiIfsLr9cJkMuHUqVPijeR2uyUrfuyxx7C6ugqVSoWdnR0Z9xQMBsU5mxpCgjMuzGQAAUjXIWNjYwN7e3sIh8Pyfbnx9vt9McH0eDyYnp5Gr9cTTzmNRiPvx8WXpQsCYKXp4+7uLjweD86ePXvMGNdkMomTN138WcomWxWJRGRzpraGYnB6GR0eHmJ/fx9+vx9er1f8nWiMWiwWcXBwgEAggOnpaTQaDaRSKQHIynuDQmreW263W2QEV65cQT6fx2233SYlIPp2kZ1iJxxwpIHhc8Bkand3V1gcAhR2DE9NTWFzcxOZTEaeR+CoFHv27Fnk83kB+BwPQ2aI7ByfBZbKCJK48bLTjSBFacastK4g88PyMKUNZED5/cko0XZA2VjE6wbgOQCM/2fTjnJqAUt9yi5Ii8VyzESX3cTK7kKj0XgMaBFkcD3iWqEsLSqrDUp7CN4LZI34HkogpdSZKVklVjq4PhB0ERATmLGrOJVKHdNmEXwpwTDvFd7/vKb8jhqNBsViUZ5vyi/4fZWdphqNRvS6nEfKUvDBwQHUarX413GiCxPXUqkkvpNk+NiQpNVqUSwWkUqlUC6XjzF2HN81iVdP3NQAbGZmBouLiwKy5ubmYLFYUK1WZeh1u90WESbLZOPxke+UElyxY4Zje9jiTq0Du1LYIcTSDgERxapKQXqhUJDMjRoamqR6PB5sbW0hkUgIsKIJKEuNXMCA615ibPnP5XLodDoy/65Wq8lGQGqdiweBBt222e3HTX44HIo3WrVaRbFYFG1YuVyWxUGlUgnTwjKVVqsVY0s69JM+V3b70PuLwnzq0VwuF+LxuCzAXq8Xy8vL4lzNMhIbB9gwwcyR4IziVJrcarVamVwwHA5x+vRpzMzMoFAowGw2iynpYDCQMi3ZFnajLi0tYWFhAYlEAvl8Xty2ubn4fD5hVTggnCUnampYsiVIZ3clu3ZZkopEItDpdNIFZbfbxWzVbDZjdXUVnU4Hh4eH4mulHEVE9owdmdwIbTYb7rrrLgyHQ+n6I7NFgEXBMAEq73Vm9ErDXBrZUkDc7/dlfmcikRBAyKw8GAxieXkZ+Xwe58+fB3DEilCzSF8ylpHUajVyuZz4Uen1enHZ93g8WFhYwMHBAba2tjA/P4/Tp0/LpkrQSXsWJlpkeGigSd1mMplEqVTCcDgUsT6Zje3tbSSTSSl389iomyyXy8K8kCXifwQIHEhNgKwc6QRAkiqWIakxpS7OarVKIkN/NIrMQ6GQABiW+wjq+PsEdQQ1wPXOQwIaBoEUrT44+Dmbzcr6SVaZQJZaNwDHXO6Vjv78PCZALP3zNWQ+WRZUJsZMBpR2F0wACMD4dybQZP7Ynch1heeSx6AEh/RwHI1GYvBLI28eA/V9ylIkWSweX7ValWeE0xmY/BHIUv9FbbLRaBQmlueQTVwAjjGCw+FQgD0TW1Y8CDL5PFEjx6SaMpxJvLripgZgd911l2xCLNWk02mUSiV56LiQKUcB0ZOFgMliscDpdAprotwcgeOiVWZJ4/EYsVgMkUgEfr9fAEalUhHNBzucer2jURnRaFQGQJ8/fx7Xrl0DADEqNBqNiEajUsbiCB2LxQK3231sk6OBJjfEQqEgn6NWq4V94wLINvZOpyOlIwDCFhGsMRsjM8HMlyUpjjgiyzAajVCr1UTH4/f7j30+TTLT6TTy+bx4sHHRIi3OCQPMTKkjYqnB7/fLSBx+LhdpZswEY/l8Hru7u1IuJRhhKaNSqchMyIODA9TrdUQiEZk7yO7RbDaL2dlZrKysiFM+gTo734CjkhO1bMygW62WmNXSNDYYDEKlOnK+52tZHueiThYiGo3C5/Nha2sLDz/8MBYWFiS73dzclFmYzH41Go00cAwGA9GSUJNGFvXq1atwOp0y6/TEiRPSdHB4eAiNRiMla2VphCVfek9Fo1EZFE6Qe+LECTidToTDYQHsHONFfR83V5VKhWg0imKxKICQGxwA0e2wQ4wCfCYhfM6azSa8Xq9sLmr10XSAra0tYZqV5qKtVguBQACBQACJRAJPPvkkHnvsMdRqNbzmNa+RZ2p6ehrb29tYX18XPSYZP/qLlUolYZ04+YBglfc+Hf65IdLPD7jOsgOQzk+WPAkImPSR+eJmy3WJgI/CfYKoVqslXcvP1mHxfFM/BQAWi0XE5gShfE+l7yCTN2WXIJkxWmmwrMjvyuMlg0sxvvJaU8hOVk+ppyJjqNSDPTsIbpTyEa4lRqNRmkl4XxLAsQxPzR0TVVqPaLVHkxgIdPgMUIpAkMPOSmUXPdlJZfc7u9mZJFcqFVSrVen4BSBlefqGMdEk68amIkoumGiwq5hJB9cVMpUTDdirL25qABaJRI618FutVhlY3Wg0UCqVRPDKzjoyXzQ/9fv9Qs0SwFFUr2y5Vgr0B4MBAoEATp8+LR1dXNzpvUXGgBoAblrNZhOf//znxcSROiyW4YbDIUqlEkqlkpQuNBqNHLvBcDRnkA83O+GU9D1wvdyg9C+jx43dbpfvbLfb5RyUSiWZj0kfIrZvM3NktxYXFeUmwnNA1szpdMJqtcJsNoutBccEcRGlZoIAJ5/PS3mQgDgYDAo9z4yS5rpk1fR6vZjRUuzMDQI4KkVzk2YGv7CwIOfcaDRifn4eOp0O6+vrSCaTSCaT6HQ6WFlZkdJVp9PBqVOnEAwGsbm5KZumx+OR+5Dmu9TJqNVH7vFf+MIXZKwOgT69f+iVxVmFVqsVhUIB165dwz/8wz9ga2sL9913H2677Tbs7OzIeBUyXzRcnZmZkTIly1FKD6NisYipqSnkcjk88sgj2NjYwN133y3sSb1eRzgcxvz8PC5duoREIiEbL883B5lzdBYTgMPDQ/T7ffEbMhgMMl5I6QWVz+dhNBrFEoNsJSdT1Ot1bG1toV6vI5PJiNkxtUVmsxnz8/MYj8dIpVLY2trC9PS0gAZ2MubzeVy5cgWpVAqnT58+ZnLa7XaFPT537hy2t7ehVqtxxx13wOFwwOl0YnZ2Voah06aCQIs+VBSME1SSMWGpjpo7JipK01D+nlKvSWsB6gapmwsGg/B4POKZls1m0el0pMOWI3oIwJRrgrKRh8BHuW6SoeMECHb8kf0kMKBWj9omJWNDkE89G/VpwHUZBxMnJrEEQ2R5uLbQFJpj0JQTE/j8KgEkS9ZkHnmOCayURrVMnskAs/mF6xdBHIEey4VkMJV7DYEnGzyUrBP1pzzvPEay37T44JBz6gKVgJrJMAEjWWk2D+3s7KBWq8kaq7Tz4LXhc+h2uyXpnsSrJ25qAMYZkIPBQG5kdk0927Sw2z0a1bO4uCh6J5ZK6EnEjSMYDCIajYoIXjkSxWq1YmZmBoFAAC6XC7lcDvv7+2IzoRRfEriZTCZMT0+jXC5jc3MTyWQSbrcbU1NTx7L/w8NDMffj4gdczxyDwSD6/b4MD1eK+tk2r2zZZqcnF3ylvsNms8l7sPOO1DU3bZVKJeOTKpWKdCRSQMySZ7lcRiAQwO233w6v14tKpSLeWSwb6fV6af1nxqJSHRkYEmjZ7XYRcB8cHODMmTNYXl5Gu90WPzed7mheIXB90Dk7nsxmsww0Z2NFLBZDPB4XiwJ2h1LI7/P5pLwAHPlWEdhkMhnxPGOphO3hkUhEdFL0WmNnH8EpmzR4DZUAmnYR7HwiMA4Gg2i1Wjh37hySyST6/T4WFxexs7ODnZ0dnDp1CqdPnxZRLrVbSgNOlhJbrZZs4gQ5AI597lNPPYVarYZTp07B6XRifX0dNpsNZ86cQTQahdPpFAaEMzzZdGGz2RAIBDA/Py8lN+XYIt7bHOFDpuPg4EDu0UAggMHgaHzT7u6uWDLE4/FjdiQs9dIOhkPM+b4HBwdiUaI05NXr9chms7hw4QKmp6cRCASOCaltNhvuueceRKNR8Strt9sIh8PSQZ1IJKTbjawtm3uo6eMzQ88qXgPKIMjI00aAIIilYXqXUWPGNYtefalUSjScBCkWi0WaYAKBAKxWq9hs9Ho91Ot1AQ9KzSXZfN4/PBdknPkfmZdGo3GsHM3vWCwWkc/nxe+Q9zr1Yfx9Am+lhkrpBE8LIJbdqQVkdzF/j6U7pT8YWSiltQXvO4ItZflTqYejpq5arQqzyFIrj4n+aAcHB2I9o/TaI8uUzWZl8D2ZT2UCTNaL2jr+nVILNkYRaDHx5R7A17J6QKkIy9fKNYbNOfx8lnozmcxLuwFP4iuOmxqAfepTn0KxWJRBslwEer0egsEgTpw4Ifoebvrs5jo4OJAOEnZo+Xw+xGIx6HQ60Yaw5AYc3eChUEjGBq2vr+Pq1asyvJn0r5KKd7lcmJ+fh1arxZUrV1AqlaSDjF0xXPAIlCgo5aLo9XqPASSKk5Umi8rSXL/fl/dVdggREDLLYwciy5v8vvT8oX4lGo3C5XIJVc5MiqVGLpTz8/OIRCJSgqpUKjg8PESj0RCWje/DjPLpp5/GwsICotEowuEwgKOSHjUL3Hgpnuf4o/F4LIue1+tFLBZDp9PB+vq6+GrR9DQUCsmGVKlUBPAp2/2ZlWazWeTzedECFgoF0SgpRwgFg0HxLWPHktPplO7IQqEgZTSObqI4mB2z4XBYrg+1YzTCJdChXxu7GFnK7vV62N/fP9aGT00ImUK20VMLx0SCm88dd9wBp9MpZUS3241MJoMrV64cG7E0MzODSCSCqakpZLNZ7O3t4fDwEJlMBtlsFjs7O+KHxkSh3W5LN/DMzAx0Op10J66srIjWicCA9ybF0OFwGD6fDz6fT0pIyvIFy200JAUAl8slzy5ZFY/HI7YZLKlyk6VOUa1WY3p6WphsdqCSVVpYWJA1o9VqyWB06rfIZlF/xHIvS6SNRkP0QWTIyAKRLeOxkdlhEgYcMUhsrCD74vV6ZUQVGVSXywWXyyUTG9g9TUBBZlkJmPgMULvl8/lkXBlZPHZeE3hw3BXLfOwuph6R15ZAgAOtlYwX2SUyTEyICXCVzDpBBAABVWSU2BnKNRfAMY0rz6PSDZ/7AX+PthPKcj6ZwWaziUQiITotAh6lrpaAnsCbM4iVHZ68jmSpbDYbwuGwyBfK5bI0cPD88JpRDkI2kswvNY16vV60eyypAtf1fv3+0bxTaj1vZExKkMfjpgZgV65cET+aw8ND6PV6BAIBLC0t4fTp00KHt9ttFAoFGYHCzkVufoFAALFYDOFwGPV6HQcHBxgOh6JfobB3fn4eTqdTWoU3NjaOmWFykeGCRB0PPYToqcSsjgwVRa5cLJT6D5VKhXQ6LYJ2lUqF6enpYw8rF1JS8GyVJ8iiZQYzVmb2XMTZGUk7BtLyvV5P5p2FQiEEg0EBPcpFk4sCR6kwS2fpl/ooXpulpSV0Oh3RulAnRlE7yxPZbFZ0NtTvdbtd0d6xE9LhcAgDCkDKZOPxWEo3+Xwem5ub2N/fh8lkQiwWw5kzZ2CxWKRUrVKpkM1mkUgkZDMnS8EM3WAwyGKmVqths9lkADaBGjuc6KfGRhClPo8bDUtubNZg5yTtIQjCgsGgjBA6PDwUaxCyTtzYyCQGAgFppqhUKqJ7on8YGZTFxUXk83kZY7O8vCydpOw4TSQSiEajiMfjGI/HwiKxy5L3ijKZILinxo3gg0DY4/EIG8NuM4/HA71ej/39fWxvb4vtBsd+sVNPqTHic8Iyr9JjTpmcKBkR6ouoD6SnmMFggNPphMfjkfvA4/HA6/WKjo/6PrKf7EhTenWxO5j+a2q1Go1GAzs7O8fMVckOAddnERKI8ZlWMl4ENixbkukjY5TNZqX0Ty0UG3oI3BqNhnhCMZFSnheCU4JlevtlMhnpDna5XAJWyZCx25VAmqCU65NS9E+NJf/Me77RaKBWqwmAUoIwvo/Sz4wsFY8fgLD8wPWuRZ5LHgPvV/6cDQfUfCkbCDhyiPccrXPG46NxXFyDaISqtMbhdVOWWQGIfx/lDyqVSqQg1JlxsLderxcbJHbQs1zMZjECep4j6uzIyJIxez7t3CRubNzUAIz0cKVSgc/nw+nTp7G6uopgMCgO+YVCAcViUYTP1Oswc2ZnGkdB9Pt9zM/Pw+fzoV6vi85naWkJdrsdW1tbuHz5MvL5vIjFqa1iKYjDsWl0mkqlxPqBlDszAT4kym4ilmE0Go2UwpjtsIxKzRZnXPI9+N4cDMtFjN2DZEQoSqXuhwafSnNBvV6PVCoFh8OBQCAg2XG5XH5OZsdskmVBv9+PWCwmhq4s5547dw7xeBwzMzNiF3L16lX5PAqLyeLQHoIjY6gL6ff7CAaDsjkrN4LxeIz9/X0p47Bsws47sqCpVArz8/MytYALdK1Ww+7uLvR6vTA/LFdzo+ZGwE2A+jWWqEOhEKxWK7LZrHRe+nw+MYMlc9bpdOSzh8Oh+FrRvXtxcRFms1nuxe3tbdhsNiwvL8Pv90t5ipoUZrzU8S0sLIjFgtfrRTKZFCZHySwQfNPaIpfLiWi+WCyiVqthY2NDmErqzJSzOwkwZ2dnoVarZWQQN3a32y1dWvV6XY6PQMBqtWJ2dlbK1dR80Y6ErAU1QQQ5oVBINjN2oMbjcTFvJYBWWkDwmPiMUCNJdtrhcKBarQp4AiB2JRxjwwSq1WrB4XDIdeRGqGSt2FXHzZxMM1/PjZfrANcHXktluXk8HguLbLfbZRA8GT1qsej9xASAhqMstymF4rz3yYSxXE0wQwNhAh52XZJVJbNIEENWicmrUpjOdY73KnAEiijfoB6Oa6PyfCjlCzx3ZIG5/tGnLJ1Oy+eSveJ3VJYjOQuXTVMsWVK7SxBMYEYdK8GO0uGeLBUBOa0yCB6VWt16vY5EIiFJBb8vryGbKAiwlfo0Aj9lV6USYFFPxiY1HtskXl1xUwOwfr8PvV6PO++8E3feeadQuleuXBEvJmbz4/EYMzMzUkfnhkxdEADMzs5KyfLw8BDJZBJmsxmnTp2C2+1GIpHAtWvXUCqVZDgxFwC9Xi/lMZvNhl6vJ4O5lYNYgesiUjpt8zi73a5sBnyo+/2+jNuJxWIIBAIYjUbY29uTVnqdTodoNCrt0rR9IPAhG8WsjOUygkZqVWjASaBIJsVms8FmsyEYDMJsNmNra0v0WABkIwQg+ifqqzhwmuaR1WpV9FzRaFQYCzIJ8XgcwNFoHG6Q3BSXl5ext7eHXq+H7e1tYQS42TcaDUQiEQF+dFV3Op3SZcnyC7thqbtSWncsLy8L8Gi1WkgkEpL9cpPhZsBsPxgMQqfTIZ/PY2dnRxZUmtUOBgMph1qtVmH/stksRqMjB2ufzyddkB6PR17DUm4qlRI91tWrV8VugpYpZBWKxSIKhQKmpqZw1113weVyCcim51OpVBLAy823Wq3C5XKJLo5AHoB0o7Jrq1arCWPMci5FwzabTcqZu7u7wuAtLS1hcXERc3NzSCaTODw8xObmpgyUZ0JE3Rg3e4JUrVYrgmOW+rmhkQG12+0CsCk8Vm7iSnaAQIJMJp8DMtJkr7VarQAnt9stnWjsziXzoUyc+BlKEOVwOI7ZC7DkRQCmdE1XsnYAROdIEMDuVnZxs2SvtIzh+sT1rdfrHesK5lxUMm1KuxE+t9Sw+nw+sabh1BCyOwTZZOkikYisUQSqXPN4HQiynq2XZBWBr+H7E8Bw7eb5oRZSqZsCcCw55Pngd2OH47OBOMGo0gSXVYRkMimJAPW0bDrK5/MC8gm2KGHg2q4sKTMROzg4wLVr15DP5+UZ5nOmUqmE9WVyzf2iVCpJEkCZiTKZopyE+xI/dwLAXn1xUwOwxcVF3HvvvVhYWEC/38fGxgaSyaSU0ziE1O/3Y3Z2VrJMk8mE+fl50Xa1221p6d/a2sL+/j4ajQampqawvLyMXq+Hc+fOydw+ah+YpbF1PRQKYTAYYG9vTxYYtquPx2PRs1CDw1o+gRIXX2Y01IKxC8pgMMhAaZaXhsOhjGIh4CB443/UoeRyOYxGIwEd3MToL8SMHIBsJtwY6Q5OjZyyLEqQNDs7K9/R6/WKkeXKyoosavQIo5aKuhRm52azGclkEjs7O1ICy2azIginAz81W2TxDg4ORKMWi8Xg9/vF54kgMplMIpPJyKJOsb+Sjej1epiamhJ2aWdnB/v7+zAYDHLPUN+0ubkJAKJ7i0QiqFQqSCQSyGQysmA6HA6Uy2WkUikZ0E2wQDbHbrdLUwbtP6rVqszhpC6OrAcAYfeoVSPYnJubw8mTJ6FSqZDL5WRoN1lMlrkJ0lmSrdVquHLlyjHTSFpQcDF3uVwoFotIJpPo9Xo4ODiQY6YguVqtynf3er1QqY784yhMdzgcWFpakq7YbDYrncUcN7W7uyughUaZ7HJmJxgBMMth9PmjFcVoNJLj4AbF5wq4bgxK9oKbtrJDj5s2W/6V3lA0wWRiQ7DHjZyAWNkgw/fmZskNm2w4P5MMBo+Z9zvF25VKRRhXl8uFcDgs6xA/h5IEZZl/OBzC6/XKxk4tmtI3i8dLFpDdnwQNNAalZqzZbKJarSIUCsHn88Fms6FUKomND0EVcB0QkT0mI82kkHozHv+zta1cV/me1A+SxSKIJGgjq8/1h+CN/l0s8bPUqvQ5U+rxyKCxJE7dMO9Fat9YceD9xgYfsphcf1kWpD6OchmawLJETbkAAT2PmWVz/r7Sq45rDs8lrzElGjcyJhqw43FTA7C3vvWtCAQCSKVSsjDwYaVokrOxCLDm5uawsrIinTp0r//85z8vY2hcLhdWVlYQiURweHiIS5cuiV8RMxHg+hgNClA5coWbDGv3pIyXlpagVqtlcVJ6jXHR44LLxQmADMTN5XIy1oelFGbABFLValXKAMr5cXx4Dw4OpFuTOhpS/2xdJrBi6YWLcq1Ww+nTp8UqgbPTmCVS48OOPnaGkuVRtqaTbudCyetBkTc7r1SqIy+i7e1tGAwGBAIBRCIREbVz8+WGQSaEm3W32xWBKwEPBbQqlUoAET+fgI2u0qlUCuFwGFqtVs69x+OBz+fD4eEhqtUqksmkgGqfzweVSoWNjQ3s7+9jdXUV999/PwaDAXZ3d7G3tweXyyWbOl3gy+Uyrl69Cr1eL2a+ZCmsVquMC9JqteIlx45Sn88nA7D5nmTwqtWqdPmGw2GxjuB3YAcXnd9nZmZkvimBLsExE4F4PI5QKCTPGjc6NgHQEJebm7IJgO7/U1NTiEQiojdKJBJYW1uTMijLupVKRcTdZBvJenAIdjabhU6nw9zcHKampqDT6cQ3S+lfxftUKbLW6XTyLJGp4SBs2lbodLpjUw9YmlKpVNLAQX84u90um2W5XBY/LoIAk8l0rAOSmzzLU9yk+XNlyY0AkRszu/DoW+h0OhGNRo+BVCaJZLr4nKvVavl+ZJ+UXZHK7kQmm3xfAsjhcCi2HtTFjcdjYYSsVivcbrcwaUoBPO9/JkIEETwXSksGMqEswwHXS2xK8Mpjox6Kr2WSwjVMOfaJmlWXyyWsH8vV9F3ke9JEWlkOpM6Ox8fnhZ5bnHJA7zCWOfl88H53u91S0uW9SQBH4EkGlE1P1DIqG79ICNA7j40/ZPgn8eqKmxqA7e/vy5BklgCoDVGr1eJkzgfytttuk7Z3dj+eP38ely5dEj3X/Pw85ufnYbVacfXqVZw/f140T1xg2ApMJqxWq4m4lmUbCuG5UbBL78qVK8hms8c6EQEca59WdsaxzDAYDKT0o8ygKRpmxx0XRZYfCOycTqd0hnIoMUsJ1Acoyw8AZFElEwYclQapr0mlUshkMsKshUIhKT9xcSyVStje3kav1xNDTpZ8AMi18ng8YiYLQMSlFA0rAQWHrLPURgClFNly06RGhoaSLKOSxaFPVblcFm+uw8NDAEA+nxcdC0u4BJh6vR4rKytoNpvink3Wi51+qVQKoVAIoVBI7CtoVMvSZL1eh9PpFD2MzWbD1NQUnE6nmCyya5Klk1wuh+3tbYzHY8zPz4vRaLlcxs7Ojgxx9ng8mJubQ7/fl3+n+N/v94uoHAAuXrwIs9mMlZUV3HHHHf8/e38WG2l+Xvfjp8hicSdrX1nc915n04w0tkb2jOQ4cQDDMXyRiyzwhQEnAQzbMBD4IjYQOLADOAGMJECAIAoSOL4I4jiG7diSo4wsjWZ6eu9mN3cWWcXaN1aRLO71v6A+T78cO4mFX/LXzO/XL9CQppvLW+/y/Z7nPOecR6enpzbwfXh4WG73i2HnAFUcmgAmSbb5dHZ2mrOQBHVaJxgzTk5OrK0OQ4lcIJlMqrOzUzs7O6rVata2hR0k+mN8fFzJZNLiaHhnyAQjl6xSqRhb5GTAJFnrzOnYA0yiNQIIAPp5R2EFWQtwCzoB//7+vm2OtO3QRjndek5HopOF4w//RoGFEQBGs1wuK5/Py+fzaWRkRJFIxIpDDqeGC3OEx+O5EpoKe4NuzDnsGTAMs9NqtaytiRQAkMr1lnTFmQpwQHKB09mZ28e7RvSFs9Up6UoYM21iGDQ+H5+LNjUMovRitiZdBjoSTqDENXcyisfHxyYtQV8lyUwRtORh7gCGgGWeX0AvewDgEzCHAB9tHGwg1xwAClhzXlfy2ph5zDVFJvLy+PQcn2kAtrq6qvHxcfX395s4m5ygV155RZOTk1Y1ODepbDar1dVVPXr0SCcnJ0okEjYSZ2pqShcXF3r8+LGWlpYsK4sFGZEk7BMhoDzohOVRdfn9ftVqNWUyGa2srFjyPE4m6cXAWl4mFmlYHSptFkKqHjbYeDyuTCZj+TNOQfnFxYVtmsFg0HK5+vv7LdYiEAhYqjttp8HBQWsFOPPItra2TFB+fHysra0tra6uKhwOa2xsTNevX1coFDIWBP0SonK0KwCVUCikqakpSbJNuaenx9jBYDCo/v5+VSoV5XI5PXjwQLVaTe+++64ZJwBR0uUiQzRJMBg0BoxYCq41zFKpVLJhzcFgUM1mU9VqVblczsAP4Z+0bbhnBOj29PQol8tZ9Yv4OplMqqenx7LSOjouA2RPTy9nyaH74dzJ+oHRmpubUy6Xs1bF0dGRstmsHj9+rJ2dHbXbbaVSKZXLZc3NzRngcGpYPB6PTVdAj9XTczl8mrbF2dmZtYMB7mNjY/L7/crlctrY2LANY2ZmRrOzs0omk7Zh0ja8f/++stmsQqGQotGovF6vaS5J4qZNSYvl6OhIpVJJHo9Ho6OjCgaDZgzB1RwKhawNSjQKw5W9Xq8BQOfkBTZXfjdaR0Is0Y+xadE+coZVwgZTeMEaOV18tJO7urosLZ0A3omJCQvjBBQcHh5a+wgARFvGCTA4PilEp3Aql8t2vtyHYDBoA8QZSSXJCjJADGAEvSGtO8Cgk1VxgjdaW0QeMBOXNQKHNPcBUxJTFAAATvbb6UbEcMF9g8GEwQHAOdu2BMBKsveTtRrQxBgip6wD1g2XK6AYIIizHY0a15CC0+v1mraW76XoIdanUChcKXAR5ztBNm1sZxEDgOV8jo+PLRSc9xuX7ydz1lirnW1MgPzL49N3fKYBGDERCLCPjo7k9/t18+ZNxeNxFQoFra6uWr5UOp3WxsaGVldXJcms2L29vebM29/f18cff2yRAoy6gRaXZHT8+Pj4lWBFIjFwnQ0MDGh3d9cGCOM0Q4wvyV4e6QVVTqZSR0eHBfuh33AKM9F8MDMR8PlJ2z1i+0qlosHBQRu1QuXH70Moz8Y2NDRk0Q9OZ6LL5dLnP/95JZNJy0ZLJpMKhUI2B432GQJUcomazaZdbzQksD2SrIVYLpdNm8fEAzKJVlZWFI/HdfPmTdObHB8fq16vX8nlgZ3q6urS4uKixTZw3drtts3W5JoCqADujLNCdwE4dlL+iUTC4juotmkVFAoFZbNZjY6O6ubNm6bhSCaTunHjhjY2NrS+vi5JtpnivJ2fn1csFtPm5qbW1tY0ODh4ZVOrVqv68MMPLZkedu3o6MjYkHq9bgv95uamqtWqmR+Iljg/P9ezZ8/0Z3/2Zxayi2A/EAhoenpa+Xxey8vLSqfTcrvdmpiYsKgNokC+9KUv6dmzZyqVSqpUKvY88RwzYgUdjM/n0+HhoZ4/f656vW5jg5y6ROJUAFK0kdAgEtcB0wLITCaTZojhPWczdLaLaI2zOSOuhnXi33AuOmMfAM7n55d5YGz0bLgUKnw/m6kzR6tcLluBxMHX87NZGwBGTkYI9gf2nZ/jPFfYOQAebTHuAffQCf6c7Jsz+qCvr88cvxQbAC6nK9KpueJnsB45QQRsGp/baaxg3aWQYH2VXgyWJiiayBxAEZITjCbOyA00WTBSgGenDhc3JO88LBjh06yPuLvJ34KlZr3ERAVDyDPCeg4IdbkuB5yzz/T09FjRTduakG7uLe8C6yvPHnmCADKKKucz9v06XmrArh6faQAGwMFFMjU1pWvXrqmzs1Pf/va3ValUNDExoYmJCaXTad29e1elUsmsylD4k5OTNktyaWnJNjQqPZxAfr/fWC4Yn4uLC6PXARGjo6M6ODjQhx9+qEwmY05EHGVOtw2sQ1dXlw1qJmw1FArZwgX7FQqFTFjMuJmdnZ0rVT/tLKcIV5K1kBA9s7hJl65P5uodH1/OU/T7/RobG1O9XtfKyoqq1aqJst1ut7X4lpaWtLKyokqlIp/PJ6/Xq/Pzc5VKJRWLRWs50hYiS8jtdlvFubKyolKpZJlsbHxsMPF4XKenpwoGg9Y+QGs2OztrGglAKG5BFr5CoWCO2GAwqNu3b2tmZkYTExMqFovWahsaGjLt34MHDyTJWFGs9lSwxWJRLpfL7gNsGYLpZDJpIMHJMMBUDg8Pa2ZmRn19fdrb21OhUFChUFClUtG9e/d09+5d/eAP/qCmp6fV3d2tfD6vvr4+hcNhjYyMmKnA7XYrnU4baER7RT7Qzs6O6t8d9E5cxM7OjoGISCSiv/W3/pa8Xq+Wl5dNX8SmcuvWLc3NzWlgYEAPHz7UBx98oPX1dQUCAdOTDQ8Pa2xsTLdu3dLm5qaWlpZs8yFzD3kAczQ9Ho/m5uZs7uLz58+1s7Nj4Bz9H8XV8PCwAfZbt25ZixAmlnZUs9lULpeTx3M5modNT5IxETjcnNokmBwCkp0uPNYCgBCgABMFoNsZ0bC/v2+jzyQZSAH8kVdH+4/DGVngZKwQkMMSOdlYNlkAAgn1hCjz/bRTeZ4B9PxB2O50cfJ9TkCEZovnEEABOHS6VD+ZwcVnA6jAEPL5AA98H7IOANHQ0JAmJycNADuBBv8fnZ4zPR7ggiSB9wTAzYQIABHmAowFREfw+WAyOzo6NDQ0ZOynE+SxJjvNHE4tHAUAINbZAeEzwIYRzsrziznD2fXg3J2OYoDXSxbs03d8pgFYPp83dxqjVNbX140V+MEf/EHduHFDm5ub+uCDD3R0dGSMFRlTb7/9tiYmJmz2n3RZPWazWbVaLXOVUa3DHCDsR2S+vb2tWCymSCSizc1N0z0Fg0FbAFhwpBcgjEUCYMNCTKJ1T0+ParWaJCkcDisWi2l4eFjSi1E+6E54uZ16EicFjRsNpoUFAgrfKerc3d1VoVDQrVu3TGcWi8U0MTFhWg/aPrlcTuvr66aboJKlvcAC5/f7zSUpyVqdXD+Ez9jjaecy6444B1qbpVJJ4+PjxrwlEgkTJLNBMN4ln8/r9PRyZA0DpW/evGkmBkmmnWLQLdlogPpqtWqtrJ6eHvl8PnNS1ut1RaNRXb9+3fLTNjY2jKlDVA4jWa1W9ezZM01PT2tqakq1Wu1KFUwLqVwu6/r16xofH9fm5qb29vY0OjpqhoVWq6VSqaRyuaxyuWwZVswu5NnCJYbrEX3M0tKSNjc39e677+pv/a2/pT/90z9VKpUyEwNxAkNDQ3rjjTcUjUa1vLysbDZrrGkul9PY2JiN/onFYsbAsQF3dXWZeSCfz+vhw4fa3t6Wz+dTMpnU9PS0ent7bRQQqe7oXqj8+SwdHR22+ZCX12w2rZCBxTw4OLB2KBs9DINTZ+N8T3h2eBed43n4b+JcnJodBPw419CNki8I6AL08b59Epw4tT1OKYFTt0WEA+OJAE9strTA9vf3FY1GzSXJOsE15Xy5Fs4YBGcx5wRvGG7QjebzeVUqlSuGHKdeTdIV1s6pcePz8jv5A6BFo0ZbldmLsVhM1WpVW1tbqtfrFnsDUAL885lpgcKk4UznPJxRD9xnQDsBxR6PR8Vi0a5xs9lUrVaz2a20/xiGzudmPaYAAUzx7zBZ/LezDc51gL0l/ghXI4CVZ8QZ0eFsywPkXh6fruMzDcDeeOMNc7fk83l9+9vf1uPHjxUIBPSTP/mTCgQC2tra0p07d+T3++V2u23YdjAY1Je+9CWFQiF99NFHajQaVjEQ7TAyMqJkMqmOjg6l02l1dHTo+vXrGhkZ0fn5uZaXl/X+++9rb29PY2NjllzearU0MjJibSgGa6MfYcFjc+TFwIJMlUaiNxlT0qVovVar2cbGYsELRrXo1FI4A/v8fr/Gx8ctARwgyAJ+cHBgcw53dnZULpd17do1vfvuu2q1WkqlUlpfX9fBwYFu3Lhhuq5r165pbm7O8sUajYbNscvn8zaHj02WduzFxYssLEa8ZDIZxWIxzczMqKurS9lsVnt7e9rc3DTtE7MAa7WaFhcXNTs7K7/fr7t371p4LA4tHFkMPic/DdcYOpCjoyOtra0Z8GLgMy2MQCBgAKzdbmtyclLHx8daW1szk8DW1pYSiYTa7ba1JhhODJMzMDBgqeRPnz61GW3cI+I02NyfPXtmsQ08N05tE4YE4lck2WZ3enpqLeJMJqNvf/vbknTluUFbNjs7q7/6V/+q3n//fW1tbcntdisYDMrtdqtYLFpocTwe18bGhunemDJweHiomZkZzc/P6wtf+IJp6Zzho0NDQ/bnm9/8puXYwY5eu3ZN1WrVWvYAEYbcw/AxRgwxfiKRuGIOQECOwYIsOVrxtJ1pazpZJQoHzhm3IDo8MsyGh4c1PDxs80gJ+pVkLHu7fRmayoYMuED/h3GAFrsz/R3GBJam0WjYOdO6uri4zImjUGGeKhsx7B0An5+LxhQXKODDqf+CkYENg41CsI7GyOfzWe4iYA2GjVay0+kJG8TX0rJ1CuwRzDvDZGHLAci0qAHAtEmdonyuAZ8TCYYku6+sR+TZ8W/ofH0+n4aHhy0uiMgKgCQjsXA70q3gXgLApEvJCS50iiIMDXy9syiG1eOPc1IGIntnG9UJwHhGnG7a7/fxsgV59fhMAzDGwqytrSmVSqlUKimZTOrLX/6ywuGwNjY2tLy8bOnosDqzs7N65513dHJyoq9//etyuVyanp62hQURPdVyKpWSx+PR22+/LZ/Pp1qtpq9//ev65je/Kb/fr7fffluRSORKQjb9exYQp8WdkSdOO7KzemWOWKvVMpeYx+PR48ePtbm5acGoVI28gCyqQ0NDVypBfi40P3ZzJ4MGZS5dLj5TU1MKBoPmXKPFh1ORwb2Aung8btMCeMnITBsfH9fU1JQBLrKocDn29PRoZGTEhOm08wYGBpRKpZTP569s/s1mU6lUSul02lxgaD6Ojo5UqVSUSqUUDocNwBJfwD0iv0y6fIFhVxKJhGq1ml0rMs+c+hpaFaSwY18/OTlRPp/X3t6eZmdnbaQNUR9szkdHRwqHw/rCF76gk5PLETKNRsOcU6lUStVqVTMzMxYuXKvVNDExYRMQ0P2lUimdnZ1Zq4+pAMQjMIJkaGhIfr9f09PTZipwu93WPgWkj4+Pq7OzU7/3e79nOsS+vj5r76ZSKY2Pj2t2dlbBYFCbm5tqNBq6f/++Hj16pPfee0+hUMimLRD6SgI/TuOBgQG9/vrr2tnZkcvlsvl5kuwdYx4hQcSIpp2hm4yqgtkg/gR2mQR9NmjaOzBtmD18Pp85ZCUZI3ZycmLvL/pL2C/eo/39fZuz5/V6rZjhmSHRH5DFMwfwcIJlGGPAD+uCU2ztjFdoNptW7EgyIBYIBDQ+Pm6OWoo/TAVoiChSGC9GIedcOzhHNHGAPNbIeDxu73uz2TTmED0Tn4UWrKQ/J5NwXheYMTLPMLJwvflZHo9HExMTajQaSqfTajQaCgaD5mhtNBoG8GD20MsxDYVgWc6JVuz5+WWYKvl12WzW1kl0pgA3MvpgF/9nLT/YKvRrvb29Fm9DqxkW6y9iQBk/RJFGDiB640ajoXq9br/bqaWD5X15fLqOzzQA+9rXvmYZRx0dHbpx44a+/OUvy+Px6OnTp+Y4PDg4MK3MW2+9pbm5ORWLRa2vrysYDFqLCQdduVzW7u6uuWwmJiZsvMp3vvMdvf/++8rlclpYWNAXv/hF+Xw+bW9vK51Oq1gsqlgsKhAIKJlM6uLiwlgwwEAsFrPKi6oO0AKo4WWGPWBjp8qlCkSgiTiVOArYCTYfABRO0Hg8ro6Oy6yiUqlkQ6MZUtxoNDQ2NqaBgQF9/PHHZjcH8EkvZqphd3eGAfp8Pt26dcucheRNjY+P6/XXX9fZ2Zny+bzpyhArn5yc2PU6ObkcQH58fKyFhQXFYjHt7u7q/Pxck5OTthmfn5/r6dOnVxZ0p0NPko0QQjsB6xIIBGxjxHE1PT1tkR8IiwE6tHL8fr8t9r29vQoGgyYmPz8/19jYmN54440rzxRjo7hOtDqpgtEEeb1eA9cAn1qtZro7p0Zkf39fa2trmpyc1Pj4uH0mn8+nfD5vw4Q7Ojq0uLhoxgm/36+zszOl02mbD8rsxWg0qh/+4R/Wt7/9bWOjiOE4PDzU48ePTTfp9XrVarUUCoX0+PFjPXr0SAMDAxofH7eoi5mZGeVyOeVyORu/NDc3p0QioYmJCbVaLZvFR/Aw2qLV1VVrteHIw1EZiUTUbDZVKpWUyWQMaAAoyXtyjhuSZGORzs8vQ0kLhYKZB27cuGHFDQDMKZhGTwaLwoYK0KD1iFuaVh8J5uVy+cq7QxsVRgXGCwDnjEGQZPcepp5nGUciBSSmkOXlZSUSCc3Pz8vr9dqGDFsuyYwRXC90kzAy/G4ABJ8RkBQKhSRdGiAAEUxucM4phVV0sn68k2jYWMNoxztnRJ6fnysSiajdbmt1dVXRaNSYdRg1mEVMOV6vVxMTEyqVSqaVZfRPNBo1w4iT6XOaAwYGBmyaCW1pzgetLk5MGFqeFTSJPEvcU2InnCCYdZ8oHKcDlTXCOb9YkunaBgcHjQFzAi5auDCETp3hy+PTcXymAVg6nbaX+datW/rhH/5hud1uc2tJsrl2Z2dnunnzppLJpGq1mg4ODvTWW2/ZLLtSqaT79++rUCjYJr6wsKDr168rGAyqWCzqD/7gD1SpVOT3+/XWW29pYmJCZ2dn+vDDD/Xs2TNrNcbjcS0sLEiStTzdbrempqZsZBAZUCzCMFi8NDBgzHyLRCK26EsyYHV6emqp306KX5ItKFDSkoxmhzni+lEVAxZoLYTDYU1MTFiUBNU5mxObXl9fnwVihkIh9fX1aXZ2Vr29vfrOd76jWq1mIC2fz5sW5uLi4goIdbZLSHTHLYmzDOGr1+u19PNyuayOjsukaiIynJtKu92+ojeiFZzNZq9sAGdnZ7YBobFA9L+5uWmaikwmo0KhoHg8bgn+Pp/PYhQA/878IUYzAUYR/9MWDgaDJp4eGhoy1y5V7e7urt58802Fw2GFQiHFYjEzMsCa8XOCwaBGR0d1dHSklZUVLS0t6ejoSJFI5Ep4Lxsl3xcIBDQ7O6v5+Xm1221rWcJ09PX1qVarWUbY5OSk3nnnHWNPqtWq8vm84vG4MYnov/r6+oyBYawNABa2am9vT9vb2zaD0+12W0YbLcTl5WVjXfmezc1Nra6u2jiqGzduqL+/37RXTkcjgMbpqBseHlaxWNTz5881PT1tmz9tNlgc2utOQESkitPdDHvKRo54mjFGtKqcjAnsjFPP5cxuoj3IeaALorWLQcftdttkhEajYUzs66+/bgAenZaTGYGlxxj0Sf0ZoB8QBmuDrjafz1uoLVNIcGXDbPE7nfpUgJ6zAIUpDwaDKpfLyuVy6unp0eTkpNrttmk50cTG43ErduhCcP+cGtdWq2UZhCcnJ1dkBTghw+Gw5YHBvlHkDw8PG7sMaCaDEmbcqcXl3XHqCwFJmEicujhYcmdsiLNlC5MOiGPvooDkd+HGB7BLL0X4n8bjMw3ASPm9ceOG3nvvPfX396tUKtlA42azaZbcmZkZTU5OXnHguVwupdNpra2tmYssGo0qmUzq2rVrikajki4dgktLSxYlIF0u2isrK1peXtb29rb29vYUDoctU6zVahkb5vV69dZbb2lsbEypVEobGxumI3K6i6h4qFCdNHooFLIFCiodgDA8PGwDpVmcYVPIu6Ft4nRwoSlA/I8mA6EnImk2I9goFtK9vT1bKDs6OvT48eMrjAAxDu+++661ANfW1nTv3j319/fbQFncbmhAGGM0MTGh4eFhC9N0hi2ibRoZGdHFxYXl61AF7+3tmYi9s7PTqtSDgwObeYn+jM3Z5/PZ9WMBbLVa6u/v1+joqBqNhpaXl/X48WP5/X5Fo1Fj7Hw+nwYHBzU2NqaVlRU9efJE+XxesVjM2h2AxUwmoz/7sz+znDNMFrB15XJZqVTK2qB8plQqpSdPnuhzn/uchQoHg0HdunVLz54909ramjKZjBUA8Xhcg4ODNvKnUqnYZ+3t7TXX8PHxsfL5vHp7e20Rv3Xrlq5du6ahoSH9zu/8jh4+fKgf+IEf0MDAgLGmjKQZHx/Xq6++qrGxMdVqNYsQwQ3oZE0PDw9VrVaVyWQMtLndbhtSzjt7fHw5fJ4gU9gnRrZQvGCgAPTn83lls1ldXFzYdR8YGDARP5vk4eGhjo6ObDNbXFxUX1+ftre31Wq1NDs7awG0nCfvKmCdmAEE/ZLsMxOHQORHrVazUT3k1qEFAiixEfMznNodGG9azABL6bKowsyDiSWTyVhbsVgs2vM4OTmp0dFRRSKRK61INmdYLsAVf8/agHSAdrlTl4RzHLAFSKBNB/DlPXcyOYBPPtvx8bFpNGGBh4aGbAJDoVAweQdFYbPZ1Pb2thlymB7BwTOIhqpSqdhkjfX1dQt1xtkqXcox+vv7tbW1ZQadcrlsurl2+zKbjsKNth9GCUAYbKYzPoR5uny99EImwn1xGhtYJ5lLyzkAvvi5n4wp4TydMSPfr+OlBuzq8ZkGYKenp3r99df13nvv2dyyZ8+eaWNjw7Qv/f39un37tt555x319fWpXq9fca41Gg0dHBzY/ECs7sFgUBcXFzbOwe/3K5PJmNux2Wzqgw8+0LNnz0zYOTY2prGxMTUaDQubvH79ukUNfPjhh1pdXTV3Gi8IeiwWd0kGJhAd88IyoocWEtoSNiLodwANCxC2ZHQZ1WrVQFlHR4dyuZzR7EdHR+b4C4fDNjuOiIq9vT0zMtAq7ezs1OTkpLV7SqWSzYdjjuXg4KCJdWlhoOEZGhqyyhIGKp/Pm9OuUqnYpkaLFlBVq9WUTqdto6cFc3R0ZFMHRkdHTfOxtramzc1NhUIh3bx5UyMjI3YtuWZHR0fq7++37K1ms6mhoSHNz89re3vbNmW0R7hLJVlII/EMnDPtAL/frzfeeEPpdFrZbNaE6mjQyEuDTXzw4IF2d3fV0dGh3d1dPXjwQMlkUuPj4wYEJycntba2pmfPntn9r9VqikQieuWVV8wAUKvVLDOOAe/7+/va2tpSoVDQ6empjW6an59XMpnUF77wBe3t7Wl1dVXJZFLFYlF37941Ju+tt94yfd/U1JQSiYQ5IGkHu1yXA7a7u7tNPC+9yD5j7FEgENCtW7c0NDRkLcmRkRFjl3hfuru7DWDHYjF7H2ZnZ1WpVGw6A61kct/IcCKGwik6n56eNjMGeXdOBhkmh5bh6empjRXDjUgECgJyr9drrC6MEE5VtHrk7TnF+s7YEpgWNnOKLtq/RKPA9nZ1dWlsbOxKGxF9IAUD+iauAWsRbSwAJWsSmzxxN06hPd9He39nZ+cKswWo54BJ42fzXnd2dtqYHdhuTB6wbk7gCQikzYy2k/spyUAghRjrIC7ndDptLDYtcrfbrbm5OfX399vnDQQCkmTxJPzhPvF9rLeAPIwWTuaL64bkgevQ0dFhRYFTD8b/B3CxzjiF+Z8EzYB3GEi+/+Xx6Tq+ZwD2zW9+U//0n/5T3bt3T7lcTr/7u7+rH//xH7d/b7fb+tVf/VX963/9r1Wr1fTmm2/qX/yLf6Fr167Z1xwfH+sXf/EX9R//439Uq9XSu+++q3/5L/+lRkZGvqdz+cpXvmJVeTab1b1797Szs2NxB729vXr99dc1MjIil8ulhw8f6uHDh9Z+Y6TN9PS02dQRWJKgnslk9OzZM62ursrr9eratWsmiGeDazQamp6e1uLiovb29rSzs6NIJGKi/VKppN///d/XnTt3lEgkNDU1Za0j50uL64ocGWzTuGRgv2A19vf31Ww2zb0Jy0HVAyvkdHSxANfrdWNsnC24ZrN5ZeHAMg3oIVRweHhY165dU29vrzY3NzUxMaFkMmlC1ZOTE1WrVdvgcEIxow+BNSDz6OjIgkOZLkCiPa1ZDhisZrOpYrFo8x2bzaZGR0d17dq1K8Lx8/NzDQ4OGhNSq9W0trZmALOvr8+ygVi0YCMZnZROp3VyciKfz3dFeF8qlbS+vq56vW65WNPT09aaIH4Eg0GxWDTWZHp62oBnuVy2aQZo6NDkkUu3tLRkobCpVMqE8O1222aNut1ubW1taXd3V8+fP7d0dCYKkEoOYzAyMqI33nhDN27cUCaTMTauXq9re3tbfr9fiURC7733nur1urlJz87O9P777+sb3/iGzs7O5PV6LUz19ddf1+DgoLLZrGnvpBeWe54hNiCCcfm8gCMAK4wIjsaOjsspAwyeppUjvRgJw0SBrq4ui2NAqNxsNu1ZdqanwyzAksJOSDIwBPOVy+WslX5xcWHBnENDQ4pEIqa3Oj8/v6IZg11BKsC/O68Hf49OjKKKliRsC8+tM7KE9rXTvEOYsJORhOlyngNsitOQAwAgMxHh9/n5uZk8aJMxP/X09HL0FS1XpxOP/wXUOaURRKcUCgXT+cEIeb1eSTLhO9ezWq1aOxSAAVjk2evo6LAQaT6PJDMwMAVld3fXntlAIKCenh49evRI+Xxe4XDYimHAKZqt4eFh0346ox6cui9AJwCJNi8HHQ/GnnGN0cjBBKMvdLYmnYU759bT02MAkesOgP9+Hi8ZsKvH9wzADg4OdOvWLf3dv/t39Tf+xt/4c//+G7/xG/rN3/xNffWrX9Xs7Kz+8T/+x/ryl7+slZUV673/3M/9nH7/939fv/M7v6NAIKBf+IVf0I/92I/p3r1731NWyVtvvaWenh7t7u7q7t27Oj8/1+c+9zkNDg4qnU5bDkytVtO9e/ds2LHX69XMzMyVoc4sol1dXSoWi9bSIbxybGxM77zzjs7Pz3X//n1tbW0ZqLl165a1JtPptCKRiN58800NDAxodXVVd+7c0dOnT80p6BxkDYMkXVZrCKulFwsJFDLMmd/vt1wc9AfOqtvp9sHCDksGc1CtVm3wNFqv8/MXc8ecolbm49ECJM4hEAhYPAEAg5RtZhcCvhhBw2ibw8NDTUxMKBqNWkp7o9EwVgl7PUJ6ft/Q0JAODg6u2OzRUUmyXCxatqFQyEwFpI5z/12uF2GLaGtwlLHREiw7MjJypY3FQri7u6vl5WXt7OxoY2PDBnlj8ad9SosMZu/g4EChUOgKY/Po0SNFo1HNzMzI4/EYI3h+fq5wOGxRJvl8XoVCQZubmxZR4nJdDoandVYsFpXNZlUul5XNZo0FWlhY0Pj4uAEhQDpTJMbGxmzTdLaIeD4vLi5T7BcWFlSv11UsFs2ddXR0pDt37ujg4EBzc3OWz4amsL+/X52dnQbGJVkFL8lacSSZA1ZgE9D1wIiGQiF5PB77+bggaVPRXozFYleypQigZbYoMyYBWPyB8UJnRZECsCeXypkb5XQAnp2dGQPDQG5a6sVi0dh1ZxApmzPAHeYLkPRJ1ySRNbgyneNxtra2bORWNBq1wqWzs9Py8RB2A9JoGToNDLQeKYQAhej7+HeAASOoAAhOUbkzCoP7wzN2dnZmzlnc1Li6nUJ2QAn3x+VyWSYXoNV5H52AiRYo50ZXAMONJLtGrBnlclmJRMLGIfHMEmWCOYlOBs8q7WLaf07Qi9nBaahxPkO0IAcGBq44zgF0xPjQCWBuLSCWzwzI5XxeHp+u43sGYD/6oz+qH/3RH/0L/63dbuuf//N/rl/+5V/WT/zET0iS/t2/+3eKRCL67d/+bf3Mz/yM9vb29G/+zb/Rv//3/17vvfeeJOk//If/oGQyqa9//ev6kR/5kb/0uXR1dalcLqtQKGhqakpdXV169uyZWq2WXnvtNSUSCdXrdUvFn5ycVDQatflyjUZDqVTKhgGXSiV961vf0tOnT61CPTo60vXr1/X6669LkpaWlrS1tWUvY39/v65du6Z6va7NzU3FYjH9wA/8gDo6OnTnzh3du3dPpVJJXq9XN2/e1OjoqGq1molaqTZhYrBEOxcPFl70WzAKtGIAC05xqySroiTZohkOhzUwMKDDw0Ntb29rd3fXWA1nUKWzaiNWAJEt7iN+LosYLSdcaLQLaAWtr69re3vbmL+BgQETrT9+/Fi9vb22WQLY2KjPzs4sWT+TyWhgYEALCwu26Pv9fvX09CibzWp7e9ucid3d3dbeITRUko2wIleMzYAxN2jvMpmMXC6XfD6fRkdHTW/T3d2tQqFgQYy0X6LRqI3Pod3WaDS0vb1trepYLGbmjEAgoK6uLvl8PvX29iqXy5kIe2Bg4AqYu3Hjhh48eKDHjx9b8rvf71csFrMhyEReBINBjYyMGKsI0Hz69KmBX6I0CGZNpVLa3d017Rggc2JiQrFYTKlUSisrK3avw+GwRUTQDqW1XigU5PF4zJ2J6L6zs9MYaWfsBJEKgBvea5fLZS1GJ1uGoxCBNdeddlqpVDJGhL/jHaJFjZsNzRnvU2dnpxVlgAcc0d3d3TZX1u/3WxAosQ+0AwFRuBRhNYh98XguZ3Ty+xF1876yaUovAkHZTInUoP0FGwZ4YwwUDDoMLu8NQb2ALwJjAViw8mhOnesPYP309NQYVUaBsS7xfg0MDJgT1AlOnIyF09HJZ43H47a2u1yXCfVut9sYZT6/JDNEONnBRqOhs7MzRSIRe8Z5vwGwSAhodRMuDJsKW+12u5VMJv+cWSEQCFhbkqkOTAChgGMNYF106uYAZ6xtn4zhgHXk+wG7rVZLjUZDExMTNh/SeS1Zt50GCpfLZVKJl8en6/g/qgHb2tpSPp/XV77yFfu77u5uvfPOO/rggw/0Mz/zM7p3755OT0+vfE08Htf169f1wQcffE8ATLq0pMfjce3t7Wl3d1fBYFAzMzOKxWImdB0dHdUrr7xiDBfZPcxlOzw81Mcff6zNzU2jvwOBgDEet2/f1vn5ue7cuaN0Om025lAopFdffVXJZFI7Ozvq7e3V+Pi42u22nj17psePH+vk5EQDAwMWS4EglwWHcwiFQhZcyAuLUB4BLtU3bAeLEDZ5KixeQumqs+ns7MwCKSORiCqVigqFgvx+vw1CppKk6udrvV6v5U+hocE5xEZBGjmaqkePHsnlcpkubn9/3+hwdG20ahKJhFV8HR0dls/D5tdqta5sVAj9Od9MJmNVc6VS0fb2ti06zLI7Pz+32ZYDAwMG2qjyAUHEYKCfoIWxvLwsj8djxgQ2J1qPABDpUmyOMxFAIUkjIyNKJBIWLXJycqKRkRHNzMyYLgVXFwJu2D6eCc6dzZah4qenp4pEIpJkwPCLX/yi8vm81tbWDLTcu3dPu7u7plmMxWIaGBjQ6OioPvroIxWLRU1OTpqrDhaIPLVsNqtqtarBwUHTE9L2m5+fV6vVUrlc1tramk5OTqxtzzPgnCnYaDQ0Pj6ueDxujNbZ2Zm5ZgnPJCSUzQSNJmCBaRCwF263+4qxw9nG5jrCZlIkYGrh3wBE/AzaJ85RRgA5dDqk7ANYnCwtIAXwv729bZotwArn7MwQ45w/uUnzdTDVPCu06ADjPMu05dFvEsUB29nZ2Wk/h9Y7gMFZkAHM+HyYhZj60dfXZzpZJAlO4AObyHMN4wPT6WTf+XkI/XO5nNxut82FRKuK5OHs7MzkBDBEyCqQBezt7dlnlV64I3Eyw0xRIFAE0XJtt9sKBoMaGhoy1yXXAqaOZw6pCyYOTArcN55XJ0sGM490AgBGVBJFEyYgXPBOtotrzHrJs/39Pl62IK8e/0fvCInebAIckUhE29vb9jVsYp/8Gr7/k4dzDIgkY4kYyEtlNzs7a1VwqVTS1taWbRRs5G632xZCspLu3r2rVquliYkJLSws2O+jndFqtfTs2TNls1lb/IPBoF577TVFo1F7oRcXF9Vut02LRsXearXMHVksFm2RdbZz0Dw5Wx/OzBhe4P7+fguW5aWl8kO75Mx8YXEZHh6+MuC1r6/PhiyXy2Vjo3CpSTIWCVDR09OjSCRiQnzycdh8SOnHbViv1zU2NqZwOGyZUFTE5FKx6J2fnyudTqtSqVzJfcLFxnkRTjswMKD19XVzW2GDp3rFBUlEBmCys/MyvHN4eNhCKt1ut8UjDA4O6q233tLU1JT9PmelSnAq944sLMATAKNYLCqVSllSO9lJnZ2dyufzVmmfnp5qe3tbLpfLsrU6OzttSDjXq7+/X7FYTD6fT+Pj48YmeDweG6tSr9dVKBRM90XLkABb3I5PnjwxpxytOhb/27dvq1arWYuOjRnWcmRkRH6/X8Vi0TR6ZCnBcDGvcWlpSU+ePLG4CiZTFItFhUIhy/ki3JUB35KMXTk9PTVdJXEWQ0NDkmS6I94BSXatnJo/9FuSroiSJdm7hZgcUO1kEPg+7p9T2D4yMmJTCDBy7O3tmUyA9xH26Pj42Dbh7u5uVSoVE8I7gZWkK20/J/hyRjiwRvBvMC+EfDJqiJYXrBfsHiLz/f19a0mTT0cx5GS3kTnQfiWaAXAFQPb5fFZQMv2Blh9rk9PpLckyw4hrob1er9etzYmRArkEbVLuKxIJJBHO9ij3QpIxlU6XNGA4GAyayxAAA/PXbrfNJEPrEokGjCr3FjDJXsfaCCuHxgtgRhHB+u3xeGzNzGazOj09tYKy0WgoGo3aMynJDA2AQacLk/vy8vh0Hf9XIPEn80acC8j/7Phffc0/+Sf/RL/6q7/65/6esFVnSxFLciqVslwqMnJISGasSTab1ccff6xGo2FzvtAseL1eq+ju3Lmjzc1NBYNBeb1eRaNRhcNheTwe7e7uamtrSyMjI4rH48pkMqZdoiUQCAQUiURsM2HhxK0UDAaVTqdtNJD0IhMoEAjYYs5CxsgLZ2WOruGTi7UkuwZut1uFQkHVatU2qcPDQ/vdt2/fNu3U4eGh6vW6/Tw2G3Qpd+7cseiNWCxmdHkkErFQ17GxMRswzkIPC4WIube318AKhoeOjg7dvn3bYgBgBnCQ9ff36/DwUJlMRuFwWDdu3FCr1dLq6qoxdLS1EF+jcfH5fAZYiNvgGhUKBRO0wxCgxUHw7/V6ja2oVCpaX1/X1taWhYnSGnXqQGhfEApJyxUQ53xvzs/PTRzMaCYYvVwuZ9EQPMu07hhT9I1vfEMffvihZmZmrC3odl8m3jv/+9GjR3bOxWLRmJxisWibcEdHx5WkeVgLACVMIJEojLByuVz2nC4tLSmVSqlWq1mOF4wuzxs6LwAKGUm0Rrnm1WrVZg7i6uPeOXU0tK5pJ56cnKher+v09NT0SU4xtLNNz/kBKhCuE7hJW8mp2QHEM2UBQTguXZgQxueQWt/X12dMCBIBfqZTnA4AcrJeHIBGgCTFD0VXIBAwdhkwhy4JxoQcNj4bIAcXKsALIIPMYH9/3zZ2wB7vE/eHsFgcygMDA1e0omjoaLUNDAwomUxqdHRUp6enlpcYjUY1NDSkUqlkQJ93AAnC2NiY/VzYU/SG6Ac5J1zBkkxOIsmmoVBQMFmDNdbJJBF+y/3hXjtbhzBSGKYA//wbQJlnxGmW4vmmWGPNZgRZpVIxIwrkAhIJZwuc1uunhSn6tJzHp+H4PwrAyM0i/4ijWCwaKwZtSovE+TVf+MIX/sKf+w//4T/Uz//8z9t/NxoNJZNJdXd367XXXpMkq7yr1aoePnyoer2uxcVF3bx50+hqZnnhkPn444+NBaCSRI8zOjqqw8ND3blzx+a+DQ0NaXh4WOFwWKenp3r06JH29/c1PT2tsbExlctlra6uam9vz1opnZ2disfjNjYJyhrdF2GZxWLRFiOv16tkMqlgMKjh4WHTJZXLZRtMDSPmPHcWP1xOvNCnp6cWL8GQZH7H6Oioic/dbrcSiYTi8bi2trZsTEy73baAx3Q6rZWVFdPBLS4uWsVJKjnjd9hcm82m8vm86b9g23w+n6XkSy+yjmhTOul1/o2WJeGkfHav12tDqcPhsKanp+05w+HGxpJKpSwDiLYXYvlGo2HtPFo1uAsR3fr9fkvhT6fTFuHBiCZnlhKtEdxVgAc0eP39/fr85z+vdrutbDZrLEW1WlW9XlckElE8HrfNF6chTO3Z2ZkymYwFVX7pS1/S2tqaDUiH6QgEApqamtK7776r27dv21Dtra0tPX361FpI2WzWfi+5Xtvb2xofH9f09LRpFwFa4XDY2sUAzlKppOPjY83MzGhqakqlUsk2QFp7bMoul8uKFkAH4b6np6fa2NjQxcXFleHc6JbY3Gizw6C4XC5jpTGBHBwcGJuBbpH2MowBIA7QRTgsDBHvA45HNnTWH6YFoDUE+HPOGGGOjo5swgWRCLTFCduk3eXM3eN/nQ5Cp3GGAoPNnggXmF3YbGc0COwRDB9xH4j8AVVOXaczq4qNHcc0gPGTAnNagsFg0GJHWCMBwU6gyudwMv88P4w7ItiXIokCl+sCAHGe88XFhQVxn51djknr7+/X6uqq2u225ufnNTg4qO3tbVUqFdMMs3ZRyCKOl2RdC4xA/B0MNkUAzyrPP8+RMzLI2ZbFFZzL5ayogdEngJXcQgp6GEq6OIBB2qMvj0/X8X8UgOFq+9rXvqZXXnlF0mVV+f777+vXf/3XJUmvvfaaurq69LWvfU0/9VM/Jemylfj06VP9xm/8xl/4c3mIPnn09fWpWq3aQOVWq6WVlRV1d3frr/7Vv2oVOWwFmo1Go6GnT5+qUqkYi9Td3W22dcSNiJJhBKgustmsAbfbt29rYmJChUJBH330kb30pNtjZ2eTIVF5YmJCN27cUHd3t5aWlpTP541FQPR8fn6ura0tbW5uqlar2eLE/wKaaD92dHRYqj2WcHRZ6XTaNqO+vj6rmNnsGDAsXeqUqCTZpKjcNzY2tL6+bi1BUstPT0+1tbWlhw8famVlxVqvnZ2dGh8f1/DwsA4PD/XkyRP5fD6999576u7uVj6ft3gKGDIyqTAFeDwes9VjJz87O7NWBfc2kUgYg4FejVT9g4MD+Xw+Y51qtZq1Eljkp6ambHNoNpsWFgqzQeI0DEZPT49tKLlczuZVOoMt+X2vvvqqzUSEmRgeHrYYE1gJrjdgn9gIUuppsTvBTH9/v7a3t9Xd3a14PK5EImFCclpFR0dHJhh/9913FYvFdO3aNe3s7OjZs2cqFotaXFy09sb6+rol6x8fH2t1ddVANQs6zxAp9263W5ubm1pZWdHJyYnW19dtkgTAolgsqlQqme6JFubKyooGBgY0NjamaDSqsbGxKywtYMq5AZEZRVsNNyUbGSwPQIExNDwbaLkAHZ90HWLOoG1IKx7dkyRLfkcgzqbXarXMKQ2o5ncAUmC92BidAcFolThXwLZT68PaxRrAc8IsTVp76PQwZ7CxezweA3ik9WPccerkcFs7dakUsug20SoRA0IBw78zKxUASDFC0YhuCfadawm42d/fV6VSMYaUvEDa/61Wy0ab+f1+Y+fQ8qInI3IEzVcsFlOlUjEJAjrdnZ0dhcNhvfXWWzo7O9P29raFnTpNC072yVkY0H5EcsJ14D1Hl+o0TVG4UfR2dXUZiHO2wnlHGbfERBKiVTAdwErCCDqz2F4en47jewZg+/v7Wl9ft/9m00XI/XM/93P6tV/7Nc3MzGhmZka/9mu/pr6+Pv3Nv/k3JV0uMj/90z+tX/iFX7D22i/+4i9amv33cjQaDY2Ojppe6Pj4WLdv37bNjbYRcQT9/f3K5XL6xje+oefPn0u6pJwTiYQFNdJawHGH3RdGg7/v6OjQzZs3NTs7q1KppDt37ujw8NBGG8E6IbonyRnWJZlMSpLu3btnTAjAizTrzc1NbWxsmEaB/C2qb1xZ0ovICuYqwkRkMhmz2JMnRestlUpZhYqQPZvN6uzsTH6/X2+++aZpaMrlsjY2NlSpVDQ1NWUL7Pn5uVKplI0BoX2bTCYtdRzRNa0agOD29rbu3r0rl8ul+fl5BYNBMwnAEtJemJubU29vrx4/fqxCoWDXhHNH+9HZ2amtrS2tra2Z8JvrQuuMWYW0QDKZjFXvzop7cnLS7j+VPjNCg8Gg5ufnFY1GNTExoXQ6bdfq4cOHuri4MBE9guyTkxMb2jw5OWkuzd7eXls0WdSxuR8eHprTMZvNmmsXvQ5tbJfLZWGQaOqornt7e03P8vjxY+3v7+v27duanp7W22+/rUAgYELy7u5uTU5OWqxBq9VSIBCw9hxDzjs7L8dpke7Pc+52uxUMBi3wuNFoaHJy0kCRs6WHRnJ6elrtdlulUknVatUGhNNuoWiBtWq1WpeL13c3GK4pAME57QFdD5qw4eFh20j39/ev/GyS2J3mF55B3J2sD2TAwWbQSpReZDHV63VlMhl7T2nLokfE2IIsAabO2SJkQgCMjnO+JMYbDlivoaEhRaNRa4FiJMBBB6Bi+gMuXufoJRho9Ji01zhgqZ1AlvXx+PhY6+vr2tvbM1CMUaPdblv4M/opWEjaik49FcXI4eGhcrmcmZkwt0gyvRcxPTCPTqDIdSdKxBko64zAABBfXFwoGAzK4/EYM8717uvrUzwev5LhR+A0znWeG4o1p4uTtnWr1VI2mzVmjpY4aw2tR2euI2CfggBDGEYap1ba5XLZ1zpNWy+PT8/xPQOwu3fv6od+6Ifsv2kN/u2//bf11a9+Vb/0S7+kVquln/3Zn7Ug1j/5kz+5YoH9Z//sn8ntduunfuqnLIj1q1/96veUASZJb775pm1apLojsv72t79t1O1rr71m2WB/+Id/qPX1dfX29mpubk6jo6PW0mJ0B+46p3aKjRK2JhwOa25uToVCQXfu3LFNmRcDDQkbcDKZtMWSLKBUKqXNzU11dnYakGw2m+bQbDQatqBQuVE5AWR42fn/iURCQ0ND2t7eVj6fv9IqILaAKhMmCaDISCGXy6VkMqmzszMDsKenp5YdRPI7GqB8Pq98Pm8uOIAFYIUFDbYmFAqp1WqpUChoZ2fH0uYBoePj48YOYAhwuVwm8s9ms/YZWPgkWRVOEvzQ0JDK5bK1G2k/kZ+ElosogPX1dQUCATNq1Ot1XVxcjloKh8O6efOmgsGgMpmM9vf3jTH0er0WQbC7u6u1tTWVSiULypSkVCqldDqtWq2mi4sLAzg+n08DAwMGmvjcBwcHkmRtj76+PkUiEWNLSW2nXUH2E4zc2dllqC6gB1ZUunRofvDBB3rw4IFeffVVzc3Nqaenx8JgecYxbDDixuVyqVAoaG9vzyIzDg8PdePGDXNMJpNJnZ6eKp/PG4uHUDoajRqggAWDDQwEAibmPzk5UT6fv+Iq410i8ZwoB4ap884yLmt4eNhYYb/fb5svAnU2dhhSWj8wcxQ0bLpsiAj+CWMF6BIkizC73W6bRqlWq11xRvLzOzo6TKoBQHYWQTByTr0WINPJvlDwAbIkWZAv8TSwNvwe56ijTCZjLT2MDkQs0KoktwtmEXcixZkkW4NzuZwxM7CLXJtKpWJMDABZepG4jyaKdQlmlSLJ6/Vqfn5exWJRKysrVjScn59reHjYZBu4lCksAMm0d09OTqx1HolEDBwBIEmd39nZ0fr6umUC0nZkPxgYGDBdMI565+8mIwzgh4Oxp6fHfpf0IrSVtZ1nBIAFW9xqtcz1LsnkIYlEQh6PxwoKJBEA+09qB79fx0sX5NXjewZgX/rSl/6XH9jlculXfuVX9Cu/8iv/06/p6enRb/3Wb+m3fuu3vtdff+UgRykYDKqn53IQ8NOnT/XkyROVSiWNjIzonXfekc/n0+rqqv7sz/5MlUpF8/PzNmcwm81qc3PTqhJ692ToUBVDz7fbbU1OTmpsbEwHBwemnyGR2uPxWPWJkwaNAoAQt9Tm5qYCgYANHYZZ40UjZ4yqy+mKOz8/t6wfNnqE+rRW2TTRhAHEeClZlKh0cR5hk2fB4Gez2ZdKpSvMEL8/GAxqYWFBfX19SqfT2traMvYLkXUgEJDX6zUdDDMrd3Z2TPQLGPP7/ert7bWEaPRu/F2tVrNxOE528dq1a0qlUnb+xG2Mjo5eWdi4v2yUzWbTRrSwEbDZO3UvbrfbssXK5bI8nsth4a+88orGxsbUbrf1+PFjY0gwbBAo6RyrA/sBG4dmpN1uW5jq06dPNTk5qVdeeUU/9EM/ZAJirhltH7KCEomExsfHtbOzY5scGkPs67RYv/Od71g7nLBJnIpEXBwdHVkKN/EEwWBQLtdlAGalUtHg4KAikYg6OjpUr9eVz+ctcgZN5DvvvKPZ2VlrST98+FDFYtEy8EZGRkz/BogEFBFNIck2XAAw7lA0NbRcadPgHuNewwbw3/wuZ5wL764zABWBNpMMkAEcHBxc0ZfCYMLC8QyenZ0Z+4xbt1AoXJna0NfXZy5X3Ly4cbnXACfWYYozSTbM/OLiwvSSnLPb7bZ7RCEiyVpwiMMZSUWqvjPA1Kk/A6QuLS2p3W7bu88aBpt8cXGh8fFxa+8Xi0XTfDmLIfR0gB/GNdXrdct8Yy0LhULK5XJW7JZKJTNhVatVW1cLhYIqlYoBVAA0gI+1OxqNKpvNamlpycAckhUCaqemphSLxVQsFo2Fh8mFYOCzSzJweHp6auBSepFLh9718PDQ2pBoWjF1YISClaWjQEeGCAyAmtfrtVYt7y6A+X9nhPv/x/ESgF09vv/BIP8PDueCsrW1pW9/+9va2NiQJL3xxht65ZVXNDAwoOXlZX3rW9+Sy+XStWvXFA6HdXZ2pqWlJe3s7Fi0AS8DQADWRpIxTy6XyxiX9fV1c85Jl8zW+Pi4ZRxR5ff399sMxImJCaviR0dHTa+zurqqWq2mZDKp3t5eCz2lrw9AY4E7PT01HQXzK2n78LJRdSNiJ0upUqmYjVqStQFIEgfEkcDOAkLYJK0R5s09evTI9FG4zRDJS5e6l0AgYGOP2FiIbWCBx3BALIHP57sSk4CGjgVKkm326XRa6XRaf+2v/TWNj4/bNfX7/RaG2mg0zHXYbrctSsPpnqWNNzk5qa6uLjWbTXNx7uzs6P79+yY+J96Exb+zs1M3b940kT4bPAt+PB5XNBq1yJF0Om1sKgGOznw0ScY0/fEf/7GePXummZkZRaNR08aRkL+1taX19XVjDrzfnUFYLpfVbrft89N6ISAV/Vm1WrXE9mAwqGKxqIcPHxpTcHh4qGQyaczLxcXl3EdS/dfW1gyE0cp1ugJLpZL+8A//UGdnZ7p27ZrFxty7d08PHjxQLBYz4wlJ/TA7BwcHKhQKlvEUCoWsoDg8PNTW1paBMP4Avvf39w28AOSdejvAFW1Lp2CbVjkgwbkGSC/MLp2dnTagGdBGy2poaEjxePxKfAWMHsaO3d1dW3coMmDOabX39/er3W5bweJsLaJJOz8/N83YycmJxsfHLcRUko1y4v709/db5iGfmQ0SNhYmlrUCFsxpDOCZAii32+0rsgenOYgWK19Le5eWIewh30O0Cmw06wBOakk2wxfnNlE24XBYkUhEnZ2dKpVKV1ihoaEhm6wBGGu1WtrZ2TE5A0VTLBbTwcGBIpGIzQpFbiC9GNiN+QHXOrmEvAPOnC++n2vOte3u7rZnjWvJ9WYvQidK+1u6BN609/P5vOVLejwec0o61+SXx6fj+EwDsNHRUTUaDT179kwPHjxQqVTS5OSk3nzzTXPBffjhh/rmN7+p/v5+Xb9+3bK/EArjaoN+l2QuHtwpBGyy6R0cHOjJkyfa2tqyTYfcIrfbbSGLzWbT8pj29/ctD4vQzouLC5XLZe3u7tpIFL/fbxZwKOmzszPt7u7q/PxcU1NTprGh+gFUEVAq6c8lLjut2Qhiu7q6rE3DCBIW3Z2dHZuJFwgE1NFxOX/v4OBAuVzOWCfiCAKBgAEB9AoTExNqNBra3d1VJBLRyMiIenp69ODBAxUKBcudQtvGphiLxdTT02NjlzARrK2tmeMHZoLE7eXlZf2n//Sf1Nvbq7fffttoeRbYnZ0dZbNZMydQmbOYYSoApOIcw5Cxs7NjWXaIwwFZ4XDYzBf1el39/f26ceOGjo6ObL7c8fGxwuGwOjouU8kLhYIxDF6v19oUx8fHFstB3AnsITEd8/Pzpi8bHx+3LLJqtaq7d+/q4OBA7777rj2LxKzw7MLEhsNhxeNxCyiGCaKVPDs7q4ODAwuvDQaDqlarWllZUaFQsHZysViUJGtH+f1+TU1Nqb+/X+vr68ZaFgoFff3rX9ezZ8/0la98RbFYTG+99Za2trbUaDS0vLxsbUJmudJ+7+josDgAv9+voaEha7+jT0NLBEBn6DUhqES1EEvhZKOkFxoyCgKnI41N0pmIDhPB72o0GqpUKjaInggPZ7ju9PS0SSW6urqsdeUEPJgFeE/5jJ90uyFU5/pKsja9s6ByCsZhlwA4uD0RfCPARz8EY8tmD/ACHAwMDGhmZkaS7N9pWVIs4mSmQ4ATFNYOIMiczkQioXQ6LenFeCqYeKJT0um0aZvQbsEanp+fK5vNKp/Pa2ZmxnIANzc3JcnarGgCg8GgBgYGzJkPUGRt5V1nmLzf79fOzo6q1apcLpeZz5i+QMHl7C4458xKssiVdrstr9drTnCfz2ejpVjbYOFPT0+toOSZB6x5PB5zfbtcLtNlArS5J9/v4yUDdvX4TAOwu3fvWt7X2dmZRkdH9aUvfUnhcFiVSkX379/X/fv31d/fb+n4e3t75vqi5YgAE8EvVl5aeSSPE2yHK3FsbMy0Lwh6ocslKZlMyufzKZ1Om3bB7/fr5ORyIPbW1pbS6bTOzs7k8/kMoDDag9BCWo3ERjBe5+TkxL6eyiscDl+x3bO5AMzImGJxwX1ECwAthCRz/KHdkmSiYZgZ2jvJZFLlctnSr2EiqL6o9mgB0eajhcVizYZENRkIBCyZXpJtImx8VIfRaFTb29t68uSJBgYGNDExoYGBAcXjcc3Pz+vDDz+0MUa0Cxj4S94VDko0P85xNDgtr127Zu2K5eVlhcNh019Ishai3+83Ro9rgrA8FApZHhexJr29vUokEjaf9P79+7p7966SyaTC4bC5nDC9kMUEKAkGg4pEIvof/+N/aGRkxMT5hULBtDJ81lqtpm9961vq7u7Wq6++qqmpKXO5dXR0aG9vT6Ojo1pcXDQ3cCaT0czMjOnmyI4jOJWIDoTBaJCGh4e1tLRkoLbVaumjjz6Sx+PRD/3QD9n0gfX1dRWLRQsNff78uXp7e7WwsKCZmRkDuwAkHHhou3g2AAGYHmi7O1vwzmRxwNUnGQqeLafbEGDG88yGxjkxE5NnnMILF2UqldLFxYWSyaQVawAx2C5+H5+PtiagzDmDEVCNiByw4NQw4u5DRuCUHnAArjBrENNwcnKizs5OY4qccQro7mjNwlbCegLcWGNh7nBfwgB1d3crEAhoYGDAwCutPUw5rIU4RGHv+aw8ix0dHaapZAoK95JxazjHCYZF4+b97rzX7e1tY6K5t2hiYXfRKXZ1dWl2dlbBYNDctDDeFPOcK4AeLd7p6alKpZJFnXD9KORp9bO3bG5uWtwIOjCKbJ5zzsnJTAJ4Ke5eHp+u4zMNwL7+9a/L4/EoHo/rtddeMzfV7u6uHjx4YHlXtDd6e3tVrVbl9Xrl9XpttA0MAy8sIYOHh4c2UBhKutFoWFSEy+VSLpfTysqKaRZ4IVi0yuWyenp69Prrr2t0dNTaBKurq1pbW9PFxYUCgYAt4gjd0Sdhn4dRIoSvWCyadmhvb89obhYANjKEq7FYzAS4MAUwX1RL0osZlOhgWq2WjZkhEJPzwTa+v7+ve/fu6ejoyCz2sCzz8/NXxv1gBGC0z9bWlhKJhObn563dWa1WDWThvEPI7BzZ4gyupIok0oAZlzMzM5qfn9cXv/hFc3GWy+UrFTV6CUTnTkYDHRetwu7ubptriOaO3K3u7m4tLi6qs7PTNoVYLKYf/MEf1Pn5uSqVirWWJalcLmt5eVmhUEgjIyOanp5WNBq1Denp06eWq4b7Fffs7u6uheYWCgVFo1H9wA/8gLknd3Z2NDQ0pPn5eaVSKT19+tTabDCMxWJR5XLZWFXadZ2dnRb+yiZ1dnamx48fWzv24OBAKysrWl9fN7v90NCQZmdn1dfXZ/M4Ly4uFIlErCU4MTFh6fEffvihbt++rVgspuvXr9tsRVpx3Ae0gwSK8kwCQr1e7xUmi4gOxNTOMFRnNhgtPGesA4wTzLGz1cT74SxQ2AzRTznT5dEwsskzxB5noCRrrwIQnYn9g4ODqlar2t/f18HBgbFfsNYANiejRfsaYFar1TQxMWHvCusD0zCcI85gzAAoGA1g7xlaTho7IAywS3YdbCRfi6SB+0lhybt2dnY5UgudIYAFF6AzngHpxcHBga3b6OuILAE0ZbNZ+f1+hUIhKxQp7qQXblW6DehCY7GYsV7t9uWECXSctP56enoUDofNCENMRrPZtGKVwnZ7e1vHx8eanJxUPB6XJMvtAxh2dXWpVqvZu0bXgqIabR8FHkJ/zgddsFNH6zSNcJ9fHp+u4zMNwEKhkN58802LKGCz3tra0v7+vlmZh4aGLB/l8PBQ4+PjOj8/VzKZtAVzb2/PAJfL5bL2QSgUslEuzCOUpEwmY/Z3wjXZ/NCK7O3t6eTkRG+//baJs6vVqgEPgga7u7tN7Ep2TzAYVEdHh0qlkvb39y0Jmllu0M9ut9tcXYhiYQCo1qmoEK1LskUWTRSOOWIt0CNQ1ZGnFAgEzEpNIvjOzo5KpZIxhW63W7u7u6pWq+roeDGqBIs3LQRagouLi6ZrQgMFEGMhgZWCWWMeIawLbRR0J7TDaElMT0+b3i6VStnizzxLACUC5Ww2ayGggBBaL4VCwTRrzvyfdrutWCxmuXRok1h0yRJqNpsKhUJ66623TMx/enqq58+fq1wua2xsTIuLi9rf37d27cLCgrkdr1+/bllLPp9PkUjEWqeBQEAbGxv2uxYWFnTt2jVrHZFT5AzRbDQampmZUVdXl2nqYPGowmOxmPL5vOnBYI5gVtHrMfoLZphEdFpgDK2HGXz48KFOT0+VSCQ0Ojpq4I3Q0lgsdsW0wTMHyIAtcc6MBEw5nwkYL+4hLkCPx2NxBE6my6nz4RmAxeNZZLNzfj1tIMTZLpfLHJq0hxh1I8meG1g1XIxEdPj9fkWjUfv8kgyg8Dl4BtlsnQ7CZrOpXC6nWCxmRgNafjBZgE3nFACiJ7ivADzAMYYMrhcOQtYQHOQIzWHoMLvwXNH6phByZmyxviKEpxgmzgJDEyyax3M5lgsDAuG57Xbbiszu7m5r7WL+4B5SHDLiB/a7o6PD3NIwkpiVmF8JM0e7v9VqaWhoyMZ2EZ6McYN2N1pajD0nJycqFos2emtyclIjIyMGQKUXOXcUtMfHx9rZ2bE1G3aP56per7/Uf31Kj880APuRH/kRA1ErKyva3NxUKpWyiINaraZr165pZmZGp6enevLkidbW1izFnCgF3EgsilDFQ0NDlhpeq9X0zW9+U/V63UZQUEXSQiKwj9mFbrdbr7zyimkkKpWKPvroIz158sSEoLRqurq6TDzp9/sNsLDIw1JQvTq1FBcXFwacABOM3Onv7zeRd7VaVa1W+3Pp3Aht0cRQWeFEhPXhJcb4gECaeAzAJPlNaHBg92AjMAfE43GFw2HL4kKL4fF4tLOzY1EgOONwtBEmiaaNxYZkbCrvcrmszc1NnZ5ejqAhyLS/v99m1OEmovIdGhpSOBxWJpNRJpOxGZ5nZ2cGyGD+aMViQ/f7/Rb1QFuD2AkW0JGREdv0u7u7FQwGDZTBygBm+/v7lUgkdHh4qP39ffu9uOZopT1+/Fgez+XcTIDO48ePbVPGeQqr6rTEFwoFpdNpdXV1GQvZ0dFhgakAqcHBQS0sLBhjs7a2pt3dXdNA8vwDzGF0Li4uLNoCPRdaQp5TgFRfX58SiYQCgYBNfiAq5fj4WLlczsbZSLJwWhgUWmlsinxW2jMULbTfpUvw4HwWcC9TvDgLGoAdLBUbL+AM6QIGCly+LpfLtEWwcrDJAKH19XXTBAEQeNa4nqwrx8fH1pbs7Oy0wtDlupxQsLe3p2w2q2g0qmg0au5p3mU+N0wfzwltR9yOgFjnTEgKGq4zBQ8AxAmCKWb29/ftPgDyncG2zqHZvAO8Q5Js3ByFNLMhSeYfGBiwmBnWQJ/PZwwxWjlAH2wZ48po26Gl4j3CdQtQPDo6Ur1eV2dnp2KxmIFnGDS6J2gHucfHx8dKJBKmFeUPif/8Pkw7ToaX9xy2lwIK4wUTDCji0JwhJ+B+Sp8OrdRLDdjV4zMNwDo6OrSxsaFcLqdMJmNDcWklJRIJXbt2TR7P5Uy69fV1W0icmUHhcNgEj1Q9fr9fs7Oz6u3t1e7urpaWltTb26t4PG7jhAYHBxWNRjUyMqJisah0Om36l1AopPn5eSWTSfX09GhnZ0d//Md/rLW1NXMRkh9E23R/f99YFarZ4+NjTU9Pa2ZmxqokhLPOhGrGpVSrVY2NjZkriCoMdolND/0LLbaxsTH5/X6Vy2Wj94l2kC7nbhK8SdhssVi8kvUDdU416tRYOCtb2jyIU9EwwLjQ1nFm79C2wxgQiUTU1dVlg7ElGTtDK5Rk/cPDQ62trUmSaa4Ad7SR0Y/MzMxYq3lyctJAEJlA5+fn1naAHWHBpRItFApqNpuampoyu3w6nTZhNOCt3W5rZGREg4ODtrCjp8HA8ZM/+ZPa2NiwQfDk3NXrdQP8tCTGx8cVCoUMKNGGy2QyKhQKln3V3d0tr9er2dlZRSIRVatVm2k5OTmp27dvq7u7W8+fP9e9e/fk8/kUj8eNjZqYmFAikbBntFgs2vmT2YS9f3Bw0AAD5whAYcPhvjabTbtvo6OjikajqlQqNk4HVgZ9FuJkGF4YVkABLBBaLWeiPYy1s32D+JoWI8CPYsQJoDBdoMOCFePvaWk7BzcTF8DmmUgkDPTBzMBod3Z22kB7WkcwS7xLABjeDdqMFDEIr5nGIcnOk3Yzgm/aVs44A2eGFJs9o5PQeKHr6+zsNCDuNEwAHPncTkcmUQwAKQAkbDNA2hkbUalUVKlU1Nvbq2AwqGg0agUYIB6XJOfocrlULBYVi8U0NjamcDhsaxJ7Abl5MLjkiTEeypkNRwFP8Yq+kzW32WxqZGREPp/PzFUwwYeHh1e6F8QTIYngvrP+OsEZUUF0M9AuRyIRtdttYwwxYUiy4lj68zOaXx7f/+MzDcD29vZMp7O3t3dlsOvs7KxlfcGMkUd1fn45ELpUKlnVxQYM+8U4FKzikUhE9Xrd+vk4H9FCQBtD39+8eVMjIyPq7OzU0tKS/uiP/kirq6vGlvT19Vm1zaxEfr8kW3iGhoYsugKXFXqTs7OzK+nVzrRqWCAyj/b29nR2dmY6FFquJycn5nTkmtbrdRNhMwR5d3dX6XTa3HPYrqkMcSDRziXSwO12m7GBxQVwgKV8f3/fWgadnZ3m8iGnB0ck2TipVMo22KGhIdO6scDTnoE9qFQqev/997W9vW335a233pJ0qcVIpVJqt9u6fv26pqamVK1WzTXq8XiUSCQsuf/g4MBaGVxbUrqJ80Bj2Gq1tL29bSn7p6en2t7eVqPRUD6f1+npqUZGRqyql2TuvqGhIU1NTWlsbEyJREJ7e3sWgur1es1ocnR0ZPZ6gM7s7KzGxsbUarWUy+V0dnZm0x66u7vNqs+Gcn5+OXfu9PRUOzs7FqFACx/dHIzXyMiI+vr6TJ8CsKBqn5ubsw1fkoF9mECuBwUPsgE0awCJrq4uE1UTb4KB4ZNtLfQ5Thcf7TJaNUNDQ7bR46KFjeU9Bhg4YyPYnAFbpL7DOPA9sLz7+/smkO7u7jYmm5YZOsCDgwNNTU1peHhYU1NTdj3ICaRAARzybvOz+OyAWMAS54imFKYVzZTL5bK2Hs8dJg7a9rBZOIQBnTCDzjY07TS0YU7whASB/EIACO+3k210ZmbBdgN8eGdg23AJwv7gCIdxI38O9nB/f9+mc5C/R/QFZgVYJJhHjBROjSjnx/tGO9UJTp3XihgX2DUO2rdkyBGtwoxQpgtwfZwRIb29vWZ4QpsMKAPEwz7DYsKCfr+PlwzY1eMzDcCY00glODg4qL29PQUCAc3OzmpgYECpVEr1et1aF7hPzs7ONDk5aW0saHHcMLFYzMDDxsaGlpeXVa1WTWjsTPbf3d3VxsaGaQ1ee+01jYyMSLrMCvv2t7+tk5MT3bp1yzRVtCxYNBHnsgBLlxvX5OSkDTBnMYUyR9vizOthY6I1BHAjlJNYBQBbZ2enRkZGzJEoycAHFRltFDY4NkZcXltbW7bgoJtjVA6ONrQlAF3aRlTGVOUI22GX0F0Q0om2D7YoGo1ae4rEdxZ7cuL6+vq0t7enTCaji4vL8SCvvvqqOTTn5uaslUvg4+npqbLZrAYGBizrDXBJCzeVSqnZbBrjQXsRUfP29rZGR0c1MzNj+kTuGWwJbaTDw0PLDGJMDJtKOBw2ZsTtdttMy6mpKfl8PmUyGYtuwMDAgPdUKqWjoyMlEgkbDVWv1y1yo1qtmnYMlufi4kKbm5s6PDxUOBw27SHuL8DU4OCg6S/D4bC1KslI4zlrtVqqVCoGbGlrwoi6XC9m9vE9AByADWwMA6phXTg/5wYFgOLv+Ln8nfRi2DRsGqANNxttMH4vGy6AhXcBAMz7ymZAMeQccQRLgrsPZgJm3alTpQihzcznOj09tftLO9oZeQDoBJQw5YOJFgALfiYRE41GQ4FAQPF4XI1Gw55H2MWLi4sr9xBWkSIUUFOv1zU8PGwyDbRxf1EbDGYRCQjXnzWGlixrDcfw8LCNbIOlRO7g8/mMUUJsT8udFmulUtHW1paxqqenpxaVAUvFWC1iHChypRfifdh7JyPm9XrNCQpzR4uYVjgAD3ci2i+AbF9fn3VzmGzhfL6caz/xOZLMHc99wY1KJwUN4cvj03N8pgFYtVo1mhj6F1dJT0+PZbkwXJrKBUYA7QMvEgnKXq9X3d3dKhaL+vjjj/Xs2TM1m00tLCxocnJSkkz4e3R0pM3NTblcLk1PT+udd97R5OSkLi4utLu7q2fPnqmj43LkCJsbCwIbQE9Pz5X8IelFxlAikZAkm5NHyB+MFWGmuH+gtSVZRVqpVOTxeGy8EOGZZJyxwUPvMxqJxRH9FWzBwcGBvF6vuQhLpZJOTk5sjBCzAo+OjlQuly3t2+1227BgxO/VatXcbWw+hA/CIuzt7Vl2Dxs2zANBrc55eVjQWZAnJiYUDAZtoSPH6p133jGwTkVP9dxsNo1Revr0qTGiLLbcO9rNQ0NDBpJdLpfi8bhGRkZMVMtnGx0dtYHwjCZyLppskExKcLlcunnzpg4PD5VOp7W4uKhYLGatM9pU3KeVlRXt7e1pcHDQ9CBnZ2f66KOPtLq6qq985Sum8wLcrKysmLsXcD0zM6NcLmcbQyQSsXYN16bRaOji4sKiM6jaOzo6TFNFm442P8wH6fCAiEAgcKUdDcuEyw6QxobiBFMUB1T86JkQZMMEcz7OIFanWJnr4Qy/BEDw+1wul7EwTpE+5+UMfQUQcU6AorOzMwWDQZv56tRisUGzydJWZzYqQAtwQ2sO1oPzpuDhWSbXL5lMWuHUbDatTcv65/f7jWEk3gcwzc9ivBTvvVPzCfDgelFQ0Fng2gFAnEUBf+ccKA0ARTeJdhKmCZaPtR9tKG5Mni+YstXVVaVSKe3u7ioYDNrvKpVKGh4etky9s7Mz05o5k+edOXMUWgyb594B9sju4n5xbSgIaIV3dnYqn88bIKNwdUZ6OO+9JMsO5D13dkVYSyRZYQwAfnl8uo7PNADr7+/XwcGB8vm8bdALCwvy+Xxyu93moqPygWbmRXLSslR1aKHq9bru3r2r5eVlSw6ntVKtVo16ly6TmG/fvq2bN28qHA5LuhxS/l/+y39RqVSS2+22TCRob0Yd9fX12cJNhUrVBSjLZDI2Gw8AguYI/YJTA0FLjo2En1WtVpXL5czS3t3drenpaavaCoWC9vf3dePGDUvRduaCjYyMmLCVzBpCY9m0aZugbSuXyzYvTZJt6ASQwuixadNWAsCh58JYwUJPu4J5a9wLqnsAbjabtTYmLeHt7W1lMhk9efJEr776qs7Pz6+M2WG49+joqJrNpunoPB6PyuWy6Z04/1arpcePH6tcLttUhdnZWXV0dGhlZUU7Ozs22Bd2wefz6dq1a2o0Gqah2t3dNefT8PCw0um0UqmUOQQZuwSw9Hg8mpubM3Evz9WjR480NjYmr9dr7ZPx8XGlUimlUin94A/+oLq6umyyAqnlbDqYNziePHmi8/NzGyHU09NjgbaNRkMbGxvW0hoaGrJWDqJh2CJJxnzxd7lcTrVazRhnmGG+ng3MyQ46w0nPzs4sNoCfCSDDrMAzyGeFTXGywGg/Ya4A31xXWDIYM36P2+22sFqeX+IiAGM8x7Rfyb4DXCAOp4BgkwWU0UJysic9PT12Hbh3xBAAHGHNnEGzPCNkhpFMz0ZOtwBgks1mTVhPO5gir1gsan9/39hVAJhzs8dQ4MweIzwV9ymOT5j3aDRq0hKKUlqy8XhcfX19qtVq2tnZsevPmgMLxf3lOaSVWa1WLepnc3PTArFpM3u9XhP0MyUDjSgjspi0ASNLXAbGhHb7cqIK6fvOwGznXkD4LMC+1WpZRmOj0bB1m2eIggT9aigUktfrNekNhYRzYDstZwr97/fxsgV59fhMAzA2rO7ubk1MTFj4JpsaadRkzJTLZYufcFLUVGswHFTnFxcXlkPkrJxpObEYJpNJY366u7uVz+f1x3/8x3r+/Ll6enoUjUZNGEs7A90JlTPVLqJSQI0kGyEzMTGhSCSik5MTG2QNOwR97Rxm29XVZQ6mra0tCxns7Ow00SdxF9lsVrVaTcPDw7p586bR+4w7CgaDSiQSlrkEO8Yw24WFBRvEC6OXTqfVarUUjUbV09NjLk+v16tgMKhWq6VMJmMtSBYuQCQtHlqz5XJZwWDQNFawLc4Nl4XO6aBiNAnaqYWFBaP2W62W/H6/tWEY4zM9Pa2enh6trKwYm8A14We3Wi2Vy2U9f/5cq6urVzZz73fnEgK4yuWytX8zmYxWVlbk8Xh07do1ff7zn9f+/r7+4A/+QB9//LE5axHkF4tFYwO2tra0vLys0dFRy2by+/0WPQDAwXW4s7NjzlPCLvf29qxICYfDWlxcVKvVMoapVCpZ0UK1n8lkLJqFVHcYM9hD2sfohojrQO93cXFhOWCcj9frtVw2NmBYNtp0n8ymQ1NGqv/W1pa8Xq/p3Mi6gtGEdZN0hU1BoH16emrtYNhj2un8O7lMHDDBmH7QByGIZxN1hqfyrAKEcHWi/+H34SgFYJJVyHmw4fOsAer4X9hhpyEAIAZDBrDjGeXzUdSgpR0ZGTGAQ44cYBhwXK/XrYgCVMD+OXOtYI2dQcjcS9zgBDTDiqGvQleFe5HIh87OTovTAXAg+pdkblGXy6VYLCa/329rJdcLcMKYprGxMdMoErCNFgyW8eDgQM+fP1ez2VQ0GjVzRCgUsjUHMIx8ACkE7yrGCwpAQB0sLM8TTs1QKGTaXQAvsSM8PzwfTvaN9RBN48vj03N8pu+Iz+fTW2+9ZZtANpvVzs6O6V5Y7iLsBQABAABJREFUsMmJAghQQQ0ODmpwcFClUslyWtD2PHz40DRR0OKAp5OTExvxEovFroT07e3t6e7du9rd3TX3mHMcUblcVmdnp4Xv4fjD3eX97gBdFh60F+RJOROjEbdjh/b5fMYoIZImCPDx48dqNBoaHBw0ESugqVQqaXd3Vy6XS2NjYwZOGKnEoGSv12usFzo6Ql4Rhh4eHlqkRrFYNCccgBjAiQsUwEvV7xT2AsQAZyyYAFrcgufn5/Z3xWLRQADCcEnWkq7X69b26Orq0ubmprngWLT9fr/6+/sVCATU19ennZ0d9fb26o033tDi4qLq9bru3LmjdDptQGJmZkbZbNbuAwwNQn42aYD8+fm5PWNut1vT09N6++23rd1B0GN/f78KhYK+8Y1vmG19bW1NDx480NTUlCYmJmwkEPljt27dUqVSsQgSGI+bN2/aPUB8DODkntLGYXNot9s2PqdWqxnAx0ARCoU0PT1tDCIAKRQKmSAawCBJxWLRQC9aqFgsZhsRzCLOV9pzmGeGhoZMQ9lqtTQ5OWmtc4AFmjVYASp/2BYYK86XZwG3MBsXoI13nmfQqW1yHgAmvo4WGSn3vKuADJ5v2B02YTRIjCMiGw9ghVZKkk27cGaDkYDu8XjsvuZyOeXzeZ2cnFgbHC1Td3e3jftBB0jbk2BWnl3eTfL+VldXtbq6quPjy0kdvM+Ab2YxwibBKqK3Yhg6Pxf2ELc3Gt9PCtGJZcAcAWvKs+nUDpIRB5PNep3NZq8Ae57dYrFo7VHWIWJhKGgzmYxyuZwikYjl8FGc9/f3q1QqGSBDRyjJ1lVmhMJm016l6MXBjPlEkp0ne1J/f7/y+bxpHtG9UvAQxEshA6v8/TxeMmBXj880AHvjjTcUjUa1s7Ojx48f21BbNlREvs5Fq6urS+VyWbFYTNPT09ra2lJPT49ee+019ff3q1Kp6IMPPtCzZ88UCAQsCI82V7lc1vb2torFouLxuCYnJzU2NmY2+FQqZewGw6tjsZgJWOv1umUd4bqCaWCmIiwDoaHBYFALCwvW3tnd3VW9XrcXHkYK7QKBqJ2dnarVatra2rIqkxd1ZGTE3H1ra2vW2pqbm7Nh4dls1mIXYrGYJNnXn5ycaHJyUolEQqVSSZVKxWY30nJgaDmaIUSje3t7Ojo60sDAgILBoGWJsWACGGA/EDmHQiHbIBiJxJgV2AMqXthJFh1alvV6Xc1mU+FwWKFQSKlUSo8ePdLk5KRu3Lhh0Ry4DTFT4IY9O7scqUQ7Ex1RPp/X06dPdXR0pMXFRQ0NDVlEBhs/gbaARIb1Li8v6/T0VIuLixoZGTFGAaYAnVBn5+VYmJs3b6pQKEi61ADihMvlcjo/P1cqldLa2pp8Pp9GR0cNLB0fH2tjY8MKDoT2Y2Njli2XSCTk9/s1OjpqCzgCezaDVqtllThgJRgM2qbszERC5M39Ia5lc3NT4XBYMzMzNo8SBoOK38k4AYxwoDqLq3g8fiVY1Zn3hFYLvRq6PQwVtVpNfX19ZtZwuh/Z0Lh3RIDQQiTfCpcgvw+gwrmyAbfbbWvZA9wBbWRXOYX5vN+019l8nXogpx4Mt7AzPkKSpbRjDEGXxyYO6Hc6o52uUHRQfE6Px2MgORqNan19Xfl83hgsZBXNZlPBYFDBYNCYLgZm067j3cLRKcmAGcXLJ2UXtVrN7vXJyYmNYZN0ZY4mzwlieFyNvb2Xs3h3d3eVz+ftPeQ64J7O5/Pa3d21c8BEgemns7NT09PTppnDlc96w2eD7XKC6L29PYvDAEw7zS2dnZ1X3OMU6bTCAdgwmrBePDuAfvRkLyMoPp3HZxqA9ff369mzZ7p3755pKqhmWcQvLi5MJ0RLy/vdafepVEoPHjwwJxfga319XX6/Xzdv3tTc3Jw5AXd2dpTJZHRwcKAbN27o1VdfNW0Ji/PS0pL9vmg0qlAopLOzM21ublryOzP12KCd7EGpVLJWKmwSA2UJLKVF5KzOyJeiskIDhzuQ1mGr1bKF0+W6HNdRLBbV09OjZDKpWCx2xeHkDK3E0bixsaH5+XkFg0GVSiU9f/7cwKokWyiy2awtIk6BMvEfMzMzikQiajabNmwckMxmiePxkz97b29PpVJJkUjEBM1s3vw7FmxE1ATe+v1+Gx6O2D0QCBhohY05OTlRJBLR/Py8aUA2NzfV2dlpgIODOY6np5cjT2hvo51heG8kEtHc3Jw5WgHjrVZLDx8+1NTUlCYnJ6+IdycnJ22cDywpLAGxHm6325K5mc0ZCoW0uLgot9ttwJPKnsobhyvXkZmnOCNjsdiVtp1zksLw8LAkmcuR68yGilaJlhqMKQaXXC5noBbjjCQDHtKLTQmGwKkJkq5u1oBdrh1g2ePxaHh4WPV6Xel02tg3WJNGo6FyuWzaQq4hjtq9vT0DLV1dXTYkvVAo6Pnz5+aMpG0Ea4tOjP+mFUaRwb2j1QSjCMtHUQaYA9g5mUVMIxSbjUbDwCbap6GhIcViMbs+sKr7+/s29BxdH+wXRSHAV5JFkhCtwfpAXtvm5qYmJyfNdASLg5aLdQ63M2wSg6739/ctLBY2jGLO4/GY3pK2LqAM8Nvf329MO6YomC4iiE5OTtTX16fR0VGVy2VtbGxY65H1gffDqa2qVCrW3me+Ku+2dAly19fXLZYCLWlvb68x6TC0mC0wHNEmJ/AVfRjrf1dXl+1vSCFwibI2k32JCQ0gCnAkFuf7fbxkwK4en2kA9vTpU62trVm7BQ0RLTioWVKI2bDcbreePHliAufXXntNPT09Wl1d1e7urvx+v+bn5xUKhUx03Wg0lMlk1N3drXfeecfYHdpdVCxUZyTpn56eqlAoKJPJqKOjQ4lEQsFg0FgaXmqAIhonnHDOOYoAPNpYiPhZzEhelmRJ5U4hPu4bUuHPzs7MKRYKhTQ5OWlaB+zVaNPIAtvZ2ZHP51MymdT5+bm2t7clyVyZZAzV63VtbW2pr6/PqkZysxiJ1N/fr8nJSRUKhSuOKunF0O/BwUGNjY2ps7PTDA1sDGxgMGnOHCRakOQyEVjI8OfDw0MVi0VVKhVrbcCawbDx/2GwmAlaLBat7eLz+TQ1NaVoNKqpqSnbYGHCnMLb7e1tAwOYFtxut9bX17W5uWnVKiJ/4hVgSGCI2Ejq9brd82q1qkePHml6etrE0Gh/Dg4ObGOCbeIaYbdnI4b1QF/j8/nUarW0ubmpTCZjrlcCNmmXZrPZK87U7u5uG+OFSB4WpL+/X9evXzfQQQArAM4JdmjJOcXKDIw+OTm5MhJHeuEkdWYxwXr4fD6dn19OhDg9Pb0yCB3AzWbO39OiRHPEBgKzjlbIGdBKGx1HGiwGYm9YD6/Xa6yRk+1zts8Q5qNnQ8cq6QpD1tPTY3Ez6NDQRQLoYO4AhmQR+nw+jY+Pa3x83FjMcrlsLUN0SABAgqrRmA0PD9vXYdghPoU2LowbzmnpRT5cT0+PfD6fGo2GMZdEacBIAUiJ/XBGk/Den56eqlarqVwu27uOHILnhvs8ODiomZkZFYtFZbNZm7Nar9e1urpqZqzZ2VnL+Ovt7VWtVjN3KMAVWUU+n1ej0dDIyIgx5864IZy3vL/cc7RbsIesu+xVrD1HR0d2zwChPAOsDT6f74oLFCE/cpaXx6fr+EwDsOfPn0uSoXuqPgSh5EDRhsHBlEql1Gg0ND09rb/21/6aksmkifWTyaQxGc+fP9fp6akFmXq9Xr399tsKBAKq1Wq2QUkyRyVtBZ/PZyNvEFHPzMxoamrKbN+8TMfHxxZeeu3aNcXjcVWrVW1sbCiRSKinp8cGNzebTQUCAY2MjFxJ1o7FYqZDqFarymQyxvpQKR8eHhog7evrU7Va1cHBgQYHBzU+Pq5gMGgiXhbMvb09VSoV7e7uqlgsyuv1Kh6PX6nKxsbGFI/HrRLBUUYLABE44l4WAq4ZmxDOH1xyiURCyWTS7lk4HJbP57P5mGzULKwwH7BkiPdZiBjTsry8bHMGR0ZGTLjLIosjD2cV5w0DxXDfjY0Nra+vq/7dYb0czGd0tkZv376tsbExu+aSrD3X19enyclJA2nvv/++JiYm9MUvftGGgqdSKXP8DQwMaGFhQclk0sIpaZMwJy+TyWh3d1c9PT0WwgmA7+npsTFQkpTNZm1KAtfC6/UaQ0Sbzyn0djJa6OvQMZKJhB4zFovZmCdaJszcCwaDkmTMFRlQaGYAwGxWtF8QwbNRkRheLBaNSQW8wjYNDg5qdHTUCiU2UVxzPD8wPAAd2GUGo6PrcgrK0ZzSZuR54Zlxun0ptnBHwibBgMCewdjDcqHvQuAOIHVqU2EcAVkYegAoABA2ZlqFjGJjwgdSCHSNmIYikYgNaoc14o/L5TLmN5PJqKurS/F43Nh2ZhQSiQHDS4vMuW5MTEyYMN7JiHI9AMpMXOjr67Mh3VxnxoDhcMRoQruV54TrCmuEc5j3IJfL2dpGGxanLPo9j8ejsbEx7e7u2nOBbo33gcKc2bCAU+QrhNmenl6m46MxpkVJK7ynp8da4JgiWOt5F5lD3Nvbq6OjI9MKvjw+XcdnGoAhunRuti6XyyhsqkzYJpxkHR0dCofD+sIXvmCVCtUL/x89BItxJBLR5z//eRsW3NHRocnJSWvvsDjTmkHjUSwWVavVLHZgZGTEQN3Q0JCKxaJtGgTD0u70eDwGira2ttRsNjU2NmYvZKVSkdvt1ujoqFW4MBjxePxKxcqizpgNwgfJywoEApZZhQCdhevx48eq1WqKRqMaHh7W2dnlXESP53J+ImygJKu0mIWJ2Bcg5Bwrgv7C+93pAJKMjbt+/brcbrdyuZyWl5dNPI8BgXvf29trFTDxAE6NDLlW5AAVi0W5XC4DJTMzM6aNW1tb0/DwsBKJhMbGxiTJBMjLy8vK5XIKhUIaGxuzET6cl8fj0e7urjFAMFBoPwAR6EF6enqsqvX7/arVamZ4uHXrliTpz/7sz8x9GggEtL6+rlwup7GxMd28eVM3btzQ8PCwPvjgA62trSkcDtsYKoKFo9Go4vG4IpGIBgcH9Y1vfEOPHz/WwsKCtSfa7baxwxcXF8pms7Zhzs7Omi7s+vXrmpiYUCgUsnejr6/PRlMxuBggIsmkADAoaIvW19e1s7OjhYUFBQIBuya817ArTgaFdx1tFSwxGXVEEuzt7amj43LsEvEDhFGSF+VkBmCvYIwkWeCrJHt+cSijySOOASDIM8hz42yjAk45f9yN1WrVDEKJROJK2xBm3Cn4BxiipWQT5vfwWdFdwnqThg+ggHlyjuti7BMzNvv7+zU2NmZTRmgFBwIBc3iiXTs6OrIOAWCbgFXajKenp+b8zefzKpfLxgazdlMA0pLGFUs7jWvAjFDWeiIhGF/W2dlprCwgBbDEGs+7i6Fnf39f4XDYjADcCyaH8BwT+I3ZAskCkTkAQKKAWA8bjYbJB/j7er1uz1GtVtPo6KjlI1Jc08In6R5TD/eee+lM08eRDQPHc/Dy+HQdn2kAtr+/b+OC8vm8CUY7Oi7HweAuwXnD5kjUwszMjDo6OrS6uqpvfetb2tvb0+joqD3QVL7RaFTz8/NqtVr6zne+I5fLpZmZmSsOPSpQqhSCJcvlsrq6unT9+nUDSoQr7uzsqFgsyuPx2CZ5fn45JqnRaMjv91uOzMDAgBYXF1WtVg3UXVxcaHFxUX19fSoWi/Y9kqyiZJFEKxOPx415QWTsHPfRarUUDAaNhUAw7xw1kslkdHR0pFdffVUTExMGrghJZPYdsQ2wGlThkoxhQgSdSCTU3d1tc9UAg8+fP1c2m7UWCzobfh4p4yzu/AyYAknGcOA4ROxcr9eNIaKdsbKyou3tbb3yyivGjkmyjDCE54A32sSA2GazqUwmo0ePHpk7kjyli4sLix1IJBIaGhpSrVbT6uqqCoWCsRzcs1wup8HBQX3xi1/U4OCg+vr6dO/ePT1//lylUklvvfWWZmdn9bnPfU67u7v62te+pmKxqB//8R/XD//wD1tWEzlkzuHeFA3d3d02Hikej9v4LtxYGxsbZnRAe3L79m1JL3KWaCkzmgrnnPN70IQBkhkWn8/nLf4EkbQk2wS5/vwvrA7gjM0JFiAajRroB2CQMUdcBgWJMyyWZ5JCBZ0WzBzOYT5LKpWy7C/ADgftU74PptcZ6sl1QtD94MEDpVIpM/YAdHEz096F+UGagOQA9obnx5kdxrVxasrS6bSKxaJFKOC4476cnl4OvXe6M2GtYO6r1ap2dnYst5CCDXPF7u6utra2rmhzBwcHFYvFrGUn6UpEQ7VatVFwoVDI2pRoOmknox+D6czlcioUChavQ15jpVIxzS8t0ouLC3vnZmdnNTIyou3tbQNgtHyRlvT29qper9t5kZ+GBo1n2uPxqFQqWWRHuVy27LlSqWRaM0AazxsGCZi8oaEhe78KhYKtq87WOgWGdDVqg2fR4/HYO4NOzPmMfr+Olxqwq8dnGoCNj4+ro6ND29vbFiZIdctCh3aDcRydnZ2amJjQzMyMvViPHz/WycmJRUawIMHwBAIBpdNp3b17V319fZqYmLBFAW0LolFYgZ6eHm1tbVnCMjPfABbLy8umN2NuJfZoFhE0BoCVTCZj4EySpqenNTY2ZrZ1ScZ6seEQvNhut839gzsSvRytD9pQHo/HXHMdHR2KRqMGSknDHhoasvar06GGFb6np8docCpMALAkc5qhk8EE0Gg0tL+/rwcPHujRo0c6Pj624d6SLCGbyg9mifYRbWE2IjYV2mmwHmiL6vW6+vv77XloNBpaXl62TCq0UoiKAXDOdiqL4cXFhTY2NnTnzh01Gg29+eabGh4eNj3N/v6+4vG4GRQ++OADdXR0aGpqSoFAQJlMRuVy2VjEUCik8/NzbW1t2ab+wz/8w8rn8yoUCvroo48sPPiNN96wa+O0rm9ubiqXyykWi2l0dFRzc3Py+Xz66KOPdHh4aO/QxsaGjUwZHh7WxMSEzs7OlE6nlcvlLDYEtoj71tHRoXg8bgwzrSoYJ5ie4+Nja82Q5YSWhRFHgB+ne5DWEICFDdcZiopWSnrRonQGkvK1Q0ND9vt4X2gfATJ4nhmkTUgtnwXw53RZoknkM3A+gI7e3l6bHMD3c6446WjV4+yjzY6ommef3811gAGkGEHjVq/Xr8QxnJ6eKp/PWxZeJBLR9va2CoWCRkZGzFABmEVjh6wBvarX67UCks9WKpXU2Xk5PYN2HdowXM7pdPpKZh3yAd5TAntZqyjo0F9eXFyYbhSjDv9GIYfzOhKJaGhoyFyHBJbimKQoxy3pjKYYHh62dYvfmc1mzSSEScLv96tSqWhnZ0fT09P2PNDOdIaqojFtNpva29szwEpHgsIb5hcDVy6Xs/UAEM4kAdzsOHHRHGJqwTkK84UG7vt9vARgV4/PNACTLh1YkUjE6HuYHUJC9/f3LRF/f39fkUhEb7zxhvx+v5rNpj766CN1dHTYnEaYiEgkYqL0paWlK4O0YUtYGI6Pj5VOp7W/v28anWq1qvX1dbVaLb3yyiu2iJbLZa2trdnYo1u3bpnrsbe31+zECFabzabW19e1vr6udrttDj6Px6ORkRFjrrD6I76UZLoYtB+IbGEEnS0RFlmATbFYtHgIqkLGYuDkjEajtmkeHByoXC7L5/OZbmpnZ8eiI2q1mp0H2hUEqWil0LTlcjltbGyoWq2ai7RSqVhYI4sjFSQLKgnzPp/Pzp0FiGeD82UjLJfL5oBbWFjQ2NiY1tfXlc1mzTGKKLlYLFrbC3YNrdzFxYV2dna0urqq8/Nz05oUCgVjDXZ2dhSJRNTT06N0Oq33339fyWRS09PTmpiY0ODgoFZXV6+MQyLElYU5Ho9rYWFBlUpFGxsbun//vrlEBwcH9fz5c927d09LS0uKxWKamZmxrLv19XXF43H19/crHo9rbW1NXV1d5mgFKKbTaUmyPC0mEMTj8StBtM6UeRZBp7OPiREYJCqVinp7e5VMJi2iwu/3a3Jy0mIe2EgA0870bpgJ53giTBls+s5cL1qO+/v7tmmhx2KcDqClXC4bE0ER43SZSTLzC8UGjBL6xnq9boUNn0WStdJ4/jm/w8NDAxWxWMzkBTA8FHIUKxQOnDMtfTIJARUUXoRzwoBJl3o/QPvQ0JAymYwBODLeaKPCgsKcwMTwXg0MDOjWrVva2trS0dGRZX4xuszn8ymRSFhYLYCToOJIJKJMJqPt7W1jlXEw7+7uWjsZaQX6skQioc7OTgNr6B4HBgYUDodNcyZdsmvECOXzeWUyGXtenOsE2kX0uRSggDEkB++//76CwaBGR0cVi8VUr9e1s7Oj8fFxO4d6va6TkxOTM1CY4sTP5/MaGBjQ5OSkGTFY+8hkQ6c8MzOj6elpk89gViKhv91uW84kIn0kN9KLXEWA2cvj03V8pgEY4aAej8faIGxaHR0dajabKpVKtnD29fXp9u3bmpqa0uHhoT788EPV63XNz8/r5OREz54909HRkcbGxjQ1NaXu7m4DP1NTUzo5uZwLuLi4aFUz7Q00AjAhu7u7ajabmpqa0iuvvCJJyuVyun//vlKplHw+n+bm5qwliIuTzVqSMWUsUMxYdLvdSiaTkqR0Om3xEziboLIlWfuSUEBJqtfr1gbipST5WpKxCZVKRS6Xy5hF9BZUr4h5aTMS0NhsNrW2tmbBjmdnZyZ6hYlyDvmt1+vWUmATQcDLQkL7CsfQJ51ZjFqq1WrGIJGLxjNClcm5wzRC3efzeUWjUS0uLqpcLl9JoUbQGovFrNWJqL/dvgw1JbiUc6Aqd26MbOCtVsvck7jbYG7R+wUCAbtO/IzV1VXF43HT2hAcTG4Wo6IAUrC6CJqLxaLpiA4ODvTHf/zH2t/f1+zsrFwul4n3cXfCJtDaQNsCSxWPx1Wv1228E21ImFSY1WAwqLGxMWuzstGjFaTliGMQZyMTHQATHBQOPLu0pdmYeSZ5B1KplLHckUhE3u/ODoWx5VlEz0fuG7EbZItdXFzOQKTNDRByjtwB7DhjI5znB3Pj1KgeHByoUCiYRhOWB5czbTpJFl2Abo1WLdpGWEH+Fxbx7OxMuVzO5iDOz88rGo2adpUEemfgKax+R0eHMT58tv39fWPUAN28c7BA0iXbHQwG7bnk50myGaYMdAdc0uKrVqtqNBrWasZwQXFAYCtrAey7MzcLxnVvb0+FQsGAK9M8isWiMblMGqlWq0qn0za2zVmc8+xHo1GTf4RCIXV3d2t0dPTKRIh6vW4FBcGxMM8AUrShLpfLnienIciZT+bMOqRA5LnkvYbFpJih0KBd/f08XjJgV4/PNABzuVwqlUpX5vNRNZKijWCy2WzqlVde0ezsrG0YLpdL169f1+npqdbW1pTP5zU+Pq6pqSm1221tb2/r8PBQc3NzOjk50ePHj68Mi2WxZdaX3+83kevh4aFu3rypV199VV1dXdrY2NDS0pKN5vH7/eYcQoBMNUblDgggDRr9ExZ5qjNs/lSqVOcI1plBScYP9nkGmFN50a4oFArWBqjVajbbcHBwUPV6XQ8fPtTOzo5u375tY51cLpfC4bBlhWWzWcXjcQUCAWv1sVDCep2fnyubzZopgo2YwEM2Atg9MoI4NzYHQBobHQCazb9ardooKFiEvr4+DQ4OmsAX8B4Oh+X1erW3t6dcLmcBvJgjWOQ4T4wWtIJoCyHWjcfjKhaLarfbun79umZnZy00l8T9drute/fuGfPq8/mMcRsZGTEAxWYOWGYj4e8QmC8sLOjk5MSEzoFAQNFo1MAaGVmzs7OWLdfV1aXp6WnduHFDfX19qlQqevr0qXp7e/WlL31JXV1dFtyZSqX0J3/yJ5Jk1xrTByYHUthxyiLWh1FBh8fzJsk0WzjQ2HjQVzqHGRO9wv2H8SDgE60MzOHNmzdVqVRsEkU4HDagwMbt8/nk9/utXcPGDwjnd6FzBPCz4UkvTAcwhc6/d8YFSLJNFaDqBFq0fEkx5xw/CYzOz89tZBBtSSQXgFd0ekQkSDKmHTapUqnY9QMUwB7iFHXmlwFua7Wa/H6/hoeHzYQSCoUUiUTsMwEuWH9g253rJ8GpXGf+++TkxEBeo9FQtVq1EGcAamdnpzGaMKmwjQD6oaEhDQ0NaXl5WYODg6YJYwIAMSzVatUKRBhNp56UebgbGxsWntzT02Njk6TLSB5Y8mw2azpgDC43b95UMBg0hycxITzrLpfLjC/OvyMY+unTp3ZdKLYpMAlxJkKJWZ7k/L08pH/yT/6J/vN//s9aXl5Wb2+vvvCFL+jXf/3XNTc3Z1/Tbrf1q7/6q/rX//pfq1ar6c0339S/+Bf/QteuXbOvOT4+1i/+4i/qP/7H/6hWq6V3331X//Jf/kt7x/4yx2cagGWzWcsSIp0ZhkR6kTNzcXGh+fl5ff7znzfxNVlbjUbDxuYEg0Fdu3bNdACSLDH5/v37Ojo60q1bt+T1erW7u6vT01MNDQ3Zy0h77OzsTAsLC7aYF4tFra2tGRvVbrc1PDysZDKpSCRiTMDx8bGNHqnX66pUKmYLZ/FjMULnQzvn+PjYFsDh4WF5PB7l83l1d3eb/Z+5ZuiqaP0xZsSZLN1sNs1Jh/4Dep5KHn0UAn8qR84ZNoGUbloIgE82Xr/fbxUuTBJtQypJfj8aOfQjTms+AJQYEDZo2mKYCDBYOAXaXPd2u63R0VHTjHz88cemwUBgD4CkdSDJTAqIdp0i4v39ffX395vgGbYrGo2qWq3q448/1scff6yLiwu9+eabBs7q3x2MDQvJNaTlDLsB21StVrW2tmbBj2zstJe4x05nV7lcNrBG6O/U1JRlj7HR0SJxZnwtLy+rXC6rVqtpcnJSU1NT6u3t1fb2tm06sApoD51Du6nmmfogXUZBMI8QpoyqntbLwcGBaWi6u7vV399vrS0YCgTfuDzRag0MDBjbSTEFqJdk7RreL3Q1bIJOLRfaMCfzQBEAIwVTxwYKs0VsjrPNitYNVggAA/MLQ4LWDMBKKxahPCJ5ZxEFgOY+0tZmwgaADAAEiyldbjTFYlFjY2MKhULa3d01wCZJpVLJ2m88l9wPQqJhJVl/hoeHNTo6qqOjI5VKJcv9kmQGppWVFZ2enmpyclIul8sMV81m0zLoYKl5fnBE4pCkJQ0DiEaRCBZa/cgreMZhuXj+nG7SoaEha9kTT3R0dKTd3V3LlmN/goEnjJspABQIgGsYPQpOp8yCz8AoMlyuXq/X3J08izz7oVDITEt8Zmdw9P+Xj/fff19/7+/9Pb3xxhs6OzvTL//yL+srX/mKnj17Zsz7b/zGb+g3f/M39dWvflWzs7P6x//4H+vLX/6yVlZWLHrm537u5/T7v//7+p3f+R0FAgH9wi/8gn7sx35M9+7ds+fxf3d8pgEY9HIgEDD9FJsTfXcYpzfeeMNE45FIRHt7e/rv//2/6/DwUOFwWFNTU+YUky71UzBl1WpVfX19lp0E0PD7/VfCIbu6umyRrdVq5hLa399XqVSyxY3RQmhjnBbz/f19ZTIZa20B2A4ODkw3wdDowcFBa7VmMhltbW2ZrgZrOxEMbObYpXmp9/f3VavVjHVAeM6mDWPEwuFyuXTz5k1rd7GZNJtNczSySCFcp6pnIQiFQmZrD4VClsHFda3Varbp8LNgxGjhOcfW0NYhSgP2ARBGoCPXX7oMeiU7C9cecQqwMHt7ezbZ4NatWxag66zqW62W0um0tXTJc4KlBDDQBsHun0wmdXFxoUKhoM3NTRv4u7e3ZzEgBwcHSqVS2t7eNk0PYNc5eJvqnPsO+4Er88GDBxZ2ChMoXeZbsUGgfwGAsNhvbW3p4cOHNq5laGjIhtYDPnGV0cYKhUKKxWI2NgpHGgn0sDQABe4ZonGYSgCHJDO3BAIBcybyfQibYUw50JmRBQUAw8HndEny7vGecH15vmlZ4SIEhNTr9SuzIZ3RE5KMkbm4uLCCCq0VzxmCcVpGsJnValWSrK3mBNFObSfvJUCB88AoRDI+1w72uNFoaHNzUz/0Qz+kRCKhdDqt9fV1nZ+fa2xsTF6v1zLCiHBBwzg4OGg/l6H0xFxQAFarVWv78qxQYM3NzSkQCGhiYsKMHfl8XgcHB2YSqNVqxhIdHByY9jWbzapWq2liYsImfjSbTXt+eTYAsOvr69YFkWQFDetvLpezddDn81kxFQ6HVS6XVSgUzNQwMjJiMRf1el0bGxu6efOmzWjFmMVawHPuZOxxRTulJzCrML0Uv0yO4L0kXb/dbisSiVxxC8OQkz/pzP5C4/bykP7bf/tvV/773/7bf6twOKx79+7pi1/8otrttv75P//n+uVf/mX9xE/8hCTp3/27f6dIJKLf/u3f1s/8zM9ob29P/+bf/Bv9+3//7/Xee+9Jkv7Df/gPSiaT+vrXv64f+ZEf+Uudy2cagB0dHWlmZkajo6MWWVCtVo0Fq1ar6u/v182bNy2zC5Hn7//+72tjY0Nzc3MaGxtTIpGwzQ3ND8nvBBui+6BlNz4+bmGvhBQi2F5ZWTEdElR2IBDQ2NiYjdXZ2dnR+fn5FTYulUqZhk16IWpGNJvP5zU8PKyxsTH7nv39fVtEBgcHrdLlpaMV43QLoo+A0iePJhgM2uBlFrHz83PTodFiY2QRrAOaDdp7aNkQhQKKyGuTZBoYWjvodtCvQK8jaIaJoWrm58AC4dgka4fNj8oXtqyrq8taogAPGCtJV9pc4+PjyuVyOjk5sdYF7BfnjS6I82P+nXQpVAdAIiyn9ZnP51WtVhWNRg1gwJA5jQ5ORoVWMq08HLEwS5OTkwb0yuWylpaWzKU2MTFh7A4VGnEtMEPZbNYAGIG9AClYx0ajoVAopImJCZVKJXN10Z6ixY2pA3s++jVm5yE4B0hTFJyenlryPcwOGzjMDGCaPDtAKc8Bc/Gcpg/ckwAErh/PB7EGFESciyQDuzA8TmE8rAPvmTMNX3oxF7DdbluQq3OCRTweVzKZ1MzMjAFSAnppM8LQcA34Q1HidJGiMYR1Yi3g2qAl297eVqVSUSaT0Y0bNxSLxax9KcnuO3mDkq7kndGSdo7tQVfKelkqlSyXEKCAvoqctunpaZu7yGxcWEUYZdzoZIuhl6OIYDB7X1+f1tfXdXBwYI5Lppi0221jPXmuaDHTBu3q6tLo6KjGx8eNLZRk+sBkMmmAWbpswaZSKSuYaPm99tpreuWVV1Qul81YxBDug4MDbW1tGfh03tfT01OTz0iy0UY9PT0m9SDGg7gYimqKZgpFuhRIALjv38/j06gBc3a7JGlra0v5fF5f+cpX7GuYgPPBBx/oZ37mZ0wy4vyaeDyu69ev64MPPvj/BgCbnp7WzMyMMpmM7ty5Y0yEMygS0T3sQaVS0R/8wR/oyZMnGhoaUiAQ0NTUlFUHHo9H5XJZjx490tbW1pXh17du3bKwR2e2TjQaVSwWU1fX5diL58+fm66GxSQYDOr27dsaHx83LcTR0ZG1QGq1mp48eaJKpXJFrMsLFQwGLTMsFAqZwPb4+Fibm5vmRsMQQIIyCx5sE0Oz2dBofzjbsiQ24yykBeh06VB9AUAGBgYskBC9FgwZAAItGnZoHJNQ/bB+aONo37AgsSFJl8CGSpxcpmazqdu3byuRSFzRftBGoCVD2wjNVDgcNodSo9EwVqRcLkuSJicnLfMtmUxaHAcuLPREjEHBiYVQFjNBtVrV8PCwRkZGLLg3nU6rp6dHIyMj9mwRIcL1IYsN0TcbEywvTjicrTA/zkRuwjRbrZZWV1ctIqDRaGh7e1vBYPDKmKHT01MbGTQ6OqqtrS1Jsk03mUyq1WrZO7K4uKjx8XGz7mM6ICT12rVrNivv7OzMRMuSDDhxrrChsEHOlgytIZ4Fj8dj1x/wATsMCIVdoGBAlCzJCgjSz7nmtDdht4+OjlSpVFSpVMx1h3aHz0jWl7NNyP2CTaY4Yg3Y29uzc2fEF88BxQjtT6eoH0bXKblwui7ZeFlnjo+PLUOQ+9fR0aGlpSXdv39fXq9XsVhMN27cMGdks9nU4OCg5eEBVIkPoSjiPjCfEucy7spWq2UhwxR0+Xzesv/oCtCyhyknz4silGKW9YRnHtCCKYSQVd7rw8NDra6uWtQNGkHE7k69H21XnjUYU0nm6GaMGe9JLpdTtVq1LC9+Pt/PdTk6OlIgELANf3x83AT1rDkwwMPDwwb+eT6QojASjOtLcSbJXJXISmjV08n4f/NBMcrBs/W/Otrttn7+539eP/ADP6Dr169LkuXTRSKRK19LdAtfgwv6k1/D9/9ljs80AIvFYtrd3dWf/umf6vnz5zZkFaZnbm5O8/PzpoMh82t3d1der1fj4+MmiqZKrtVq+u///b/r6dOnVjGdnFyOJ0kmk/J4PFalETBK9dtsNk08XavVTITNyBv6905nDozcs2fPbKCxJEuIRtd2584dXVxcaHp6Wo1Gw1o7T548MSEs7SdiLKDA2UxZPAF4aDVwHdHmYhNxurlYbFhwYaVoweDwodpi0XK6oQhEhRqPxWL2Gbu6Lsc9MYFAkglZ2VDYnNHk0JZhzNDGxob131999VWbMUerkKyjRqNhv4PQ3t7eXrOph8Nh9ff3q1wuy+12a2xszCzgzKGjbdvT02OMzubmpjwej+bm5pRIJLS9vW2bPVlXtMsJvUVcDyikTYzryuv16vj4WOVyWaVSSZJMW4V2DoBCfhu6lO7ubhPkc48AL5IUDAa1t7enp0+fam9vz2JFtra2dH5+bqze+Pi4OXA7OzuNhRsfH9cXvvAF3b9/XxsbG3K5XJqYmNDk5KSBj0wmo3w+r9HRUS0sLNj8TXRcAHBYLrLvYCzZvGBIuVdoFQH2bDQANeIQcEEDZthY0Se6XC4zn8A6I4aXXgx/7+3ttTYsERIAekJo0YGhX3SGoPJ70BGhQ+J5TqfT1tK8efOmgRCKus3NTYXDYWNWcQrzXlIM0ZKkrQqbVqvVtLe3Z0waBpJgMKh0Oq3Hjx9b+zCRSKhQKFibHjYZsAMTCLMK80/W2OHhoYLB4JViC0Y9Ho8rFAppb29P6+vrds+RGgCwCKHd2tpSuVxWLpezYo4iA9f5yMiIOjs7TVAfjUbNqcqaQ5uauJiLiwuFw2GNjo7aaDl+/tramg4ODnT9+nUzGFAYoc2l4CXx3wmwAKw4VYkGYh9gWDjgqdFoKJvNGuBkDWW6CU5h3ic0sjxPsM9nZ2fmdHSGYXPN2Fu+n8f/TQaMZACOf/SP/pF+5Vd+5X/5vX//7/99PX78WN/61rf+3L8Bap2/55N/9xedy//ua5zHZxqAPX36VCsrK+rs7NSP/MiP2MxE3GPkffFSI9QmzXhubk7j4+PWdmw2m1paWlI2mzUnDM6ojo4OhUIhW1yTyaTN7WJRo+Le399XKBTS22+/rfHxccvQqn939AXVDflEaCzQc5H1RCvvo48+UqVSUTQaNWGyJD179kxra2smzKdK9fl8tsk6U6hZkKlOWVxhmqhkaSUhkEbvhMidDRNxbr1eN1E1gnWS64+OjqxCBNyhaWHj7OzsNOEx4mBn64k/CPpp8Uiyz0ZS/OnpqTkwQ6GQfD6fZmdnlU6nDVA2Gg0b/Itmxhnei1394ODAxs84YwAASIi0+eyIgwcHB7W/v69isWiLKInWXV1d2t3dtSTyQCBg+UnMdHRqibgvtPmIg8hmszo+Ptbg4KCmpqaMFQUkwRo4nbRkNf3oj/6oBVJSkW9tbZnGxe12q1KpKJ/P6w//8A+NzQwEAvL5fJqenla73baWbavV0vLystbX17W8vKzZ2VnNzs4qHo9rcHBQxWLRbPAAVphSJ1gHxJA3xZQGXIFsJOQ40fpmooMzYZy4Fkk2Z1WSAWKeYf44tVuwTwA3wE2j0VC5XDajCWwzfwBbHPxcihzMHrDzztDmrq4u5XI5fetb37I5taFQSBcXF1dMKnw+GBM2Ylgw4jpwTVJ8cm0qlYr29vbMwDMxMSFJKhQKun//vo2yooUM+0Y7ErPR6OiouYydUzJarZbW19etjc06QUusr69PMzMzpntk6P3U1JQ6OztVKpVMckEAND8fXRh5h7CWmJLW19et6Ort7bXCdGBgQLFYTIlEQpOTk9ra2rIcs+7ubs3OzqrRaFjkCxNMtra2NDc3p1AoZLmNfr/figK3+3IMHKPK9vb2FAqFLMYE9pPWIBmFhNnC3FWrVe3t7VkcD1/LM97f329sc6PR0NjYmJLJpBWWR0dHFkcE+KYYcD6HnwYA9n/zoK3N8b9jv/7BP/gH+q//9b/qm9/85hXnImPx8vm8YrGY/T0aSL7m5OTEdIPOr/nCF77wlz7nzzQACwaDJhqHYr+4uNDc3Jzeeecdeb1epdNp7e7uWqo47phoNKrx8XFLaUdw/eTJEwstZRQHrAe9+Hg8bhVFq9W6MpYCATKONzQ3zPxzu92m36GlgIi0XC5blg+b0pMnT0wvMD09bQsKjkZAjDP3iA2KBYDNh9YQG3IwGJTP57MBxXwN9DZshHSp/aBSZYHG9uwcgIyonvOh6uazUqUDmABkCI9hKFjACMNkcHM+nzd2zukMQ6vBNT84OLAEfFgk3FhoTWBaEH0jvOZ+B4NBYylarZa8Xq8tZLR+cE663W4L6a3VapaXlEgkLJgyFArZwh0MBi2YFBY2m82qVCpdCarl2abapQUNcEPUu7u7ayyCsy3h1CEB5Hp7e42FGh8f15e//GVtb2+b+WFoaMjAOveP1qTH49HTp08ViUT01ltvaWxsTNevX9fQ0JD29va0vb1to5JmZ2etFc6mRLtakuVKUWhQyHR2dhqAZqyQ9KJ6ZiNBs4k2ES0WzJbb7bZnlRR9njWuD21LRPCsETB0AFdMJmSp4Tw7OzszPZQzf8zZLgVMcu7OwFmYZ5gcHKkwkIFAQMFg8ErgLyOVAFWYDxithNgbEwFC9s7OTss/a7fbxirBHFBgwWzBIsLIECZLe83lugyBLpVK9t7TUgOA8fyen58bIzkxMSG/329fRzFICzCdTuvb3/623nvvPdOjco3RceL2xC16dnamTCZjzD8mAExDvPvj4+Nm2JIujRqLi4v2XhKzQhgxEoeRkRFjAdHZMquRdu/w8LA5Hvv7+y1TjBYnDBxatVKppHq9bmxaMpm0OZ0E06JX5Jxo13O9yKuj2KW4pDvDvsV1+X/zQdTI/+5ot9v6B//gH+h3f/d39T/+x/+wIoRjYmJC0WhUX/va1yzD8+TkRO+//75+/dd/XZL02muvqaurS1/72tf0Uz/1U5Iucz6fPn2q3/iN3/hLn/NnGoAx6FWSCTVfe+01JZNJdXd3a2lpSR988IH8fr8SiYSWlpaUSqV048YNvf322/J6vapUKlpbWzPLMNlVgUBAlUrFQgLHx8c1MjKiVCqlfD6vyclJdXd3mxsqGo3aIklgJtZunHY8HK1Wy9qBxWJR29vbNitwbm5O165dk9vt1pMnT7S0tKSpqSm9/vrrJmLv6OjQ+vq6SqWS2fvZnKDmYVEQfe/v7xtIYWNCeF2pVJTNZuV2uy2PixYIn8fp8HS2MNHGDAwM2GbHhkmSNM5BSbYw0LZgwwUs8P9ZPHD4AQSdY22cWidAGZsaABdmEtdTsVg0VxEZUGy6bJq0tRKJhCKRiFKplJ49e6ZkMqmxsTFzysViMYVCIRNes2nl83mtrq4qmUwaWKClQuV7fn5uLTjaZo1GQ7u7uxocHNTs7KzC4bBOTy/nOcLcwAYdHBxod3fX7nG5XNbp6am1vM/Pz7W8vGxhrAig8/m8XaNSqaS1tTXdvHlTi4uLltxP/lOhUFCpVFKpVDIHGe3Qjz76SE+ePNFf/+t/XdPT0+Yeo11LGjwMLG1jQA2L1dHRkY1DojXoDOnFwYoDkOcX9otnnlgI5AFMdODnnZ2dqVwumxvN+TxKL5hGfg732PkexWIxm/mJmw8pAgw37AhuXUkGwJxCeN5//sCM0QLe2toypoMJE7T1+vr6LI4E5pYWXCaTUW9vr4X4OvPvdnd3rQhC+5ROp5VMJhUIBEx/xP2BOdrd3dXq6qp1BHh3GA7Nhk97FFMKOkUcwUSW+P1+9fX1WbYXDsVIJKK5uTmlUil97WtfU39/v/7KX/krGh8f18bGhjY3N62oYswPovqFhQULVc3n84rH4+YOn5ycVDgc1tramiRpZmZGhUJBu7u7ljc2MzNjrdOzszNls1lJl+ypz+fTyMiItra2lEqlNDc3p3A4rI2NDTMskHPHfFfAGO1fv99vsRsAw6GhIQumZS3hOajVahoZGTFAeu/ePcuprH9ihi2tYdZkii5a4ID4lyL8y+Pv/b2/p9/+7d/W7/3e72lwcNA0W4z/c7lc+rmf+zn92q/9mmZmZjQzM6Nf+7VfU19fn/7m3/yb9rU//dM/rV/4hV8wZvQXf/EXdePGDXNF/mWOzzQAW1lZ0a1bt2yWnjMBPpPJ6OnTp+bYy+Vy9jK+8847GhgYsI2Vao3Wx/HxsQqFglwul1GTzWZTT5480eHhoWKxmLxer+lqEFwzWw0WqaenR7FYzBLPEaUjokWMygiUcDis2dlZdXdfDkh+/Pix/H6/bt26Za2ps7Mz7e7uKpPJGOBxthQjkYiJNckKarfbpllyu93GqORyOa2srGhzc1NnZ2cmAG6321Y1IRh2znXb3d21lhLVJi40rPXkarHpEZRIdUgCPZubU29Gdc3fU/HRsnAGujrbTrQoEVVLssUtm81qe3vbnFpsxLRAYZdg0DAwNBoNraysqFQqmSOx2Wwa68eiCxOK4wnxcbVa1eDgoGUFca+JhTg4ONDm5qb6+vp0cXFh4KO/v1+Dg4Pa3d3V4eGh/H6/zRM9OzvTo0ePtLGxoeHhYYVCIRMfP3nyRG63W6+99ppisZh2dnYspwndkyQzZWAA+fjjj9XT02NRJzMzM/ZMP3782AYLI/4mJuHDDz+0jbvZbMrv92tkZMTYQO6LJGvrwcQkEgkbrUKLjpaSy+Wy64n7kwR+WDW+h8KCdjLvK78bPRQ6O/4e9ogCxqkhgz2ifQaAom01MDCgbDarYrFojLHT+Qj45J0F6DkZXt5bjBls3uvr69a2RzROyxgdJsUYBhXAEn/H88d7TIvw0aNHNuqKc8tkMjbhoVKp2OxE6UUERmdnp4rFoo6PjxWLxQww857wWYLBoDlTcaI3Gg2Njo5qbGzMvgcmmTUMUBcMBhWPx239vnbtmhYWFrS3t6fl5WXrXiwtLVkHwOv1qrOzU2+++aYePXqklZUVvfnmm7px44Y2Nja0u7urWCym3t5e3b17Vzdv3pTf71epVNL29rbldjH5Y2ZmxsA/kg/0WbDrtHZLpZKtT8FgUH6/3+I5cGmjoaNY3NnZUUdHhyYnJ3V0dGTvMNegUqlYMv/p6ak2NzcNWJM1SaHBPMuuri4NDw9bxBJRLazLfN3LQ/pX/+pfSZK+9KUvXfn7f/tv/63+zt/5O5KkX/qlX1Kr1dLP/uzPWhDrn/zJn1gGmCT9s3/2z+R2u/VTP/VTFsT61a9+9XsyO3ymAdjnP/95Xbt2zWhf6Olaraa7d+9qfX3dKtqTkxO98cYbWlhYUKPR0OPHj20m1/z8vPb397WysmItwOHhYbXbbe3t7ZlzcGJiwqqKcrms7u7uK1Q8IlD+d35+Xq+++qqFaCKSPjg4UCgUsoVqb29PFxcXNl+sVCrpzp07Ojo60uc+9zl1dHSYjubw8FBLS0v2ckE9x+NxG+jd19dnAmXABtU3LjgYJTYQNiXnxgGDRBU1ODhoMx1hIxAbp9NpRaNRY/nIu2o2myZgpkVDgKIkA1BUcGyY3DdE3+SyAR7Rh6FzoEUCm8XXouGjekVzxNfSGoPZI+aC/11eXtaTJ080OjqqkZERc/l1dnaai5F5i2zYFxcXFj5K+4vWs5O1obVCAjf6Kkl2n1g4YSbIpRocHFQ0GrWN3+12K5FIyOVy6eHDhyqXy3r11Vf12muvWWxFOp2Wx+NRMpk00XEkElGxWFQ6nbZRSblcTh6PR1NTU7p27ZrC4bBWVlaslXxwcHBF91OpVKwV3mw2tbW1pUQiYZsQmiXYHxjOWCxmblqeDd4pZjOyKSaTSTOVPHr0SP39/RYJAsMGy0YbkgM32vDwsGlmeMYoOABotGwIGJZkbBt6OmIPMNs4Yyto/3Oge+QZJTLCGT9QrVYtDgTtaKvVMkc01w75AMJ6BNYHBwfG1tLagzmmndXf36+RkRF1dXVpZ2dH6XTa0vwrlYoCgYA5I2ECCUjt6+vT1NSUcrmccrmcXC6XMe/84X2bmJhQPB6XJNus8vm83G634vG4FWJM5HC73Zbiz7s/MzNjY9j+6I/+yHSVsPG04WjhJRIJyyJDv/ngwQO98847mp2dNYPK3Nyc7t+/byxzLBa7UqgeHR0pm80qkUjo5s2bevz4sY0p4llm7QmHw+ro6FCpVDJNaCgUMnb2k8yUJKVSKR0fH9v7UqvVLHOtv79fW1tbev78uWkWAf9MIoC5hikmX5L4IWZGElBOxE69XteTJ0/seft+Hp8GBuwv8/Uul0u/8iu/8r8U8ff09Oi3fuu39Fu/9Vvf0+93Hp9pAHb79m0NDw/bIoI766OPPtLa2ppOTy/H0szNzZlocX19XUtLSzbfkMqPHKzR0VF1dHTYCBVJNgqmWCwaA4bAj5evs7PTBK6tVkt+v1/j4+O20TOLb2lpySouBnMfHx/r9ddf1+c+9zm1222tra2p0Whofn5ekUjExoF0dV2ONEIcLsn0SmiCYAYk2WZNfMT5+bmFEiKype3HhgNz5GwBQnezEMHQ8P1ObYuzFUQQo8/n0+npqSqVirUe0KMxUNzZNqRdhsAdRo92Jv9OqCRaN4TqMEzMqkOT42wVAjZpCUgyEwRMDBvE3NycZmZmNDAwoGfPnmlzc9PALqGn5IaxYSWTSdPEIGpPpVL2THq9XrMyezwera2tqbu7W9PT0+rp6bEATenSQXZxcaHh4WHTbsG8sZHB5s3Pz9si/Kd/+qf6zne+o5mZGS0uLmpwcNDYkXg8rlKppPfff9/EwbS2Tk5OVCwWVSwWtbi4qMXFRXm9Xq2trdm58KxQRLhcl6OoGMK+s7NzZYNGHwho4HyJLEHrIsmeJafIH6CKexgg73K5bENGDwOzxbuJgJ22L0CIwgzmCpaLNh3Asaenx4T3jK1yRmyQI8V7x89g40XLODw8bMwi+U4ez+XM0EqlYqwxgIxgYIoV/teZAVYqlZROp1Uul3Xz5k1jpGDaEOk7GXmP53KWKe3dVCqle/fuWTuYlimghHcrHo9brAVgHBCO5mpvb+/K504mk9ZGZag7zBHucsDb1NSU5YI9e/ZMbrdbm5ubevjwoV599VX5/X4bUs19KxQKGh8ft+dneHhYX/ziF1WpVPTw4UPdvn1bgUDAdJu09UihJ6S6r6/P4kdoUXu9XjUaDWMre3p6TG8VDodtzBqt0M7OTis4WXspVinw0Gmx3hFoXKvVlE6ndXR0ZKYq8gb39vbMIcp9ZD0FpOOuRXMmybpBvKf/p4HPy+P/+fGZBmDn5+daWVkxZ8jFxYW1Ccn0ef311zUzM2MxBel0WsFg0DQe0MfY5LPZrAEFcn6KxaJSqZS9CIuLi5JkmoHe3l6tr69fGVMzOjqqcDhsYtnDw0M9efJEJycnikQiOj091crKis7Pz/XGG2/onXfeMXo6nU4rEonoxo0bZrWm+qeyYWHlcxKMystGu6dWq6nValkEBtUSkRUsEOi9nPZm2j8TExPGYFHRO/VTzrE8OK7YuNCioBMZHR1VKBTS6uqqsV+I9bFbM9aIlhip24ifyWfyeDwW3YCrqaenx0ZLZbNZBYPBKxo8DBBovXA7kj4PECFHh6kFPp9Pu7u7WllZMYCWSCTU09NjQ3HRybndbmMemWywsbGhdrttFvXT01PFYjG5XC7TkQA+fT6fbZbtdlvRaFQDAwP23Kyvr1u23NDQkDY3N1WpVOxeUGxUKhUD/eQoUTgMDw+r0WjYBsrzIV3OuxseHrYWfavV0s2bNzU7O6uVlRVjgth4cV0SNkxQZCAQsIgXJ2uJAB7NCi0xp66PLDjADQaN/v5+G/HldDZz7TG6SDLwe3x8bEnmaNAAe7Ql0TViBkHY78xPcoJHRP60LHlvnAYIpwni4uJCxWLRxNY4sWnVrq+vWxHIDM3+/n4DPblczs4J1y9uPxjf1dVVJRIJM2LwnuJkHBoasow673fnnS4sLKhcLuvJkyemQ2w2m/Y+9Pf3W2wCYdM49pwBr1xv9GW0jZFoxGIxc4s6s/xgnchTDAaDikajmpiYsKH29Xpd9Xpdvb292t/f17Nnz660iXFr0r6+uLiQz+fTysqKnjx5opmZGftZXD9MJgjj19bW1Gw2FYvFTGbQ3d1tEhQmXKAzi8ViFtbt9/tNvgE72Gw2TX+JJpKiFSkHExckWQuTTg73AQctbW+v16uenh5ls1nToQYCAeVyOZtz2mw2LfTZGeSLNvf7eXwaGLBP0/GZBmBLS0vyer1aWFhQq9XS2tqaOQovLi60sLCgubk5dXR0aHNzU3fv3rUMlZ2dHUUiEduYDw4O9OjRI7Xbbd24ccOswsViUc1m0wTGPT099kDTzqrVatre3jbAEA6HbW4ZsRXZbFZ7e3sWxLq6umptouvXr1vMA/P3ZmdndX5+rs3NTfn9fh0fH2t5edls2FSggBw2GkmWVURl5HTqOas8Nj4WUjZUZ3AkLTxne6ZQKOji4sJmDjJayJklhhYMoAOYiUaj2t3dVaPRsEG26FH4TOjAcCXikoPFIEaAFgcOtdHRUfl8PtNRwOaQOI5WC2aJDaK/v9/ATCwWMxE6rSuP53IwMWGvtEMGBgZs04UVHBgYUKFQUD6fV7vd1szMjHZ2dvTgwYMrOjpiAIjyoP2GQSMcDqu7u1vZbNY20FarpWfPnml5ednYQ7/fbxs0LikYIu4NzNDp6amq1aq2t7f13nvv2YZOC66rq8vyvrq6urS4uCifz2fFSiQS0dTUlI6OjrSxsWHaMkn2jvX29iocDptgnQ3DOTUAVodCwel+JEWfdiXaLPKiAG2wmc4MLJ5fnnsAEG01nkMnUAAkw+rxzrpcLwJYYUZrtZq1DgGHtOI5B6eZg3/H1MK7Mzw8rFgspu3tbWUyGd26dUtvvvmmtWJxB1JEYZhptVrWmqQ1NjIyojfffFOlUsncdD6fTycnJ5YJh3Mxk8no6OjIijWcv4wv4zPkcrkr4vJPupTPzs7UbDZNtMya6PF4rEVGe/f8/NxiN/r6+kxuAbA9Pz+3dxUGa3h4WPPz87aeo9VaXFxUMplUoVCwnEXE5bCHLpdLuVzO2K79/X3duXNHAwMD5myHtSQdn4IiEomYez2bzZqOUJIFrOIgxtXIGoqzESMN7VH0bpKM4e/o6NDx8bHNTP3c5z5nXQLc0LhtuX50d3AQl0olVSoVc2b39/erVCqZoYGCArMHRcjL49N1fKYBWDweN6FiOp3WxsaGdnZ2FAgE9JWvfEXXrl0zh9izZ89sKPL5+bk5v6LRqJrN5pVwVo/Ho6WlJX300Uc6OjrSq6++qlgsZvZwWAs2tq2tLZ2dnSkSiai/v1+Tk5Mmpof9wslWr9evpMkHAgFVq1WLyYCtOj8/18OHDy2JnpYelDcMAeJqaHI2EJgGZtyxsaHvYHFmMWTzAoAhdk0kEpZZxkbKZgkDwTiQi4sLax8xWw2GDR1HtVpVLpezIEgYA3RgaGQArjBLsBEI/Wk5saljDqAViEMIpxgWcxgG6bKHTzyBM8IDU4FTG0ZlOzU1ZZsWUSDkV11cXKbdE3NB5ttbb70lt9utfD5vjB2t0mq1ateWmA/s4ozvQedxenpqzzhaQWYLBgIBY/3q9bqi0ai120j99/l89uytr6/L7Xbr7t27Ojw8VCAQMPE1ovVIJGKFRDqdNnfZtWvXNDIyoufPn9sgetor/H6AD+1F8o9gNwEpgCz+kEHH9UA7SVvnk7EaADoO2DJcsQAf4mYA8bzHuJ4IzeTnsk4gpOZ8EJY7E9mdxhFn+8/Z2qe9yTMyOztrIairq6tqNBoKBAIGXPr6+gxo+f1+Cy0mL65YLF6Z5uD3++094PkkrgRXZ6VS0ebmpjkaJyYmTEvEOwh7zUgiBN7o2HhHW62WyQMwARBmXK/X7fNT4CIpaDab2tnZMWPA4OCgdnZ2tL6+rmq1qs7OTn3pS18yzenTp0+Vz+ctBoapJffu3bOOwPn5uenZkGKUSiVNTk5qaGjIYlFoQSMzAMw2m01NTExobGzMNMSbm5sWMlur1Yw9BmQheSFGx7nmrqysWB4jzweGG4pk1msKL/SsxWLRshBTqZQKhYK58iWZ6QJJQnd39xXDycHBgekxWcN4Zz6ZFP/y+P4fn2kAhqCz2WxaLMPCwoK+8pWv2ANL1RgKhRQKhayyhoVwOhC9Xq9SqZTu37+vJ0+eKBAI6NVXX7Wk5JOTEw0NDRm1S8tif3/fHGLhcNhcXGz2JB1jR/Z4PFpcXLQICvQE9Ph9Pp+1j27cuGGzLi8uLmx+IhuZ3++3kEpJVr2jBWBxRZxL5VmpVKxVAI0PKyHJqlGXy2WaFCdAwJxAywt9D9lebEJOVx8uK8ab4OahGpdk2hzEx1jOK5WKuecAXXzdwMCAORQPDg5sYSOzhxYe1vRms2lAjRR9tBlEchDoiIiZOA+C+XC+kTN2cHBgLlgy3/b39/Wd73xH4+Pjunnzpl555RUdHx/r8ePHOjw8VG9vrxYWFrS4uKj9/X3Lv+ro6DCtXyKRUCAQUDqdtoo5EoloeHjYnkNaQ4Aet9ttZoh2u20usVarpf7+fgNZiURCu7u7pkMkbgIzxenpqba3t5XP55XP560FRzxCIpHQ9PS07t27Z+CX3K5yuWyjWXD5AUi4j4Avp1gfxssZkMrGDWDj2XJmYPF1PMPEtaAV4rp80mULc0aLHSalUCgYMwsIZFQSkRzObDY2OVqj/H9+F2w1YOzZs2e6ffu2Pve5zxnThqOTdrHf77cWPQwcoCUajRrjU6vVrI1MgKjH47HYiIGBAXtHTk5OlEqllMlkJEnz8/PmkqNg8nq9NiKNYggAifAbTSY/n6yx7u5uY+T5/LAxgNj79++bmJ94H6IuvvWtb2loaEif+9zn9Pbbb+vs7MyGXANcR0ZGLAKCNYx2K6PUtre3zex0/fp1raysmC4PDR9rEi1Roini8biCwaDFVEjS/4+9P4uNNE2z+/ATDO5bMCIYKyO4r0nmVnt1dU13a3paGs0iwYAHtq8M+EKAZQGCJBgQdCMBggTpwjYg2AYMCJIgQ9aNLIx9oZmpGU1Xd3V1LZmVWbkzua/B2FcyuEXE/4Lze/iS3bI19vxdlUB+QKIqk8FYvvi+9z3Pec45z+rqqubn540hLhaLBqjOzs60u7urUCikYrFomXmDg4OqVqsW7cO9Njg4qGg0qng8rkajYQO2a7WagSlY9uPjYxvRRA4lxQOtVEJuAbxdXV12z5fL5V9w6H6Tx+sW5NXjlQZgZBK9ePFCtVpNP/zhD3X79m3TN7VaLR0eHpp+KxAIaH9/X/v7+6pUKorH45qfn7cRLI8ePdJHH32kra0tTU1N6e7du5qdnTVaF+1Qo9EwkSS5SZIs1JSsGrQLGxsb2t/fVygUUjKZNDH1kydPdHp6qqWlJR0eHlquGaDx9u3bSiQSqtfrunfvnsrlsiYnJxWNRlUqlawt6roKW62WzcJj5A7VG2wN1SJaJpw7VFdu4jZpv2wguVzONulWq2VRBmdnZ8aiAbzGx8dtULLP57PWxsLCgsLhsIFfgJZ0OfoFZxIaJaYRuJlUVOxsILB3fE/7+/umuaGViDhckrVYcd25A3kZ07KxsaFSqSSPx2PZQLBgfr/fgMry8rLW1tYsNLbVuhhNtL+/r1QqpUqlopGREdMlSjIACUtKrMHe3p62trZscy+Xy/rqq6/01VdfWfsKIE2IJwVBMBg04AFLA8vHLEBJlt6fSCTsXgFgk0dFYQAIBozt7u5qYWFBk5OTmpubU29vrz799FM9efLEJh40Gg2NjIxY5MjQ0JAxhtLl/Eds/bwnksRxG/NZ2GT4jmB43LmhuCG5xgHpMGC0g9DCAPxgrnge6TK0uF6vq1wuW4o8mhpa9m7WF8YE2DRYCWJRGo3LeY+M30KgTZsyEomYoJ9/g7XDoUpRhYie16CV2NFxMRs1k8loZGTEAm0BouPj4+rr61M+n1c6nTbADXB5+fKlxZGggwUcEwTc1tZmOWB8BxSQMC0w883mRZo/rMydO3e0trZmY3oCgYDdEwS7NptNTU1NWUsT8wNF7OzsrE0scHVdpVLJABZrNusk7VgmmEgyLSetdtaucDisVCql7e1tTUxMGAAcHx+32a4HBwd2Tb548cJMR/39/UqlUsZQ4gANBoN69OiRyuWyyR6KxaJKpZKGh4fNkUwOYbPZtBF2RFcMDQ2pWq3a90UHA82t1+u90mLnfqDge318u45XHoAxw+4HP/iBAoGAdnd3tbGxofb2dtPzkLj97NkzW/CGhoY0Oztr2TSfffaZPvroI5VKJc3NzWl2dlYjIyPGIiGMjUQiisfjFp64sbFhOq2xsTFLyS8UCioWixaO6PP59M4772hgYED5fF6ffPKJUqmU3nvvPWO/SFre39/X5OSkEomE0um0PvnkE+XzeU1OTlrwJ85EMr7QGcCKQI2z4ODKwQZNTo90sXmMjo5qenpa/f39Ojg4MF0Mc/jYeJrNps2kq1ar5vpks7zuIkJU3mq1TAjKyKB0Om3tQqh5Kj0cVpFIxFpQ0qUjsKenx/RsLHJUfQizyUADyMBooCdDq7a7u6tyuaypqSlVKhXb4Pf29qw1gjgfPREAgo2INvTS0pJisZg2NzftWmtra7OYk5OTE/X29ioWixlzyXtisUS3Ua/XlUql7DMHg0Gdnp5ahc1m/eLFC4VCIY2Ojur8/Fybm5s2Wy6ZTKrZbJrOhjYlLSQAViaTMccXAmzOO8nln332mWXfraysaHl5WaOjo3rjjTf0m7/5m0omk+bmZNIB0QGff/65Cd4bjYb6+/s1NzdnmjjiIQC/uAy5JtAkEnjJNcIGiyEEXdnJyYlFsTCSCs2RpF9wPRLtQCFBgUVLFMaX1jfAhxYvawSBuTBstL2bzYsJCRQP5FJhFABM877Ozs6MpWHs1cDAgIFqScZCcb/x+N7eXsXjcdPWoUfi/qT1Xq/X9fz5c21ubmp8fFwjIyN64403tLa2ZoVDX1+fzTYlU4yxZ4wFo/Bk6giGIUbrsPaQSdXR0aGJiQm1Wi3t7u6qVCppdnZWoVBIP/vZz7S2tqaf/exnun37tmn/mCiSz+fl9/s1Pj4ur9drphDYTPS66M0A4NyLRHnAfpPZRdego+Ni+DdzfJeXl80ww3tnzcpmsyoWi3Z9YsSgiGw2mybyJxYECQeRKZ2dnapUKlfGRrkA9vz8XNvb29YByOfzxm5SRPM9wwZSRLtyDhj/b8PxKjNWf9bHKw3AaAnRGvjqq6/08OFD9fX16datW2Yb5kbw+XwKBAI2gicYDKrVaunhw4f6+OOPdXp6aq0V2kq0D6HMeZ729natra1pc3NTrVZLIyMjGhsbU39/v81HhLpnNNHAwIDS6bS++OILra6uanp6Wnfv3rV8LrRWPT09isfjqtVqevLkiUqlkhYXF01jQaU7MDBgmzbjYyQZc4UonoVBumCYyPPx+Xyanp5WIpGwVhc5TmwM2MXRePh8visz8NiwcSayCRIqGggEdH5+bjoMsrGofmFISqWSif9pWRCsCwOAOJsFCGv28fGxLe4IyIk+cGfkSfqFrDOiQ6gg+X4BrWzCmAporbARwrYODg5qZmZGgUBAjx8/NpMF7W0MBuivdnd3NTc3Zy0IRpcw94/MNyrp+fl5zc3NWewDwCwSiaitrc3yjwKBgGZmZvTuu+9adV4ul+1x6JYKhYLW1tbU29trcwf5/DiAaetUq1WFQiF973vfM2BEgZHP57Wzs3PFCQYTQOo+jBRzIM/Pz83IMTo6au5brlkcXxg0aElS4UsyRsjVj7HpcM8xtw3wj04RsMP9CViilUg0Bm448ppoEbq6Ll6bDZeB9BRBXq/X5hCil2q1WsZg09LntXEtuucN9q6rq8vmQ8J6b2xsaG9vz4wmMOiI93Ezp9Npa10BIGKxmDHRxWLRwC3a2LW1NQtELRaLlruGPOHw8FC7u7vKZrOKxWLmlsYEsrGxocPDQ8XjcdPZMb6He/ns7Exffvml1tbW9Nu//dv6jd/4Df3BH/yBtre3zWXMKDHapMxtDAQCGhoa0vr6un0vkUjEAOL+/r6ZaSg4KfBgpLa2tuwc02Lf3Ny0AGR0f0w0AWzR0maeLOBsb29P5+fnGhsbMyAViUR0fn5uLnqAdzabNWkHjF2j0VA0GjWh/vDwsBKJhPr6+rSzs6NSqWQsMe1PCknWNToBXOu0z2F3Xx/fnuOVBmBU6txsxWJRb7zxhsbGxmxBh2Kmz88mdHJyYhUpQYE45fr6+oziT6fTlkL8wQcf6O7du/J4PJbhVa/XNTc3p1/5lV+xNtuzZ8+MvRkaGrI2Yblc1oMHD7SysqKOjg6bS0bl3Wg0tLm5qWAwqIODA6XTaZVKJQWDQVUqFRWLRQv19Hq91mJkIyAxXdKVfCg2Hlqi/f39un37toWLIjxns0WwX6/XVa1WbQQRrSg0KtFo1EApCyrifFparVbLqHxs3zxPo9GwAEq+K3K6ELB7PBdDrrPZ7JXxG93d3Qb4sG2zmeKSw2SAJbuvr09jY2NKp9PmSsW6jkkgGAya0+3o6MjaaJKM9cBRxIBt2gmlUkl7e3sqFouSZAwqVTxtXQoBtDMAHWJMAB+8JqAV0EfrjdZ3OBw2cbHX6zWwvrW1pc3NTfl8Pr3//vtqa2vT7u6u5SiNjY1pfn7eWnXS5TBqbPr1et3aof39/dra2tKDBw9M25JIJJRKpXRycqJwOKyFhQVls1ltb29bOCqOTM5TZ2enstmsKpWKtre3LcpF0pVYCX6HnCUeQzHhft/S5QxUN4FckjkLXcemC55cEEWbknuGlhr6Jtg6NjrclQBo2GU2wKOjI+3t7enk5MRMQwzvdXVuACPa7pKMAXVdgqxPXHfMdN3a2jKzBiNwSEQn1gG3X6vVsmDiGzduaGxsTI1GwzLc+H1ctNVq1fSHXIuSLKuMAGLAzeHhocLhsJaWlmycGmsFAn6kET6fTwsLC3r48KH+5b/8l/rwww/1zjvvmDmlt7fXvhPeHzl7AFHOM/cUmlNE7Thy9/b2bFIJjBhtUr4DGLtKpaK2tjYL5UaiQWsc01U8Hrd7rL+/X3t7e5Zltru7q1wup1qtJr/fr5GREfX39yubzVoBBQAjY5Iij3sZhprYCtZYCkw6FVzbmHuQGvh8PsvQo9X/+vj2HK80AINSZ+7c9PS0CYC3trZsM3n58qXS6bTm5uZ08+ZN9ff3W5sShw7RDlRJxAPUajVLRl5YWFBHR4fK5bJSqZSq1apu3Lih73znOxoaGlIqldLnn39uVS8bApUU2S6tVkvj4+NaWFiQJBOMk8s0OTlpCyjsyPn5uYaHhzU8PGwgB60OTibceIDLWq1mg70ZFru0tKTOzk7lcjmVSiVls1nF43GrnmjvERCJkN3dsLA5s/ji3iSOgVYnGVnoqtjkSBFnRBHtELQkkuw9sGifnJyYeBXAwnfl0v60HckJImEbkbN0OVeSTB4YuGKxaPPs3M/Y09Nj2Tq0sMLhsKWjAxoKhYLNpQOMwTwAYKUL9+7k5KTNYyR+5OTkRENDQ/b/JycnSiaTmpiYMJ0ZejKYgf7+fhPF02at1+tKp9Pq6OjQ/Py8MTnRaFQLCwvWcqGid23xsKOA1/b2diWTSZuwAJt0eHiotbU1ff3114rH43r//feVSCRMOzc2NqZisajd3V0Vi0WTAqB36e/vt++JPD3uGa5hNFku0EIzx/XKNYlmCg2iO/7HjaagJQNjiw4M0Ma5cLVkMLps2tJlOKzf7zeWJRaLaXR01EA47ISr00Izyqgf3L+AYIoZdE2wVbwftzgAbExNTSmZTKpcLl+Z93p6eqr9/X1zIkejUXs/Jycn2t3d1ePHj611CruFk06SCdclmUEA9ylhuWTI4crjNYPBoAFyxivBZlLk3blzR9/73vfU29urjz76SE+ePNFf+At/QeFwWFtbW/b99ff321SI3d1duza4nsbHx23d43FEUkxPT1vLE2YW4M+aROxLrVazHL0HDx6oWCxqenra9Kd8T5i4EomEnU/W5FqtplKppMnJSdXrdet2wF5ubm7atdzT02OFNgANhpuYC2QmBEMTVwEwhDEFwMPu9/f3G7ONxOD18e06XmkARlr4y5cvtba2Zjfq3t6eje5Bd/T9739fS0tLqtVqSqVSSqfTNp7EHZHCrD3A2MTEhN5//30bHQEgGR4e1s2bNxUKhUx7du/ePXV1den27dvq6+tTLpczu3gikbCbJBqN6ubNmwamGGPUbDZtMG4mk7GFGaaIihHGjGgC3IX8HB1DtVo1gSmLMAvA559/foUdZBNikDGtu9PTU+XzeWPraI3hxKQtur6+rsHBQZsk4Arr+/r65Pf7LdPKXQxgKQCqsAkc0Oss+Ay0docq446jPSDJWkJ+v99ycNBIBAIBG8pN5crcNzZnDBxUjrSMaOOgp6KVVq1WTXsTiUSsMCiXy9auIt9rcnJSfX19xlCRR+T3+629yXfLBkGMA4/BSEHeG1X/2tqajTPBlQvDiWYKoF6tVpVIJBQKhZROp/XZZ5+ZYeTs7GKw9+zsrIGFWq2mWCymX//1XzcGK51OK5VK6fnz58rn82pvb9fNmzcVCAQMjBI94grBmYhA25ANiPsPbQtOO3cyQ61WU3t7u33HkgyIu9ER7r9xvXAP0NLm2nEdlm7bBj0RuW4wEBQFTGs4Pz+32I3u7m69fPnSxPuAZEKBCVOmFQ/4ue7cBPyQL8cQdkT6TIZg4Djnl3Wjra3NohUwMbh6OYod1oTV1VV1dHQoHo9bYjz3uyQrbgCUHo/HtHE7Ozt66623FI1G1dXVpWfPnunhw4e6e/euTY34+c9/bvNLY7GY1tbW9Ad/8AfmEGYk18HBgQ2vR2ROe3tzc9PGf42NjenBgwfa3d3VjRs3lEgkbNIH7etaraZ79+5Z5A8FOm1X9JmHh4fWdge8wnjRXuScHh0daWVlxUDtwMCAqtWqNjc3zdjEfjIyMqInT55Ysc+5r9frFi0CUYAujb2Hexx9GYUGgJn1jgIAZhL2Eh0moA1N5Td5vHZBXj1eaQC2vr6uzc1Ny12Zn5+3bJd0Oq14PK6FhQW9+eabGhwc1OPHj7WxsXGlhUGV2tHRYXZfmI1YLKb33ntPfr9fOzs7evLkiW7dumURFwjE2YB6enr0/vvvWxjnxsaGbTzhcNh0CDMzM5qamlJHR4dqtZoODg6USqUMRKBzIOYAZyNZYYzOgSWQLsSdmANghtCedXV12SaI5mhkZMSCPNmMXaBAK5J4BoSruBNhEFxW7OzszHKf2DBdIbIki3eAweI5OKgKJV2JVWBxgfWQZNQ754ifuW0hktEJ08QdR6sB3RzDcNnEvF6vuZykC9YKtuLs7MzE+myGaJxw4uEChClJJpOanZ2VdNEe/uqrr/T8+XML1kUfxftksX3w4IE2NzfNbYjFnO8a7ci9e/cskJE8r4GBAfX09BgIrdVq2traMnC1uLhoNv/V1VUVi0WLOpiamtLs7KySyaTy+bwePHggr9ersbEx06RwXnd2dkyfQro5Y75mZ2dt+gGt40qlovX1ddVqNavST09PrWUGQwkgwrmI1o0sPXQvtClpRwEu3NiLVqtlbX6uOQAu1zwZV+71RVZYJBKxjZl8MoCsO7+SCQbu6DJYPphQv99vrDKjhwADrraH9889xmd1W5SAAvSegAUOgCXxBZLsMzFdAQ3X3t6e1tfXDTjCng8MDFiLmVBmIh+4T7LZrB49eqQ33nhD3d3dCoVC1qr+jd/4DYXDYd28edN0dLQyv/zySz148ECRSER37961gob3TvsWOQLgPJ1Oa3p62q5POh5oVZE7UEB+9tln+pVf+RXLP8OYwqifR48e6ezszHSDXV1dGhsbM9ZLko0wwklPBhfAkbYfrCBFrMdzEfzNZ2bdcYNte3p6TM9MK/Tg4MDyKV2dGUw+OYl0N1gPWq2WySTY05BqvD6+XccrDcB+8pOfWBDlxMSEQqGQUqmUvF6vFhYWtLCwoNHRUZVKJf3RH/2Rdnd3ze4LBSxd2N8TiYRVjjs7O+rr69Pi4qI6Ozv185//XA8fPlR3d7fee+89o6F5PNXlwsKCent7tbq6agsJLUIWwTt37mhsbEwdHR3KZDI6ODjQxsaGuYlg7aTLHCEqxqGhIZVKJQtrxCmIY811ufj9fsuKOjo6Mk0ZwlXyw7xer7ENrr4D7Q7p6UdHR7bwA5xYEFlIOC8AJj4H75cNhkoe1gDNFi6r/v5+a9cGg0Hb4HBc0p5BGDs0NKR8Pn/lOQChJEUTp0FwI5uHJGuPuvqevb09bW5uanR0VJOTk2aUOD8/t/YuwY4sosFg0FoIaO2mp6fV2dlpWUC5XE7Pnz/X1taWTk9Pr4zGaTQapuujwuX3aKXu7Oxod3dXU1NT1nLBoUoLe3FxUbFYTPV6XaU/mVWZz+e1vLxss/Pa29t1//595fN5A9RLS0uam5tTq9WyYEjME8fHx5bphY6Llsjk5KQl5G9ubmp9fV17e3tKpVI6Pz83TZo7usbv91sEBQ4vQAl5Zbwv2nd8tzAxbtCqq3XkvoNFAoRxLbrAXrqsygHyh4eHZjZw25zMcMW1Rvgp7SA2vpOTEwsxpj19enpqRQ33NlMY2FRdQTXnn/dbLBbNKYf+C1cf4AtJBm1a/k5LDvDIfQyj3NfXZzqpRCKhSqVi+k+CnyVZfAI6NNYIwFwqldJPf/pTLS0taWhoSH/uz/05bWxs6Ouvv9bs7KwGBwf15ptv6uDgwNjCX/u1X7OIk2KxaIwdureenh6Njo6qv79f6+vrxhCurq5qdHTUZu7m83lJspxF3Manp6eqVCpaW1tTT0+PxRRFIhFjH9ESB4NBWxPPz89tBjDn3m37HR0dXRkgzvnkMaw53d3dmp+fVzqdVrlcVjAYtBF16JG3t7dtQgHfFVEsgESkMrhBMQfQUm+1WqbPBWwhIeD9Uax/k8drBuzq8UoDsMPDQ83OzmpiYkIjIyM2QDsej1sw5/Lysl68eKF6va7JyUlLxKbiJVNlbGxMBwcHev78udra2vT2228rFArpq6++0pdffqnj42PduHHDqGgWw/Pzc21tbdmYnVqtdmUwOONtOjs7FYlEzA5fLpetVUqbBQE4LAKtCEATOpdwOGwaMRxtxCLwuQA4sEGwfTgPyYihfcLGIF2G15IYzgWOYNjN/GKDcV1p3Oi0e2h/sniia4AeZ/MATA4NDVkLAJYCizgbOcN0+/r6VCgUfiGywHVoco6Oj481ODhoVT+AinYO4ZsYMHp7ezU1NaVwOGz5Ve4oEqYU0KIhIHVjY0P5fN6q7cnJSdOhlctlDQwMaGZmxqISaD9LF+A2l8tZ+O0HH3yg0dFRm2+Jc43ru729XcvLyxbKG41GVa/X9fjxYzvvXG+pVMqYvk8++USPHj3S8PCwfvCDH9hrkNLfbDb16NEjvXz50tpcAHKmC9CyHxkZseoddo7zm8/n9Qd/8AeKxWK6ceOGVeO4Gvkuc7mcKpWKvF6votGomUtclyLtGzYvWmoAKTfSg2uB//IY2pgUL+jauA7QefG++INI3RVqwyZvbGyot7dXCwsL5g6Ege3r67OIC2JE2OBhmhgETUHjtsE5VzBPLnPCVAiuGQYzBwIB+wxku+GYhJFPJBI265FwadpYyWTSCq98Pm9AAtDtgmN0Z+fn5xoYGFCxWNQf/uEfanFx0UAoaw1g0+O5GMXWbDZ1+/ZtBYNBra2tWZAx+lukG2Qfcm3COK+trSkQCGhyctJYXswMsVjMWnhTU1NqNBp6+vSpgsGgMdXk49FqlnTF3QqQ3d7evjJ3Ee2lJDMp9ff3m3bYbUOenZ2ZcYhcMoqrkZERY/txjRIjJMnCcyER0JnCkMPmR6NRK6Rxg3Ldc83goP2mj9cA7OrxSgOwpaUlfec737nSBhwcHJTP57MRQWTZRCIRm6cG64KuBx0Um+x3vvMdm0X28uVLq1RpZ6HVqNfrFtMQCASUy+UsYZr4guHhYQ39yYBmgNvOzo5yuZy1y4aGhpRMJk03hm4oEomYnR524Po4CzacsbExy9tis/X5fCZa50bHEMDMuEajYYssDp/rgAr9EREPpP0DbtCeuRZz6eqN4Yql2VypHmEQabGxCLmuNjZCFhUXqAEY3fYT+jA0RqSyDw0NGXBkoeL3+Z47Ozs1Pz+v8/Nz05K4WjUWeYTKHR0dpsFhTh2jnBgA7vVejH+i2uXz0iamjbK7u6tGo6GpqSmNjIwoEono5OREGxsbxqDOzMxYvMDS0pImJiYsr4jvm5yvZDJp7Aei4CdPnuj+/fvq7e21maOMt9rd3TVt2N27dzUyMqLV1VXt7OyYM2tnZ8cqa4KFP/74Y3V2dmppaUljY2OKxWLmut3e3tbBwYFtjI1GwzSOOCNpC5EXhcYIhswdDwWogPnigJXl/oZxc1kA9zullcP1yj1AS1K66rgk5ZxpAqFQyO7PUqmkg4MD28wpNhgufj0EE62ay9qxrvAzNHsUH2guYWpPTk7MvQsLSgsRYBaLxZRMJq04SKfTFnmzt7dnRhTaWdw/JOtPTEzY+6E45NoFIAwODtqon87OizFujx49su+RdQfQGIvFlE6nlc/n9fLlS3NCkg/49OlT1et1HRwc2ID3SCSiRCJhmY44o92YCwZlV6tVi5mAUX/33XfV1dWl3d1dBYNB1Wo1BQIB08eura2pXq9b25WRYaenp7ZmMN2iXq9bVBGt8UgkYuu6G5jKEHV0pqU/GWLOWkPmGGOo8vm8rcFoDrmeuY44l64shOucaxpGGf0fYPP18e06XmkAhgA5k8noZz/7mQ4PDy1aoVAoWKXR1dVlPXGv16vh4WGronp6emyR7evr04cffqj5+XkdHBzoxYsXpnsZGBjQnTt3LAOMwE+v12uZXfv7+9ra2lIymbTFiE2aBYxhrcfHx9bSaGtrMydgoVC44so5PDy0Ch7BPjcUAAyARLbW/v6+6SHYpDo7Oy2yAx1KV1fXlQwgt+KmFcT5YoFva2uzIdwIfgElCD1xyfF8kkzHA9sFi8DmQ4uWTQhWgO+H8Uzn5+caHR1VMBjU0dGRDSh3w2dhQKQLVoQcJ8bkSDKgh9sTXQqvyXXlbjhuSjwLHxXu+Pi4IpGI5XaNjIwomUxqd3dXq6urCgaDxuQUCgX7HLT12tvb9fTpU+3s7Oju3bu6efOmxTXgSnMdoLCY6XRayWRSP/zhD9XR0aFCoaD9/X2b7ei2IbH/Dw8P6y//5b9sjMEXX3xhIuqJiQk9e/ZM+/v7SiaTtjF2dHQolUqZC66zs9MCOkt/MleUtqarTenu7jZGpdW6GHzMZybVnYHKXGMYS2jL8jPcjLAoCJ1htCg0XNb3uv6LxxBcSkva1ZDBJBQKBWMUWq2WRYrAXvf19Skej+vGjRuanZ21Frckuw/IVIN1gcm7XrXjXkWPCrtJ7h5AFMOMK6rmnvX7/daaJo+sWq2aXolWG61bgDDvhTXBzT/j+kaHROwB1xOJ+bgUm82m7t69q0wmo52dHSsumI96+/ZtxWIxnZ+f69mzZybD6Ovr082bNzUzM6O5uTltb2/ryy+/1KNHj7S5uWkA7M6dOyY5yGazWllZMTc2oHt3d1eZTEZvv/22fD6fubCXlpa0s7NjnQEY0FqtpvX1dTWbF8G2yCd2dnbk9/stc7Gvr0+ZTEbb29s26YMOAXE8XBMwdQDxcDhsa/fw8LCNNMrlcsrlcpYPxvvyeC5GwDUaDYsPwRiCPpC1lNgbron29otRRszapbBhrNI3ebxmwK4erzQAu3PnjiqVip48eaJwOKz5+Xklk0mVSiV9/fXXZsWn7VCv16+AEio9Kki0UYVCQc+ePVMoFNLIyIixJ7BQ6I/K5bIajYZCoZAFZBaLRc3MzJjOiAqwra1NhULBRttEo1ELe8QpBaCjJZBOp80MEIlEbGHk5iIccXR0VJJMCA3wJLwQWh2LOe1RFkZXvHvdzg/TREXm9XrNbo6uhffJZyW7CKAmyUIvqfhpP1Lpu5ESVG+EpnZ0dNjoERhM9BGwMgMDA2o2m5ZHhjUcTY+bkE5LiM0RMCbJ6HqYRRg4xMw8xn3vLJgA+rt375qODTcu4mDEv2iI6vW6ZaQlk0nLZWo2m/bvbiRBq9VSNpu18M22tjZVKhW7fiqVirlgHz16ZPlQjMIaHR01N1Y6nTZzSXd3twqFgt544w2bPbqzs2P6k8XFRfX29loa/ve+9z3bSOv1uiKRiPb29rS7u2usDC16WASyoGC+pqenDSjRGgeYw/hwzl22CxAjya4X7nE2JenSpMIfgBitWQ63de4ybCTyAwhv3Liher2u3d1du2/Oz8+1vr5uawW6I0ATrlPpMtKB6w3GFrB53cUJu0KhAAssXQ6NB0DC9DIDlJmItN/ZmGkz0oKcmprS0dGRKpWKgQ+S8rmnAHLk8LGWnpycaHV1VcfHxxoaGrL2eCaT0eHhoXZ2dqxt2NXVpS+++EJra2v64IMPrrBejx490rNnzywKZm5uTslkUtPT0/r1X/91PXjwQKurq+rs7FQikdDi4qLpDZeXl/XHf/zH9vipqSkbOk6YK3EwmEJarZZevHhhRi2cwmS+8blx0FP04MYmWsZdK2H2KLoBsMzO5PnRd7He7OzsaGNjQ5OTk6b3JAus2WxaWDP6VxgxnMDumso602w2jamjyMLl/fr4dh2vNABDPzM8PKzJyUkNDQ1pd3dXP/3pT5XJZKxl6Lqdent7FQwGrdJl8UokEurq6lKxWNTKyoo6OzuNfqcaLZfLJp49Pj62aiqRSMjj8dhcQXRmhFuGQiETQsM2MaMNcTGVDm0QVzgL4MvlcpJk6cvt7Rfjd6jccO0lEgkT0FJduQwA1WImk7HsKvKPWLTPzs6sVemKoAE4OIIIED0+PtbMzIzloWEfl2RaLjZRdC+wCjyG1py7EbNpoZ2D3UOTxPtloaSSlC5nLQ4NDVnuGAJ9SWYK4DPS8nB1bnwfACHOIy2E8/NzC6r1ei+CE30+nyqVilZWVkz7U6lUtLm5qUKhYO3loaEhczHWajWNjY3p7t27Ojs70+PHjy0ouFgsyu/3q7u7W+Pj45JkmqloNCqfz2dxAI1Gw9gChP5uEjvfMwu6z+fTm2++qf39fX388ceqVCp666235PP5VCgUtL29bVlUU1NTpo+Zn5+XJAuOdbPJAG0wuCz+hO2iXero6FCxWNSTJ090dHSk8fFxTU9PW1Ap3xP6RAoaQLH7/fN3mFauB+nSTQuj9svcjzgP0Zlx3/F8rm5oZmZG8XjcPj/XMI/3er02ABujDiCRcFoKEDdfr7293VqArq4NFhn2judqNpsmmGcThnWEsYF5ly6CU3HhuTMtCUx2M89oH9KepYgBXMICd3Z2am9vz3LjiCzBBdtoNGzWKJ/93r17phG7ffu2xsfH9eTJE7sXnzx5ovPzc7399tvW6vvyyy9t7SSiol6v232wublp+8D4+Lg55IeHhzU3N2fX2s7OjhKJhKLRqBm2pqam9NZbb6lYLCqTyZiDETMHOk/0mxTOtDFZL7l2tra27JoH8KP9ovNCO5eCGPDqah+np6c1NjZmmWkEAgMoGY/mFiaYIxiO7rbrXZPWN3W8ZsCuHq80ANvZ2dH09LQCgYCOj4+1urqqhw8fKp/Pa3R01MJPAR5ubAIXqQsoCoWCHj9+bOJrUq0JyMP+TnWE07Ctrc1yyObn59XR0aG1tTV1d3fr1q1b6u3t1YsXL0zYTfaU1+tVrVYzxx8LFRsA0QSTk5OSZFVsf3+/bU4s4LRRACCZTMbcZbxnqmlaapFIxBgkRP19fX224DB6BMbInV3HYoM2QZIt+GdnZ8ZgAQLJqmEhwFUIO0CVR/uFzdbVaeXzedPZRKNRq/phvtDI8O8Ivd1oCDYUwDibtnQ5oBoHHFUoTCdtGHfzaWtrM3EuwbPPnz83nRRJ+rRz0G8Q1dHe3q5EIqHe3l6Nj49rYGBAhULBLP847nC6ZjIZbWxs2CSFoaEhm5LAQs1cyng8buCqp6fHEur5rvx+v4mJE4mEtTufPXumeDxuTACBwNPT0/rt3/5ttbe3GzPE60oyrSLjoRhzxJgsGKG9vT2LzBgYGNDExIS1ZVdXV62IcVt3xJ6cn59f0XRxwEJwP0iXMRYAGMANwJ71gP+S7eXqCml/ArJwuXV2dqpQKCidTtu5RLMFyEOofv0eheGCmQPgE7HAdcpnd5kx7r/e3l4bcE3xUigUbKbs2dmZMpmM3fP8HV0UgOHw8NDuGz4n74UiD7YOMA3gRfJAKPX29rZu3rypSCSisbExMyEQ+uoWYU+fPtXKyoqSyaQCgYDNBaX1/+WXX+rf/Jt/o7ffflvz8/NaWlrS+vq6rdlo0BYWFpTP57W/v2/6LvS65PRFIhFNTk7q0aNHNgrs9PTUomXa29v1xhtvaHV1VcvLy6bzxRzlro/xeFzDw8NX2oJc0wDh9fV1nZ+fW7cFNp71DlnD9TgQ1gSIhXA4bHpO2HW0uLDIvb29Vtgjr6AQ7ejoUDQatSLh4ODgz27zfX38mRyvNACDwiZ5mvERU1NTV+zDBHDCbFA5nZ2d6fbt20omkzo+Ptb9+/dVLBa1sLBg1fLk5KSGh4eVyWSMXqbFQ6ulWq3q2bNnFhvAa83NzSkSiSiXy2l7e/tKwCPtTMZGsOBTLQGUEomE5fAAWtwU6FAopMHBQdPmDA4OKpfLaWdnxwAIwAdnl3Q5tBuWp1KpmM5GkhkEuru7bbwG+qWenh67wd3AVZyJLI5ocdz2iruZUgUS6kp0BxumdClWLhaLOjg4MDYBIO0uYq5OyDUCuJoXQBULI1qZwcHBK0GZLJK0WSVdcX+58yFdZml/f19ra2uq1WoaHR3VwMCAXRcEtbJAShfi5M7OTrPaM8QYEfT8/LxlONEioQCQpLW1NRUKBdOf0KJcW1vTyMiIlpaWlEqltLW1ZZMdAD/FYtHOF5EdRCsgUodNa29vNxDYbDaVSqW0u7urra0tlUol20iZLID2LJPJaHBwUPF43OJHYFrI/WJET7Va1fr6ulZXVyVJs7Ozmp2dNd3S8fGxAXhYAiQBXNMc6AHdlp7bngS483zcD9x/AAXuAe6dQqGgSqViAcwUGaVSydgkWuC8jtve5Lp34zEAhRRVBKy6LkkKE94v4BKWCvBEuDBtelyT1WrVzDnSxSSHUChkMShomHitWq1mmjc+e7lctvmF6E7RRXLNHR4e6u2337br5PDwUMPDwxocHLQ5ueStETjMPTUxMWHJ8PPz8zo8PLSh9sQM5XI5Mx/AyMM0EtDq3qP7+/sGmpLJpA4PDy3vzOPxmDMV3WKlUtHu7q6xd2gmAbq0+DF0lctli/lBZ1epVOza2N3dtfBuchX53tGaUgSSD8cYJq/3YmLA3t6eFWXkQxJN1NPTo2KxaOynJFtjWdsB2G4+3Dd1vGbArh6vNABDwyRJvb29qlarNlZmf3/fqjQYEjcPhY3lu9/9rjo7O829hhC8s7NTU1NTarVa1nsfGxszFxKVKtqvarWq9vZ2qzoXFxc1OTlpQmluMnQMg4ODOjo6sqpVkomkBwYGNDY2pnA4bGGTgBfmBG5ubpo2hTYquhsW8LGxMdtUcCeyGVC9QrNXKhULmHVD/3hPsEowEG6iM4CRTQaDAJvDyMiIaUwQ+NM+oVJsNpvGQLEhkmPT2dmpSqViLV3XNEG7CqaBP26LmXmPsCxUlAjj2dzINIIFXF1dte+VxZGNkQHosETpdFrpdFqFQsEA19TUlLFmaNfIXEL8L8mYp46ODnOTBQIBRSIRdXR0mDUfNmZqasrs8cfHxxZsi9aLlu+Pf/xjpdNpTUxMGCiFySF01dUgUlGj2SFTCBv93t6evv76a6u2z8/P7btaX19XJpMxUTbC7IODA62srGhvb8+AHDEebPA3btzQ4uKijSgKBAKWPwVzguvW1eux8HIfkisHoESnw3eOxpKfc524LWw2KVgu2uwUXzDCACiAhQt4MEnQkqQI4P2i2+F6pm0J64uEgXuP8UJ8V3wGNJusb8FgUKVSycT8gCqyv7gOCczd2dmRJJNqwIJRsJBPRtwB7DqfhwgL7h/GLD1+/NiKKj5fLBbT1NSU2tralEqldHx8fKWIAeAwm7der5sshHY+Y36QOnR2dlqxsri4qPX1da2srBhzhCu0s7NTH3zwgZlk+C5IqCeuhsK4VCpZmC5rKQYS9MCwSjDKgH20t2gtmT0J4+jGixwdHVl8C2s91wX7kCstqFar2tvbU6lUMl0arDrxKXQpYDExRIVCISUSiT/D3ff18WdxvNIA7MWLFxoZGVE4HLYeOzkpVBoAGEmWYXR2dmYBm7AO2WxW4XDYtFmuSNYNNmQBIzjV4/Ho4OBAHR0dFuvAjUwrg6r/5OREt27dshlke3t7V5gqNEqTk5Oanp6+wl6xcQaDQWNvmC8GO0B+DvolSRa0iWCajYcKmnYFn5PMLYBIOp2WJMViMUmyxUqSVcU9PT0Kh8NXAh6vi59dpxBtGFgcXFvkTdG+dIXSBDfyvbKZuKnc6KQISz0+PrZF0zUnUDWiSWo2L4cmx2IxxWIxY8Q6Oi7nQR4dHVmrY+hPBhH7/X7LguK80Sbls2EWYFGkFbe/v2/FAKYCwj95LMLozs5O7e/vq1AoGMhlqDqDuVdXV00UDYPz8ccf2xzI7u5uE4XT5gWg08bElNLf32/gkbT+Vqtl8xx7e3s1OTlpeWaMmXJ1Tmji+BwbGxsKBoM2kJpASc5to3ERbHvjxg1r2QDSYLoA5LTmuHZOT0+tDQn75W6KAEY0YzC9sNIANu4jmFWAMk49QIjbzidDi3gTWDTAPT/n+gPkubot7k0AkCu+hxl3W2EAAXedomBBzkB4KEOq3egOAlzT6fSV9hTBx61Wy/R6e3t7Wl1dNeMIsT98X4lEQh9++KHK5bI591hX3DgEv9+vW7duqdFomNubfDLY4VgsZlMbAHrIRQqFgnZ2dmzsWigUsnMejUZVLBa1t7dneWhobLe3tzU2NqbFxUVjoogSoXNwfHxsrwnw9Xov8toQtLe1tVlMBMHdqVRK4XBY/f392tzctGsaXVc2m7XCDN0nCfcAY3SdFIinp6eKRCI6OjrS9va2SSAwIQUCAWO0mGDCtTI4OGj74NHR0ZXr6duQA/b6uHr8qQDYP/yH/1D/+//+v+vFixfq6enRd77zHf2jf/SPNDc3Z49ptVr6e3/v7+l/+V/+FxWLRb377rv6H//H/1GLi4v2mJOTE/2tv/W39L/9b/+b6vW6fvVXf1X/0//0P/2pETrJ9p2dndra2tLy8rJpd9wFFVEswINwu87OTnMutrW16datWxoaGrLB1lS8bkCiC1y6u7u1v7+vzc1N2xRv3LhhIABhqquRGRkZkSRtbW1Z8B5Uc09PjwKBgEZGRuzffD6fufeo6mD6otGoent7jelZWVmxDZvq2B2ETbVHZY+wVpJZl10tFYsb5+16W+f09NSCAMPhsG0AnCPYAPQSLHoej8dALIwdInfakwA32sdY6BGp8tlg0agaAXc8FsBQKBSumCRwkAWDQTNq7O/v23gcKP5wOGyOMl5rampKk5OTJsBtNpu20QEEyI3a3Ny0xRGQK+lKSCq6MuJBAGlffvml5ufn9eabb9p5RGdHXly1WrUYh0QiIb/fb+0vLPNEmaBjlGTtahycboYWzCXnCc3hxsaGGo2GzWvMZrPq6urS+Pi4/H6/mQxIKidXaWJiQrdu3TIB/dDQkBURADuynVwAyDXERgybJV26ImlXo+XEMMK55XVg9mDLYJS5piVZSx2Q09nZaSwruhxACPc4v8fn4HUBVjBlrgaTx8DEcO7d9rvbXsc9S2sYgODqN5EmMDaJYoV7G20bnxnWk1YVuVtkiLlmlqGhIc3MzJjeDwBPBwI9Itc2hRS6uvb2ditGg8GgFhcX7RqlFX5wcGCOcpx7ALSuri4rBFOplJ48eSKfz6fvf//7SiQSxvr5/X5tbW3p4ODAru3h4WGVy2XTodJ1OD4+1sjIiJmrzs/PlU6nzXxBjA7FGaamarVq6xGaVN4vzw3DzXXL9TE1NWUOSTSSsIybm5uq1Wrq6upSOBw2M9PDhw9tTW21WvL5fJqcnLxCLCAPgQllbSMHjjm+dA6+yeN1C/Lq8acCYB9//LH+6l/9q3r77bd1fn6uv/N3/o5+9KMf6dmzZ8aK/ON//I/13/13/53++T//55qdndXf//t/X7/2a7+m5eVlYwX++l//6/o//8//U//6X/9rBYNB/c2/+Tf1m7/5m7p///4VHcf/3RGPx+Xz+ZTNZi05mTYYXzSMB3qJ4eFhtbe3W8UPc0V1i/6HzR/rN/PNzs8vx4EcHx/r0aNHKhaLGh0d1Y0bN2wjqlQqNpT58PDQbgTGh7x8+dJuXsAEI1bYdHgNhsWiQensvAyP5X3u7OxofX3d9CfuYk5VDhBob2834OmCM9fl5w7kBqDAGrAxIih3LfFuXplr1UY0zAByV6QK6wMDwGbkivb57/WwSv4fJswNruR7p1KlTUUoIQPB+UxoBDkfbIIsXFSrVJK0dnH/AVbR7fG7VNnhcFiRSMS0ODCVuVxO6+vrBqYADljWYfTy+byazYuB7QSX1mo17ezsGAMMsAqHw3rzzTf1wQcfGFNJq3lvb097e3tmjCAMFnAM6GLjRE8Du9JqtWwY+cOHD7WxsaFEImHaSlgPRhTB0gKcnz17ZuHBGEb29vasAGg2m1ZYoWPBZs81Ksk2N4Y2cyA1AOBQiPFdkXHnOpWlCyCFzo+ZlbAIjKrhfqCNyOG6F/kMbAzuvcj65hZzvC8KCRd0AlR4Pu412pS0phmFRFwJgIDWOPcE78Nlq1kDMWqsrq7K4/FYEDAZgvF43LRbp6enmp6eVq1Ws+urVqtpdXXVWl6zs7OKRCLWluM8BAIBvfvuu7ae0WZGn/bOO+8Y+wrzDbDs6OjQxsaGsXcTExOSZEzUzZs3de/ePdNF4VTO5/NX5u0CjnEQ44KlCORaY40jhw3NZi6XUyqVUqvVUigUMkZqZGTEYmJwc09PT9uYMtzHGBVg5g8PD20/WFxc1ODgoJ49e6ZyuWwue8gENwLHNXlQrMKA+3w+e91SqaT19fX/6L319fH/zfGnAmC/93u/d+Xv/+yf/TOFw2Hdv39fv/Irv6JWq6X/4X/4H/R3/s7f0X/yn/wnkqR/8S/+hSKRiP7Vv/pX+it/5a+oXC7rn/7Tf6p/+S//pX74wx9Kkv7X//V/VTKZ1B/+4R/qz//5P/8f/+bb2+2mamu7SCOmOmRzw0l1fn6u8fFx3bx500BHT0/PlcHR3NTQyLAZ3JRnZ2eW9n56eqqPPvpIy8vLunXrlt59910bqbG+vq7R0VGjtmnfke+VyWTMVUcFBnXP+6Uts7q6agn3uVxOQ0NDFjYLeGPQNhZ0Nn4AAtZy2ook+NNOxDnVal3MnKO9isaAn7kOM6hynoNzw7mErQHs0CIkLBTQgm7J1ZDBfgHmAMFuC5X2Dm1KV/QsXWwwaGfS6bSJV3d2dgzEwZbRLuK7dltBxWLR2i2AI5it6elpjY6OmjO1Wq3q8PBQwWBQiUTCNnhE5WjcGDVEe2l7e9vafC9fvtTR0ZG+853v6MMPP1Q4HNb+/r6y2ay1KD0ej+7cuaNwOGzM4rNnz/Ts2TMdHBwokUhoenpasVjMxP0AKrQ410NIy+WytTvC4bASiYSGhobU19cnr9drqfrofXCtMjMzm83K6/Xq9u3bisfjCoVCam9vVzab1cOHD6+MTQHIlMtlcwYvLi6a2QDTA9lpMFsukAHAuO1GWGoYJh4jyRgplxFzWTAeQ8uS3D+eV5JdL7iqYT6kS7MIf+e/MGC8T0wwAEPc2S6jx3cCkKIQcCNXeP/EUpyfnxtDI8liUWhTunEWgLj29nZLj+/p6bGcuK6uLn311VdKp9NKJBLGhBPuS9EEu47phliEra0tM5+49ywGk3w+r5GREU1MTFhB1NXVpVKppBcvXqhWq+ndd9+1oGpS79vb2634gb1bXV218W+pVMoMHe73KcnWQFqmbkArawDnmfulq6vLxhmdn5+rUqmYaxvAhFYX7W53d7e2tra0vb2tYDB4JXLixYsXdg9jWsGpyX/n5uZ0enqqzc1NPXr0SKenpxaEDavFNIZkMvkLbW9MRTjNkTZsbGxoeXn5P3pv/f/X8ZoBu3r8v9KAMbonEAhIkjY2NnRwcKAf/ehH9piuri5973vf06effqq/8lf+iu7fv6+zs7Mrj4nH41paWtKnn376pwJgL168UH9/v6amptTT02NDkKmgFxYWNDk5aRt5JBIxEbDH4zGafH9/3xx8gBQ0Mgh/T09PLdiVjKTT01O99957+uCDD0wwvLW1pfHxcY2Pj1vbAMYMVx0i9/Pzc2UyGe3v72t/f1+JREK3bt2yWXr37t2TdAEmyIBhDiCgqFKpKJVKWZUKOHEpaRZAgEez2bSFmlZIX1+f6WwCgYAGBwetTYiYVrqcpYdjivYcVnhJV8T6gBmfz2djeQ4ODjQxMaG2tjZVq1Xl83kTAkP7IziG2QDkuAnjtANofaBtAaxxztFxpdNpvXjxwjYH2izu5s3GzWZ3cnKiVCqljo4ORSIRe89ra2tWgcKikN1Gbpur1+FaW1tb08bGhrV1GMYuXeTFRaNR9fX1aWlpSX6/314nEAjYKBav12v3Xn9/v8bGxkyP9sknn2h5eVmpVEqjo6NaX1+3DY7NA3aQljDf0fz8vAEGQidTqZTW19fV0dGhO3fuKBQKGUPEZvbGG2/YCBaYq8PDQz1+/Fg7Ozt27rzei5mGExMTVghwL9DmZMMCHLhCe9c5CGji3LsCfOly8+X6oCXEdXJdC8lGzfUGmKKYcVuJboFH2xsmFtMP7Tuej+uQz0F7jud2WTUMA2jgYPZhmzHiYAYCpPHZKFhgZdA9keyP9giNV19fn7kkMRuQZk/BiYN1a2tLkmwNkGQAA0f27OysstmsudLdc9De3m4uxHK5rImJCX3wwQf2GdEKEhURCoUUiUR0fn5uTsNm82KMXCKRUK1WU6PRUDgc1vLysh49eqTx8XHFYjErajH8YOzwei+me3R3d9s94bb5KMzdSRKsk2iEmdWaTCbNoML6R+saxp7CD+CL2aRUKtm6jgyDQd0UNmNjYxZm3dvbe2XmJ9c0zBhrENcqAB/tIUTA6+Pbc/w/BmCtVkt/42/8DX33u9/V0tKSJJmQMxKJXHlsJBKxG5eZcLjX3Mf8h3JK3ARoSdY6QnxIfpZ0AQbn5+f1/vvv2xgPfl6tVvX5558rlUrpRz/6kTwej6Xe9/T0aHR01BYVFi9YIPQW6Hwk6dd+7dcUCoWuVDEE9RGdQGBeIBBQR0eHVUCABTackZERffDBB0okEjo6OtKjR4+0v7+vxcVFq6DD4bCBLG68dDptmhzcNq5gmeru6OjIBPyVSsUGHx8eHtoijO2dBPB6vW4VNwtfuVzW0NCQWfQJJTw4OLBNB8aQ6pcqvNVqmdgaJoWWi8fjUSgUslwqRLBU22x0bFTS1XYOwZIwcTBROAdp6ZIVBTCt1+vGrrD48vxslpKsdQUwYNNjw0eHQpu2WCzaeB7mLcJ4dXR0GBOFoBl35OjoqIaHh5VKpfTVV1+ZDubw8NBMI+hiaOfxuW7cuKG33npLjx490k9/+lNLFuf9ky8F+Ozo6FA8HtfU1JR952wEbDgErAaDQeVyOWudt1otzc7Oan5+Xj6fT+Fw2PSBtLtv3rypW7duXWnjuueTKA4X6Ljt7uvMFowQLi+uAdfZyDXBf11DAwwT3xsMKq8N4KNw4FoG7MH4sja4rJXrYmTjA2ihXeO1+Mzo1Vx9F3ouHn98fGwRNbiNAc6watedkXwe7rVW6yIoFJbq8PDQwBHP4/F4bE2AHUskElfGIB0eHprpie8Ydh6nNxo66UJUXiwWFQ6H5ff7NTw8rGg0qlgsZtMycrmcAcrOzk6NjY1ZJ+Hx48fq7Oy0EVnd3d3W4jw6OtLLly8VjUZtOsj09LRWVlaUy+WUSCTU39+vWq2m8/NzbW5uanV1VbFYTNvb2wqHw5qenrYOQldXl/b39y29X5K1D1dXV7WysmKyiVgsZm7YqakpC1KmqGi1WjZ/mFiJSqVikS/1el37+/uWkUgXJplMKhQKKRgM6uDgwMLFAc5cYwRnHxwcGBFAVBGFHiO0GAMmyUJyv8njNQN29fh/DMD+m//mv9GjR4/0ySef/MLPXPeadCko/b86/q8e8w//4T/U3/t7f+8X/p3efqvV0szMjN5//30lEgkNDw/r7OxiqOuTJ0/U19en0dFR7e7uamNjQ4uLiwoGg5aPBAMgyfRPVEeVSsU2ZFdTRFXy4sULra2tmQOMFmC5XLZEfTRm6XRaKysrxtyQUUZg3szMjLxer4FUxn40m02zPrtCc+zHzGFz24Msqmia2BDOz8/NKQrjg36EKIu2tjZjcySZvgubeDAYtDYubJMrzqeKh0UAVKEDYQGB7eJc0M7gvfB8MCWDg4O2aQGGYD3I4AkEAhoaGtLa2ppthIA/BKrupkV7CYCNiwiQBcCivYhOaGFhQbdv31YwGNTOzo5WVlaUyWR09+5dxWIx7ezs2My4iYkJA19kqg0PD6tWqymXyxljgpuKSv7s7MyAT3d3tw1nf/nypT7//HMNDAxYq+/Jkyc6OzvT+++/b8nfBDRmMhkbiwRwfOONN3Tz5k3LwNve3rbrh6wxChwS8J89e2bsMjlQuIq5/t3rjvY0Uw0AKlTuroYPwwbgHIDlOiDRR2KSkWQslLvusMhfZ4ZcvRdMNM/NewGI8/5cfaSkK45HwJl7HQPeeD7XUEKhcf29ugARBpt2Fyw2xWA6ndb+/r6BFpcVc+MsuNbd+adoIHt7e82pure3Z8aU4eFhHR8fa2trS41Gw4pkCkzuZUAChh7auswEZWID8xR3dnaMrRsaGlJPT49GRkZspBmuvbOzMwsHnp2dNT0VkTsMv8bhmc1mrSW6tbWl9vZ201hKMlkAmlnyEWl7S7L7x+v1Wn7Y9va2Hj58aEV5q9WykON6va61tTUrRBH4M1pre3vb2GE6Krdv39bOzo5peWlfdnR0WPzE2dmZRVJUq1Xlcjm7x1j/Wq2WrW/u2uHGC3HwvRwcHOjo6MikOq+Pb9fx/wiA/bW/9tf0f/wf/4d+8pOfXHEuRqNRSRcsl0t3ZjIZY8Wi0ahOT09tvIr7mO985zu/9PX+9t/+2/obf+Nv2N8rlYrpUSYmJrSwsKA33njDAgihyrPZrAmBofKXlpYUi8VULBa1vb1tdHNvb68xQc1mU/v7+/Y5cA1Kl+NrlpeX9fOf/9yqIq/Xa2OOSMk+Pz+3oL9cLmcZLliaj46OTFvxq7/6q+rv79fa2ppKpZIGBgbshsXuTeuAvj95SuRukTqNu5BND3eUx+OxRHnExQAZ93dwSLr2eIAl1SEbBUGUtLV4TYANugs2PH6ODozNzXVlAqhckIRDyLX1U1HyOu73B4OGFgJQB8giSLFQKKjVapmQmSgR3JBscvl83rLAuru75ff7LSCUQMaOjg77LtAn7u3t6ezsTD6fT3t7ezYdQZI+++wzdXR02PihcrmsP/qjP9KjR4/U29uriYkJbW9vq9lsWjBkd3e3bty4oUKhoLW1NUky5qBYLOqjjz6S1+vV5OSkacEajYY2NjZ0//59/fjHP9atW7c0MjKijo4OffLJJ8rn8+rv77fW+czMjInd0ZwRykooJYAA7SR6SxhXmAc0fIeHh5Y/1d7eruHhYcutu669c/8OEwWwl2QaHVfvxXXhthOlq4CJvwPqKR5ouWISgIkCWLkOaAAdz8um5+rIeE/XD5cVoygimgWmzHVx814R5fM9A+5cJx3jxqRLgT2MIgVFtVo1Bravr89mD3K+2touR3nt7OxYMDKMa7PZ1PLystbX1zU3N6fFxUV7jxSHhE4PDg5qaGjIXN9kch0dHWlmZsbuU9cNeXh4aK1XCh7Y12KxKK/Xq8XFRXV1denBgwcGfhKJhNra2pTNZjU4OCi/329rI6B/bGxMfr9fBwcHymazkmTzR9HIeb1ejY6OqtFoaH9/X5lMRv39/Wq1WhofH5fH41Emk9Hu7q5JJ3CE4jREv8v5Zk+KRqPyer3WkseMxYG+bXh42NYkRnq5RS6MJMabyclJRaNRY8gAwlwTsN6sVd/08ZoBu3r8qQBYq9XSX/trf03/9t/+W/34xz82BwrHxMSEotGoPvroI929e1fSRaX58ccf6x/9o38kSXrzzTfV0dGhjz76SL/zO78j6dJa/I//8T/+pa+LVfr68Vu/9VuampqyC5o05Hv37un8/FxLS0tKJpM6OjpST0+P3nrrLbW1XSTINxoNxeNxA16u8+z+/ftaW1vTwsKCjdNwK0nswdlsVuPj4wb6otGobUJer1fJZFLSxcgkRMiAORLwfT6ffvCDH2hkZETpdFrb29uKxWImsmXTpWKj2id4E4CGNo3IB7e9AnhjMyQDi0HbbB4ez0VKd6VSMQNCq9XS4OCgzTekBcSCBQPBe0TbxntH/EuFRlUmXWj/qBKpzgFNMB4AJxZL1x7P41i0uUbZzIi2cO3eMAQsgDjEZmdnLUcM7Q0bIe1Z2DamGMDykYUWiURMYN7T06OZmRnt7u7q8PDQYkNggx49eqSf/OQnmpmZ0czMjM7Pz/X06VObmJBMJuXxeDQ8PKyZmRmr4Le3t7W0tKS5uTmbA7i/vy/posXwzjvvWPZQuVzW8PCwtZ19Pp/+s//sP9O7774rSbp3755+9rOfWdQCTNnIyIgBC8wYDP/GxQVoh+FCTwSgpvAAQHCNEQWwt7dnA56j0agl5QMsKHgA44BvAA4gzRXlSzJQAyNH6xM2E9aKVuz1SBVXY8b16oJANILua7r/74r6uQfYINx2JwfP7bYymYLg9/vtfnRDiGGiYMPRFtHOpAXlMt+SjO3L5/PK5/PWGsRRCTMOy+/3+61NLMkKllQqpZWVFTN1oM/s6elRIpFQd3e3zVUlVsJl905OTiybDADPPNVKpWKtXFySAP50Oq329ou4n+npacvXq9Vqisfj1v7z+/02Gmh6etoiM6LRqHVHPB6PSTaGh4clSdvb21aI4mJOp9Pa2Niwe5eihGyzjY0Nk0jQwu3u7r4yxN11kFerVfX39yuZTOrx48cW5EoAcX9/v05PTzU5OWktXdZMNHAAVNZbGNRIJCK/369sNmsgLhaLKRAI6ODg4Irr+/Xx7Tj+VADsr/7Vv6p/9a/+lX73d39XAwMDtpH6fD5jWP76X//r+gf/4B/YxvIP/sE/UG9vr/6L/+K/sMf+V//Vf6W/+Tf/poLBoAKBgP7W3/pbunnzprki/2OPnp4eC88D5Ny7d09bW1uanJy0BeX8/Fyjo6NaWVnR9va2ZeaEw2GFw2GrGPr6+kwgOjAwoPn5eduUacEdHR1pc3NTJycnFmlBwjsCZVicjo4O5XI5E6SjqyKfamBgQB988IHZkaG9BwYGtLW1pVwuZ9ZrmAjpsvWKe6ler6u7u1s+n8+qKgIp0X65Fv5qtWqCaRge0qiva8l4HFEItB3YrPh9rM/oiKi0SRGnTXV4eKj9/X1bcDl/mUzm4oL8k+encqfqK5fLZg6gZQlLAONw3fkGw4hjjQofxg0RNAwNuV58V9ls1lg1XFE4T2lp1Wo1lctlxWIxGxtE9lhPT4+CwaAJ3tkAt7a2tL+/byxQNps1oEEILOd+ZGREfr9fhUJBy8vLpqWBbT05OTFh7tnZmebm5jQ/P6+RkRG9ePHChhy3t7drZmbGZud98sknqlQqevPNN9XW1mY6la+//lqPHj1Ss9nUwMCAbt26pUgkYg5Zqn6AQ09PjwFpMrLQqbnhuqFQyGZmYqRgvh2tVyJIAN8uIOJ+kvQLDJcroOf3+APw53t2244wTy5rRjvNBVQucEJ3xWZ/nalzgZvbguT/XR2a2+oErPGZTk9P7RpF1E0rjGgaRvK4LBaMMaAM5qqrq0tDQ0PGpm9sbOjly5cKBAL2h8KCooT1GtaT631qasrMP7hfe3p6NDc3J7/fr2QyacJ/zuXZ2ZlGRkasqKtWq2o0Gva9o+tz58rOzs6qVquZc5IYlLa2Ns3MzGhubk47OzuWhn92dqZCoWB5jMx1HBkZ0aNHj5TJZPT2228rEAjoyZMnV8Db7Oysuru79eTJE9OCNhoNMyJ4vV4DZYA0ZrUWCgVj3AYGBqy4owgA7MOcUajDLEsyxlCSxc1Eo1F1dHRYu5XHEssRCAQUjUYNOPI9Hh4e2noaj8dtnJzLuH1Tx2sG7OrxpwJg//P//D9Lkr7//e9f+fd/9s/+mf7L//K/lCT9t//tf6t6va7/+r/+ry2I9Q/+4A+MFpek//6//+/V3t6u3/md37Eg1n/+z//5n5oi3draUjweVz6fv2JTnpyc1I0bN6zSpDr58ssvjXJG6EpKus/nU3t7u2q1miYnJzU7O2vOvHq9rnQ6bRqXo6MjSTIHHTS43++3Rf/09FR7e3u2GLptsba2i1TihYUFxeNxa5lWq1X5/X7V63WlUil5PBezyggKjMfjtsDu7OzY68JstbW1mRWcXCzYKM4t/8+wZ5gsj8djdDpggI2Wqg8hKWLotrY2Y59YhNmcJFlbSJIJ/hGNupoF177PaA0YT0axsMkBZqns4/G4crmcBS+ib3GzdQYGBhQMBi1EFEBHiwNAwPcjXc7wI/W8VCoZOOB3YB8Av7SAd3Z2VKlUDCSRNE6b7/j42IAr7qx4PK633npLyWTSco6IeqBtCwOTTqc1ODhobNeXX36pr776yrRAtCD9fr82NjaUzWb1ox/9SPPz8yoWi3r69KmWl5dtMw4EAkomk6pWq6bXoS2/u7urvr4+hUIhPX36VI8fP7aAYBxzbW1tKhaL8vl8ajab2tzc1N7eno0mSiaTOjk50djYmM3OdJkpgEk2m1W5XLbN6OTkxDZXrg+uPUCMy67SgsGU4Qr/aSvCdnHdu+DOBUTX9TK8luuuRPMF2wyIorXmAjG3Rcl96LLEMMD83H2/FD+M+mEjBiwy+mlgYMDYItacarVqjmc0V+imMpmMRTxIF5s1rNjx8bGBChgfYje43oeGhpTJZJRKpWzNOjw8NO2S2wqFGee9cc9RIAGyeP+s0+Vy2TSJMH98PwMDAxoZGVE+n9fDhw/NANVoNBQMBm0ObyQS0cTEhFZWViw1v1armUbY5/MpmUxa3h2sOS3hZDKp8fFxdXZ2KhwO6+DgwHIh0UTG43Fbix89emTrSTqdVqvVsliTeDxumYHsBYVCwTImNzY29OTJEytqCoWCDg4OtLW1ZQAPM1Y4HNbg4KBSqZQCgYD8fr/lqZ2cnNhUA3L2WJe/yeM1ALt6/KlbkP93h8fj0d/9u39Xf/fv/t3/4GO6u7v1T/7JP9E/+Sf/5E/z8r9wNJtNq4BarZYF8ZH7BRhaW1vT2tqaTZzHgXJ8fKz9/X2bMt9qtTQyMmIDhQEdLIKwALQw2RQmJia0tLRkIn02AxgeSRYyyELm9/tNi4Y4G2FsZ2enzW5jgbt586ZCoZCJUGFZwuGwhoeHzUbu8/kUiUSUzWbtfbIx8V9YNkbYNBoNA5W857Ozi2HMUOKuYN+15vOctEZdvRobIYsZuU8ej8deh83RNQnQhpRk+VcweC54AxDTggZI8l7ZoBCwo1Ni+DifEXbOdeHxmd0oDDZu9B/JZNJE9rRaYS4wTjD+xc3OQlxN/MjJyYmFSk5PT2tmZkapVEovXrwwXRwzKSkAmNKA9i4SiRjz8OjRI718+VI3btzQd77zHVv8P/30U5v/t7CwYCDv9PTUUv/7+/tVLBavCH6JmGC+49ramgWnTk5OqqOjQ0NDQ2o2m8pkMtZWHBsbs7YIOjomNXAfNJtNA/4IlAEuAEq0ijCXgCB0US4DyvUEUOceRZfnsmMu4GHtcnVfLrCj3QngArC52jAe64r/Wafce4afublcAEY2fdeIwn3GlAPAMfcEujlarxQgtBJ5jkKhYO+tre1iCHqz2TR9ar1ev5JET8Dt8fGxOXVpBXs8HsuJQxQu6crMzVQqpa6uLrs/tra2VKvVND4+bqPLMPLUajWtrKzYjFVYZ9rNBM76/f4r4HpoaEiDg4O6f/++/H6/vvvd7yoQCFj8y8nJiXZ3d00jt76+rt7eXssT29/f19bWlmVmobnyeDzWRiQeBr0gjnbWucXFRd25c0eDg4Pa3d218W+lUsmkKd3d3dZxYLZlNps1JmthYUGdnZ1mdGEo+fr6urLZrLkl4/G4MfTlctmczktLS/J4PNrc3LQOBvsXIeXutf76+HYcr/QsyM8++0wzMzM2LmZyctL0Lmzkjx490s7OjpLJpEZHR3Xv3j3LRsIyjsVZuhxMC+WL+DyXyymdTsvr9Vr22ODgoDEd7e0X4awsUmgo2tvbTZdF0ndvb686OzuVyWSuAMFms2mV4+npqV68eKFKpaJ4PG5hfKenp8pkMiqVSsZ2kH3FjUZrC+E6rQVJtjGRkNzZeTHrrlqt2kbKhjMwMKBYLGYtUJgxgJN0mR7OWBweR5uQjQD3GvlBaOrcdhKsJOew0bgY/5PL5Uww7LYaeV50WpJsUwZ0JBIJY8PYMNB3wbDQwpUuU/8xD0gyjYV0GU2BTgVAy7nt7OzU6OjoFSNDKBSyTKEXL16YbZxKnBgPMqQAi7du3VJbW5v29/eVy+V0eHhotvaTkxNrkcbjcS0uLtrnOTg40Pn5ucVElMtl/f7v/75+/vOfWyYZbZBAIGDXaygUsiBIXF+5XE77+/sGXtEnhkIhayOXy2UTbXs8Hi0sLOjOnTvWUkmlUsbkAQjC4bDlgXEfIhS+DmYkWTwDGz3/HggE1Nvba3/HAOJeVzAmbiYegIrvmogHmCpaP4CYVusycoTXv+66BGj9MhG+y8bxGrQlXZelK/inAHDNJ2imMDkA+NEjwsChm0NjCUhrtVrWvpQuwB+DrTnPjKuCHaQIYv6p66qkzUZ8i8tooo0tl8vy+/2Kx+Pa3d3VysqKSqWSXeder9cCpknFZzwRoIhCkBBdpADEvGxvb+vg4EDb29tm+mHtKZVK2tvbs7DqRCKhUChkgcTcN6xpZO1JMp0jWqvu7m6bEEB3BPdiNptVKpVSf3+/jUI7PT01ZzddglqtZiQAY/Hm5ubMpBKJREzKQJC1x+Mxhh9919nZmTY3N1UsFq01KklTU1N2DT5//txmvMKcfpPHawbs6vFKA7BYLGaJ341GQ7u7u5Zf09fXp0ePHunFixfGDPzsZz/T06dPNTQ0ZCGdo6OjmpmZMbFytVrV1taWTk9PNTIyop6eHhvjUK1WNTs7a86y58+fa2tryxax999/3wIlaXH19vZqe3vb9A3kyJydnenly5emQyiXy0omk4rH42q1Wtrc3DTRKWNCms2mRVkAUq5b3jOZjMUE0EqAxQHQAMBYZGhToDNBB+WKYbm52UjQvsAs0HZlUWTDIlyShc5lNVx2gYqTRZbNms0GNxI6LCpzwmDdBYZNFYBZKBSUyWQMjDLn0N28XI0d44zIOPJ4LjLKqNhxk2KDJ+GfVilsEFW2a7VHS4J5AY1JKBSyFhDC3zfeeEOTk5P2Gvv7++YIpb2cy+UUCAS0uLiotbU1vXjxQvF4XOPj44pEIsZW5XI5awUSRRCJRBQOhw0IAywjkYg53ogAoEWIhuX27duq1Wp6/PixDXQeGxvT2NiYQqGQTk5OtLq6aq5ZmGWs/gADN6+Og5YbUxmIw3AT3N0QVJfxcQXwtLxchyRtXMAF+imADyCCx7njtwA8PM5thfNvsFyws0RbwDS6ujFeq9VqGRPrxlGgJ4UVA+Dx/Axm5x7DsOPmarnnjKO3t9cE9UdHRxoaGlI8HjetFp8T9nxwcNBGTZEHxvpzcHBgbC8teBx4N2/etHPNKK5EIqFisXhFJ+tmqw0ODurg4MDWG+a4UqB0dnZqenpab7/9tmnaBgYG9OGHH+r+/fsWlp1IJDQ1NWUFTi6XUy6Xk8fjMf3txMSETZmIRCLWvqvVapbUv7GxYUwXzBRzJLlvEMbD1lGc8HqM6Ort7dXs7Kyi0ajGx8dVKpUs969arRp7xfUWCAQUiUTs8x8eHmp9fd1YNQoH6cJAwPfY3d2tjY0NY+MwO7jXwOvj23G80t/Iu+++q0gkonQ6rfv376tarer73/+++vv7lUql9PLlS9PokFLPjVSpVEw4TWRGJpMxtwijWKQLF2M+n1c8HlcsFlNfX5+Wl5d17949tVotRaNRa0exIZyenqparerg4MB0CENDQ0bx7+3tWfYSIXzT09MGRAqFgg2FBgiwAPr9fvl8Pr148cJGz9C2ZCEGTOGQZJMhRR5Ag9gWxkqSPRciZQT17oZFlc7GBOV9XTuDuJZkbhyP5NzwXmDHYCJ4P2xACF3JAmIjpPWIpoqNk1gBPpN7DhAkezweHRwcGGVP24dFjfBSNCySLM8ol8upWq1aO+L8/Fzb29umsXHDOg8PD7W7u6vd3V17z+5GBXBoNps6ODiQ13sRYjo8PGzaD3QogEk0Pqurq9rZ2dHo6KhV5rSWvvjiCx0cHGh+fl43btzQysqKKpWKJiYmLAqAFsba2pq1fWBPt7a2lM/nVa/XjX0bGxvTxMSEAefp6WklEglzltHqom11cnKiW7du2TUKaHKZElhg140r6QqL4wIKXtvN4LreToTpBeS47Uhew42UuL45udpFHus6GF2gx/O41b3bUnffn/tY7iHYUsAC8gWAIwDNNQtx3cN8SZcGFklmOABEUdDQ/obZI3j37OzMnNKcLxhHChzYf3ISaf3jZnX/uIDTNTTQnnZz/igKADo9PT0qFovG/DBUHhH75uamAoGApqen7fqIRCJaWloycX0ulzM3b3d3t00X4frJZrMKBoOamprS48ePTRsG8Jdk82uRErC+VSoV7e7uam5uTjMzM8pmswbaKHZLpZLlngGkuWaIhZifn1dfX5/W1tausJcuq396eqpsNqtisaharaZsNmtriKstRAPN+KSnT59eifzY2dn5VgCw1wzY1eOb/0b+XxwdHR1aWVnRj3/8YxUKBb333ntKJBLK5XL6+c9/rpOTE4VCIcvTIuPp8PDQrMAk2+/t7dmMLdcSn06nlc1mrYXZbDa1sbGhBw8eKJfLWaDl9PS0aSby+bw2NzeVz+fV1tZmwAtBdz6f19OnT1UoFDQzM6NoNGo3R6VSMQcNIu5kMqmenh7LW4KqRzeGGLW7u/tKuxEGyB1BxEKLm6uj42IQLyM9yM2CxWLzxW0lXRXNw265mhkqfJ7HBTGNRsOG+Y6PjxtjRJK2y+ZJlzcsI4ymp6dtESVzh/dMzERXV5eSyaQ51XCRNRqXcwZp7bS1tZluhffZarWsRc2MxPb2dtPloQNpNpu2AabTae3t7V2JyUC3ReSCz+ezDbu9vd00UkdHRxoeHja94tTUlG7evKlarabf/d3fVSqV0tLSkl0rfF8I+8/OzhQOh22TefTokYGtSCRi0wBGRkY0MDCg4eFhu47X1tZUKBTU0XExrimbzRqIpTB5+vSpjo+PbVSWx+PRF198oVarpampKUWj0SvnUboUkROpAHsBICXqpNFoWJiyK7CGKaO9j+YPYMW1TEvKFcg3m01jfQDy6He4dngcuj+uba43vifOBezGdfOIC6qkS2DmtiR5XZ4b7SEsmSsqd80J7n0GsOdehp2jqEJyAaB1w2rJxurv77dIA9ZDHM6AYMKYKXRobbVaLQtRhQlCdkErlzYZsQw4Jxl2jS7UZRHpFtTrdbu2kZQAzsi0I5srn89rZWVFHo9HU1NT5sAdGhrS0tKSlpeXLXIBWQiFLeGnFEUk++/s7FiETKlUsrm9RKywxhCFg4HC5/PZe0KfxhrP9AE+OxE0BCQnEgnT6pKNiWszFApZZ2J/f1/ValWZTEatVss6KOS6Aay5vtBRDg8P22cdGBhQOp3+s9p6Xx9/RscrDcCePXumVCqlYrGoxcVFvfnmm2o0Gnr69KnS6bQGBgauCF3RMjF/jCnzpDWPjIxY+4ioh3Q6bSOKYCfS6bRqtZpu3bql733ve5bZxE107949PX36VO3t7QqHw5qamrI5ae3t7SoWizo6OtLo6Kii0ajllxEVIF2wL7gi0Uqcn18MriWtulqtWmVGddbZ2WmVUigUssWMTYfNhgXEFZqjU4BdYWHg8bRcYHbYBNEu0dal+h4aGrL3R3sEXZTrzISBuM5kuLM5sd67ehyYCd7f6empsQZ8ZlpPkiy89ujoyOIooPhhsdgweXy9XlelUlEgELANhfe+u7trTkx300EQDevA6zFu6+XLl8ZysEkigi8Wi6YtbDQaGhsbM8aPxG03EgO31/HxsYrFog4PD7W9vW3mEtogCwsLWlhYsAiU7e1t5XI5dXR0aGFhwWz4q6urun//vrUn+/r6tLi4qEgkosnJSaXTaf3hH/6hTk5OtLCwoK2tLRNCDw0NKZFIqKOjQ3t7exoaGjJ9EcAYpvDJkyemNQKMhsNhuwZcthVg4N7DR0dHarVaxsZIunJtukAKoASocrO+pEsxvMt0uWwcQOiXsWju70iX1fh1jZh0VQd2HaBxL7j3gNvOxNAC0IGd5jriOVqtll0jAF2mIQwNDSkajdr1wnrCa1P88Vok6bsFSn9/v8rlsg4ODhQMBjUwMCC/36/NzU3LNUSrhGYTBs9tBxMvQduPaJqZmRm7f3BO7u7uqrOzU/F4XJFIRJVKxSZNHB0daWlpyRyXQ0NDmpubs5R4rhk+e7FYNK1ttVpVOp22SR/r6+taW1uzzDDWJiQJAGWuweXlZU1MTNi9yvXR09OjaDRqLDjTAWjB9/T0KBaL6eDgwOIiSqWScrmcxsfHlUwm5fV6lclklMvlrA1P4j2FHNpfCmWiXyiIScAHgLqTIF4f347jlQZgDEpNJBJ6//33FQqFtLGxYeNeYD36+vp0dHRkmxsBrV1dXdrb29PLly+tOpFk7jIEkScnJ3r69KlKpZKJMW/duqXbt2+b7ZlFBrEn4tFAIGDzzTo7O1WpVCxnBxdiLpfTxsaGEomELcy0GiYnJ61t5/F4NDg4aO0Kl4o+PDy0hYtMJ1frRYvQbWv8hzYC2nnSxaZGkrx0GUbpZm0xosfr9VqQo1vpu+0QwBitEjK62LAYSUOqdDgcVi6XU7FYtNYfGhfXvs/zue4xmA+CEAECR0dHNvx8dHT0iqYKPRHsGa5U2qUwfczSI3sH6zosAecLJyobGlloaDPGxsZ048YNjY+Pq1arKZVK6ezsTLu7uwbS79y5Y87Nly9fmpaNUS8HBwc2uPf999/X+++/r0AgYMPUMRywmWcyGT148EDValXvvPOOObDQ89Ce7u7u1vT0tDFmTJeYnZ01YEvmUHd3t5LJpE2GwEzCpnF4eKi9vT3t7++bswuQQXvW6/Xqxo0bV8TW7pghN+4BRgqzBterq/FyhfCutssFTFwzbmuEe8EFRjzOjZqgvc11zOv/sn9zc7oAIq6D0nU8EsHC+ZFkTCFaxXq9btcVhR1GBYAHrFcgEFA+n7fcxmQyaWPOXHON1+s1Aw8FFlo0ClJ3zE6xWDRGqaenR2tra9re3jYmE4cu7VTCmmHKcGDjUMxkMtrc3LSA7+PjYxvBRoo+2Y3BYFClUsl0v2+++aZCoZBNWpCk3d1dVSoVY9WGhoZ0//59MwAwx3djY8PWJFrd6NRI9+feJYcMbSyB1RSbrVbL9Le9vb2m2Q2HwxbXwuxK9MM9PT3a3983xg29H+OoMAHBgo6MjFwZJ0cR6vFcjMBj/0kmk1paWrIoCkxFr49vz/FKA7BWq6XR0VGLaECfg+OMFoV0AdYCgYBu376tsbExE1Ezx5FNR5ItjoAWdDbt7e1mGWbwKsF/LIpQ37h+xsfH7eaUpFwup83NTXV0dFgoZavV0vDwsDlraNeMjo4qFAqZVdtd+Km43OrGdY3BCLmJ9gAS/h8ghZvJ1adIMlYKGl/SFeE8eqnOzk719vYqm81aJSvJBgm77Q30HoA18qQGBwetzQEYQyPV09NjbSp+3+PxWHabdBnCSYaYy1zA6vX19VmoLvMVGYfDJnJ4eGgtnd7eXms7YgxotVomDp+amlIymbwyqgpNV7VaNYaCcFWYBUDCzZs3bWh8tVrVs2fPVCgUNDo6qnq9rt3dXQ0NDWlsbEyNxsXg7c3NTXm9XgMpyWTS3JSPHj1SrVbThx9+aHox2lF8XgJpaanPzc1Jkr7++msVCgWNjY0pmUxqbGzMKna3bf/WW28Zg/H8+XOdnp5qenpaoVDIcsAk2fcKSKb6Zmbe5OSkPB6PDg8P7XVw4LIhA7Jombthpi67BFBx/w0toXttuG5DF4jBmnF9S1eHcF8HZ7wHt/3oCuQ5rmu++B2XiXZfj8cC8rjPAWqcH9qCzCt1tWmYU1y3MGG3aB4lWTuTgoDzTAuNz0phARNdLpfVaDQUiUSMIabAIQdrY2NDqVTK9GCwUNfNDXyfZH4tLy8bqBkcHDSGbHJyUt3d3frpT3+qly9fKp/PG7tEXtzq6qrGx8ct8NjVrSKS7+/vt7Zfq9UyBo+xdH19fbp586a1Rgk8zefzlu1HoYA2jP2CQGSYfn4G4861ALOby+WMGZdkwa8MGXevDa7lwcFBDQ8Pq7+/336fwhe2fX9/X8ViUWNjY3rvvfdscLrP57OZvN/k8VoDdvV4pQHY6OioEomEent7lclk1N7ebpR5uVw2dw5MRTwetzl35XJZz58/19nZmUZHR81ej44Laz3sgt/v19TUlG3Sy8vL6unp0RtvvKGOjg7LdGJmGRQ8mg3ymwqFguWO0dfHgs0GL0nDw8Oanp62DdNtG7JYEhWApR49GLo2d3GUdGURpEp2gRnCdVLKYTMYbstmAKPGAF2cnzj6WHxgMFwKn8Wi1WoZSOnt7TUNlHTRCqF6JrLCraRhE9B/8X03GhczHGOxmE1ngJbHQs6ihjUcvcfY2JjOz89VqVSMdcEpSf4YbBrjUiKRiEUouAG0nCsYPzZOQDJu14mJCWv1MiCYVnWhULDkfwBMJBJRvV7X5uamXrx4ob29Pb355puamprS4uKi7t69a2NicFetra1pa2vLDB0I6aenpxUOh7W7u6vV1VW1tbXp9u3bWlxcVDKZNGMEYuLe3l7FYjFrexAESygrrAdsKCwh6dvuOeY8ShdasYWFBdtM2SRc/RXXuAt63BgJ6bIwcBkB93Db1Pw+bUUOAAcsm6vHckEOv8918ssYNPfx7gFj6+Zz8dn4mXQp/udx7e3tpuFCz0ZR1Gw2r+QH8lzuc9K6QsuJxg5pwvn5uQFddHyARdpuTGqAHYPRqlarNvvW4/FoZGTkyugogC7Fo1vgAYBHRkbU19dncT+dnZ0KBAJWwI2NjanVauknP/mJCey5ngcHB01XOzc3Z/pV1jmuuWazqXA4bKwfzmUMR5ubm5qZmbGxRhQFrtGIkWTE8fB9Z7NZa8cDdre2tnRwcGDzVQGatNDZB1h7yfbCODUwMGD3FTrOrq4updNp05gBFlmPT05OVK1WNTo6asU5ew9jj77J4zUAu3q80gCM+VyPHz9Wo9FQNBo19yOg6/T01PJrEEQHg0ETAcdiMY2Pj9si7/F4TBvAqI2hoSG9++67arVaevTokVZXV9XX16e/8Bf+gkKhkDY3N/XZZ59Zi4LncjN8PB6PJcvHYjHLawoEAhoeHtb+/r6xYu3tF+nWsEK05dB8AcjclgqLX39/v6LRqIn0E4mEotGoAQ+v13ul3SHJtB2MvUEg7AZa0ipwF3gWDo/HY/Z0dGC0snBqkQMFe8bnYZNFvEvwKO0raHcMDa57ym0joY8BMPE+3NFIbFhk67RaLaXTaQ0PDxuQwn1EOxQbPL+H7Z3Knt9BkEx7YWhoyJL4aVH4fD4tLi5qcXHRpit4PB75/X5b1GFRT05OdOfOHQOmvP94PG7i/uHhYWMKJFm4LuJ30rWz2ayZQm7evKl33nlHoVBIDx480Keffqr29na9++67mp+fV39/v2n/CNlE2/j1118bi4emhXYVgAHBcrN54ehMpVI2aw+NHvcJByAEHSNAgA2OFrYk0xUBGrgO+AOYoehxGS8XvPE+rjNobuvSBWHXXZfcS67eTLrKkPHZ3P/yHt2MMVcWwL8hL0AOAKvHa8HIUkThIgWoHR0dGbvE+3eZffd30SnClmNg4DvhekTDxzrGWuCGGDP2zHWrup+L8yrpCjPGmjcwMKDOzk4tLy8bEDo9PZXP59Ps7Kz6+/t1//59ZbNZc1MyozabzapSqdgcYrocsGG0DmHNYKsYGM5EFe4bmKf+/n7LbIRJi0QiV/SymUxGW1tbCofDNkCcgmtqasp0W66zFGMP557nppVKi5fWZigUUqPRsM8EeEXXiXaS+9Dj8djMYFjL18e363ilAVij0dD6+rrpYqRL55l0ueDjEiEgj/56LBYzkTNaGQADWguv16t33nlHvb29+ulPf6rHjx/L6/XqjTfeMEbg+fPnJtSGGgfUwCixOVcqFW1tben4+FjhcFiLi4vK5XLKZrMGEtmIWHRhXXDguTcfVSZOOloDqVTKqlcWbYAJnw1dA9EUzJ1Eu8WCNDw8rOHhYQUCAfsdSRYoSyIzertms6lgMGggB7cOG6skWwAlGejBPUhSP5u6x3ORw4Umgg2KBYbz4upy+DkLqdtu8vl8yufzSqVSyufzVxYmKlMqzWw2q5OTE+XzeXk8HmuRMhaGhZjNBX0Wm1AymVSlUrHpB11dXSqVSqaFamtrUyKRsHBG6aKwWFpastfa3Ny0lPBgMKi7d+8qHA7b0F2+J1ojoVDI9FpcEw8fPjR2cH193Zi5H/zgB3a/MAdvbGxMo6OjSqfTKhQK9r2Ojo4a0Gfz4by67Wiu3/7+fhup0mg0LAWfFjEMjN/vvxKB4mZbsXG7QbiSrjBDsMTSpQbLjcXgPaJv5HEuUONxLjC8rtFiE3c1lLyvXwb2pEsmz9V58Xda1IAj2r3MnAVY0z6nsOE6BiQBYJAcuJlmnBsKLoAgLJo7euf4+NiYYsCKa4ZAhN5qtSzkGYamXC7bSCOChyk+AQXn5xdzXQ8PD02rhdvSdaMODw/r5OTEhmC7YGd0dFQ9PT366quvlMlk7PpgNiXnC9fw4eGhzePt6Oiw1ivfE0YpiqWDgwMtLS1pcHDQgBHTBRhHBruIVgu34dbWlrU2JdkkEcKOWXcZG8SsVGQrsJKdnZ0m58CRf3p6as+LHo5II7THADqkBuSPra+vKxaLXdEzflPHawbs6vFKA7BSqaSNjQ2VSiXFYjHTIqFRQWdQLBZtdpbf77fwQapI6WrLqNlsmtB0ZmZGPp9PT5480f379yVJc3NzSiQSNkKG3r90ufgyEogKrdVqmaulWCxqZGREN27cUL1et0DW0dFRAw7ugomGikUIJiqTyajRaCgej18RztZqNdOMUN1yLli0ad/BVLExSpfZXZJUq9WsNYvIE6s6ujcqRTZyV7wMq+WyCR0dHYpEItbOJeQUxokFjnZIvV63tistJDea4Pj42DZ5NiA2JqpzFjY+I++dTYrvnqgPzgtgK5VK6ejo6EpyO6GPsE5o2Dg3sBh+v9+ACVZyNo+RkRFzxp6cnMjv99s8PK/Xq83NTf3+7/++njx5onA4bOBoYmJC7e3tunfvnra3t7WwsKDR0VGbeQm4HB0dVTwe1/e//33V63VrjxA06/V6zQUai8Us8mRvb0+///u/r0KhoFgsppGREcViMdOgSJfu1Xw+b3ozNDi0h3AIAzYwOGBywI3KZ+ZevB4N8h8Sq/O9w9rynrjOXV0kz833D/jh566GzL0GaZu7rBpgzWXfuP9d56ULwNyfuTqvSqVi4NDVNOJqJW+rq6vLzisgiXPLewFsutMk3Kw/iszrIJE1ge8VppziBpAE++lqOwcHB6+I12F5WI94fkmWwSfJHIkUntyDZ2dnisViZnACpHG/RqNRffDBB+b2pTUIOHr58qUFaZO56PV6tbKyop2dHRPgHxwcKJvNGmOFO1S60DCybrP20AJ2jRSSrLgaHBxUMplUOBw2N+X8/LxpHSmI+e4PDg60tramVqtlEgvWvEbjYrqAK6Ug54s9IBgMyuO5yElzdYnDw8MqFArmft3f3zfT0uvj23W80gCMBG42Ylw9tKFoJRGSRyIwvxcIBGwxqlarRptD4ft8Ps3MzGh3d1dffvmlms2mYrGYaX8Yr0NFCoXf0dFhTATvx618+/r6THidSqVUKBQsQZmNgY2pUqnYtHuS+Ts7OzUyMqJms2ngK5fLmfCVxQBXJJudWymjWaJdBZvE5kOyNcNf0da5bVDpcvF224JsfmykLGpskADI8/Nzc+qxKJOYH41GbYPs6+uz6vm6ngwQjdgYLR8sBflmtEp5fwMDAyYsZnPBwo2+A91UR0eHjfeRLhi64eFhy/dyW7QArfb2dmsjEC7JhsaCnUwmNTk5aQG76AcBiWiw/H6/FhcXbRN68eKFbXxvvvmmJicnbXYpw7MJ72XYcigUktfrNTNEPp9XV1eXxsfHNTw8bOxUR0eHNjY29Nlnn9mA7ba2NgsU7uvrsz+JRELBYFC1Ws2usXQ6bWn5jEEqlUr2PfX29tpoGelyYDubEtcM48Q8Ho+luxNLQsXL9+6OnQGUwM5Jl2DL1SIB5lwWjOv5eguT5+R3XWG8y7JxXGfJ+Bm/iybJFcpj6OF5JVkRJ8mYo2q1am1wlxXjXnbZMQB/rVazQgOJg3vvIDdoNpu2vvB+JRm7CThytbLoHnt7exWNRq1QAyDCUFMAolPr7++X3++3+bDIAlwzArooBrJzTxB5AjPIc5AvNjAwYNfdwMCA5ubmDNBns1nLQnTd4uFwWEtLS3ZPx+NxYwUx77RaLTMW8Fqc62AwqJs3b2ppaUnd3d3KZrPmxF9fX7fvmQDbg4MDPXv2TBsbGyb/cM91e3u7xsfHLaoGgIyhwuv1Kh6PW3A43ReugVqtpmKxqHK5rIGBAY2Ojmp5efnPYtv9f328yozVn/XxSgMwRmKQz8TmwmKDgNnn8ykWi6nVahngkWR5K4wJonIEVI2OjqrVatk8LapQQlUBFAg1aQ0Gg0Gj4NFnQD0DDs/Pz7W3t2eMC4up1+tVJBKxcTClUkmpVErt7e2Kx+MGSkZHR20u4MuXL5VOp1WpVCwNnSweWqJ8PkbvNBoNq7xduz/aOGzPIyMjFjrKqB3cbjjxEJZC6bOIt1otq/wAyV6v94p4vlAo2FxLSfa8bJaE0R4eHlpQIYuxy0DwHQKAuR7QPwAIaUVwzgFgjcZFSOvh4aF9BtoM2WzWnLIwFB0dF9MLAG/M1xscHDRAi6MSU0EsFjO3Ls/NXM9SqaRms2lz6Xi90dFRjY2NmaaH0EdE8ePj46a32tjYUL1eNxZuenraksth+aQLHRUMMeeE8/jjH/9Y9+7d0/n5uWKxmAFhBiSz8btjnWjh7O3tqdlsamxsTNFoVDs7O1pbW7MW18nJiXw+n6ampiwwmHuE647rpFqtXmEFuL+uj9ZptVoG4qTLViBgjs8rXUZAAKRccTWP5fcpIlznJKyYy3i5Am/39zncdiPADJDtsldHR0dmfAHQMwyaYgFWxM1CA9jw2jyn6zamEDs7OzOpAICZjZsC1tXDIVkgnoWCoq2tzaZ+UBTUajUDygj60WXCWEuy94FOidR7CqWenh4Fg0Fjw9PptA4PDxUOhy2bi+uA59/d3VUoFNL09LS1N0OhkM1WbG9vt/V/c3NTu7u7VkxwXZ+cnCgYDF5h+5ByEMBNqzAWi2loaMhAfFdXlyYnJxWNRjU0NKS2tjYtLi7afMlSqWTXLcARxz7sHlmKXCvoL5Gw+P1+JRIJSRdaz3w+b+vVwsKCAoGARXnguiQL8MaNG1e0rK+Pb8/xSgMwwjPRgpyenlouTltbm23C0MJEBczMzNgGTEXCIswfKrK1tTUdHBzY4slmAhuC65AN9OzsTMFg8ErqPhUo2iaADIBEkr3/np4em+fFwF0qJ34HV8uLFy+uuCoBJLAMMDlsMmSLdXR0qFqtqlwuX9FewX6RxN9qtWwEDzc8FS7MFboENzySg2qOBZ3WYG9vr1XcOBfdWWos0NLVcE3OF6wULBhMF1EibGRHR0fmaqQiZ4Hr7u62Qc4uOwIwDYVCpoPb3d21333+/LmJz4npYKNAx8O55Hqs1+vyer0WwJtOp+16SqVS8vl8unnzpjmZACAMI0bLGA6HjVnl57ASfX19+u53v2s5cehWcEyh80PbgouKWJVkMmlAc2xszAI62YgqlYpKpZK1QRHn1+t1pVIpY2PJn9ra2tJnn31mw+RhEAqFgjY2NqxICoVCZiZA31itVk1zCDhgk6YNBwCQLtuK7v9f/05dc4YLyABl0mUQK9erqwkDjF1vJ7rxMG470z34uashA6jxHmCHaK/S7uN+l2R6n6OjI/t9BN20xnmPsChdXV3GnLnAT5IVNRhACC92W5McfK9tbW1maiE3a3Bw0MYZ8b75XTfvj88LmCa8mPvZZQZpC7a1XUwjoXDEmUxBAkjiuhwZGbHCsq+vT0+ePFEmk7H2+uLiouUxjo+PK5FIqNlsant7W52dnRag2mg0tLW1ZVMwmLZQr9dtmL3LysPUA5jcKRs+n88ciolEQqVSSfl83ogBOilcB4BGphQwzB4W7+DgQKVSyZi68fFxm8SBIYD9jy7Q/v7+L702/78+XmvArh6vNAAj0JINjkXBregJu5RkYu6Ojg6jtbu7u1WpVCytGF0JrEImk7GF5fz8XMFgUP39/RbUR8AfLUUybHDg0X6r1Wq20EuX2S4s2kQxINikdQRAJHSWaAZXq+Y61xDWsjAikG21WiZsB3Du7e0pGo1aWCtMFroPgBLtIBZ7NxSWTYUWiTv2A3YFBgO2jAoerZx0WdXSbgTs4VZjgWIj5/ullUKLEb0LwJy2musC47wHAgFrw5ycnJhjrFKpKJ/P22bLOBIYNc6Va3IYGRkxwMm58fl88nq9CgaDFgaMu5A2Dnb7rq4ubW9vX5npJsns7YVCQXNzc1pYWND09LSi0aiFYdKiYGZpuVxWsVhUoVCwAeBuCjfnDENDNBrV0dGRlpeXVSqVzEAhyQBPIBDQ0NCQXSsnJycqFAp6+PChOjo69M4772hgYED7+/v6yU9+YoLlwcFBez+0swEO1WrVvhPa9oODgzaaC/AkXRRbsCtusC8tY1dYDYClIEET5k5lcDVd/L/bTryu5XL1Ya5gn99zry/WGn7XBVUUQ65WjeuJYolCjNFEro6Mgg4tEAetPthsfkZLDlceIAgGhyKGQhAgyeu5ukqeCw0YpiKfz2evy7XBdwDjzbnhu8Y45IIw2pycA6/Xq3A4rP7+fpudyzrJOu/3+zU7O6vNzU0tLy9rbm7OisxKpWLaKdi7RCKht99+W3t7e9bWGxoass4HhSSyBRh12rdIV66PMeOebrVaKpfL2t3dVa1W09HRkSRZWDHTLgCqsVhM5XLZGHTWzEajYeaafD6vZDKpYrGora0tM9iMjY1paWlJkUhEa2tr2tjYsPNGYdze3m7Zb4FA4M9i2319/BkerzQAQ/jrshu4cc7PzzU8PKzR0VFLr2exWV1dtb45Iyiki1yxtraLOV6VSsVS3s/Pz6/Q4IioaUNBI+OsRGsDCHHdWs3mZf4WizUZSIT7nZ2daX19XYVCwcJZi8WiVU3t7RdDd3E8csOiS+Nxkq7EV9BCY2F1WzRdXV1WiZVKJfl8viu6MFf0zHO4MQDQ+a42xF1M3HPA5koLEPaPdlKzeTFPk3YXzCKLNxuadMmQ0Wbh37geaPe6QmA2ZZ/PZ+0avgdE6VjNaQfALJAddH5+MXsObeHx8bGlcgPAYcDcsSws+LQXyNXCqSvpShExPDxsbcWuri49f/5cz58/VzQatYHxw8PDxkxgJCA2YG5uToFAwEwOpJn39PRYyn2tVtOXX36p/f19A46RSETT09PmXAW8wwqm02ltbm5qf39f8/PzV8bh4AJ123IAXRgQWj8YFOr1ura2tkzLh47F4/GYWYDngeWULluQbhuR64TX4/9dsOSCqP+QmJ579LrI3f3DmsLzcwC23PYkzJSrX+S98txukeK+J9431xDPx7WCppPzguaIcwaYcNtcvA8AF+eOa89lrVwHJ0ziwMCAjRhytXVuS9htcbvif9dYASDlXua1MFb09PQYK0u0iSuz8Pv9CgaDevz4sb744gslk0lNTEwYsNva2rJCEJnGyMiIisWisdtdXV22XmezWet6BAIB0x9OTk7q7OxixBqF+/7+/pU9gPT8vr4+bW5uXhkRND4+bhM9mOHK90hBl8vlTOtVq9XM3QhIDAQC5gpF+7m9vW1hurRm6Sig/eScftPHawbs6vFKAzAXDLiVV7lcltfrVTQatYqo0bgYAv3y5UsVCgXdvn1bg4ODevnypZ4/f24toHK5rK2tLRsrk8/njfkZHh42ZoAhx+hytre31Wq1NDMzo0AgcEVLdXR0dIXNgWGjgnZZnPb2i5Tk58+f2ziLeDyuSqViziMWMITW+Xxe0uW4EpfVWVtbU6lUstadywYwHsnVfMDSkcfV1tZmuigqS+hwBNowTbReOzs7jf1DA4LWDDDjiq/Z5HED9fX1KZVKmf5LugywdNtE7kLuWuWly2RyRKws8lTegFg2H7RhtHInJibsswLmAZdu+CcRGycnJxoeHjbgCqva1tamWCx2hZVxN+vz83NbdFlcS6WSHj58aJsS8xwpCGjBJBIJdXZ26sGDB/r444/V1dWl999/X4lEQgsLC5ZJtLKyop/97Gd69OiRisWi/H6/FhYWrHWUSqXU2dmpH/7wh3rrrbfse4WBYtwQ72ttbU09PT1aWFjQr//6rysajZqdfmhoSLdv39b9+/etNcl94raW/X6/JiYmzNCSzWZtRJEkBYNBxeNx9ff3W2sUPSLZR657DGaUA3DjAioXZLjX0S8DYFyztDIl2bXi3i9ccxR4v+z3magBuwPIIruLDddlyHlO11AAe8b1R5ubggJNFPcrf1ymzj0AmHwuiiA+L0ULcg2u11/2fFzPFEfcz67RgMNlAd2WKEUr3yvtPNj4aDSqzs5O5XK5K+AYvW5/f7++/vprffLJJ/rqq6/0xhtv2Gzder2u0dFRpVIpvXjxQnfu3JHf79fu7q7y+bwNqkfMf3h4aGsv1xZrEbIF1j33+gZMAZhCoZAl1Pf29tpMWQoZPjsFW7N5ERbL8HG0kKVSyfYhplW89dZbVqw3Gg2NjIxYcHJ/f78WFxcVDAaVzWZNY/xNH68B2NXjlQZgVIAufS9dMBmTk5N65513bFDy3t6evv76a52fn2t8fFzj4+M6Pz/X5uamhd+hBchkMkomk6Y7kC4cSWQ4wXxQsTx//lz5fN7SzaULi7VrmceVRCigO0qkv7/fnIb0/AuFgkKhkLEPvb29KhaL2tjYMCDjZuicnJyoVqtZq4/QRNeez0LYal1Yq+PxuC28tCxYbPicLDqAnGq1qt3dXct4IiiSdGnAFK0i6TLbqaenxwAMi6vLVvC+iArhe6Xqd9tMbotIkn3/tC8Rig8NDVmAIpuNy5SxCbgCZFpwnFtavcFgUOVy2ebLYbun4kVMizCdNjQVKIOOpUtHKC2Ocrmso6MjxWIxO4ds6Ij5I5GIXSt9fX3a39/X6empwuGwIpGIPvroI21ubuq3f/u39eGHH5qLixEsnJ/Ozk7Nz89b8Go4HFYsFrM5dIi/GWG0v7+vtbU1ZTIZ9fT0KJFI6MaNG5qbmzMrfKFQ0Pb2toLBoAKBgL773e9a2DAaHkwhtGIwlaTTaWUyGQ0ODioUCpkgm2iBQCBguj0qfFgXzh/g+jpj5LrqXIaK6wiw4AInNlH+7rYPXebY1YvBHAGIABC0zbn/3NcEVHGv0M7innHb5zy3OzLMvZ5hXtxwYJg0FwBdB0puMeOuoy5Txr3htljd1rCrZYPRIlMM8MQ5ovChaKHdCmAGYAIeOc+0Egn6pWBF8wTr/9Zbb6lUKmlra0vFYlFTU1O6deuW3Qder1dffPGF7t+/r8XFRXV1dSmXy9l1e35+bux3o9GwuY7cy7w3SWaOkKRUKmVxHJVKRdVqVW+//baFbDcaDcViMU1MTJghJRAIaGNjw8B5o3ERuUEeIy3IQCCgYrGoRqNh8RisbSsrK0qlUhoZGbGsSbLH/H6/Fa4EXb8+vl3HKw3AGo2GubyazaY5sSRpaWlJiURCHR0d2tzc1LNnz9RsXmSkxONxSRfDWj0ejwkzNzY29OzZM52cnCiZTFrlysKH/oEbjYwVn8+nt99+28Y/pFIpa+MBvlxWiDgMmKRQKKS5uTkbT5ROp+3zIcDGsu2yL1TglUrF/vj9fgNugDdYNzZ1RLmENLpsHIspiyjnFs3O4eGhDSWHfWTTQCtFO4hQR5gmcrwk2UaMs5TFF4YvFArZYututNLVTZFF3W0lkcqfTCbl8/mUSqVsU2dzcvUquBLdc8Dzci4Yq8Mg9VAoZEAYzRK5TbBybHxsRmdnZ/bdIzaGAWQDrtfrCgaDNv+Rx6+urhoAy2Qy5sja2dnR4OCgbt++rbm5ORuf8uWXXyqVStlzvfHGG3rvvfcMBKEHIY1/dXVVBwcHNiwYBo9Bxbdu3dKHH36ohYUFnZ2dWZREpVLRl19+qZ2dHTUaDQWDQU1NTZm5Y3Z2VktLSzo7O7NcIjR2OEw3Nzc1NDSkaDSqvr4+00KikwNMu2J52DmczwB4vj+AyHVDh9tGBMC7/+a2HK8L7vl9igg2ZMwX3A/u9U/YLG1y97ldoxAFgqtvdFlSrlt39JirQcQQwFrjgiWXsbuunwTMAQ55HkkWscBaBKMlXTpL3SBaQJObOQjb5QIwCkQ0mxhE0EMCGtra2kx0D3tIkUdxifNyfX1dpVJJs7Oz+vN//s/r008/vXIODg4ObEJFLBbT2tqaaWAPDw+1u7trzBZZay7Txvut1Womy6BVDrPtGg9gqpAnzM/PW3HF+3Jd19eLCQo9zoEkOwck5C8vL+vevXtWQO/t7amjo0MTExOq1+t68uSJent75fP5bP94fXy7jlcagOHq6u7uNo3U/v6+7t69a0zU5uamvvzySw0MDGhhYcEswZVKRTs7O0b/5/N5PX78WJlMxuYyMoYD58vk5KRmZ2fV1dWlg4MDLS8vK51Oa3R01JyVBwcHevnypSRZ1hUiTjQ+5EVlMhlJF7ZmGKt8Pq9cLidJpslis3ODAmm/1mo1a7Gha2JhhR4nCoKqlQ2EjZTDBR60zDA44ORk40Zb4OYXubPODg8PbYAzOUCdnZ0mGEeTxiJGe/H8/NwGnANQXA0OeilXUCxdjQi4zgYg4ieNn4Xu+PhY6XTa2rm0WQGFbOC1Ws1ofN4L5xtgQL4RxgXYLlpNkhQOh41to10hyfQ0rVbLxNGNxoUTl+RvgOXKyorq9brlsj179kyxWEy/+Zu/qe985zsqFov68Y9/rI8//lj1el3j4+MqlUqamprS7du3LQD37OxMm5ub+ulPf6pMJmOMLI5R3I2NRkM/+MEP9MMf/lC9vb1aXl5WNpvVnTt31Gg09Omnn+rhw4dKJpNWhT948MAGmbdaLYXDYQu6HBkZMRAD2IYRw0iBro3vEBDEQfxJq9UykTOuVDZcrgNYGFeTJl22LVxd4C/TfLlFCY8FYMEawQBLl6OSABDu4bYOXaaItqSbwQUzzHNwL3BwP/O5Ojo6rlxXgCWAINosF3y5xQbFgiu+h6FzGTfAFGyZWxSxtvAzt0XLY1xXqTtGiZYf95TL2tHqc1lKPpfXe5HbmEwmbfbi6Oio7ty5o5WVFR0dHZm2lv1idnZW2WzWIhskqVwuK5fLWcEH64XsgwPwRfeFNY/PTwuT4hb2CWDKdVWv1y1Im3W6Wq3a99fe3m5MmDvYm2R/csxevnyp4eFhpdNp24uGhoZsbQOgAgpfH9+u45UGYFTGlUpF6+vrymQyev/99/Ubv/EbCgaD2tnZ0R//8R+rv79fMzMzJrKUZENfp6amdH5+rt3dXa2vr5uYUZKJyYeGhnTr1i3dunVLJycnWl1dVblcNgem3++3TQuqmEWFBOXe3l4LpAwEAqYPQuxMe+/FixfK5/OWulwqlWzzHx4eViaTUblctg2fChKxOGaAk5MTe21YNPczYSJAyAmQocpyF8b29ou0c2zhAwMDtihUKhVls1n5fL4ro1QkWYuNFh7U+/n5uQXGNptNmwkJG4eODfDGe2dxhP1wNT2u1gyRNu1jVz8VCoUkyXRNhJS2tbXZYooQOJvNqtVqaXx8XJFIxNyptKkxHiCAdgXItFnQytGS5ByTV0TrplAo2Ps7OzszhyChlfV6XR999JHW1tY0MTGh/v5+raysaHJyUm+99ZYGBwe1v7+v3d1dLS8vq1wu6+7du7px44ZGR0ft+0qlUtrb21Mul1MkEtHNmze1u7urTCZjYauExH744YcaGxtTV1eXHj58qB//+Mc6OjrSX/pLf0m9vb36oz/6I/3xH/+xwuGwhoeHLRSTiry7u1vValWff/656vW6ksmkbt26ZexyV1eXDeIGfAA8XHE44IYWS61W08DAgPx+v43Ccdkil82EMXLBhaQr4MNlimi3ucCH7w1Aw+YLmwNou24IAWTxnBQZ/C7XGs/tMjZct/wX0MPhuou5N7jX3M8JG3V8fGzTNPhsbvvSzfS7PmTcbVe6AI9i7XoLlzYs79193y5Lyc+IniELyx1X5RoNAJqSzHTE50gmk5aPtbOzo9XVVetwDA8Pa3Jy0opVBtI/efJEkjQ7O2tp/AAl4oxWVlZUKBQ0OjpqsgP3dXHgcu0w0YIcSNgzfgcDD2wlDk3WCq/Xa0CM/aPVaimbzSqVStl94vP5TBdJoUMG5PDwsA4PD9XWduE2JtHfLba/qeO1Buzq8UoDsP7+fh0eHmp9fV0HBwe6efOm/tP/9D/V2NiYtY06Oy/m8W1vb2tjY0PvvPOOJGlvb88S5fk7qcgs2oVCQc1mUxMTE7px44Y8Ho9WVlYMcBABQSAn+U5tbW3mduKGPTs7s4GpCPMJuwyHw9bC2t3dVUfHxcyyoaEhlUolbW9va2hoSLOzszb2hnYBCybuQGjyfD5vAmUWSoARVnYWNhZ/mCU0VwRvck6IunDZCjRmrpsRBx50PhWedLEgk+MEW4S+hd+/Hq7b399vjiYqXulSRyVdznXDoQUIRSMIi+XOu8Tt5MY+sMCjyyAOwI3OOD09VaFQsORvdwMBMCKqdd2bsBWu+4uNmvbf8PCwjo+PVavV7PtHrP3mm29qfHzcRPjvvfeeubSePHmi7e1tBQIB3bp1S7/2a7+mqakpnZycKJfLaXl52e6VQqFgOWjDw8O6efOmxsfHVa/XtbKyIq/Xq7t37yoajaparerf//t/ry+++EKlUkm/+Zu/qenpaX322Wf63d/9XfX09Jie8unTp9rY2FB3d7fGx8cVj8c1OzurcDhsidzFYlHFYtF0KkSluPcNRQzXA/9PFAr6H0YfwSry/Ui6Mvnguv6Jw2W4+DtuV7c177YMaR8DTNwxWbTlYGfPzs5Ur9fN0ALg4H6V9AuACObHBXCu0YRrnfPiivABUIBW2ttkfLnxPNfbscgsXAaK9+ICT5fNck0x18Gsy5pRFLl/3M8L6OE9MLvVdTZTFPEafB6+v7a2NhvjxTik9vZ2i9uhy0B+WCwWs2DS/v5+DQ8P2+dkXeO7Yt2TdOXcwH6zbrrgcXBw8Mq11Gq1LLqlra3NoioI4HVz2pB7VKtVyzXLZDIWBk4QLN0VjAo9PT0mU6BIpatCAf36+HYdrzQAOz091dramk5PT/Xbv/3b+tGPfmRieuzR8/PzkqTnz5/bQs7suxs3bqirq0vr6+taXV01oTA3GUnFzN3b29szfRY6JcZF5HI5u7lw2dGS8nq9pp2q1+s2v6y/v1+xWMzYr83NTZXLZRvuSovx8PDQ9F3j4+MaHBxUpVKxjTSRSFhYJmAjk8mYc4+WCODA7/drZmbGWh9sNDwG9yY6C5/PJ5/Pp93dXUv5d1s6fE6XZWDRoe0HA0NgJKwURoS+vj5bfAGLaCQQYKPBo83JpsNrkrNFVQ2wBNDRtnUF3AClYrFoI05g5zo7O20hpuLn/bNASjIGx23xShdOPxcEu5Ua1TPOW/QZVNSjo6Nmld/e3laj0VA4HNbCwoIkaW1tzRK9t7e31dbWZmBqcXFRHo9HX331lf7dv/t35nzEQBEIBKwN8/TpUz148EDhcFjz8/N66623FAwGdX5+rpWVFa2uriqfz+v27dtaXFxUIBDQJ598oo8++khnZ2d688037T5sb2+3eaa1Wk1//Md/rNHRUb311luKx+Omoevt7TXGolqtamtry/QubGDM05R0hfEk0wpmAEYX9onz74rc2TBd0MHjXHAFW+G+JiDHjQxwLf2uFs3Vf/FYgDmMDQ5oXhMA5boxKSzQtblxL5wTrnHer3tdAWpghGHl3PbidW2Wu0bw3tyJA3xWnpf3yzpw/dp2we51wOWefxcQEo8iXYwkI4aEIhbZAPeJa2rg+bu7uxWJRMzIk8lklMlklE6nrajN5XLq7+/Xn/tzf86y8kqlkk04OT8/VyaTUXt7uxKJhEZGRq4E+LpZbS4obrUushY7Ozut4HSZer7Hk5MTLS8v68GDBwoGg3YuCaStVqv2XH19fTYy6eTkxMKOWevdvMXj42MLyyW7EeZrcHDwW8EUvWbArh6vNADb3d3V8fGxbt68qb/4F/+iDW+VLhYLet/pdNrGugSDQVWrVXV3d5vj6tmzZyoWiyYCJTfs6OhIfr/fRv4wrw9m6+bNm4pGoyqVStrf31dHR4eJnNFdQecDohD2h0IhxeNxhUIha3e9fPnyyu+USqUrN9ru7q4SiYRRybzm8PCwstmsaWpOT0/NVeemM6O3IdUZZoWNgxaadKkxQcfFpggwgSKHNWSzkC4dXGTgAFrYbFiEaQvCGB0dHSmXy/2CZo7PRY4ZLR338Hq9ltbe0XGRGu+6LwHWvBYLuZtFJl1uEl6v184TOsOTkxPLw5JkTBXtWjYLNl7eJ8wEmycMKAXBwMCAsVUwEOPj47YJ3rx5U+Vy+crIqGAwaK3agYEB03blcjn93u/9njEAPp/PdInET3AesPVXq1Wrznd2dvTy5Uvt7u5arlg0GrVYjocPH2pzc1OBQEBvvPGGwuGwzcPkmmYcC8HBp6enWl9f1/r6uvx+vyYnJ431y+Vy2t3dVbPZtGKhre0ieoPRRW1tbTYMnRl96GharZYxpJx71gD3vLvXy3VGzGWeOFxQBvsEi+G6BN3rBeBDOwk9Hfcr591tW7qbhyvQd0EQRhDYaUDVddaJz8Z75j6meHDvbVdT5gIrdJhs6u7nhxVEI3qd8eJ9u1oz18DAvecaaFy2z33v/f39V1hnwB5aN/cznJ+fXzEZAcQk2Zg4wk6HhoZM1zk4OKhYLKZKpaLNzU1j/dGC0gLkO3DfH5/RXQv4fq6voVyLRBth1kFLyrXpmpoogtF8nZycaGlpSXfu3LFWqt/vl8/ns6Ke8wRbx7WWSCQsjf/18e06XmkAls/nNTExYZZ7tCInJydXhicfHx8rkUhocXFRzWZTKysr8nguBN1bW1va3Nw0xxJaFhK2A4GAabsYIeH1enXjxg0NDw8bxV0sFjU7O6vu7m6bRF8qlWymI5UYFc709LSJsuv1usrlsprNprFNHo/HMp8kGUt2cHCgyclJY0/y+fwVx2EgELBRR+jCWHBYCBGy87y0TGB+wuGwCVHR8rgMFG7MarVqIIBNyev1Xhm0DU0OcPJ6vTYmiZEq2MOp4jo6OsyUAGvIosvizcLnsgYHBwe2SaMNYsPgs6HzAixdnwWJEN7VrLGwA7Rp1UgXwFq6XOxg5CRd0d6hi6KNhcuQRR9zBxs47ttSqaRisaiVlRX5/X5FIhENDg6qp6dHH3zwgbxer/L5vF6+fKmNjQ09ePBAGxsbikaj+u53v6u3335b0WhUn376qTGJ/f39NvqH4FXclui1EA8Hg0GNj49rampKx8fHGhsb0+joqM7OziwsklYXz8F1ODExoVKppGfPntmUBO7Bzz77zDKOYI1woyGcJ6gVAwrFBvpIADlgBLG5C1AkXQFlHC675mqY3E3T/TnXI60eSQayKZQ6Ozvt2sHt5ordrwNxV8MoXQIw2vJuixGgBkBjbQMEuHEQ19uAruaK3wF0uc/p/j5gwH28+4fX4xy7AnsA5vW/u0DYZSSvH9znbmHHegXjh8zhegQJ4NA1JCB3ICpncHBQn3/+uY6Pj7WwsCC/328jhQBdFHTknwGMAMUED/N9wXC6gNkF0RysB729vTa7slgs2vMDtGu1mo0c8ng8NlC7p6fHiAEAKSn/tFL5N/SngUBAJycnSqVSv3Cu/78+XjNgV49XGoD19vbq7t27puOCUs9kMtrZ2bE2xubmpgYHB62lVa/XNTs7q0qloqdPn2p3d1fSRSsN99XZ2ZlpjshxIcV4ZGRE4XBYx8fH2tra0ieffKKuri7bmFKplCqVimnDTk5OVC6XTdCeSqV08+ZNa6Uxwsjv99tQW7eihAFioahUKpaoXi6XdXx8bG0+NDWtVutKcCjtPQAIiyIsDU47qH42HdqBMBFuOwbGDMaivb1dIyMjV9gu2jq8HkCF9uR1Hdf1jfD4+PhKsrqrF3Iradqu13U0tGoCgYACgYCBKXeBZ3Pls9FCBgSwgOLmhH2qVqvK5XJm9UYPAhglkwwWDVDsggf0Q+g/AHGNRkOZTEabm5s2R+7s7ExbW1tKp9Pq6enR/Py8JiYmbMHGzTg2Nqbj42NtbGyos7NTU1NT8vl8Wl1dtbava5CghTcyMqKJiQnLmINVODk50c7Ojo1LYWQX8RqwxFyrh4eH2t/f18nJieXFDQ4OWrvFbTkODAwYYM/lcgZOAYq0bEKhkEKhkE29cM0i7kYo6QoocB2NABmXFeJ64I8rOAfYuKDGBXVs+DDtXI+AUn4XnSBsjXQJ7K4zYJLsWnDbeB6PxzZ6XveXaa5cbZZrILjOSP0yXRmyA64NV+TPY9zXcf/fBbL/IQbuugDf/U7cw/1O+DuuZOkShHE/Xmc4XXaS9c79Xlgrt7a2FIvFrKipVqvKZDJWGPN77nlE50XR5rph+b7d8wtIck1CsPnd3d169uyZyVHQnqEPpmPBNUS4N9cakhkc2eT+YUCiICAI9nq7+vXxzR+vNABLJBK6c+eOjatxKVh69ltbWzo8PLS2x/7+vjo7O5XP57WxsaFMJmO6MUbE4Kwkn4cFrVgsqqenR3fu3FEikVC9Xtfz58+1t7enmZkZo8cJkBwdHdXg4KB2dnasyunr69PU1JRVKpVKRblcTn19fYpEIraxUEHjmHFBU6FQ0MjIiGKxmDo7Oy2FnQyddDptC58kWwTcCp6Wn+tYYuEAVAHY3EqcTU66ZHWY4+Za990WyXWGgU2IzZB2IAAaRg0xLjlLBLm6olY+E7Q9z+OmiMPy8P2w2bRaF7P13JYvI3JYcBFMezwemwG3t7ener1u7TM0giyuvAfAMqCKRbVSqahWq11J1OZ80gbM5XIKh8Pmcmo0LkakPHnyRKurq5qbm5PX69Xe3p65bH0+nxKJhKTLluwXX3yh2dlZzc7O6u2339azZ89UqVTM6MFEh6GhIYsBYFNDHwMI8ng8isfjikQikmRt72KxqGw2a8UN197x8bFisZgSiYRpU168eKF79+7p6OhIQ0NDpqvr7u7WyMiIpqamJF22hgHIsF6APK4995y7LKQLwmBbXHDFteqCBzZa1/GHM++6u9dlxtzoF65bWtKu5tAFGtdZuevaNNhirnX+uJu6+zOuf34HUOACCe4Vt5jhPMA00RZ3QSjnkvuWn7v38vV7nO/A1Z657+E6GHPBpnuOOR8u8OW8c755Lfc5OaeAIsAaIMzv9yuXy2ljY8OMQsQ6eDweJRIJcy6zXrpzclkfrl8PrJHn5+d2P3EdAiJhl0m3p6ijSCcMua2tzbSzmAGYNkK2YXt7u61h6Lxw5odCIdXrdS0vLysQCCiZTOqbPl4zYFePVxqA3bhxwzK8EOjSSvP5fMpmszYCglYci8fW1pZ2dnYkyQJQ2QgajYbFVBD3AD09MTFhrcavv/7axrK4A1rRS8G47O7u2uZ++/Ztiw1AgJ3NZhWNRuXz+UxfxWZCNeSKzVn8qFKhqHt6ekwHBVPAAuRWPy7FzyLJos7G7GpOms2mnR9aRuTm4PYBgLltB1ekel3j4WpFAEW8J3RyMFKuu81d/GhLcgB+y+WysSeuFhDBPeNOaHsxVJy8HxKx3YWfzZhNTrpY4IeGhswp6h68LwAyQEO6mGG6t7dnw6lpdwIuWIhbrZa5HokEiUQi1jKs1+tKp9Pq6upSNBq1JHvG+eAaxEU7PT2tH/3oR1fS2mlLV6tVra2tGZinRVmr1VQoFNTZ2XmlVXxycqJ8Pq9SqaRcLneFLenv71dfX5/Gx8etAmdDgRkKh8OmoeTf2ewJk+X7YlPjGpJkgIcAU9rk3N9cu67WymUx+HNds4P2BoDvtv6q1aqBepgJmDcKJIoFChdXP+a+HzZi9z4ESPGz6612rjnek6slAyhwj1CEEPfB9+Pqlq7rKN3zCrhptVq/ELnhslmuhol72H1+Hsf7clk0vgPWhOuaNpd1BMyyziArcCcDuFoxV0/FAZjnc1KUAiJha2HUBgYGjDF2Z9hSbLnXI4fbRWB95nFe70Wu2M7OzhUdK1M3CF+VZJl8fX199h7L5fKVGJ3e3t4r84CZ6DI+Pq6joyOl02nrjjD/8ps+XgOwq8crDcDm5uZMHE9eEsGGR0dHNtKHBOKBgQHFYjHt7u6q0bgM93PjDhhdUygUVC6XFY1GrX/OqIv+/n7t7u7qZz/7mY0gIskcAWc4HJYkZTIZPXz4UK1WS7Ozs7p586aBr0wmo1wuZxk92PIBReiBXAqf9gBhfF6v10IG0YkdHh4aIGu1WuakRFd2dnZ2heljgUPv5GpqSPDP5/MaHR01owP09/DwsIlcWWTcqpY4ENxf7gYpXepT2JxwkyEMdzdFd/G+Xj2ziR0fH6uz82KcEcYD1wV2fHysbDZrIl+v12vfu9frtfPEUHLG47jOSeY1NptNxeNxY8xwkdVqNaXTaR0cHCgUCl2xuHd3dysWi1lQbUdHh/r7+20CAuCkv79fR0dH+vf//t+rVqtpYmJCc3NzGhsb09OnT7WysqKOjg4Di2TFjY+PW65ZIBCwipnvN5fL2YZcqVS0u7ur8/Nzm9pwcHBgYvm2tjatr6/r+PhYt27dUm9vr549e6Z8Pm+aGa4DhskHAgEDlXz/X3/9tY6PjzU7O2ssF0ys2yoDfMOU8L0DbDgoLmjJuRsi7BisJv/utiEBbGyAfG/ortzWtss8u1MMeE7uhaOjIwOBHo/nCjPEe5D0C8/La7lRBi7z5LZA3VbbdV3Xde0X9wUsvutoBBS5ABKAy9oDKAYQwqS5YMrVX3EeXemEKxPgdwFn19uX18HY9ZYvB/cZz0mUjlsoua/Fdc/r0N4nR4s5o4ODgwa2cGByj8BYwRLyX5dxA2xf1+Px3dJdaDQaBuyePHlixhseg9OT65rInUwmY0UkHZOlpSUFg0HlcjmVSiW1Wi0DWoVCQbVazQw9tO9fH9+u45UGYGdnZzZior+/X8Vi0eZwYT32er1aWlrSxMSE0um0VlZWzEVCmw+AwEYLi0R+DiL773//+5qbm1M+n9enn36qdDqtRCJhIaswbP39/VZtsIFNT0+biBIHIRWKdOH0Q3/GxsPzsdCjp2JA8fn5ub1ndwECJAwNDamzs1PlclmNRsP0TG6lCbBgw+MmR7RKFc2GywaHoJUFg7+7TBZORhZ46WqFDZ3PZk2wrfs4hPJ8V6enF4Ny3UqWzXFsbEx9fX1WxbKhuloNNHfBYNDeOzouQCwz1BjEzQZE9s7Z2ZlVpgSPAghwRpJaPTExYRozmFjA0eDgoEVVUDjgxoWRI6ssk8no9u3bSiQSNoQbcHJ4eKh79+5paGhIb731lhYWFswGTzHipmhz/lzBNQYRtwXZaDQ0NTWlRCKhvr4+VSoVeTwec5RKMr2Yz+dTNBqV3++3LCNGgG1vbysSiai9vd2A/+bmph48eGBjv2hTRqNR29ilyxE3bLwUOYBIt8UFcIGtcDdy6VJX6AIamAjANb/jMrYuQwPwAmCwwcPSuoDRZZL4LBwuywNIcYGRdNmidxmU6219V8Pl6o74N5f5ds8Tz8l55H5zdYpEgvBYwKZ0KWu4rjPjmuP9u21EHusCLvc74t9YF65/f7wu54XXdB2ctIpZ1zo6OnR4eGgsFt+5x3ORBcbw7l/91V+1de/09FT7+/sWcApwAojzXl2QR6HnXrfXmcBW60KXGwqFtLKyooODAwP+BKeiNyWOxj2nXV1dpieenJy0Qs6N3qE48Xq9CgQC8ng8NlrpuhHlmzheM2BXj1cagL148UIjIyOWLF8sFi38UJIN3Y7H4yqVSvr888+Vz+eNlWAz5ubELbK+vq56va54PK62tjYVi0VNTEzojTfeUKVS0U9+8hOtrq5qdHRUY2NjNiyYxXZmZsZYkkqlooWFBU1PTysYDKq7u9vcfrSnoJa5icmlgiGBGSoUCpqfn7cFNp/Py+Px2OBY9GS9vb0KBoOmX5Euref9/f1WxTWbzSvVLosZQMzj8SiXy1lb9tmzZ7p165axa2dnZxbVcHp6agCSRTsYDJqODFDhRggQdEprAfBIK442AyGKtKTcgFU+i9/v140bN7S6uqq9vT0Tbbvp+TwfKdW8BkJ5N7OLcyXJNlXATr1e19nZmSKRiGmuYC/YaKampgx4pdNpDQ0N2WfFieWGtzabFzEMsK+lUkmSzGoOQH7w4IElYEciEXk8Hj19+lRHR0c6ODgwF29/f7+2t7d1//59HRwcqNm8GDJO1hnjsAAzbMY4Dzs6OqzwaDQaSqfT5lpEJ9fd3a1oNGrjs9ChZTIZffbZZ5ZPlkgktLCwoKmpKWUyGX311Vfa29sz0ALz6vP5jElCDI4+h+uTKp7NhM3adeW5bUd3Ewe4uJpGFyi5G7vbFuX1rrec2ITd/CqXheLv7h9+zxWZ8ziA5vXW6XUg4wr52aRhsgBOLrh2X9/Vnrn/zprC+fplj+PzuqyO6/R02S7+//o5gSW73nZ0WT/33LnfC8DCbS+6791lNq8DV4A7jP69e/d0dnamyclJra+v62c/+5l+9Vd/VT6fT8fHxzo4ODCXPa70621t3hPMvhvV4R48lvVPupggkkgkbG4w63K5XNbIyIh8Pp856aVLfeP+/r5JU7g/vV6vOZbPzs5snqtbRAwMDNi++Pr49hyvNACTLtPw0+m0AZZ4PG40b2dnp4rFor766iul02klk0lNTU1ZKjiaIXdECq4UwNfs7Ky+973vqaenR48ePdL29rbGxsY0NTWlnp4eG49ydHSkGzduKBqN2k03Pz9vTA7uRgSZrdaFKaBarWp8fNxuwrOzM4tfgD5ns6bS6enpsawv6WK4NW0xspfc6INms2mjLth8ARYuo4VuqFwuG5N1fn5urKAL1Nx2Ivli0qU1ntYAMx/JB3PdVgBBnJ6ATQAWeiLAFxs1gJOh3mxAzNcMh8NXtEAwidLlHE20cZxjwCSbLxsGcRKuwBmnFAYQ6ZJxcCvttbU12xBhD9h4jo+PbUwJLWxiDba3tw2AYNAIhULmou3q6rLW4vz8vEWnoInMZrMaHBzUyMiI6vW6VldXLWyW2aKAMQS76NDQ7/l8PqXTadVqNdMYtVotY+tgoB88eKC+vj4Fg0Ht7++bMSUSieidd95Rf3+/dnZ29PXXX5t2kPu0vb1dc3NzmpycVF9fn7XjyQCTdOXauL5xX2eGuP7clh//ddteHG6rHHDA42FTuFav66hcpgkGzX1N7hWuPxgwt43O4WZk8QegA/BxX5/fB7gALli/3PvLZWJ4Py6oc0Hl9dafy/S47UUXELqtUfc5XSbv+mvwea6/7vXncL8zgKd77nmvsJKsTa7BwNXAwjLVajU9efJEt27d0o0bN1QoFLS6uqqlpSXL/SuXy+Ysx1QBAKNl6zJ7bnQHB4CZbgZFbzwe1+npqTY3N+1xmDfQENdqNWO3cF0SW9NoNJRKpcyVzP1H0VipVHR4eGgEBev9N328ZsCuHq80AJuenla5XNbBwYG8Xq+NQBkcHNTLly+1urpqIt+zszNNTU0Z+Mrn8zagmXwlt8oZGhqydtTS0pKGh4dtZuH09LRCodCVHLFKpWLp5a1Wy/JbOjo6LHmfSgxquFqtam9vz9iL+fl5Ey0jEIcRAwiweAGsuNGGh4eNLSGl3a1gaR/gJJQu07TZ/Fmkjo6OdHZ2Zk7PTCZjiwz6MtoTvA8YLjYCFicYPHcBYvwGoml0NZwvN+aCUTtsPrxHYiBg/46Pj5VKpdRsNjU5Oanh4WEDerAEiPTRdOAcwnXIoHFYNVqD7lgpgC0/W19fVzgctrmW0qUIemNjw9x+zH3s6+uzoE7OAfrEaDRqC667oa6srCgejyuZTFo1zjl48eKF0um0IpGIlpaW1NXVdaW9MTQ0pBs3bigQCCiXy11hPRqNhkqlkqrVqlZWVkxbQ8xKOp3W5uam0um02tvbNTg4qEAgYCL5VCqljz/+WL29vfr+n7Tnd3Z2NDg4qN/6rd+ycFTGqPj9ft28eVMzMzM6OTnR+Pi4zRB1GQWAJKYFcu3c64rzfB0YSZcMk7t58zvX9WQc11tpbusacEex4DJCXGPN5qVzkdd0waH7ey6zw6boskrXAYsLVlxNGM/p6pu4x/gc0iV7d72ld51l432wxtDedIGQ277lvfEafBZ3hBDfA+wchQ8/4/mv/wF4umDXPddu8cb75R5H2+Z+DpcJHBoa0s2bN1UoFPT1119rdHRUP/jBD2zyw+zsrBWxsKuuq9zVCHJOXFeqyyC6Yc2MzOJzFItFew5mRJLzlU6nTZDvXgesWTim+/v7LdKC84ybPB6PKxwOW9ufAvD18e05XmkAtr6+ruXlZY2OjmppacmqzIcPH+qLL75QX1+f5ufnr1D0W1tbOjg4sL+fnl7MNgSYZLNZVSoV+Xw+JZNJdXV12SafSqW0urpquiDpIrqgVCqpr69Pc3Nz6uvr0/LyshqNhuLxuMrlstLptAKBgGWxuOnMVKqMMmJchiRLGEfD4A74lnTlZxxkwLiBowAk2ktUW4AnXHY4iljEXVDEJkI6PSM3aGERTMjvIjKHzfN4PAqHw2prazNtGdlRVJPS5eINq4bFn3NOuxIgRLWPxioajSoUChmAxajgti9I6CdcFlDhtoEk2Tkk8Z/wW84p4598Pp+azaaJtGkdAPBXVlYUDAYt4JZzmUqlTJtHe3R6elqJREK7u7s2DJxsrv39fXNwejwXGrft7W39/Oc/1+LiogHwrq4u3bhxw8IY4/G4RkdHrTVBdhxsFkJtglaPj4/1+eef2yI+Pj5uIAgNXjab1fb2tvr6+vSf/+f/ub773e+q0WgoGo2apf7ly5eWTwTIRGtHC2ljY8OGDAPo0fvR6j08PLRrDTbDZZ8omq6Ds1+m43LbR7AmPN5lh9hguS5coIFuDCAASwwb5eoT+T02Zfe5XfOA+1749+sCdB4DmJJk94XLdrn6I9Y5VxTvtvgAKy4Ycs8d78VlpVwA6rJzADbX8OCyfS5IvQ7krrMYLtuGNOG6psoFwXxm2tXu5+QapNA9Pj5We3u7pqenValU9NVXX9mA7mazqf39fVuDKQZgt9zrxAWU1xk93pc7ZBst5tOnT7W2tmbtRDoMrVbLWoXcj5wDt63sGpO4zoaHh03n1dbWZh0H9LN8B6+Pb9fxSgOwJ0+eaGFhQTMzMyYubm9v19ramlqtlpLJpEZGRkyH8+WXX6pSqdisMNicpaUlRSIRY46i0aji8bgajYby+byBhnv37ml1dVXT09NqtVrmwOzq6tJ7771nI2POzs4UjUbV1dVlIyIkKZVKaWpqytpFpIN7vV5LO4dGbjab1ipEP4DTjHllZMOQh+WGbEqXugHYHBYSABcVYy6Xs/ZUW1ub0fHt7e0KBoMWk4ATiPmOuAxpWUmyTR3BNsPDmVPGhs8ijYDZHdvD4omrzGUa2GiIFTk6OrKWGm20rq4uM0W47BkMFCN2AAOxWEzn5xdDa9E/8X7YjEjeBoRJMpOD22ItlUp6+vSpisWiRkZGjGHjO3ejOgAV7ubD58ZQgMNwc3NTm5ubGh4etucllf/GjRuam5tTs9nU2tqavTeAIBsG19Pp6akODg7MudpoNFSr1TQ7O6tkMqkXL17ok08+0bvvvqulpSX5fD6tra3ZIG9coxMTE3rnnXc0NjamSqWiL7/80lymqVRKbW1tisViajabBvhGR0fV39+vg4MDff755xbKiqNzbm5ON2/eNJMDIN7NdWs0GhYt4rI019uUHG7rCBDgAmlX+yVd1UFxrRJhQEHitrdgTCleXBbM1Wtdfy88DgABSwPAcAGX+3vXgSSCePffXB3SL2Oe3Dbndb0W50b6RVehe15dU4PLMrn6Nt6v2179ZaDLBWOSroANtxh0zxu/B5i/rkdzQ5Fp+bNeFotFnZ6eKhqNKpPJ6KOPPtL3vvc93bp1S4VCQYeHh1bIHR8fW1QE74Gi9JeZPVzwR/GL47dWq2lra0upVMqAKACNzEn3/PLe3TBrj8ejUChkMhucjm4eJufcbWNf16a9Pr7545UGYFNTUwoGg3rw4IEKhYIikYi1tSYnJ3Xnzh319PRobW1NT58+1fb2tiSZqLnZbCqRSGhyclJe78UQa5/Pp4mJCZXLZRM8w+Lk83nFYjGFQiETyvt8Pt28eVNTU1P2O6FQyIao5vN5JRIJu0mp/vkZmznOzaOjI2MMyAVjU6ZtBi1+vR0JEyXpysLDYgGIoV2AFqJQKJioGkCWzWYVCATMZUk1xfuHzqYyQ3wPW8VCyILiVqaARxxYbpaUdDWglRwjwB6J0bjyWMSg5wFsBIgODw9b5Uq7k6wsfpc2GVEgADAYDTRmhCTCigAw/3/s/WmQZOl13oc/udReuVdVZu1dS3f1vsyCmcEMgMG+CCBEAgQRkihRssOU9UlhKeywHLapCEu25QjZkiwj5AjKJG0ThCkSFAUKwADgADNAz9ozvVdXde1b7nvtVZn5/1DxO/VmYSiT/EOeaXtuRMdMd2Vl3rz3ved9znOe8xy6pejChQVsaWlRLBYzbdDOzo7pmXCyJ0vmu66vr5t+KhwO273r6OhQb2+vDWlnnbMhsX6Z3VksFnX//n0T+GKQyigfdC6sa4asJ5NJra6u6sqVK7py5Yp9l8nJSRtwXKvVFI1GFY/HFQgENDs7qxdeeMFGNCWTSWtc8fv9ZofR19ener2uYrGoTCZjthVk+Bjmwi62tLSYgS4Mg8uUujowVyPFGnI3dbfUx3pk83Y3aspOrCl3HbrlLNdCwt3sKD3x7y6gcs/ZfR0M8DuJuN37y/PhAjeeORf88F3cEiNMmCsW599PlhoBl1xH156D10mykinnBEvF+fAd3Xt38j3c7+meg/vdXU2mK5/gure2tlpiB1DmevAcNxoNm0bBAHtYJ7SHL7/8sjyeIxNW2FiXqXNBvLtG3HXoAmZ+h5iEByFlUD630WiYTx7XVTq2+ujo6LB1ARBljjDJXVdXl+1JXL/+/n4zZz0JcN+t430NWPPxSAMwnIyLxaJ5faEBmpiYUCAQ0Pr6un7wgx/o5ZdfVkvL0QBiSSbyHRoakt/vV7lcNkPOSqWiW7duGdvV0tJiDMjIyIiZ4fX29srn82lkZERbW1u6efOmMpmM4vG4tre3tbS0ZIGXB4Puu42NDav7U3ZbW1vTxsaGlZEYpUR5jYDi+vkgwJRknlKU59AfSbI5ZZRuXKNA2DEofHd4NgG6s7OzyYsLjRkB0A16vE97e7vy+bwkGau3ublpQJRuNzpByRRdgAjLga4KtozZmZRQ+I77+/s2MgdmCtDqdhwS5ACG+P7AtvDeBIy9vT0tLi7q4OBAZ86cMaAJ6KV5odFo2EQDdGM0ZXAOdERiulqv103bsbm5qenpaXk8R23yCG4BMQcHxzM40+m06UEqlYp1LHIfMMmljCsdsxZcx1AopPPnz+vq1asKh8NaX1/XtWvXbMpCoVBQMpk0JpUsHeDo8Xg0Ozurw8NDJRIJAxPxeFxXrlzR1NSUGo0jf7G33npLXq9XAwMDxs7FYjFNTk7amseqA22iqytytVsEXbeUeJKRcJke7qFbrgI0ScebPZs8jBvPIeuU5EI6LjO5+rF32oBPsjkuKDr5XQD9/HFLcVLzDEUX1Lgb28nr5Mot6DJ29W5u+cwVs7Nhu+fiAipAASUxDIdh2vnO7jVy2cGTTJHLYr7T9+KcWL8wotx3XueymyebENAWer1e8/wjDqBpxHOPrl63zOiWNU+WHrl23GNeJ8kqHu3t7RoZGbF4Q3cx15GEwC3rwnDipYh+lOcQz8OFhQXV60fdztFo1J4p1sR7gQH798HEvRe+15/3eKQBmCtUjMfjSiQStungOHzv3j0tLy8rFAoZo0DpKRKJaGhoyDRLdJDNzs7qwYMHkqSenh4rM3V2dmp0dNQGbrMBIurHwJIJ9dVqVYODg+YphQ0AmX0wGGwa4VOtVrW8vKzR0VFjv2C6YIso/5GZukOQDw4OFAgErBTLawCB6A1gHAhQLmgDTHk8HitpUt4jCKMFKhaLBhx4b5eRcH1rurq6bKKA13vUyYhzf7VaValUUiAQUCQSsaDd1dVlmWhra6u2tra0ubmp3d1dm1KAgagk884qlUrq6OiweycdsU+YKiJu9/l8BoT4noC8k51UXV1dGhsbMxYMRpBzRaROCZqNwefzmUgWDZ90FJjxIAsGg+Zld3h4qMHBQbvnBwcHZltRrx8Ndd/c3NT6+rrW1tZsPRQKBa2srGhsbEyRSESFQsHAC79PACeIB4NBnT9/3pgp9JCVSsU0KqlUykrUvb29unjxojHLfv/RqK94PC6v98hN/+DgwIZ3Dw4Oan9/35KZUqmkkZER+f1+0//R3QmTQ2mRa0Xnl+t157JGriZJUpPzuVu6gyl2DXVdbZMLVNxEAxNTSuxusOf3XU2oWyLkvXkv1w/MZXpcfyl3oz+pZXLBovvHLYHxnV1gA+tFGcwFOBysc1fnxDnwXPP8u0yaywq5AJPDLfu6bNFJQOOWJt04wuGCTkAYyQCxDmBGAwufwXXjvgaDQZ09e9Y6jpFBSDK2fW1tTQMDA7YeT15f97u49+ekWB8ARdWDdVirHc16TSaTNgsYfRxEAkkyzSwQASRPXV1dCgaD6unpMfulgYEBY7ndkqkLft8/3jvHIw3AEANfvXrV/KkODw+tDR6dEN5BLGgE5F1dXebrVK8fCdOXlpa0vLysw8NDxWIx9fT0GMMhHWVXuVxO9+7d0/7+vp5++mkVi0XdvXvXSpTValUrKyuq1Y6GMOPpxQgjMjHX9gCwUigUtL29bXPw+vr6bIo9Amhcyn0+nzFJdDJCs7PpEyRgyVyBLQ8zbdJoGzg3SnYEZcpeBDaGUdNlQ4AG2NHtiJcN39HdANmYEFsDhihJsTm52TZZbKlUshKxCzApDQeDQbtnfE+MbNH2SEeBMxKJqKenR5K0ublpY3fca0wpj25LtHsAarSCADTADsETm4lKpaJ6va50Oq1CoWAu/NLRWJKpqSnt7e1pc3NTuVxOk5OTCofDNn4EZrJYLNo1WVlZUS6X07Vr1xSNRrW1tWUdpHiDMdcS/RdlxaWlJc3NzZneihmQ1WrVTFXr9aPRLJjRoper1WpaX19XNptVR0eHRkZGzM5laWlJmUxG8/Pz5tFH0wG2KwBDmCm3CxbNoWvhIDVvgmgCpXfu9nMBB2yEWz6CYXLL85IMDLudxzwPvIa16f4u73XSfNg1LXXLZx7PsVeXC17c5/QkoONwxe4cJ0Gfy1a514LjZMmU68v65jklvnC+LovMubgNAZQhT3acumXXkyU7zvOdWDL3ngJiPB5PU9cfoJ147zq/c+9oZCFx5/MZ/UUHNZ/hVhu4vpyTC8oAgMRz998PDw+t1J/NZpXP51Uul61Rx51Awvfj2iJFYS2664mktrOzU5ubm+rv728yQ3afmfcKS/R+CbL5eKQBWLlc1kc+8hF99rOfVXd3t3K5nP74j/9Yd+/e1ejoqEZGRozeBXC5D6fH4zHfqHg8biALg8mRkRG1tLSYQzstxYwHunLlinp7e3Xnzh0T0EMHl0ol9fT0qKOjQ8lk0tgat7wBi1Cv1w3YuBl4Z2enJicnlclktLa2ZhofskksDNAfAc74rgQQVz9BeVA6tptwdVx0NaIrcLNeMl+ABIwV38EVF7e0tNhMw0gkYuJ2n89n4Ifv6W5arsYoEAhIUpNomnOFTXNLbic3VUTUZMywSQi8T5blCIB0p8KY4pjP98cvi/vo6jsqlYqdK/o9mBQ6Cb1er2k+hoaGbIYp2TxDdNPptK3ZnZ0dpVKppu5PvNlYNxcuXDATYEl2LpRGu7u7bcA3ARkzR5hC1tfKykrTVITTp0/r6tWrko4MkBn8ns/nrXt3cHBQtVpN8/Pzphf0+/0aGxvTqVOnzP4DsFuv181bjusCCGxtPRonhYaFZ/MkAEIfJB3P4WPT5DsCMniNpKYN0y2LuAyLpCb95ElAdJJlckuGyAXYXPld99zdsj1Ak/c9qWU7CaBOgig+06ewHDUAAQAASURBVAV3HH+S9gcwxbXgOcT7jBjF8+wCDJ51SrswzNVq1YasMy/TPX9X5+aybgAMruU7nftJ5o/1yXcH2JFwueDW1WItLy9rbW1NS0tLam9vVzQa1cjIiI20Gx4eNmafc3Rd6U+uI/7r3iuXdXM7Gmn0crWE7lplvezt7SkQCJiVxMrKiqrVqjo7O42V83q9Nm/Y5/Opv7/fZBesrXdi6t4/3jvHIw3AnnnmGX3mM59Re3u7crmcfvCDH+jGjRtWDoJKrlQqisViko7pbUqSKysrltnTucfmCKMiHZWwyuWyPQTnz5/X2bNnNT09rfn5efl8PvX29ioSiSifz6u1tVXDw8Pa3d1VtVq1shfMwuHhoQESsk9KjJubmwZY8IfCL2xra8todEo/0tHGyYgdvGFcTdNJM0vmAkpHGwhdPrCE6JQIKi7L4PV6rWQWi8Xk9XqNEmezQauE7g6WA4CJyBo2h4NASbenJLuGADD3dS5rQdYKmHDLVC4dj1mhdLzBwc7s7+9rfX3djGXRerm6Ga4nWSsbB0ACYBiLxay7iYPzqNVqCofD6u7uNmsNV8MEU8Q0ho2NDc3Ozto93dzcNDasp6dHfX19Gh0d1ejoqG2mlNMZczU1NWXMIiWLQCBgQve5uTktLCyoq6tLZ86csdJoJBLRqVOn5Pf7dfPmTZu0sLKyonv37tl8RxpPYrGYTp06Zc0rrgaIa4SFRiaT0fj4uN0TdJInS4Q8u4iWWQcuUHCZJIC3+/tuF5kLZE6K3t3/uiDLZRR4FlyWBtZHUpPxsnv+e3t7Zh3jmmxS+neF7C5IdDslOfg7AONkmcz9/u7P3FIZa/qknomEgXjn2nGQYLhl+p2dHWWzWaXTaRWLRe3t7SmRSDQJyDmPk+J197u41xVmkX9z76PLJHLPKd/RcMJzR9LW2tpqzSDJZNLev7W1VUNDQwZmKNm7CaV7798J4Lr3y73ngFNkLxh/u4PEXfDP92QPGBoaMv0WlQm6r2G/+L7IOtymFZc9Prl+343jfQas+XikAdjFixfl8/mUyWT07W9/W/fv37eHAbYKLyT0ASx2wAJu+eVyWRsbG1pbW1M6nVZPT49aWlrU09OjfD6vVCqldDotSbp69arOnDmjubk53bhxQ4VCQdFoVOFw2EpzkUjEzCqhtymZ7u3tWYcK7AplQvRifX19lolGo1H19PSoVCqpWCwqn88bO0PprlKpmB8YwAGmTjrWpgD6tra2tLKyora2NiuzuswRZbNisWilOzdguiNhGAQLYNvb27NsuLu7W9Fo1HRjbKDcJ+m4eQCBPOybq2NDnybJvnOj0TD/MVeHwTWllOiWVin3sgmih2MyAd/JtengvNyslVIjZVUGQMfjcQMilIJd7RIbKlk1maykptIOGa3P57MRQIODg1pZWbHy9tmzZ80UF33I8vKyNTK0tLTYzDiSiR/96EeanZ1VvX7k5h8IBDQ5OalTp05pbGxMiUTCvMbC4bAB81KppOnpabW1tRk46+7u1rVr12wgeWdnp4aHh9XS0qK1tTUb6YWBK9+VUijjoij91ut1W9fuBkuC4upY3E2RDRYZAZsqPz8JQtwynbthup/JOoD5csuUxJCTvlsnAQHaH5cdgYF2GTEYVCxk0DDBSLMuuA68v6uFc8GYe3DuLijjGjQaDUuA3PNnA+fZ3tzctMQLaxDWtmsbw3N69+5dLS0taWpqSn19fWYP4zLR7vmePD/ey2XMSI7cBI4xZ+Fw2L4Lz6Eks8Px+XyKxWIWY2OxmDo6OnTmzBlJsqYWDJWRbwDATpYdXaD6Ttebf3PF8ugl0+m0MeJMMuG7srYoP/b29ioWi2l1ddU0qi0tLU0d0I1GownA82ydfF4eZZDy/+bjkQZgnZ2dSqVS+v73v6/vfe97ikQiNoIGDU5HR4f6+vos6+7p6TGX9OnpaW1vb2tyclJtbW1aW1vT+vq6idsRB+Oyvrq6qosXL2p0dFTb29u6ffu2dU/i0VWtVuX1ejUyMqL29nZVq1Uz8yOA4aTOw7qzs2MPFkEPFmVnZ0ft7e0aGxvT3NycCoWCUqmUhoeHrRy4ublp45Cq1aqVbShruOVNVzRLQOvr6zMwxyBpzFwlWRkXEOZqadA2JBKJpmBJAEDLhudZe3u7lT1dihxGo62tzTp4CEouU0DgA0zDdsAs+Xw+9fX12YxPNju6iLimlUpF+/v71oDA9/L7j6YfoBPkewLiarWaMRvMl6tUKha02TwBlS6wJOt1WQ83m280jiYobG1tmTYKoIkmTZKV/xqNhjKZjM0SJVPmZ9Fo1EoguVxO6XRaa2tryufzVooZGBjQ1taWCoWCTp8+raGhIWMsfb4ja4t79+7pwYMHyuVyNpibdvqenh55PB4Vi0Xt7u4qEAjY3FIY3Gg0ag0ViIZhXQFYdJ4CSt2yGBu324FHCYoRK5SIuV8wtu/E/LjGv7ze3VT5bN4HgOCWmwBKLqvGz/hcN6FxWQ4+h/fg82B7XKsYvqsLWNxz4XMBkv8u5uukJuyk5ouf81+6sElG0Kgmk0ltbm4qFotpaGjIkjfWZ7lctnV26dIleTwek0a419PVnp1kJAEjgENXk8r1ATy6zBMVAjSZNL8Qz2i+am9vVyaTsWYn6Uj3F4lEjE3lXrj2IdxDrtHJe851ddna7u5uk3uMj4+rUqloZmZGm5ubTWvWTR57e3vNXJnuRklNDRVUHhKJhD03LnPsxnqu0bt9vM+ANR+PNAArFos2zT4UCmlwcNDYAtctns2QrL6lpcU6KE+fPq3R0VE9fPhQd+7cMT0Lrbwej0fLy8u6ffu2Jicn9dxzzykQCGhmZkbValXRaFS1Ws1EobXakQP+wMCAVlZWtLi4qGq1qv7+fntI0Dm5mRJ1ff4OSKFdGUE1mh3pCIAyxJpuw9XVVUUiEcvyXE2KazCKxoz5kQBUPpsjFAopm81qa2vLNmU2JzbeYrGogYGBn+qyYQNrNBo2ZJnyl1se5fzw8YJ1gdHY3d1t2lQlGbCiQw3tx+HhobFOWGWQFTMKpFqtKpvNGrguFotWImDjYZ24ZVuYMbfE6vF4zA0fAO33+83nRzqeZcgfQBffT2reKBmmTccuAn7A39LSku7duye/369Tp05paGjINrfDw0MtLi5qenra5ntubm7q8PDQhrWzmTM5AL0kHaHFYlHpdNrGUx0eHioYDJrIvre3V5lMxrzr6AwFcDJ2KRaL2TxHyuaUrAEfbKiwSYBcBN9sIjwvfr/f2AnYVu67q01CsMyGCGBm03aZBHfjdzVh3Cd3zZEosNm6GwrncfI9KHEDWnl2XNaJ9Q2Ad0t7fJ7bWXtSb3ayJOaW8FyJA7/vgp6TrBgAiGeS55eZu8vLy1aq39zc1MTEhHy+I6d3xpYBQmCBOAf0Se4zBfAAhLisHkw2TTBoW93yv9tkk0ql7NyRY3i9R+bSWPh4vUdWKPgu8jwXCgUzDnbLpi4IfifGy73u/B4JIuXb3d1dsydi/BeJp1vC5jszo5XqBjGaWa7IBojrPBOuDYt7j90k4P3jvXM80gDsrbfe0r1790zjIh0Jj+kuJAixSVcqFRssPDQ0pDNnzmhgYMDE+8lk0qwLMMlbW1vT7du3FY1G9clPflITExPWJUaAo1uRTTAajSqfz+vmzZvWesyDw6ggNkeMU8n0WltbjbEYGBhQMBi0EmBfX5/ZEPC5MFeUWuk2czNr6dh1nWzw8PDQNiCCJRsIInVXu0YWRaB3xaNsipLMEBFdjlvmAai6nmbu+cGSSMdZOSDNDdxudxnUO4GaoObOhIOBJMgjSnc3YII2o6LQV2F74WopKCO5wBYGBsBHyVWSAUGXpXCBANeSUVGcFxuhu0mjDRsfH1c4HDbvsNXVVdOEwOwBKMnyS6WSgsGghoaGFAqFTF/I981ms+art7GxYQCsVjsycP3Qhz6k8fFxW2+sP4/HYx2nPBt04LK59PT0mJ7N7/fr4OBocDralZMbF/eWtUkiBSvq6vvQ/rChu+ALgHey0/ck+DrJYnCcZGhcdonypAvguGeAbvf+srbd17nAkvd1dV4ua8X58R78PteZv7vXisTKZb9P6rB4PxcUoU9y9UPd3d1KJBKmDatWq0omk9YtDjCnkQRGl4502HyG1QO6uebIRNwSHOw5Vg2sH86XuAYTmslkTCpC4xOJBfGN9woGg8pkMiZFuXXrVlPTkNtlyuf9SToqF3y514/YXq/X7VrQOX2yDAw4hjzIZrNNOkqv12vnBrAk/nPfXYaYdXGSDX03D1fz9rN8z0f1eKQBGGL7K1euKBwOm+aIB9wttWHvEI/HNTU11bRw8QCjVML7SjIbgGeeeUYTExNaW1vTzZs3VS6XTf8TCARs8wQELS8va2VlxR4EGBY66A4ODtTV1WWbI6/B7yqXy6mnp8c2CTy+Ojs7TZSONxSgCusCBN0EOTfgYiHA+AtYGjaq/f19tbW12bBxxPIej8fG/PB6gCDdnoAddFzRaFTFYtEAjyTL1FwdCIDGpf9huwB+rh0Bmid3I+Oa8vtcT5gxNqB32nDJ3vf29kzgCsPF9yE4smmEw2Era6CF4RrCdAAc+DvMxu7urnVa9vT0mHAfEOcaytIoQdfc4OCg+vv7TWO3vLysg4MDDQwMqLW1VS+99JINyL58+bKuXr2qkZERRaNR7ezsmNYFJnBzc1Pz8/Oq148NHhnajpHj3t6eTYyo1Wp6/fXXrcu3vb3dPOLy+XxTOXZ/f18bGxsmJsavaG9vT/Pz85qbm7NB97u7u8pkMjZXFQaXUguzRREfu7PxYMJcnQ4JAWuDRMzVxbggjsMF2QAjfsdlMflzUuPlNrKw4bNuSCDcNcsaB0S4ZS23cYQY4DJikpre0938SYr4r1uG4r3cjQuw6JbbGETvdhDHYjEFAgGlUinduXNHS0tLKhQKunLlivm//ehHP5Ik0yUuLi6aPikajVoCBvuDdALwgRZuaWnJ1ikdtBcvXlQikWiSV2xvb1uneSKRUHd3tzFHMFxcq9bWVlWrVS0sLKhYLCqXy1lSWCqV9PDhQxte7wJ7F9hwf06yXy64hTmu1WqqVCpKpVLWnOAC7HdqSCDGFotFq0iwBt3ElyTHBYe8j5sonCyZvpvH+yXI5uORBmDnzp3T4OCgLfByuax8Pm9lEL/fr8HBQUlHC31wcNBczGdmZuTz+TQ8PKyhoSF9/OMfV2dnp+7du6d4PK7+/n7TuIyPj2t0dFRLS0u6fv26ATZKUmzCm5ubFkxo7UdXhdaLQAt1Dnjr7u423REgDcaCIIXfFDYBDAnnoWRz471dWp8gjVA/FAppc3PTNhgAxc7OjoGXtrY226Tx1QKsSMelUhizSqVipquu0BhGzmURCAYuje76PsGMAX5cobl0xBbBTgUCgaYsuF6vm/M+EwhO6nsodxIAAR+UUmH9YM5OBlu+syTTp7hWBHiBtbW1NZXhYId6e3u1ubmpjo4OM+QFWAO2sJ5gbBUbqgtODw6O5o7u7+/rrbfe0tzcnM6ePauRkRET4O/v72thYcHKhouLi00JCqCGofSwsZFIRIlEwjaiw8NDvf7665qdnbXMnPmSrPu9vT1j/3p7exWNRnXu3DljTlZXV20d4LmUTqeVyWRs7QEYANlswIzooiPvnZgwNiCSK8peMM0nmVwO16YC4OVuii7bJR2XNk9qvKRj2QPPJWDO/T0XUJGwuKVJt1ToatIAgjAJ2C0A8AG+rm+UGwd4P5dB4/sRN5BKALwoNe/s7BjoputxYWHBxO4kMHt7e5qdnbVnwZVMAIJgIWGwuCdY62DGCwOey+U0OzurmZkZ85ojJtHgcfv2bTUaDQ0NDcnn86lQKDRdc6oGyWRS5XJZmUzGGFcSQoyss9msQqGQJVcuC8o6Ofn/LrsDO7W5uak33njDYqAkaxZwS9TEJje+oAHlnvh8PpshyQivRqPRlBi7h7vmXMnL+8d753ikAVgoFLJ2+NnZWWWzWXNtP336tGKxmPb29lQqlexhIxjQzXP//n3FYjF1dnbqiSeeUGdnp3V4YUQZCAS0sbGh69eva3l5WdFoVNJRMI9GoxobG1O9XtfCwoIBILI46HTKIDzIuVxOfr/fhJ9oHAYGBrS9va07d+6oWq3qiSeeUEdHh/b3982VmzEt6MSwjGAzZ1Pk89lQXXGtO25Iau6KkmR6nnq9rnK5rFwup/7+fsViMbW3t1s7NUGNLjtAB5k3XZ+BQOCnWt+5NrAZrlEnP3cDBxsCrBTUPZ2Ofr/fyn+wX3QZ8bmURhEOE5zQkwHgCIZks7wXALBararROLZM2NraUrlcNvsMzpXripaGa+W60nPdAV8ej8dYzFgsZo0R1WpVklQqlazMMzQ0pIODAyWTSbW1temLX/yird39/X1NT08rk8kon8+bNss1avT5fAaOWltbNTk5qfb2dm1vbyubzWpnZ8cc7R88eKBqtapr167ZJtHX12cAkeYVwMXw8LD6+/sViURUr9etYYCml2w2qzt37mhtbU2xWExnzpwxQTEltVwuZ0Jjuoy599IxuACYnLQ04Tl1yzEnxewuuObvrE1XS+XOAD3JhLjdlJSW0YW6m7PLlLmaN/7OmnTZPekY1PFZ7vfku7mNAfzcfS9X6A+jwnmjPSoUCurs7LRqgJtMrKysaGlpqcm/LBgMyuv1KpVK6e7duzpz5oyuXbtm/7a5uWlgjsYXzung4MA6vTlX9Lp4KtIQgy630Wgom81qY2PD7jfjq5gx6vP5FI/H1dHRocnJSU1NTeng4EArKysmfj/pOUbiQNyjm5B77AIrjpOlPe6za52BFY/H47EqDGCM0iRrjQSOEmupVFKlUrHXAdZhD5F0uI1N7rm4DOvJc3+3jvcKA/bSSy/pf/gf/gfduHFDyWRS3/zmN/UX/+JftJ//yq/8in7zN3+z6Xeeeuopvfrqq/b3vb09/d2/+3f19a9/XTs7O/r4xz+u/+V/+V80NDT0pz6PRxqA4T01Pj6uRqOh4eFhxWIxDQ4Oqru722jyhw8fyuPx6OrVq4pGo2YjgLCUMSljY2O6cOGC+vr6LOgEAgHt7+9rdnZWyWTSMtR6vW7jj2KxmI2UwJ4As1EycLe0IclG7zCKBa1OS8vRLMr19XW1tLToySeftIDHptNoNMxCobu72wZEA+LoIoMRazQapsdys2y0OJVKxZoJpGNvJddhf21tTadOnWoakSQdbYBdXV3q6Oiw0pWr0XAzc+k4GwVYIWzGuqK7u9u+L9cQgOK24wNmuBaAKjfL4/uyufG7Pp/PzHLJcN3r6uqDYN88Ho/K5bKJ8HGlx6gSEW8wGLQNgWvHvUUTw3WDeXPH8CCk576Fw2EL1hsbG8b49PT0WNci1yKTyWhjY0Pr6+tKp9PWLdXW1qaOjg5jdmEO0EYGAgFjANHQPHjwQB6PRx/60IcUCoVUKBS0sbFhLCfnDhCLRqMaHR21Dke6IL1er5kE+3xHc1NpRPH5jrzzRkZG1NfXZ8CL5IXyN0yEq21xy3OAYhe0uE0xrhAd1k869rT7k0AVn3VSjM9r3Q2NZ84tLbqJg6vn49xhmvkdAMVJGw0XKLjn4F4Hl4lzv6e7EQPciCMwO5S4aAJaWVlRPp+3EjRJH/eN59LVUjYaDa2vr6tarSqRSFgii+Ac+YXH47FEBiDmsnj7+/tKpVJaW1szgIH0ore3VwMDA+ZB6PV6bZxQPp835grWu7+/3zoNOX8mc3A/AbSSrMlkYmLCnjtXN+cyoifXwklm35Uw9Pb2qlKpWELjJmZuPOP+oGEtlUpNRtKYXzPGCxsc4pQL9l3GH4DyKJfqftbH1taWrly5or/+1/+6vvSlL73jaz7zmc/of/vf/jf7O88Hx9/+239b/+bf/Bv9zu/8jmKxmP7O3/k7+vznP68bN278FMP+Jx2PNAAjk+zs7NS5c+c0MTGharVqdhJLS0t66623JB1dzGg0qpWVlSan5I6ODiuBhMNh04sheq/Vanr77bct+w8EAmo0jsaaIGZmLp8rGgc4uJ1xgABGCjEMFmdyMplUKmUsRz6fN4BCoJTUpFmihFmpVBQIBKxsJh07KxN0yc6k5pZ4ruPh4WGTZosS2sbGhm30eGqFQiG7jvjyEKAQ+nM+rtjXNTZl86Zc4Ho6cd0Q7pKVAsjYEEqlkgFdtzxFCQfK36Xk8bYCvCL4xbqCjc0thcKCUBojKHq9xwaw3HsCY61WsxZ8slm+C6wGZRtYCe6z20hx5swZxeNxzc/Pm7Fjo9HQ4uKi5ufnlUwmjSULBoMaHR01LSRaNAAjrDDdlYCvxcVFEwnH43F96EMf0pkzZ+y+Dg0NKZ/Pa2ZmRq2trdaZC7ifn583hqFarWpyctJsOuhOpszeaDQUCoVMF0bSUSwWtb+/r9HRUZurCoh0hdqwuu6ahv15J10W8YLD3bRIFk6yWdwD1gK/5zIdJ0uFUjPg4ucu20T5yWV3YfboKIXRZj27YnsAmbvpsnm74M9NOLh2gHsSCYAuz3Z7e7tisZiWlpb09ttvKxaLqaWlxWbf0ujivh+fsbe3p3w+b00zPEsACp5tnifpeHanq29yWSnmpdbrdS0uLprdTyQSsbjH8xuLxazkDlNWqVTMn69cLlvCiDaM++vz+RQKhexZdcHXyVj5JwGZk2vK9Q8slUrKZrOWWNIsxnvzWj7P7e6l/M/+4+5R3D83kQSEc06u3ODdPt4rDNhnP/tZffazn/13vqatrU2JROIdf1Yul/Xrv/7r+t//9/9dn/jEJyRJ/8f/8X9oeHhY3//+9/XpT3/6T3UejzwAc7v3eGBcOj8ajerMmTMaHx+3uY9sCm5wHRkZ0alTp1StVnXv3j3LgtAwkN1BecdiMXV1dSmVSmlpack65VyNiVvW4pxol8YnZm9vT7FYzAIq3UV8v2KxaPV+ghcal0qlYnYJZJtumzYLs16vG+PgamOYE0iJTjp2C+fvBCw66thU0ORQLmRwMv5VZK9kqq6vGfoeNkz0ZwQrgA3AwQWUnNNJ0THMG8JUXu+WFVkjrjD54ODAytaAJNg213KA60i5CMB7cHBgpTGCKAdrgfIs1wwQiREvINHdLAmu6Bu5FvF4XOl0WgsLC0qn08Z87e7umscWXkdsntxf1gHfhyTg4OBovundu3eVTCZ19uxZXb16VefPn1c+n9cbb7wh6Uh3l0gkjC2FfaIc5Pf7TZsXiUR04cIFeb1elctlW/ulUknLy8vyeI6mUXD/yPRbWloUj8fN9gJgS7mK6+SK8F0dERs6B+DnJDPAcbJ8eJI1ctmNd2It3NIm7+ey5NJxiZB74dpVuEwwncCANZg9Ygcgzc2uAYJu8uPaYPB6t+wPe4LJKgbQkmyQ88DAgNLptKanp23EjSQzfIY95xqRiBB/Go1GU0MOY8mIjQAVyvvEJe4h3xl2jZjHBA+eeXRk2OQkEgmbIQvjho5ybW3NOqJhWtHeogFDOgDAP8l6ndRruffBXTskl/l8XslkUqlUSl6v17wiYbhZBzCjxCn3cJN5pAt4BXJfAfruGua6nVzT7x9/uuOHP/yh+vr6FA6H9ZGPfET/4B/8A/X19UmSbty4oYODA33qU5+y1w8MDOjixYu6fv36/zcA2P7+vsrlsi0sABVeRcPDw8pkMmo0GlZ/pyXZ6/Wqv7/ftD3YP1QqFXV1dam/v986s4aHh/X888/r/v375nuTSCSUz+e1vr5ujISr3ZFkbBH6LVgiHOoR7ff395veAo3ayXIM89UQwyJMHR4elsfjaeoMo0xFEHcBIZsI50wXERlxoVCwoEypCQ0GQRXj2N7eXnk8Hps40Gg07HtIMsCDhg09FsGCAMTrXIZPOgY8bnkHJgvRMv9lg4ZFZE2gxaELzQXIgIczZ87YnE5ADxsmAAAQmUwmjfkE/ALKWUsAKLpCk8mkgVWfz2f6LFg8mB6CJYCEMgTNJfv7+4pEIhoZGWnSvKETK5VKTX5NLS0tikQiGh4etnvV2dlp95uNBkE1OsjnnntOV69e1cOHD/XHf/zHWllZUTQa1cTEhMbGxhSNRrW2tqbFxUXt7e2pr69PwWDQNhy/368rV66ora1N6XTaSik8g93d3Tp9+rQBkGAwaIHO3VS57gAWOgABz+jW3OYH6aetJFydo1ui4zgJXGDGOXiWKB25rJe7aQNI2EhZhzBFsLSsZe4hcgFAbHt7u3XxkcC47BblVZgxgKDb4ejq4djU3c+DmZ+bm5PP5zOmOpfLaW9vTxMTE5qamtLu7q6Wl5fV399vDBnyDff92fBpLAiHwwqFQtbJ5/f77f41Gg1reJmampLH49HS0pKKxaK8Xm9TknASbHJdaWYJh8M2TqtYLOqtt94ykXosFlM8HrfEAxaqu7u7yYn+pM4UJskFyC6j5ILzk5IHAJXrwVYul5XNZuX1ek3vS6LnHi5whxggjuzt7enSpUv64Ac/aAmmazLtnrsr1WCNuEnGu3n8+2TAGLDO4fr2/VmPz372s/rFX/xFjY6OanFxUf/lf/lf6mMf+5hu3LihtrY2pVIpi93uEY/HlUql/tSf80gDMOno4rv6Hx4OZmYxyHRmZsbYDsogLS0txl61trYqm82arUU0GtXLL7+smZkZXbp0SU8++aS1/weDQW1tbWl5edk62aRjfx8CKaU3ApPbdUQQymazGh8fN5p6fX1d0lGDQT6fVy6XU6VSUU9Pj4nnCf5kvpTAaB4ggPBzXnN4eGg/hz3o7Oy0ske9Xtfq6qqKxaI52Le2tpo3Wr1eNwNBtE4ej8ec6NnY+J4IQ11Gh42VoEIwgak6KWzFhBWgBTtAQEGftbm5KUk22BqwSzkR4AaLAFsDiKFrtKXlaPg646J4fwIxbBw6Qj7Xpf9dsTV+V8zFo7QASGaNcN1c3zE27Z6eHgOvdPlevnxZh4eHunfvnpVc2FQBqsViUYVCwTbvSqWidDptTBzMwdLSkg4ODnT58mU999xz6u7u1htvvKHvfOc7mp+fV29vr6SjLHttbc10ZHiNkbzw3s8//7x6e3u1tLSkdDqtra0tLS4uamVlRQMDA6Zdo1Gkra3N1qV7jySZwbBrrbC9va3t7e0mXZErSud4J5DlCr657m6Hocu08nv8cUGXe68plQGqXd2Yqz107QR4H1fDxZqGCaQTzl3zvBesOt+BzzvZ1UgCRxxivZGYjYyMKJvNanV11SxsONdLly7p2rVr+uM//mO9/fbbdq4wUW4MdsEvfw+Hw7ZuAch0UEciEV2+fFnnz5+3f6PMRhe4yyYCVvh3PpdKBhrCQCCgXC6nra0tGy03NDSkoaEh647f2dmxzwRc8/mwxa7tzcnS9cnDZUUBp5ubm8YuV6tVbWxsmEWSKzc5qS/jmeD+1utHM12j0ah1NSMRQZPE/XXX60mg814AX/++j+Hh4aa//9f/9X+tX/u1X/tzvdcv/dIv2f9fvHhRTzzxhEZHR/VHf/RH+oVf+IU/8fdcNvxPczzyAAyg4Hb/8AfmCGYCESM1dBar287MYODr16/r4cOH8nq9ev3117W6uqonn3xSp0+f1vT0tObm5pTL5ZoE3gQ1AjClJheUEVQJBLAh9fqRRUQ+nzcmibFD8/PztmmGw2HzPKNs197erkgkosPDQxOPwp5ks1krmQFmKBO2th4N3XZb1tkE6KhEz9Td3W3iVTIs1yCT68CGRvmA8gRlI1ePwGJl40UjBAPARkvGz+YIYKLEIclmO5IRu2CX7JvvwgPS3d1tTQUui0r5k8/k3hweHs3lBOQlk0kVCgWdOXPGtIG8ltLb9va2IpGIbYAwGniBZbNZ+3euCdMAAJEMGA4EAioWi3rw4IE6Ozt16dIlXbhwQZVKRdPT0036HFcfBVPMvFO/369isWhgDYZsfHxcLS0tunfvnm7cuKFyuazz588rFospHA4rn8+rXC6rtbVVvb296uvr08bGhm7fvq3l5WVFIhF96UtfUjwe14svvqiNjY2mjt3Lly/r2Wef1alTp1Qulw34jY+Pm6aNNUgzytramorFonp6eowFpkxJY4e7bl0Az/u518MFUKwRnlnXBNgFSy7bxOtPCqd5LZshnwdwZw3z2bwWHShNNJwziZZ0bC8D8+wyvrCYyBk4QqGQaYwYzwPw4rywL4D1zWazZv6JxGF4eFjhcNjYSxIrwIo7+saNdcg9SLK4DjCu+GTt7Oyor69POzs7NuOU83YPEjRJ1oAkyVjpra0tsz0h8azXj2xoXKaQhiBXf+XGKNaXu6ewJqRjAHgShFNKJ2Yx+osOcuIY0yi4JwAlV/9F1QE2MBaLaWRkRLVazRp9WPtoUl2N8UngxZ+Tpc134+B6/azfU5JWV1fNv1PSn5v9eqejv7/fJuZIMuufYrHYxIJlMhl98IMf/FO/7yMPwFwa/CTyJCCjiymVSkqn08pms6rX69Z1KB1txhcuXFAgENCPf/xjTU9PW3aPg/7i4qJt8lNTU/L5fCYadgXfvB+MB+DMLb+xMboABnE7ARAvHHRhGBkODg5qaWlJ5XJZOzs7Vg5zM113YwDgUCIFjACgAGc8oJubmyoWi4rFYk0dldLxonY3PGhyArOb2ZHBuuOhoMoJ3jBZ6KVccbFbMnKDi9uZRtBko3JZQl4L8IIVwCcOb7FyuaxIJGLfdXNzU4lEwuwzCJj4FLlBzgWblPfI5rExQaPWaBx1UJZKJS0tLVmSwIbsCsZZKx6PR319ffJ6vRocHDQglUwmNTY2Zt5lfr9f6XTazrNer9vGtbq6ap5tbOR9fX2Kx+Pq7u7W8PCwotGocrmcNjc3NTIyoosXL9pm1dXVpfX1dWNUcrmcXSev98gu4+Mf/7iuXr2q1157TTdv3tTAwIASiYSNYbl8+bK8Xq/u3bun9fV102cyqxWWlmkOxWJR2WxWPT095v7tMhM0vLhgy93EWDtuQwXlf0AB15hn1GUPXB2Nu4Hxd36fUj5r3xW9u6CNdURJjnI9zSBY0rCW+V3WmCsKZ9OhJMjad0vd6C6RO1QqFRUKBUsceaZ4NgcGBpre7969e5qfn7fu8nq9bhNAXFbObTIgeeEZcO8HCSnC8t7eXo2Ojqqzs1PLy8uq1Wrq6Oiw83evM4CNOMREExglkm/XCw3wtLOzo/X1dYsXbW1tVlEABLrA6+QewnGSUXXXCqzh4eGhstmsZmdnmwAZ55nNZlUul20NuvGZtU3VgVJyKBTS5OSk4vG4Jc18fmdnZ1OZ9uT6556cZC3frePfZwkSC6Z/H0c+n9fq6qr6+/slSY8//rhaWlr0ve99T1/5ylckSclkUnfv3tU/+kf/6E/9vn8mAPa1r31NX/va17S0tCRJunDhgv6r/+q/sm6CRqOhv//3/77+1//1f1WxWNRTTz2lf/7P/7kuXLhg7/Gz8M7ggP1yKWk3QHGz/X6/enp6bNo9eiM0XqFQSBcvXlR3d7d+8IMf6Ic//KHNxkPoHgwGlcvlNDc3p3A4rPHxcT399NNaXl7WzMyM2Q1Iatp08QXjv+54lY6ODhtijUEqhq5kmwQsGLNAIKCBgQGtr6+b947LrLiCZMAWIM4tV7hdQGhu0BjNzc1pYWFBAwMDpo0i4LtaDzZCQB+MgttVBQBwaX5Jdh14X9c52tVkENTcz+bgnAEUsHJuu790rO1howB48dpcLmfddjQnlMtlMxo9ODjQ1taWaXhgyegyZLPo6Oiw+8CG1NbWZmxWPp9vugYkDfyX30NHA7DjtWxuNC2sra2pUCjo/PnzOnXqlOLxuB4+fKiZmRlb1+7MTJgl3otrg7h3ZmbGWKmDgwNls1nl83kTxVcqFSvnJpNJFYtFcx5/5plndO3aNc3NzWllZUWXLl3S1NSUent7reuTc97a2tKlS5dsI3E1Qvgm0ayA4Sas687Ojra2tmxzgyGkK85NQlzwxWQBd+OGtXVBv6vr4nCfG/7uxhvWJOuA/7qMPNea6QaAajr6SqWSzVVlQwUIoWeSZIanABE0faxP/nC9uWckdtvb21YabmtrMwsZSp7t7e06PDy0BpPDw0Mb7ePzHXnIubougADPMyCT+ObaubheW7jmHxwc6Cc/+Ylu375tM1UBQjw7bW1tKpfL2tvbs/XLZ3Jd2QdISClPAjwAI4BSjIJ53ohBxBwXgJ+MPS4wd/9OnMbgmpIr7BeMHOfDvWL9udeK+EkzzdDQkAYHB+07c23cRJOEwC2bsj55tt4/jo7NzU3Nzc3Z3xcXF3Xz5k1Fo1FFo1H92q/9mr70pS+pv79fS0tL+nt/7++pp6dHP//zPy/piGH+D/6D/0B/5+/8HZsb/Xf/7t/VpUuXrCvyT3P8mQDY0NCQ/rv/7r/T5OSkJOk3f/M39cUvflFvv/22Lly4oH/0j/6R/vE//sf6jd/4DZ05c0b/zX/z3+iTn/ykZmZmFAgEJP1svDM43AyJTdtdxG42QJdLIBDQ+Pi4crmcHjx4oL29PT355JPq6+vTK6+8ohdffNGYC8SQ6GfIVhnk/dRTT+nChQvyeDy6f/++1tbWbOAwzAlsC0DDZZ7Q0cCW4KqP7QRZHA812SEdPru7u0qlUhoYGDCvJ1glt42Z4MBcSc6J0oe7gWDSmcvltLOzYxmF24VFVxqZnwtsXJbH1Zjt7u4a7Y82zGUITt43GAuYAPf6uU0GZNps4AQ3NHiwUpubm6b9g/WjXMj7ogXs7u5Wf3+/Go3jrjo2VjbXg4ODJksKwDWbO+d/eHiocrlsTAFsA2aQXAe3XEbApCuOzQWvNEasuLox2KHJyUn5fD5rdQdwMATb6/VaE0FXV5dGRkbU1dWlhYUF5XI5Sx4ALDw3sG58Jp1cTCJobW3V4uKi9vf39fzzz2t0dPSnxtrU63UNDw9rYGBACwsLun//vuLxuCYmJrSzs6MHDx6op6dH586dM0AOa7u2tmabsAt80KG5TSjueobdgqmFYaBblsNN2KTmeYzuenTZYACeC/xdMb0rQWg0GqYTc0t/lHTpPoT9w9AYoLm1tWWd17ApXq/XAOn9+/ctocIChrXORg5jTzxCiM750QSxt7f3U2VLWFXiE88t31+SefhtbGzYeuc6ETt2dnY0PDyssbEx+f1+vf3223rxxRetGxzGnU7IwcFBtbS02PzEnp6eJiabch7f1y31w/wgwnf3CEykAaGSmjzO3mmfcZ9RF3wB7gBRnB8lXZehI5adTGDda4Q8ATNcYgDxzePxmNSE78szwbny/zwHdL2+28e/Twbsz3K8+eab+uhHP2p//0/+k/9EkvTX/tpf09e+9jXduXNHv/Vbv6VSqaT+/n599KMf1Te+8Q3DMZL0P/6P/6P8fr++8pWvGJn0G7/xG38mHPNnAmBf+MIXmv7+D/7BP9DXvvY1vfrqqzp//rz+p//pf9J/8V/8FyZS+83f/E3F43H99m//tn71V3/1Z+adweEangIQENe7AZDg6WqR3JlhnZ2d+vGPf6yXX35ZqVRK4+Pjtqki+iVzogRVr9d169Ytra+v6/z584rH43rttdfMX6m9vV25XK6pRElggfbHd4sNbXZ2VpFIRL29vdY9RmljZ2fHOt0Yj8QmjB5HOg4I29vbBhoLhYKBlr29PbN+ACS5Aveuri4bGu7qHqTjgd78nXMn6LBpcu0AgLAQ7kYGoOEz2BwBOABHNF18BgddmwBAd0PF28stXW5tbamlpcW0FXh+bW1t2T0tFotGY9PdyMMEo8Am7Pf7bYQQwc3tWEK/gbAZ7R6BlGtTqVS0tLRkvmpuR5+rSWEdMDKIknZ3d7ei0aj29o7Gv1SrVfX29uov/IW/oLW1Nf3u7/6uUqmUPvShD2l/f1/pdFpzc3M6d+6cLly4oJ2dHW1sbGhvb09TU1PGWnC4QIExU4Azr9drvkzMzmOTrtVqVs50N5X9/X1VKhV5vV594AMfUCwW0/b2tubm5tTa2qrBwUHt7e1paWlJuVzOBhEDtJm52tXVZQOgWb8u2HFZYBeAuUCatX1yUzgZ0N3mGnczdvVjbuxhQ2bzJwa1trZqYGCgSaMkHXdn4p2FZgjmfX9/X0tLS7px44YuX75sXdPoV8PhsBKJhE1S4P4sLy9renraZBPo7Pr7+zU0NCSv12vGuzs7O0omk8ZcASobjeOh9bAorAmkFFx7NGlcQ6xFWOvValX1el3j4+M6d+6cAf18Pm+2Iy5o8Pv9TfNBP/CBD2hgYEBbW1taW1vT7OysNjY27LXEI1hszg/WkPd19XkkHFtbW9rc3LRKBvdXatYQEsPc9eZ+LklytVo1zz7kACcPd98ieXWtPfheHMRUt5ubP5wj5+Wu3ZOdnO8f0vPPP//vBG7f/e53/2/fo729Xf/sn/0z/bN/9s/+3Ofx59aA1Wo1/e7v/q62trb0zDPPaHFxUalUqskXo62tTR/5yEd0/fp1/eqv/urPzDvDTt7JKlx34ZMMmKtFAhzBWND9WKlU5PP5TM/hinJ5wCTZe3g8HhPI1+t1PfPMM/r4xz+uO3fuWEmHbNMtt5EpwZagPclkMjYn78KFC+YgXalUbPPo7Ow0p/t4PG4bVTqdVl9fnwVYt+ZP+YzMm00EIaybuXHPcOZ3O8ZcapzNKJfLqbOz0zr8AFccbJy4WVOOxLmeMh0sC9eWDBHQRCmTe+nz+Zq0PJw7+iaAayAQsHNieDr3cHd3V2trazo4ONDg4KB8Pp+Vv2CT3BIBrBXluZNZPV5p29vbKhQKTSUZQLckK0PMz89rfX1d+/v7VuZ1GRPEu6xDGiHoEsPriPIRoPv69euKx+N6/vnnTaD/xhtvNAFeWMXl5WW98cYbev3119Xb26vJyUljQdznAJDe29urUChk1xUmVpK1+a+trZnjN40ejUZD6XRaKysrOjw81MjIiJ566ilz4E8mk/ZcZjIZ3b17Vw8ePDAwDzDr6ekxXRmD7N01zyZzsiOVNez+4ecnmQPY4ncqMbmvc/VIrh7R3Qil41mn/Iz1CDjgtSR4wWBQIyMjevDggaanpw20U8760Y9+pKmpKV28eFGSzGOKkmZPT4+CwaD6+/sVjUatA3VjY8PYXMDrwMCADg6OZnqSoMGotrW12fxBwMjh4aElLAAmtymBDlAAJX/n/uzs7Fj5cWBgQPV6XT09Pbp8+bJpC2naAHBvbW3J7/crFotpampKfX19ajQaSiQS2t7e1v3795XP561Ey+e6XdckdPyc55brD2PlMu3ucVIg7yaCkppiGIkic1fdCQ4uM0uSwLnwfOJNFolEmoDwyQ5Nrrm7v5083CoBQPrdPt4rDNh75fgzA7A7d+7omWeesYXyzW9+U+fPn9f169clHflguEc8Htfy8rIk/bm9M2AZOPD7wPQPjROdjpTvcNDmcKlBhh1Thjt79qyi0aj6+/tNsExGwkPgZj7YFvj9ft29e1c7Ozu6fPmynnrqKfl8Pt28eVPFYlGdnZ0mbIWtI6uF8ubh2dzcVCqV0sTEhKLRqILBoNkc8NAxHLurq0vDw8NW4qNDEaDSaBx7XSFWpqxVqx35U4VCIQu40WjUNvv+/n4b0USHKN+VkpAk61RkI3M3JTq8tre3mwwEXRpeOg4erhM8wIpuyHK5bJQ77EGxWFS9XjdGq9Fo2L0uFovmgA2AY62w4QEyGKwOy5ZOp3VwcGDlHuwmYEFdsT1C9FAoZAwQYIvAyjw77DAI1pQSmT1HMuBq6XK5nGl0XK0dwZTSicuGoWVkrTz11FN6/PHHVSqVtLq6as9LW1ubKpWKVlZWTJdF6ZuNF+CM4SMsCuDK7/crlUppZmZGCwsLdu9bW1s1NDSkqakpVSoVffe731Uul1MsFtOFCxc0NTWl1tZW/fjHP7ZO48PDQ5vdeu/ePducAVro3FwfLSw7AK+cN6yhy1QBINxN8mRJideeZA1cwMaa5WBjdEs/lDldWw2XcWNtu119Ozs7Nk8zEonozJkzikajevjwoTKZjDo7O80rrlKp6P79+xoaGlImk9Ebb7yhZDKpYDCoSCRieteuri6Njo5qcHDQ7nOxWFQul7MSMt+9paVF0WjUrgGsvdtgw7ND4nQSwMCGu8mGyyIDpJBnUP4MhUL2nm6iUKvVNDIyoscff9xinWsXgx62r6/PurRTqZQqlUqTv97u7m7TWDOAOV1se3t7SqfT1mjgAiLA5J/EkJ5cI9KRYDufz2t4eFiJRELz8/N2vXgvV/vFOuBaoYEkzhwcHGh9fd1GMcH4ci6cA+fKv/OHqs/7DNh77/gzA7CpqSndvHlTpVJJv/d7v6e/9tf+mn70ox/Zz08icTeT/JOO/7vX/Lf/7X+rv//3//5P/Xtra2uT1xYsANkTgdmlcQnC0WjULBiwghgdHdWpU6d0//59E6lTKnAzYJcmxwV9ZWVFGxsb+vCHP6yzZ89aSUk6GtyMSJZzRh/ACBwE5YVCQevr6zaiB+3C3t6eOfGj4RoZGdHY2JixMgi3CR6UfmCLXE3YyMiIdYZSPqO9ORaLaWNjQwsLCxoaGjLbCko46KYoNVGqdYMC+iXYFgTxBHE2Kl5PlubS/h6Px4J0b2+v1d/RrlAacal6BMduxkrZDraRzez06dMGctDbVKtVY1w4f5oKOjs77fp7vV77vWAwaNcZEArAdQ1a0WOxeQDIXCDhaulgGaRjgS32INJxw8bW1pYCgYBGRkbMe6lcLmt2dlb1el1XrlzRxMSEGbaura1pc3NTHo9H8Xhcn/jEJ5RIJDQyMmLu+Zja0hkHwKVsg5XKzZs3devWLdXrdZsoAMBaWFjQ1taWhoaGzPuLMtvc3Jz+8A//UPV63WZBBgIBMybG1ykajZqxMuJ/PJyCwaA1u1A6ZfPm+XfLgSRVsCLST3fTnizhcO2JL6xxd825OiDAs8uiEYMAd4A2foeSNIB2ZWXFrEGIH9ls1srxra2tKhQKKhQKSiQS+oVf+AXduXNH169ft4RlYWFB0pFYmGsZj8ctEevr69Po6KhpWplW4Grp2PxpVuC/fGfkDOhP6eyGTUN7S/wkEWppabFkM5PJ2OxGrhEap2g0qra2Nkv2cePnOY/FYrp27ZrC4bCtqXq9bjEUaQFMKz5tLmNVLpdVrVZVLBbts3itC5JO6r7c+87aIQZvb29rampKIyMjxnIDtPh8dy85mQwQ3/ANY9wSVkkkagBF99xc8OuCvVqtpkwm81N76P/Th8tK/yzf81E9/swArLW11UT4TzzxhN544w39k3/yT/Sf/Wf/maQjlotWTenIFwNW7M/rnfGf/+f/uYnkJJk/DZkn2S8POEGImj7DW8kQXF0PbAIBhTFDjOFYW1tTMplUvV5vKgHCcEjHVhjr6+uamZnR4OCgLl++rGAwqOvXr2t+ft7AS2trq/L5vO7evav9/X1NTU2ZZoMDMTRmldVqtUkvhb7s/v376urqUjweb8oc2cAxi0VjgeAfVkU6euARuNINyZDw+fl55fN5TUxMNAk+KQGi+XG1dpIMvAYCAWMn6dhBR8WDSPBwBe5kbmSCMFHcPzYN7AsINGzMgARYtba2Npu3KMmuESVLdEkAcTZ1N/slwMJgwqih+0EMTZv/wMCAjTcCMCOsBsy4eh23M4prDHPGfaWDrFwuKxgMKhwOWxAeHBzU5uampqenbWPk+VhcXLTS32c+8xkrd1EmwbAW5oz33N3d1cOHD5XP5+XxeHTq1CljZlj7165dU0tLi5VbRkZGVCqVNDMzo2KxqOHhYZ07d06bm5vKZrN2PdLptIaHh03Hxcy1RqOhwcFBSTI2KBgMmu1GrVZTKBRSPB5XJBKxcij3ljJLqVSyTj3uk8uIupveO+lpXHkDf2gAkI61RAA4dIW8x0nNECDetWcAhHF/29vb1dPTo0wmo0wmo0qlomg0qsuXL+u1117T3NycWlpadO7cOV26dMnAUigU0ujoqMbHx/XWW28pnU4b20sjBgDV1WSSFGD/gN0N1wigIh0lWyQNLhjluURSgM7QTaRISrhGXq+3aTIGTT2sKUr6tVpN8/PzVkH58Ic/bN2E2DG4jH5PT48lUrdv31ahUNDIyIii0ah2d3ftu7rfi0SQ71StVpVOp431d8G6e5xkV12tKRUKGr1oMuJwdWQw4xxoJdva2hSNRpVIJDQ2NmYsezQabWK63KSX/7pAked4dXVVd+/e/anv8f7x7h7/f/uAAVzGxsaUSCT0ve99T9euXZN0JBr80Y9+pP/+v//vJf35vTP+pJEC5XLZABilt9bWVvPvwiiTEhv1eK/XayJzdCruxk/AppR5eHioVCplZn+BQMA2e4JytVpVf3+/4vG4eXZNTU2pWq0qmUzaxstQ262tLXV3d+vg4MAsDzwej42qcEGRq6PgoaLlntZtskUCHt8Drya0MlwrWtT54/P5FI/HbcOKx+Pq6elRsVg0hoXNgw4kV9js0ueUF2hrp4RHJnZSC0EwJxABdAAFrtcNrB6dYXxPNjlJTWsCgEaWzprl3lOGrNePO5U4JzZKgCHCXrrQYCfdDJkNDuDHRs3nc55cO0b0lEolu4Yej6epY9Rtt+fzudeAPM59bGzMmMxKpWJ6ukwmo9nZWQ0MDNhkg0qlotu3b+v27dva39+3Eh+gkvJuoVBQf3+/enp65Pf7tb6+3iSsvnjxohnazs7O6vvf/74WFhasyYRNCuYUr7LHHnvMmMbOzk5ls1kDhK6dwPz8vA1rJ5FgXcDWUequ1Y7MKldXV7W/v2/r2J0n6uoUT5aTiAMue+CW3VwWi02c+81aY227bDmglrUJeHMZI7/fb8x8tVrVgwcPlM/nNTo6qscff1x+v18PHjzQa6+9pmQyaZIJhpqfOnVKkUhEb7zxhrLZrGKxmM6ePWuxA0E6MyAxleRcJVm5DumEa/vggktiJNfFbTSC5aQkCSMI+KIb1NUDwQTyb5ubm6YB83g8eumll7SysqKJiQlNTk5qe3tbd+7cMXY+FAqpt7dX+Xxe2WxWPp/PtGF0OsKCsib53q5usFqtanV1VYlEQrFYzO4Zzzng5+Q6IQlA/+nxeKypgb9zzwGjnBf6N9hyEn2ST8q2AGCXbeW93YYQ1jXntru7ax3K7/bxvgas+fgzAbC/9/f+nj772c9qeHhY1WpVv/M7v6Mf/vCH+s53viOPx6O//bf/tv7hP/yHOn36tE6fPq1/+A//oTo7O/WX/tJfkvSz887gKBQKTb4xbr0b8FKpVFQul1UoFKxshYiZTZdWX7fTjuDT39+v06dPa3FxUQsLC03MFwucv0ciEcu23nrrLU1NTekDH/iAEomEstmsPB6PstmsgREYJ5g7fLcikYhyuZxyuZyCwaDi8bi8Xq+Wl5eN1YlEIjYkN5fLaXd31zo7ec3U1JQODw9NhH1wcGC0NsAK9gfm0C0pnDp1Svfu3TMNFhS7dLRZbW1tmQ5JkmWSnZ2dpq3A7NANBlwzNgXeBwNKApzbyYrehP/W63VlMhmtrKzo1KlTam1tVblctrmVABSYMa/Xa6U7NiACIkAPYO2KdAFhWHic7HxDR+ga8La3txvbCPjCUoTPpFTKJgVoJrjC5MCaumNH6N6lWaBUKlnHLWXQYrGoVCql3d1dM32lW9bj8WhyclLXrl3TM888o1KppMXFResIBqwnk0nl83kNDg7qscceUzQaNb0R9xVAKB0x2bVaTR//+Md15coVu5ednZ0aHBy05xEdSyKRUK1Ws5LaysqKstmsarWagbelpSVVKhVbm+Pj44pGowbOOGc8xbjOY2NjVu4neXPLMm732cmyyEnRNYfrlM+my0bo/jtrxO22gyXiGeFeukywyxbBgK+srOj+/fvq7+/XU089pVgsphdffFE/+clPNDAwYLNt0bFeuXJFV65c0d27d7W2tqZSqdTk+UayQSIFK8tnUw6lJOpqDTkAkSSVXDN+xtrluZdkSRuNRYFAwBJPQJH7WknWWOX1ejU7O6vvfe97unLlip566ikVi0VrUgiHwxbXYPXQ+DI6zufzmaidZ9bV/cGs5fN5m8dL/AB0uwmkW4qFgctmswZeSSjx0yPpOgnCue8kjKFQSCMjI2ptbVVfX5+V59njuMZUJHgflwFzQQ52GG4MeTeP9wFY8/FnAmDpdFq//Mu/rGQyqVAopMuXL+s73/mOPvnJT0qS/tP/9D/Vzs6O/tbf+ltmxPrCCy/8zL0zON58801Vq1VNTExYdn6yew1wtbGxYYGcIIO7OGJU94FAMIquZXJyUvfv31cqlVIqlbIym6tngmHx+Xyanp7WK6+8oi984QuW6S8vL5umiQyILKenp8c2FMYRkbHiBwOIOKkBYOM6d+6clYQIstFoVH19fUomk2ZayQYNQ0VAgVHzer0KhUIaGhpSMplUNps1rzG3rIewnuAiycqGiIwBWbBvZPsEXdgxNjc2PIIj98QV/KKtKpVKyufzZqgoHbvst7W12WewKbNpEugxFUV4HolENDAwYCwADCLaPTJcOhe5D66ejQ31ZPdbo9Eww9hG48inaGBgQLVaTd3d3YrH4/L5fMaIwtKQMbsZu1sy29nZ0dLSkrFS+JPBULIOGFiMofC//tf/WtPT07p27Zo+9rGP2cbDuc7Pz1s5/dq1awqFQpqZmTFBcblcVktLi5566imFw2G9+eabeuWVV9TX16dLly7p/PnzBhQbjSM7kc3NTSWTSXk8HhutgudUKpUys2AAwfLysg4Pj0ZwoQWjyQCbFsTmMB908QJOuQdcw5OaGa6jy4q4JTjWIuV2l9FyEwnusytWdz//nUqero7QNU3mmcD6o1AoKJvN6syZMzpz5oxaWlr0yiuvqFQqmfauWq3q1Vdf1cbGhp566imdP39era2tWl9fNxd8dE5uswIlUNh+9FQYP/OM8/qTDLErQHefL0CGC27pbKbk6TLEMLU8w+imONDa7u/v6/XXX7dpHblczp5lgCKxhu/hxit3NJLbfOH3H/ktrq2t6fTp02bz8SetGz4HXSk+jjQcYUVB5YRYJ8nAEAkD7CEgHYnIxMSEJZOsG7dj3L2HHG5XJedLcgvj+f7x3jn+TADs13/91/+dP/d4PPq1X/s1/dq/YwDmz8I7g+PNN9/UjRs3ND4+rosXLyqRSGh4eNjGeUCREwzoTgGAeTwenT592kSaMGR0xHG+ACWMAGu1mjFrbtDhwcrlcma38NJLL9nootHRUaVSKS0uLqpYLJp2a29vzzoj3dKYdOTQ6/f7NTU1ZTPzEI26m3Amk5HX69XIyIj6+/tN27W7u6tYLNZUWmU4LNob1+iTAdNoEK5evWrXjPIIQaDRaGhjY0PZbNYGV7NR9fX1GbuAn9PQ0JCBMDY5FyS5gY6SRSKRsOtLCau1tdUaFDY3N1Uul83ckqCLYFw6tgIg40evgmaFxo1AIKBIJKJMJqNCodC0DmBWAQdkzZSICaKAUbcE6XabsZkRWDOZjGnH4vG4lQsbjYbNkHT1MegZYR8oX7ChM9aoXq/bfd/a2jLPMtdZHY3f4OCgQqGQhoeHNTIyomq1qoODA01OTuqJJ55QLBbT9PS0vv/978vv9xvIJ7H61re+pZWVFWOyvvvd7yoYDOrSpUu6dOmSdYiybvBxKhQKVgLHeJPX4WJ+5swZnT9/3rpJt7a29NZbb6lSqejChQvmfzUzM6Pt7W0zogWAo/fBRd0tQ7rstdScnZMouCVoQD4Ai/dx2RHWsHvAkrA+WA/ol2Ce8vm8lWBhQPjOyWRSt27d0tLSkp599lmNj4/rpZde0p07d2yixvb2th4+fKhcLqdnnnlGExMT6uzs1M2bN5XL5ZrGgaE5JCZwPdzh5gcHB/bMuVIItwLA9XSZxZMaK5c13traahqgnUqlmrSZ7vWixMZ1Y72Vy2VJx2wzLDH2QjSYwNLT8QzQIsbwTJNUurYX0rHXIHIBDpch51pQdaED8+DgQDMzM1pbW2syJCa5B+BHIhElEgmzGwoGg2brg7wDEEvMOrm23qn06AJfn8+n+/fv68aNG++8kf4/eLzPgDUfj/QsyJ6eHhtvwtDS4eFhTU5OamhoyFgj/Ira29sVCoWUz+e1srJiZpLM0mOTd0WlLrPjbm7BYFAzMzMqFArGbFA2wcrg+eefVzAYVCaTUUdHh4aHh/XRj37UGgIoffHASlJvb68mJiZ0eHjkoI4GZ3d3VyMjIwYAAoGAwuGwWXLQhv32229bd2YsFlOxWJTf7zffHcSopVLJNAc81HTilUol063BzFAawVSQoHdwcGDDknngyUDR5DAwF5PX3t5e00MR4BDeup2N3IdGo2EO4QDivr4+5fN5y9SxYnDnZrrMhZuxu2wYHZHYR/B6XscG5Hpxcb3YHHD65/uwuQJA0XFhaQFjsba2Zt1a0WjUACMsnhv83ZItpZR8Pt/EWMIsch9hz9hgKANRwqasOj09raWlJV29elUf/vCHNTExoY9//OPq7OxUPp/XCy+8oNXVVS0tLWlsbEynTp3S0NCQFhcXNTMzY1o0AH+hUFC9XjfbGbq3otGonn76aftes7OzZhbsAh9EzJcuXdLExIR1pzLGp16v69lnn9XAwIBt+lNTU+Zd5vF47PVcR8q6LhPlMoQwVjDobOBuGYouZDZCGEeACwCSctZJlhcrk4ODAwWDQbtnnA/g5+DgwJo8VlZWdO/ePRvaXqlU9J3vfEeXLl3Shz/8YZ0+fVpvv/220um02tvbNTY2pvn5ef3whz+0zu7x8XFrDqH5g3XGbEf+lEolxWIxBYNBM3/mYE25neXoHl0tpNu0QALR1tamSCSiVCql9fV1FQoFhcNhRSIRYz45iLk8D3wuzSIn/az4XWKAawCNbIJ74p4relfuPx3Gra2t1rVOvOGeu9oqvmO9fmSHgwi/VCqpUChoeXnZErJGo9FkyEqSiW0ITU3MPeX7u5UWNyF0Dxdsufo0GPs33nhD3/ve9x5poPL/1uORBmC06tP1t7Ozo5mZGXuwKE2htZKOZ2i1tbVpdXVV9+7d0+Lios6fP28golQqaXd318Z3uBtuKBTSuXPnrFV+ZmZGr7/+ug4ODqxrDY+ojY0NjY6OanNzU7Ozs9rZ2dHp06f1/PPP69SpU6Z3YTbcyU4ZwEd7e7uy2aw2Nzd1/vx5K6vu7e1ZYEZHhL4pHo+b5iaZTMrn82lwcNA6CCkjIdCH4mdz2NraUrVaVSwWs8GwiHld0EZgQLBNsHRtIXD/RutF0AYgeb1eY90Idjiuu2UOAiIgJh6Pq1AomF6PoAlIIpsl8EDdu+Jori+laEk20xF2DKCKnYZbdibD39vbM88qMlkCIWwM5TjX74jzZeQKJQM6henGqlarKhQKajQaVjrnnoTDYbW1tWlra0sLCwva29uzOZCBQEDVatW6T0ulkm1glEEoFbW0tCgYDJqG5bXXXtO9e/fsfS5cuKAnnnhC8XhcDx480OzsrGmwyOTpcGxra9PAwIBaWlq0sLCgO3fu6Pz587py5YoZ1l64cEHnz5/Xzs6ONSJwnYeGhmw4OiwJnZxjY2OmQTs8PBonA+PM5grjQUmT9QpYZy0A4NjYXR8r6ZjhYl0D4iidssGiLdre3jbNkSv2p6xGZybd4DCZMGMANZI6um7xY5NkcUuSTp8+rZ6eHv3e7/2eXn31VfX09FhH6f3797W/v68zZ84Y0HatUljrW1tb1hnLWqYZqLOz08qjbsmU5wpWEykDsYjyPIkNMaOrq0tzc3MaHR3VmTNnmsTn6DLb29utQgHYIQ7weRxoJrnWro4SZhJwI8mkDK4HF2XnaDRqyeHq6qqi0ajGxsaMiXLZJffvgL3Ozk4NDw+bfpfnic5zn89nrBpaTkT4rm3R2tqaKpWK2coQT4g37h++q9sIRFWkUCjoJz/5ib75zW8qlUppcnLyXe+EfJ8Baz4eaQCG4JPNrlKpKJfLmYYKJgXjSTJUvHRCoZBWVlaUTCZ17949DQ8PKxgMNgEJV6vgih5heMLhsCQZ1c0okUqlooWFBXk8Hj3++ONqa2sz/7SrV6+qv79fkUhEy8vLmpubk8fjMV8yOml2dnYsYJPRLy4uGiuFpo0H0N3cAQaMLUqlUsbEsNnAlrCBk30DOjE8JRtnocPCUUpkXAjaEqh5NrlIJNIEVCqViiKRiN0fxvyQubIhsqmedBone8VBnk4o977xHU52Mrqbq1uSwrrBLcvA5NHJxWBjmFC0bQRiMn3X9BCmZGtryzRWrCfOC80H5+d2n7k6OMb60NlFifLw8HjOJwa0sVjMtG8dHR0mPKaDsFKpWJkHEHX+/HldvHhRHs9R19kf/MEfaHh4WNeuXVMkElGtVrOk45VXXlGhUDAGgKaNqakpm+XY2dlpHY+UgLFFQLPGKKXBwUHztKNLrlQqaX193Yaij46O2jPqdtbyvdlE3QYMQBXNCJKaZjHCcgHGXN8q/vAanhHuq8uaU8amhIyGqbe31+Zwor+k05T3gV3L5/MG8pmO4Y58AnzUajUtLCzo7t27euyxx/TEE0/o+eefV7Va1Y9+9CNls1lNTk5qf3/fvODGx8fV0dGht99+W6urq8b2Uh6jQYCGkUqlot3dXTNnzefzlnw0GseNLTxDXD9iEWuWZwmTbNiltbU1TU5OKhQKmRcXI4FcRuek3syVBPD5/B1gCZjkfLif7n3k+nO//H6/WZtsbGyo0TjySiRZ4b24By47CqvGs8ZoKNcKhRhHiZlnAgNkJh6sra0ZmOfZj8fjxrDyXd0mApcxRM5RLBb15ptv6rvf/a5mZ2ftmXn/eG8djzQAQyjf0dGhZDKpjY0NTU1N6cknn9TQ0JCKxaK1346NjSkejzcBsUgkYi2/zMhj5IqrWwGEueJH6UgYSlltfX3dWK/FxUVVKhUNDg7K4/Eol8spHA6rv79flUpFMzMzunz5srmtE1wBhJubm+rr69PFixct02RDWl5elsfjUTQa1cDAgCRpbm5O2WzWAIzreVOtVhWPxxUIBJTL5dRoNIzhKpfL1pXW2dlpJSo2KDZLsnACD0EXELu+vq5isWi6r83NTbNoQH+BKNbNtt33Q8xKmUE6Hisiqanjis23paXFhkpTGgGo0r3lWnK4pVSXDYORkGQdbdLR5hiNRpvsAgA7AFDWoSRrIef78HM2IkpjfCZmtdFo1DZdrlkgEDA2yBWr8/3ZuLGvQHiOFqa1tVWlUskYNubwwdKxKeVyOaXTaZ07d04TExNqNI66HxcWFkzzyMZ08+ZNzc/PW/kZ3d7p06ctU69Wq+bvRZLiJgMAjUKhYJMS2trarDQdiUQUi8VMFC1Jg4ODOnXqlIGDra0t+/75fN40bjyP0vF4GP6bSqWUTqc1MDBgLf6wxif1h66mzmUW2OBcAT6An8/HToHJHCQcAEVAGusBeww2bmQT29vb1uEMQOH1PHe1Wk1vvvmm9vb29Oyzz+ov/aW/pEAgoD/8wz9ULpfT6dOnFY1G9corr2hnZ0eXLl3SxYsXtbm5qRs3bqi/v7/JJgSQ4T4r1WrVTHLRtjL9ol6vG3DgWpLABINBK5m63dKUBZlPOzw8rHq9brpD7od0DHoAwoApYrCrGYQN5b6w7nleiCFuAw7fHX3ryMiI/H6/6T9JiN1GIJIszs/Vep6UCQwNDdmaYl4sRAFsaTKZVDwe18jIiEljhoaGlEgkDHDClJFg8v1OliPRTlYqFbOduX//viRZEvZuHy64/lm+56N6PNIAjNJXOp3W/Py8IpGInnjiCQ0ODmptbU0/+tGP9Oabb8rn8+n06dOamprS2bNnTTCOviYej5ujPp0r29vbRsPjPeSKrqVjQ0/Ezojyh4aGrBvr0qVLGhsbM5odVoVuFzZL2Lx4PG6bQaPR0PDwsAWJbDar6elp+/yWlpammWGI+NGdsJk2Gg0De3SVhUIhE/4WCgULbuhm2traLOiTJRMEXKsIyk+AIDJ0BgozPxB2jPN0S5knA5sr3GWj5V5heLqzs2NBnnNvbW1Vd3e3bXKueawkAzKwDCfHk+TzeUWjUQOjsJKcNwCAB57gSjAEaNHU4DJubFLu2CDmeaKBQyfm2m+4fltsJNwXVxTu8/kUDAbN94nvyobNIGzOA1ay0WgYi9Lb26v19XUtLCwYkLp27ZpisZjeeustvfzyyzZiyNW97O/va3V1Vb29verv71ehULBWfhhWZhTSWTc8PKy9vT0bi5NOp/Xw4UNb85i+njt3TpKa1g2fzXWjg5UmDK6ZO0cVmxFKrPV63ZzeuT/cS+4tzzgbO5srh1uKBpRJx9KIrq4uW1s0t3C/YUXp3ms0jrwEiWuUyWDKGSiNJx5JGMLvl19+WVevXtXP/dzPKZFI6N/+23+rhw8fGkCSjkadXb16Vc8884y2t7c1OztrTS6SrOSOrQUsOc82ZUbW5vb2tlUb+N78zNVe8p35O5KK119/XR/84AfN+Hh7e9uaBGDCePaIA4AxANjJEiQHgJ5GHZ5fV9DOveSZg3kKhUIaGxuzuMvvnbQbcdnyer2uSqVioNvr9Zpr/f7+vrnQs8YA+e4EkEKhIK/Xq6GhIXV2dmpzc9OSXcCWq190Wdrd3V2znslms3r77bf1yiuvWOLjrs/3j/fO8UgDsGAwaONQgsGgPv/5z+vJJ5/U1taWdeeRvdy/f18rKytaXFzUyMiILl26ZM7Jra2tBmQymYxyuZxtIi0tRyMvcN0OBAJNbdxofCKRiM6dO2ceUNlsVn19fXaO2Wy2qQS1uLioiYkJxeNxKzcieHXZNlerEQ6H1dfXZ7oX6UjHw+ZGFknWD21P8IaNADRxXhsbG5YdsYFRMmKzQFdEaZKSHd1MXq/XMlcCdjKZtHOkxMEGAkMEQGLDIYBThoJRg8Wq1WoqFotWNsCPCN1NW1ub6dz6+/ubTBABeug1CN6AbIxLfb7jYd+UQCXZJsm9cEuxHIAhQJ5bUiOAw7jAnrLRcR0ADej0XCE/BrSMKaK5ggCbSCRULpdt8HUoFJLf7zcWBYDItQqFQhofH9fo6Ki2tra0srKira0tnT17VsPDw+rv79fNmzf10ksvqVgs6umnn7Yh3/l8XsvLy0qn0/L7/ert7VW9Xtf6+rqxkWwAlOe5nmw8/f39lojgxo4nnt/v1+rqqpkcx2IxhUIhA0k0YyCGLxQKNnEAcTneeu78PJ5btwTN/QVUc7hgm1jhln8QO8P+uN2DlL8pidO1hx6spaXFuq9doJDNZs3agOcZZujg4MBKeazrWq2mjY0N5fN5ffCDH9QzzzyjaDSq73//+3rllVcUiUS0srJiJc3HHntMn/nMZ9TV1aWFhQU7P7RddAYCbLi/sMo0KDH2SpJdd0p7btdlJBIxfRely0AgoDt37qinp0eXLl1SLBYzdg1miu9JB6t7XxCoc44uAKPhiUTKLR2678F9JTGdm5uTJH3gAx8wIbxrdEpXJuvC1YGytrERWlpaUqlUUjgctthcLBatVC3JNH6Hh4fKZDIql8vGAMMOAzrd83UbiVh/GxsbunnzprLZrDKZjG7evKmFhQVrMnATtnfzeF8D1nw80gAMS4fOzk595jOf0ZUrV7S7u6tbt27p7bfftuybTXZ7e1uvv/66deGMj4/bmAcCD3oNxPhsnNvb2xZw3BldgAnYl8HBQRNlUhql7AYlXygU1NnZqUKhoMuXLysSiVigp7TW29trGWMul7NNlw5KQAElzMHBQd25c8e0WO3t7WargQUHrtLMDwyFQlaOyuVyTeahnZ2d6u3tNaG+G3jQa9GV093dbcAAAXRvb6+2traUy+XMloINjK422L/u7m4DLHwGLBvlFjZZSaaT6+rqMpAFsPJ6vSqVSlpZWbFZhtwjArHr34VGqFQq2XenbNTa2mpdpIBPdHmM7YHlct3qOdhI+GxXoMzPYG/QB7kiYb4zujKsLri/lD44J0mmseH8OfehoSH7f8AlDC9+aJSo2RB3dna0vr6ujY0Ns6kYHBy08uatW7fU0dGhj370o4pEIkqn08pkMgYssUaB6YIZyOVy1um4s7OjBw8eaGtrSz09PTp37pzOnj0rr9ert956S5ubmxodHbV7RickprvcF1efSImaeYquZUepVLImDtgW18PPZTVdsbWkpnXkisNZR4B8QKbLlsFEuoL9UqlkjT78Ha0U0gd3Fu3m5qYx1MgMXM1mpVLRiy++qPPnz+uxxx7T2NiYwuGwXn31VWMM33jjDZXLZX3uc5/Ts88+q1qtpps3b5qe0D1fV/5AUkZ5EvAKeHmna0USCfhFzhAIBMx3bG5uToFAQMPDw2pvb9fy8rJpp7iWkkwv6PP5jAnlc9xnDQZ1eHjYQDnd6fjhEWMoHyO7aDQaZulBogg7BQsPWOXvkuwalctla/zZ3NzU0tKSxsfH1dLSYl5u29vb8vv9pq3zeo8mA6CJHBoasmcZ4T4g/GQDEf9/cHCg+fl53blzR9vb2yqVSqpWqwYOee7eK8ejDJh+1scjDcBu3rypQCCgT3ziE3rmmWe0s7OjF198UTdv3jSWCOrYpYv9fr9u376tjY0N7e3taXJy0uaK4b8Ti8WUTCYtKMKO7O3tqVwuq6OjQ/F43Jz4EfcDIqDoESgjpKcECD3+7W9/W/F4XB/5yEfU09Ojrq4ubW5uamNjQ93d3QqFQlZWy2azBuwYt8SGzvxGrCsAOwT0w8NDeyBTqZRtYHTfADzc8p07rJuuRI/naEC2S7NTls3n800i8ba2NhsuTGchgY+uL7qqQqGQZd2u6BahLcEdtpJu05NlAQIV7BNBttFoWIs4P5eOhfg0JxBg0cHRPYj32P7+vt1PNsHW1lbrFtvd3W0SJru6DfRhBCDYKMqFXHuEv/yXjSwUCjVt0GwqbEaAQIAtm9XW1paJ08vlsj0bsLd+v18rKysKh8O6fPmyJOmtt95SMplUb2+vwuGw9vf3zZ4AW5HDw0NbsysrK7px44YSiYSGhoaUyWSUzWbV09NjGsGuri6Njo6qra1NxWJRa2trKhaLmpubUzgcVjwe1+nTp9XW1qb19XWFw2HrmqTDE7DIhguz4m70Ozs7xixzXdEZseZqtaNZmHt7e5Z0cc9Yf2x0ACDA0EnWhWcR4AwzCnOC9Qf/Bsg7WQJDFJ/JZLSzs9MEEOhK5tnEaNYt7/HdX331VU1PT+v555/XL/3SL6m3t1ff+c53VCqVFAgENDMzo0Qioaefflof/OAH5ff79eabb2pgYKDJs44Eg02cP5hHu7ICzp1rABilmxOGzW0S6urqspmhP//zP69z587ZPaNpCIDCPeMc3MYa1gIjrZBPUA7EH4y5wBgVU8L0+Y58+cbGxgwow7658gi+nytB4fnm37mXwWBQY2NjmpiYMMPo/f39pq59kk6eX8bl0RRDcxkAzO3A5Bz29/dVKBT04MEDqzi4I9KINSQL7x/vreORBmAej0d/9a/+VT3xxBOSpDt37mh+ft40LsyWw84Bh2I2JjKVN998U6FQyII9QaW/v1/BYFDFYtH8ijhcYW0oFDJanmDT3t5uJcXW1lalUimtrq4qn8+rr6/PxrEsLy9rfX1d0WhUzz33nD2EMzMzevXVVxWLxXTmzBkNDQ2pVCppenpa2WxWw8PDGh0dVUdHh9H1bNJkhaVSydgLOt+4DmgOGo2GAQuyp87OTo2OjjZtIpJMp9TZ2alyuazV1VX5fD719fUpHA5b5oXmi7ITAA6LBoKo6yDOPUFDBlMlNZf1AELBYNCYvL6+PmPBtre3rdTLAYA7ODgaC0KZmKAGKCKgAkxLpZJSqZQxkp2dnbaWaB4g6MOwwGCimyODRYNC8Cf4AowwkHQZNBgTrgFgDa8z7jPnja6RTkc0O4C/1tZW807jO0SjUZVKJQ0PDysUCqlSqWhjY8MmR2B9ACDmOno8HvX19SkUCimbzer27dvyeDw2T3JnZ0fXrl3T6OiopqentbW1ZVpLym+tra0aHR3VpUuXzLfO6/UqmUyah9jq6qoODg4Ui8UUDoeNRUQ35QqhpaMNietAaQ8dH2VzrisJCUDYBcscsLHcF8rX0rHQH0BMGR6GEQDlJkQuQOFaUA7FBwyQ7YI53peB8dvb202deG6JMJvN6nvf+57m5ub01a9+VR/72MeUSCT09a9/XWtra4rH4/re976nvb09ffSjH9UHP/hBYyJhRV1mj1I/CRIgjYTIBWokTdwPkgQ3meK+BQIBJRIJHRwc6O7du2o0Gurp6dHw8LAOD4/8FLGnAQATN1wtlMvaNxoNLS8vW7zgOWJt7O/vN+luXUDY0dGh3t7eptFLrujdLYPyO6wPzgeDbJ/PZ40omUxG+Xzeuh3ZN1gHgMSOjg6lUinl83lNTU0ZewvTjSYU4AnDx8gw7EK2trbsGWUdvlcA2PslyObjkQZgv/Irv6Inn3xSknTr1i299tpr8ng81m0IKCEAEGh5TX9/vzY3N5XNZiVJy8vLxmy5GxiLl44egja+W1DbsGGIk8kK9/f31dfXp8cff1zlclmhUEiDg4O2IZfLZY2MjNgD3dHRocnJSXm9Xi0uLmp6elqnTp1SLBbTxMSEdnd3rdXddYPGAbqnp8fmGjKfbHx8XD7f0YBa6YglQcwtybpneIgBGOi3MDREc9Xa2molJ4JHJBIx7Q02BIhrKWUCVF2rBkoy7hxGAgiBikyUjYbPSiaT6u/vty48AAQsoyuSdt+XjYAgzUYqydgxTHYJmpKMxSOwsaG6rJTbpOAGSzZk2DkAJfozACiWFK5mjb8zViQcDlunpMvMoGejMQS2iCYSxgG1tLRofHxc8XjcXPG///3vKxAIaGRkxEokMApYJFAaYgNiw5mcnNTIyIgGBgY0Pz8vSRoZGVEwGLQuxt7eXnm9Xm1sbNg6DwaDxkbl83ljf/n5wMCAdVVK0iuvvKJMJmPaLspF0pF/UjabtQHflLm7u7tVLBYtEeB+YSOD75l0LJKGwQasAvoo/QOYC4WCPXckX2yqpVLJSugw17AuzERkfcK+t7W1aWxsTMPDwwboKfe7zHE6nTbzX1h56ah02NPTo8cff1yzs7P6p//0n+pv/I2/oeeff16hUEi//uu/runpabW3t+uFF17Q4eGhPvaxj+lTn/qUWlpa9JOf/MQaUWB6XYDLcwIQgwWTZIy81OyfJh2Xdt0GFZKtbDarGzduKJlManx8XMPDw1a6W1tbs0YpABN2PNlsVsFgUJFIRKFQSOFwuCnpo/EExhAAhzUEQIn4ViwWzReNUiHAjO55d7MHEKHjIn729PSYBcvS0pKWl5dVr9eVSCQ0ODhoTU/IDgCUzMZsaWmxOb9UCLimxJ2dnR0lk0mtrKxoeXnZGGeaS4glgLCTTQrvH++N45EGYFevXtXa2pru3r2r27dva2try0aRYHpJQCA4kNWhrygUChoaGtLo6KiSyaQWFxfV39+vixcvGgXs+rjwoLl+WjzU+/v7xojAFOEP09vbq3g83lSSrNfr1slHxl6pVGwg8sTEhBKJhGmrvF6vYrGYnnjiCTNcJYODZmd2nHQ0/Hx0dNSGJNOWTWBEr4BQnxIpmjXABeN+JJkGjWzx4OBA6XTagg6MD2DKbd1n08OY9KQo1M3YTpYZpGO9h8935I2TTqdtCHVvb6/9DMbA1c9wjRDPS8cBjQ7CgYEBy/5hG4rFooFZSgfSsWO7K+x1QZ1L/fN9KGXwPSjpcn1ckOka0XIARjs6Oiw5QABOmQ2mjFK2u7mjbzl79qxOnTqlwcFB80G6fv265ubm9OyzzyoajapcLjeVohuNo7FT58+f1+DgoDo6OjQzM2Ode5RXk8mk7ty5Yw0TS0tLSqfTGh8fV6Nx1JV78+ZNFYtF0+awXsfHx23uaq1WUywW09TUlJnJ3rp1S+vr6xocHNTZs2dt8/T5fEqn01pbWzOdpCTTQ/IcJxIJ+zlASZKV+NxEBrDsegCevN5ovWAJYcEot0ciER0eHhpQk450PYj0iUl4bh0cHCgcDisajdrn0S1drx8Nn89kMlZ6p5xGHEC4T5n+3Llzmpub07/8l/9S9XpdzzzzjP7m3/yb+s53vqO33npLfr/fmLBPfOIT+shHPqJGo6FXX33V7inlPQ6SSuKGC854HWywm4RIx0w37DANQZS1FxcXbVwcDSAtLS3WFFWr1QwI0cne29trZXCSOuY/cm34TGIf95DYDGO/v7+vjY0NW9OYXnO+fB+eRZgmkmziUmvr0bi3ZDKpdDotj8ej4eFhXbhwwYBZS0uLuru7TccHmzg8PGygnTXqMnVUZ2g0W19fN6DJeZEIAp4Bu5z7u3mcZJh/Vu/5qB6PNAC7e/eulpaWtLu7a+270WhUyWRS5XK5qWOEh5ONqNFo2My9WCxmHUkLCwt68OCBbTZnzpwxp3hYL/yyyK4wdsX+gUyvq6urSQcWDAbNq4VzdksL1P7D4bANTobhYgMn4FGqZIP1+/0Kh8OmH8H9eWxszEpUXV1disVipl2iLNbZ2and3d0m+vrhw4caHx+Xx+NRPp+3Dh3KR4jtXTYJHQ4dXZTZuN47Ozs/lfURZNyONAAQTBusEeAHC4rJyUljdOhoRc+BEJtrKsm0ZgiaCZ4nS7eUllpaWoyRYTA7uixKlwR0SgWuhs1lylyNmnTcccf3cRsAKBO7/kroGflOaFhYE/X6kSEo9gd4vbFp8RkdHR169tln1d3dreXlZd24ccNAnmvIS2kSxpcNq6+vT9Fo1M7p8PDQSpHb29u6f/++6vW6BgcHVavVrOSJVmV5edlMPdFFJpNJKyNPTk4aEKaZAE1LW1ubHn/8cY2PjzcBD9bkwMCA+aHRAEBigOM4m5ckY3U9Ho9NDtjd3VWlUrEuP9fziT/4m7HuKYvR0ZhKpYyBdZsrKA1T/ocB8/mOzKIZwh4Oh60zljI7mwzrAADI/XcTSiZldHV1qa+vT0tLS/oX/+JfqFqt6vOf/7y+/OUvKxwO68UXX1S9Xtd3v/tdNRoNffGLX9QnPvEJ7e/v20QRnltKkWitAL5cGxJTJkew+cMS0UUKyy8de3EBcmAF7969q2QyqYGBAWMNGVTONSLGAuDX19dtnQQCAQNkNBYBulwRPfeSPQJmlyYqtzQNCCLZqlarymazFlf39/cNOPM9arWaQqGQsVkYzeJ8z3oj0U0kEopGo1Zp4NqeLKXu7OyoWCwaIOS7uN2372Q7Qfx8/3jvHI/0Hbl+/bomJiasDZgOFx42SjcwVdC+ra2tVk4cHh5WLBazjZSOuNu3b8vrPXKgRq9Fp5C7eVKO5P9djUe9Xrff4d/JunhQOjo61N/fr5GRkaYgz7G7u2tgggDgWglIxyU8ShI+n0/FYtHKeq5Ogo3D1cG4gJLskYwLAIHPDCJmgiLjSg4ODpTL5YyhYfPq6+uz2ZPLy8tNZrF+v189PT1NYmfpWJRM9yldYmhmMpmMif3HxsaUy+WsW6per5ug370veDmREULR893csSuuFQflQ8T4fIbbdUUAp9zNd3AtNk7OuGRToHQIo4rupa+vT5KaNggydHR73D/uEWuDDbDRaFjZjZL1+fPnFQwGlU6n9e1vf1szMzO6ePGiotGojbWKRCKmvyoUCgoGg7p69ar5x9VqNc3NzenVV1/VqVOndP78eSuHdHd36+LFi4rFYtrY2NDKykpTmSydThs4yGQyWlxcVCwW03PPPWcjvnZ3d/XWW29penpa/f39evzxx03bBlBmXI/Hc+QFtri4aL5nXKdEIqFYLKaDgwPTLLqNB2yK3d3dOjg40Nramt0vV4+FpgnWWjpmY10NGcw6jCnTMcLhsE3MAKjQLMP9omtUOh6XtL6+bs7olEr53M3NTbunJFI9PT02H5b4h6HnxsaGfuu3fkulUkk///M/r09/+tNm2urxePTaa68pkUjoE5/4hL70pS/pO9/5jt5++20zmCWhw2W9WCzaOZEoAQBWV1cN/Ljduq7OkutINYCuTzS3+/v7WlpaMvAMqwgbSUIN2KBEyz3hNZSK3YRPkq0DNGzIEySZrRBaMZfVdkuAzLB0WXZAER3aJDBer1fZbNYMiEmeDg4OFI1GNTQ0pOHhYUt68InjPN39Y2try0B5qVRSNpu1+I/u2E0mOU5WHN6N430NWPPxSAMwNlscmsl23E4+Apwkq/0DMGh/DoVCRj3v7e2ZWaPP59P6+rq8Xq8mJyfNIHFra6tpQ0e/I8kefChm/KYo65GxYn3hHrBBbMrS8QbMv7vlNZgadBmFQsE6FCkvjoyMKBaLmQ1FX1+ffD6fhoeHlc/ntbu7q2q1ahl1a2urmbbC5nR1dSmfzyuZTGpqasq+AxsImZwrDEdD0tvba6OZdnZ2lMvlFIlEFIlElMlk1NbWZowHGyECXXRLbuZ5cHDsan758mUNDQ2pr6/PdBzcV9eOANE79w2QyLXk+1CiZlOlnIxOBWNF1pC7kbCxuF1hAC02ScA369IN/DBK0WjU2EhKtaVSSW1tbcpkMlpfXzfmyWVk3HI5gA+QxvqIxWKKx+PWLcfkhnPnzqmnp8fG5sAShkIhlUoldXd3a3R01EAlHbmtra3q7+/X/Py8fvCDH2h7e1uf+MQnrGtxaWlJyWRSQ0ND6u7uNjPhQqFgur2PfvSjmpiYsA64dDptw6f7+/t1+fJltbW16cGDB1pfX9eFCxfMSgOQnEqltLGxYQw4m1ogELAh1O7mynQDRNEej8fMnOPxuAYHBy1RIXYA1N177DKbLqsLS+paj2ApQ3nedeN3N1vkALBqrAE2dsqM+G8BDgBIgUDAmGrE68FgUD09Pbp7966+9a1vKZfL6a//9b+uL3zhC2o0Gvr93/99bW1t6cUXX1QkEtGHPvQh/cW/+BeVTqf1xhtvKJFImH6Ppp9MJqOtrS37PL4P7BM6KmIiiSElWbcrWzr2WePZrNfrKhaLNm8TnZ0bJ5A48LzBQrt6PmI0CSeMN//GXkAshX0nDhEjYKooB9OYw3WHgSIpCIfD1rnKs8+Qbq/Xa5IWv99vCXh7e7tWV1eb4iefD9vOOiMmLS4uKpVKmVURVQe+m7tXvq8De+8djzQA6+vrs7IZAsx0Om3GfTzMLS0tJmYF0HR1dSmRSCgQCCibzVrQQ3j75JNP6ty5czaO4s6dOwoEAhoaGjKjR0ZsQKHDkvAwer1ec23HxZxNrFar2UbA77ibNQ+Ma6IHowdTA4vDZkLGTilkc3NT3d3dOn36tGWoaJai0aiuXbtmnwdAJMhKR2aK0jEwzGazWltbsw0avQHv4YJbaPTOzk7FYjFtbm5aOYoyUTqdtizS5/OZWBo2MxAIWICFjaNctLKyorW1NU1MTGhoaMi65wqFgpVj0cnh8wOLRFYpyUTWlIlgEwBwwWBQuVxOmUxGQ0ND6u3tNXYJIETmi47D7TpyrxMsCRsqZaO1tTWVSiUNDAyY5QN2JYeHh0qlUtbJCsOBHQDMJqUpV2fW2dlpXXPnzp0zVg328OLFi6pUKvYs0SJPaYyOTe6RK7pHLJ3L5fTd735XDx48sOHKW1tbZora1dVlzQIM2x4dHdWTTz5pHnyNxpFFyMOHD1UsFhWPx3Xx4kXT7LHZnzlzRgMDA1Z2gaHg/Ht7e02PQxPB6uqqcrmcPeeUxAH9kiwBaWlpUblcVmdnp83UpLQqyUZQUcJmc5Wa/cNOsu6wy17v0XicgYGBJjnC7Oys1tbWLEGDGUeKEI1G1dHRYVoyvjuZP6VpEiBiEmuBEtmVK1e0tLSkH//4xyoUCvqP/qP/SD//8z+veDyu//P//D+1tbVlYOyzn/2sfvmXf1nd3d169dVXtb+/b0lRKBQy2QZMDps/zwavJeYh1gcU8AzwTPMHMLO/v2/aQ1cryTQBl+HhmXUHhUvHo4KImZSxSUpcAEeSx88Zp+b1eg00u8kwzz7xLpfLmXUGSS5VFqQJgGKv98irMJlM6vTp01YloFEE/RusXr1et8SP/S6Xy2ltbc2Mw9EFuvIX9hZJTV3n7+bxPgPWfDzSAMzr9drIk56eHlv0dPfxUJK1uzQ2XTNuJ6DLRCQSCU1OTmpvb08vv/yy3n77bQ0ODurpp5/WxYsXjZ2h5MR7HhwcmE1AKpWy+n8sFmtiT9xsiUDAw86G7eoU3M2eRQwLA6vCHEs27Vu3bun69etWesL4ku5Nt0OL709wYnNpb2/X8PCwOjo6NDs7a0HD6/XaeBXAIRtpo9EwrYvH4zFnZ7RpBGO0c2R0XEtsPQhaABnuOfMT+Z4tLUfTCni/ra0tm14A8CUoIbB1M2+uHxsg36WtrU2BQED5fN6yYn6HzZZg7+pLpGOnbNYGvknutQa0M/pqfHxcXV1dxmAC7HHLDwQCGhsb0/T0dJPoHmaURge37ImOEBC0ublpXX+Tk5Pa3t5WKBTSwMCAKpWKbt26ZawJrANsHWAsFAppYmLC3Le3t7d19epVXbhwQYODg3atA4FAU3fnmTNnrFsSkMN8vP39/SYX8Gw2aw0oaL+6urrstTARPB9olZiJymt3dnbMAb6np0ejo6M2P1aSMTUdHR0aHBy0zT0YDDb5QIVCISsbA3YQvHN9MSbmgFmhI/Lw8ND0XZSwYUNcjRVrvVgsWmMO4744b6wuKpWKgQnWEya3GEvja7i4uGjzcOfn5/W1r31Nv/qrv6pPfepT6urq0je/+U3duXNHf/AHf6D29nZ99KMf1V/5K39FsVhM9+7daxLlUw0A/ABEOBeYOZIOQHNLS4vFSaYh8J4kQLxPrVazhiSAnXSsZXIF3TyPJCEwkXRCp1IpA4Iwz7wHIIWGCjSTMHWc9+HhoXXWUupGSkIJkKQD8M8UB0qtxCNm5waDQR0eHiqbzZo349mzZ63rlxhKzCA2ZLNZ63B3pRBtbW2mL6NkfrK7+9083gdgzccjDcAikYiWl5ebjDC3trZsGDN09c7OjrzeI38n18AUfy8eUrLXCxcuaGxsTJlMxuZJoie5d++epqen9cQTT9icOvxlCEKu/UW1WjXxLayLJNu8XVoYLZC7SMkMCSy8N6zV6uqqyuWylZD4Dr29vXryySetazGfz1uJluDD8FdAkuvVBdjAqyoej9vw2Hw+r0QiYY7oh4eHikajqtVqpv9gRIykpqCSSqVsk3M9fba3t00Dg2s7rCUbFSULQBi+XmzwdNDlcjnT+3F/ELdLahql5Ga1lDvJzltajoZRo7GA9SSLBUgTIAE+J7VibrkFYAbLhnaHpgjuOf51bW1tTZ2ywWDQyjyUamAfYVpYIx6Px/RIGxsb2t7eVjAYNL0cNhGUrnO5nHy+oyH1/DsGxADrYDCovr4+bW9va3V1VaVSSVNTUxoaGtLk5KQkaWlpye5Hf3+/hoeHNTAwoFqtZpMg7ty5o93dXSUSCU1MTKit7WiE1M2bN9Xd3a3h4WHT+LjlVoTY6XRajUbDNqrDw0Nj7GALy+Wy1tfXVavVND4+bsAPdtydZQiLnEgkzCuQzYvfAUwBqNmsGbnjygcQ8yPIdp3JMfmEERofH9fExIQODg6Uz+eVSqVso0czlk6n7R6w8RMPKG8C+AALboKKFQig9Pz585qbm9P//D//z/rqV7+q5557Tv39/frGN76h1157Tf/yX/5L7e/v6wtf+II+/vGPq1Y7GndE044rWqfkB+hyYxpABkkGNg8077i/D6BGPoGxtdu9xzpwWWc3Rrp/J5YCUpEZ8Hku6w1z5I6Z435KsnhONzTJz8OHD3V4eDTyqqenx2bvlstlA0jRaNSaQDY3N22kHOurq6tLExMTkmTSB2In7B+GrZVKxfSWvD/fAdmMa4fCegEov3+8t45HGoCB/oPBoFpaWkzTBHOBfxNZPyCBMg9B7GTpYHx8XOFwWIuLi5qbm9PAwICuXbum1tZW3b1715zF4/G4BgYGjI6nXk8GRLCjIxMWzH24YbE4XFM/DjcwUTKDBg8EAiqXy8pkMla+q9VqikQiikajmpqaUjKZNINaTCkpWXm9Xiu7xGIxFQoFra6uqlKpyO/3a3h42Epfp0+fNjEzNDuMABv30NCQisWi1tfXrazGtYF5SCaTWl1dtZEw0nHnI75hZNUej8cCKAGytbXVADYAztXBUEYgKJfLZXsv7jXXnRIKAA0AxnVuaWnR0NCQgWl83ABv6DPYcNEMkY2TEMCYsgFzXymP0AkHQ0dSAfAslUomHB8ZGbF/d0sU6NwQNLOuyuWy3njjDXV1dTWBcn5+584ds3BgeDrXhcw6k8mor69PfX192t3d1cLCggqFgn33jo4Ou++hUMh0ZbVazQDgysqK7t27Z92J/f39NvOxXC7rzTffNLa2XC7rwYMH6u7uNoDGyJaZmRlJ0sTEhAEjzhlwmc/ndf/+fbW0tOjxxx837RvP++HhofL5vNbX161s3NnZademXq9b8wiNJ5Ro6XbjGQYglMtla36BaeYeYSdBRzPlOtg7kgrYlVwuZ+sZwTclOBqGYOuRF7iaUHyvuCfpdFpnz57V+fPnFY1GlU6njdX+7d/+bW1vb+ujH/2ofvmXf1mhUEgvvviivvWtb6m9vV0f+chH9KlPfUr/1//1f2l2dlaXLl0yqQKstVuag5Fxu/dITAAwCPrRQ8Lg1Wo1MzDl+WStuvonGDGu5clGHukIvGSz2SYZB+VdzIL5XZJymH/0bMQmt0MbNg+wS5ynK5fh8h6Px/YHxnFJMiKAsiFsMbEJr0b2LzTFNGYsLy9rcXHRkiOmJhAXialcb5LG9wIAe58Baz4eaQBGLZxZinTn0BXDQnQF3S6NzcYL47O3t6d4PK5EIqFGo6F0Oq1gMGhzxVZXV+XxeHTp0iV1dnZqY2PDdE5u6zW6Fko+6XRaKysrxraRZUHVu91/bBJoTFygQFmMwEPHSyQSMUpfkpn0RaNRDQwMGHjLZrMWfGABfL4j7xoGKTNUF9bM7/fr8uXLZlAbj8dNh4HgGaaHIcrt7e2an5/X2tqapGPDwuHhYXV1dRmD4brZs0lx7dxRSe7GQzbH+2IBQekxmUxqd3fXRrVQfqbMxCbKAVBymUH+DcYI/zZAIJsujAYWIpQN+/v7bcOBbaMrTJKxb/yp1+vK5/PWSBKPx9XX12elGwIn8xLj8biVRNlUCLBs/JR22MRHRkasTN3W1mbt8CQwrKWLFy+aeH13d1e9vb0aGhpq0s/wvQAY4+PjisViKhaL1q0IMFpcXNSdO3esDH/mzBmzhnC7dxcXF9Xa2qqLFy8qm83q29/+tvx+vz796U8rHo+rUqmYzqZYLCoWi5k2ChC+vLys7e1tRSIRtbS0aHBw0MpOrDHuGed+6tQpA8CAVvSQzO/z+4+88FZWVhSNRhWLxUw/tr+/b+X1paUl5fN5m73J/FMYU0A6yY07mguGCDuMSCSiUqmkTCbTJABHd8X6cz37aCJy/ehIkvL5vN5++227VwD4sbExzczM6F/8i3+hZDKpr3zlK/rFX/xFBYNBff/739c3vvENtba26vnnn9eXv/xl/f7v/77eeOMNm7MJc8xzw30HqLilQ7fc6CaeblJE2V06loTwzLtMJTEFVovYCTh1mSu3A5N/k9Qk60AKsbOzo1AopEgkYnIPrqff77e1wnfEow4fOWYP87tdXV1WTSFhiUQiVo6EHUUfxvxVWDLWlySl02mzSEKr7E4FaG1ttRL3zs6OKpWKlVGJpe8f763jkQZg4+PjtqnQ7ZRKpWwuGt1ePPToiVj0aHvcOWd9fX1qaWnR3NycNjY2LCvDmBIw09nZqVQqZQ8V1DwbJsGmtbXVxOSHh4fmtkxJ0hWeUs5yg3u1WjWwRGYuyc6rWq3aJtrb22vdkJJsphr6kXQ6bSOU2Eg9Hk/TRgxoY/BvPB6X3+8388CLFy8am9XScuT672aIaFpwp6brDKDp9/ttQwcIbW9vm15JktlnAIhpdUdED+gjCyc7lI5tO8iIYTrR2MA2uJsCGwdlHI/nyGgWn5/BwUFNTEzY/TzZNLG/v2/lwEqlor6+PrvG0rHfFLokymWIapk5Skmpp6fH9EesD64TINvVkwBeXSaXzQgtzNjYWFOnHhMLYEkXFhZM24MmpaenRxMTEwqFQjZKC9aWc4tGo+rp6bFno1wu6/79+1pfX9cHPvABFQoFtba26rHHHtPIyIjK5bIWFhbsWctms2aki4v+97//fe3u7uojH/mIRkdHm8parP9AIKBYLKbOzk4Vi0XdvHlTqVRKly9fVk9Pj5UqYYtx3IdBBfTgC+gK8t2yTygUss2ZTZiOSO6925UYjUZthBmsLLIHDt7j8PDQxOt093KPsJSAKYEVYTMGvKA/c5lS3tv1DaRBZ2lpydYfM1Xb2tp069YtfeMb35DH49Ev/uIv6nOf+5xqtZpeeOEFfetb31JXV5cef/xx/fIv/7LFx56eHjsP4gnPJusBIMK9Q05AnHPXKWsLFsotswJmYMgBmgAiPt9NpHhGOT+AGs+4Cx5ZE5ih0snJGuG8EfNzz3m20VECitvb222GMOOwhoeHLV7TJFSpVKzkDXuYSCSaqjfsTawx1x/QZfxgEV22EWYfcPluH65u72f5no/q8UgDMDI4HqqWlhYNDw+bKLulpUXr6+uWNbHp4bXCQ4WGqL+/X7FYTJlMRnNzc8rlctYaTBcMzBmjfOiWcTd2t/MN3Q4PsXRs9OnaEZDtuf5llFYlNZU7AJP1el3d3d12/u74kM7OTgvabW1tNuoolUoZY8T5S0caETZTzqezs1N9fX3a2trS/Py8ia9hpWgicLvvCMDQ5vl83qwwsKJAo0Z2BuBpNBo2hoYSCkwY78d1RtMXDoetdAdDBeAFyCFKJgMka3YF2DAGbpbM51IOZFN3NVYEZM4NE2B8nxCHHx4eKhgMmobKBYGsA0ABYACvNDY5nO0xCoXN4vWujYlb3gbAsDnTiEKycHh4qImJCTUaR87rlFL7+/vV29ur1dVV/Zt/82/k8Xj0sY99TH19fU0b3/Lyspms3r17V21tbZqcnFQkElFPT4/29/etm3R2dtaMdOfm5pROp9XZ2anTp09rcXFRL774oubm5vTMM8/owoUL8ng8TUCKjtYzZ86ovb1dqVRKc3Nz2t3d1cTEhM6cOaO9vT298cYbkqTLly/bM0NDAuV32BpYTDb1QCBgpe/Dw0MDQzyfsOU8WwC8/v5+ezYZ0u7z+WxOH6J9t9QIcGdQPPGE15NgBINBK4FvbW1ZuYk5rqw7d/oD94c/tVrNkrOenh5bB4xuunfvnr75zW+qWq3qq1/9qn7hF35BkUhEX//61/VP/+k/1d/8m39Tzz77rL785S/rN3/zN1UsFq3M7H4OYIf4Kh0BIICE2+QAWCQu8npiJGsUgMJzCnhDR0op0gUh7vnAMLodkpRA3YYtgCGdwHQO0zSD/2GxWLTkknvKDEkYUZhozpHkn6H0NAldvnxZg4ODFl8w8C2VSlpZWVGtVlNvb69pMFOpVJMvG9+JfQMbG+QRJB2uNu/9471xPNIArFqtWkkJytbtCPF6vfZzNhoeINgQsvitrS319/dbp9P58+c1PDxsY4ZgzOr1uulk0um00um0PWjnzp0zoECAR8NBUGWDBIBRXkLgSVYFI4RomuAFfc/m6orHAQQEhEgkYgO4w+GwgZrt7W2jpHl4u7u7m7qIKOVGo1FJRw83rdNYRmDASkAkAMAI+nzHI2JoqUYU7DZNEFzZuNAwSbJWcLfjMxAI2JBe2vOZoea2ocMEMWQcE15ALZ1Ho6OjxkJSLgIUejweAz6wVwAI2uTJ0mFKS6WSEomEnQfiaUqZLS0tttH6/X4NDg7K6/WaR5DLGribA2a8rrbGdeZ3mTyALeuZa8Nr+S9rEzaD7B4GM5vNmm0Bmqu9vT0lk0nNzs7a5lmtVtXW1qZPfepTGhwc1JkzZ+z88Gjb3d21Ls979+4Zw3n27Fltb2/rzTff1Nrams6fP2+lR3cgOokGrvmpVErr6+vGYMXjcSu/DA4OKhKJSJLZULgCa0nGCqAtk6T+/n4Vi0UtLCyopaVFZ86csbI4QKlQKKherzdNDvB4jryfWlparNzpsrUtLS3GarhlOdhNEgT83nB3Z5ZmIpGw9VssFg0EYmGSSqWMsaFDlPXidl8TT3Z3dw0A7OzsKBKJ6IMf/KDu37+vF154Qel0Wn/1r/5VffrTn1Z3d7e+8Y1v6Dd+4ze0v7+v5557Tl/+8pf1B3/wB1peXlYikbBGALcrm0SAuALbhvaNJhb81YjbxEMYKa4Xsd1lr4nhrrbW1YMRjyht8rmcF6/j9wC8KysramlpMa0rZf56/WiOJ9cM5pnr6PF4lM1mlUwmrRHI4/EYKCOxCQQCpi0EAFMJIMHd29sznatrP+E27LhaQr6bq23G684t7b6bx/sasObjkQZgdBei4QLUSLLMAG2WSzVTZnQp8vb2dqOCoZXJgAmyCGERZwI4pqenlcvl9JnPfEZnz55VOBy2IEQmCiXNJgKz4tL2kixQksXA1iDedkuQiFf5DgQ6Phuwmc/n7TOi0ahpjhKJhA0ur9WOvGzIFPv7+y1rBaigi9vb27NhyAQeAgXgsaury7RKbOxslGiFpOMxPFyrQCCglZUVG+dCVlqrNfuBdXR02L0FHOKS7pY8MEQ82anKBokHFEyW6/SP2BxWlaybLJZyHaUCMn/ekw0VDRK6KQJ6vX5k8zEwMGAdna47PmvBLZ+gJevo6FA0GjVAhKD3ZIcYeq9G48hYlbXn8Xisy5FOTITpkkwH9t3vflc7Ozs6deqUTp06JUnmYJ9KpTQwMGC+VjimZzIZzczMmC+XO16pWCzqpZdeUiaT0ec+9zlduXJFHo9Hb731ltbX13X16lV96UtfsoaQ27dv233MZDLWUFGpVBQOhzU0NKRyuazFxUW7T7lcTsvLy3rw4IFisZiGh4cVCASsfMtaX1xctIaISqWiwcFBHR4e6tatW2pra7PZfaVSyRhH7E5gnGEcKANLx/M/0XgBmHmGsd9w7WaIA+3t7cZOwbLVakcjnTo7O3X+/Hnb5BcWFkyIHYvFzPWfOIDujPPp7Oy0zsrd3V2LAe3t7fb+Fy9e1PDwsF5++WX9k3/yT/S3/tbf0oc+9CGFw2H91m/9ln77t39bXV1d+sAHPqBPfvKT+ta3vmXlLndQvdu1DHiCzXVBFudG7Ha7KwFVsOtuKY31hNM+QIjkw9WBUbngWSFuIJ7nOSRJ6enpsW5dElES2b29Pa2srJgJLfeHbmwAcrlcNvaMRCsUCkmSJTazs7O6ePGiGo2GVVii0ah5OdbrR5NUstmsgetwOKy1tTUD7m6jTktLi1mkkHSx5lwm/f3jvXM80gAMkTWHyxqwEbvdiXQL8QADAtyOMES7fv+xIzMbGiwRLENXV5dOnTql+fl5VSoVLS4u2tgiV9OF8BLtDRsprAnBhPZjHnoXEOIlhNEfwAtwybkS/GA2KH+4WhOCmMfj0cWLF5uAIhlnJBIxEOrW7ekQnJ6eViwWUzAYVKlUMuG7JNvoXK0b7CPMWLlcVktLiw3RptzGdVlfXzcPMjI73md7e1vz8/NKJBIaGRlRT0+PdZ7RLYgzPKVcSU0lWv5er9eVTqeVSCQkHXc8UYoge6UEIR13F/FaAh+bsOv5hk4FzRAbI4asBGd0bjB9bNxs3gA2EgKYKjRKdDvRHcra5r0R0bsaN7/fr/X1dY2OjlqJ0O/3m7Zqbm5OS0tLGh0dtaHYsJ4jIyPWsNLW1qa5uTm9/fbb2tvbUywW05UrV3RwcGDsJ9d6YWFBmUxGV65c0dNPPy2v16vXXntNmUxGjz32mJ5//nlFo1HNz8/r1q1bSiQSGh8f1/LysnZ3d9Xf36+pqSnrzGSjmpqa0sjIiDKZjF544QVVKhUNDQ1ZMoCHFRYlXu+RTQZJ0dDQkPnkPf3002aSi62A657v2j6w+UmyTZtyJ5sjscBtruF+ujNYXSNV1iogDCC7urpq7PbVq1fN0JjEyxVewwbCOrBmATn5fF5tbW02lH1+fl6xWEwDAwP65Cc/qR/+8If6x//4H+s//A//Qz355JP6G3/jb+h3fud39PWvf12BQEBPPfWU6vW6XnjhBWsUkI5ZPVgwV5NIDHLjLskN58nzx3Vz7T94BkjAAFe8N+9BKZIYyXOF1Q6JEuw6f/ASJPEF/ABkYKqWl5clSadOnTIGrFqtqre31+b5hsNhZbNZVatVm90L65zNZi0+MUJOOiIOisWi6U33949GMknS5OSkYrGYxQOmlZBQkoixLt29ilj+bh/vM2DNxyMNwMieeIhYxDw8gAoCDtkuwn28e1wtDu3OvC8UOaU/SZax0SE3Njam/f1964BZWlqyh5/gwgZA0CHwSsfz0MiwSqWS4vG4MWl0AWazWc3NzWlmZsbKq/F43DqzMILc3d3V2tqaNjY2tLe3Z12SaEjq9bqeffZZXbhwwQxTXRNEzoOMrLu724bEov1ZWlpSLpdr6tZhs8HhvlwuGxNWq9WsAcDn86lUKqm/v19DQ0Pa3d3VxsaGDdTGu213d1elUqlpBiFailqtplQqZW7hw8PD5vsE4yMdlcZKpZJpTgCqXq+3aewP5bJSqaRisdg0WoU15ZplkmXzGhoXCL7cN4APQIl1CzNFE4fbPYvA3gWsrrgW/7VyuWzXixIdG6yrBWNd8/mMZKrVasYgU1Z2y/WSzGcLPRcMaWdnp9bW1pRMJnXnzh0VCgVduXJFzz77rJVFAZ9ci1KppEAgoM9//vN69tln5ff79frrr6tcLutDH/qQRkdHtbe3p9dff13z8/MaGRnRmTNnrLvw/PnzZu66uLhoI3by+byB0GKxqEgkonPnzsnj8dhzQYcZjB/duIVCwYCkm6CVy2UbwUSyxnOIuSyJGdrH5eVl1et1DQwMmPGtpKbZoiRVGxsb5s0XiUTU29triQuM6ubmpulJaSyhdB2JRDQxMaHBwUGFw2Gtr68rk8mYBQVlKuIZAIYNGxa5UCio0WhodHRUfX19unnzpu7du6dTp07pC1/4gl588UV97Wtf0/r6uj7zmc/oP/6P/2P9q3/1r/R7v/d7CofDevbZZ7W7u6sf/OAHKhQKxuBQ7gRIAXCIc8RuYrZ7XjDN7yQyd5MUYhHPB1owSVbqBNC5sX5vb89GqOEY7z7XPFcYTtNxHI1GjW2LRqM2xgrWMpfLWWJJWTGbzdoz6wrz6/W6PVtMYZCOdJ5IZADsDKX3+/1NiRbPKd/bbe5hn6JRic95t4/3AVjz8UgDMOnY0BQARrB0y1aUQMLhsLWuw4y5Lcxs+BjcuZ0mvM79XB52AgCu3pT7YNMQ7PMweL1era6uamtrS5OTk5aN4XrulsDW19eVy+VMdwAVv729bVoUNoxoNKpEImH/tr+/b12EtLN3dnZqfHxcU1NTZswKbc2Im93dXaXTaWMJECvDxuEwzWwzwBFsTVtbm3WTsaEfHByoUCiYUBVn9/b2dq2tralSqdj3pyxaKBRsIwkGg7aBtLW1aWpqyjqE0JAwlw5BPpu/2xzhMpNtbW02zw3tBYakZNbBYNBYFlhHNnvOGaDhjpECtKPjYEgzG6rP52sKurFYzJzYYVgASbitw8S5yYZ0PBCaa4E5p6QmI1tJVo7lmvDdJFnnHgC0r69Pw8PD2t/fNxB+eHiojY0NXb9+XQ8ePNDm5qYikYg+97nPqb+/XxsbG/L5fJqamjIN4sLCgmlQzp07Z7Yxq6urqtVq+sAHPqCBgQFtb29rdnZWxWJRV65csU0tmUwqEomYbmZ3d1djY2Pq6urS22+/rbm5OX3uc58zsfrIyIiKxaIlJugAYedaW1uVTCaVSqXM9iMcDmt7e9u8vAABS0tL5oFG6bZSqWhtbU2FQkG9vb3q7e3V3t6euenzPGBP4IJgzJ8pe8Gwulq9UChk0w9c/RDAgWdpe3vbvNQmJyfl8/k0Oztr3lesHzoSYUco69OcUiwW5fcfzY29fPmyZmdnrYT2cz/3c5qentbXv/51lctlfeUrX9Ev/dIv6Tvf+Y7+6I/+SF/+8pf14Q9/WMViUT/+8Y/tvbHccNkvV6uELKHROJqaQXkR9splx4gX3CtkFJVKxSwZXJaeqoDbZAFr5vV6jYGEoeezOM/NzU3TEG5vbyudTlt85f3GxsasIkK1Aa/AYDCoWCxm8TWfz9v6QR8G8CaZJ9Erl8sKBoOmbfV6vYpEIvL5fFbRoBrjVnEAuTSQUPYlzrhd5+8f753jkb4jJ5kv/o3MlM0GoaPb+cPhmmOWy2VNT0/rwYMHkmQPp/uA8pBKarKbAIBQboH5ApRgOkpwohPT1f34/Ucmj52dnSoUCnr48KENCXeFouFw2NgCghjflZLC+fPn1d/fb9kf4DMWi5kYeHl5Wel02rrjGo2GqtWqaRikIwEzo0N48AEeZPmAAs4HRiGVSmllZcUE8GTvsVhMp0+fNg0VjQdk+TiZl0olc1zHjwoWi+DsCk/x4WGzpVwnHRlz4lzO/SOjdoEIGycBNBKJWJbJ+8ImwWiitUN/4/oweb1eC5aSbLNgXQDk8ZKjuYDyJO9BIGWtoENyy/AIr12/MzZ+kgkE3LBdsKYtLUfGq+iTKIPhwo9uam9vT7Ozs5qZmTHW6/HHHzfNWCgU0tjYmNra2nTjxg3Nz89rb29P4+PjZogKoIjH45qcnFSj0VA+nzdN0qVLl9TV1aVMJqOlpSWFQiGbrNDa2qqenh7Nzc1penpaS0tLunjxosbGxtRoNDQzM6NisWijcugMRLMHM0gnZX9/v9rb2030Tiltb29P8/Pzeuutt4yJjEajun//vpaWluT3+zU5OalwOKylpSVLbEiGeF65x4BxnlEYFBo5KpWKlpeXjUUhAaTTl3IpDAvPCrYn/f39isfjxhjOz88bMwgAJcHi31wm6uHDh1pbW9OFCxd06dIlPXjwQK+++qrOnz+vy5cvKx6P6/r169ra2tIv/dIv6atf/ar+9b/+13rhhRf0cz/3c/q5n/s57e7u6vr16xofHzcJAzILkmA3nvp8Pvv+6ENd4Og2LPCsowtFU0mSTSc6DBAJFPERO4m2tjYb7+SWO6PRqE6dOmWf19PTYx3BxWLR2Heei3K5bOXr3d1ds5kgRiMtwAUfQF2v17W2tqbBwUHrDKfJir2LZ50KDGVp2DB3H3KrPH6/35Irvivgk+ahd/twgfLP8j0f1eORBmDSsZDT7b5zrQEIvO/UBQVTcXBwoHK5rPn5eaXTaStVlstlA1auEFo69o/ifaTj0gEPNqUhggL0NYwGguGNjQ3LQCklkSnB3PHAra2tmRM9Wh2//8jUEc1Nd3e3aXNgd2hYoLU5k8mYvUZ3d7d1TvGQEyRhk+imoaON8hW6FVdvQns9ZpLJZNLOZX//aKgv8x4R91NCgCZPJBJNWrVCoWB2AAiU+TxX8AtAg7kEoBEsJTVZBQDQ3EwWAM09JJME/KD9osMJIIedRb1eV6lUsk2Esonry0O3LJsp70kiwCxMXs/7FItFW9d0lbE22chbW1sVjUYtEXHLQNFo1MTFaIl4LtCwoZuam5vT7OyssRqDg4P2PWq1o/E+V65cUTAY1MOHD7W1taVQKKRcLqdKpaLXX39dpVLJmMZarWYlX4xZPR6P5ufnjdXk++RyOVufMMjFYtFmV6ZSKaXTaY2OjurTn/60fD6frl+/bqVJ2DIsG9isAHHDw8O2Fty2/kqlonw+byXhixcvWnnwD//wD5XJZPT000/r3Llzdj9JmtLptIrFogFXQLWkpjIzDCwbJiCBEVCumBzNJp50lNLdDstKpaJyuSy//8hY9oknnlA8Htcbb7xho8ak42STa8E1qtWOJmfUajVzcB8bG1OpVNIPfvADnT17Vk8//bQ+//nP66WXXtLv/u7v6qtf/aq+8IUv6Hvf+55efPFFfepTn9KnP/1pbWxs6PXXX9fFixcVDAabnkHAAEwxZTbsaUgWSMDca0DSsrKyYnpArmsgELAmG/YCnikSDLq+ASiw0jxb7rg4ACBMWSwW0/7+vs3PpYmB/WdgYMDiIrOH19bWzEfR7cxHH4jon0oHXdxUUKrVqhkBk5xynQCv0vFMW9Yy7DqvIcEDsL5/vLeORxqAkU2xebD5EmgBAd3d3cZ+uP4ysAylUknr6+t6+PChbep7e3um4XBbxsk+3EDGH8pFZC8Y57l0OpseYk88pnC/JhsKh8OamJjQ0tKSMWswF5w3LFQkEjHWbHNzU+vr66ZNctkWt/Mvk8kok8kYSwe7QQDk+gAoAFbhcNjeh4yVDb5UKimfz2t/f1+Dg4Pq7u42HynKM2R8ADF0STg+M7GABgc0cWSt6Ero5JSOZhoCWNBToHuAUWMj29zc1P7+vlKplKQjjROGsZJMd+EaQLJ+eH+39Aw4R+ArHScFMGIc+I4BCOiMWl1dVTKZtKG9AE+3TCnJGD42ptbWVutyY24en8s9wl+OkjxJRLVaNSDotuWHQiEDwqlUyrROmUzGPOA6OjqUSCR04cIF+f1+ffvb39b8/Lx8Pp+N7MrlcmayOjExoVgsJp/Pp2KxqGq1qoGBAStD0gkKS5fL5XT9+nXduHFDxWJRY2Njisfjdm3ZdC9cuKBnn31Wo6OjNmMUZgFLGsr5zOBzfcCYnAFbcvv2ba2srFjTwc7Ojubn57W0tGTdl08//bTOnDljpUTWOOV5Ot/QFcLQuBKJUChkswHz+byVmtxngbVKyRWGlYSS77W3dzRuKJfLaXt7WzMzMxoaGtLZs2f1iU98Qi+//LLu37+vfD7fNLNSkiVPfC7rKp1Oq7+/X88884wGBwd148YN/e7v/q4+9alP6S/8hb+g1157Td/4xjf0C7/wC/rc5z6nl19+WS+++KI+9rGP6S//5b+sTCajH//4x7p8+bKNfSNxpfsbPRaWGIAen89nNigkPzTruEkQ70tp1u1K5Zq7YA7mEd2YJPX19ZmQfWlpSY3GkZVQa2urTYLweDzq6elp0hczFcTr9ZopN888Gr9QKGSxjlLs1taW0ul0UzMUySulf+YYc85ovvBsJA4D3E7q5AB7rlSBBqb3wvG+Bqz5eKQBmKspYGPBhBNWiIwa+hWfJx7c3d1dZbNZPXjwQOvr601ZJ3SpW2eX1LSpuofP59Pm5qYJQwFr7nkSdNF64LfEkFpX38GDXKvVNDg4qFqtZmACZopNgu8GPV8ul5s8oqCxAQwYP7oZJsJrbA7QpVC6oPQH84OXF9oVriXZfDQaVSgUss65vr4+DQwM2IYBU4iuqFarGRh1y2s8tLj800ARiURs9BBMA27xLnOFaJZyJ0wHgW93d9coewxqKY8A1skoKbm6m6VbViHjR4vB9ZfUtDmQrbq+TYj1yXy5D6y71tZWC+y0rRNwYXi5R6w/1iXXkbXJ92D+IWwEDAADlBmiTaD3+XwaGhqyhOaHP/yhfvzjH8vjORpij5FvNps1sOsyskNDQ3Zd6Brr7u5WLpfTnTt3rBsVNikQCFiji99/ZJ6LU3mj0TDriLW1NS0vL8vn89moINdaBrBJPKDVX5KBgHPnzml8fFy5XE63bt1SMpmUdDRzEg+0/f19ra6umkcUHbA8H8gBarWaNjc3VSgULBlsa2sz2wc2Z64R921zc9OGcbuMJGCfMiQMGmAuFoupra3NjGnv3bunxx57TJ/61Kf02GOP6aWXXrJRTcQC1hUyCnRixEOGrD/33HN6/fXX9eqrr+rZZ5/VM888o/n5ef3+7/++PvvZz+q5557TSy+9pNu3b+vq1av6yle+YvIKnikSZHc9Eqdd7z3OhZmo/D7sEJpeXu/GNyogjHTCE9Htyoah4pqT0DBWjrjHH669+zyRFHH+mLLSbU0i7nYdkwyRDKMNJBFj7bjd15wDYJwkwL13AE2uLTGcg8qQ2637/vHeOR7pO+LWyymPAS4APDwICDPRC0BF7+zs6M6dO7p3717TooYFIwtyBf4EdF7HucA6kHHwWkpjUOzU7AuFgpLJpIlBE4mEQqGQ1tbWtL29rXg8bnPDeB9KmF1dXVaugrGBSoeJARgR7KRjEBkIBNTf36/l5WUbFi3JvM/6+/tN7MrG4PpmsYm4/jL8P105AJuhoSEb+Hz58mW1tLRoZmZGW1tbmpiYUCQS0a1bt+TxHDlP+/1+K1dS1hocHDTxO2WBkZER1WpHDu90a4bDYfM9I7skMELnR6PRJg8vHPfD4bCxknTHwZbAOBHEKVFQSmQjISslIXCZD8oUkiwBAIQxEYHXY5tCSYpNAPDgltIBwTQW8O+uCJl2dIAeJUwYAbeMTrlzaGhIklQoFMxKgzJYV1eX3nzzTf3kJz/R3NycHnvsMRPO03175swZBQIBnT171kYaAf7cjej111/Xa6+9ZoC5v7/fjFxhMbhOIyMj6u/v18HBgSUaqVRKL774ojFgACIMKN3nkO46NipiAIA7l8vp3r17amtr0/j4uNrb27W7u2vdgu3t7RoaGrJy/vT0tFKplIaGhnTmzBlls1nz1EI/NDQ0pIGBAfPWW19f19ramgEowBlJR29vr+LxuCUelNuJM5QtuZ+cU3t7uzY3N7WxsaH19XW9/vrryufzunz5sr785S8rnU7rlVde0Z07d7SwsKDBwUHT/pE4sG6KxaJu376tTCaj06dP66mnntKtW7f0W7/1W3r++ed19epVra+v6w//8A/1+c9/Xs8//7xeeeUVPXjwQJcuXdKv/Mqv6J//83+uZDKpqakpu9bEV1g+SU0AjMQOwNloNFQsFpVOp9Xd3W1yDth5QBT+avV6XaFQSIlEQvF43FjCUqlkpqok5Ggi6/W6BgcHzSuOCgPnCngkdnIOxNlkMmnl5mKxaGw4QArQRxzI5XLWbe3xeNTf32/XnXiNHML9vL29PdOHnqzKkKC6Xf2uSN99xt/N430GrPl45AEYegtKTnQNMkYFjRQCSNeBPJvN6rXXXtOdO3fMdZpuLVfgCD3O4Yqa3TIdHXqALX4HKwsyOXcEB/V5uhzxeeno6LDMXDqeAQcNHYvFbKhxNps1XQ0PsaQmXQVBFsBK4Mrn80qlUqZR4OEvlUrq7u7W8PCwfQZMCCAQfxvc8gG56CTo2uzr6zO7CUoz9Xpdi4uLNsoDfRNCbsqObDbQ8q2trXZ+wWBQoVDILC/ognPnQaLN4v5Xq1V1d3erp6fHylburE7E1Mlk0sCnJCtJM0OP1nD8ePjubCywFyd1XK4VAAGRfyPAshkSdLe2tkywvbu7awkGDCTrFHG325zBe0sy9qBSqWh1ddXWGpoZviusHmUfWJHbt29beYus++zZszp16pR11qJ9mpqaMiCKVtG1Rchms3r48KF1GXo8Hk1OTto4HsZwra6uKp/Pa2JiwjSA29vbSqVStpnSsdfb22uDuHt6egy8uoALrRzPN00eHo9Hp06dMgAEsGPDiMfjmpiYsHWUSqV0584dLS0tqa+vz0bJPHz4UNVqVSMjI+rq6lJfX58uXbpkwu10Oq1CoaBwOGwduxhnIvAOh8NWbkP3g26RTmfAfD6ft9+HPeZ5SyaTevDggZaXl3Xp0iWdO3dOX/ziFzU5OamXXnpJ+Xze1gbla/SDWNcUi0XdvXtXo6OjunDhgsLhsO7du6darabHHntMkUhEP/jBD/Txj39c165d0/T0tCqViq5du6bPfOYzevHFF81LDgDsMleAHbSy6NQoJ5IMbG5uKpfLGQvc1dVl95XOUPRPdBKzhkgcXKPmer2uZDKpfD6vrq4uGzXlWti4jDJmu4AdKiwkp9hM8Ex3dnZaswnP/ebmppUZ+QyA2tbWlvL5vAYGBuzzSP4Zc4SdEc06JFNoCZHPkHTxX5e9e/94bx2PNAAjwCHKzmazamk5GhiNZxFBmjIaDzQbQL1+5Da8vLysra0tAz5soJKse4kHV9JPMQs8lAQXsnk38EMr12q1puHJBFeABbowBKQwX4Apj8fTVN5whe88oGRSlFDJOikPwroFg0HNzs5qY2PDBPvoQqrVqpLJpHV20pnFAaDi4Xa79QA7hUJBsVhM586dUy6XUy6Xa+r8QuNz7tw5zc/PWzdnvV431qG1tVXpdFo+n8+64TY2Nqx9OxAI/NSQcxhBHOq7urqs1JXNZjUyMqK2tjal0+kmo87W1lYDHgRb2BfuFQabZNGU6/g55aGWlhZVq1Xlcjm1tbVZqznXicwfkAmbBjimCWJtbc00Lm5nHRoaGinQebjMHPfGLa17vV7F43FjPSlHsgFyXu3t7SqXy1pdXdXCwoJp5WCnHnvsMfOSk2QlOjbEcrlsrC7rvVQq6eWXX9Yrr7yi3d1dRaNReb1ejY+Py+v16t69ewqFQnryySfNGDUajeqxxx5TOBzW3t6eWaRQAuzr61NHR4fm5+d1eHiop59+2jRnJB48t7CqGxsb2tzcVDqdVjabVSwWa0qEKpWKxsbGTF8IyJ6fn9fdu3e1srIiSTp9+rQuXbqkw8NDvf766ya8Hh8fN02f1+tVOp3W9PS0MS4I1Bmj5fF4DLTBSpPo+XxHnlqnT582UEnHMgJt1gVTLSjHXbhwQfl8Xrdv39aDBw/0wQ9+UE8//bTOnj2r27dv6+bNm9ZsRMMDzClMDgafnZ2deuyxx9Tb26u33npLu7u7euKJJ5RIJPSjH/1IH/7wh3X+/HktLi6qu7tbX/ziF7W5uak/+qM/srmyxEmkDCQt0rEGjQQSrVgikVAsFlM6nVYqlbIxSmihiO3d3d2anJzU+Pi4ASp+5paBYaToCEXmQSmRigX3nIoJ156YEon8/9j70xjX8/SsG7/scm2uKru8u8qufTn72uvMdNIzWWbIZB7Cw/MwiPACQV5EghdEJAIBL0ikKIggQaQAL4JAg0AQIRIkMizJhMySmZ7p6entnD5r7YvLLtvlvVZX2f8Xpc9dX3uakJDwdB/+5ycddZ86VS779/t+7+91X/d1X3fI9jp7cnh42PYkhrj9/f06Pj7W3t6exffh4WEdHBxYwk9lQZLt697eXrMRgvF1JTRoAmHnuq2SSB7cc+qjvp4zYJ3XMw3AyGgqlYrpmcbGxhQKhewwhJmC9err61OtVlMul1MoFNLi4qLm5ubk9/u1tLTUoZHgIii5WZB0MWsMalm6aAwgeALE+Lp0wXjwWt2HpGu4d3JyYvom9DkuU+WaHrIZKXHitg4DBpPEQYvbNu99bW3NvodO0oODA8ViMXm9XhPYM/MOfQ2f1b0fAE/8x1KplFKplLa2trS2tqbe3l7NzMyYqeXc3Jy5elNSLJVK2tvbs042NHIAStq/Kfeg/XL1TgAoSVbSgiGKxWL2Wtz/er1ugIy1QwmIUT4wWIDNdvvcxoIsFeCLzuPDyteAeRegsz4R1EuytnNJdmi5fkS8f5IA/o6RLe+PgwK/MkrYxWLR9gvviYONIezZbFZe77kn3OzsrDEt7CXXbJKsf3h4WNPT01Yawz38v/yX/6JvfvObdqjWajXNzc2pp6fHfMUmJyet9BaNRq2jjvuOtpDfz2F48+ZNjYyMdABo/kuzDYfb7u6u4vG4ZmdnFYlEdHZ2po2NDQ0ODuqll15Sq9UyCwuA1DvvvKP/9t/+m3p7e7WwsKDJyUlNT0/r5ORE9+7d0/7+viYnJ/XSSy8pEonYDFm8zHgeCLN5b/F4XOFw2GbPZjIZlctl8zRj5BRrN5/Pa39/X/v7+7YHaM4pl8vWgevxeKwMOzg4qL29Pf23//bftL6+rhdffFEvv/yyJiYm9J3vfEcPHjwwvzVc/yVZdyRsr9/vN5uRp0+f6lvf+pZu3bqlsbExvfnmm7px44bm5+etSedP/+k/bZo65BGAYtY1I5JoWiARJhloNpvGTALuXYNi9FXE7XA4bOsFBhkwzrnhdqkHAgGTWNDoBNChdAezyF5vNBrK5/MdjB16VuIGiZqbqCJrQP9KLKVqUCqV1N/fb4bBsVhM/f3n80FhRhlz5nosIjchlnOWEHNgCD8O17MMmP6kr2cagFFG3Nvb08jIiKanp+2QpBREIII9qFarWl9ft7p/IBAw7dCTJ0/MYDIajdowX34Xh7JLoVPqkfR9YMvVMXTT7xzKHCpcBBteDyEoQYksCwCFJgqNELQ5olUX4LmAggOZFu6DgwNls1llMhnLxiORiH1eXof3TJmMQwEAQccdjvN006H3Ghsbs1IYDQXValWRSMSsJ9CYjY2NmT9UMBi0cSWUEHnfMJ2Ifik7NZtNRSIR07n4fD5Fo1E7vDgQCHZ0ILrPAB1HJpNRs9m0bjxExniFSTJwjLgfTQYmqpQOAWgIjQmQ9Xq94zDivXCYuJYidFKhE2N9w5zRacqac9lZLhgAl7VlrfN5RkdH9ZnPfEaRSESxWMwOUIDte++9p69//etKp9N6/fXXNTs7a88ecX+j0dDq6qp+4zd+Q7/3e7+nnp4eG7SNtcXS0pI2NjY0MjKiarWqhw8fKhQK6Ud/9EcVj8fNmJdkwwWv6D8p6bO+Ya7RfZKE0cxCYoF+hrLg0NCQ6vW6Njc3LZ6gQ8KYOBqN2lrKZDJqNBrWkYwp89bWltrttqrVqra3txUKhUzTyZ6laxPR/unpqWZmZqyL8/j4WE+ePDFWhdL06Oiodeehe+WQ5+DnfqATm52d1dHRkdbX17W1taXJyUndvXtXP/7jP67bt2/ra1/7mtbX183qg9dmv5OATU9Pa3R0VHfu3NHW1pYeP36subk5DQ0N6d69ezo7O9OlS5c0MDCgqakp/cRP/IT29/fNsw2NJ3GQRhiYOyQcsFyuTtL1BWON+v1+Kw/HYjFjeoeHh200XLVa7bDP8fv9VoKXzlljj8djAJ7XJma6nZs02UgyHSWxCV0n8RgRPWVkdMoAcGI+4BEQB9vFM/R4PKbJJS55vd6O58574Nzg/5mQAQB9fn18rmcagBFcMVklaCCWhUqX1FEWga6Gxvd4zucFujPgKE0BqNBWuUwDND0AzD3kXMBCtueyYNL307GuqB9gRhCmY801GnTHGUky5kaSlSfo1AGE4auFhgimjDILZRG6zbifPp/PRg6hQ+J94peDfqVarWp/f9+Awd7enra2tlSr1XTjxg0lEglls1kVi0VNTk5KkpUU6fzDJgF6nucGqEM4j3iabiQ8mQg6+GFRCojH4yaC5RmhteFwY83AdrndicySozx4cHDQMRRbkrX6A/zcLNU1SGRuZTQaNW0ibB+6DrqkeJYEW9g6PNvQxPCcCb4u6Cfg80z9fr+5bGNnQTeVJPtcHBb4ceVyOdMJLS4uGoszPz9vhru8DuADhgXNz+bmpi5fvqyxsTFzpQ8EAvZ+mHU4MjJimq9yuWyMAGwmmrm1tTXt7OwomUzK7/fr8PBQ2WzWynmBQEDxeNzWDQwXr0056eHDh+andeXKFWWzWT169Mj88yjjww7z3CgrnZ2d6cmTJ8YSStLm5qbNoJycnNTU1JQ9QxcocugSg2q1morFokqlkunyQqGQ4vG4NYy4uieStWQyaZMjSE4oq46OjmpxcdE+V7Va1YsvvqgrV65obGxM3/jGN/TOO+/YM2Q9ARzq9bqePn1qurerV69qe3tb3/ve9zQ3N6fJyUl98MEHJgXxeDy6c+eOms2m/uW//Jc2e5ROPlenyP5GThGPx5VIJOTxeLS3t2cCd/Yp65YzAPNk4ixJF/eChIc4yzgfyuacGbBLlPmItySO0jnoJ75JMpacJhzAE+wZcRKrCiZXEG/RkvL+iNmwnUNDQ4rFYtrd3VWz2TSNHtMdsJahc9dN6pGoABo/yut5CbLzeuYBWDAYVCQSsbo+oIUs3rWDILPe2dlRq9XSxMSEpqenjb4fGRnRnTt3FAgEtLa2plwuZ9ooV3zOAQY9DcsFMHMZMBd8cQC6V/fP8TW37V+SidoBFu74Er4He4W9vT1tb2+bpozMPJlMGo2OHo6gEwqFdPnyZR0fnw8g/uCDD9RoNDQ3N2djNCR1jPGgtHl6ej7nj6wT9/tXX33VHLHr9bpWV1dVrVZ1+fJlo98poZydnWl7e1vz8/OKx+NmxYEOqFKpaHd310D24eGhdRH5/X7LfKHwQ6GQeTUBLDkkvV6vcrmcjdEJh8Pmdg3oIMOmvEdgx/qD10IM6zZtuGJ2dBr9/f3m7N9ut01PBYgg4DPKxC1lo60iqOLajxYOk1zKI4BG2AL3Dx19hULBGB8YMzq7AKX1el1ra2vKZDIWwHd3d83dOxgMdgi2YeUI9AiLDw8PTcM0MzOjs7Mz83mjaQNmKBgManZ2VsPDw8rlctre3jZB/MjIiKampgyg440ESzo6Oqp0Oq2RkREdHh4qn8/re9/7nrxer6ampow1BpiiuUETmslk1Gq17Pdvbm7q29/+tjWQSLLPjBCeMiCdxC5QCgQCJvQ+PT3V1NSUpqenrVEE42SPx2MdiTs7O+YXRxMKSRKGsDy/k5MTRaNR09nheQXzAliAPQoEAqpWq8Y8xmIx07Tt7e3p2rVr+rEf+zHNzMzo7bfftm7sg4MDY7UZn1Ov17W+vq5YLKZbt24pGAzqN37jN3RwcKC7d++apc+NGzcUDof10ksvqVgs6jd+4zf0+PFjLSwsWGKEZALAh96J30+DDj6Aly9ftpiBD9rw8LCZrbpm2dL3J8aSTDdJhyTzNV3hPfsNhprE02Wee3vPp2AA1rjfkkwWQXc0a4MGg1wu12HPAXMJi0lTR7VaNaA3MjKiSCSidrttDCsAkxjmVlVcwoBu8OfXx+d6pgEYTAGiSUo7kgxgwGAx2gf9z7179/T06VPduHFD165dUyQSsYxkdnZWCwsLymaz+u53v2v+ObBZrp0EAAsg1s2A8W9uKbC7FNTdUuyWV/g317LApeGDwaDdA8S8u7u7pr1yqfByuWwdRMfHx2ZfATjEoR4PJ9gyAgPvBUGtx+PRzs6OZd4wOqlUSsViUaurq5qfn7eDo9VqWaPElStXFAgEzLIimUyqXC7r5OTEPle9XtfAwICCwaC9H5i2vr4+NRoNHR0dKRAIKBAI2CxFgvnZ2ZlKpZJpp6SLQez9/f323gF1DEcmgHs8HnP45/7QpUfAByy72S0/C2A7Ojoyny7KOK4zNyVP1xIAlpI1Xi6Xtb+/r3A4bPcIVorPBYhrt8+nJ8AOkQBQSmHMisvQutYAsICUNziYOPABC1hplEolHR4e2gBiGiiePn1q1iHr6+umrxsaGtLc3JyuXr1q9h+uJqfdbtskiGg0qoWFBU1PT2twcFBjY2P27IrFor1H2Bc6Q2u1mp4+fWqlQdYtz981ND46OtLa2pp6enr02muvKZFImCD9k5/8pEZGRrS8vKydnR0NDQ0pnU7bAQkbi5M6ewEWFI1jOBxWOp2Wz+fT3t6eisWiHdwkN7Bq0rlB8MDAgDY3N630XCqV5PF4zM8MsIG2zefz2Z6XZAkb8QYZBXtneHjYHP2Xl5fVbDZ1+fJlXb16VclkUm+99ZZNOGi1zh33a7Wa6e9OTk6UzWY1NDSky5cv60//6T+t//Af/oOy2az+1J/6U1paWlK5XNanP/1phcNh/ak/9adUq9X0n/7Tf1Imk9HCwoKtORJLGO3Dw0Pt7OyoVCoZ0+QmqQBYxjShdZMupAAuAANwu5ULXo+SsmsWCxsMs03HZfckC/Y/ZVo3KUfrNzg4aB3p7C23ecLVmQWDQWP5OWPQPHo8Hi0sLCgUCmlra8skEIyH4/Vh3NzmExLHj/p6zoB1Xs80AJMuNAQEGoALBwkB6v3331ez2dSLL75onUrvvfeeHj58qN7eXt26dcsCNcaekUhEL7zwgtrtc+8WsiI0IzAT7u912RBAkgu4XGaD73dFqa5dARuZzArRKRQ3HT0cOugoKI15PB4lk0klk0ljhwqFgmlf0EVwWKAJo8RCdxwdQGgVOCja7bYFSA78nZ0djY2N2SDl3d1dhUIhRaNR05nVajXl83klEgnz40K4DYjo7+/X+Pi4isWisWyYx7o6t3q9boxmPB6316PciON/IpGwpo3T01PL5Jnrh64O1gHRPc0GAFZKJJRA8CDK5XLWWID9AhYbgC7MGslIKVlRYqT0QtDkEOBgB8S7zRjo9HhmXAR21pJ00bkLy3B4eKjd3V0T/rfb555LdIAlk0kTYTNh4fj4fBakz+ezcmC73bbvbbfbymQyun//vplibmxs6MmTJ3r8+LGKxaJ1Kb/77rsmTk+lUpqamtL6+roKhYIB67GxMWOt6P7lXqXTaTu4wuGwlVHZQ4uLi5qYmDC3eqwBwuGwms2m8vm87ZNUKqXp6WmNjY2pVCpZ2TUWiymfz+vdd9/V/fv39YlPfMI+M2VDYgUWAwx439nZ0fb2tiYmJnTjxg2Nj49rf39fm5ubpq+jc5mDc3x83MpOMMDoA4+Pj7WwsKBIJGJleZqPWJ/tdlvhcFiTk5PmqXdwcGDzXQuFgnp6zg2eJSkWi3VY0jx+/NjmSn72s59VPB7Xm2++qdPTUzvo8VkjYXj69Km8Xq/m5+f1Z/7Mn9HXvvY1/dZv/Za+8IUvqFgs6r/+1/+qz33uc0okEvoLf+EvqFar6Rvf+IZyuZwlA6xnyn3sd9bhwsKCsbwwX4xgmpubs7gIOALo8LrEy27PvVarZaCneyYt+k/XLoaORIAZCTng162O8P0kFySFdFUeHBxYKRkD7/7+fnteh4eHHd3yQ0NDHUCRuL2/v69isahWq6WxsTFLPBuNhq2rdrtt4P759fG5nmkAhmgSFoDDBooYVmxnZ0crKyuKxWL67ne/a6JWMpjl5WUdHBxoampKc3Nzxij5fD6l02n19/draWnJPHdckz6EmWw8V8flCu7d99ytBXMZLVcXwQFKhkXQdxE/hw1t53t7e6a1icViZiRJxsf4FbpHcajnEAY8VatVZTIZA3iuv1m9Xu/owpTOx3qEQiGtra1pZWVFiURCQ0ND2tvbU7vdNl+2hw8f6v3337dS2fj4uAWdfD5vc9IAJiMjI3bY4AhNF9Dk5KR5eW1ubqpUKlkJiiBL9smBCtAMBoPmF1Sv1zuYRbdrFrErGbQ7wkc6Zxmw6aA8wfOivEtJD2f2SqVif+ff3edIEwmsRn9/v3WvokMhE2eNc9C4szrdDlBXWI8VAiXJnp4e0x1hE4IWr6+vT9lsVl//+tf1zjvvyOPxKJ1OmyZoenpaiUTC9mC1WlU+n5fP51MymbQOukAgoHQ6rXA4rNdee03RaFT37t1TtVrV3bt3NTw8rIcPH2pzc9PWGyVMGBHKsMlk0kAxpdydnR0TKp+dnc8MpKSINsh1wmcaBSVBnv3p6cX8QA5XOvrm5+d15coVpdNpNRoNbW1tmV8esYBmCJIfQDydbTR8oF9yhdp8L8ysO0YNMEDZnm5HdIMAomg0ak0Ekuz+pNNpnZ6emm4U/0TiZU9Pj6LRqNkjkFi98MILikaj+va3v618Pq9QKKRUKqVSqaSNjQ1riLh//75arZZu3rypaDSq//gf/6PefPNNvf7666rX6/ov/+W/6POf/7zGx8f1Uz/1U+rp6dHXvvY1eTwea2yBiT89Pe0YJp/P51Wv183rDWYqlUp1+H1JF2X/bnYXBpvElwvhP0k8zUVucxWAi7gEcMN+hv3pljdx2ncTV6aUkGDQNT08PGyj1kgy6vW6eeiRmKL/rdfr2tjYMOsYqgKwgq5vJYDWHQf1UV7PGbDO66N/In+MC+qZ7JjSHRuY7rW3337bjFA3NzfNoHBiYkKBQEDvv/++vvvd7+ratWs6OjqytngOqXg8bn5Z9+7dMzaM7MbtjOxeDK73ilueBLDBSHAAuzV7DmRYMg5Wt8sFDx0O7aOjI8Xj8Y4B4GS7ZGnLy8vq7e210pHHczHvzO/3m5v5zs6OcrmcpqamrLTnZogIwgEs4XBY0WhUW1tbOj4+1tTUlGq1mjY2NlSpVHT58mXdunVLtVpNjx8/NnZtcXHROpKy2azW1tZMz0Lg4+ChBRwtHofR0NCQtZJzqLrGrJlMRqVSSRMTE4rH49ZNOj4+boabZLTovQASlMj8fr9px9wDjLIrjRI8G5ex6gZMrqbL1WjBrLGOeY6UD9DHAKw4AFywBUPA8+J1PR6PBWJAIYcE644RShw0JycnWl5e1uPHj+1ZATLm5ubsYMeOgskFMGrb29vGoNIUcePGDbVaLc3MzBh42djY0O7urpLJpCVVZ2dnpnlptc7nRlJCxD4FQX8+n9fNmzfV19enXC6nzc1N1et1Y9Dw+GKvAMAYCP7o0SMtLy9rcnJSCwsLVkql4YK9ND8/b+B+fHzcLBu4b+1227qqBwcHVSqV9K1vfUvValVjY2Pmeo4/FSxhf3+/NVUAgtzXZe3DdLP3iEOU4cPhsPb395XL5VSpVFSv1zU4OKhgMGh6MWIlrJEkiyPMmWw2m9YYkUql9IUvfEGPHj3SgwcPVK/XFQwGdefOHZtBWa1W9c4771gZ8y/8hb+gb33rW/rOd76jH/qhH9Lx8bF+67d+S5/97Gc1OzurP//n/7wajYYePXqkYDCo8fFx+f1++91er9fGXWG1w54BjCCEh5UnYXVZUOIEYI4zgsSZGA14IbGC4We/uokOzTAkACQ/NP9gA4NMoVQqdUhVMB4eHx+32IIL/+bmpnp7e5VMJs3WBfkEyczJyUkHEKWzk7VfrVZtjQD42U/Pr4/X9UwDMDJV1+eEkoTP59Pu7q7++3//71pdXVUikVC1WjWaH3r29PR8rE21WpXP51M2mzXxKO3hgLfLly/bz+CeTVebW3bsFtO7X3NBmAvcJHVkXe73uyVJXheLAbIbOrqg2dEtwArwuvwsbA7BAHYGJ+xgMKidnR3TaAEA0TmRlaENQdBOdry/vy+f73zG2cHBgZ4+fapcLqfbt2/rlVdekc93PvyY5xGNRpVIJNTX16d79+6ZT1M8HjdGBE+03t7z4crSOQ2/sbFhHmB4Q9HNxHMhk2Y0C/ME6ZKkDEDgxHHfvY8cUjj2p1Ipeb1elUolHR8fm6koz41DAB8sShTuJARJBrZg6KQLk1uCLOuDZ4guDTDF84Fd5edYk+wN9HGwdQBct1kF3Vw0GrXSFjYU6JFeffVVxeNxY+R2dnbM341DPJ/PG9Bgv01NTZl9w9HRkR48eKDt7W3r0AsGgzo8PDQwNzc3p0AgYFYmi4uLVgJutVo2QJsZi41GQ9vb2xoYGNDt27eVSCRMi0NXM2wTjMQ3v/lNffnLX9bm5qbu3r2rz3/+80omk1Zyy2QyyuVyunbtmskRsKUYGhrS7OysgsFgR/I1MDBgTCWHNJ169XrdSlBYpUgXnm+wmCQ7+/v72tra0uHhodkqcHCzLpAxsIaHhoY0NjZmRrJumaxSqUiSTRBhHbnlUYygaWYYGRnR7du3NTs7qzfeeEPLy8vq6ekxw9tSqaTd3V1jGWOxmH7oh35I3/nOd/TOO+/ozp078vl8+vKXv6yf+ImfUDwe10/+5E/q3//7f68HDx7YZ+rv71cqlTJNot/v140bN9Rut03nBGvkOuejMe0W3Hfrb9mTsG0w3i5jzb5nPwLwGo2GxTq89EgIiZ90tA4ODhq4Z68hxm+1WpZEwGDxs7lcTpFIxNg6EmT2OXGNCgZVAeIvQAuZBlcgEFChUPhfOGX/ZK/nDFjn9UwDMOhitB/ShdD0+PhY77//vlZXV63ExswugA/Z98nJiS5duqTp6Wmtra3pO9/5jo6OjvTJT37SNh+6oqmpKQ0PD+vNN9/U+++/b23AiKJdQMXvcUuU/JdAIF2ALVcH5l5uaYpA6/q6QHnzedAguS7TfX19KhaLOjk5UTqdNsYLZ/i+vj7LyOLxuCYmJqz9ndZquuvwmsLXCqG1JPM6ymQyymQyBqzW1tb09OlTDQ4O6tKlS7py5YqVh99++21dunTJphrE43FtbW11AK5qtWqMUF/f+axGgEIikdDKyoqVEvf29iyw0SVEOS2fz2t9fd0CPoEKgTrZI676HFisg2g0qnq9rkKhoFAo1OG5VKvV1G6fj4ly7z33zmWcXNExlh4cqnS6cRBKFx5zMF4EU8qf6PlorafcwJoBvJHh81/KUhzeAEWSAQ6OqakpmzqQTCZ1enpq47uOj4+1tramw8NDW1elUknr6+vWtDA0NKRLly4ZK/rd735X3/zmN7WxsWGvn0qljMGByY1EIvY8sC7A761er9vcz1gsptPTU62uriqXy2lubs5Y01arZWab+Xxe09PT8vv92t3d1d7engqFgtlUlEolvf/++x0jtvj/kZERffvb37ZDeGdnR7dv31az2VStVrP7DUMJ4I5Go5qamlI6nbYyZV9fn613WB/sb0qlkj2vfD6vQqGgSqViCRIzMEdHR+Xz+azbkY5USpUkSXQ2Mze2XC7b2pqZmdHMzIxCoZA9X8pxfB5JqlQqarVaikaj+sIXvqDNzU1985vf1OrqqsLhsF599VVtbW2ZVjCfzysajerWrVvK5/O6f/++XnjhBR0dHem//tf/qtdee00LCwv6c3/uz9n6gVUcHBy00U5nZ2fm1Uj5niSbUi5xn/+SdBELu5MiGEC+h/jmxl6AF92NaA/545YXkUUgfEeWQLmQTlcalSiHEttcr0i6+kkeaVpBF8fnZOA7XdbMGf3Wt75ljDBelsPDw9a9/vz6eF3PNACDzek2mavX63r//ff15ptv2riira0tlUoljY+Pm9iR0gJCz5OTE21sbOjBgwd2UC8sLJhtAgE1mUzqB37gB9Rut/X48WPt7OyYyBcQRjbGJgdkueUpt9TI55EuWofdn4UFcy0OYGcADzAm7tXX12ct6wA1ymyhUEjpdFqZTMYONQJFLBZTIBDQ6uqqisWiFhcXreEAsbHbEYQGaHBwUDMzM8pmsyoUCopGo5qcnNT6+roePnxopaxbt27pzp07CoVC2tzc1Nramrxer/lAjY6O2h+v16tsNmuddAzwbjabVkqiDZsyKuxDvV63klF/f79mZmZUqVRUKBTseXm9XtOInZ2daXl52fRKlD1GRkasbZ2uxL29PRM0HxwcaHd31/4dWwyYQMb6UKLigKY8RImRAwFNCewdzuqsh9PTUx0cHCiZTEq6cMnHZsNdJ1hb8Iyki4MK6wuPx2NZPyVs6TxzvnLlilmPnJ2d6eHDhyoUCnrhhResOw92GZBA2/3w8LBmZmbsGWUyGW1sbOjRo0fy+Xz61Kc+ZcwUuqhYLGYGrbyP8fFx9fb2GhN2dnZmDGlPT4+VMz2e8xmW8Xjc9tro6KjK5bLeffddTUxMKBQKqVgs6itf+YparZYWFxc1MDCgtbW1jnIX1gvT09OSpCdPnpj1wtTUlDGpJDbRaFRer1ePHj3S5uamenp6jE1dXFzssI+B0T49PTVLA+kchMPaNZtNra+vq1gsGoNVKBRUr9dtnbDOE4mEOaV7vV4Dl4xdIhGR1DHl4oMPPlA2m1U6nTb9J4f//v6+NZ709vba3Mx4PG4NC++//76++tWv6vDwUBMTE5awVCoVLS8va35+3sq2H3zwgW7cuKGRkRF99atftaaVH/mRH9GXv/xl0/vFYjENDw8rlUpZAkgJmHXrMrdoZmF/XSaZRibXkqUbfCFh4ZkjyGev8R7onqZzkp+lUQWdHqObMA6enp5WuVw2r0MYsUQiYVIAytFXrlyxbk785eikZq0A9Lxer01MoFTbarU0OTmpYDBoZWlYvg+zQfr/+nrOgHVezzQAo/sHEAK1nM1m9Y1vfEP5fF6f+tSnNDk5qUKhYKUb6Vw/5hrc0c1zfHysS5cuqdlsWjZRq9Vs9AgZSyKR0Oc+9zmNjIzonXfeUS6Xs7IF+gpKht3lxG4RPv/vCvq5us00XQodmwM0YpLs7wR3WpLZxATvaDSq8fFxjY2NGbVOBxzZ98jIiIl6JZkZar1eN9Dn2geUSiX5fD4TXK+urqpQKGh2dlbJZFIPHjzQysqKlShfe+01zc3Nyev12hB16Rw0cKhHo1FNT09rZmZGe3t7Wl1dVW9vr65evap0Oq2+vj5tb28rEAhY0Mtms1YOJGOlAxGAQvt8IpEwnd3JyYnGx8eVyWS0trZmLALPhjVH12Y+n7cOOBiZbDZrnlkcmrCRp6cXo0bcMjKlIXQnNGQAHF2rENaWW14kuLJ2AC0ANbJ7PgP3hTKuJK2srOjo6MjsHvb3903gzMigRqOhlZUV7e7uan5+XqOjo6rX6+YQj+6OEtQLL7xga3V7e1ubm5tqtVoaHR3VlStXrMvr8PBQW1tb6u3ttfE4jx8/1tOnT5VMJjU+Pm5NGEx/yOfz5v+Hzx17AL2Z1+vVyMiIWq2WNjc3lUqldPnyZWOvzs7OtLKyYlorkhV0iJcuXbL7zp5YXFzU/Py8HbY8g3A4bHNkYe3oOJybm1MymbRZgIzZevfddzU0NGQdkuxVHM9hhK5cuaK9vT2TVWBNQfmL51kqlUzYD2jAuqW/v980QcfH5wPo3bmS29vbKhQKxgxHIhFz+3eHSNM5vLW1pVQqpZdeeknz8/P6/d//fb333nsKBoPWgLO/v69Hjx5Jkl5++WXt7e1pZWVFyWRS0WhUX/va1/T5z39er776qrxer7761a+axg17GHSMxDtKnm6yS4nOrYK4EgC33MgfxPquqB7tGWV1V5OJ1MSN2TBxfB/VlVarZesrlUrZXEpJ2t7etlmW+MdRena7wgFblEQLhYLFCt6Pq2MliUTSwaSJTCZjesGPA1BxrTH+JF/zWb2eaQDmdhy6onQ8UqRzLQbdR3j7oMvp6+tTIpEwgS8iXQ6e4+NjbW1tmafVa6+9pqmpKRNa+/1+vf766xoaGtIbb7yh7e1tA2Fkud3Xh32tWzfmMmMuU8b3uAc7TAMAyy2ZYoFBmQ0tEKzI2dmZsSaU9KC68djBhqFSqZjHFOAOhgBmhE7Eubk5zc7OmiEs5c2FhQU9evTIPMq8Xq8++clP2mBszDXx1drZ2VGhUNBnPvMZTU5O6tVXX9XTp091dnam3d1d+3x4QwWDQeXzeZ2cnJgxp3RhiDgxMWEjjzB6pMsTsfnU1JQuXbqkcrlsnbKtVstKewjVC4WClSe43wRU17gXywg3K4dVA7CiFUFvyOtxOBBwAXHoHdF1dYMssmOp03qC37G/v28lM3RTdNDRtECpg0Nsf3/f2NBwOKyxsTHl83n97u/+rvb393X37l3Nzc2ZtxnMWj6fN00NWsVisWjsVq1Wk9/v18TEhM0LBUTXajVNTk7aTFNYD1dX2d/fr2g0qrOz81mO9Xrdun/xCDs9PVU6nTZvuW9+85uKxWK6fPmyisWiarWaUqmULl26ZN2X6GzYb5I0NjamcDis09NTK+kFg0HNzMzI5/MZk5vNZs1jK5FImGceLA4AnnFIdLDlcjnl83kD1zCnfr/fxqZdunTJgFWxWLQJE5VKpaNsTemVQ9zj8ZhJM0kTz5k143ZV4rm2sLBg5VM69Zgm8OjRIzWbTd2+fVs/8RM/odnZWf3n//yftby8rLm5OV26dElra2t6++23dXp6qh/90R/V2NiYTk/PZ/aura1paWlJkUhEr732mkKhkNbX162sL6mj9OwmGWii3L9/mHSDZwgg4//5OyDOHWuGxox/AzyRDLlMM15wbucuCTpNGO60FZ5tIpFQIBAwDRvaLn4HCRjPkDhPEtJsnk91ofEJtjmZTGpnZ0e7u7s6ODiwBoJueczz6+NxPdMAjGyFgMycM4SMfA1wQUcQlC0+RvgTQW27QWloaEiFQkFf/epXdXR0pOvXr1urNdqSF154QZL0e7/3e8pms5YButqtD+t6dDeEK5KXLtgyvuaCMA7l7g2FmNTVHnEhxB0YGND29rZ2dnYUjUZtmDWbHAG71+s1BqjVaml3d1eSjAFkZA5ZNK3evEYikdArr7yiTCZjGW0sFlO73TZn8I2NDY2OjuratWt2GNMxt7CwoMHBQdOISVIikdDc3Jx5nZXLZSshT0xMGAvB8z4+PjZh9/Hxsba3tzsO+3fffVdvvvmmLl26pEgkokKhoEePHunFF180zya6EF2NCXYBBG1AI2VATFVhXJmtR7kR7zECNoJisn+eN35cbqbPc4YVkGQHIsJdDm+AGIcwDROImdE49fT0WANErVZTs9k0UOOKm/1+v7E/MKaAe7oFDw8PVa1Wba5oo9HoWLMkATBm6+vrevr0qT75yU9qcXFRzWbTxu/gwk5pCnuUarUq6VwfFY/HrYS7u7trrC17AbaETtQHDx5oa2tLIyMjGh0d1fT0tAEaGItAIKBr164Zs9vT02PGzuwF/OjoUKVc+Oabb2p7e1t37tzR66+/bvuHEhNgKBqN6vDw0MYGUZpickUkElGz2dSjR4+0v79vJUJiAqVdysOlUkmZTEaVSqXjGbOuJNneAjggFgeY1+t1G9EFEL93755WVlY0Pj5u94YGDfRN9+7d0/j4uObn5/VTP/VTHW73V69e1cjIiN5//30FAgF9+tOfNmuMF198Uaurq3r8+LGuX7+uu3fvanJy0gAgTJbbINIdR7tjJoywK9lwtbforQBsADQSDRqNeO5oIvm7dF7Cda1RSI4o62Jlcnp6aqODYFj5PtgsLJPcTkd+D4yca5uDXpSu87GxMWtK2tvbM90u3c5oC/nMH/X1vATZeX30T+SPecHI0ImVzWY1MDCg69evm/gXXU5PT491OzKaZGVlxVrloUdhkgjAPp9PtVpN3/72t60N+9q1a5qfnzeNz4svvqienh595zvfsdlyuDfzu3m/0oW41Q0i3Z2SkgwIuuVMDmSCEuJsWBi3K8r1EyPzpcREmz42E4wKojxBeQ8Lh3Q6rVQqZZmZq2vDaHR3d1c7OzuKx+O6e/eurl69amOIent7bVgxYODp06dqt9s2R7BWq1mZlFLM8fGxddTBzNHxw9xJDuiRkRHlcjkVi0UdHR0ZU0ZAhxnlPjx58kSVSkU/9EM/pMuXL6tarerevXuanJxUKBSy5+/aN2CzAEtGJylasGw2q9HRUY2MjFiZD6CIVg1mCG0OWT6sLvfAPYg4TMiOedaSrKSIw373gcNzIiuWZJpBVx9Dt1mj0bBMHgaOEUQ0KzBseXV1VW+88YbK5bLm5+etNMJB1W63lcvl1Gyez7CjY7BWq2l/f9/mN3JQ1Go1bW5uml3E0dGRxsfHFQqFjFnc29tTq9XqGB2Wz+d1dnamYrFo46joKMTXrtVq6fr167px44b8fr8SiYRWV1c1PDysRCJhg9XRiLbb51YXh4eHZkWSSCTMLqPRaCiXy5mhK35hN27csA5gPheHOeuE7kP2ESU3ukgB8HRmb29va3V11YTcV69eNdbR7/drYWHBgDf7IpfL2c8AMNCCEp/a7bb56l2+fNnWq3Supy2VStrZ2dEHH3ygUCiksbExxeNxDQ4O6vbt21bKfeuttzQ7O6svfvGLeuutt3T//n0tLi7qB3/wB5VOp7WysqI333xTn/vc52xdp9NpGzNGhyxrhvfAGieeUf4jfrrVA2Jk90HPPnDjr1tSxPCUZ43OCkNc7I4o50my7kfYUtYCnmzY5eCxh65ta2vL1gJ+gAj+SZggA5B/uDGgVquZwF6SgW9GhmFRwagur9erSqWicrn8Pz9Qn1//n17PNACjfRd/FNy2vd7z4cCAC5gXRLahUEhDQ0Pa2dmxERyIGNEM4UAOPR2LxRQMBpXNZvXbv/3bNlaFQNTX16c7d+5Ikr7+9a+rWCwqlUp1COndUhBXt6bLzfLcf3f/7gYWgpTrkyPJSmNQ6G5rciqVsmBycHCg8fFxxeNxbW9vmwkgrvIYPj5+/FiBQEDj4+Md+gQOeQxfT05OtLS0pPfee0/b29t66aWXNDY2pomJCSsRUoKklPj06VONj48rmUxaF+PR0ZFpIwhOBETKLXQaMrAZvVIwGDQX/KWlJW1ubiocDmt2dtbGCZ2cnCiVSml4eFiZTEaPHj3S/Py8pqen9fDhQ62srCgej2tubs7YVJhFj8djQ4Jdz514PK5oNKq9vT2tra3ZgcjPSDLQ4vV69eTJEysJuQO8aXSoVqtW7uBZoyeDiURgTIZ8fHysoaEhy37RRdGsIslArXTRYevOiaPZAfCFrgtBOd1cKysr2tra0r179zQ2NqaZmRk1Gg3TAfX29pp2CKaItvmNjQ2b7ZhIJMxqYWJiwtgGZje6Fhp8Lx2vaGQajYaxCNVqVZOTkxodHbWDx+c7H7BNeYcSEvNR3VEtLhCF6YRpx6B0aGjIkqtwOKzh4WHrfsXQmFIy+7+np0d7e3uqVqvyer32cycnJ9rd3bUDm666gYEB8xqTZEBuZ2fHDG9rtZqZiVKOjUajFq8otR4eHppjerFYtIYQpBiMmioUChYbstmstre3rSkClmx/f9+Gll++fFlXrlzRzZs3lc1mbS0g13jrrbfk8/n0wgsv2FSBhw8f6urVqwoGg5ZMAXSYxQqgdf273CoATGo3iHWfH/EPho/X646raInxN8TeA4kFMYimH0rfgCtkHwA3LE6oRgwNDZmmjvXA0Hqahnp7e61Bg1FPboylKkEnJDpHpiH4fD7Nzs7q8PDQTIiJyyQiALaP8nrOgHVezzQAI5s7ODhQrVYzYMABGIvFVCqVNDMzYwERD6BSqWSZMpuYMg3loXb73JyTDRIOh+X3+7W1tWVZczqd1qVLl0wvhRnkW2+9pc3NTZ2cnJjPT7fvl3ShR4DpcpkyV38A20Vm1g3QCC5oClwhfjcoQ/fUbJ4Pc0Zgimng3t6elVHj8bgmJyftoIchow0c8SqeNmNjYzo+PtbS0pIePXqkRqNhmW0sFtPExISJlYvFonp7ew3goZNAjwKVzsDdiYkJTUxM2NDZcrlszRWtVksjIyOanp7W9PS0otGomU8+fPhQb775pjY3N3Xr1i3rDAqFQtZx9dZbb+nk5EQLCwsKh8N6/PixzcScnZ3V2NiYlWUwFO3r69PS0pLq9boZUr7yyisG/lhLjKwBFNEx12g0tLu7a2U7DlFKZru7u1ai5FnTwTs8PKx8Pm+msHS/0j0HOKVZgrVGSYMAD3OHB97m5qZGR0c1NTVlrvLtdttKHLBr+XzedHHJZFKJREKHh4d6/PixXnzxRTNCzufz2tnZse7KTCaj1dVVZTIZLS4u2qBlbDTwsJuYmNDBwYEdXAAKr/d8sDZdnWjiKLcODw9rYmJCo6OjOjg40Ntvv63Dw0NduXLFuld3d3dtPU1PT5tVw/7+vkqlkra2tpRIJHTp0iXt7u7K5/MpFAoZq9Tb26taraZsNqudnR3VajXFYjEtLCxYme709FSbm5vGliJXgP1zwQRl1qOj88H02IGwpyRZuRMzV7RkmUzG9jUTI5BboA1FuxaLxQyEwvR88MEHKpfL1hzT29trwH1gYEDpdNpmMWK3Qaw8PT3VysqKyuWyUqmU4vG4XnjhBZMS0HzzwQcf6MmTJ5qbm9O1a9eUzWZt/QBi6C6UZJol6WI+bnfy6lYG3LKdy4K5wntiK6ALQMb/u12v7mxOYjbaYSwkhoaGrNGBkmK73bbPQYfx6empzakEqLE+eN40SJyenprdBokP76/7fbj2MjT9SLIJDbDajGwaGRnRxMTEn9DJ+/z6k7qeaQAGZVypVNTf368XXnhB169fVzabtZbxN954Q48fP7b25lQqZRsPN+X9/X15PB7T4tAxJ114c1FuyGazxjhA8cOo4Wh969YtW/zM2kMQzvuWOk1W+Xp3GZKN5AYe3r9blnRZNFgUxNoIOwFu7pimw8NDlUoljY6OKhqNWvv62tqafV8oFLLZYuiS+F0AMAwDA4GAQqGQwuGwWVVAkff09Ghqasrmn3FIezznrdvvvvuulY4YMovIlUBGxxsBDo0f9hEPHz7U1NSUXnjhBSttXbp0Sfv7+1peXtb9+/fN5wx/HNq/0ReRMW5tbRnI+9SnPmVBDjaBAbscqoVCQaurqxobGzOggoCdMnir1bJus8nJSUUiEXvGdJnR/cj9pYzsll0oW2xvb6tUKmlxcdFsMo6OjiTJRh6RrfPeCeRk0wjWNzY2tL29be8L/VK9XjfNWb1eV61W0/DwsK5evaqenh5zXadcC5N8cnKiYrGo9fV1xeNxpVIpFQoFra+v6/Lly8aqeDwem1BAwjE3N2flSZoUAAVDQ0N2gFHCOT09VaVSsU4ySjU8I6/Xa5ql5eVlG3tFiQfrAMT/zHbM5/MaGxszJgNN6IMHD5TJZJTNZtVoNHTr1i0tLCzI6/VqdXXVtD/j4+OanZ010ELSAyDlWeARBnDmYMcAFOZ3f39f8Xhc6XRaZ2dnGhsbM1BOZ6Tf79fAwIByuZwKhYL55VHSwjF/aGhIP/iDP6hWq6VHjx5pY2PDhoTDro2Ojnb4YGEATPcp4L9YLGpjY0Onp6d68cUXrQSNWz3eYCcnJ7p+/boxypTdAJiscWJYd1NStwbM9bgDBJGouFYfxBk3vgJseA2SSl4f/SNrlJiF1pXZmzBmrj0G8Z84zeehSxGwzCxZmGM8v6SL0ivxDxYMLSGxAXa71WqZ9UgwGLRmKxpeiAsf5fWcAeu8nmkARinl6OjI7BK8Xq8mJyd1/fp1bW5uan19Xaurq+bBUqlUjP0IhUIdehi3BZg6PoEeHReGrfPz86ZXCIVC6u/vNw0A5ZHbt28bW3ZycmJlK+miQ4dsRrroWHNZsm69g6sTAlzxfQQd2CoOKDYnAYdgSqZFuQ+DzXfffVf5fF7pdFqJRELtdtvKEZTfpAunf+4fAYKyQXepoFKp6OjoyMbUACZ5FmjOHj58qEQiYfYgOKmvr6+rVqvZcO9IJKJ4PG4zA9E8NJtNvf322zo7O58bmE6ndfv2bcXjcd27d8/m90nnB280GtXCwoL8fr92dnbU29uriYkJnZ6e2iDt7e1te0bNZtNYJEpkPT091lmJuDoajdrnpkWceZ1LS0u6cuWK3fNarWaNEei7YBzwIAJIt9tts9B4//33VSgUzLcM9sD1DnIvGF2yfMAJGjdE1sfHx3bYUso4OzvTkydPdHh4aMyRJGsyuHLlivkWuaVH9klfX59ee+01vfTSS2bTQdfo9PS0dcHRjXd8fGwDvsvlsmKxmMbHx1Uul/Xd735XPT09+tSnPtXB1ExOTqqnp0dbW1s2Rqtb+xOJRDQ6OqqZmRmNjIwon8/rK1/5inXg3b1715KRtbU1NZtNXbp0yUxxaQDhGYVCIevWXFpaMpB/9+5djY6OGgggPiwtLWl8fNyGia+trWlzc1ORSESzs7MGrrEp4R5EIhFVKhXrliM5xBLD4/FYIgjQwlyWg5n41GyezyeElZ6fn9e1a9cMbGUyGWtooosVLR1TBSgz0/mNxOA73/mOLl26ZPdkZGREN27csHFViNJdjSzAxZViuLpEt4zb3f1ILOJ73J9zn3139cH9OtUFGFWkJ8R+YrQ79YSyP6V12GQMgElCeF1A8P7+vv08+iy0fzCe/Czxn/dHufns7MyMdbEVoSLDdI9isai3337bJB8fBx+w51fn9UwDMLx2SqWSJFmWwmK+evWqFhcXzVR1Y2NDT58+VTabVTKZtDluiB7z+bx1z6HnwYcI0SOHicfjMf8sWIiTkxPl83kNDg7q+vXrevnll3X58mV95Stf0crKiqrVaodPGO+ZDe2WFSV1gDO+lyBCeQtQJXXS9W7Q4OAG4EF38/r1et2yvZGREaXTaa2trVmZNZlM6vLlyya2dwEggIB7SCcgTIh0HkwQ89IAgF4Mk9NEIqHx8XFFIhE7gHw+n2nR3NZsSXYQoL2BxTk5ObHxMVtbWzbTcmJiQn19fWZBwR+0Sevr60omk6aTIIjBWFWrVe3u7tqYEJ4jDAtl2bW1NdNxUCJBTI1vWKPR0Obmpo6Pj/XKK6/YrE5YSl7P7Rp0S20cROiY8IAaGRmxTlCYBUAuz4nnjhUC7EC7fe5lRWmv1Wopl8vZeBaPx6OtrS3T/ly6dMkMO7HuuHz5snK5nB0czBHE38zv92twcFCPHj0yoFUoFAyMBYNBeTznPn540gG+Ga/j8VyMdpHOHcEDgYBNXADUraysqNFoKBaL2VxFBM6Li4umv9rd3dXS0pINfObwRXf1ve99z5oJSLJg3ZAdoCOr1+va2toyZ366rAHtJCAzMzOamJiQz+dTpVLR2tqazZfd2tqy51osFq2ECOhIJBLmN+X3+409RevGSCz2JGbGk5OTZu9CMlWpVPTkyRN97Wtf08LCgpWSr127pps3b+ru3buqVCrWiEMTw/DwsBmsYslBUgvzlclkrMwL2GLvUG6FqSO57Wb/3XjnAk5iHHuBOOj+DLHOfT33Ija6r4mOy41vsPVuyRgtqHRRLqWETcmQ0qDX6zXvPxJ8Ki6UMbG9gEHms/EZYOX29/eNTXO93Twej0ZGRjr8DOngvn37toG+j4Nf1nMGrPN6pgHY4eGh9vb2tLe3p8PDQ5XLZeu0Izj29fVpZmZGqVRKCwsLSqfTBsJwQ0dnhOh1fHxcd+/e1czMjP2e/f19lctla62vVCoGSAggdMtcuXJFV65cMcfpT3ziE/J6vVpbWzMNgCRjF9yszy0ldmeD3R2T/IHlcMuWLl1PQGBTcwhjM8DGxQ1/cnLSDrF6va5XXnlFo6OjSqVSpm0iSLnmhVgwnJ6eanZ2VpFIxDL0fD5vhz6fmaCL8SkUPyVdv99vHkSABtiEfD5vhxYDl2kgoJTIfT48PNSjR4+M/UC8isifrBJ9h8fjsc4214maEUcYgFK2Ojw81OjoqBmGShfsrNvSD+A8Pj4237RIJKLLly/bPUEwjr8agIBnygHh9Xqt85N9wGHLmuCZc896enqs5AnwQqMGC8pEAATtlCjJuPf29ky3FQqFNDc3Z5YOuVxODx48sPZ47t3GxkbH5IYPPvhAGxsb2t/fV39/vy5dumT+U0xO8Hq91m24vb1tfmndvk71et0OfrRNfO7h4WFjJ+hobbfbVsouFAq2Jxmm7vV6bVrG9va2hoeHrdOQ7sJisWgH3OLioqanp40pT6fTmpycNHDC4cthDDjHFiOfzyubzXbYQJDw4O9F7Mnn89YNWigU1Gq1tLCwYLKJ6elpY1mkThaFxIhuzytXrqjZPB+4/fTpU0tk9/b2zKYjFotpcnJSly5d0q1bt8wqY3d317oe8/m8sT7ValXJZFLXr1+X3++3LtejoyP7vLCPxCDYcpIN6aJj0bUucUvwxL1uZsw93KkOfJh+rLucSaLi7hPkG1jOAHTYMz09PRZ30bJhS4OmjPdPqZUSII0aVCO6RxIRP9rt9vdZ4BA/3RIr8bdcLmttbU17e3uq1WoG3uLxuJ1ZH/X1HIB1Xs80AEPAzIgI5oRVq1UNDQ3ZXC3q6jAsly5d0pMnT5TJZCTJSkPoTQjUBMe9vT2Vy2UzL6UOTwDh+3nt2dnZjiHW8XhcP/ADPyDpvMMMITyjNdzN5LZaS+oIOlwwXdKFCL+7S8hlAwk4mGHCHmDTAfhwtRCpVEp7e3sqFot6/PixLl++rEAgYNmm21EHyON16bzp7e21wxmTz2KxqN3dXdPVcSigY4DRwRUfUSmfkzInv+fk5ESrq6uW/aOPINgBjHl9XgugdXBwYMxRpVLRe++9Zzoz5hryzGFq0O0glMUMc3FxUS+99JJWV1e1tLRkgnHKMH6/X0NDQ6rX6xodHbX1wdy2+fl5FYtF3b9/X36/X5OTk8Z2uKJc7CuGhoa0uLjYMdgX7QkgjzVCMGetYPB5fHxszQbFYlGZTEbhcNj8mLLZrGZnZw380tE6NzenoaEhG7ECYIUBws5AkukBKWXRJchgb8o6DH2enZ3V5OSkMQ+wc+hyKDOj6WQ+IvcTu5JQKKRGo2HsgiQzEvX5fB0mrNVq1Ww3hoaGzFNpYmLCmlBKpZK2t7dVLpe1v7+vRCKhiYkJ66ZmHWGuilC/v79f6XTamC3WJ+B6fHzcNKhnZ+ejbWDHmAaBvpBk6v79+x3DvlmfNPywT3jurAPWEvIDxpFxT0kSdnd3tbq6qrW1NU1MTGhmZsZmTTKKaGpqStvb25bIYXNSq9WsY7parapQKFiMOTk5MXbO9daSLhj/buar+9+Ic64e1gVkxCX2bXdC636f26nK73Evt/zKs3P1V9JFdyrJD2wv759yPUwyDJWkDoNlt1MZAMh+5WtuWZWmM7qTiX+Mk6rVajYdgff5/Pp4XX8sAPb3//7f19/5O39Hf/2v/3X9yq/8iqTzBfcLv/AL+rVf+zWVy2W98sor+qf/9J/q2rVr9nPHx8f6uZ/7Of27f/fvdHh4qB/+4R/WP/tn/0zpdPqP9PuLxaKxXcwtc9mqbDZr5SLGa6BdSCaTHZ5ZMCrr6+tWAkFMTNZD1xgZk5vh9PT0aGxszGb2kbVJMmH53bt3dXJyovX1dUmdNLi7obmPHyYc7Q5OLvp3S5kELDaoqzmju9ANVoCSVqtl7fdzc3NaW1vT7u6uhoeHNTk5qeHhYdVqNeXzeTPnJIBR0vL5fFb27evrM1NJtD2UI5hdBwBznePRN0Cz4/tE8KbbCJF+uVxWT0+PAoFAR0v43t6e2u22otGosXMEVNerSpIdCEdHR9rZ2VGxWFQgEDCnc+k8YMMCcK/9fr/29/f15MkTjY2NKRgMGqDNZrOmGQOUopUhoPLeeY8HBwfa2dnR8fGxpqenTUfEMwXc8zxxZ5cuRiU1m01zFKd8RgbNYdJsNrW9va3d3V2FQiGlUik1Gg27B4BTWLAbN27o1q1bVgZut9s2U5SOQNbV/v6+7TmYU/aTJCvZcg95Jjs7O2YfI0l7e3vGUsJubm9vG/sUj8etPIzBsjvomJKsx+Mx3zm6e0dGRhSPx40FhLmjwxbg1NPTo3w+rw8++MC8865fv67j42N99atfNVH/wsKCPv3pT2toaEjFYlGVSsXWQLFY1KuvvmpjigDI4XBY8/PzkmT3JhaLGUhCHkCHL3uUg5nk0Z3AwGxCwNH4+LglDENDQwbUYV2DwaDC4bAxvdhBXLp0yfYSjBc2Nul0WouLi7p586bJGnZ3d/XkyRO9//77KpfLmp6etrmyzAklieCzsoZd9onKgFsy6+4OJ37C9BJLATLcX1dfSwx1mXsXlLqlUBIvKh18D7HHfc/dFQjeJ7+X84FYi1kr9h+ArePjY3u2rj6Y91Sv1218F7GSDl5AYCwWMyaNxhR0pNls9n92pP5vv54zYJ3X/zIAe+utt/Rrv/ZrunnzZsfXf/mXf1n/6B/9I33pS1/S4uKifvEXf1E/+qM/qidPnlgX2c/8zM/ot37rt/Trv/7rikQi+tmf/Vl94Qtf0Ntvv/1HEgo+ffpU4XBY6XTaFiidKYAQAuno6Kjy+bx16LkiULqMstms9vf3zSOHzkW308YFQC4TBBCkzEaGyUb1er1KpVK6e/eu2u1zN3hKVC6DxR9+1vWRIuvrfi/8PBvPBWCwddKFDQZBh03vCl3d2WahUEj1el1LS0sGptLptDm4E5joAnTvDZT62tqalpeXLSOHPgf0Eigoe+G75AIFxO4ELzJ114Ue80ZmnwWDQYVCIXMFx7UdMOIGPEBcPB7X6Oio2u226dcODg46OsfwHEP30tPTo3A4bF1na2trxhJgT8GYENY264v7AfuAb9PU1JSNcWKmJQcmpSTG+lBiQsTssgzc50qlYjozgj8lFBgpnPopgwaDQaVSKQWDQesyZPYjoAg/rWQyaTYwdJXR+t5tmYEeDLDL2uVAZk8xpikajZqekJINHny9vb027sjj8Rh7XS6XTe9EUgTzAINRq9U0MDCg2dlZ89JKJBK6ceOGmfyOjo7aqCY0UPF4XDdv3lSlUtHv/M7v6Ld/+7dt4sL169c1Pj5ubufZbNZGMhUKBU1NTWl2dtYO8NPTU+VyOW1tbamvr8+Sx1KpZE0caLwGBwfN4d/j8ZidBM0NaBd539xnQCBAenR0VLFYzF6XbmhiDSytpA6N5/T0tOmLCoWC3n//fTWbTU1MTGhxcVHRaFThcFjxeFyFQsGMhxGWd5eOAUqsXzf2SRdsnatvdeMU14f9DKDMlW1w32Ch3MYk/s2tKrAOXTDD+3STb7f8SRna9Qsk7vJzgD+sYuhOdAEm388zQPfldqFS3XDvEcw8TDdnE8as6BqfXx+f638JgDUaDf3Fv/gX9c//+T/XL/7iL9rX2+22fuVXfkV/9+/+Xf3ZP/tnJUn/6l/9KyUSCf3bf/tv9dM//dOqVqv6F//iX+hf/+t/rR/5kR+RJP2bf/NvNDExod/93d/V5z73uT/0+0DYzCHstvsPDw8rGo0qlUqZYzozshCDMjF+aGhIt27d0pUrV0wLUyqVtLy8rEwmYweamzVygHm9XqXTaS0sLCgWi1m2A3OGoJlN19vbq1gsZoc+30/GRamJ+9lNybv32qXhXXAldQI2DjyYHsoMABk3q4SmpqwFsKzVatra2tLAwIB1IDJUm2AASJLOXb3n5ubUap0P7yVz4/0iwCag4IcjqeN+oTkiQBEIu0uqBFVKSfiXzc3NaX19Xe+9956JVbnHfGa0Q++//76VgOiQ3d/ft+/HcJfybiAQ0OjoqDUdtNvn3aL379+3AwixfrVaNYaCQ8cVMGN9wuDxYrFoZfK5uTnTmfBMJdk8wunpaesCZG0A+gGfMBBYOABqsDeQpHQ6rYmJCYVCIRMH06WFczqHMBo4j8dj+kC6Nzlw6d6cmprS7u6uSqWSmZ0ODAzYH9g45jgODw9rc3NTe3t7evnllzU+Pm6lw5OTEyWTSV26dEmJRMIGzOMOzlrF/whwAnNIsvD2228rkUjotddes3gRi8XMCmNpaUmZTEaXLl1SKpXS8vKyNjY2TB/25MkTvfnmm2o0Gkqn03r99dd1+/Zt1et1Azvohubn59Vut037RcmOexUIBLS9vW1sJWavN27c0OLiYkeMYK0DFtjfNG+wVk9Pz+dfAiIom6HJBNS7gm9+XtL3AWNXowqrSZemyxgODw+bzxXxixjj6lDdYdj8PnftfpjOy419xAmAvAvCYJz5jLy+y0K5r81/AZt8v6u9dX/W/Rm3OsE6crVcfN2NXfy/+z7dZwCQc0Eie9adJerGbu4fc135Heig2+22yTo+yus5A9Z5/S8BsL/21/6afvzHf1w/8iM/0gHA1tbWlMvl9NnPfta+1t/fr9dff11vvPGGfvqnf1pvv/22ms1mx/eMj4/r+vXreuONN/5IAOy1116zw3xvb0+SjE6nM2lkZEQbGxtaX18313E6xiKRiAl40YtRMotEItrb29POzo5tBg4xv99v2fvAwIBmZmYUjUaNRYvH46bFQLOzv7+vQqFgtgY4JRNQEalyyLobleDr6gG6y5Bu0HIvl3Vi87rt3gRYl4mi84xgOzU1pa2tLWNdBgYGFI/HrXxGAwMBjHsTCASUTCZVrVatpAvYwnSUQ6Fer1u3Gu+B9wFoQOPAz0gXAdE1Uzw9PTeInJiY0I0bN6zUzPdJsrITwRJmrd1uG9VPece9OGCCwaAJ6ik5wmTB1PFcYVoJos1m08Acz1iSldfGxsZ048aNjgyXDiv3UKFs4pZ38L5qtVo2UoqDCvAGQ4SmiYMTHZnH47GuXkpGrK9QKGSzC2EPef/u72d6gCSb1ZnJZEy3yZpDc3h8fKxKpaLFxUX19PSoVCqpUqkol8uZpqXVOp/NGovFNDMzY2wdrGe5XDZAzAXjBVuHbm11dVVHR0d66aWXzEU8nU5rZGREa2trevLkiZVwnz59qnK5rKmpKTMrPjs7s7min/nMZ3Tz5k2blFEqlex9YjrLPpJke1ySjc+KRCKmh2y1Wqa3Ys10M0QwIq7Oj+QJdob974K14+Nj07KyFv9HCZ4LNthb/D6YM0mWpPA73O9zQYzL9LN3+R73ebl/d1l/wIwLjvh3fi/3y9WXuYksrBvvFWDqXrBiLqsP++we9nxG3qebHLk6XX4H9xzQS7UBUE5ZkrjA+3QZOF4TNo/7we9zk2xAGo0QzPN9fn18rj8yAPv1X/91vfPOO3rrrbe+799yuZwkmU8UVyKR0MbGhn2PS5m738PPd18cxly1Wk2SrJuJwb5nZ2fmmH16ej4I9d1339XW1pZ1XDWbTa2urnZ03BHseL3R0VH5fOfzHefn582gkiGxQ0NDOjo6UigUsmwQkAXLMDw83LGBK5WKtZVLF0aA6H+4HwQ9QIYLHNyOSTaj+19+3s3ayL7oUkT07I49gclwL7J0SlMITBlC6/FcDBSu1+sWpN3ZaGdnZwZYCoWC6vW6PB6PieLRwkgXYIIgSnkQ8bMrwKVLENatr6/PusUInPv7+1pbW9OdO3eUTCaVSqW0trbWQc23222j/wGpkuz1KWfQ4u22idP9SMma90/puVqtmtg+mUzK4zm3cTg7O7N7CiDweDzWucmfxcVFvfjii/bM8aWDCQsGg2ZoyeFLsCWz5j57vV5jIHmuZ2fnHmkTExMdYJ6gHwgErBPW7dzFpBbTT35ueHhYlUpFT58+tXvGHpBkSU21WrWy6s7Ojk1PYERPvV43Ubm7RphbmUgkrDv04OBA6+vr6unp0eTkpJX2aALp7+9XLBazjlRMhA8ODpRMJi0WzM/Pa25uTj6fzzrJZmZmdO3aNWM1L126pJGRES0tLVmp+rXXXtPw8LBefPFFhcNhFQoF1Wo1ra6uamJiwqYutFots4TAFBcWCod5hPYkM1js0IRCKb1biM5+YI8DqFyg5q4bgJUL3NxkhtIyiZRbymNf9Pf3GytH8tJdmnMTMcC/ywa5QKIbLH4YK8b3uq/n/psr3eD7XIaLr2Ebwb1yy4+wiMQ/Ymi73Tb5jAsiieHdpVL2aze76AJvziI3Vrv3ya2CIBvg96GzJB65ps80dzBmDm2gu04+yus5A9Z5/ZGeyNbWlv76X//r+p3f+R0zjPuw639ULvuDrj/oe/7+3//7+oVf+IXv+zo6pJGRES0uLtrmxQl/a2vLPMJoAz88PDQAR5cIrtiUnmKxmM0oSyQSCofDWlhY0Pb2tjlfe73nQ5ndOX143HCItNtt7e7umhEehy0dZ/fu3TPQyGen9CepIyC4WrJubRmL2qXDYRfczhlJJspH7E2JgpKBdJG9IYymFEkpA70PTBDlI1eM6mat+NxUKhVVKhXTyLiZp3souO/H1bUBcghC6LzcDjgun8+njY0NbW1tKRQKaXx8XNvb26Yrc7NJSrG8Poc/5QAE790l6Ha7bawXTA7NAZQjyuWylSSZ04j1BwAS1gnwxM/Mz8+bqJYEggyX98p6RnzO7282mwZiXVAsSclk0g5junth6zye88YGAOPTp08VDAY1OztrzC9NCAcHB7aOisWidTGm02nTOtHMwBBnjJNLpZL5wg0ODioQCJh42uc7n/kISCRRSSQStq9Yi5SGsWbwer2KRqMmzmff4/HFIcz8R8CQx3NuXAnzB1BrNpuanJzU4OCglpaWLHGTpFdffVWpVEpDQ0P2WolEQoFAwBg19iIHKcamfAZ8y2AH6Rzmc52enlq8+TB5gcssuaw6ewuGlDJ+N5PGa7mNQO5INvd98jspMbtdzzRqwKq57JYLLFzw6IrMXc2qG89cMOn+HO/ZvVxQBmPb3WXePRO1+32wt7obAdwysCvrcFlvGDZJVjEhfnGveD80LbHeeU3eM/GQ72s2m3afXCaQ2AVAgymHNfP5fLZeu9n859dHf/2RABiuui+88IJ97ezsTN/4xjf0T/7JP9GTJ08knbNcY2Nj9j35fN5YsWQyqZOTE/MRcr/nk5/85If+3r/9t/+2/sbf+Bv291qtZloVupYwdqSMlkgkNDAwoK2tLdOJeTzn5ndjY2Mdoney7L29PY2MjKhYLBobxiw6wFkqlVImk7ExEgMDA0b15vN5mxWH5iybzerk5MSo5UajoUKhoI2NDTWbTSWTSY2MjKhWq6nRaCgcDmt0dNSCHlm9GyBc+t293K+5m5AgitkqDB3iaknGiuDVxMEElQ97gmaI8hSiXTQtZJhu8wB+VYACgF93ZuoCLzcgu2av2HeMjY2ZS//m5qa9f34nMyZXV1d18+ZNxeNx69ikQ44yDkAWdoJMk/fL8x0aGjK/H0CZ28wAA8qBB4CrVqvyeDwmSD49PbV5lq5vFc8bMJDL5fSJT3xC8/PzZlsBE9RqnXer0iUnyZoVYHLQOyYSCWtVd7PyUqmkd9991wbH4yPlAoRoNKqJiQklk0krMzPf0eO56KCjXDw5OWkWFPfv37eyYjAYlN/v1/b2tp48eWIHBiOu6GBsNpt6+vSp2u22zVjF84pDH+0lPnE0BTBTE7CJ5gqmEpaTtcw9gx1DozczM6NqtWqWMT6fT7lcTmdnZ7p+/bole7AK3KvR0VEbJn10dKSVlZUO93nABL8HnSOvxfsol8t2yKKvdL3hXN0Q++bo6MjAJk0FgAXAEzHBZdXdRI49z31xtUeAMvaEq4F1G2zcf+dzwZQCYIhh3axPN6vFXnbPmj8oUXfBZfdrdlswALCwzpBkia8Laqg8tFoXxrewU+iFmcBSKpWsmYZkiBgBg+jebyxzXGaK+0lJHuDGOnA93hqNhnVhIjtwh4e7P+P1es364qO8njNgndcfCYD98A//sO7fv9/xtb/8l/+yLl++rL/1t/6WDV/9yle+ojt37kg6zxy+/vWv6x/8g38gSXrhhRfU29urr3zlK/riF78o6VxM/MEHH+iXf/mXP/T3fpgWRzpnwLa2trS+vq6lpSUrES4uLury5cvy+Xyam5tTNpvV48ePLYi6nT+wBWTluVzOMvZgMGiZfTweN00ZxpkcrgAyWCHKn+iD2EwbGxvWaenxeDQ5OalQKGSZEPoMAhUZsFsW4MBnQ3eXI7qDl0ut829k6/wOAgx6Hjd412o1tVotc4Sm9CbJylqYm2K82K1PABgw3JlxPQArF3i5OjIAJ3qxcDhsQ7bb7bZqtZrp9FyjRDdT3Nzc1JUrV5ROpzU3N6elpSVbUwRUNyvm/RDEIpGIMT8AJsCgq0GhLEdnGtkv9xiBOvYdMGJ7e3smnAXQ03V5dnY+3xIzRUoLABW8t9Ab0gFMqzxldbra0IXA7PT09GhhYUGJRMK+1/WI6+npUTqdVjKZtDIHLB1zHDGVxEKiXC53AIjLly+bCzssAR2SgUDA5hWGw2FFIhFls1m99957CgaDNsqHQ5Q5hbBOjUZD0vmhxVB2t5RPkoGPH7YjZ2dnZjLMuCiSCYAaP4+JaiqVssYe2CKANGbQdObu7e0pn8+bFg7QdPXqVU1PT1u37ODgoMLhsGkzT09PLWnjZ0ZGRjQyMmIWLhy6lN6JA81m0+ZP9vScz1xNJBIGABqNhsrlckcJkuST/cZn4nezL0mC2CP1et3uAR25rrWJW4p0GR+XbXKZN5cFI0a5ZVH+zs+5CR57tpvV473yuiRDAF8mYVQqFWOxALnETEaduaw/1RPYrv7+fk1NTVniyUi3QCBgMaGnp8cSIFf365Zp3ftwenpq5Wi6kI+Pj228l5u0AvJIrkOhkIFgmryq1arZi3zUF+vvT/o1n9XrjwTARkZGdP369Y6vDQ0NKRKJ2Nd/5md+Rr/0S7+khYUFLSws6Jd+6Zfk9/v1kz/5k5LORfI/9VM/pZ/92Z9VJBJROBzWz/3cz+nGjRvWFfmHvT744AMTLEYiERszs7a2pq2tLcXjcV26dEm3b9/WzMyMnjx5oqdPnxpTRgYL/cssPXReWAdgyoiYNhKJmCmpexBzaO7u7tomwT2cLjjKVcPDw3ZwIdxls5RKJSUSCZtv6QpTYQLcRde9kaXOkRxk/gQmNwvmYHUFtwQJfp8kY3QIhJTEOICTyaRpryh9kT0DILmffr/fgC4sAFkz9xNQ1NNz7l0UDofNSmFvb08bGxsql8v2rBjXgjbp7Ox8fuH6+ro2Nze1sLCgcDhsQDAajaq/v9/KKTjjAyr9fr/S6bSxkbVaTdVq1QT16JP4PAxrbzabdqienZ116C9gFwKBgA2M5j6TdaPVYV4mNiClUsk+P67WmUxG+/v7qtfrmp6eNs1Vu902LSLlDIAFQdnj8VgDimsEybiggYEBKxPipdVqnZulwnBzr+n0DQaDVv6jlFksFtVqtRSPx9XT02NGpVtbW8b4AfJxzsfDqVwuKxAIKJFIKJ/P6/d///eN/ZRko6d8Pp82Nzdt30xOTnbM0WNv41f23nvv6fd+7/c0OjqqV1991dY+2k9Jxga6wLFSqWh5edkAM89geHhYPp+vw/yXUjJdjqFQSDMzM9boAzOEJQQH/vHxsR48eKDZ2VmFw2FNTEwonU5rc3PTSuqFQkGpVEqTk5M2/eDo6EjvvfeeHjx4oMnJSU1NTRmw4/AlHkjq0A8ByJjVyL6GpSQ5Y+1iZ0AChLQAfSm2P7j2U1J1hendYAxmxy0dSrI4xcHdrW11dV4uWCPO8TW/32+JB/eAdUfDSLvdtkQTv0gSgKOjI/M/RMZBRQAgBOMKaHabZADLrHPuA/eA/ydBoQzP2mKfEcu5l3wW4ojX6zWgRfwBZJKwPL8+PtefuCrvb/7Nv6nDw0P91b/6V82I9Xd+53dMxChJ//gf/2P5fD598YtfNCPWL33pS8Z4/GEv9F9sZtgJSTZ78MGDB4pEInr11Vf10ksvKR6Pm6getoLDPRKJWMbLQYgVQrFYVLlcVrVaVaVSMY3Y8PCwZmdnjc3I5/MmCN/f39fOzo75NUkyEEamzgw3hMJutlmtVhWJRMwuA42Fq41wywrdXTn8tzs7pDRJNumaqaJV4T0A7rB3oK2ZDMvVgw0ODlqwd8sZMHCAZZeKZ75cKpUy3c7Ozo4xdHRSBoNBm25QLBa1tLSkqakpvfLKK2Zd0Gq1rMOLz5jL5TQ8PKzj42MNDg5qfn7edHYEY4IawIj7xLqi0aDZbHZMQAC8UT4j8AM8dnd3DdDAuMXjcWNzDw4OOgamU8bj4Ea7xQEAYPR6z13PJyYmrGyF6Sn7gmAO64XfG8CMkjHMSalU0tjYmMbGxjomQzCTkfIMTBxgmQHkS0tLOjk5UTweN3f9hw8f6uHDh5qentbnPvc5OzQ5kI+OjkyQv7e3Z+s8mUzK7/erWCxaSRf5gFtecocYB4NBK43DaEnnBxJGv9L5oVSpVMxoOJ1OG+tBMgZLeHx8bExZpVKxOa+3b9/W3NycsYGAkocPH2p1dVU+n08zMzOampoyFg3gns/ntbe3Z40HNAQVi0Ur31LOpkuuVqtpcHBQ6XTaSk3sI6w3Go2GsSWtVss+49HRkTVAASiwH3HLYm4M3d/f18HBQYcEALCN7o+h8SRJ7Fv3s/b0nI9zSqVSNjmhW6DvstVcJJndiSF/XNDC3wFufI+rFWWgNiyVJNOlHhwc2HDzs7Mz8yKkSYbXAagCWqkOUApk1ND+/r6NxXLBEsmPW0aEpSPWkXjS9OIy9LBbbqLN54OJ5tlDHiBvqNVq9kw+6ut5CbLz+mMDsK997Wsdf/d4PPr5n/95/fzP//z/8GcGBgb0q7/6q/rVX/3VP9bv7u/vt4OEAwZqdnh4WB7PeZv6+vq6jo6O9Nprr5lfEgf54OCgxsbGbDgvIzI4TAcGBhQMBs2Kgbbyvb09G7jLqB2XVqccwebhv3SAkh2m02nb8HSsuYAK7zCyHTc7RJPhZpKuRsLVVHR39fDvaDDIrtggMFDd7efcI0poADBX+4XGS5KBWH6e9z40NKRkMmn+V3hdua7iyWRSQ0NDHaU0gNanPvUpsy8AQPC+AoGA6SgIeJQ3ORj4bICJQCBgswYppcLmcU9gAVnDbvs4bJjH47FyKCwEoH5wcNBc7s/OzoeQY1bJvaHM2Wg0VK1W7fexRtAIzszMGOvo6tlYI5JM38KBhIUHrfh01YVCIdsrksx5nwTn8PDQrBVoLmCtwoZSGo5Go9rZ2dF7771n8z8l2WeqVCrmsr61taVAIKDDw0NtbW0pFosZ88C1t7ens7Pz0V137tyR13tub4Kmh2eSSCRMk+ZqpzgAYQimp6f1l/7SX7Jh0LCKJFZ0k+LonslkbETZyMiI7ty5o5mZGRM737x5UysrK1pfXzewyzB4r9dr1YFLly5ZZykgmOSv3T5v5njy5IkikYh+6Id+yMBOoVDQBx98oPHxcQWDQU1MTGh4eNjYVle7dv36dU1NTXWsCeJRs9m0CQAuK5LP581I+PT01PRMLtvUbrcVCARUq9Ws8zSfz9s+xgeRZoF6va7t7W3dv39fh4eHevz4sTY3N3Xnzh2lUilrJOF3EKPcy41RvAe329AV5nO5gM39miRLwFyvQdgqwBPlPM4Qfq7ZbJqJMwkRulCsSjgrkBcQp9m/MP18FpIf7jf7tdVqmY+cK9TvLtW65wTvl9/V09NjcYvEHd++59fH6/ro+1L/GFe9XrcMDk2EW/+XZLSw1+vVo0ePbCjz/v6+MpmMzs7OrDwxNTVlont0P7zmycmJGU5Siy8Wi2Zu19/fbxmHdE7VDw8Pa3Fx0YARmR2b2WWEoJldOp4DFbDgtme71DvZKyCjuwzJ97itzdLFeA8AmHQhdJUuLC14TQ40NFludw6ifFgwAg6v6bZ7ozUbGBjQ5OSkotGo+TrRdZlIJEykyggpXNg9Hk+HHo1gw395DWYTMo6I909ZDK0WYAqNiM937hgeCASMpSIADg8P2xglr/eiU5Rutb29Pft5yqIcDATSsbExY3n29vaMMeQPWfvR0ZGVd+hMxB3/+PhY4+PjarVaZubKmuO9NBoNc+F3Xf55hrCVJCA0kWCtQfkP7dXMzIwxBJVKRZFIRGNjYx0lbfYc7BIlQtYxJqmM5PH5fDYGDBPaZDJpXWQI/uPxuHUo9/f3m3ns4OCgGQJT1qUjlX2F75wrcKZpo1wum8anUqnY8202z938x8fHNT4+bnYSuVxOzeb5CCeP59xkNRqNWtl0fn5eJycnevjwod58801jlFhLMIrsL9b33bt39eDBA62srJgVDkwtelKfz6dkMmlSC1hByvQkAYAEWBdc8yl9ojEEbMIAknQBSohnvFa73bZGDO7h5uamdnZ2NDMzY5q7iYkJJRIJ061ls1nV63U9fvxY7XZbY2Njxu6wdtw4RSxz2U7ilvt1LoAJMc21d+D7SCRdVo8EhbhLvCV2HB4edoBQN3GjWgEzjKi+m1EEXLlxnmYntxrBRblSknnquYwhr0+ccw2a+dzoeJmKQLx8LsL/+F3PNABDWAlQcLMpgombOaFlYKMRYIrFonp7e7W8vGxZ8djYmCYnJxWPx+2gdTUEBwcHVpYjA6Ek5Y68cbtQeK8fZqBIRuXxeL6PacHRmEMOMMZncjc5F4HHzQbdLkH+zX1C5gudAAEAAElEQVR/BBGobYAX7xvxvatnwJ+qXq8baB0ZGTHQ8geVEPj9Pp9P1WrVstRr166pp+d8/h5goV6va3l5Wbu7uyak7e/vNx0hXUcuY+b1enX9+nXt7u5qc3PTZnwyhgavqLOzM5vZiBEvXlTd4nt8m9BkUCrms8RiMQuA+Xy+g7XEpgA25eDgQIFAQNFoVKenp1aO5mJNIBAn4Pf19ZmQG20hWjj+3T1MuvUqlNgZIQSrlUqldHx83CE+5zXp4sM2YXNzU8fHxzbHsNFo2OzBeDyu8fFx5fN5VatVbW5uql6v68qVK/L7/crn84rFYrpy5YpNDmCYOXNa2+1zi4zt7W3T62WzWXk8HpuZiaXJ6en5mCRKbDAZ+KLxX1frVK1WNTk5aaUexkBRTpqcnNTAwIDNlQX4Y/kBkF9dXdXc3Jx6e3u1sbGhnp4eTUxMKBaL6dKlS9rZ2TEj4UwmY0wgQCwcDuvatWtKJpO6e/eufQ+aKXS3sBcwzS5QYR8WCgWtrKzI7/fr5s2bGhkZMRNkkoOtrS0bG0RMOTg4UCgU0sTEhCVOrmAd9p6OWwyRAQ4Ad8x9x8bGdPPmTQNii4uLHU0+NBp1lxK53JhB0tQd41yWvzvWwR7x+sRol+E/ODiwxMZN3H0+n5Wy3YQL6xhXgwZ7SnnW7WoEICHb4HNIMibeLT26iZF03mF5eHhoya6r6XUTaNYC94rXAlQDBimPPr8+XtczDcAACN1GrS4Ac0tLdHaxud0RJWSCp6enWl9f18OHD62VHRbN9UmihBQIBGwTYG9AAGAzIGAFzHAou5Q52TmvxWerVCq2wck6P4zlIiCzYTkk3MyJjeoyaW4gB4y55Sq3BEfG2Gq1jDXiPiPyJgtGOwUIJiBJMsAkyZoUHj16pJ6e84HUWHPgMbW0tGQsWiQSsdIsYI7fT8BjMLCr16C0zD1HWwHoAjwiqAeo0CWHzoLfdXJyYjNEi8WiMQejo6MGeGBFOMA8nnPbhWKxaFk8Im0OQpguStXYT/DZOHC5Bz09PWZOSsnb7YyC2WSNo/lCX0apXDo3WXVH5ACCBgcHO5oGIpGIXnnlFUkXOrbDw0Pt7OyoUCgYW0WWj4aut7dXY2Nj1jxw7do1K8tjm1Gr1Wz4+fLysnZ2dmwE0tnZmY0dGhgY0Pz8vHXEsubdJOfg4EClUkmbm5um9ZHOBdmxWMyaNVx9IqBidnZW8XhcH3zwgY0pYqgx2ruRkRElEglbL9euXdP+/r7i8bharZa2t7fNzLlUKlmJiqHixCXe7+HhoaLRqKTzBgMAPskG77XZbFojEQPTKal5PB4tLy+rWq3qpZdeMlPqVuvctsT1MUR7hr4oEomYtIDfBaiQLnykSBJprnj8+LFJJfx+v7LZrO7du6ednR2bKZpMJjU2NmZxiPjtsjv8IUZ2xzgXmFGadMEXP+MK/YkRAEtY+3K5bJ+d6kAkEpHX61Wj0bAEmiSbpJqOYumiAgOwhHl0R70RJ933IMlipAtAAYo8Vwx0YeBcplD6fnkHz6Snp6fjrGKduxrhj+p6zoB1Xs80AIOG7xaYe73eDjbq+PjYutzYRAifESz39fV1tFfz7672CY0J5QgCN7SxG1zIdvh5t2uFDe964LRa5+NroL7L5bJ1rDF0mo3tBhgXdJGZud2KgCqoakAZ2RzAiu/n3wgeBBSCHoASoEbA5r5zGLqddW7glM43DAdIo9HQ2dmZZmZmFAgEOsqyOO3z/ringASCJ4zhwcGBMpmMeUt5PB4DRNhlLCwsWJs9BwCjqCgxACrcgcw8M4AV5WdKLojm3XZ3/ID4fiYGwGwR7ClbksFyKPv9fuvK4mDhvWB3UKlUOuwgWBusdTJhQAxu7XTy7u/va3193cpgaAFhOavVqtbW1uT3+zU+Pm7rBHBAWz4dWxsbG6ah8/l8NluRNYUtw9bWlnn1+f1+nZycaG1tTfPz86rVahodHVVPT0+HKXI6nbZ74nbNsb+3t7eNiR0bG7PnSWPMxMSEra3Dw0OtrKxYOe7k5ES7u7t64403VCgUNDY2pr6+PlWrVcXjce3s7Ojhw4cdTOHOzo4xgrlcTj6fT9PT06YtarfbpnejpEcSFgwGdXJyYjNRPR6P6cPoxl1YWJDH47FyMB25e3t7ymQyVobmsO3r69PLL7+sWq2m7e1tm2SRTCbNsBgWEdDosuq5XM4SI9YorC6NNzS5jIyMmIbtypUrxq56vV5LSNivS0tL2t3dNR2cOynD1aSytogdADBXlO8yYd2HOf9GXHCTW/6NtUmTVr1el9d7Ps+Xnw0Gg0okElb5qNfrFsuZVEAiht7UTbLq9boajYaBVxdwoj10m6kAZS4Y5z5wf1ygxRru1ru54JTXhbEk0Xt+fbyuZxqAcTjDbrABXOE1mUixWFQmk+kYDUMXHxkDXl6IspPJpEZHRw20sUEIWq642s3G3HIbF0DJ7/d3MEJHR0cmuD48PDSfHz4bAYRN63bMsQEJZFxosWAFAHuAJtgPdx4hzIp04dbcDbz4HIAwSn6MSXG9dyh9oWMCgGJE29PTY8xCLBYz9iWfz6tQKBi749pYELSr1arR7IBSgA/dboBjV38H+OV50jpOt9bS0pLpxSg/8Hl5dlD5Ho/H9IIu4+n6pAGeyLwJ5CQGgEdACGuWdctB4WpZhoaGDBQC8ADRaFPo7nW92NDcjY+PGwOEBcTW1pZyuZx6enp06dKlDusTgjYgnn2HLgydEWaq6XRa5XLZhM29vb1m6UAnGfqg5eVlzc/P65VXXtHExISGhoY0Pj5uXWk3btzQ7du3TdvGz8Nu8ae/v1+7u7t6++23rfkBE2E+68rKilqtll5++WUlk0kDxPh7HR8fa3t7Ww8ePNA3v/lNLS4u6urVq+rv71e5XFYul9OjR4/08ssv6/j4WO+8846Wl5dtnNSTJ0/Ubrc1MTFhYJpmIJdprtfrGh4e1uTkpIrFoqrVqvL5vDwej2ZmZnRycqJCoWDPbWBgQLFYrIPFpnTZal3MjDw+PlY+n9fx8bHpF2E7SUTwtCPR9Pv95uFHsgnLRaJH7GE/7O3taW1tTfv7+2ZPcnh4qKWlJR0cHJgFBjFGko0SI/lg/bM+XT2X1GlRQWzi6+73ftjPEQfcsiNNB4eHh8rn88Zc53I5tVotLSwsaGhoyKxRmL4Am0zySnLmNiZQbaFRxWW2iD9IBGDi0YG5ZUXio9t5znMAfHWXVd1KCPGN+EEsgJnHMPyjvp4zYJ3XMw3AoFexonDr4WQVg4ODNrJmYmLCSjy07Pb09Jgn1enpqcbGxhSLxRQOh00n4YIQggMHoGtL8GE6hu6A4XbN0ZJerVZVLBbt87gBB4YNsERgdkEXGTPibQAdIAdGimBCwOjv77exRPybG0BcTyaXeXNLgAjHmV3Hfcf/Z2hoyMbWkCGPjY0plUpZh06pVNLDhw9NjO2WlNxGAdcmoLs8yjPn0CPAo+2gfHR2dmai5t7eXoVCIfX29hobBxgicAMEKYcCpAAEuJ27+izup9frVblc1s7OjnZ2dkz74YJhPk+tVrNSKOwOIAyTSFcvh4cahwPPqLe313Rb7fa5jxiGk5QST05OtLW1ZUxwMpnUwcGBdnd3zWyU4I5QHIYLFsDtHsWmpd1u2yxWxPzNZtPKydgqMJgaC4eXXnqpo0GF+0jCg2YKQTfGyF6v13yZmPf59OlTW7eHh4fWODMwMKB6vW4dYUdHR4pEIhofH9fJyYl2dna0t7dn0w5qtZqZMbdaLevORHtWLpfNl+vOnTu6cuWKfZ6TkxMFAgHTA+7s7Cifz+v+/fva2NjQjRs3dPfuXaVSKS0sLKhcLltXZG9vr3mmEVOQSZA8AZoePXpkprfo0cLhsFmFeDwe7e3taX9/3yZHIFJ3WXLuuyuKJ5Hhd/N+3PItey6ZTKrVaunx48daWlrS4eGhefeNj48rlUqZL5YLLLDCcS+3w9u9ugX4gFo36eUieTw5OVG9Xlc+n7fuasAge8Yt6VIWJlFkTFcwGDS2GTZwYGCgYz4nCcXZ2Znu3LljzC/myOwjkjViGs8JIEpiTWwAjMHo8XkBZCRK7FXAJ2CMGEyM+6iv5wCs83qmAZhbhqDMgSeTJLM4CIVCptWiE4kOIDJIRNZ4SkHrc9Cw8AmCLGwWf3cgYdOhrfJ6vcYG4ReGCzhMh2tlQHAkoANg3EOKTsp4PG6B5OjoyLoFARAEAjY+dg6UOwAT0Oh8dhgEWAcYMXdOIgwa7fA0K1CyRcSK5cf4+LiZeFLGKxQKymQyVqIDmLn3n8OBg4H3xH12yw8EMS5XJ8jrAwqwQAAY0mFXLpdNhwOThRaEjJSRL9w3DjMOJjLmTCajRqNh64rslJImwmQOeg4r1g5aHHzQGK2DpoT3w/fBusF+er1e066cnZ0Zo4H3XTweN2aA5+J6pLXbbe3t7dleGhoaUiqVMnAQCoVUrVbt87EXAQrtdtuArcfjUSqV0tjYmEKhkGndXJaPDsRaraZyuax3331XzWZTn/rUpzqmDQwMDKhSqejevXva39/X4uKizSSFMW42m6pWq0qn08ZiMNOVzr1cLqd79+5JOtcnzs3N2Zru6ekxEDs5OWkTANB1wqwATjFa5XBkfWNpQnLAZ6xUKhoaGlK5XDYBO0a2AEm0jAjtMb8eGRnR8vKyNSDABg4NDSmTyajdbpstB3sFCwyAEOacNCQ1m03bhzSpsDYpcaNNZI329fXpypUrmpqasrI8cWh1ddW0rH6/X1evXjWtGfvFZbrc0uWHJbFcrvaVv7tSh7OzM21uburhw4cql8sqFAoqFApmHeH66RUKBRuV5ZY5Kdnu7u5aRzGNGK1Wy8r8sVhM7XbbfPxWVlYsVnq9XquiEFP5Ou8bdt+NvS7ApETsAjdYeZJaqiW8jmtfw54iOX5+Sd/4xjf0D//hP9Tbb7+tbDar//gf/6P+zJ/5M/bv7XZbv/ALv6Bf+7VfMz/Tf/pP/6muXbtm33N8fKyf+7mf07/7d//O/Ez/2T/7Z1bO/sNczzQAc+vo6C4wVXVZHRgHOuSGh4dt5AcmddFo1DIj6aJThY1ACYYDkgAidYpEpc7AAZOEyDibzWpjY8MYt3b7vLOOMSmULFyNFqCM0iP2AQRnmCwYLL/fb23vdOi5XaE0LJAhIuSlnOCyLTBflB0pdVEORaxOaY/DicNAkvmkwS5gasv7Y0g1YM+l4gkqBFW3fZt7332PXFGqJDO2dJsNpPMDNpVKKRwOW2ZcqVRUq9WMHeMg5D7AAmAvAKvkBk9AGcwB4AftmVtaltShqWq323YoAvZh8shiEdPzPZS1AZmI6tkTGIW6TCWDpxkkDatYLpeNLYNtiUQiqtVqWltb08OHD202JECD58B7o9NUkh34LqhOpVKW4fPc6A7EnLRYLJrrvVvC5aCULrzF6vW6vv3tb+v09FSTk5OmoeH3SeesCGO/1tfX1Wg0dPnyZfX396tUKmlra8t0UYzMkmQdvUdHR1pYWNDk5KSy2ax1O1KiBOjF43FduXJFQ0NDVj6EcQMYwTSyHiWZlo9mCp4twB393dbWlhqNhubm5vSJT3xC09PTpsmamZkxII61xtDQkGZmZgx0Sxf+cDRAsM9J4IgX1WrVytQkiSRtHs+5BQbJL8+TfUxpvtlsGrjP5XLKZDLy+c7Nc+fn5zU1NaVQKGRNMB9WbuwW1ndLPbjcZoadnR29++67evr0qVUcSEp4XzRSwDwdHR1pZ2dHjUbDBse3Wufdn3Ro0xmLMXOpVLJGlbGxMRWLRRuNlkqlTDtH/IDtYn25XZMkO5xrlNrdjndX5wv4cl/T7brmWbv38aO+Pi4M2P7+vm7duqW//Jf/sv6f/+f/+b5//+Vf/mX9o3/0j/SlL31Ji4uL+sVf/EX96I/+qJ48eWI6yZ/5mZ/Rb/3Wb+nXf/3XFYlE9LM/+7P6whe+oLfffrtDEvQHXc80AINNoLZ+cnKiTCajTCZjJSzoYkAYwlxMBWGSAFCUdSRZQOHgpSQBwwAb424At1wJgKAVP5PJmAg6GAxayzxBgc4zgiDAwhXskz25IIw5lq6ugo4+AKUrAiabQmxNdxnv3223Rh/C5+S/biclpa/+/n4LbNgbMOOPJodcLqf9/X01Go2O1nrX0RmtF8+WAApwcX2UJHUAMAISgMQVqrIe2u22tWSjo8JId39/X6VSyUZC0bzBOgLgEiQpEwLIAcMEdQAXjCsAku4onqt7AFHi46BotS5GRgG4KfHBTMAOwj719PQYIKvVaiaw7uvrs0kNrHc+m9/vN98xd8gwwA8dSaFQsA4wSZY4YHA8Pj6uZDJp94NyLCCSQ5syfDabtYOGBo18Pm8dgYwiqtfrVgYELPDsnzx5olarpVdffVUzMzMWG7i3lMcHBgaUyWQ0MzNjLAhaxdPTU83Pz2t6etrMcrHicPcVPmnNZlNbW1s6OTlROp1WT0+PzZ1khqB0LvxmXu0HH3yg5eVlLS4u6q/8lb+imzdv2r1gpixTH/B0KxaLOjk5MUd5EpfBwUHFYjHTJeL5Njg4qBs3bki6mDrhmkKzH1xmH2CDuLzRaKhUKlmMYmYsJeSjoyOTT5ydnVmSUSqVOsr7MJ2f+MQn5PF4LOlDC+bua2KeyxBxAcI+rNLgAoxms6lCoaB79+5Zw4GrbwXoIBlAH4xeEUnA1tZWh/YKgMpIMADx9773Pd27d09TU1MaHx838N7X12ffQ1JHbON5s/6ki2Yt99zgPXP+cPF3t6PfBSHdshcX1D6/zq8f+7Ef04/92I996L+12239yq/8iv7u3/27+rN/9s9Kkv7Vv/pXSiQS+rf/9t/qp3/6p1WtVvUv/sW/0L/+1//aRij+m3/zbzQxMaHf/d3f1ec+97k/1Pt4pgEYhxHU9dnZmR3slHZcfy6yAVgLZhNyMEgX3Ypopsg2hoeH7WDFcoFDmM0gXQSE3t5ey+h3dna0u7uro6MjG5vibiIOXcTl7sYj4+G1AWquYR8ACtCAwJ7v4T2SjbksDPcJATmHPVQ538Pv595yn9x/l84zuv39fdVqNcXjcSuBcLA3Go2OUUewOWj4+Ky8L54JgIxDwM0wAEJugOEz0R6PzYNbfuFAQutHgIzFYpqYmDAwlslkrJGAzJR1we9GdM79ZX3wfOnGhWHkM8DMEPg5RHnPGLVy/zmIEDiTTLjsHyUIDjIYChpO0EzR0HB4eGjGtxyQ7gxTSn79/f1WbmGdUPKKRqPWxUrS4AqI3a5VfLnQpqG/CofDZsyLGWixWDRvNj4THcFYhoyOjioWiymTyej99983YT0sDgAZIBQMBjU9PW1mtYx1evjwoXK5nAKBgFKplGk8JZmJLTNLK5WKtra2ND09radPn2psbEwzMzOmu+RwZP+vrKzo93//99Xb26uFhQV98pOf1PT0tCSZ39nBwYGVqyTZfafsiib0+PhYuVxOR0dH5leWSCQsISRBgGFzO5rd0hXMoysHoLwWDAZt6gXrFMaF9Tc8PCzpnL2Dycdc+OjoSFtbW/re975n5rpoBnk+7mt260yJf8SC7v3tsmCAEBij7e1t88gD8FMSRJjO/nXZZu4PYJVyPRUPzhd88YaHh7WwsKBaraZ6va6dnZ0Oq6CNjY2OTkY+E1rUDwPAgEziqhtnXODcLXVw2S2SOpI+Xpv18VFe/zsZMFh3LpK/P+q1tramXC6nz372sx2v9frrr+uNN97QT//0T+vtt99Ws9ns+J7x8XFdv35db7zxxv9/ADDKghyAdFi5Lbqub9aHCdpZnAAKDg+CA6VLBh1T3gDUuN060sU4mbOzMxUKBS0tLZnlAmARloiNyHvFpwwgAlDoBmCU/nw+X8dYEwTRHKAckLxXOnFcdgRWhn93dThumZWAgCjX4zlvt0ZXw73ld8F8ASKq1arZasAeujoJV/vBPe0uNQC+uNx/d5sSXDZof3+/49mi2zo+PjYjT7ycXJapt7dX4XBYly9f1rVr17S2tqZHjx4ZE0SwdjNRl0Xgs7rifLQ/rigXqwjAKH5CsB9uCWRvb88GQdMqf3Z2ZqwDYC4ej1tn39HRkbG8dMWhbYPJGRgYsMSFIfGMKqKDDF+ioaEhxWIxs7HgIHeTGHREpVLJyrmSTLfW19fX0anHIVWtVk38Pj4+bmsVzSVgl848OvlmZ2fl9Xq1tramSqWiXC6n2dlZSbI1Fo1GbfLFyMiIKpWK1tbWJEkzMzOam5vT7u6uyuWyWZcwCsjn85mwvVKpqL+/X1NTU/J6vZqfnzeAFI/HFQgEdHp6amVe6dzCJpFI2Cxa3POXlpbU29urWCxmjQ9nZ+ezKlOplKLRqGnDPB6PYrGYrf3R0VF7jlg78MwqlYqBNZoxuI/sFcxG9/b2tLu72wFoWdehUEjNZlMPHjzQwcGBpqamNDMzY+uXw21wcLDj905OThrApmsQgHJycqKpqSkz3UU3CHvlykAAJC4jRGwg0SWxPj4+Vrlc1v379/Xuu+9qc3PT4ih7E6BGjJAuDmn2LQk6TVrscyoFdHvz+yKRiDyec6uQQqFgFQMaXfr6+pROp4015GwA1LGmXXDlvjcXjLnvx2UMXdbf/TcXuLrSk/9TL2xmuP7e3/t7+vk/YCTi/+jK5XKS1DESjb8zVzWXy5l+tft7+Pk/zPVMAzAoXA5Fr9drWiNAFFYT+E25oIkNzuEMqOEPZRk6hFy9CweOS/Ei8j44ONDm5qZWVlZULpctYMBkABQoLcL2MDbCBT14YHGIdVPuMAs9PefWD5OTkzo4OLAW91arZXoTPj+fwRXXu+aBlFtcsaorlIWRImAdHBwYGzk+Pq7Lly+b0zut8S7oJAt1GTYAVHe2SJbIn249RLdgl/fmlohdOr9Wq2l3d9f+ndKJK97FKT2Xy2lhYUE3btzQ6OioLl++bAwMXmOwfwAqDhW6qJrNZkeW7WazeAwxtJt2cZip3t5ec/iHncATimd3enruoM8orVgsZiV0QC+CcQTUzBXEKw3tH2N5RkZGOkp8vFfpnF1JJpPmIA8L6nYq0iHnHhqS7DDotkSBQQP0A5ALhYKBHBi7QqFg67JQKBggYnZoo9FQJBLpmIGJTUO5XFYsFlNvb6/ef/99tdttK28ODw/bBAbG/8C+IeBn/NDk5KRu3bqleDyuaDSq9fV1bW1t6erVq4rH49b5mslkFI/Hdf36dWv8QH/11a9+VY8fP9adO3f0+uuva3p6WplMRuvr6/Y70NgBrE5PT03LFwqFOhLNUqlk99gdSu7GNfY9OkBG7dDhOTo6qtPTU21sbKhQKJh1RSAQUKlU0tLSkqrVqqanpxWLxaxrk/cA40hZNxwOW1I5Ojpqgvfl5WWtr69rfn5eExMTNgrL7YDsZsL4rIAMNw6im/zWt76lr371q/bZGAVGbHEBGEw2zRGuJpHORX6GpImYR6w7OjpSuVw2k1ymAfCesBXq6enR48ePrYGFBMmtKHR3df6PyoXdiWh3JYI/LqDj9f5X2KA/6et/JwPGbFmuP+7n/YPK3X/Qe/mjlHqfaQAG+KL7jg2Dx5MLMjjMWbCAIrcTp9tUFeZAumCfXJYGsMBiHxwctJLK06dPzVuJTQndzu9xwQVgq5tad7vRXC2YS5m7HS6wdehv0IvwO2FVXH0ZgYXX4T2SFbOQXeDIOBJ+9uzs3Ex1dnbWbAhc8MWhSomru+uN++Ae3NwH6SIbdku27nviM/Ff1xOM7+OgCQQCplei3MofumTJbnd2drS6uqqpqSklEgn19Z3PVMPuw10LrVZLu7u7ymQyHcEdxpJDk3IATBnaQrrHarWarU23FOwKdnd3d/Xee++pXC4rnU6bnxqdgYyfcUu2fFbuO2CvVqsZQC6VSmq32+ZaDptIKRLBtM/nM/YUzVa5XLbnOzIyoqmpKfl8PksGMIQEZNGl7PP5VC6XtbKyIul8YDbzLhGT8+fp06cKhUKanJy0MmCr1VIkErHnt7y8rHa7beA1HA7r5OTEmMtCoWCAKJlMmsaNrr9Go2FlT2QIfX19KpVK6unpMVd3Sdrc3NTa2prZEqDH6unp0dzcnEKhkJWfNzY2LKl79OiRlpaWND8/r42NDSuNY6+BgS6sEwyW62+IrxNdztKFh9/p6akNMo/H49ZZCQh29yxeeGifWAfLy8sKBAJmkpzL5bS+vm6JSTqdtpmS/f39CoVCVnYFvPMckCGgHSwWi3Zgjo2N6c6dO7p69aqVersF491/dxmter2u73znO7p3756Gh4fVarWsWQHZyIcd/LD5WBNRHSFGslY5Z9hLxOKBgQFLsNFwknwQQ2GZ33rrLbVa55MDXn75ZdvrbrLPvujWsrrv3a0GdFtYuKCRM4N43dNzMZD+/9SLuP7HvZLJpKRzlot9Lp2P3IIVSyaT1nDksmD5fF6f/OQn/9C/65kGYBsbGyZ4hGkga2X4MMJj11eKjMEFIdIFHc1GRCQNqnWZIJcRkmSbL5vN6uHDh0a3s8EkGW3vlinRYwBmKHt2l/4AFJRUACIuWwQTgRcWLeTt9vlIGYK4yz65HZcAJPfgJbhy6ELFAzpw706lUrp27ZqNImk0Gsa88N4p/wE2ed/cBz6vG+xgtciyYeYoZbrCfEAk94WyZjAY1OzsrNLptOmLCJgEL+aujY2NWXZarVat483n82l9fV35fN467MLhsDmUw1bQig7wRVyOOS5AyAWTvB+aNRgKPTw8bPeMe0GpkPWC6Pr4+FiZTMY0eJTkua90C5KgULb2+XzWkBAOh20MFPYndLwxf9HnOx+9ksvlDOThKI4eq91um7UHBrscSMfHx1pdXVUulzOAg0Go3+/X2tqaJSKpVMr0QujFEG+jz+np6dHW1pZ6e3t169Yta05Bc9jT02OddpVKRd/97nfNUoK4gXXF8PCw7ReADOWow8ND9fX1mV5rYmJCs7OzBjh9vvOh4nNzc5qYmLDEkESkv79f09PT1hRw/fp1lUolayjY3t5WPB7X1atXLYliv/p8PtMYHhwcdGh+3ISK54VNTbvdtnFaeL7hAcg6zWQy1tktyZIFv9+v3d1dra+v22xObEWy2azW1tbUarWMTSUuRKNR67alYePx48dmPwPoQZdLGXR9fd1YNZ6vyya4LDhxt90+b1h6+PChvv3tb9sMTJhcGG5YJuJLN8BzRyxRcXCZI7wTiUOuxx5nAnpSSuOMjyOWEQthmnd3d23KA+weMdGVNfCeXRbfPYsoefIzLvsC4eBWOj7q638nA/YndaFZ/MpXvqI7d+5IOpcyfP3rX9c/+Af/QJL0wgsvqLe3V1/5ylf0xS9+UZKUzWb1wQcf6Jd/+Zf/0L/rmQZga2tr1mKN9olyxtjYmGWMrkiT7MQtQ6KXcE0gXdredWzmtaCwAUq1Wk3Ly8taWlpSNpuVJBtTwe8B7Lngze0O4zB1mQrABeJ/WCUXpEiy/wfcceiNjY0pGAyqUCjYMGq+z+3KcSlqVwCKeL3ZbBoopUy5sbGh3t5eG7yL6SRjlGAZeW9u2QmA5IJS6eJA6Rb3c4Dz3oeHh02bxP0EXLtWFQMDA8YiAHJKpVKHpo1nMjw8bGVgRt+MjY3ZYUq5hpJKOBy2NcYzcDUX7vBbAjnP1dUe8rP4FPl8PvOK46DigmnAQsMFkDizo2HjeWLGyX06PDzU3t6eIpGIJiYmlEqlLOmgcxZGkOeN8SjWFRjYDg0Nqd0+94yCmaTcvr29rZGREU1OTprXG12w6Mywf+nt7dXExESHUzsWBplMxtzEI5GIJJm+ihJ4NpvVwsKCjesh+WBNwLrcu3fPzGHZ4+vr6zYsHMuNk5MT9fX12SHJs/T5fMrlcgqHw0qlUnr55Zfl8Xi0tbVlz4EsGTA5MTFhpcn33ntP0rlW5LOf/axGR0eVTqetBAjwImmBUYEt5f+JX+wRnh3PHgbbnZxBQ8nw8LDi8bgxNAD3vr4+TU9PK5vNGqAolUrWOXt8fGygmJmm5XJZ169fVygUMgBId3YoFDIbDthgJpG4zRpMpKC7102M2S+uZMSNCXt7e3rvvfd0dnamRCJhsy/p2iSeuPGF2Mu/0aTA3gbkS+owpEXaQtMTCZ9b4qRBiz3tNvrEYrGO0WThcNhAkwv6+IwkypSRXVaLtc8aQG96cHDQIY8BoKGH/aivjwsAazQaWl5etr+vra3pvffes0agn/mZn9Ev/dIvaWFhQQsLC/qlX/ol+f1+/eRP/qSk8+75n/qpn9LP/uzPKhKJKBwO6+d+7ud048YN64r8w1zPNACLRCIWcKGRk8mkLXRXqMl/3bIff2ejA7zcA8jViEkf3iZcqVS0u7trnTewALAU0sVoHLeTyC19chgABN2Mxs1a3SyQ9+MKVF3tB4fzwMCAtcWvr68bq0Op1i2lArrcAAWIxRQ1GAxqa2tLw8PDun37thYXF014TGBy749rFgqA4jO4rKILKF2mkf/nvVICJVOmhVy6mAPnaqoox6AF5L1QfoBJ6u3ttZLI9PS0Jicn7ZlgLzE9PW2B0d34+A+dnZ0ZuzQ0NPR9mj6eGZeriYpEIpqfn1ehUNDDhw/1ta99TU+fPrUWd7zipIsOTtYxbA4MiM/nMz1WqVQy0ErHIFo16RyQ4ajP55VkJrJra2va2Ngw25bJyUn19/dbeZU1DEiggYGmAywS0Hhhb0FXptfbOSyZ5oihoSH7e7FYVG9vr5UHMPaE5azX69ra2lIwGLQSeL1e1/T0tAYGBsx8dXx8vKM7FYsM5j9OTEyY8TH+aScnJwb44vG4MS+YI/NsWq1zN/hMJqNr165pYGDAGKtSqaS9vT3bT+Pj4/rsZz9rQnmSC8A+h7urz2QtuWJ0fh7ROJ227vpEKzoyMqJ4PG6Ap7e312IBjQRHR0c2HB7/Lz5rNpu1siVaU2w3KP8Qf7BsiEajeuGFFwwEU85mqDoJWC6XU7vd1uTkpNLptJkRu7pOYgdg7Pj4WA8fPtTq6qqi0aiCwaCq1ar5yAFC2SfdOivWALGAJoJuPSrrBfa+m31yuzdhsrolEfjYcW9caxb2ndug0x0jXN0vF+CLtSKpY6KBW9Fw9/XzS/re976nz3zmM/b3v/E3/oYk6S/9pb+kL33pS/qbf/Nv6vDwUH/1r/5VM2L9nd/5HWOKJekf/+N/LJ/Ppy9+8YtmxPqlL32pI2H+n13PNADDtwgn6omJCaN02bxkPi7r5bJOZGCU2TBvdfVi3YsfNk06p+w3NzdtDAfmlbwHdxZcNz3MxgBoUUYgm+H78PAiwyTLJQgAKFzdkwvqAKJohHCmx+KADjMXsLplLwAH7+fo6MiYk+npacvoEP+SefMzbqDi/nULFV3KnWDBewJ8EsRh19gMaJgoDfr9fgWDQYXDYSuvoPdyATTlJywXstmsTk9PtbS0pEwmo+3tbU1PT1vpqLstvFuv5q4rd93xrFwQ6t5f6ULw6ff7NTMzo1gspvHxcb3xxht66623VCqVlEwmTfgOUCczdj2vuP8AnkgkorGxMSWTScXjcROx8yzdEUqnp6fK5XJaWVkxw2D2GGN5VlZW9OjRI7VarQ4mCa2Jz+czLQZidg7ASCRiQIE5iDx/mNGDgwNjo1kzMJutVst0R4eHhwoEAnrhhRdUKpUMOIXDYZ2enpqr/NHRkQEwul4RjbPeq9WqlpaWlE6nbbIEnaiVSkWtVstc8NvttuLxuM1BlaSpqSltbm6aUSO6EIBpIBDQyy+/rM985jMWb9zZmnt7e99XPu+2F+CwJmYRowBPeO1tbm6ajQRaUJIn7CrcEma5XLaJD5VKRZlMxqw5YCtd/0OaRvD5cjuzXad97DLW19et01GSidEBm27SRCJEhaHdbhtb5sZft/R4eHhoXoNra2va2toyxh0WyP1ZN9axtlgP7Ge3Qx7tLGDa1WLysyTHMOh0QQMU6/W6KpWKjcUbGhpSvV43DSRlSwAnz5lnRMxw1wUAkf1D/HEBIHvTldp8lJebmP9JvuYf9fr0pz/9BzJnHo9HP//zP6+f/wO6KAcGBvSrv/qr+tVf/dU/8u/neqYBGEJUn8+naDRqZREOSjYUmwnw5S5I2A1MNt0yEQFOumC+2LSIytF8LS0tqd1uG+tBKQfdj3SRHUn6PuaNoEpw4HfSWeP1eq0ricwYISZMhFv+YkNKnWMo0CyFw2Ftb2+blsctWUmdDBT3gXJGKBTS5cuXFQ6HVa1WtbOz08F8uWydm9G5mga3pNBNvbtaBsTq/G7KpQxo5oBBF8XX6dDC5sH1M3LZToLn3t6etre3lclkDHgsLy/r+vXrBl4RvLqdRu57JpiyPhDJ8tnw2eI5uR2d/D9Bc3h4WC+88IJSqZT8fr/+03/6T9rZ2TGdHUCFkg3rDMCXTCaVTCY1OzurRCJhruUHBwc2WgemDnC4vb2tJ0+eKJfLmbVIKpVSIpEwLRhu/rwWuh+YLVgHtGuUsPkdGP7C1sKYuF3GHOQwLBzEPT09xjrBGPT19ZnGibExDLtutVpaW1vT3t6eMaZ0vLkaUNYZfk50/sFgNRoNKwvv7+9ramrKhOvFYlG5XE5er7fjWZRKJSs9YhsCAwEzwdo+ODjo8Elj/bCuXPscN7s+Ozuzzkmv96Jb1AWugCuSFfRdDKTmfhAjYVgPDw81OTmpyclJ65Ld29uz5iQ+B9Y6JycnlsS2223zduM1YeEoZY+PjyudTmtiYsJKrrC25XLZwD2ggX3GvqlUKnrjjTdUKBQMCEpSoVAwOxXYMldD5R7WgGn3TCBRdGUC/E7WLhUO9LhuWZh7AkDjtYh7sGCFQsHW1tnZWUeDhfvHTdx5n24sdTXGxFQXYLtJ4sdBA/b86ryeaQB2enqqaDRqLtzSxaYC6HSXEcnSuw9iMng2It/vAiIugkE+nzfN1+joqJX0XE0QHVHu60qdQkoX5BA03I3N5xgdHTXPHTyVEAfDhLA52XQAIeliaDfswszMjEZHR7W1tWW6Lfe9uYGDA3hubk6XLl2S3+/X1taWdTwRYF19mguyXKDRDW65V2R1UqfBIoEdV3fADQPHXS8fN+gC0GFL3JKGW+5lsHoikdCtW7eUyWSM1fzmN7+p09NTvfzyy+Z2zusQvGFEAbkuQ8F7pRTA/XBBtissBkgDxMbGxvT//r//r3w+n37/93+/Y125vmCI1XFHZ9gz3ZXu86chwOc7Nyfd3NxULpfT9va27Slm/QGepqam7B67ZUcOQ8AWJUGkAV6vV6VSydZd90HoJh2wNDBhW1tbyuVytmcRPsOotVotKy0PDQ0pl8tpc3PT7tvS0pK+973vmZ4Pew3sYPj/vr5zY9rR0VHTFXm9Xs3MzBhQ4QCu1Wq6fv26Dfcul8sWOyYmJmwYdjweNwAB80kDBX9YM7Aq0gX7i96Izw5jy/5iXzLaKJ1OW8mX76URB3bY6/Walg4tIe+fZGRwcFBXrlxRo9Ewjee1a9fMm44GE0yNGdfFPR0eHlYsFlOpVPo+UBmJRKxMSBkSY1zmS5KkHB8fm76Qz0vsZf/QJdvf369sNqtCoWBG1C6wZm0CRtwEnGcD0OG1YbKJzcQh9iXPxU3GXNZ/cHDQPjtJG+ueCkelUjGJBJ/DBVhuouoCMpeBdxmx7mSW1wEoEt8/yuvjogH7uFzPNACT1OHY7AoUoZFdVsAt+/l8vo5uR0CMq52SOkc5uFkNwWNzc1Ner9eyTjreyK7ZSC71zcZwR+WwWdlIBFGp0yzQPaRcbRuMHweZq21wPwfMA4cwmpBSqaTt7W0zV2Wjt1rnnXfValWvvPKKrl27pqGhIesyI3jxGTgAujUMPBNX3M/v4CKouuAMQIJDOGUfGgPQ/iFAdlk2Aiav4z5HV0PD/R4dHVU8Htfly5c1PT2ta9euaWlpyQx1/X6/0ul0hy6NbJPsGWBMlySHlsve8flYLwRLgKurnUOv8/nPf159fX36yle+olwuZ8AKlmtiYkKJREJjY2OKxWIKBoN2X10xbzQaNa3W/fv3tbS01NH5hrlqpVIxg1emGXg8HuvsJCHg/uF5JMm0ZqxRV5uIH1e1WrUSDZ+ftUBC5DKMbqkFwHN4eKh8Pq/T01NzhGdg+MnJiWkSYS/5XTCqkky+kE6nbV+zRmBzC4WCenp6rNzK565WqzZ2CbDVXdaiG9C1k3GlBjTfuNY06DOlc61bNps1gToldjppw+GwNjc3reu4r69Pm5ubKpfLunHjhq2/QqEg6RwYBAIBDQ0NWUKHPUQoFLIGhsHBQWWzWW1vbysQCGh6elrpdNq0Xdg8nJ6e2txLNIJ0H0YiEdNZAgDQLTH+Z3Bw0HzF6FqnIuHeT+IIYCObzapardqagbUmzpIkuKyqy0i5SRAgxY2nLivmGnm7wIfyJGueZMuVuaD/a7fPu0iRVOTzeT158kT7+/u6dOnSH5iU8v+sIxeEuoDGTeaQo3SXsJ9fH6/rmQZgDNZG0CjJDkOXxXGBF4HO7RSj69Cto7s6gW6xY7VaNfPFVqtl5S43i3EBjLvR3YuAQHkGwOYe8C4T525G6aI84bJoBHiCBpuQwOP6fp2entpYI+wX8HPCwBNA9IlPfEIvvfSS/H6/zazjtWF4XI2TyzhKMn0OAJX3zKEEqORwBDjCXOGjxRgnhMqNRkOHh4fa3t62AIX/z9DQkAmUCYa8TzdISufBic4uytmpVErJZFKrq6uq1+va3t62Q4vn4IIqAB+HQyQS0eXLl83x3XWiZp3ixeU+U8A5oE46t4j41Kc+JZ/Pp3fffVder1eTk5O6du2aLl++bOVYyt6sF9aYdA7ostmslpaWtL6+ru3tbfX29mpxcdGGTmP4iccUHk7lcrlDr4M2CmAC6KPJIZPJGJDyer3GZkjqKKdxsJC0AF5dwOquK+wYOOgpU/p8PuskDAQCKhQKBhBgQfkdiMrZE6OjoyoUCtrY2ND+/r6mp6c1MjKi/f19aygYGhqy/yaTSWOQWOuuBQrrgudKKZqvSzK2cmhoyOIF77Ver1tXntfr1e7urgFxkknAAJ2GPJtwOKxWq6VCoaD79+9b910sFtPm5qZOTk6sHMpnL5VK2tnZMVDrfm58rLiHZ2dnSiaTCgaDqtfrZtvBfgfcE8eIs+joaJIgbmF50mg09OjRI+tij0ajikQi9nnPzs6MpTs4OND9+/dtVmVPT08Hw0yywdpjv/FMXE0rDKSrx3WrEbBkbvmOxEG6SBZI4l1tIVYfxWJRg4ODNuPT6/Vqe3vbkkJGUvEeOXPcM4L1zx4hVrhgjM9DXHPPRRjYj/p6zoB1Xs80AAuHwx0lDJeeZWFSVgQcuUwXhxWgyS0JchGk2byHh4fKZDJaWVkxETCCXbdtWrqYYegyYO7lHo4cNBzMbhnUBQruz0oX9gwEP0oUgEgOMxeIdb8O94S26N7eXhOlHx8f6zOf+Yxeeukla8HHcNItO7mdjpI6AKmk7xu7xGc6O7vwyGGUC2M9ODS7wbP7XPAWon1+d3dXOzs7NtKG54sOjEPBfb5oMzwejw1LR/c0Ozura9euaXV1VWdn551hAByPx2NBF33P0NCQdeFVKhU9evRIk5OTisViGh4etrIxZQjXlsTV//CseJaMs/mxH/sxXb9+XQcHB4rFYtZtCJjt1p1wlctlvfHGG/rt3/5ta5vG74xmDMACIGtkZEQjIyN2MPT19Rljyn10QRb3p1wuWxlPko0YgpVkv+CVxD1EJ8c9oeQMm8jwZO4dDQdomdAv1Wo128f9/f0G2vCwGhkZMeADiwrwYCxQJBIxsfTU1FSHxs49BNG4uQJ6158OJt41dIV9phTZaDTk9XoN5NItOTMzY2ax6N3oAGZN+Hzn8wxrtZp1A8ZiMc3MzGhzc1Pb29uq1WqamprSxMSE/W5iw8TEhEZGRmwWJOCJhiQaGnZ3d+19YUiM5o8mIaYSDAwM2L3jHo2Pj6vdbtsEBdZUpVLR9va2yuWyrTnuE8kI7Db7ArNrWCvim8sk8/PEGPcPsRjGGhbvw1hW1rmbTLuSAfYLiTT/7zZGUcb3eDwKBoMmyuf9sA7cmE+ywH2CIeZ9EEe637NbvWGv8D3PNWAfv+uZBmCBQKCD5eLwJ/iyiVxrCdf53GW/XCGjK5rv3kyYBh4cHFiQ6e/vN0Gzq2GSvp8KdkuQbtB2A4Sbvbm1fTJN6eKwdktpMAIcNnwmAhpAybVR4LAFvHIwASiDwaA5N6P5guVwAwRaC/ceumJ6ngXCYzJaQDHt76lUykokZNi8Z2Yich85vMkuZ2dnNTU1pYODA2Mo6bIDTBDMXcDdbl8My6ZE1mg0zFX+ypUrSqfTHSJtGAyPx2NlRz4rJTuv16t8Pq/79+9rfn7eDnLePyyPG3BdUCvJynYuyzY3N2fPl69j/AoAxAIFfxu6w7BXQI+EzQMlH/YFmT8l3mazqWKxqP39fTOJHRkZMTbS7SgcGBhQLBaz5+zznZvnuo7j6IjYG642xmWGeR+MIEJrCbh2S4vFYtEEzX6/3wDy0dGRNYns7u5qd3dXd+7cMRZtcHBQt27d0s2bNzU5Oal4PN6R+LC+ea8cahzIJB/ufnUtbbi3JENMWtjd3dXDhw/l8Xg0NTXVUdZlKLjf77c5lNJ5YoOpLuVLRvxsbGwoFAppcXHRBsxns1m71273HjMam82mxa7l5WVtbm4asBoaGtLMzIxOTk6Uz+dVrVYlXQj5u+fuuuCUde3KMCgLu/o3GPFUKmVVCLdr0dULcr8B+VQOYGv7+vo6mgrcRMutjHAB4olFPFPODpdNYz+554Er2neBEK/N7+T7p6enNTY2Zo0lxEmGoY+MjBiodgEdscJNXN2Kh5tYuyVIRr+5koaP+nrOgHVezzQAc0EN5bXuGj6HPIcKQIys1BW6ShedJFwuUGk0Gtrd3VWj0TCrA34vB1c3ewY7wmEPYCFzckWqdNK45Ubeg9RZw3cBGOUEAAQHBe+FYEKAcjciP8+9I3DhS8WA09XVVe3t7XW4O7PxXfDlUvjuZyKgS+cmeK1W5zBqDhs6ANHaIIKGoWE0Sk9PjzY2NsyfqtFomFA4Go1qfHxce3t7NpOT0lqr1TKdiTsOCgaH+032uL29bQwbPkcHBwcdUxP8fr+9P0m2rmA91tbW9OTJEw0MDGh2dtbKoozngYXksOI+UpZmLXm9XtMBAchpd5dk3Z8HBwd69OiRHj16pAcPHqhUKikYDCoajZoFBYxRLBYzryeCtSsC7uvrM0NhSnd4e6G5cQ9gt0GBtSKp47nyudAOwWoi3OZgZT3ig0fzCSwJGijYtGw2q2azqUQioWq1qkqlYp+LZ7K4uKjBwUHduXNH09PT1sTD+neZaIAEf+ezd+9L2AnigNtNzf2gaeL09NS8wtx7sb+/byaz4XDYSn6Aof39fQM+gLDj42MNDg7q7t27xuLW63UdHh5ackmJrr+/32xiYrGY/e5SqaTT01PFYjHF43Gtr6/bAHWaKbCM2N7etu5Zr9drFjbdVYW+vj5LYk5PT1Wr1azrD+DB6/f2nnsMuqOqaIqYnZ3V5OSkeYxhI4OhtM/nszVLUwkxhz3sJuGsU+IXJWAX8LkgzWW8WG8Achd4uTIGYqpb6oQlpXzdarXM3qVUKqlarSocDn/fmeCuL/YZr0uyQmzn5/hepAE0p52cnNi80I/yeg7AOq9nGoC5wl13MbKxuzsc3ayU73H1FGwmd/G7C56OK1r36S6Tzjcfoync2rzLBrn+Nu6YHn7eZZDcy92QrpgfUAW75f67S0O7XyPb42DhtQBU3M/h4WElk0kNDw9rY2PD5hMSuAkG3UyaWz5w7w2gY2RkxMqYvDev16tgMGit/wQQdDcwNcFgUHNzc4pGo6rX68rlcsbGuXPZYNLS6bTGx8c1Pz9vvmdkpTBnH+aN4wJXTEA9Ho/m5+ft82O6ShAfGBiwbjlALC35ExMTWltbUy6XUyqVslIvoAUAy3rknnI4uVo+fj/vj/fr8XjUaDR07949fe9739Pjx48to2bQdbVa7WAe2+22lUJc3YzH47GynMvgcnjScIKujVIfbM/IyIgxfe46Q8vDQezz+awzEtaWz8yadUeCYWNQrVbVaDTMlf3o6Mg8xZrNi2HyfX3nzu5DQ0OamJjQxMSEeVTx7NnflHNcxsbjuZjZSbxhbQBq+D2w3+wnhO/1et10QKenp5qdnbUEABd8GDFACZYhLjtKZyAjm/r7+w3gjI6O6sUXX9SjR48M+LvaWJIJtFyUar3e81FRbmKyuLioTCajdrtteklc9g8ODrSxsWHxjdmYON3jtN7T02MzMVkjxFhJHcaz3H/iGKODkAO0WufjjnimJycnVn4kVrisGzGIs4H478ZzNHtu6d7Vhblxi73hvs7w8LDtD9hQV17h6v0ou+/v72tnZ0dTU1MGMj0ej3WD9vb2WgOMm4S7XY7EXMC5z+ezvcb3u5pJ4grJy9bW1h90nD6/PoLrmQdgrmaqW/vldtK4pTu3ROD+O8HTLS0Q+Gq1mvL5vLXTu6/ltg67r+EyGnyvK/ZHg8Lm7RbM8z7dUh8Ak/93RaJuZsaGda0l+L0c/ARnvGlgq8LhsOnrdnZ2lM/n7WfQLPC9vG9JHc/BBTqAPwBGb++5Iz9sHQcRnkndGi3Gs4RCIUWjUZ2cnGhra8tsEySZISaaC7y9bt68qenpaWPEeJauWLXValkZgwMMR+l4PK56va6DgwPlcjklk8kOoAxQgJlwdRkcArClKysrOj091Q/8wA9YOW1wcNBYPkmmNaKkxwHgMjHcI57Z8vKy+V+Vy2XlcjkVi0VjMFyNCqVEt8TtamXc7jHAIQ0XsDUwYu66cj8zoIN7GY1GzT6l0WhYZt5sNo0dAXwxiouGCrdUgzUDwLBSqXRYq4yNjamnp0ezs7PWVBKJREwm4B6wsFKuLoivu/ddumCQYc3ZlzBdrEEY6Gq1qnfeeUePHz+Wz+fT1NSUYrGYxQn2xf7+vkKhkOLxuPL5vO0RmkboLA0EAkokEtrd3dXq6qqazaampqZMSwVbPT4+bqXW3t5eY5/QHsFKkUD4fD7b58QPv9+v6elpNZtN5XI5bW1tme1MIpEwXajb7QzA7unpMVE+AIxOT5IO9rbrUM+6y+fz1lVOnNjc3LSyORpA9ilx0gU+buLrVh1cWYmbKLogjM8FGHOTBpfh9fl8BnrQKrKOuCe8t7Ozc5884g7NErDn3Au3EYd46Z5LriyBpI/P5TJtLmA7Pj7W/v6+JW5UID7K6zkD1nk90wDMFR5yIMF2uSUAt6ul+/s/rKwnXei/+O/e3p4xKC7TxusCtvh/AB8ZOYCPYEEWjssy2a7LZBEUOOBcUMeB7DJpbkbGv7kaADeL4/0RKBAhx+NxJRIJDQ4Oamdnx3Q1koz1YS4iwIDD3dXLuF48fB12CBHvo0eP5PP5NDExoampKQMLWIpAs8NWDQ0NGfhaXl5WNpu1jjhXi1Mul7W8vKxWq6VaraZr167ZnEA0ZfV63VgXgjpg0tWowXQcHx9re3tbBwcHmpubM8bHDcAEXz5zq9UyxgVLh/v37+vk5ER3797V2NhYR+mEciQaEDcIo/fgvcLOUGq4f/++Go2GPv/5z+v27dv68pe/rI2NDRvszaHe29trJUcaWNzSjHsh+KYZAv0cZUJmFwIU3LmcMHqsA0Y8oeNjr8ViMY2NjRk4ALBhMotRsMvAjY6OKhQKmZg+Go0qnU4rEolYSZPXZy9xb91SDoex+0f6fjYdFsplHHmtcrmst956S5VKxZKIg4MD5fN5VSoV20upVMpKyAyE39jYMBYDEThrPRAIWIkKsNzf32/dq5J069YtO2RdTRZrEK0e9y8Wi2lnZ0dnZ2dKp9OmQRsdHTUNVqlU0v7+vtmI5HI5s+mATWy1WsbW0DkLEESXi04PcMEzIOnCUw3/MAaeY3qLcz7MLoAcbRMaTZJhV/aAzpD467JtkgxEY5VBOdUdxO4C8m4tlpvkuoe/KyUhgeL13GYkfgbgWyqVlMvlTLfodtJ3VyhceQqleTcWs4dJnrBRIU4+vz5e1/8xAAw2q5vZkjqZKlfzxWFBxsVC7w7S9XrdwBfMSDeb5ZY+XS0C3+tqZaQL3ZpbvoER6xbnS50WFoA8ghpdXmRw7kFD0ALMuVoj2CkE7qFQSDMzM+rr61OxWFSpVLKfl9Rh4cDPuuCL+0mQJCgSHLiazabi8biBA2Z3wjZyf3g2Ho/HdE/7+/uWxaOPgR1BKA0bVS6X9fWvf93cwOfn522MjXuvea6SVK/XbayNm1lD7e/u7tqBOTQ0ZJ/fZUAJkgjC6eKbmppSLpfTzs6OLl++bKUQDim3hIK4mcsNvm7JZGBgQNPT0yoWi/rv//2/a2dnRz/wAz+g//v//r/19OlT/fZv/7aePHmiaDSqVCrVkW2zHkgSOIgkmc6Hz8c6pzTlluik84OFNne/32/u9Kenp8rn8zo6OjImkwOUtdTf329CeTQxZ2dnxgrynIeHh21eYzwet/FSrAH30HTZZA5KQCwMgdvsQuwgTrgldtb0wcGBHWY+3/n0jcPDQ62srGhnZ0cDAwOanJyUx+MxAHNycj5jkHt4cHCgYDCoeDyuWq0mn89na/L09NRmNLbbF3MqOZQZRl4sFk2I382Owvaxv5n3Wa/XrTmkUqkolUopEomYzxiJERMyJGliYkI+n0/1et1Kfj09PTbvFgsVSSb4Hhoa6hiv02g0VCqVrFzqJhEAGxg0SvnEHFcrWa1WlUgkOpI8d7yRyzp1J7zEHkCiG8NdcAdjSyLdbrc7yuWu3ssFZXydBJqzSbqwDGINYcxNTDs4OLA5ocw5RRfLvoI1c2MB95Kklj1AMkjZsVAoKJPJWML1cbieZcbqT/p6pgGYK66HFnZF9q4w3C1N8sfVaLGhXAEmwYHOPwKC+zsAHbBKBHDpQm8AHcx75r255Un+AOI4KFyA4wJKV3uCbgbDVJgR6SJb59AhI+MwpwQaDoe1uLioaDSq3d1d63iCWXI/4/DwcEcgkmTlBhfYuiawfA5asPv6+owJoDuRQMM94zkQNGG0yCI5MLlnDPAlAB0eHqparapYLOpb3/qW2u22za7DNoLfwQEXDoeVTCaNWcDiwB0bU6vVVKvVbCQJn49MF2CCYJ9h6HyN8g7lXrc8gp8ROr3ucgSvwe/j4HrppZfk8Xj0L//lv9Q3vvEN/fAP/7Du3Lmj/+v/+r+0s7Ojhw8f6smTJzarDsDCQc+a5HmTEHBfWMOUKF07CL4PwHF0dKR8Pm9moZSXWK8ej6fD0d3Vn7EeAOnBYNAsITg4XVE169edowdw7N5jrC32DBo07gVsBKBpZ2dHfX19CgaDOjo6MkaYpCEQCBiTTcPG3NycgsGgZmZmtLu7q3K5rGw2q3w+r0uXLqnZbCqfzysajdrn4dmWSiW779wP1hC/J5FIqF6vGxMJ6+RqEN21BAvebrfNRy+bzWpgYEDpdFqzs7M6Pj5WJpOx4ePXr1/X3t6eenrOZ9nCpBDT8AG8efOmwuGwsVSw8e7UCexYAOckZTC/fO6DgwMDJWgXSXDcMjxrxfXUc6UPLjPsVhzcBJ34L8nKtS7rzH503yegx9VluRUT1jFnCCXgbmkKkxco00qyuZvRaLRDh0iSxPNzzzsSQsA3MaFcLuvs7Nzah308MDCgWq32sRDhP786r2cagLkbymW+3PIBmwHw5YIu6cIhvhuV8zUCICwJB46bRbFZeD13Y7IRCQRsJDc74+ddNgWNFV/vZphcATi6GUpxzKw7OzszQOKWxngtdBqJREJTU1MaGRmxbBX9GBmVdDHPEMDh8XjswPD5fKa94v3xX36We9FsNs1jaGxszDp1yEAldQRYAk6j0VA+n7eGABeouho+fsYF2g8ePFB/f79eeeUVc4MHAJDdHh0dmQEmdg50lnFA0JLf29trGTtMKKU1fiet67ATw8PDCgaDOjw81MOHD5XNZvXpT3/aXsstWQA8XW0j95uA7ArI/X6/bt26pR//8R/Xb/7mb+rf//t/r42NDf3gD/6grl69qomJCT1+/Fhvvvmmzs7O7DMC5tkvsDGsMw4a1j3JxdHRkYrFoprNpmnZEJfDGheLRQMwJC5c/G7Y0kAgoMnJSY2Pj9trwLRyP13rk0aj0cFIAMLZT4B2NyH4sHIuIBsz0q2tLTWbTTO2HR0d7dDFARL4e29vrxmHNhoNbW9vmx6LWamNRkPZbFZTU1MKBAIql8s6PT1VMplUo9FQpVKxkmomk9Hw8LBmZ2etfIhmi/Ik8xMZFk4J0OfzqVAoGCiFiZyZmVEmk1G5XDbGc3V11UDk/Py8abtINGH33GRtZGREV69eVX9/v5aWllQul9Xf32/TB9D/4fknXUxzAEi4eiWX+eI+Mo2BmOE+J35fT895hzBrgD3LfnETCdgrt/mKNQPrzdisTCZjDQEuCPb5fKbtPD09tc/nylhcLaxbNiSmk+S4SQPrUlLH7zw9PTeYJulz9W2SOgyt+X7OKfYIyUxfX58ikYglAx/19VwD1nk90wDMZbZcxsfVbkgXQI0gDcXvfh+HgXswn56eGhvUDZzQWrlaEzfQox0CqLj0O3oUl13oLjHx2Qhg0oUewRXxSxfmn/F4XKlUStlsVisrK9re3rZDkAOXnyPLCgQCunPnjo0jovzjdjq6gn8+rxugCJiHh4d2iNE5xWdxyzoMrJ2amlIikVAgELDMT7pgDnt6Ltzx3d97dnbWofHgdQl8LqPZbrc1MjKiSCRimrNbt24pFot1uNDD1BCsASaUG9GNDQ8PWws5TAZlHjJR1hkgnYPG7/crHA5rdXW1o7FjenrawI8rPGeNuSVe1gBBnD+Ax9dff131el2/+Zu/qbffflsrKyu6evWqXnrpJb3yyiu6evWqnj59qtXVVRtxQwkXxgrmBLaJAO++n97eXsXjcTug0QKxt+gYbrXORzwFg0FrPKCMGIlEOgTuLlhyS/ZuowqMlssy83fuMYcde5/3T8KRy+X0/vvvK5fLyefzmYYqFApZWe/s7EwTExNKJpMm8vf7/Vb+Gxsbs/0aDocViURULpd1cHBg43tCoZByuZyVgViPNEK47Ep/f7/i8biZqtZqNRsszx7iOQEIYMYQdwcCAY2Pj6tcLiufzxubOz4+rp6eHlUqFbXbbSWTSWP6vN7zTkjpHFRns1lj1CgHwtQQQ0dGRjQ+Pi6Px6NarWYlVhoe8B2D7XYZa1gbbGbcRBQAmM1mrYOy1bpokHnttdd069Yt687O5XIdOkYYSWIEAnTWcbvdNk0mejxKpD6fr0M2QVx25ycSZ3gm7ngzkjPiulut4N6xrvms7tdh9l05Az/P5yMxdmUlaNewH3HLsMR47j1x+vn18bmeaQDWTSm7JTwXgLmAxS0bugDOLT9ykNdqNWOT0MdA+7st+PwBFLjaLTfr5yCB9uY98D0u2AGAdL8mv8v9rHwN4Ws4HLYBxE+fPjU9DIGo1Tp3kx4fH9f09LTC4bB9ThgvV2zqXmRXvHdYON4H5ZhisShJHa7XrpYK1gqA4w6h5vO7GgeXuYMZ4T0QuAA9PE9+R7PZVDKZ1O7urh4/fixJevHFFxWJRIzFIHBKMj8iAph0MTQa4S66JpcF8Pl8ds/IxFlPfB5AN2xlsVhUMpm0Ug7vm0yWrJp1yzqGhSQZ4F6NjIzo05/+tA4ODvTlL39Zh4eHevz4sR49eqRXXnlFP/IjP6JPf/rTunPnjt577z2trq7agYWlQSAQsGAOs4d+pVKpWNkLTZMLGkk2PB6PMSzJZFJjY2OKx+PmK+U2H7hsqVtCczVdrDsOKO4J+4dYwEHoek9hnszMw0qlor29PRvBxWtgL3N0dKRKpaLV1VV5vV7F4/EO9hk9FM//4ODADmViw+npqaampoyhwCojGo2q1TofIh6JRGztUbZzWVMYVQTd7pQAt6TK5+3tPTdOZj6k67uVSCSsYQLwgOmqCxIwzG21Wia5QIvHXoINHhwcNO0fVYKpqamOewV7xz5j/ZOc4C2H3QtNMl6v14Cg3++3+06nZigUsqSJEiMlaLr9eI+wV8gYAJKAchIk9hasMmuP58LP9vX12Z5wtWF9fX2WqLjnEABIuuj+pJx/fHysUChk55cb55EhuGCP+0CXN6PjWCtUBri/rda5xxpNXh/19ZwB67yeaQDmgiqXAQN8EaxdkERw4HBxy1Z8PwEQ/cbe3p6xHGhIXLGlW5pxwQnvj4yGA5XNydf4PsodbobjdhG57Ay/g4AsXZQm2+22ZaOIanO5nAVVftfk5KQJcfEsQu/ltmO798299y6lT8ljampKPp9PKysrymQyxnZJF5sPVs/v95vzs9vd9z9i3gg8ABlAL/PqPB6PAoGAlcTQW9EdmkgktLOzYwxhf3+/RkZG7N6xhihbcL/57PxODpNKpaJarWbCZ1dL4gJlABOlxtHRUdPF4AEVCATsNXg/3fefdeqWUciIacjgsP3sZz+r/f19/eZv/qZOT081PDys73znO1pZWdGtW7d069YtffKTn9Tt27f19OlTraysqNFo2HxV9HZk1pSPTk/PndwLhYJ1OCL+DofDNgqHcitGqR9mL8JaBXS5rBcHIp+5W2rgrj32T7PZ1M7Ojh49eqRvfvOb9tzr9bqq1aqV7UZHRzU9PS2Px9NhJOoemDA5mUxGQ0NDSqVS9pyxQ9ja2jJWFEYVVgdbFLoT9/f3lclkrNSIsJ2S9Pb2tiVPdJsC5mBoupuIOJgBDZVKxRhAfPEo6RETabjBwoMh2JTz/X5/R+MN8Y29yX3CAHhwcFChUMgOe1inbiE9CSMssSQDuuxzwCBrhc/Cz5BsBINBjY2NmaEwn//g4MBis6vXopuXv/N5XEmIW8pjr7ueXNwPV9zvWmG4rDS/n2SQs4AyIXIHzGenpqasfAuw4zlTjoQx4x4QL/kasYLzhvdKDKOE+1FfzwFY5/VMAzD3cHRr4mwa6fsF7vyby6b8/9j7s9jI0+u8H3+qiltxK1aRLK5NdrO3menuWSXNaCbyxLHswImdGL4wYN/4IhcGfGXEgoPAF5EAQYF94QQI7AsDBmwncIwgQALo59iWZNmyrdFY45FGs6qnp1eSza12VnEn639BfA6f+g5HthIlM+1/f4FGd5NV3+X9vu85z3nOc87L56C8iVBqtZpqtVrHPl04SCYShoco1cXjnnrjO0RX/MxBGg5CUsdidSqZ7zgD5hoyjDLAjUgSA46BxRGwJQqLnPtK6sYcRLpRJork96Ojo5qfn48UAMAII3d4eKhcLqfJyUkVi8UQpErqMIwYMq6FIUILA/AcGhoKJghqP5VKxTgCDlutljY3N9Xb26uNjQ2tr68rn89HugpgCNgDzHItn2MHBwchmPWGjA7AiFiJ+gEL6FXYCxC9FN3qmQ84HgCcg9Tk4RpEIvfx8XE999xzunv3rt58801lMhmNj4/r8PBQf/mXf6nXXntNjz32WGzd1G63tby8rI2NDZ09e1bd3d3R3JS9FQGIbLw9MTERLBesBFs0sT6YOx4cnJb+52eun3MWwfU2zCPSR6Tc7t27p1u3bkULiDNnzsT7m5iY6Gj5wnvxlE86nY55IynYKJqk0kV+dnZWg4ODUV0GAHUGr1qtxv6iMGz8nq2gaLWxt7enarWqSqUS2jEYGkB7vV6PbuqeGqZ1Ayk4ArzR0dHQcJJKY+wpWAG0kApGp+WBAEEgqUjafMBazc7OamJiQlNTU6ELpILPbSbvDFYMZntkZCTmBpXMjClsG41Ht7a21NXVpQsXLkRD47t370ZacHx8XBcuXNDm5qZu377dIUXwFDbv3OdmUubh8wJ7gJ0iQHfHzzrlPD7XXZO2vb0dwST/53OwzNgdl60488V73dnZCY1XpVIJMEsQf3h4GEwYKdWHx0freKABGAbcNV7e+NO1M56+STJeLEaiHCLp69eva3FxMZooSgoxMU7LaWNSYb74kptQu34MAIlDoMs10RQgiq7dLC43Em5kXNTMdff396OvEJ/d3t7WwMBA6DcajUZEXoAsT5/xf8Y1WYXH8wJw0J6QnmEPQJxQoVDQmTNnNDExIUmxTx3sAxWIfg/OZAGSAGA4JErzcc50A+edUTxRKBSC3SRtmzS6vEc32jht0pFUg3pfIgAEbJSDBubf4OCgCoWC2u12nAPtHdsxodkg/cM4+L99DhCREzz09/fr2rVr4XjffvttFYvF0G21Wi19/etf16uvvqq5ublIJ66vrwdrQx+prq6u2KmA7YtoQ8A7dUDlLDKpb2ftvGiCCJ00D5/1YAnwvb+/r1qtptu3b2tpaSn2+nRmJp1Oa2xsTJJC44NDYy739fUF4ygpQFY6ndbs7GzcD+k62K779+9rbGwsgARVnw6ASeNRUQkDRKC3tbUV1YyAHII8nCT2gJTbwMBAzGXpGGR7yop1SAUtDO7Q0FAHewwApEHt6uqqbt++re7u40auzrK7toz3wXMCdO7duxepWgoM0EeSPtzb21Oz2Yz5cRqjjhYT+93T06PNzc3YKBu7w8bds7Ozmpub09mzZ3X79u1Iid6/fz/AWalUispOUpIe2GEvYbNg//Ene3t78TtsAXMRMOZyCt4bsgzsqUsSAMLYB0nRtoPgQFKHfuvo6CjaUrgtYL0Q9E1NTWl3dzcKY5wIwFa7HObDOh4yYJ3HAw3AiDhwrp72SYIuHCdO1vUkOF0crqQo2/WeNRh5UmBJsTdRLZvbSuoAFtwPCx2GA5BEagO6mgiQqDQZXXFeDDUREobLNXLoUIjyDw8PI2piwWJwXDvCQXoOAMm1cXpe4UNl3JkzZ7S+vq6bN2+GYyANOjo6GuAtWRZOmpFrMkaAM78n13yR0qCyra+vL6rz2LaE72UyGa2trandbuvy5csaHR2N+eE6KwfrGGIcBeAER5/L5WJckvODAxZseno6tvRhzrr2DBaA9Anzz6Nvv1+MtTMM2WxWjz/+uOr1ujY2NvTd735XxWIxHJ47F7azGRgYCFYDIbanvlqtVkd1JtfkYC146pXghXtlE3veBeCI59jb2wv29Pbt27p165ZKpZLq9XoEDdKJvtK3BCIQ8lQ2oBSb0W63Y40AYJlblO7zvLRg4L2l02ktLi4G8AdsoYMEKE1MTATDRVsFxr7RaGhvb09zc3MxfgcHB8rn89GOhaBrZWVFkjQxMRH3Kqmjoe3Q0JBSqVRsaQOAwonzDtCq0VqCxrcEKxTN8L5Jz8PmAnS45tzcnFZXV3Xnzp1oSzE9PR0i+kKhoK6uLlUqlQhYEOJjg7FLLjVAZ0samCCiq6srNFYTExO6dOmSvvGNb0Rrh2azqdXV1Vhfy8vLWltbi6pRnsX7z1EARNGAa+1YH5LCVmMTYKyceffUJ0EIwA3gB5sM60kRktTZ88vHg2um08dVuV6A4wUPkiKgI0Xs9iCp5314fPjHAw3AcAKnMV78jSE6Te8lnbAKTi9De/f390dZPNR4vV6PXjXcAwsWww6AcWEo13AWDIeDONRbPuzv70cESTGAgyCqA2G0MJI4HdeHuQienlvr6+uq1+th3LxowbU6XhTgY5TU1jkr1tvbG9sGzc7OanV1Nc4D++VAgPd3dHTUUd1Ft23XlOAsPT2K8BaGjLGENWu1WpEaY/cBQDvVe6Ojox3CeYwoYMIdNWNEqgVD55o1F+i6yNcNfFdXVwhkpZPtVXi/zJHu7u6OrZIA3a4/8ffiQDWXy+lHfuRHtL29rf/1v/6XWq2WFhYWNDo6Gv21JiYmguGiEpIxJ1Dh/8l3wVgk0/y8GwTBzkIyr7wNBHqre/fuqVqtRpd0GngCsoj2nWlj7HHsOJukPXDnyJqAsRoZGQltFqwVVXysG5z1ysqKtre3I41HU9CxsbFgYh1AULTAe+/u7o6Nx72Z8GlC7eXl5WAgYcicHWQebG1tBYsKQwvIhFWH8SFtXy6XJXXukIF94llJOTabzZgH9Bpkk2xS7tvb28rn8xoeHlalUlG73Y6WL+vr6zFnWPeAKlhfKo2RJfA+sQXo95h/MzMzeuSRR/Ttb387dI5ra2vq6uqK4o/l5eXoxedMJQATtm1rayvWIWPIHGK9+eE/c12bSz9cZsJ3GAPeDax0d3d3tJfw7EVSV+bjwj1jf3guD0o9BZp8hg/jeMiAdR4PPADz1M73YsCczZHe3//LJ2u5XI6ojbJl6aTlAalFfg64w6kS3aysrESaicXONdvtdkfZsJ8DI8t+Y97jhYWVyWRUrVYDQAI4uB8MiLMAfX19GhwcjPYHRHzJ9BvP5UJcB6ru+FxTh1Go1WoBdsbHxzU2NqZSqRSR6czMjHp7ezvGgudCvwZg4plgE5JpX+7LS81brVZE/dvb25FmhcrHCdJTi+1W2MoFIOXpSC92YP5wPZpd4nj5uXQCilxT53MUHQ7aHsTubOTt0bOzmh5dOzDkHQGKmKc/9EM/pIWFBTUaDRWLxWhL4m1VvMM9ht5TFx6ZezUeTqW7uzsALoAVcIFjKZVKWl1d1TvvvBOVievr63F+ggcAF8UavlZhHJIpeK9Ag2FFu+T6TioM9/f3Q/DO+COMPnfuXIy5A4fd3eO9Ho+OjltHbG5uRvqyUCjo6OhIy8vLkhTgcmdnR4VCQZKCdUJEnskc79cpKTqjN5tNFQqFAKZTU1OxaTZ7QwLG2KeVMd7c3IztdXhvgH7YJNZAJpPR6upqVKayzhgj3qkXBfAZWPnBwUE99dRT2tjY0N7eXlSXItTv7j7u78WzsPsCASSpXNhg5hM7MtC+hLl1eHiomzdvanV1VUNDQ7p8+XJHMUStVos1zHzCfnpgwNpuNBqhr3MAClhKMqnJ4I+1CehKMvj88b6RBB4Ug4yPj0tSR8BFJoMUOu8X+4jNdHuCLcIeAqrdXj88PlrHAw3AknqRJPPlIMydFE7bF6KncOjqjOFCBOxpChgqznV4eCwupxllT09P6FWIztjkGsPrqUlPxzmYI0KkZNmZNfRO3hwVp+2OFLYGoTDaLBYkjo6FLZ1sleSpWs7ri9oZIownjoeGjrOzs2H4p6amND8/H0JtSe8zesnUJs7Ex5rUmbNKVO+tra2FKBwQy3OXSqVgIxhnUl19fX3hvDg394dBdyaQ79NfbWtrK9Iarhdx4O2RKSkr10JJinQGjSK3trY6wBHpaN9oPTn/cQS899HRUQ0PD2ttbU3lcjn0VK6hxGnynKSwfL1xn16FiLaO+UGwwHYopVJJS0tLkRKitx5OiHT90NCQxsbGonEnKXXmKY6NZ0w2d2X+EBh4RTIOzNMxzGHumTFk67He3l6dOXMmroNUAEaS1gSrq6taW1uLvSAzmUykCAm0mD8ABAIyNGv5fF6pVErlclm1Wk1jY2PK5XJ67LHHYoxqtVoHa+bs6uDgYPSRS7Z94P2wNRRgFKH/5uZmR4C2v3/crR+dH+uT3nDZbFaVSkXr6+ux7tLptFZWVlSpVKKacX9/P1Ju2B7vtefNeBuNRgD20dHR6CNIand5eVnlcllLS0uq1WoqFou6cOGC5ufn9fjjj+sb3/hGAL5arRZsD2sZJtIZe+YWRVZu2wBVLvfw6nTppCWJC/R51+l0Op6Buela2b29PZ07d05nzpxRd3d3sHCAW/4QeDLuDrTwfzwnaxU77oEzPuzDPh4yYJ3Hh/9G/g8OojKifQdcyT/SCfDyiEE6AQAYaO8iz/cwIhhX35qDCJkFTWPUJ598MtpA3Lt3T6urq8rn8yoWi1Gq74sJA4KjwyngeLwpIo6Ee4Mx8mozntXL4+/du6f19fWI+LxMnIOFnExvAT48mnLdHOdAN4OQlSgWB8vGw1DppOt4PoCN9P40M8AkyWim02nlcrlg21qtVrSIIH0E68beb8yNg4MD3bx5MxyO9xECVHgVGMYao+ZOCvDikTPMFZ937RHOiIgX1o/tZjiPV6j53CeA8OIS7hf2NJ1OxxZAqVQqnAQFCt440tcSTpzvMVecDeRa1WpVb7/9dgjkEXDX6/WYC7zP3t5e5XK52Jswk8kE6wGzxDx2QbIfPBtzLOkYSWO7A2K8AfS+kwGAilR2pVIJ0NpoNLS4uKju7m4VCoWY3+12uyONVa1WY4uher0eVbUO9mgHMD4+Hqwb84N1ClO4u7ur0dHRaCPAe2TsnS0lDU9nfdYDIL7dPmkkDfhgiyxkDLCpzEXXzsIW7e/va3h4OFLGvuE4WiZPzxOAEHChAwOgjI2NRa8uBPxstt7d3R3pvWw2q5GRkQ47sbGxoZGREc3OzurcuXO6e/dufJZUJuNP0MycZW5hQwlqnfXGzjL3PAhOMtysU54/l8spn89HcQtrDv8xPDysc+fOaXJyMnRwNApG/8guCuVyWX19fZqZmYmWH4C/JDPvNoj7c1nFw+OjdfyDAGA4PKlzKyDXgHE4qGDh8O+kXopIE+CBYSF1RUUignl6XmHcenp6dOXKFc3Pz+v8+fO6e/euFhcXde/evQ7tmNQZTXl1IRS66yzoo0WFD5GWOyXSi9xzNpuNsn3SUuhBPKWaHDupU1sknSxkB5/eAoSocnd3N/Z0owv82NhYfMYZQCJMtj8BoBHB1uv1iBzpP4XDYPzQ4UxNTenmzZsxbtVqNbp30/yy0WiEYerqOt7C5fbt26GRS1Yi8R5gfhz4kJLzZrLJtDTzMskstdvHDX9v3bqlbDYbW70gXoYFSKfT4YAY+2RhhM99T03ybvv6+jQ5Oam9vT3dv38/9FU4IMAQ1yfgcFaULYY2NjZij9T33ntPlUpFi4uLwXDAqnFPMMOAaAC96x4BSMxn0lakFV3X6AED852DteSOiaCGlCbgk/EkIINNwinCnsAAIaCHZcjlchoYGAgBNQw16cBMJhOd5vf3jzvgo1NiDqJRLBQKkZ7b3NxUOp3WzMyMUqnjjvMEMs5k8JzoJXd2dlQul8NmOZhFlsBcIS3qAIx5Dyja2tqKIhaaz9KKpFqtRnNZxnNnZ0fFYrEjjc0cY/x5h9yLt2aZmprqYPS5V0khLN/f39fS0lJUQLJpeLvdDs0cthSmEMDs+kCvKCegTWp2+Tz34joqZ8KZmwMDA7HHLXPG+xdiS86ePRuFFc1mU61WK7S8Xlm7v3+85RKpZwC1i/7dDmCTsMEe6H5QG5v/l8dDBqzzeOABWFKfJJ1UW2HQ3VhzeDTAZN/dPd40GOOH0ebcTt07A8F2G6Ql3UjS6Xh8fDw2F757925oNyYnJ2MR8nnXOGHA/P+AQxYkIA79gHTSTJRIf39/PyJWDC2slEd+SdZPUkSJSYPjYwNwZOwBU94KolAoxIbZDsAALTg6DCbAgDQJjAKL+DTwDYvhFV3tdjvSEq1WS6OjoxocHOzY43N6elpra2taXV3V1NSUCoVCjDepP/7Pc2PQcJqAzr29vahM4365x6SImu9zPgTdPDe9j3iXpAl9fHgfniIF6EiK8SZiRpOztramvb29EEED5HCyMCF3797V6uqqarWatre3tbGxoWq1GoDD1yJsDO+DOQGQ8d/xN0wHTg5nmUql3hdY0AjVU7ysEQ7mAUCf4Agn2NXV1RHY+D1yzUqlEkLydrsd/fMIGA4PD4OtgVmllx73Avhk3sOiA6QA8wB+fj86OhrOmNQ979CBe7vdjvECJPX392tjYyNYQIJCALsHPGw+7wJvlzIcHR1FNTHAmHXd3d2tM2fOxDMDILG3sGYe5LTb7QiA9vePN6Tf3t7W2tqaent7NTo6GjtnYEdYG6x37Bygj7k6NTWl8+fPB9Pk3fPdN7hcgrVN4M39ejrPU5LMUZe4sC4pLgLw1uv1YDFJcaINnpyc1MLCgsbGxrS1tRWSEC/govCAtDBAtFwudzDj3v7GWXRS5thvSR1r5MM6HgKwzuOBBmDJw0HDaQJFj+Q5PCrZ399XuVyOvd9cq5U05miz0umTbuxUsTQajdDJELHBWD311FOanp7Wu+++GyLkycnJqMJzsTB/WMQwCTBJXtnjIMqBIhvcLi8vR/WWp7G8ZQFGj8jfDZGDWBy+M1+AhK6uro7xxAgMDg5G89ckS8n9YkwBA5zH+3t5KxE3hjgWxrlQKEQKFwfGu+F+cOTNZjOqq2h3UCwWJZ00k2Rsd3d3g9Vy4MkYeLqNKBvBOO+LMcMJ8k65f0Bbb2+vtre3tbm5GVoYGFl3Fh6xM26ue+I+uafBwUHNzc1pZ2dHa2trWl9fV6vVCmAFuysd688qlUqAE09vI45nbvrBu0+mSHAGDuKd8WCsYSdIX8HIkKLp6enpSJ2i62Fe8/yuGyTIqtfrERTg8CUFW8Jc3NjYUF9fX3SrZ00TtGxtbWljYyPmOqCUdzc3N6eurq7YgqlSqai3t1fj4+ORSmIuuZYxlUppaWmpo3UG68qrpzc3N2Nj+nq9HuejsSo7EMDmMe7YCPoBSuoYOwIB1ktXV1e0aQFYHR0d74E5MDAQejf0Ttvb2wHOAeEAbeZ4V1eXxsfHdXBwEEVPpMN9M2+fV9hcximXy6lUKkU/skKhoGvXrum73/2ulpeXw36fli7EZjKW3maCa/vBuuU9AZppRkyq3gMUmD8YSPzKJz7xCV24cEG9vb0B9HlH+BhJESTTo41ttcgMHBwc737ADgD06aN6l11PmPsPU5AfveOBBmCuDfKfufaL/+NAMMouJGfSsxiIOjEWCPG9ospTFzhz12l5dI3TYOud2dnZaAHw+uuv69atW6Fn8BQnKTcc0+joqKanpzU5ORlpj2az2ZEaxNGSuurr61OtVtPy8rIajUawKxxorvg83aipsHQGiPFkHD1t5ayO/5vvA8DQm/k7g/GCPYBtxHBj0JMpY0An/z88PIwGpkNDQ2FUXXh8eHjY0QkfHR4R/ubmZlSG5XK597GrnMs1ULALMBr0WyIa5llOM4CpVEq5XE7T09Ohu+HcgDzXfgHkcrlcGHbXniSLJLh3QBi/pwKrUqnoa1/7Wuwph4aRFgM4OmdFvGiCd+ljzJjwc0+5ukPkHMn0DI6L9AuOl/PQ742eVgQCqVQqGEjGg4PrOxtHGhFNIUyrdAIgAbFcGyE+8xxgMzY2FvoeGhEDjAD9BGMANCoBm81mpIilTl1lMs3PnIF1GRwcjFYysGGkwgBgrVZLlUpFk5OTyuVysd7QnjLOvGuCHElRkESgwZzmvRMYYD+dafLCGZ6/UqnERuOjo6OSFLo3bBPjwlzyIIcq8b29vWCsV1ZWlEqldPbsWV29elUjIyP60pe+FIwrtpv14AEVc8PnB/fP3277GFt0vASsbFlHEQvnYhzwCc8//7yeeuop5fP52MWhWq2GHpd3nrxfmF8CkZ2dnWBRl5eXValU1Gq1ohchNhgW3YuuPszjIQPWeTzwACz5ty8WF+C7s0hSzizocrkcTIvUWXZPxE3EjMaAqMgr3VwTguNKp9PR3Rnx6cc//nFNTU3pzp07un79uur1etzv1tZWMGlPPvmkFhYWQm8yPDwcwmFf8M7IYHwbjYbu3r2rSqUS4wKLg+HG6FLqTRqKEmgAoIMvFzRLnd3Nk2nIdPq4+SYVhmikuGeE12NjYwE4eT84FXdC/o4Zc+4FUTz9mNwJcE/SsZFbXl6ONAyM58bGhpaXl2MvTVhOAIaPsbMraKdwiHQQl04KCRy0cJA2JoXAu4HZIpWAk2YeUq3GVjYcHu37z/09wSBMTU3piSee0ODgoO7duxetIbzNgqQOfRX37uymVx26TgYAlATKHgiRRnFQ5oAHwEUqFn0YaT9AhDtOwIiL3/3euR/mFHMZBps5RRCGlginCIhkLNEXslbK5XLcA/vIOkDb399XsViMiuFGoxHOlZ95+u20eyb4yefz2tjYCPANC8Z6pv1EqVTqqFjkHbbb7WDq6dzP+WFbYf2pAGV8vLEzOrJmsxmsrmcAWB+Hh4cRxHId1496kQTgnOfH3gGUKYQqFotRNcr+lk899ZTu3LmjUqnUYYM9LerMsAda7k9cP4w/kI5ZvuXl5bCVXh1PuhQmmsDkhRde0AsvvKBisdixHyeBgDfQ5d5YW9gDB6nIJwCwZGFg01KpVKSNmY8Pj4/W8cADsGQ6K/lHOolwkmJ8j4J8UQMQSDmxcN0BOk3MIuF82Ww2HCRpE+nEkQGMSJdNT09HpHrjxg29++67oZd44YUXdP78+aiU476907FrtGCB0FqVSiVVKhVJCnaL53fDSNTb09Oj8fFx1Wq1iMwkdRgtBx8YY4BEMo1DlEyLjuTB+dHPEZFjGLe2tsJpYaC4TwdiOCsXvJMu8cjP/43TbTabkWJpt48bbebzeU1PTyufz0cVGowaTAifx8Dz7Bhl/u/gNcm+Mm9hN2AivDkn75HUG0aaOYmxd2DLOHmU7/dCMQMtQSYmJnT+/HmtrKzo3Xff1Y0bN7S2thbjChNDiow15YDHU9/JZ/XIl3fkUXpynnE9rkFaGlY5l8vFvIHN4jg8PIwxcYeLOJ75n9R+OUgkPcWccVDj4BYGiHVPYOTvwHvEkV5DJM2G58kCBa+GBRSQLnSxuKcMYUlIBTqQ8ICFOYGD57zVajVSg0NDQwHGqGgFkHmLBv54vzaAOMwNa9D7lwFqsUNUjcIuejUugYADaIo6AF4HBwd69913o1p1bm5O2WxWt27d0u3btzvAF/fIu8ZXJBlqDzjReAIgKWzx6tukvyFoy+fz+uEf/mE9/fTT0avM2WJfQ95+h3MwBmRXWP/Mx/39/ShyggFGS3Z4eBibyD/cjPujdzzwAMxTjUxMr5aSFI4J54QBI90FoKEqCYOKIcDgsiiI+jAMRGoALu6LSkgqugB09XpdlUoltE2pVErDw8O6evVqbBC9urqq+fl59fT0qFqtRkXm4eFhpCZpVohhwjHR+oHruG4Ew0alDUaVsaEKke2K6G2VpK8BIaQGiJhhc9AsAFSoBPOKNt4PKV4fY0/b4QD7+/sjhekd2zFggBgcGGMAC4ATQV/kqbTV1dVwItVqVe+++67m5+c1NTUV5/QGqDhtHKBH87AG9Xo9CiA4AOwOonACntZmXpGO6OvrC+H8wcFBVETSvoBKK87FuCZTtZwb8O6sIm1C5ubmdPnyZb3++uv69re/rf39/ZjvPgYEBL7ufH44gOLfrCdPK+EQXbPF/TO+jB1ODzAI85JkvQDLzrSi3UQ47aDHGTmcqqf9AB4ALu47lTruOL+8vKz+/v7YSxJQCePM+imXy/F75nmpVFImkwndIbpHegm2Wi1NTEzEc/Eee3t7VSwWY51xn8wl5ler1dLY2Fhs8s0c4Xzp9PGeqrBxBHAAyp6enpABIGugGpSxxa7wzAByJAGw/6R7PWAaHh7W7u6u7t27p42NjUh/866QSBAoEtgw9yVpaWkpbCotLJBrXL58We+88070EOPdur7Ti5b4vYO009hUlwx4et4r4i9duqQnnnhCMzMzOjw8jKbDrCHmNVkGDyS4P9Yz64a1wfxmfXPfjBXMHMCOhr8Pj4/O8UADMEnvcyxufDwCd8PO94iIdnd3Y/F6KsSjdEkd7JH3EMIYsOhYDAAcxL5srsw2K2guaNA6Ojqq+fl5nTlzRqurqwH2AEhEYexR6ZEiz4QIt91ua21tTdVqNUAQi9VTQDggHCIp1PHxcY2OjgalzWdd84VxJh0C+Eo2iSQqd4Dk0SLXT6VSHUYG4aozhw4qvEqVA2aALvy8FwAcQAqWzUXlAOjd3V3dv39f9+7d0/z8fIfh8igZsJ5koHBQzvgQ8ZIOYB4xR3HepJQ9AHD2DFE+wB4HwNhKinMkGV/+JLUvMGDsjlAoFDQ7O6vZ2VktLCzotdde03vvvdeRrmHsnY1h/fEZ1kEqlYq9OP3ayco7Z6U4l6dkHPgxXxlTT7d4UYBHx97yggOQ5+lhZyzdRjDXvX8XKSZ0S/SeowM+AJoNqmFrCHRGRka0srIS24LBdDQaDc3NzcX2SLBbpKO53+HhYc3MzKhcLsfcpl/b0dFx7y7sEMEW8gZE2rBe7fZJ09hWqxVzua+vL9YGDl5SyDVYR9gP2Bnv7E5rDar2HDhQIZ3JZCKAhRnjfbBOXZ/GO2Asp6amYk4ODAxEherZs2c1MzOj1dVVLS8va3V1New9ABSADePE+VnnLo1wX+L2g38PDw9rfHxc58+fD/tB/zfSxu12u0P7x9xkTXB9wL6nN0nHuj3m8GAOOQZgOOkDP4zjIQPWeTzwAMwPFi4GVDp54U7HO8hCg1IqlaIjtNRZHYlR8R45AJdUqrOlA84YmtpZAPYyGx0djQgZBw64GBgYUD6f1+XLl0MMT4oFI0gTVV+gGL18Pq9CoaCVlZX4roM4b//A9RkT79fDfa6srESVDVEhzB4pQ9dvOPBlbHx7miTAlU7aJDgDwd90TPfiAcYLBoz37YAKx9put1Uul+O5AAs8r7cn8HTC5uambt26pUcffTT2zuPACPJ53jmsjLeVkE4chadDeXZnoZIAjffm7J50DCRID6MH9LHgHTjg5mB+e3BCSmd/f1+Li4uq1Wpqt9uanp7W1NSULly4oFdeeUVf+9rXtLq6GkCC9+3PmVyPLl6WTvY5dGbutO8x3p5K9c95ZZfromAhk8/LeXBkXsVKI1fGzpkOd26Inrk+QVRvb6+GhoZUKpVCYE7jUQIRb9LsusZ8Pq/z58+Hxo11BfsEWCFdx3UBaWNjY1GIgGygv78/gg1YUdZfJpMJQIHNcF0gbBP3zRqnwrfVap2qQ8XB816c6ST4RAdLipb3k06nQ+rhLB3nJP3MmujuPt4knf5ZycKo/f392ACd9GlXV5fOnTunCxcuhNyEJsGSorBjdXVVGxsbodFijidBenJt9fX1aXx8XBMTE9FvkEAP1pqeagR/AFr6/OFnkoEhAJSx8sDGfZszdS7V8Hf88PhoHf8gABjAS+rcq9B/z8HvEQuTKqL9BA6FSBEHQYk0ndUBYXwWJ4oRABwRASPIlxQGElaEqJ9+MUSdh4eHGhkZCYFsrVaTpI70IXRzJnO8p9zMzIwkaW1tLZ7HwacDLjcgzuyQOmArn1KppMHBwTDEgAwiaI+6nIr3aJjrMjb+bnhmjBTGt9lshiCcSlP0EET4OF83UjwL17l586ZWVlZi6xOeA8eOEWPe8L333ntPN2/e1OTkZDg0nAqAj7SWdAIKcJyNRiOYGtILzgImD2cSGEsXhQP0m81mMGzObLqTSs57Dt55ck1kMscNQ5vNppaWlmL/wp6enujYvbCwoK9//et68803tby8rMHBwShScKbq6OgoxoC15oEI487YO7vl9wSDwvM4G+3PinNk7PgMc4A55nuL+hZVMNvu1NBt4hwBaTRXdZYWW4JDR9OJ5IDncSa21Wrpu9/9bjQRpbjH9YS7u7tqNpvK5XKxfpAg+BjQ1w7Gq1arxW4TFDBsbm5G4DQ0NNTRQgObBjhw/R1pLdYXUgsPSFl3bluTbCUFAKTuJHWw5S5D4Pxo0JgHABLXOLlNGxkZUSqV0tbWllKpVNhAT/NjW4eGhjQxMRH3AtCmYKFcLkfDYXR42AkAcrFYjEpcnoW/JUUfN+yE+xyuh36PueyBka8FB2DMa7d5buM5knYfrd+HeTxkwDqPfxAATHp/6tGZLqlTV+M5c1imjY2NSDOwOJjYRIaAKhYCUSXGgU71zooA4AYGBjq0IES7kiJyxwHt7+93aKkymeN+XhgrytqTuhvKnZeWlnT9+vUOXZEboaT2yNNSgEq6U/f29r5P64Ph5fMYQHfAfn/J95J8R4BUdFrSyXYyR0dHAVhJdcJkuH6JqBhmkXdPCvL+/fuh2eO+eZ8e5cKIsB3NG2+8oUuXLunixYvBTjF+GE7vM+bl+jhgrouoGiNNyxFS2ZlMJtJWMHVciwM2hCabgGzX/vjnk4drovibd57JZDQ1NaXDw8Moq6esfXBwUJ/4xCc0OTmpmZkZvfTSSyqVSiGu9oO1AIvDWJF6dhDGnE8yVZ7eZM56atA1R562BsTSb45xcSZWUjAngH3WrI/lwcFBFFTw/prNZsw3f69dXV3RbJXAi8KN4eHhjqBvfHxczWZTN2/eVKvVUrFYDCDLs5PmZPsftI+kEWGMqLhlvgwODnZ0S280GtECh9Y2AC4KADKZTLSncHApnaTVYFkbjUYEZsxNZyg9cDg6OgqdKulAKiGTQY+vAdLsBwcHsZ2Sa+pYr84QwmSziTkbdmM3uP+k/tN7rWUymdi27OzZs9G4GTtEepB3D8sOmK/VagHcvKUPlY4UvjC+COiTsgDsMs/H+vGgGbueDCKT2R9f4w+Pj97xQAOwpFGW3t/p2BkIDiYsrQo2NjZ09+7dqBz0NBmGzkWjnsLzie06AUkRcbpOxStmACPulFzzAEPGcXh4GFoRWkcQ2Y6Ojqqvr09LS0v6zne+0yH0Blw4+OL8Sf0AzqO7+3jfO3qT8XvSNj6eOAan5tGzECHiXJIsGNE52itYQN4dABgD6voGPx/jCBCCRcBZ4sAwbhgzb5TKuWDhdnd39e1vf1sf+9jHdO7cuQ6Qyb+pTILRhOXAyNbr9dC/Aai9xw+O0wX43ugXh8j9wUgkAZ4zOi4ePu3g3nifnAN9z5kzZ7S/f9ypHF0k9zQ+Pq5Pf/rTunTpkl5//XV9/etfjw2YHZh7OT5Rvs8bHAdjDvvEO/eUuVfFJte/gwDeCQCYfzMulOrzzpOpZIIlUvB7e3taWlqKFCNMuFfzwqjABjI/uK/d3d1YQ4jtYVB8Hbfb7Y4mvpOTk5E6I33nbLWzsYDCrq4ulUolpdPpCPhoicEejBQsMEbs2gDDh+7S1w+gj8ahzWYztjGCEUImkcvldHR0pFKpFGxfKpUKET5rEXAIO0ZKlMAE2+yAj3mAoBy7zx/eJb/n857G9rS1rydJseUPYNAlD8xlbxXDvN7b24sgmmIZNv/2+YAvgBljzhP4O6PlPiIZUPEzD0yS/iiZ4fiosEQPGbDO44EGYB7hOiBwbYv0fmGwdGLENjc3YyshJqzrqqDRPacOsIJW9vuAQXORcjIvn7wv7odrs/BhgXZ3d2PLit3d3dg3DUYIp0DqBOPJM/sfHx8YQGe/HCj19PSE4d7d3Y0KJj8fz+rVYvw/lUqFHsb1ChgdGBwMJZV8nhpyIOLvnWdwAEuZPw7GjRrPw7M6E+qaCn/+bDarRqOh9957T1evXg1g4loxN/xehYdR9JQDDJePD6woTtTnkI8v53Vwube39z6xP99h/iQZL96Rj4OvG+kYVMzOzobeiM3pAZk9PT169NFHNTY2pvHxcf3RH/2RKpVKzEXmNWACR8W4+Lxk7nuaEbDqY5t8P+6UPI3POFcqlY7x6+rqCtG1s2iADBwy4JZ5R2oOkIUMgTnizCxrAOfM+z46OorNt2l70t/fr1wup2w2Gw45lUoFg9XV1RVBDACANSMphPAe+MD01Ov1WAMEE7xfLygAcHNNwCnzi8/wHUkBHNGCeRqS9CS2ifnnjVUBGIwvwQj9AtHV9ff3B4ja2dkJRtBT0Pxh7TmAzmazyufzYV+cKXY7QiNm1jVjg9xha2tL6+vrcQ+SorqQOUq2oLu7O6rHee+cE8kIzCFVpYyxM/bJoACg7b4OG+n2mIAlCUjc533Yx0MA1nk88ADMU1lJ9O+ak9N+R7VPu92O3jdodfb2jvfIo2+NV6ft7e2pVquFoYDhwMHDEvk9Sp2dkaXOfjHORmBoMBY4BDZTbrfb0TfHhZaNRkOrq6sBQk4DXknH5ZVoSRaup6dHIyMjGh4ejqqb0ya7GzZPL0LVOzvJuev1emyfQrTOfRwdHQUbALjEubnWyJ2msw6wQBgpfudieMbDwbrrTLjW4eGhvvOd7+jpp5/WzMzMqSlbotlsNquRkZHQ7+3u7sZWITjprq6TikgcRiqVCrACG4LhTaaRORiPzc1NVatVpdPpEEa7HswZWk9TJI2xg1AYn8HBwShQANzzfUkaHx/Xiy++qImJCd25cyeCkSTrimYHsIlz8dYUpNcldWySzWf9Pfp8Zk46YHXg5akq2ph4+tcBqgdfjF06fbKVldS5zREH8wYgt7q62tFDr1KpxF6wd+/e1bvvvhuVxjA/fBYQyXYy3uomk8lEVSPbIWF3eObJyUmNjY0pn89HSg7NmLcQ4bkqlUoH2E2ljveuZL9KgkCaw3olN/OcYKTdPtm1gXUBe05qHLBENSmMO+93c3MzGuKytQ7vGPaI9e1SAJgvbxbL2uE7ACMvnpIU9h7NHXswEoBsbGwEiCOFyN69qdRxRTxrDqZwZGQk5j6V6zw3z3xwcBABC8EoNpLgkWfD3nkw53PXA0q3t/w+aQseHh+N44EGYB7ZcbBYPdLCkLLIHfh4k8Hh4eFwjlQqHR0ddYjIWcyUm7uQHj2R606SqTful7SkN+QjysSJsOgQ2mez2QArRFVoEdrttjY2NkLLdtq1faFikBxUJceOiI79xQA0/N4rdXwMOA/XdmOIk6JXGQaHlBU6NkS2vjUK1/BIz58JA+UG2iNeF4L78zrwTL6ndDqt+/fv66233tKFCxc0MjIS78fTiKQxh4eHQwDtqRGeDcOLEyC14RpA9EeAIU97Mo+lk73xAGFemepjcxoY92f29gyk7Lu7uzU0NBT71Pn8R9DLe3n66ad1/vx5tdvtYHgADHzP2S4vluDeALukZH1dEwwwn09jQ31+MZ9cl+cBmYNI0sb83lN7rE//LNfmnbAG0+l07OkpSUNDQ7p27Vo05J2eno6GxBT0JCUHbpfYUqirqytE4IODg9GUeGBgIFijgYGBSIcDrpKsNiwTPQQJ6ra2tqK5p1dvYgPRmNHzis848ILpQsJBWpF57pXYLgvxAh4HzfQ+k461Y+g7OYcHay6r4GdexentPtiuBzZKUoAyngO2luejWjWVSoW+l/nl65dmu3y3q6tLc3Nzof+TFNkLAi7Gj+ADvRi2l/dHmtQLeDxwTgbWbveccXrIgH30ju8LgH32s5/V5z73uY6fTUxMaHV1VdLxQHzuc5/Tb//2b6tarerZZ5/Vb/7mb+rKlSvx+d3dXX3mM5/Rf/2v/1Xb29v6kR/5Ef3Wb/2WZmdnv++bd/CQBBtJBsDTTc5S0ZuGiM2jSqfNXXPE99FKAChwXO4w+blHLA6IMBJESlRpcZ/cC73CdnZ23sd+YbTY75GUnWtgklovvz9nFZwV6+rqihSk093JlOpp7wRRMvovv6akAJP7+/uR4pJO0jfSSToqCb6S7KYzYpzHx9tFr9y7GwKcAIenFxErv/766/rYxz7W0TvJ6X7mT6lU0tHRUaSJ2awY503qK6l/4j0QRZMa9oaLfnD/vr1Lq9XqaJgKmOb+XH/l5/H5CkAmsu/p6dHKyoqOjo4CXNRqtUgPo60DpO/v7+v+/ftqNBodYI/36Nfhdw6MOFzH5mOcnEvJ+cfvYFGYKz7+nr7y7/i9eaCWdPYEV4wdQQqfp2qxq6srNmdeX18PJp2qWteVAbhwzJ6aazabGhwcVLPZVK1W62Dp/J12d3dreHg4evdx3zhs0vqst56eHs3NzWl/fz8q+ZBlMA/a7XZoomDW6VXGfQH+mL/MHb7D2DF+3A9sLWsAhgjQ5YEqn/HCCIKEgYGBAK0wcIBSdHGI+n1uuZ1xQNlsNgM0UhQEu+caSwIvL8g4ODjQxsZGSANmZ2fDHtKGBPkA4NZF/YBUQB6gD5tOIJ58p64pY+75evIA/+Hx0Tm+bwbsypUr+spXvhL/d8fw67/+6/qN3/gN/e7v/q4uXbqkz3/+8/rRH/1RXb9+PYSrv/RLv6QvfvGL+sM//EONjo7ql3/5l/UTP/ETevXVV9/nZP6uw9Mdvsilk3Qfhh+jSlTLgsXReXoKGpkyegcbgAKMA8YMqp0F5mCD9gPJ/lBuYKHGEbn29/drfHw8HCrpUSqZXMx7eHjcYRlWznU4VAkBYpJiY1Ix3C/RJek/hLQerUqdbBpgkPOT8vHeXzB9/r74PFEmxoR7YINwF9Mznsw91xSRMnAhd1KvJXVGgg7CkvQ9DrW3t1eLi4u6c+eOZmZmQjfC5xgHwAgsA2wZaQTGHOcGa+Nps52dnejfRgPYrq6uOAefAxgQzbfb7Wj0WCgU4ryAEGfjmAPJ9cPY8Pm+vj7l83m99dZbWllZ0dzcXGzNxFxl3z9nfw8Pj6soK5VKpNQBFtxDkrHyg/XmTgQ25bRoN6mFSYJx/7nbjaSNcPDOvbqt4Ry+DnCYpM4WFxcDlGEnDg6O+32NjIzEO6vX61pcXNTOzo4WFhY0MzMTNgg7srW1pbW1tZgD1WpVm5ubmpqaCkCALgntFOlcGOuZmZlgK3lWb3nh+iW0aP5spGsPDw9js23sILaTdba/vx/FIcxLlybA5LieD3tIyp4+agBJClx8jQJasVMUFBAEERDzPcbHd89otVo6ODgIv9RsNsNWEUjw7trtdlS0ogdmvsN889xuf8rlcth9AqR2ux3tXaanpzsKYrBhpCl5R7u7uyHs59n5A2NJEQTXdxvo6/7DPh4yYJ3H9w3Aurq6NDk5+b6ft9tt/cf/+B/1q7/6q/rpn/5pSdLv/d7vaWJiQn/wB3+gX/iFX1C9Xtfv/M7v6D//5/+sT3/605Kk//Jf/ovOnDmjr3zlK/qn//Sfft8P4MwXBhsD7gyKp4uIdlqtVuTyXaANqMBhbm1t6c6dOzo8PNTc3JwKhYKGhoY0OjoaTpPFiUaDNI1Hed6kE4PVaDRULpfVbDZVrVbDgF+9ejXSomNjYxFNehQKu1QqlXT//v3QTbCQcXI4Y0AQY8ECZtyo3iMCx1E5o8KYM06cj3PzHngvSYZC6gRCaB+IztBhpNPp6P0FAHO2hPN62iqZykimPJwt48CBcC43WjjQ3t5etVot3b59W4888kj0RPMUrOuZJIX4HrYC0MjvGQfuF4PugAkGy1NVOBBPM+GUtre3tba2pna7HeyKBx/cq48BLKgzw4wLTuKxxx7TN7/5Tb3++uu6dOmSxsfHJZ20ciAAQYMmHaeO1tbWtLS0FFo/B/r+vii193vwVGES/CQZLJ7D2THXbPJ71/8xbtgBDtaxV0k6MCdoAezwLPv7+9EEdXx8PNbd0NCQZmdnNTY2pq6uLpXLZd27dy/SeuVyWYVCQQMDAx3zfmxsLFJVuVxO6XS6I5VXqVRUrVbDzrCmkSPQC6unp0ejo6ORQoPVplUFY+Nsn6SoQBwdHQ0g4+AcYCad2E7ACWPNOuTg34CKra2t2HKIzvukMH1LHgpXWCcAFO6TQgMCP4T7gJv+/n5NTExEoQGBN+sGyQOghblNkZWvS9Y8qX7WoQeTBJTo+AjMMpmMqtWqVldXtbe3p9nZWQ0MDESWwTVevBeYL9LknrVhfg0NDSmfz8d8oeKUgJZ5fFol8cPjwz2+bwB248YNTU9Pq7e3V88++6y+8IUvaGFhQbdv39bq6qp+7Md+LD7b29urF198US+99JJ+4Rd+Qa+++qr29/c7PjM9Pa2rV6/qpZde+r4BWDKV4akEQBRVW66LYTLSB+d7fb/dPha8v/POOxG1Tk5O6syZMzp//rxyuVxENzs7OxGZpNPpqEbCsLH1ENtybGxsqFarxZ6N6+vrunDhgorFoqampjQ8PBwl2EdHJ9tPICbOZDJqtVpaX18P40pKCiYK0OHOzI0VTJWnudxpJYEs432aw5Y6t+bAaEqdQNnHGWaHqBWjR68ixvKDjAjXRQCOY/GIGDANcwSwchBCdJ68TwDl/v6+bty4oSeeeEJTU1PRmBUDxz14ZR3vYGRkJJwrhzt12FiuQ3EIOxAwdwFjyRQcIJ90J5H38PBwR3qWMfQ0roM6QLSnmjOZjB599FE1m0396Z/+qV555RU9/fTTsTdhKpWKIAagjs5lfHxcR0fHzUHffffdKPGncIV350wF7yP5/+TccVbPWVnmHw4TgE8/Kj7nDBnXoB0DVW44RO4Th9dut0M32tXVFTpFngsdIIzL7u5utCPxbWFooEoDVOYwqXm3J2iDANZsR9RqtVSv1+PeSYXB4jhryLtnfrqg3YNEQGitVutIrcLaAi4clAKuXB/JXGL9keLkejBcbKwOw8Mc5/C0JXPS7Yu/cwInQB4HveqYO9hJwB62Hlbr/v37HdWVbtuSqVG3T9wv400qPJvNanBwUNLxfqDValVLS0vhSyYmJgJs+bP6GLgPQ3YA6wUjCDBlDcJqeuubD/N4yIB1Ht8XAHv22Wf1+7//+7p06ZLW1tb0+c9/Xs8//7zeeuut0IFNTEx0fGdiYkJ3796VJK2urqqnpyc2ZvbP8P3TDi+/lhSNSN1Z8m8oZHQXvb29OnPmjAqFQkeUt7u7G6ke6YQGdwfEAt/c3FQ6nY6Nmre2tlSr1bSxsRGGDyofY4TjZNJzzUajoUqlEn+ziKlwnJ+fV7FYVC6XUz6f1+HhoWq1Wkd6z2nlSqWicrncQYPjqKWTjskcqVQq0ppEy4wt3/V+Rjh4xgI9GBoF1xt4xOugzcFC0oli+DCcRNGwc4AiQOVpqaFkijM5RswVB+vJf7sehMNTpj09PVpdXdXNmzd14cKFYHv4HucgJeHOxnV0ycNTijgC9Hy9vb0B9mAdGC9niZi/zDs0IbRL8OeCBfJ0u6dxmavSCVMxMDCgj33sY9rd3dVf/MVf6Fvf+paefPLJYHpgVyQFoACI0ci1VqvpnXfeiYCHe3PWmvXnz+daPk+d0ibBnbDreGDwWBPOqDBuSbaUdCm9rLxik/PRUiWVSgU7yzlplgywYceMlZUV9fT0aGFhIcYWZ0+rFzbL3tvbC4aVlg/VajVaMuBgCSTQYtEHEJsDSIIJotKPLaT29/e1vr4etos1wvmHhoY6GCEqDBlresTNzs7G+ZAiAAZgbZrNZgR8sEfYSp8PAAxn0rkvmDHmMsFPKpV63zZnFARks9kopmLdOIsHUwqoY94hC2E8mTvYR0/p+Rzm39hVPyf3RlW3g3PSzEgWent7I5NB1sMDW/5mbnMe5p03HCYwgyF/eHy0ju8LgP34j/94/PvatWv65Cc/qfPnz+v3fu/39Nxzz0l6f/Tqkf4HHX/XZ/79v//37xP/+7XcubLgb9y4oUajobNnz3ZEFSxC76slKdIIGEbuyYWQrt+pVCra3NzU4OCgZmdnNTExEVTx2tpalCITUZLuRJDtdDWGD0c9ODio0dHRju753FeSRanVaqrVamGkeBbA02ljzWa9GE5nEzxlyX25lsOdN6k118c5q5QEOc4qcS4YL9cruYH2Z/Lzcx43bqQAuW8caJIt8f9zf65H4pzJaHx7e1uvv/66nnzySU1PT4fzdpYBZwcrlewInwShGHMPJvL5vLq7jxvhwkDw/B4k+HM7MEELh2DXHUTyHbkj8XQK18QJ0gl/a2tLf/qnf6rXXntNjz/+uKampiIFBhMGq8r7uHDhQmz4/Vd/9VfRsiKbzXb0jGLs3PFzn8l0NvPUWzD4c/E3c5Ogwp8PVhpg4IymjxcHv/O9Gr3th6c80TKyk8TR0bHuS1IHAwtQgamH3QZs8F2eD22ZpLAPvt1OMuXqqSgqMh30+zxkvsNQwbIT+DGPAANePHN4eBiNVX3zet4p3fhJyft+lYA3rgtjMzQ0FE1aWbMcjCGsc1LrxHsHINPfC3tMVsHbv5DF4B4ODw+Djdze3latVguNlgOsJFOaPLgv5ng6nY6dGmA+AdLYHVKLAwMD0Q6EcSLQYE3AOjKXfE7xvhj7j8LxIDNWP+jj/6gNxcDAgK5du6YbN27op37qpyQds1xTU1PxmfX19WDFJicnY+sfZ8HW19f1/PPPf+B1/u2//bf61//6X8f/G42Gzpw50+HYfXFmMhmNj49rbm5OZ86cicWGM8Z41Gq1KMF2444hxWixcNA1QO2zIJjkOJmRkZEwfqurq1pbWwvWDsOB7mBvb0/5fF7j4+PR2JI2Bq7D4v8sJlI/pVIpqt8QwWLIMMhJvRRjBIsgqaMhoEehNKDkuXE4nsZhbL3K0EGGaxpwbBhexoEI15kLbz+BkaE/D4vYmUu0XrAv5XI5xjgJ8pMpU4y2My7evkA6Bvp3797V8vKyzp8/H4URHBhazodTSb5HPutzzY0ohRSku2DZuAdPcfgY4yzR5KCNwdg7KGeOeNoRUOOsrTvqXC6nT37ykyqXy/rqV7+qg4MDPfXUUzp79mycl4piWByYzfn5eY2NjWlyclJf//rX9frrr7+vJQFOFAbBgRLvPsmoekoKh+vtPpgfOB/uE+dFsMWaINUPiPcAAJsAWOnp6eloFwEQJxVbq9W0ubkZ6bdmsxnBDOdrtVra2NjQe++9p4sXL+rKlSvq6elRpVKJ3lH0/GIeePd1grixsbEA26wFJAt00l9fX+/oK+Vj5Qw6cw4BvM8xvlcsFkO7OTw8HHak2WwGcGLcSJky7/y9wbS5nQBEAfC8JQNr3oMpT6sDNmGiYVphnBxsktpFV0sbCW9FQVCNZo+qR9eeelrVx/c0MoLzolUDkGEf0AKzg0KxWAwdHu8H2+Q9+jxA4Zy+7RPpyg/7eJiC7Dz+jwDY7u6u3nnnHX3qU5+KDXu//OUv66mnnpJ0bCi+9rWv6dd+7dckSc8884y6u7v15S9/WT/zMz8jSVpZWdGbb76pX//1X//A6+CMk4cDL2cTstmsLl26FKjfe7ZgYNgjDWfDZ/y8Ozs7ka9POiVfXFS9VatVzc7OanJyMgSqqVQqtEwAOioZe3t7de7cuY4F5o1dAUE4EXRS3Af6MZwcBoY0FMbEU4bOtJCu81QPDsjbIIyNjamvry96TTGWDsBwKix8nKjUKdrH+GKMfEG64fAxdobTAQ/XxuDS+NGrotDOuM7FWTQXeXtqwj/Hfff29qrZbGplZSXSQjhBB1TOthLle3TOePM5Z7MorYeNxenixB10JY28A1zp2Blub29HmwTWZJKR5LmTKUH6IzFGON6nnnpK5XJZr7/+ul555RXt7e3pkUceiTnO3nc00uQaPT09unLlioaHhzU6OqpXX31V9Xo9rsszMEc9peipVAerzgb4O6B3nze9TKaoAUPe64l5xXX5Oak65hpHJpMJ/R4sG+8Ohpg12NfX17GROnqjdrsd+wfOzc1paGgo5tbt27cDSPf09IT2y4F8EuDzHASPfi+MRaPRULPZ7Oj7xbh6WouxcSAMGPaCHVJnjKnvIev2z1lVf98eMMIyOhjmc86WMiYEtcwDCpYajUYAxGRqliKmgYEBtVqtDrvBJt6+7RBsPDaLBssEvKxdDw79WV3XhT1i7JPMNs9YrVZjPRUKhWCLae9DetWrwjmP2wG3GQ+Pj9bxfQGwz3zmM/rJn/xJzc3NaX19XZ///OfVaDT08z//80qlUvqlX/olfeELX9DFixd18eJFfeELX1B/f79+7ud+TpKUy+X0r/7Vv9Iv//Iva3R0VIVCQZ/5zGd07dq1qIr83zncmeDQSGHh9PxvNlr2bTqkk0XiInq0DkQ5biwc/B0dHcWm3qnUscAWVotOyThUukjPz89rZmYmqH42cHVD7kwbzyodV+msrKzE5rrDw8Ox+DlHtVp9H0PkfaxwUJ5+JCUIsCGNlc1mVa1Wg+mSOitKoe2dOTgNIONYHYxwPgw5KdJkKgkj4pEvzgFD5Wkh9naTFAbYjVTSKLkTd6fEs+L8r1+/rqeeekrT09Md4n6+5z2RYA19fgIASDPxXcaaFMfy8rLu3LmjkZGR0NpwjSRITAYPGHbSJ7AgrpX0dBWHs5f+MwdCly5dCsd0584dvf766+rt7dWlS5eifQKOYXh4WCMjIzH+6XRa586di+qvL33pS6pUKtHg00vwXWxNKo5zOKvKXEimdl0/yL/92ZirpOscePEecYpHRyfNMZlDdIfHoeLAfX24FgtWibXszwNj9sYbb2hoaEhzc3MaHx/XpUuXtLy8rNXV1WiGShBGc1Lv+QZjxDjW6/XQ8aFxq1QqUYnHJuCsWWyms4PYFWfHJcU2TB5AOCAk3UfKDdvih69F1gMAltRwsscY7wdAxxxIAk72yq1UKsrn8+rv74/0vgd/AEpadJBhWF5e1ubmZswJ7t1tTqPRUDp9XGhA6wyvBpXUsXG5ryVYOOaQN2eFUV1bW1OpVFJ/f78KhUK06EBbid3mnTmg43eui/ywD88o/CDP+aAe3xcAW1pa0s/+7M+qVCppfHxczz33nF5++WXNz89Lkn7lV35F29vb+sVf/MVoxPqlL32pY/Pa//Af/oO6urr0Mz/zM9GI9Xd/93c7tBbfz+HRvDuUpFPBmLpjpi+LdDLh+f729rZKpZIajUZHeirp/DCgXm3JYnPA0mg0wmBkMhlNTExodHQ0OsLThd8BAtoO7guGjChtdXVV6XQ6tEIwMaQAiKIw9G48XTsAMMIYOUOF0eP9eBTszs/HjnFwh895cA7er4ZUHeDKxbdc06+bdKCwNZ6K297eVqPRiHJ8dxI+LxxYuHFPAhzuLZPJaGlpSffv3w/A4ePBszrodcflFZlJtg0RP8fBwUGkUcfHxztagSTXgB+MIQCs0WjEvoNeacm9MTZeeeVjzbN45H7u3DnV63UNDAzo5s2bevXVV7W9va3Lly8H4+DbJNFSBI3N1NSUnn/+eWWzWf33//7fVavV3leRCxPqAQFj6/ODcXcGq9lsBhhi3eKkAFwwGFQaeqrGAbqnapMpane07jx9jjEn2I+Q+S+dNB5mzRwcHG9zc/fuXe3t7WliYkJnz55Vb2+vrl+/HiAKcEPAQiofpi3JZnuTU0/twwbxnDA66F45HDzQXJUefaxdhPa+nrz1h7PsvGOCSx9X+uzx3kgneqEMzBt2nOfi3Xl/Lex8u92OvnWkKtHcttvH+rpGo6FsNqtcLqeLFy9qY2NDq6urWl1djTWCxpN7I/gDfPLvg4OD9+1h6euJFHCSecXfUPgA0GPOra2tqavruN8fRS/ed455DAhzO/bw+Ggd3xcA+8M//MPv+ftUKqXPfvaz+uxnP/uBn+nr69N/+k//Sf/pP/2n7+fS3/OaTDIWHxGoR22u9anX66pWq2q1Wh37mXkkxn5nGCFnQqQTDQnXwTEAklwzsr6+rv39fc3OzqpYLKqvr0/Dw8MhusdZwkrhiEkXYbTp37O3t6f79++rXq/HIoSO53MDAwNRQYXB9GjVUzeMEVEzIAz2jPtKsgtuOIh4eSZ/N54+9HQBQlFnFBkPT9PxeQCfF1Mkr8MY0O/Ho1EMvac6/PBUFt/xMWL+bm9v68aNG3r88cfjfl2n5SDLUwuehnRgynPg3DDAg4ODGhkZie19fEusJOjzd4Oz4jzogSi8QCPmgIH74Dk9jZpMC3Z3dyuXy2l2djZYgcXFRX3rW9/S3t6eHnvssdAFUZFMzyd3zOPj43r++edVKBT0P//n/9TS0pIkBeCg5xEO2CsMfQ16+op5ApD1OeptQpLpNtbs4OBgjJdXnAJiWDdeIMJ97O/va2trK4Aq7z+ZNgcssR6xG8gsRkZGlM/n1Wg0tL6+rtHRUWWzWV28eDHAxOHhSdNhNK4wcgR62EM+B4NCNSfvFykGfQvb7eM9GUulUgcbyTyv1WrBGjkI9lYInn7zuebMPtkK5p43s+adt9vtjvQhY46201l4mCvfNo0A1Zlnl0v4NeinhX/o6urSI488or29vWgVtLi4qLffflsrKythm1zXRlsIwC3peG8q7Qe2DpBPEOT2xndWwH+Vy2Xdv38/dJUTExOxh2c6nQ7pBfaIsfqwj/8bqdAHGVw+0HtBJh28R7nO2LjDbbePmxSyBySLFgeK4Xa9xGnpHhyhdBLZOXiQFAxUo9HQzMxM9E/r7u7W5OSkpqenI5WDGJWyZ09TwBphoJvNpur1ejgMmAqqqLzSCEPsAAyj5WlNFxzzGZ7dI1vXMDkAparNKyb9nXhKiSgSgIAz4B6J/vg+79ijQ4/O+YyzHTR0ZbsoN7pc67SF61oY/yzGr7u7W1tbW7p586ZqtZqKxaJ6eno6WBMH5+6wPQWbTNEy7vwBFE1OTmpjYyPG/bQ/H5Q2JI1D1S5OBiDMe/H36OfzlB5jA7PQ39+vubm5ALr9/f1aWVnRu+++q6OjI129ejWYJbREnmYH4GSzWX384x/X/Py8/uIv/kJvv/12XI85AvPnlXK8Swe5zC0fP+9X5Uwf4w/w8XQODtXBHusdZ8a6Z/0AdAGMBAI8c3d3dzhF15t5QAAb5dpMgEZX13F7jPHxcWUyGZXL5Q7gJCmYF8aH9B92jXlHWpxxRWAOk55Op2PLs0KhEOMJcPe2FlTAMl8AXswhr7R2ewBbh40CkHqqjneGpgtQ5u1psN9oxWDB0MtSRYykwZlcep0RHNDYGiDIu+3v71culwvZDGv4zp072t3dDSDqzDPBt4PgdrvdsbMHz+dMIc9A6hrGGQ3n1tZWFLhIx+Dqzp072tjY0Pj4uCYmJmLnAnSL+I4kU/7w+PCPBxqAeZoLR+FOnqgCYy+dlEuXSqWO73KwYGHAvAtzMq3poleoewcmNGDMZDKamZlRoVCIyIq2E1QY9vT0qF6vx0LFeAFKvG0EBpmO/DhaQBIUP+kVukyzWS4GgnQQwM4rgk4TFCfTbDw/kRtpLnqg8Y4cMNGpuVwuh2bMmQIMkHRSGJFMfxKl804xzoBNBLjsvweQciCeBD+8N2c8PB3K++CzjUZDi4uLunjxYgBUwAn3wX0i0ubZiHB5XgdPHii4c/VtVE5Llfr3uF+cJaka3mFSH+mMUpKN5Dr8HGDR3d2tfD6v+fl5bW5uxjqBnU2lUtEvDfAMo9zX16dCoaBUKhVzrlgs6vnnn9fBwYG+/OUvK5vNRjd4B8A4eH6Gc3H9TDIASLJ42AFnHhgzXx+MHQepO87haXcHIbxzgiuuB7vBM7A2cPj0afI1gzOt1+u6fv26ZmZmdO3aNU1MTGh7ezvSwN3d3QGkABzcJ/MaZ0wAimgeNgo7gR2koawHgASEBDgA+tPaQXgaEEAE2GBMGDfmO+1jfO4xht5Q1IsAAJIEmpJCh8jzZDKZ2EtzZGREk5OTUW3OWuff2Aq3GaVSKcblqaeeCh30nTt39O6774av8OCNAwDKOyKQQMuFLg0NWzabDdtHzzDYVbZEYt4xjuyMcPfu3QgqaOeDXf4oHA8ZsM7jgQZgSUDkURi/c4fiwKrZbHb0n8EIYEw8h+6LSTpx1vycyY5hTafT2tzcjErLxx9/XBMTE9GEltJij4IBFu7EJXVU4EjHhoXKMZpCOjPERrQ4C0AZ0RiVOuwvScoT7QsAL5VKxf+TjFdSrwejAJBM6pt4V7wLBLCAAGclvJqT93FaKhNgRYQMgENHQ8qAtKOfk/ly2nz6oMXMHOKdNJtNLS0taW1tTQsLC/GevE+PFyz4ffg8cgDk1wL0dne/f0ur7xXJMl8ZE+6XcXeWxdkGB3U+lkl9GL93BzE7OxvNRtPptNbW1rS8vKx2u61z584pl8vFOQiIuru7AwBQFTw6OqrnnntO29vb+su//Et1dXVFGwHu11NZrDd+7sxSUq/FM/EZBOSwWp42ZN3gtKkERKOWyWTC2cJ08L6SqTbmA/pMT90BUliLR0fHuwZUq9UAB7B7rPNSqaQ7d+6oWCzGbhG7u7sqlUqxjg8PD6PvFvo7WKmNjY1gfgYHB6MxK4zf1tZWzFnvAeg6Uhiadrsd3folxdwCFDF3kq03eD88nzNlAC8CaOwiAStBI/OPscfueGBBM1WaJg8MDMTWb/x8dHRU3d3dKpfL2t7efh8bSpCQyWSiqAlGLJfLaXp6WpcvX9by8rJu3rwZkg9PnRLkkn5mV4x6va5arRYtJzKZTIwlwNwLIViDbgvRA7o0hvVeKpW0uLioVCqlXC4XnfgfHh+d4x8EAHNDlRQ1Op1NimB7ezv2s+NzHiWzh5enZzg8TYSBp/MwjBAsz87OjsbHx3X16lXNzMzE+RGMonvo6ekJJ+DsCOyTR9Y0cwXseEWOGz4YDIwv0aOzG7B2sEmkQWDhKJPG+PX393ekMgAK7sC80aOnYnkGr8rkOfk31Ztezu1jzb3DMHEerk3PMhiw+/fvR3qMd5cEYK5hcSCSfO9JULS5uanXXntNjzzyiM6cOdOR1nOjSRqI5+PfSTDl85RrYbSlzv5JHhQ4YEo+G+ADp8Sc5l488MBhOFjlvpIgjXnCvJiZmQnBfT6fD8dz584dHR0d6WMf+5h6e3vDMcHSDAwMhFPmHmdnZ/VjP/ZjyuVy+uu//mutr6+rUCjEOnUmCwDjTpyxPS1oYizdeQGUeCZ2vSgUChHQMNd8rtA/i/F2kb4z8VwLlhg74d9DJ8QG0b6eSW2S7hsYGFCtVovCCirvtre3A5ykUqmo3KbQJ5U62ag6yY6m0yf6ukqlooGBgZBDSCcNaAkWvCUQKV6E+4A27JBLOBgP5j5jxdr1VKXPcVL4vs+l68g4J/eCH6A10OHhoaampjQyMqLu7m7VarUA1djedvu4NRHMLm0wSAOzuwOtLWDNCoVCgLCnn35azWZTt27d0ne/+93ou0Z1JXOQNYatZcxHR0eDDfM+iN7XDFvmAVTSjhHEZrNZNZvNjp6TH/bxkAHrPB5oACadOE4WC+ADB84foiZy/iMjIx2bs6LbQEQJzXuaIUdr5NQ8Udrm5qbq9XoIYi9cuKDp6enYuLtarer+/fva2NjQ4OBglEd7FRPGHgaKPePQEuzt7UWE7sYeUCadaMcQ3gKqnKVyrQnpCcS9OF2YG++Z41Evn0OrgLHnD86DMm9vrYHjdJ0U0be/Xzcu/N8NEI6XVO3Ozk5sJ0JqUupkSV2QipM8jbXjezwr99LT06ONjQ3dvn1bjz/+uEZGRjo0Jswf0kp+7mTq01NTyTSgAyTmhKSOqtEkQGLekqbx1E+lUolnwKk4y8JY8Z74OePuY7G/vx8s1tjYWLDK/f39mp6eVrvd1r1795ROp/Xoo49GwQtplMPDw+j2DXPJfqnFYlFjY2P6b//tv6lcLkeqC80Pc4b3z8F9wyA5+GdcAIFczxleUvOVSiXS/rQmkE6CCTREHvw5y5oE1Iy/Bzxo4DKZTLQ7yOVy6u/v19bWltbX1+N82BPmPQ1Yi8VibF2DONslEKQeCZw8dUvaEft1dHQUbSvoAcd98wxuQ2Bl0JQBjpJBsLPbzB0P0phz2F6XcwBGGXfS+MwB2CQYpM3NzQgMpqentb6+Hvvluh7K7UYmk1GhUIhehxRnAfQJ6sg4UFlOpgF7wB6+ExMTeuSRR7S+vq779+9rfX095BYEE7xDWMTd3V2Vy2Xt7u7G1lS+tZyPnweDnMuDKdLbtCfCru7s7OjP//zP9WEeDwFY5/FAAzB3OMno2KNTXtD+/n4AJHLw6FkajUaIQakaSmpJMNIe5WGkYazY4Pvo6EjFYlHFYjGAH+XPrkcD7BHlQKvzORwc94bIN9nxHvYLQ4iRGxkZUaFQULlcDofNguQPxg6j7FoGxhktAiAwaUyh9F27weFaO5w6ThNnJKmjECDJICQBjGuneHYAIOOKANYrC905JMFWEsT44SJvUgqbm5u6ffu2qtVqR6sVZz2IxrkPDClGMqnpckfuadbkPToD5iCMP3wGoOQVkLQ6IC1DqTvG3rcxcb0Sz8S9HB4eRoRfLBa1vr4e7SQIEFKplFZWVpRKpUKYTzNixpRdMQDoONRPfOIT2tnZ0R//8R+rXC5rZGQkKvy4B+6Zd+Tj4OMK+HHGgGdl3rG9EKACEML78nH1cfBUGvfhjJu/bxyl2wHmB+cGNGcymWgGurOzE1IHL/qAGcHRVqvVAF2MM82AHRzSxsLBo+s0Yb5d58mB/djd3dXq6qp6e3s1OTkZFcL0D6QoCbCMqNzXfnJOk4JFHwXo9MADhgeNJHMCFo2q4YGBAU1NTQU7iH11Vp0ejZubm8pms/F/1qtvYVQul2OM6Mnm79wZppGRkejfh16UXm5kI2iqOj4+Hu+Fd0NFKb7Ai3iYv9hv36PTszlJG/xBtu3h8eEdDzQA40hONP+5p2j29/dVLpdVq9VCbA+7BG3farU6hPAOAIi+6MXlWpp2ux09mzCWxWIxxNOkk2ikB3jzdhOecvK0GqAGw8K94BTYeNmF+oyFp++Ijlzfw9/SMegZHh6O6+PAocRJZ7pRYGycyeC7bjAw6jg3UmykJXFSbmAAvDha/5mnh6j+ooqJ5os8k3TCjEiK8XSjlAQwznYBRNxIct7l5WWtr69rdnY2HIMfzi55NOs/B0RxLf+ZtzTBcSc1eM7wObNHmolnBPyhSfKmv1R3whjCiPDOGC/um/MB3kdGRjQ/Px/vgvJ5rn3z5k2lUiktLCzEPKLSq1KpdOyXCWjs6urS008/rXa7rS9+8YuRIoIJ9MpcHI+PuwdNOEjmjo81DI9r4mA2dnd3I+1EytTP52PO9X09eZocITSOGgDVaDSCQQbs0PHeWVXWous9vcrO9VDeTsLfJ4ATgOTMIOlQ7gN74sGHzyWAI9XA7JsonfRCA9jSCifJ6vrPKGap1+uxbyX2ExBGuhQgNjw8HPpWQNj29rZu3bqlTCaj2dnZ2F+TQI330m63O7R53CN7faZSqdCRUr04PDys7e3teHZ2NZDUwfKT4sxkjns+Tk1NhX53eXk5Gqyura11gODh4WHNzMzo8PCwYys1/EWywAnAyPshyHLbwxz1HoMf1vGQAes8/kEAsNP0M+4s+T3tG2jOSZoBWrjVaqlUKoUOhAOjC+jCwAHAYNZobSFJw8PDmpiYiAoUFj6LFKaJhoeePiQt5elC3zORKBID1tXVFY34PAJiUXt5N+CBczhoODo66ti+CTYQ8SZVNYhJMeBQ/gAqgJ87D9d0YQxherhH/kjqAHBupN0BSidGhuff29tTs9lUpVI5tYUIHdWTYv8kK5YE8n74uFcqFd29e1eXLl0KQS/MCZ/1NIwzMZzDn8+/xx9vVnnaPbhjlE4YOKJjZ9ToB5bNZiMQSGogOQdOmWeAzfB3zFhlMpnY6/XmzZva2dmJbt2AjLffflvb29u6evWqcrlch5gbjRPOP5vNBivx9NNP6+joSH/2Z38WXfMzmUwIzXHMSXvg889Tp76uWWOAOtKSDiT6+/uD/fXvOmOXLKYAmPKHtcb/eYfoUpkHFABJnUJ0/57fByn+RqOh/v5+jY2NRR9BDyIAtfwOoI0GivdAqpK2Db6BuI8j73xkZCTeGQGja8cajUaH5IFxcZbGmXkCPbZZA2hKJ1kO/sYWkLKl2nhwcFDDw8NaWVnR4uKiisVipLC5z2w2q+3tba2treno6Cjae2xubmpzczOCQoT79Xo9AK8DcZ4BoOTsFQf3OzQ0pNHRUS0sLOjg4EC1Wi3+kDWhz+Pi4mI0Mfb3jw/JZrPRs+7w8DAaHUsnW+Phm6STQrGHx0freKABGJPco12nXt25Hh4eqlqtxkT3lN7Q0JByuZxGRkbU398fOhkcDcbWaXXSkKlUSo1GQ5VKJTZrHRgY0PT0tMbHx6MrPZ+HNsYgutGWOnVOXDOdPu7LQ9+fZANEzsn33Bn09/cHy8fPnEniOhh1QCZjCouCoevr61O5XNb+/n7o0rLZrPL5fESNnv6SFAaJ1Jbrcng/ngp1IMB7TDJLSWDjVVcUQfj3mBMIdwEUXO80yt6BvP/Mx2p3d1f37t3TxsaGxsbG4j3wea+KJGXnTItfL5nCYt6iZ3OtB5/x1BHnYV7wt4uVmTsYdXRizjA6YOYcXonLz5z9PTg40NDQkBYWFqIAoqvruO+Ys6K3b99WKpXSlStXorrTi0Nw4l7oMTQ0pI9//OMqFov6//6//0/vvvtuaCp9I3fuiflMQOK/4/+eBmf+MS8AVcwLgKcDUOmE4XJw5wGHb/vloMMDqN3d3ZjD7XY7NEgEd6wj5hxjxfWwf8gCAEOkJpNpMu4XvZWkAGGkjQFj2B1Aja89AgmcOoEa3+d9kz4FADkDyXqA/ed3g4ODwcZ5XzTmMd/hfARtpI2HhoY0MzMTDJKnQXnv2EzSs+l0OlhYAFEul4v1QYC9vLys/f19TU1NRRDR1dUVwbuzWYB22KdKpRJB7sDAgCYnJzU1NRXvmXFrtVqhM+P9Yd+8eAr7go3Z2tqK7ekkaWxsTKOjoxEsJ33Nh3E8ZMA6jwcegDnT5QJwqVM3tL+/H+wXYEiSarVaUL8sCtpUoINwdgaKF7Zne3tb1WpV9Xo9nOvk5KTm5uaisaHvp0jaxSPUJPOBw0CQ3NPTE4JyjtPofKeiMU59fX2anJzU8PCwarVahxbEz4Vz53ve4whNCiwTIArKnvt0MTT3xXmlk61aMPA8697e3vv0YzzDB+kWMN60B3DD7m0ufJ7wb0AN79Lv2+89mdLjd4wPYOfu3bu6f/++Ll68GM6VcfTUJs+Oo8dYe+sODpwl74brsusBGi1ne5L3ybvjM5nMcXPezc3NjnXAhtncE/fIefycPp48E+OeTh/vwXru3LnopJ7P5yMNA0B/66231G639fjjj3cEOQ4kWAMwQl1dXbpy5UoAhzt37mh4eDj0Y87i4XCT745nYB7yrDx3Uu+Eo+Nc3iLGReKuN+W8Dox9rXpA5/MJRpY2OaScPA0I08Jn/X7a7ZPtuEhxAkR3dnbiOwR1tGlJp9OxX62ngA8ODmKusZ8qYNptCJpCZAmkPgGspCVPm0c+foAIWKTR0dGo5CQFx/e4DiAWWz0yMhJ6T8aP4oVsNhuMPuMJu+mNdQFkXAtbB2AbHh7W7du3tb+/r4mJCY2MjGh0dFSjo6Pq6+tTpVIJ2846dtbbdanOsno1Zk9PjyYnJzuAOgFXsq2QM3+ZTEbj4+NRZY/MBu3aRyEF+fDoPB5oAMbhzI50elPKvb29KN/GCJOygl7u7+/X+Pi4Njc3tby8HAsPahwaFyaMMudarRYGaGBgQDMzM0F7A8D4nqTQB+CkPf3DwoX+7u/v18HBQXyHxYqx5DupVKrDKTsLk8vlOqrUGBMfH/7GgBI9Yxyh7YeHhzU0NKT19XXV63Xl8/moBvK0J+f2KJmoPMkAYVgcsCTTcknASYoU54IQlRYUrjnxdIePM+dhrjiD6s6Tg+/4mB8eHvdmQmALS0gFK2CSPzhL6cSISupID3BNwCUieU+rMReSjKOzIz6+3DefHxoaiv1OackCM+Cl/dyPAxCfP4whgGNgYEBnzpzR3t6eXnrpJdVqtXDudM8/PDzUjRs3AqwhASBF6g4PMTlO+KmnnlJPT4+++MUv6tatW2q1Wh1bvPj8BwjBhDlrknwenLaL5H0M+Zmz6p7e95YOvraSjJmzbqwD0mzcu7NjgAfuudVqxZZksGfJTbHRgeL0vW0CmlI+u7GxoVQqpUKhoO7u7ugo77YFTSxz29PiOP6Dg4NoCksqjGfjs9hnxhEpgzM8jElPT48KhUKw5t602d8tdgoAhwSASndYSNh67LQH7lSUSopWH7CrgBbuq1gsSlKkSHlOqlfRenkT72w2G6AMwOn2nzSkN0tmvWLbsLEOxryCnetzv+12OxpewxJ7IcWHdTxkwDqPBxqAYfRdD8DhThSdDMYdIES0zO+GhoaifP7w8DB2unfDhdCb3zcajXAYpAcp73eKHLqYCIj0jUfqGAbE7HRBR9Tp4AvWgfMAwFi4aFqIigGSOB4AB2PFGPJ/Dn6GIcCoYTxJMzkLwrvgvAhWidI5cEbo4pIFAQAHB0eePiOSJHLNZDLBSCajPQdSbuhJBfPcyVQnn+dwYMa82N/f19ramjY3N0MkS0TLXKECTFKHAcVxY1z5Pc4PRyNJGxsbMQ+cGcMoJ5mw0wCUjy/R8cHBQZTx866S7Rs4v889HxsHgN3d3bEtyvr6eoCpw8PDaEC8vb2tb3zjG9rd3dXTTz/dAcB9hwY2O+e9Dw4O6hOf+IT6+vr0pS99Sa+++qra7bbGxsYCuACsYE98PJw1dO0cTtEZTO9L5e8EUMN4AxS5V9jh5Lr2/SlhOgAHpOuYPwjxeccwTdgQ3zwc4Cwp5A4EAIODg9re3lalUgmmmnN2dR1vmL62tqZ8Pq/JyclY2/7Mrq1kfvjze58x1rUHWcgB+BzrDPuXTO0CtrEJvE+3A9vb2zo6OtLIyIiGhoaiEzz95tbX19XV1RV96Ui3whx6oLW1tRX7lU5OTnbsAkJRCftJ7uzs6MyZMyoWi6rX6wE2k4VEzraxR+P29nasN8YIvRo+ie8x9wBYyGNYX+wTy04TpEdZs+gu6/V6R+Dx8PhoHQ80AEuyOBhpHJdHsGzjgGH3qLVer2tpaUkXL17U+Ph4NNdzgEcvIMqe19fXY9F6eufixYuamJjo6GiM46XxHpGjO0sYkp6engBgGCYXxnJed4wYeCIiHDIOyClx6SQKcUYkqZXCuKLHQJdDF+j+/v7oHF0sFsNo+fPi5JrNZvztTS39+jgODi8ycAPCd3g3iNO9KqlWq3WADWetTgNZnpbya3J4qjipg6Ic/+bNm1peXo4tdgAqPT09kW7Aee/t7UXzXt49qZck6OG+AGuU0vf09HQALwdbvjaSAAK2xdNF9XpdBwcHHU07Hfwm00c+ljwTgBvdztDQkC5fvixJ4ZjRAe3u7uqRRx7R5uamXn31VfX09OjatWsqFAqxfQzOj/Yme3t70a+vv79fV65cifTQ9evXValUVCgUgq2muIVxhylJFrBwuD6GdCTzjXeZBMbSie7Ii3NOOzdAEBAGyAVI+fpnLtPLLMkce08q5gU9pWAaYXBg0Fx/SFCYzWZVr9fVbDajLyFOHhDI3GPrIeQUHmAA5lgbm5ubYWM9S1AoFCKtCfj0AIXnT65HxtXbaqTTaTWbTXV1dYVWi51HsCPNZjMACvPK2U1YUy/EqtVqERDxbIwd40N6dmRkpGOjc5q3UpA0NDSkcrnckcKlfc3W1lY0jmZjezTKm5ubqtVqHeumWCxqfHw8tFx7e3sql8taWlrqaEvkqUn6ShKMP2zE+tE7HmgAhtF3QyB1pl+kYyNZrVYDCOA0pON0YK1WC9bk0Ucf1cTERPTvYhG4BgyKm73tMNILCwu6ePFiND+FsneNkWtynLnDwHIN2CoAIg7aBb2uYXGj5tE31+D+MUIuRMY5EXF5asRBUTp9XAmaz+eVz+d1eHgYlT2ABx937o0KIUrrPfVK6gdQlEw7cl1nx9z5AZBTqVQYe8SuDpYknQom/HcY5qQw39Nxnn7ya29sbOju3bs6f/68hoeHY07weZhPBzUuEoch+yAWsre3Nzp5+zg4iPXvuIYsCWD9XTP2a2trSqfTKhaLHWxykl1mrjlD6fOX6rq+vj5NTU2p2WzqzTffVLvd1uDgYEeA8dhjj+nWrVt66623YluYgYGBcGq7u7vRE8rXRKPRUDab1aOPPqp0+rg45OWXX5YkFQqFsAe+TniOZDGBM+XJnzn76OOaZBNgGwgkYEVOcwykDP3/tVotxpRKu9NSya6VopIbKQTfp19fq9WKDvsAJO7Ju7PTLd132PB5n8lkoqGnVxUDrpw9pqoVjaXLKeiR6O17YO7QxBIUIF53bR5zGE0Xto5xHxoaUqFQ6ND0etDh75xG0khDGCPsHmyZB4a8b68aZh4j0CfogwEfHx+P4IFgn6AWHTFV8gDCYrEYwQaAqb+/P/R329vbunPnju7cuaNbt24FMBwfHw9ATdoWkM97+yikIN33/iDP+aAeDzQA83w6bAIOznVhe3t72tjYiGarfIaogKh5bW2tY0PpfD4fOgC+w75r1Wo1JvbU1JSuXLmiiYmJqAZkM1uiN5w4lXMOGCWFVsD/D5XMdhoYUah7mAImICkxxsEnO2AKA4LRQzvlTIanB/gbw0u6kDJoytV5Pp6J8fI0nzNtAMWDg4PYoseF0O7Y3eERnTsokk6cE47E7wFn4oJWB2Pu7DhvkqH7IODG2LZaLd27d0/lcjnaJPg7BlDwfU/HkGJJprmSKWIcD3OecXRHz2eTTKYzV+6McE77+/u6efOmurpOGhQnx9jXHYDYGTb+sJNCb2+vxsbG1G4fa41gfNjqZmpqSn19fbpz506kEh9//PHYfFhSaGkYU1JSsDGXL19Wb2+varWarl+/rna73bE1GOPt4MuZF9eB+Vzw+YHz9GAJQEh61St8SR16ygx2CvDU1XXcST+Xy3XojOgJ5oESwQpzpd1ux7Y/gFK3d5lMJmwQz+KNV5EqoOnc3d1Vq9VSpVIJZrXdPha1T01NaWxsLMTqgGOeDcCVfE62deJ9j4+PR5U4DJavD2+FQVDgAGJwcFBDQ0PR949rUvGM6D6fz6vZbAbI8XfM2HA9+id2d3draWkpNMAHBwcBwnguxtp3c2Bj71QqpVu3bmlxcVETExORZid9SPoYP0JAys987RweHkYRAvcJ21UqlXT//n2trq6qVCppY2ND09PTGh4eDiabLAPv0HdYeZCByj/U44EGYI7oMY7OMjChG42GSqVSpArc8aFn4BylUimExCMjIxHhICSt1+taXFxUqVRSb2+vnnnmGT3xxBMhzsTgEGF5BIYIG7DoC8+dGYAwk8mEOJP+XEmHgREj8nZWJcmAOJDg+QFGnprzlI2nl0jFYtwBr85oYeActOEAYdO8EpDnxSHy+SQTdhrD42DM9RwYz9MOf37/dxKsnjbGfN/fGT9rt9u6ffu2VldXNT8/Hw4asC0pumo728az4HySYC95z9wLDssd2Wmgyw9nBJJp6EKhoFu3bunmzZu6dOlSOEl/V7xfZ1EcVPP+APipVErDw8M6c+aMXn/9dZXL5QhQ+NNuH2/Y/cYbb+hb3/qWurq69MQTTyiXywWDQ/oIIXJXV1c44Z6eHp0/f14//uM/rnQ6rTfffFPb29uhdeKPNx719ZB0TM44edrRn88/T3oSlgfHmtQKSidArr+/P+ZIpVKJVCE2AgAGe04rBxe3w9oAdAlyAEiSok8U6Ux+h81Lp9MhqYBtopAik8lEmwRSoaSAEXV74cDR0VF0h2eOODCkAtyDCsYD+wvI8bYJSDao3hweHlalUulg/QBijLNvD8QcIr0JiGLNSYqNtev1erCnBHFURZJVIJuAnnh3d1dDQ0O6cOGC7ty5o2q1GmsfNpP0Ld3vYfqOjo5iTBkvbCRpxvX1dS0vL6tUKkXQLEkTExPRsBVAiJSBogJ8lo/Vh308TEF2Hg80AIMVIspIamdwHuw6v7e3F4Jxd1ZECAiq6XwNACPtsbm5GeBrcnJSL774oqanp4N18j5fznK4+DrpsFyrxuKGpvfCARaPi6Mx/KRhkhoi6cSJYCgdlLkhJyLmuzy3nwNtiqeR0DV4xM+zueP2NI+zlLA6ALDTwEMSNHi0jNFOpY5FwGw+y/cckPvxvVgtxijJiPG50/RWXV1dKpfLWllZico8AI6LiQG8OEAcQfK8p+nU+Azzi4o1TxP7GCdTrD6ezgym02nl83nlcjktLS2pp6dHly9f7mgpwDl4R9x7Ms3qQAKG9fLly1pfX9fi4qK2traCdchkMiH+v3btmt555x299dZbGh0d1SOPPBJOiffqTBZghDUOaMvlcnr77bc75jqpJgdCzqiedjgLCehzm4HzZox5D57KSjLC0rEuaXp6WgsLC5G6hnmi4pXKPQdxgF4/JwwLtoJ30mq1JCmYQMCwz2/Gk5SYp8mKxaJGRkYCxNK+hx5lgEOYUNY8LTIoJABAAkSwFQB0gAF2nGf3Obq7u/u+Tb6Hhobi+34PMHuuq0ML5+lQbAXM3/DwcATQ7EUpKZquUoTQaDR0dHSkQqHQoWeE4RscHAx9V61Wi0DVdxABJLdarXh2MgoEFvV6PXbz2NjYiIasQ0NDGhoaiiwEfgnABcPonfiT6fWHx0freKABmGs9pE5AQwQBoKI/DMaM75AGwxHR+oEIn+1UaHK3srKiK1eu6B/9o3+ky5cvd9DhGBv+dnDgRp8DB4PR5g/GBzGmi4D5jGskktV1nrbDWTrzB4jZ29uLzY2Tf/iuG3w/p3SyFUay/xqGBWPA/eG0uBcquEhjwko4EwdL4YczdPSxSqVS0Y/NmZokaya9f+seZ/i4XpJN8vM40PfP0L6k0WjEjgGATJgMQIpfG8Gzl+ozpp7uQQPDePqWI9wH95XUWiSZT96lp1vHxsa0vr6ut99+W8ViMbR9HiQk2UkOom/mMvNld3dXfX19WlhY0NraWjhDT5Xznd3dXX3nO9/R17/+dWUyGZ09ezbmOkwCjgimN51OB4syNzenf/kv/6Weeuqp2PT+5s2b4ZxcqgC752wE48H9JMEZ9wLo83nl684rSRk3fpdsLUGFnr9LT+8BUhy8Y/O4X9YYYwFjQ6DEe2GdMeY0Tx0cHIw9CrPZrMbHx6Oic2NjQ6urqx3BjqQocmi32xH8eXDkWqpCoRAMfVdXV8gE0ul0NHilwvDg4CCCZN+GTVJso+Q91OgBRroVmwKDy3vd2dlRtVqNMUmnj9s/MBckxZZJ3d3dGhkZUalUUqvV0sjISGQ2XA8GW0d/ReQrm5ubsaUdAnnePyCz2WxqfX09KlQB+t3d3arX61pbW+vYi5QgwvcCpQ1Pq9UKxouWLUl28LSm3x/G8VAD1nk80AAMY+kpqqS+hwnOdh+eJpFOtAc4c1pI0LmYHlqwYleuXNGnP/1pTU5Oam1tTT09PbF/IsDLe+0kI2DX+Eid6TYYLa9+9M120WvgEDEuzgA6gHHnhpYhOX5elABAZXyI6qniIwoFNHV3dyufz3ekD/1ZXRfiKU4ff6/68sPvld9hxA4ODgKIUJKdTh9r/UqlUkc7DgdhTsEnU6TOePEMDr78M8mUH/d2cHCge/fuqVqtqlgsRmROjyFYFAwl7AzXSzJIrgVKpY51KKQyvGFkEth/0MFnfX4wtrQYmZ6eVqvV0tLSUlRzcT+AAJhegA/j7Ck6ADYBQj6f18TEhO7evavV1dXQSzKmg4ODmp6e1sHBgV5//XX9yZ/8iX7sx34smDC0LIAx1hkyBNI4+Xw+GhefP39eTz75ZICItbU11ev1cISS4vlYCy74xpb4XPU5SeAldRbXJNeavyPeL2kq1if3AavJ+wd8oVd1DRtd2lkHbOMDmKItg7c4Yc7B6iBILxaLKhQKOnv2bFQ4V6vVaH3COXjXOHSA1sDAQOi0+Dm2lcpYT6F6P0LaeLjulWccHByMLYkIUjyA6u7ujqIM2L5qtaqJiYlgqWDQHFRjN7AjVI6y3yJMHmlb3glBFDaaTAFFEchAKBypVCoqlUqhHSODQHoeO0JRFwEgzKazY6QsAbA05yaY3tnZ6SikgFCgIS3M6MPjo3M80ADMUygY8iTrsbOzE5u7en7dgQcR7MDAQPS5Ojg40PLysnp7e7WwsKArV66ov79fIyMjkqSVlZWI5Fkk6AwwpiwmBzocDgCkk0o/QBgL7eDgQNlsNrZLgs3jXERmDi44l7Mszq45YMWxIrZ3oCGd9OpyQILBHBoaChGqp+mkzg2sPRXJge7EGb0ku8I1PWXJ33zOq318Hz1P07jzcTDo8+U0xsPnGIcDfJ9/OA/EsefPnw8nl5yjMBQOiNC7AHqTjBPp6VwuF9on5rWzwEmmzv+fZLBwKOhSurq6VCwWVS6Xtba2psnJyehFx7nYONu3lwIEsJUNc9DZtoGBAV24cEHNZjMqkmEM0FmijTo6OtK3v/1tvfLKK8pmszp79mxH8ES0nww0mGc4SFKgZ86cUS6X0+TkpFZXV3X//v1ookkqHW0eTBsO3c/rAJN5mGTfuR+YMP+9pGBQnC1iPrt4nWpJDyJYq/zxd0jA5NerVCrq7+9XoVAIAONaQ9KOxWJRZ86c0czMTICGxcXF2Pzc+9X5/TtriPbI+8QxnnS05x0Calwr5kFWu92O/ltUCrqkAdvGv0mD01pjZ2dH9+7diypEKnNHRkY61gNBBCC03W5rdXVVOzs7mpiYCBBFA28CRdpf0JYC++MFDvQyYz2xZmu1mkqlUowXa4txBUQBLgcGBpTP59Xd3R2gi/RkpVLpaK6NbfENuyWF1jKpH/4wjqR//kGd80E9HmgA5g7cDwdXTFgWhLM1TuvTk4XIent7W6urq2o0Gtra2tKFCxe0sLAQi5EtKIhMvCs0RgUDkYymMYCAM6JhGDQWIv2sksJ2Z7pwzFJnlY+DS09JAnTQxGxvb4f+ROpMY/lYeid+OjNTaeQgAafgYJNn93+7qFXq1NI4mPbPuNH38nEMe7lcjj3UiDgdrDmQ8vFIpuqSrCWHs5g+j/z5W62WlpeXVa/Xo20EKQMA4ujoqEZGRjqcLyln0sX+/J7aZg6hq/EUuI+vA0Q3eswnHBeABefFe71x40YwcsVi8X2pQumk6Sd6In7mLJsHOKOjoxobG1O1Wo1il6mpqWAOGNuzZ8+qu7tb3/72t7W/v69Pf/rTmp2dVT6fD90ObQ5IC7lDJsXEvq6kmgqFgkZGRnT+/HnV63Wtr69rZWWlQ1TONbw7uheJeJABiOLwuezMhae16VovKZhlgBGAAxAoKdapAxVSTLDRgGAH1LAyjUYjem+5No201NmzZ3Xu3DnNzMxof39ft27ditQYLAzgh3tIBjLYQGdY0DxiG0lp+vplzNrtY4E/a5kx5p2Q1mMNkUplfbtuln5ca2trsTfj4OBgB3tKoAxrSOXp4OBgAKbd3V2NjY2FbSQj4Ywp9whrB0gbGhqSdKz3ozJze3s79qns7++PynZsAO+FNcjY41PQQaJx9eIFSSHF8P5r3i6JVO3D46N1PNAAzBkI/u8ajYOD45Jwj7gwFg4UPMcOG0AflnfffTciIYxFLpeLvb/a7Xakkfg+Qk+v0ONIpowcIDlDRbURTh7GhLJlfk63cCIoIh+u62lQFrx0kk4BWBEhwSpxDqK6zc3NoO2h6QG0PCfXxKkn3xPn5GdSZ8NV6f2bSSe/g4h1c3NTOzs7wcCxGS4pCk8JJcEX5/H3choI4zP+znhGDKb/DnCytLQU7AOgxfU5ExMTmp2d1dbWVkTDsEkIi/1eYUW5NsCIVLmns78XA8Y9Js/r6eOenp5IDS4tLSmXy4WGB9CGgBjnCKvBmkHDw9wDZHLuTCajlZWVGN+RkZEQgqPRGR8f18WLF/X222/r5Zdf1j/7Z/9Mk5OTqlarAVjYFYB7A6jwnOibBgYGgoVot9vBikmKFNHt27eVTqd1+fJlDQwM6MaNG7px44aq1Wpob3wOMb7OFCfniTcyJVBh/dCgV1I4U5js/v7+jnQb7wj7kxSx+zZDSQ0ZFdy+h2Amk9HMzIxGRkY0Pz+vgYEBLS4u6r333tPS0pLy+Xx0Z2cdYzuS6whdHVpQNLeI9BkLtFnMCdpicD5SZLDrgK9Wq6WhoaEOW+TyCGfEGfuBgQGdPXs2UrHMB9fqttvtSHu22+3oaj8zM6Pe3l7dv39fpVIp9r/l/SJwJ/jEzriGr9lsBrtWrVZDtM/2UQSIU1NTarVakUqEuaX4C4KAfVUp8OEzIyMjkRpNpVLRRJcWGaxJ3oOn0D+sA9v5gz7ng3o80AAMw+YRIouTCA3aFgDlImgWD5oQNvdtNBqxZcTVq1c1PT0dDoLOxaSRSFWwh5hHcURcgEI3PO12O1gvOuzzLNDQiOQxnl7VAlMyMjISET+g04EBRgNDLJ1oMLhXnBnpJu4VoAJjhmEhLUCKhnsBZNRqtYigvQGpC4ed9cNZ+LUBLrAszoa5GJjWBLwPjLhH6ElQkgSDjKeDYY4kZe7MkjOvdM/e39+PyqWzZ8/GvTH26XRai4uL0VoBFo97ceDK++GdAdIBf2iAPL3jayGZypVOWGOMII6C/8MiPPbYY7p7965WVlZUq9V05syZ0Dri6Hk3vhco75gxYZ7zLorFoiYmJrS6uqo7d+5oZ2dHTzzxhGZmZkKTQ9p9ampK+/v7+sY3vqFsNqsXX3wxqktJ0dDcE5YO4TdpL7ZxSaePO6fzd6PR0OjoqObn53XmzBnNzc1pZWVF586dC5Zlbm5Ojz32mEqlUgeLTvqctcZ6BySzDgFFfAanz9Y0sOYwPLu7u9HvDJvhcw6ROswPKT+uib6NtBXzsdFoBGNGpSWMyf7+vt588019/etfV61W0/z8fLSv8MCMOXh0dLK3pK9bOrtjk9FUYZO5P6+SBLjzDDBo9A9jo3Wps08iYA79LZWAADfmuO/BiJ0jY4I9wpY1m83Yy5K+Wmzjxi4Dno0gHevVjTBQrtfa39/X/fv3NTEx0aG94/5hzLa3t7W2tqa7d+/q9u3bwUQfHR1XOq6vr2t1dTX0wF6dCTiFCfWGrxQnJO3gh3U8TEF2Hg80AJPe70T955ROsxUEBwyNb2Y7MDCgXC4X2pp6va6ZmRk98sgjsUBI00DzEk25TgQj4CyV9P4KNddeJZ8F9ot7c6fmwAi9BQblNKYGJ8h1WQCenkqmrZIT2lObLG4XDnuKL6nlSD6b6x1cv+HXcp0a/8aAAtAAbxykjPz6ScbC7yWp+fJ35WzcaRGbj5+zrxy+s4IXYzAv7t+/r7W1NY2OjkZVlFe6kiZBC+bpDm/gyvVhhLh3T+GelkblZ84WeYXqwMBApArv3r2rV199VcPDwzG3cZ6+NQ0AHAftY+Rgb3R0VFevXtXm5qbeeOMNra6uhuP2di6MFyD1L/7iL7S1taVnnnkm9vcjuMhkMtE+AEApnfTL4pyAHhiPlZWVYMjGxsai4eibb76pxcVFnTt3Ti+++KLu3bun1157TSsrK7EWYR8d5DvY4Nk9xexpM0/f0WV+c3MzgrR0Ot3RaZ65Qer+8PC495M3TmUtomXt6elRuVwOTVE+nw9her1e1+rqqhYXF/XGG2/ozp07mpmZ0dHRUVRe05aGFBnzD1uH7hLBPsAPVhSQkFy3AFnmBtWNpO48pUo6FZ0b33EbwHZpgEBJMS+ouvT1zvO0Wq3QEg4MDGhnZ0eNRiMyHtjY0yo+G42G7t27F0wUoItgnqCLgLRUKnUE5qTPqaRMbpOXTp/sJ1mv12NbLjI1vBfmBuuZ+/SsBPb2o6ABe3h0Hg88AMO5YYTc+NGAlSiLBQDAIRLJ5/OxDcrm5qbu3bunTCajubk5DQwMqNFoSFII9IlcPP3mE7+rqyvAAAbDDa8vZkSunmYisibN50yGgxMclwMwdwROuRMR05jQ95NzUOaOEweP4SW65N6JZJMpThxUEtzxDDAuntaVOitCHbQkwRwMHp/Z3t7W4uKiyuVyB4uYTHcmDwdrfjgAcwfq9+ngwhlXLyNnQ2BPUx0cHETVpjMgjBkODUaUd8l4MY9xDlKnrov380HPnGT4GGuYQ343MTER4/rGG29ocHBQTz/9dLwTom/mL/OafklUSyJSJkLv7j7eqPvRRx9VpVLR6uqqNjY2dPPmTc3NzQVTxHkPDw914cIFNRoNffWrX9XR0ZE+9alPhcOEBazVasFC+3Y+gBbYqGazGSkknOTGxkaAQxizQqGg27dv68KFC7p69aoymYz+5E/+JGwG4J936Eylrx/mCvMITSnzM5vNamJiQqlUKkA7a4h3QdUnaUvsCuvYU7xoEI+OjuJ5aMPB+89kMlpdXdXt27d17949HRwc72W4u7sbhR3Yj1wuF3PMMwaSQgCOLimTOe4ZNjs7GywZ75H5y1ZJzGWALACK1DbyEcCe2zjsB5II1hXn8TYl2Otyuaz9/f34f7vdDlBHEIT9YseU0dHRYFJpFdJqtSK1zbvd3NwM3dbR0VEUURwdHUXvLg7aR8CiUnQBg3727Fnt7OyoVCpF1S7jxtg7oAIEu630YA7A53btwzweMmCdxwMNwNzAAb48RUe+nsUPUEK8nEqlwthSel0qldTT0xNbQSwuLqqrq0uTk5Oh3yDqx3lhfIlAnTVBo+GiakkRpeKcSO05O8c5XdyLo8b4EPVLJykfPgcwyGQywWAQaXmKSlIHYHPj7w7R2ToHjgAR6G/u26sfHaTB7rmTOi0tmLw3nIxv3Nvbe7y5bb1eV7Vaje9jsE9bnDhEjtM0Cf6973Uevu/Vf9vb27p9+7Yef/xxTU5OdtwL85JrYoRd6I0j8ecGhOFAYcmS44cTYU6cdvh6cfBQqVSCVenr69Pk5KQeffRRfeMb39Arr7yiYrGoqampcKCsA8aTiB/nxtx1TR5zh8o7wDTVkePj4+FQt7a2YluiK1euaH19XX/5l3+prq4uffKTn3wf60a0j5PFWTMvSfugpUHrNDg4qN3dXW1sbIRg/9q1a7G9zNjYWLRYWFtbiznPs/icdTvD+2D+eyUnIBaAil1pNpvRV8qLb1wy4POV9Bif8z9HR0eanJwMQAqA4J0j+JYU3fb39/eVy+U6nL1v7l2v1zvWGIHQwcFB9OFzMbwL/mHNmV+0T8CG7u/vRxNjZBWAR+YF9pV7JrWalG6Mj493FACMj4+rXC7HWiX4Y59det5h91gHFFqtrKzEucmGsAk4gMglMC4/YX0TUPucOTw8rlpsNBrRKoUNuQGw2A/3NdhW5B+882RwhT13lvvh8dE5HmgA5mkmZ4fI27Nno4vuDw4OQvCJzgDdRa1W0/r6ejBiCPgpY2crDCL+pBGGIfKmd0Qo/n+iK6pz6C5N2gExMw4lqTFxehmwBMh0R41DT6ZHEP1SbUe1lzNqPrZJ/RF7qDlo4h4AF4y3GwHORfSK4UoCFAcU7gh4fsaJZ6Pax6vxOJLgycfNj6TwnfeLE0wyG/45d4A8K9V36Pj8AFQBEByAHRwchCP0NCQODCMMgAK4844x0M7W+d+njQvncB0N43758mV1dXXp5s2bWlpa0ujoqIaGhmIt1Wq1jneMc08yls7QZjIZFQoFnTt3rqOohDQcejJEy9lsVrlcTi+++KK+8pWv6E//9E/V19enq1evRvqW+cYcoIcT+jgc4tDQkMbHx3Xnzh29/fbbmp+f1xNPPKHZ2dlgf3DaTz75pGq1WlSvPfLII6pWq5HWBHw4Q4ljJxDxcSDlBYDb2zvePJ70HQL8np6e2ILHBfcw0QR1vr6xGYDiWq0WYGpiYiIAVKVSkXRSNce6xy4SsA4ODsZ5AOrMQ+arB71omdrttm7cuKHx8XHNzs5qbGysI0CkGAL7t7u7q/X19WiAjb2jqpHrJtcxdoL0HePGe6eFBiBtcHAwAl5sIzaLquXR0dGQo6TTx41ayURMTk7Gvp0UfzAOPvfdpnF93p+kDlsIAK3X65GtQTfmARrzmDQtaV0PjFlDvltC0m5SkPBhHi6p+EGe80E9HmgA5uyLpI5J22q1tL6+HsJUT4NAdbNhNlE2NDD9d/r6+jQ+Pq7x8fGOihkHU0TraBswiPwewwgzAN1PxVBymxC2sXDg5ZGPi1Fdg5bUIgFmeG5nJ3D+gEMWPNWNGDfOyXUQtVLSDRiEaaOnDsYCQ+SMGsbUDQM/477dSJLW5b68EaN/F4d1mq7utBRikqlMAkRARZK+T4Kv5Pl4/zT/BOh6BZjrSqhc4jw+Z9i7lLGFwcHoAsAGBgZiE96kJpAjyeAxtg4aPb3ebrdDy5ZKpTQ6OhrbtyBSxllQbp/L5QIcc88wCR4cMT+KxaJKpVKkWZiP7XY7GCHGn2ahjz/+uF566SV9+ctfVnd3t65evRoskqToOwVYkRSMBe9sfHxcV69e1TvvvKN79+5FQAGjmkqlVCwW1dPTo5mZGZ05cybG+P79+6pUKuGEPeDwgIh79zXA/flcOzw8jK1mks6cscSpwt5wj/zcHTrPmMvlIk3W1XW8xc3q6qrK5XLYONgtgAb3DLtKD0W0rTh0L0Dw1DfrJZVKRXf3g4OD0KPB0mxsbITGtr+/XwcHB1pfX1ehUOjYhsnbc2DrGR/mEKw+YGhwcDCKFRgXChao2PQgj4bJa2trHaANXSMBciqVUj6fD02XpA6ZAO/d24rQJiSXywVopijC/RCtVeieT4qStcKYErgRJHmgzPvhu9yTM+te4PPw+Ggc/yAAGI4Uo0D6gly8M0MYl66uroi6mLjb29sRSWUyx2XsREU4PU8h4GSk44iSyjYmPkaECAan6J3b0fLAqqHxwGnxx0EHoMv1Vw4UkuwX95fNZt/XDdmNPgYSA58EdnRfp4zeDQDjjoP5XiwTEfNp7JKDIv5PRAxAwdEwzt5Jn2vzrjiHA/UPOpJAzdOU/P6DwBwH39nZ2dGdO3eivN1Thf5e+Q5gqru7u6OLOWkF2DEvvHDwhIH31KOzmLxDPzDwAAbGlZ+NjIyoXC7rW9/6Vmh7SqVSbM3COnPWTDpeGwiFYZQAQ5wfMXGxWIw0ECAMcOPPSer5kUceUXd3t/70T/9UX/3qV9XX16dHHnkkUlowDmh1aAVC6wzm5/z8vIaGhqIX2Ntvv6319XUdHBxodHRUk5OTKhaLeuyxxyLVND8/r729Pd25cycqWQHSPLfPFQIM5mOxWOyoaubAbiXnJ+seRo33jVAb5+1i8XK5rIWFBZ0/f16Tk5PKZrNaW1uLVB5rnGCTKkKfKzwHgJ755fMb8Ig9Q19K2pi5QS+uXC4XbC4Sgu3t7dhLN8kE+3V8LGj5w1zFFnlDUg/mmG+ehXD7hr3HhpC6JmVL5oNiBtqk7OzsxLtnzbGOGWvsVjqdjhSvb8ztchPfSog1w9wgnY9GlG739BQjWCHI4Hq8Zx+LD/v4v3EfH4Xn+t89HmgA5uAAA8aCo/mcAwrpJP1DtETURxrQm7Ei8HQWisXmYILz5fP5oOmJIBG9s8C51ySdjqEjcvIqSZwu0RUpvKTB97QZ0SeGpre3V0NDQ9EB3AGMi69dQyd1UuauE+M7Sc0BziXJFCU1MThYPuPn8D/Sid4Bh0PayrUXGHV/z8m0ouvevpemK5muS46v/+3PyLhx3fX1ddVqNU1PTwe44PPMOw8ciOh93hDJEwH7O06mbD11mnwmf7bkfRMZc98wvf39/Tp79qzu3LkT3fGXl5dVLBajYpgNigFtpHCGh4d1eHgYQmqewwEevZCq1aoWFxeVSqU6quxYWwCZdvu4h9f8/LyeeeYZvfXWW9Et/8KFCx0ggspn72eFU2KMBgYGND09rUKhoPn5ed25c0df//rXo6Dj+vXrWlxc1LVr13TmzBnl83m1Wi2trKzo/v3772sm6vOPd02QSIAFQwEgcFaHdYsjJc3kaUefx93d3bHfIO/uzJkzunjxoi5evKj9/X29++67Wl1d1fj4eNwLICGdPtngmeCRZ2H9AACY254+d00fPRE9fQYg434LhUIEwc1mU+VyWXt7e9GhnhQZoIaxhHVjqx7AvAv0AfuwSt74FaaOlL8XTJBGJtjxNeTvkC72h4eHkfau1Woql8vR2oP5ADhzHS/3AHPMuHjTX5qtet9KmHBsOM8B2wZg86DYm3k7i/ggA5V/qMcDD8CSDkdSaCsqlUqku6TO7UKy2WwALBiqwcHBmOBoKdyAkv5x5suBAgsDoMC2Qb7vXCqViqpMHCjfQeTqQl4HfR4VuR7KhfoejbNI0adglJyFQbfiWgLXEjHORHMOADgPz0+qkOfH8CYXPoDVmTcfO2cNeF568mBwaPEgKQALjj3JUPl1T5tDHA7QHLwl048OwpLn5LpHR0dRDbmwsBBzy6NvjDRsqDtjj5hPS8k4K5bc8iapUWSenfacOFvmAb+jEq2rq0vT09NqNBodTTlZV/THc8eb1KExvzxo4Hf9/f2anp7W0tKSms2mCoVCtBXgcLAOS/3ss88qk8nonXfe0Te/+c1gq3FagL9M5niLMZp54txcQ0NvrnPnzun8+fN6/fXX9frrr6ter+vtt99Wo9GIakikAYA52jokgwgHkdw76SyYG9453+HfHlj6/AIM7e3tKZvNht6pXC5rdHRUTzzxhC5evKjJyUltbW3prbfe0ltvvaWNjQ09/vjjweYALA4PD6MFB737CBSwcX4vpNJ5/57yosgHHR/n9/Ya2FHAXbPZ1OLiYrC9sFFkJghasSlsck1jYMAcVYwAS2xnUmvJz6nGdVbRU+SwbKw/wFNfX582NjZUqVSifYmzTJIieMevAPwBhkdHx1WSAC5nzNj9hM+StuX8MGEUXh0eHvc+rFQqsb4GBgZiPHznl2TQ/2EdHwUN2Gc/+1l97nOf6/gZvQml47n+uc99Tr/927+tarWqZ599Vr/5m7+pK1eu/MDumeOBB2DSCbvB32zISsQinVDmUNcYjKOjo2jeyOcQJWOcXRDKgYHCcGCYWFAYOZwjhoWfcT+Ap6Oj4/47zWYzFo50sg0K13S2zKsQPSJ1RoQtKEjleDVaciyTKcEkwwbzBdBJAhJP2bkQlHuXTvRkfu+uHfHDmTJ/Np6DsUOAz/twR+Z6G855GiD0e/TrJ8GWA8YkyEuymzs7O9rY2NDW1lYUb+AMYMo4N+kI7o/UtbNmzpTxHjxq9zHx95CcE/5/FzgfHR1pY2MjgBDXcT0iHb0Z24GBgdDF0OIBgM15vWEv88zZiIGBAY2Ojur27duq1WrBJvg85N5Jsw4MDOiRRx5Rs9nUysqK3nzzTT366KPB9ACKYLaHhoZULBbjPmF30CBVq1UNDw/rwoULmpyc1MjIiL761a+GlrTdPu7Yf+XKFT3zzDPq7e3Va6+9Fi1DfC5zfbcD/B7Gqa+vL/RVzGc+h6Nl7AFqgHJfv7lcTv39/Tp//rweffRRdXd3a3l5WW+//bbW1taiWen6+rokRSNbbCHXYZ9ZtICMNyyR65z4w7ytVCra2dmJ6kmAg9vMvb091Wq10NnxDvf391Wv1+M7HgQz75GSAPja7baGhoZijjGXu7qOe6oBmAgkk5WjZCRIXXqWBBmFd7ZnHgNsGo2GNjY2wgbT7JuCFM8YwNjTegThPXMZNpBGvDBgR0dH8R6wZcwRns9ZPj5H5odKUj6ftOcf1vFRSUFeuXJFX/nKV+L/Pja//uu/rt/4jd/Q7/7u7+rSpUv6/Oc/rx/90R/V9evXO1qK/CCOBx6AYUy8Tw0Tmd40fFZSRPtsBEyaB3rXt9VwJsYZGyoVJYU+DINFmtAZjVQqFbqZnZ2dSCl4ZZvUuT0MiwzmDQNIZONOUTpZZPzfFy73geEjwvW0mX8Hhi1Z9s14JFNcDm6JNJPpVr7jXaBPO5//DAPnfXskRZoKB0AVkQvoGS8HGH44sPKf8UynsWWMc/LzOPOkXm9//3hvPSqs+Bzj4vfBH8A6TJ9fB+Dn3/OUiqenfOyTWjx/RgdmjFez2dTQ0FC8I9LxnsogLYJOJp0+rhpDY4hDg5Fzxtbnh6ToOk/RTDabDTYL1s8rgaVjg3nmzBnt7x93cn/llVfUbrf1wgsvaGRkJITZANlMJhMNNmFPYMRwro1GQ7VaTfl8Xi+++KLOnz+vl156SX/913+t1dVVFQoFnT17Vo899pjGx8eDZaKtgqeZYFi4V+mkgzvsJ60Gku+Xcent7Y1+ZYANAo5Go6FGo6HJyUlduXJFFy9eVF9fn65fv66vfe1runPnjorFYmytw1ZdiO0BwAQDBKRUUvJ+0JeROuO+mOf9/f0hIHdhPMDWgzJYH+wp84OUHWuWtQMwOjo6rrym+GNzczOAHgAG1taDV8BTsp8e6yqfz8f9ozPc2trSxsZG7OMKAKUggSIqX/P7+/sBwgDdAGtsEgGWa1jR7VWr1cjCMN9dv5u0XYwNa9MrUj09SwEQv/soMGAflaOr67i1VPJot9v6j//xP+pXf/VX9dM//dOSpN/7vd/TxMSE/uAP/kC/8Au/8IO9jx/o2f4fHzgtB2AYCrROiOwzmUwsfnr6IHiGNsZ4AHIAXcn0nEf++/vH+31heH3rIQwN1VIsEEDc/v5+LCCiW9J9iIURz2OgYONg5gB4GLoksPLPoZlysXUyLcQzOnBJgiKpU+zPudzpY3y8SAGjkNR/OVPl1+nq6gptgxsmnovzs9tBsj2Hj8dpAMzBjDvPD9KKfS8D5hEU750S+3q9HkDJ0zeeNgTcAE6ZI/55v29nAomMpc5N2JPPmBwHZ4f57NDQUDgK5jpidrYJciCG7ouWLuVyWY1GQ/l8PgAdqSfuGUbFtU+jo6NaWFjQd7/73dheCOG2p7MBNuh2AGGrq6v6m7/5G+VyuaiMRAeDcNlF0d5slAO2iZTO7Oysfuqnfkrnz5/Xd77zndgO5sknn9Rjjz0WTVXpcs65YGJ9jmMPPC1GMASbyZrl96Sv6KuVz+e1ubmp27dva21tTePj45qbm9P8/LwymYzu3bunmzdvqtVqRe8y1t7u7m5UO46NjQWLCWOZyWSUy+XCzrjzTzLbAApJwTphz5y59wDU15aDF9pfMJewDalUKja3zmazEZBgTxkrArS9vT2Vy+UIdhl/AAkgEkAGECKwKJfL0bm/1WopnU4rl8uFTwGsA1B5zwTRgFdfx9ybdJLORKfGeVk/MJw8F/fnY+02KJVKRfsS1kS73Y70bSp1UgCDb0sWYH0Yx0eFAbtx44amp6fV29urZ599Vl/4whe0sLCg27dva3V1VT/2Yz8Wn+3t7dWLL76ol1566SEA8yPpXEkPbW5uqtFodKRkYFBIR9D3yHeUZ9NdqHPYMBYcrAUgjqgGo4WzBCTQuuHw8FC1Wi328JIUC9CreCqVinZ3d8NwkoLD4Ljg1MFNkslIGlDGhu9CfTvFzXmS4+oOnMPToND2zWYz9tqDpXCgQRrADaGzSb6IHJB5OTnvESOKVoq94DxVlQRQpwGq08bu7zqS7N/3OtLpdKTIzp49G0UaPo98rJk3zFm6ZGNUpZPeU85EYeipckVXxueSurjkvWPg/dmYI4D9bDYbJfKbm5sqFAqSFCX6bMDd39+vzc3NYC9gUbwrOg6G9D9rYHJyUuvr67p//35ovZyFBRA6A5NOp3XmzBk9+eST+uu//mv9yZ/8iQ4ODvTxj39cvb29Ad5I69Bw0xkRWFuuRWsNtE3PPfecLl26pL/927/V8vKy3nnnHb344ot6/vnn1d3drT/5kz9RvV7X8PBwbImEhpSUFg5RUodswVNdkjoq6xC2Hx0dRTPRsbExdXV1qVgsanh4WMViUXt7e1pcXNTKyop2dnY0MTER12IuIDIH7OTzeY2MjIRkwu0ejBsAmvHxde1AKlmJ6E14+Z7Pcw/uEJl764u9vb3QebEGqJQEzAFQAI6SogKT8fY143MP4EvH+8HBwUgDkv0g1cu8BWiSKmR9eFYkGVwxfpI6GngD1tCxssYAfsxNAk7fHYHfkdI8PDzsWCfuV9BD7u7uxmf+IR/sWMPBOkwezz77rH7/939fly5d0tramj7/+c/r+eef11tvvRU6sImJiY7vTExM6O7duz/we37g30jSCQIGnOqWFLn4vr6+oLJ9OyIobNJ03nYCoOMl/q73qtfrOjo66tiewqMfBJboU7iud+vmMzhiN2A4MI+OcWwO0pz9AQA6sOB8OIXT0mk+rrAUfk7/t6c/EZCiJ/F34yygP4PryDwl51G2t5xgHCRFxRH6Ck/feNrzNCB2GtD0cXDD+fc5Tktd8u+9vT3dv3+/gxUCEHmqxMeTd7e9vR1OjeeipQOf52eANIwQbSCSh48D9+j6RX7mTgAmUjqey7VaTblcLgw8OiiKMIrFYrBLzmJyHWeJcWKpVErDw8Oan5+PnQ3a7WPdlWsTuV+eAcdSr9d15swZvffee/rbv/1bDQ0N6cKFC+FsaYtA+s3BRrIKtLv7uOElgGVnZ0djY2P69Kc/revXrweTWygUdO3aNb311lsR7OFQnckGOGKTuLYHQzA2rvUBwHK0Wq1oCAujUa1W9c477+jWrVvRPBVGDZtHTysHnPRuAyQCUnK5nLa2tmIucv8wU86I+Vol2ETfJJ0wwQj7mVPOlnOeg4Pjpr6bm5uqVqs6ODjQxMREMMmk27gmcxVGiaAVCQqfY774jiHocnd3d7W8vBzjxnwE+NE4F3tOxSl/ANUOyLBDPCcgybWpPLcH1mji8Bm8J3rYYSP4PLIZ0q78nPuClWOde6Xsh3l4APqDPKd0XAHsx7/7d/9On/3sZ9/3+R//8R+Pf1+7dk2f/OQndf78ef3e7/2ennvuOUmn+8PT7On/6fFAA7DTGB8amRLV0buKCYiBRHfgVDH/9hQZrJqDhqOjozDMLrb3tAy0L5R0sVhUoVCIxcCilhR7sGGwqE7yRYh+wsGg66jcebtTxaB7k0nuwR279xST3s9IOfACHHoBAL3NJAVwJYLEWEjq0Ns5mEym2LgHj7TplUM6C/0H27EkWbvkXEnOmyTw8uf+IBCWTMue9rvkz+7cuaONjQ3Nz89HDyFnqZKFEbAG6Ep2dnY0ODgYQNfTxvyMd8jPHHh/0H3xt78v3p90wrZ1d3fHzhCurcIBrK6uqt1ux3ZdqVSqI93hOr69vb2oBsTx4Jh7e3s1MTGh9fV1vfvuuzFX8vl8BCKeqmPeDA4Oan5+Phz722+/re3tbf3zf/7PdeHChXhGT7nRRoP+Z/783DMs0NbWllZWVtTf3x9bMbEeu7u7tbCwoM3NTd2/fz+q0Bgj5j9rjjQn2/Z4uxEYYt6jb6MGAMax9/f3a2xsTPfv349u+g7cPC0FAGROE8wg08BRd3d3K5/Pxz2yrpAv8P4IzNCi0piUykAHSEm7yTMkGXpsBc/Nz9mw3sER8xPAQwAGswng5v0BhN1uIagvl8u6efOm8vl8NMhGU4XtqdfrAci8KAYg5vtxuv0A6PP5pLSD+cv1mI+Ms2t1+R4Ak8bTBGnMb2cpYdz83f1DPhYXFzU8PBz/P439Ou0YGBjQtWvXdOPGDf3UT/2UJGl1dVVTU1PxmfX19fexYj+I44EGYFIni0FERO8hFhkLFWPmUZinbzxiA4QlBZzSsXHm3GjDMBAsdKLgvb09DQwMaGpqKtIzGEEcOBEpUQsGwivK+JxHjiw27u+D2CV/dnfSznokF7n/TjrZW9Mb+2GIJYWxYaGTBkiCIjQJnkb7IHCQfBYHuYir2cjWK8m8h9AHzRnOnzxOY8z83w72T2PYGFs/KpWKqtWqJEVLEmf4+L6fF0BK2o+x9utjvJ0pdK3iaWD0tGf2sUs6SrRrpE4Ya+Zbd/fx5uPlclldXce98Fx87WJrnALg23eB8Hc3Nzener0eFYuwe7AvzGVPYw0MDOj8+fNKpY77e33729/Wyy+/rGw2q5mZmY5nJW3KljOeIgMIeYp9ZGQknrvZbEZLDvQ7ly9fjtRkkqngvIwHbIUzX1RFog1iraFtIoCUjvuaOTs3Njamp556SiMjI7p9+3aMj+sCYSJJ3cHgkRHo6+uL+8Pmue4IIMPzEAQxdrBUBJvJPmaAk+Q6cQ0jc46AZHNzU6urq6FZg/EjneZ7f7ZaLVUqFY2MjARj6vtEem+w3t5eNRqN0E/RzoPdCMbGxmKcWKOkhclcOEPsDV6dkWbMAUBegMGzE3R7tgKtMuCbYAufwk4B7XY72tysra11FL/wHpPSAp8TH9Zxmr3/QZxTOl4bDsD+vsfu7q7eeecdfepTn9K5c+c0OTmpL3/5y3rqqackHa/Vr33ta/q1X/u1H+h9Sw84AEs6Q5wy26UgRpROjBCpHU8zEnkQ/XiKzVMmXAc6nJJup32lk67z3Es+nw9mQFKHrqDdbof2C+NMZInzp02GAwtn6EgNOKBhUfMMOBWE0jhRUhQO7DCeXv3p9DmgkHtAu4EDIeVxdHQURQYwWJI6eicBNtxZcS1+zrgzNjQvxHhhyAEfro1LAhsfn9MAoH8Wx+zghM+cBmz4vR+wCWtrayqVSgEGXOPBnGKuUVVH5E5DS1pZMO/REDlowLn6FkeS3ldx6/cNcHMRsDOPpIjQ2xFYEMBMTU3pO9/5ju7duxepLtfu+Xg62+b6ItZbT0+PisWi5ufnO1I7rGWCDtL0/Js1OTExoSeffFKrq6t66623ovJxfHy8g+3zlgvcH4CB8WR8WUfMsVKppJs3b2pkZESPPfaYzp07p8cff1wvv/yy7ty5o4WFhdiDMZVKBVtx+/Zt9ff3a3R0VGfOnImO5q5TZa0wxlT3zs3NaWpqSl1dXRF0SArbwlZQq6urHW1OnGmFyfKebR5YYiP5mTMqnjrivXihkgd2HthiH92uorGilxpaXGwoY7CxsRE9svL5fIckA9CInTs6OopN1tkBgANdGcyhJNVqNZVKJY2OjqpYLMb2QK1WK56ZwgJsNHYAm8SaI7uCTUFb60EKdgO75xWKvgYAX4wJ85ysChuo53K56FtXqVS0tLSkxcXFjsyMg7AkqP7/5+Mzn/mMfvInfzIqrz//+c+r0Wjo53/+55VKpfRLv/RL+sIXvhANjb/whS+ov79fP/dzP/cDv5cHGoBJnSDs6OhI1Wo1IlycFaXBXs3IwsVQeMqBRYIBc3F9kiHj2jgdIqp2ux1iZLYyQRPg2ioX6cJ8AQZ7e3sDxHk5uFP47viTQAOji2PxFCcLHINMytQjVUAaDuE04MF5qRz1tAtjLJ0Ys2Tq7u9KF7p+jOdDeAt4xqH4PHDmyu/1g6KvJGBIzi3+/0EM2GkHjmF3dzf24RsbG+tIPXIeT2E4MKX6i3eBvsdTwF4dyn36O+fZGRcfY38+r+TzZ+beWCcwNKyp/v5+9ff3a3l5WZOTkzH/k2kX7os15Q6ZsSLqZ9PvtbU1VatVdXV1RY8oru3zhTHo7e3V+Pi4PvGJT+iVV17R3/zN3yibzeqFF15436bogBPmmMsNpBOQR3odZn16elqlUkmNRiMag16+fFmf/OQn9Rd/8RcaHR3V2bNnNTMzo3a7rUajoeXlZa2ursbzA2aS3e2diaYlAo53aGgo0k8Ea/x7amoqGJ2NjY2oePR5i73zdUzLHg8IuT52hnnuDl1SR2qS90AWggBXUrCerNMkOwegI52ezWbjXbCpfaFQCAaLzbex48PDw3Fv7L86Ojoavb54Bk9VDgwMqFKpqFKpaHx8XFNTU7p//340jWUsuD8aZHMdBzQwYvRwHB8fj7Ym7msIsBwAA7qlk87+0glgcgYVBg6Atr+/H2AeMHH79u3Y/WBgYCD2xiSg+7CP/5sasL/vsbS0pJ/92Z9VqVTS+Pi4nnvuOb388suan5+XJP3Kr/yKtre39Yu/+IvRiPVLX/rSD7wHmPQPAIAlnSFVKjg4138RcWBku7u7owKFKhV3wskGhJI6SsMBTp4mQ2DLAiHKpkx6cXExxK6kYahMwplhOIgEeQ5PhyYPIl53rB5ZSyd79KXTJ53WYVBOS7lhLAFffh6u4dEu6V/v3eQAyhscMq6cI8lC8X8cc1Ln5GPh4Mwdv6dHfa4k/834JcHJ37Wwv1cq058hnU6r1WoF61IsFtXd3R0RvqeHXNsE2Gq1Wtre3g6Q6wyegw+YM9cnJdlRB2pJppF5gMjfHSWgFzaW90dqZGxsTG+88YZu3bqlK1eudDgwB7cwWul0OtIl/qywcDRnLZfLajabkXqh7L7RaASg93fFvS8sLCidTutrX/uaXnrpJQ0NDeljH/tYx5YzAEFPRcLSejrp4OBAAwMDGhkZiXTU6OiopOP08tDQkHK5nH7oh36ooyp0Z2dH5XJZ6+vrwXbt7u6qVCppeHhY9Xo9UqHM1VqtFlrPsbExfepTn9LU1JSKxaLS6bRWVlYkKdrowOwMDg5G+g0NWKPRUL1e79iLVjpJCwOk2TDdWSPerWunfC64virJJDvDif0DpADkeA+wNQ762EeRuQuDTHCKBIQglewBAKdUKung4CACA3SCAD2a8wJy7927FxIRCgFoc8O4uT/xANfXogc++BbYa88WwE75GuP3XmACC0rwxUFBCc1taSB88eJFTUxMqFgs6pvf/KYWFxc72q94EdmHdfzfTEH+fY8//MM//J6/T6VS+uxnP6vPniLg/0EfDzQAS4IGtAPQ8wh9vYKQCJs/OC2n0z1yl06iVET9vg2G61sAUOl0WpVKRZubm9FLZmdnJzbwdbaNzsfoBQBgVHcBHmHAdnZ2IvpLAoYks4RTcfaK88MKEpG5UB7HjkH06NwNj2uiMKacl58l2w4kW3rgmB0UOKvnmgov7WZM2STXHbEvyNPGJpmWPI3V41wOvl1Amzx8zJLXxMHSqJRqQVgBnp85B0iiaAIdGKDdHQP3tbe3F0wJbTk8MADIego7+RwEHDBdVMdxfwQqBBfOWBUKBaXTad29e1czMzPRrJJ7gy1DUD44OBhOEyDEffE3FZGrq6sda4RgCZ0Pz8czAHIPDg70wgsv6K/+6q/01a9+Vb29vbp8+bJyuVzHe0RvRcEObAzaTBp9jo6Oanx8PJikWq2marWqmZkZDQ4O6syZM3rhhRfifm/fvh2pLeYHY8A9FwoFHR4eqlwuh72an5/X/Py8rl27posXLyqdTqtUKunb3/62lpeXlcvldHh42GGPAOB0o/c16ODB2fvk4e/IWS8O19757z01zJzzdYit5b78XTsTxHkB6dgO1hWgg7lP7zkq2R3EVKvV+Cz3xntzzVkqlVKpVAp5hnePZ44TWKPFBLjiJ7CRBBtIPAiqYCk9G4Ed9bEC0GLnPGhh3DydeXBwoFKpFBXDk5OTWlhYCKnCK6+80tE64WEK8qN3PNAADAPPwoauhmkh2sCJSArHxkTnDwJvT5e4CLO7u1vj4+PK5/M6PDwMo+waCahzFp0kTU5OxqJHi+KGbXt7O0TEGBzXnnjFG+kLUlLSB/ex8p87YPIqH1gpL5VPns8Nqf8fg8FYwFJhmNyYS4oKHO895swM1+dIpq/csLvQGO0Z7zYJrng3/P60dK0beb++zxmeNwnwHHD5Nd055XK5aFZZKpUCAJAKcU0Jz+fvnk7cjBHpCK6PVsyrnLy9igNIZ/r4fhLM4cxhWAg0JEWHc3/HsBz5fF5LS0u6c+dOaHaI3rkGIJxgBcaDIIl7h7WYmZmJhqe8a8CLsxNeFSodV/aNjY1pe3tbTzzxhF5++WX9+Z//uQYGBjQxMaGenp5oM+ABljPEzBeamP7t3/6tisWinn/+eU1NTUWKeHV1NVKvMG8ETrQF4XkzmUx0nadH3ODgoPL5vC5duqSJiQldunRJhUIhKh9v376tN954Qzdu3IjmpNybV3qxvovFYgcDnslkooKPwI+DlJy3OnCQ5jZRUodA3W0oc4t5g11j/dfr9ajoBXQ7M+c2yO2Ma10BQ65jg0HjXgBuAwMD0WMM0T76KeYjn5WkjY0NHRwcxM4B1Wq1Y24AIgkyYfF8HGHYuDf2SWVjeOa9s4Yw+c4OwlgRYBCAMqYun2G+r62txXVGR0d1+fLlsB/r6+vBGH7Yx0eBAfsoHQ80AMPI4QzYKoIFRpku6SiieFggFhVGBk2DO3ocHI1XSRsQaWMkvKdXtVrV5uam8vn8+1pe4LC5PyK14eHhjhJrd+R7e3va2NiIkny6N0Nlc/9SZ3sB7s2FvfQao+GrR2aIt52V4hmdCcPg0uwR0OntFRhfwEUyDYKx5ZzJtBiHp0cBI7CBsG40fvR79Gtwv57u9Ijb/3Bg6D/osxxJ8OZ/uw4OkLSxsaF8Ph/i33a7Hdqi5HggrHYAQsd/3g3XB0zQS8jT2m6gGCNE+Un2woMaxpq1wf2waTzFFDzf0NCQjo6O9O677yqXywUY8YoyLyqYmJgI0OMtXbheu92Oyib2uCMFi/Cc+cyY9fb2dugqBwYGdOHCBbVaLX3rW9/SSy+9pN7eXj3zzDMx1jQP5fvYAK8+hZ3+q7/6K62srOiHfuiHtLCwoKOjY/E3+quRkZGoYL569apGR0f13e9+V6VSKSrvvAXM1NSUFhYWND8/r+npaQ0ODqrdbqtarerNN9/UO++8o+9+97va2NjQ0dGRZmZmYv9CSR1B2/7+voaHh+M+GBdsAI1HsWkwO8wLJBGsmWQA48GmBwiMNWue+cM4sO6HhoY0NTUVrS7u3bun9957L2yq2zPWvqfomO8+Z2iBAbDh2o1GI5jm4eFhjYyMRFqYNcCaZK3S/mJ2djbWJRpM1gL3w889mORdYGf5PLYe1lJSkAAOMgHAbu8I0rLZbDD9ZE4ajUaHxCaXy0Xqem5uTmNjYzp79mwQEFz74fHROR5oAOZOlXw4onVJYVidRnb2C4Phi95pZYwcEaILYF3Q6OABUAAFjaFlEcBuOB3tacKkroKfUyadLMfncIDAvbBYnfVxlojfAZ54lsHBwXg+/66zUIwZaU6ehfvnb94TxuW0dN9paUL/fZKNAjS7o/V78+9+0JFksZwV8GudNtYflLJMHu68AUVohADs9PfCKdP2gXmJUyEN2dV13JGbZ+azGGKAvHQCXpNpHuYmz8I1+BvWCweIw4Z5YS14A1OeZWBgQKVSSe+8804IkiXFc3EPVCz39PTEtl4UrXi15tDQkM6cOaNWq6Vmsxlgm5J8Z274HkCyu7s7tqW5cOGCms1mbKg7NjYWKV2ex5s3e4EHTMjc3Jyy2az+/M//XN/61rf00z/90/r4xz+u8fHxGKfe3l6NjIzo/v37ymQyunr1qi5cuKDXXntNlUol2MypqSlNTEyEoBzd6PXr13X79m3dvHlT7733nqrVajxLKnW8PY+nXXt6ejQ4OBgFCrxn3gVAlbHhvfIuACCku30HALSorC3AG/OBwIYKVWzm0dFRdPHv7j7e0glWb3h4WOl0OjRx6+vrHSCL4MqZb9YiqT++67aagFhSMMSAm2w2q4mJiRDywyJLJ2J3gvKVlRUdHByETGNzczM0lcw9l0F48Oi20tOS/I4UJYE+u6R4myRsCUENaeXZ2dkOe9RsNrW6uqqVlZUoBqnX6yqVStEI+cyZM5qeno62Gx+Fw+3RD/KcD+rxwAMw6fgF0AvGm+WhVXFa1/UXOC8HJa5pwoC4PgzH6AyEsyBMdu/wjSPj3AAT0koeOeFYWag9PT2xbYikEIf6+ZiAyRQeRtN1HDg80iR7e3vBXOCEEDy7M5PUYYg9lel/+JwbTu4lCQiTbJIfp7FKDrIAJWjOTjtf8r783B90Xb+e/zsJ7k77TjIVzNgD4Le3tyNqZUzYlsZZV9LmzDcYLrZKYUxhf7gW87WrqyvK5pPgy+cMwAknAVMK0EmmdNFKopOhCSRd8AuFggqFgkqlklZWVpTP55XL5Tq0hQQgOHuYJaqEuWfSL729vZqcnFS5XI6WBKTNXCPkf7t4mw7p4+Pj0dfn9u3bevnll/Xcc8/F/QGA2NUC4OFpvEwmo5mZGf3wD/+wvvGNb+iLX/yiVlZWdO3aNc3NzSmfz2t4eFhzc3Nqt9t67bXX1G63df78ef3QD/1QVKqdPXu2g8W+d++elpaWtLy8rHv37kVbmsPDw6iaBaDDKGG/aGrbarWiL6GnkMfGxtTbe7ypNzsyAE4AxYxdV1dXdJ93loh3B5vKvIRRIdhKyjby+byeeeYZPfPMMxoZGVGz2dTS0pL+6q/+Sm+++aZu3boVrWsAbzTGZU7zTlkzfJZ15Sy9y0fcFh4cHETDWnRerAt2j8hmswFiDw8PVSgUIoCHvSKligYRP+HV3ax/GEXXd3lWAhtKEJnUxjE3+J7rJmGGHXCvr69HRSvAsFarRQYFW/3w+GgdDzQAw+m128ctHzY3NyOVQapDUoemCmPDIvCoxRcO52aRHxwcxMKTOvU0/Dk8PIyIqa+vL/acxJhi0BDxYwj7+/tDXIzz8AadiI+TKUHYNQc4yfQbhpoDh49jd/2MayO8ICEZiZK2bLfbAdpcZ8d9SydgBxDMNU8zBkkmjOs4AHRA5NS+X89Bg+u/kilGv4fk/xkPfwY3rklw6POR6wDw3VnSTd6dH+PB93gHXhwC+8ozIRCnR5ZrxRhn5pyzBRxJvU06nQ4nNDIyEgUDPs8ICnCM3CcAkoIWfn/37l3t7u7qySefjKai/G5/f1+NRkOpVCoqCnlW+jYxNkNDQ1pYWFCz2dT6+no8p2/746lb5j1MD/KBw8NDPfLII2q32/rGN76hgYEBfeITn4j2FJyHOc15SL8y/ufPn1exWNRbb72ld999V3fv3tWjjz6qYrEYa3l0dFTT09Nhi3p6erSwsKC5ubkQdL/++ut67733VCqVtLq6GtsDIfTnu6wvno/9EWFR0P6Q2qOakeeA0RkeHu5w+NjFTCaj7e1t9fb26vHHH1d/f7/eeecdVSqVeJ8+XwBjzCvAENcDdD7//POan59XV1eX1tbW9Ed/9Ef6m7/5G924caMDdDHvmRuABQ8ePD3tYIt553P5NJ0m7B9aMG+nQoBTqVQCiDOmpODZwQF2z1k6DgdZnlFxht2Df8/M8L7cjrpMY2trq0MywxqiepX0cDqd1sDAgLa3t7W0tBTBX7IFy4d1nBYQ/yDO+aAeDzQA8wUKTeyT1hkaB10uMvWUGeeSTiI+NzaSOhyIO3QWCSmmfD7f0fWc1BnXIZqVFEbb7831ZzhYp/6TrJoDS0/3uZiWZ/E9x/gOaRAcII7M2TPGLznhk+xdEuhg+DCUp7FPydSpGzCiahwB94RzckPowOs0UOXXOI3pSjJ6SUP696XPOT9pb+650WioVCp1NIukhUfyubk+3bz54+XkpB8x/rBKkgKcEkA4O+TjwMGaIR0D6+aMCSDJgRbnl45ThkT2Ozs7WlpaUj6f19zcXKw33uPBwfH+fwQlvgGydKL7yWazmpqaimcgDedBBs/DGjo8PNTw8HBUjjEf+Ozm5qa++c1vamhoSNeuXdPAwED8nrYxBE6MN6nKZrOp/v5+Pfvss7p69aquX7+uUqmk119/XRMTEwFI0Gtxr4i+6cZ+9+5d/c3f/E0wh2xwjhzAHSvvgPPQjR+mK5vNamdnR+vr69rb29PExITy+bx2d3e1vr7eodvyIg7WJAHB/Py8enp6dP36de3t7cU9uN6WseZvwGKr1dLR0ZGefPJJ/eN//I81OjqqarWq73znO/rLv/xLvfbaa8pms5qdnVW1Wg0GGxCG7fJKQV+XzhAB3ph7AMnTbBTFIkdHR5EVIfVIGxFAVW9vr0qlUqT7PSgiGGEsT5N2eMB3GiDjQKrCeuvr6ws9G/dMepj7oApUUjDh3iQ8nU5H4I/P8FYvD6sgP3rHAw3AONrtdixmL5v3SeeMF0CDie3gjIXK4iKiQyMBkHKNgjMSsF84QV9Inrqk/UQydZIUkxLhZbPZMHA4dxwsxsdBmHQCQvycqVQqtlGiGSvifukE1HC/rpNzAIsTJYoGkPJZZwgxWqRO/PD0kQNlf7eum+M7R0dHHcyAg3E+w98YRqfs/Xf+s+S9MY4cyVRkEsRhjHkm3h3zaXNzM1qUUPnK/EILwnV5N9lsNvo5edNMgAsH1bdJ8S/pNHdinqIBDHn5Pz/n/fs40V8J50mwwLY6MDz5fF7tdjsaHlIJgst0wgAAnfFJREFUCqMGOAfk+c8dpPH+SfGRIiV1CBPma93nUbvdjvfQbrc1NjamJ554Qt/97nf1t3/7txoeHtbjjz8eAAvniqPn8PPCGE1NTWl0dFSlUknp9HH7GfR4gDnuv1qtdrAhaIoGBgYineeFG1yzp6dHQ0ND4YSlk+rplZWVACJ8j1587D1KGpPxxpZUq1Vls9lolTM8PKyxsbGwK/RIBAQkmV8ASyaTCTbzmWee0Y/+6I8qn89rcXFRf/RHf6Tr169rc3MzdGWlUqkjfY6NcPDFzx2YuV32QI15Ip300eJv7t3H3T+/v7+vSqUSz+rNNgnAeQ+st1SqU1bic4Treb/DpC1xooB7Aix5VTefh3XzVC/Ak3HzKkdakzhTzb192Ac+6Ad9zgf1eKABGJMTsaR3lJdOnAwshC9ej95ZyIhOWZzk0qFwXX/D9bkW2+PAftGp3UGhdLKvWrIknL+JUl2QLR1HqlQ/QcknWTDp/ak/qZO5AiT19/d3tLMAxBEJQrk7KPUqpXa7HSyBpyCTBgsDmWRN/MA4+u8wNgBCzsfvMD4419POl3QYLP6k8eb3SQbKv+fviL89XX3a4QAW4E67FPQoOAcvT3dRMimETCYTaXXS1ZI6UkSwFaQueTdstvxB9L8bRa/0SqVSARIldWjTnPUikkfTNjAwEL2JYBnK5XJox/ge5yWwYO47+8E8ZJ2yLyTFNmgoifgBsQBlD3DQ7YyOjsbzvvPOO3rllVc0NDSkubm5eH5PtToQxOYwJ46OjrSwsKDLly8HgDg4OIjeb3t7e9FFvaurS+VyORil4eFhTU9PR9rT9T6MP7YK+QP2huruzc1NSSfbl9G+Yn9/X7du3eoA6QC5sbGxmNtUtErS/Py8xsfHY+cB5hMMkgNaZ3ZJhT/++OP61Kc+peHhYa2tremVV17RnTt3JKmjihzdl//x3SzcPmDr3Eb4O/WD+eb3mUz/ue12PSRsJ9WQPJ8XIbj2kHniOkuvTCVA8LQ4h7eT4PpkQbD77jeSbT9Yc3TF94wLoN7ZO44fNPD53zkepiA7jwcegFER4h2fiUDZascZLyYni9mZMU9/cJCjHxwcDIEoRt4dEWXJsEnZbDbSkp4SdLYHw8oil9QBCDFGgBGPFF2w7cAomf7zyBjAQEdpntkNBykmWmlwDzhHZ6ec6eH/HvH5fUsKIECU799NMjP+Tpzl8pQXY+tpZwcaADC+k9TtcT0HZ9L7040O0vz5/UgCOIy7awhhagBgOzs7IWCHTfA0AwAUFpT2Dfv7+1Eo4eeH7cIxAqSTVY+npYv5vbNQnp4CNDGuPkcd8KK5wklJxz2g7t27FwwLejA3nElQ63MNcJJKHW+MzabMvF8KAZhfHgwwd92xA6iKxWJUHr722msaHh5WLpcLaQBjgIPmXeAs2WKMwhgKgKg+lI5TzisrKwFuent7w16NjIxoYWFBlUqlo7kt98g4uDwBBgUWEGDorXVgS3DSPLezrblcTl1dXdrY2AgWjf5kfB5WlEATLSJjii5xYmJCzz33nC5duqTe3l5985vf1Le//W3du3cvAPvOzo7q9boqlUrYStjQpIbTU3tJh+1BlLOlybXrKcrTAr4kkGbu4zvQW7lWjPtw7ZyzcqwTPse7S4JnX4OZTCbE/2hvaSdB4OHMNf92ZjiXy0WQzFh62wwH0g+Pj9bxQAMw6dhgs+UGTAyLKLnIYCG8kaGzGkl2a39/PwybG2GcBKBmZ2cnwEo+n9fQ0FBoqWDRpBPmzdkk34YEkAGowNBwz9DJvtBdlHqawXEA5lvAQK+zmGFbAKZ9fX3a3NxUq9XS0NBQOHQ3jtybswYArGSazo2Gp4K5B/6cxpQBkjkXgAMxu4+tj7cfGMpktHSacU6mFU87z991eNqTZ5MUDTip2IWVgQVztok/GHJvxeC7OzA/uS7OoqurK9YD4+z6MX92UkKelufnpVIpxPbsx4fexNu8wN7Q+oHqsnQ6HXvT9fX1RRsNd4TcH5E+z0I1JGwiDJtX6uHcpRMgB8gnAEP7BLhla6fZ2VkdHBzo+vXrKhQK+sQnPqFsNhtA9+joKNiQoaGhmL/ugFdWVrS8vKzl5WUdHBzoiSee0ODgYDjGGzdu6KWXXtJzzz2ns2fPBjBOp9PRt6tUKkVDY94ZB2ON46fYoV6vR+ER+iFsC+8glTrZSUE6CWxgXaampkJAD2hMpvH5GbYPvejh4fEWSJ/4xCf0yCOPKJPJaHFxUX/2Z38WFXhdXV1qtVoql8uxfyaMX7JNSpIp+l5rCzvghVW+7ngGD+w+KMDiZ8xJ5hXtjJhPfi2qgZ1VY+35Jtow1r7umMcwuaTgeUfMf5g0Z4DdLro2mHUIYE5uYXQaEP0wjocpyM7jgQZgGBsmL5MNAABz4JGMa5gcGEknmxH7gnJdAAvLtSaSYp8/HBhOgQWc1CWQ+gNYJQ93+iw6bxjrkaE7MY4kw4GhQizrLMnBwUFU31AdRU8dnL7vtQa1jrHxqMsNHIap2WyqXC7H8/M8vCfeI/ftqR8fA961g7ZkCwq/Lufze/O0Y5ItTIIzj8R536elPfzzp6V/eT5vK0K/ns3NzWgB4hoW3pM7Gm+Yy/wG4AGC+T3/dlbL79ELGngPPo6Mv7NnpMVIiztTw1zgmrlcTkNDQ9EYmSKCtbW1aE6cfEc+95NpYX4GEwoDhYax3W5Hk1H6qjFXGCPG0PVXznRIx+0p8vm8zp8/H20ipJP2GzjMZrMZDCaAmOrn/f19ra6uqlQqhZM9f/68bt68qT/6oz/SJz/5ST311FMaGxtTrVaLhqGMN+vLgSTvlD5aVKuur69LUhRLkK7nHtmBYWhoKKolAQiwdQRkFB7QfJS17QEe7BdOvlgs6rnnntO5c+e0v7+v9957Ty+//LIkRdPTZrOpUqmk9fX12CKL5/J15X87i55cSy4dOS0d6Wx48mdJQOlrOmkv3c762nH9lfdO9CAaoEYGhGp8zkewS5aE3UE8gyApglb8DtpLghpArIv1CfgAiYj8CcYeHh+t44EGYJJiIRD1O4MEHQ+LRSScTJ+4gcHowyYldUnuMPgemgbX5iRBmgMPjBughrQAB44MB+BskRsE16Ykr8f9OQDjd7BMsHR06vcCBFJJru/CcABU/fCoEyN0dHSk9fV1VavVKNEHcLoug7QR95gEMk7BAxySOokkePJrJFOPDsiSBpb/c52/T8SWjIIdNHuKEzBwdHSkarWqVqvVoQPxVI/vecf9d3d3d6RueD5PrcPIYuAB0K7l4zrcL+wbY4ZjY57Sg44mma6LIbWRdHy+16OkAIn1ej0AvaQOVs7BMiJ2B+s4r2w2q+HhYe3v70erDAeM/t79/34vACw2SJ+fn9fbb7+t69eva2RkpGPjboIp3nOtVlOtVos0j0sJ0PdsbGxocnJSvb29unLliprNpl5++WVtbGxEwFitVqNZ7djYmFqtlq5fv67u7m4NDw93yA7Y95SKOWxZV1eXlpeXQ0fHGDEXdnd3gzH0NcV4sMNGX19f9FojEHMw4Oz84eGhLl26pBdeeEFzc3NqtVp677339NJLL8VWSVSLVqvVYOo8YHI7m0wh+jU/iNF3tszT4sn1nmTzkoDOgZfrW/kd84/n5vdJ4MNa9wIlvsN4cs+ZzEnLGLeHnm7GVnqbCV/nDrDQ7Urq0HtyX4zR1taW/sf/+B8fbMj+HxzJDMMP6pwP6vFAAzCMP1UlGEwWdTabjZJcjExSw0PaK8meHR0dRUk9hztjmAqaIPb390fTQwwCEQrRNxG4VwomOziTckJj4bqDpJEiHXlahVCSGcMBcy5aUdC+g5QMWjcq4rhn9EYAMFg8vuvN/nzhSwowALjyKNZTCcnIlpSL94TCIXkUDwPDuOLIfTz8cIN7Wooxaaz/vgvcHX8q1dnfh3np2ib2CaSK1O/PWTHeIeML8IT99f0kJUWHeYw4OiD6cCEm5l2QEkyyuLwHB7/ei8jBjYOv/v7+SIG1Wq0OMTvawqmpqQ7Gzh0WRpq1yZi4CJ75xFhThUwHeGcOOHhe1iAp1Ha7rTNnzmhqako9PT06d+5cSANguFqtlra3t+N5PaXn2i3A4/r6uur1usbGxjQ4OKhr166pXC5HxeG9e/f0rW99S93d3Xr22We1sLCgxx57TK1WK5pqEshIx6w5gH1oaCjYrf7+fuVyOd24cSPutVKpqN0+lkOQjnK94OHhYaSKC4WCtre3tb29HYUN2C0HmK4pevLJJ/VP/sk/0fT0tJrNpl555RXduHEjzktKbXNzU7VaLdLtvAtnOE9jlP2dOwBzW4Z9S0oOAEI+Zzkfv+fafPe0QA3G2eeds/MeoANwsSnYdHyPtwqBxcJGUkACgHd5DPeELWZdePCDjUzaHu7h8PCk5chDBuyjd3zfAGx5eVn/5t/8G/3xH/+xtre3denSJf3O7/yOnnnmGUnHE/tzn/ucfvu3f1vValXPPvusfvM3f1NXrlyJc+zu7uozn/mM/ut//a/a3t7Wj/zIj+i3fuu3NDs7+33dSzqdjoXuzU5ZGIjNXWifTG95FMXfsDEsGF/QGEWcB5WJUMPeZ4eID7CGU0FfwkJ03YKzXu7cvGKSw/Vg6ID+rgiDBYwep1KpxL5iXoLNmCEy9pJsrs3nALDOPEqKzuWkMxkHqTPNkARMblw9ssXAeTqKd8x7c8bJDZZfk2u4MUumO05jw/4+h6fW/Hn8nbAfYKlUUqvVUqFQCJE9IMG1OtKJoNc3ND46Our4PmCf+wUwea8gfg5wHRgYUD6fj3FAo5N0TDyTdKKHOY0ZhE3L5XKRJmR80WuSiqQ5ZJJNdeDJ+XjnkoIVcC1SOp0OKYADeU/juoZncHBQ2WxWxWIxKiuPjo5Cn/fGG28EgAFUoZMqFArBHgGUeD7G5M6dO+ru7taTTz6pYrGo0dFRffzjHw+bQDuSxcVF5fN5FYtFFYtFPfLII5qcnFSz2QxNGLbIQQEsXjab1eTkpKampnTjxg3duHEjbA/7jtL9HTG4V8ry7EtLS1pbW4u0Jm1GGHt293jiiSf0L/7Fv9Dw8LA2Njb0la98RW+//XbsV0jVaq1WU6lUipYraO9OY6qTh883X7++Rj0w4p16Gv+06zBukqKyGhBzGiDjwH7B/tLGB5vW09MT6wp9Hr/nPj1oYZ5g45rNZlSzErgTGPN/siye3nSfgoSE9YZNdb/1URDhP2TAOo/vC4BVq1W98MIL+uEf/mH98R//sYrFom7evBkpCkn69V//df3Gb/yGfvd3f1eXLl3S5z//ef3oj/5o7MEmSb/0S7+kL37xi/rDP/xDjY6O6pd/+Zf1Ez/xE3r11Vffx1B9r+Pw8Lisme0X0FAQISe73/vCwkESUbuDkjrTdP4d/u7u7tbm5mYYSYCe09MwDkQ6VLrQL8wrK93Q/P/aO9MgSbPqPL9Ze1VWLpW1V28zAz0Ms3qY0cJiywgCjIUcMg4hWbKRwrJDWEYCI4et5QdYEQLJP2TZCoONJGPJsox/IClGYQwMMhqBsFgGZpgFZuueXqprzb2y9srPPyqeW2/e/qpnEfR00XkiKqoq81vueu573nPuuV6+OH4pjSXw4PbY3cZkjCl2WCmYEXdT0S4oFCwsjw3yXUE8n/JSDmI+nH1E8XgclrMtzt65AuH9/f39wVqHUXIwEIOsNImZRJRZDDj82jg+I+2Z7t70WEO+x63X39+vVqul+fl5raysqFQqhfHh9yZJEtg/xhDuybjM3g58Rrv7kVw+BwAWuK+IVaFt0tg/N3J8fAGOAEMsQnzPe7e2tlQul0Nqhvh9zjgg7g72xZBYuaWlpRDr0tPTE44gcjcq44gdzZ6DjTm5srKi+fl5Pf300zp37pxuvfVW3XbbbQHkw7xms1lNTk6GDQossLBGfX19qtVqeuihh0IfxHGX7Lis1+vBwGEO53K54DKChYa9Qr/A/sKwFIvFcLblN7/5zVCvpaUlra2tqVgsKpvNBtbMwzXIkl+pVHTu3DnNzs6G+CTcvBsbG7r11lv19//+31cul9PCwoLuu+++oP8HBweDwbWxsaFKpaLV1dWQK87n22HA67m+c53I+Ivjed0Aidl1QigYF+gw9AgAi/6C3cJbAHhFLzJWGfe4G/EIVKvVMDcA3owhGFv6wGNvY+OReYKhjQ520sCNFZ/n6BvmXleuLXlBAOzXf/3XdeLECX30ox8Nn91www3h7yRJ9Ju/+Zv65V/+Zb3tbW+TJP3e7/2epqen9Yd/+If66Z/+adXrdf3u7/6u/vt//+964xvfKEn6gz/4A504cUKf+cxn9OY3v/l5l8cP4EaxkReFrfAORqTOQ6odeOF6YSLkcrkOqwXA4JMf6h56H+XobpWYWsZliFtUOgjSZtcS5XGF5eDEF0UHYA4mUUpeZl98YQiHhoYCYGShlw5YNz92gxgb6ufMlS+07hrGjeQWWGzFojQ9cJy2xB3Coks/N5vNjsBv3HixCzFur7h9fSxQb76PBUWX5jbxa2hf33rOPYDI3t5eLS0taXFxUcePHw8MBZYsY8Vz29FOuKM4wodF1MEYfYPLkXgTt457eno6gq4zmcyhR5ag4B3oxkaBs7k9PQe7bT02iXx2CwsLyufz4SBuxq0vKtSN9Boe38L7crmclpeXtbKyolwuF2KnHNAxtzmMmVAFmKJms6mlpaUwpnBhPvbYYxofH9eJEyck7cfX0FZDQ0Oanp7W3t6eqtVqAKAOalqtls6ePauTJ08GkDIyMqKdnZ1wGHdvb6/m5uY0Ojoa4sNwD5LHbWNjQyMjIx3xUm6E0T4c+5PP58MRRwDrjY0N5fP5APiKxWJImpvL5ZTL5dRqtfTMM8+ov79fJ0+eVKVS0TPPPKPe3l597/d+r972trdpZmZGtVpNDzzwgM6cOaNCoaCenh6Vy+XQj7VaTSsrK2Fn6pVAlfe16+mYlfYQhJj5cgDmMb6AF8aDu1KHh4cDC5vL5QLIcnaQceaGhodcYAhKByk7iNFET1I+Z7J7enqCbmNOUx/e56mJANPo4Ngw4h0+/lyP+vx9qeX5xNS+mGceVXlBAOy+++7Tm9/8Zv3wD/+wHnjgAR07dkw/8zM/o3/2z/6ZpP1dRIuLi3rTm94U7hkcHNT3fd/36Qtf+IJ++qd/Wg8++KB2dnY6rpmbm9Ptt9+uL3zhCy8YgHE+l9OxMAMsWm6t+4LrA5wJixXEYafEhMULD1YNYIrge8rARMACYYEhHmN0dLRjNxmTkmcAcNx683gIX1zSFi9nMOKJx/NQOA4Y0yY3bQmjyOTe2NgIC7mzcVDyKDuX2O3nLjd3u1Evz2Xj38f5iQDAuDpQZixUfEYZqJuzMIiD85jZej7iyt/r6Qxqb29vcMc1Gg2Njo5qZGQkuLRid59bw7AS4+PjweoeHR3tWKjoT+8vZ+PiMeLXAaDiseX3uKvfAYGPScYLiwrza319XcvLy5qeng4slLNerlDdOOnpOchtRZ+TVPTJJ5/U2tpah5t7ZmZGxWKxI/XM3t6eWq1WWEhhJDwwH3fkQw89pG984xsBbNH+zJdisRgSreLSos8IpOaMQTYlEKDPLsVcLqepqSm1Wi2dO3dO5XI5HOy9t7cXdkoSt+csEH0AO7Ozs6PJyUlJ+27axx9/XPPz82FcVKvVcNxRuVzW9va2stmstre3lcvlNDY2ps3NTVUqFd18882am5vT9va2Tp8+rR/6oR/S+Pi4NjY29Fd/9Vc6e/ZsR7jF3t5eON1jeXk5pIhxNsr1k8+lNPbfwRhj1ucvOszd1+gQztoFyJC6hHFLfUulUggdod+oD5ssms2mqtVq2EFKWXy80h9ugDB2Y0CJh4bnpMWxxfc6g+suRdrC/2eM+3rAHI5d/V156eUFAbAzZ87owx/+sN773vfql37pl/SlL31JP/dzP6fBwUG94x3v0OLioiRpenq6477p6WmdO3dOkrS4uKiBgYFw9I1fw/2xEH+BELxMwCnAZXV1NWzXjhdWX/Tb7XZgBGB8UJ7FYjG4Ht1NyWQHFG1ubqparUpSoN8HBweDcpY644B4ryfX8wlMjI90YK24xUt5HBwBdHzHHOKg0ml7D4Qnj4x0cASR5/Py+4kFQ5EODAyE3WHEE8GAAYQAqK6YnBFJcynQF4Bc4poclFFnScH656BhADmWcOzmcuXnLuO0scI9scJ38OF9yDhhTLjr1tNKUPatrS1duHAhuK1QxLSzu/Z4Nkk2WeBoD99FyvXb29vB1Yyb3q1xSZfNE9rCNwv4uGMO+JxhLAC++Q6Wl+36tBW7M8+cORMOhvc+oM/d2IDJoX8JfGaBrdfrOnfuXGB6Tp48qZMnT4Y51Wq1tLS0FGJpYCId2BOr1Ne3fyZitVrVmTNnVCwWNT4+rvHx8XCYOvF609PTarVaoR897oxF79KlSyEdxMbGhs6fP6+FhQVtbGzo+PHj6u/vD4HzmUxGq6urgUmfmZkJY2ZnZ0eFQqEj5mdoaEilUkm5XE49PfsxsZlMRqVSSVNTU3r00Ud17tw5VatV7e7uql6vBzZ5Z2dHq6urKhaL4dmTk5Pa2NjQww8/rP7+fv2dv/N3dNtttymfz2t+fl6f+9zn9NWvfjUwhcSzAlzK5XI4DxejCIMg1k+IuxPdIPBx72PTg+G5BxeiM3ru+mUcVCoVXbx4UeVyWc1mM4ApgDg7Fhl3lME3RPFdbMDDIjNPXG/wWRyGEeskfzY6yHW7f4e+iNc27vG//buXUtIIgW/FM4+qvCAA1m63de+99+oDH/iAJOnuu+/WY489pg9/+MN6xzveEa6L0fzzYQ+udM0HP/hB/dt/+28v+xyrBEXHZPUtu7GlwG928/mgJ07JqefYMmOxIz6Dd/k1sQWHwgCUcD1KybcxO2Bz65Hn8B0D2Sf0cw3umAVEObFDCTDlz4fV8l1f7rqVDqw1jz0grszbxa89zOJ1dyRxEwBl+oZ2BAjx29NYOA1Pv7ui83cyfpyt8riV+Id6+Hjy9qA/4/fzbp7dbrdVLpfDwcTOPsYsIW3PYkPwLotnbIFTJ4KBiTeJ3RWAER8j3l4xc+G7v+g7GAcHUcQysdPQWTLGLWzQxMRESDrJe308+EJEP0sHMS29vb2amppSu93W5OSkbr311nD+4cbGhmq1mpaWlrS6uhoCz1nAfD543Ewmk9Hp06eDGxG2amhoKIBA4sFmZmbC7kU3ntAhZL7nLErc5RwTxfxyw5TPms2ment7NTs7q0KhoEajoUqlEowMQISk4GJFL7385S9XoVDQ+Pi4vvzlL4fYU4wiGGzikY4dO6be3v08bhcvXtSxY8d0yy23aGBgQF/60pf0mc98RpcuXQqbPjwxNUwsn2E8IXEeQ58//HiqBmfK0TmMfVhOYrQKhYKKxWJgXH3nOrq6Wq2qt7c37MoEiKetO7HHhLLFbryY8fI5Gs93r6Mz6jGoi3VM/Bni+snXOWfC4rl7LQCVrguyU14QAJudndWtt97a8dkrX/lKffzjH5e0T/lL+yzX7OxsuAZ3A9cQoOgs2PLysl7zmtekvvcXf/EX9d73vjf832g0dOLEiXCsBeCh3W5rZGQkBLen+cFR4vj8YSPy+bzGxsbCVmzPCu2gKEn20wAQ9+EBwX4dCwVKI5PJBMbILT3pIKVFWt6omIbH5cb7+JsFEIbJ3UTc6yCJiUvc2cbGRgC0nlk9dgFJ6mDcfOGSDoAW8XHuWvT4I3c9xcoujpfyszyx/NfX1zveT1tQH9/Y4IAK8R1rsRKMP3OJlUea8gbYOmtJOUj9gdJgJ9zc3Jzy+fxlCt4XOAenvvitr68HJocy9PTsJ2ocGRnpKKMvBF5+70NX+jHwTHMP+Zii3UmDQVoCfyaG0tbWlpaWljQ0NBQO7o7HFAaPM4++QYEF96abbtJdd90V2oEEwLVaLQCCTCYTgA3sCYu1/7+5uRlYpJMnT+qxxx7T448/Hpg12tHZptnZWdVqtY5daLAq1WpVtVpNU1NTHUAZkN5oNAJA8PYkL1g2m9WxY8fCbsVarRbYHZhHGE/OXFxfX1dfX58mJycDwPrmN7+pRqMR2pM2ZcPQ7u6unn76aZ04cUJvfvOb9brXvU75fF6PPvqoPvWpT+nixYsaGxsLYwudi5HmMVDsSmecoAPp25hld9Aaj0vGEwwwRz2x09YNCnZ/snMQwEnIB/X0I5p8o49/5iDHwZqXOTZG4+dwv78D8ftioJUGUvzdPJe1JAZgjCP6IHbXduXakBcEwF772tfqiSee6PjsySef1KlTpyRJN954o2ZmZnT//ffr7rvvlrS/WD/wwAP69V//dUnSPffco/7+ft1///16+9vfLklaWFjQo48+qn/37/5d6ntZgGPBDeB0LIHlMQPmE8YnOQpheHi4Q3ExyZl40gEYIW0DZfMA6djFhSuK2BMsV1dAKH52bibJweGw7qLz2CSpEySgBN2qd3dTzH45E8iCwc4aygCIy2QOzgVkMXOAyOSOrT4SbtJmxFjQP06TOwvjbkan7VFQLGyuEIlRIxYvLX4rtgZp3zTx+xAHh7Hrkb+lg6NGGA8OWmjLVqsVAtLn5+fVaDTC0TXU0fOa0bfO8vl45kBsFkaAEMxlDICw5mMgHG/hj9skZmEBU7ybz9jRhZubdnBWBLditVoN5+HF7hifJ7QvrB0gb3JyMiywzWZTy8vL+vrXv65araZ8Pq/Z2dmQIR+wQb9hiDAWcP+SFuDkyZPa2trS448/rrNnz4aNAzxrfX29w0W5sLAQ2gSjpt1uq16vdzCfgHPKwzj3JLUYqsT3LSws6Ny5cyGFze7ubsc5gpyJiWEL0J+amtI999yjqakpPf7447p06VIHAOHvSqWira0t3Xvvvfru7/5uzc7OanFxUQ888IC+8pWvaG5uTr29veHYKmf7AF0YSDFb5EdneXu7QYnwuTPfsI/s3N7b2wvAlYB2wBWMq7RvWJMQt1KpdBxZByh0veAxWa4/GJPMEZ/3MZPlQvugjxwQuR6Ox7jra3dnOrjzz/jfJdZL32rm6cXIt4OJuxaYvRcrLwiA/ct/+S/1mte8Rh/4wAf09re/XV/60pf0kY98RB/5yEck7Xfwe97zHn3gAx/Q6dOndfr0aX3gAx/QyMiIfuzHfkySVCgU9FM/9VP6+Z//eY2Pj6tUKulf/at/pTvuuCPsiny+AqCRFJgsj71xd6DHDbTbba2urgZrCsXNDspSqdTBJjjoYQs4LgOPeSHnEvdJCtvdOZLCYxqwyp3ZcjDjDJhPHg8GTaOZ0wakT3BnmOIM3rHi9Ht8AfbrYoaEtnZQlMbysSB5sDqAwncRuZUHWIyBLq4wZ3hiAHUYVe2KEZDFc/0eL7eD8vhZAGpXzA6aiAnjeWfPntXy8rImJydDO2Gt8w4WtiRJgtuYXEGABpgJmBkYH9qHlAYO1N1VkQY647/Txpp/FsfpAIxwvcdjRdpnAXEj4Vb0MefAkYV8ampKExMTKhQKgclm195TTz0VwBcgz3fQ0g4EkQNCiVuT9g8Qb7f3dzHPzs6q2WxqcXEx7Fhkt1ySJCGOivxdzWYzlB03H4dej42NhU04GGUwlcePH1ej0Qgs1eTkpF7/+tcHZvTSpUt65plnNDY2FnbgbW5uBtempJDra2ZmJoCRdrutsbGxUO5sNhuAHH1ALsU3vvGN+rEf+zEdO3ZMjUZDf/Znf6a/+qu/Clnt2+2DszH7+vrChgb0mx8J53M61mc+bzzFisdykWID8L69vR3K6S5O9KYDN9+RySYX7nUXuo9d7omZLljtmJ1KM3KdOYvrGQMu/z/WIbFh6PqGd6aBxPi3dG3FgHWlU14QAPuu7/ou/fEf/7F+8Rd/Ub/yK7+iG2+8Ub/5m7+pH//xHw/X/Ot//a+1sbGhn/mZnwmJWD/96U93JPn89//+36uvr09vf/vbQyLW//bf/lsHC/J8xK0oLEfy18TsCpYNSoG/s9mspIPM1mNjYyoWi2HCOwPDpCBnFjvXWGxRCrgEPIkpMSMO5ig7ExEGwRcf6cB1ipKCDWQSOtPHc31yOrCAlWOzAOAoSZJwtp7UyQChfNwdhtXr1hkTfWtrS/V6PTAs3m4OAFFs7OyTLs/4z4HVDmrI90R9ACqA2KGhIe3tHZwV6a4QrxsLkJfRWR134TprhAKM2RyYjVgxu5uVHGCMBRYxFnyeSR9Sd97Hwt9ut4O7Z319Xfl8Puz2YgzyXnJRkeKAetInzjY5QEzbyMJntCHzjB2vLIbuAvZ7SAHBO2nfRqMRclWxKxGwyNxJkkT5fF6lUkmTk5OBDWk0GlpeXg6hAWyIkRSSiZISg1MDYH0YUzGjVyqVgnsvn8/rxhtv1MMPPxzcxcVisYOpJa/f+Ph4cI+PjIwEcLK+vq7V1dWg44aHh0M6FUAUqUgADbhAC4WCvv71r+v8+fMBiLMJg3QvjEGA7/T0dNBPfNdqtTQ6OqrZ2Vn19PRofn4+hFKsr6/rVa96lX70R39UJ06c0MrKiv7n//yf+tM//VOtr69rZmZGe3t7KpfL4Xxc33ncaDRCUuo0I9DBjBvDPj5xM2KEMi98V18a6+TGXHyNM5X+mc93H/8YePzPe3xephmoPMv1l8+zWP/Ea53rFtcD8TxxYOZA1/VTLLTHtQDAugxYp7zgTPhvfetb9da3vvXQ7zOZjN7//vfr/e9//6HXDA0N6bd+67f0W7/1Wy/09R2CdclA9LMGEQcznpcFpkw6GMi4HVmAfCFlokJz9/b2hgBnFkTpQAGgNJhIMEyeUR5rzZk5LHxnJGLGwCeh08vOFMQTPLboPPGgZ4MmEJ/dobH1yv8AOJQGi1kc6MoiRFvD4AB63BJ1dok+8w0VHmeEEqUf3R3qsTweO+RtRFt47Bzi19B2scvBf3NP3NaA97gvPH7Gd9TCzPi7KR/GBjv3BgcHA9vV29sb2poxQd02NjZCv7BYutuR/31hQZn7QuV1oH4+7ngvYxrwIh3EOPp93t99fX2qVqvK5XKanp4OgfvEWDIOisWipqamVCgUJCnEeVWr1QC8x8bGwhE4gK9WqxXyfwFQfVMC5WZs+YYPGMaxsTGdOnVKCwsLOn/+fNA1zAUMhfHx8XD2Ia5omHVAc09PTzisfHl5OaSDgDmD/SVVRqPR0NNPP61GoxF2KeK63NraCuAF46xWqwV3L5/39fVpampKPT09Ghsb0+TkpGZnZ/WlL31J586d0/d8z/foPe95j+68804tLy/rd37nd/Snf/qnIScih52jA2kXDAjyycU7in3++PykjT1GFMPOY8sOe1YM/hlTPT2d5+bGC77/n8Zex79jnRHrEhfXWz4nHKS5PvU5hX7xzV8e5uHud97letDXQV8fYgayK9eWHOmzIFlcYlo8jQFyAYCx6AIinEnyRRpQsLW1pUajEQCFvyuNMva4KizW+FifeFFCYmpZOpi8aYjfgd9hFgHl5AeggsKCNYJtkzpzZ8GsEMQbtynXwaShcH1Boxxenhgc8pyenh4Vi0Xl8/mOjQyVSqUjB5nHB7JYA6o8/QPWpb/Xga6PE3eXxAot7qs0l4XXl/bBCCBehXfs7Ozo/PnzKpfLmpubC8/w8eX9z/glHQIJXOkTFnAWM5hGdhrGLONhEgMtbx9nMlk0nEmkTYmnZFzFhgKg0hccnsn9BPOz021zc1P1ej0Et9NW7XY7xEDBLMFkr62thZgxT3EQzzn6ibEDmzU4OKhjx46pXC7rwoULmpiY0OTkZDDUAMPZbFYTExOBnWSXo2de5/gfDqqmLQiin5qakrS/2JKDijnZbDZD+d3AIAcaRhVuV2LwGANzc3MhdUa1WlU+n1e5XNbf/bt/V3fffbd2d/cT0P7FX/yF9vb2dPLkyZCfLnat4c6t1WrhHMo0I8WZfg+1ACQCJLwf4vno4zUGEj5uYgPLx3gaGIvfeaV54GP7uVgul7Qyx/ozBotuoDgR4GtADCTRDXGbAe4OK9/VlG9HOY4ysDzSAIxFFlcSaSRiZsWteY8zQHkBijgo1RkCFgF2ErGjiyM9UNgEIXsMgS8MBK4CeCib/2DBslCi9GIGzCd8PJjjGISYyYkBJm1B/BA7IWNQg4JioZA6QRcLPe3AwgTz5e3i7Br14h2UBeDqcSDt9v7xRuVyWZubm6HsLJYodgfkWIzujmbsoNj43pWcx0e5xJap/x0r0HgBYes+7eP3LSws6NKlS5qbmwsxLLjOAL/0bZIkIf5ofX1dpVIplAO3NwwS7Q5r5pn1KVea+FjzBcRBF/fjMvI5xfgfHBxUsVgMYJH+iRfJdns/LjOT2Y/B5KiebDarXC4Xwgw4r5BnASzYHMJB1WStp2yNRiPMfX4DIJiPzpZnMpkAHOnTXC6n48eP6+mnnw4B+QCKTGbfFdrf36+JiYnAgmHktFotLS4uamRkRBMTEwFcw04yjmdmZnT8+HENDw/riSeeULVa1dbWlmZnZ3X8+HF985vf1GOPPRba3plU5h9/UzbSbhB8TvLR5eVl5XI5/eAP/qBe8YpXaGNjQ5/85Cf1h3/4h6rX6wEIeroPxiH9yG5nz5OVBr5iA9e9BPGYSWOYfF65OIj2ue2Gi98XL9axK/JKBkmapM0NbwOuSctt52yh6x3Ara8jMTvNM2i3w9g7r2d3F+S1J0cagPnuMOmAZYgtc7eonIkAZJAFm9QUPqlQ8mS0Xl9f74hZgMGAgfAM5DA/KCgUrtPtvB9gxj0o0TRKP83iSmPM0hRiPDH97DMsadwLLrFSjS01QKifP8dxTp7jjPaUrpwbDKCBUuX/Wq2mcrmsdrsdzhqkPz1oPg7GjceNv98D4hEHR7F1nqbY/T4Wdl9YAZ24jTzGo7+/X2tra8Ed5QaCtxdKGaDV19cXEoNWq9WQGZ7x1dfXF1xZ7LD1el5pwfG4Nb/W3ZJxrIwHB1N+ScFVv7Gx0XE9z8tkDnbN9vT0aGJiQlNTUyHFC/nCVlZWQlZ4X9R9cVpbW1Mmk9H4+HgH6wR7ur29reHhYRUKhY4YwTgnIOUkeSrlnZ2dDUdITU9Phx3guBrX1tY0Pj6umZkZra6uqtlsBrDXbDbVaDTCAeqAYm8rNi309Oxnrqe+Y2NjuvnmmzUyMqJ6va6VlZWO8wXdECAW1o/J4fxHQii45xWveIVOnTqljY0N3X///fqv//W/6ty5c+GYHnZws2vZxzmxY8+H+XIQ4uMnBgUAMvcsxPM3DYwxhrwM8TXS5Wkg4niwNDnMCDnME+GGmJfF48jiOnPvYcyYz1Xq6gCN3xiWsWcmzVh/qeSFgtzvZDnSAMwnnQcqSweB9x5Ezz3SgcXDgomVCLuDeytJkpA0kYBQco0RDI6SR+E7wACgxcwDZUQBe0xETB3HtO1hNHrMqB0msRuSAGB21flmAbfaaGvfSYcFygK2urqqCxcuaHNzMyTY9MUWIBKzalInEHNl5wvs5uamms1mWBSoqzMrDgx8y3i8O8uVe1qbukJPA15xG7ui9t2HAHU+Z7wwTtglyHmEuAspM2DOA5xZZDnwnAScmUxGuVwuLJC4zPP5fADVMMcxaxcraMoY19sXCWfS/AdmGiYPdpr2d6Olr69PExMTOnXqlE6fPq1SqRQOEa9UKlpaWtK5c+fUbrfDzmOfGz5X2+39XIAE0S8sLITykzyZWE9ylJHWBEbbjTpciNJ+yMPQ0JBmZmb07LPPamFhQceOHQv1JRh/Z2dH4+PjyufzqtVqwY2ayRwkZS0UCiFkgj5pNBqhjgBs+nJjY0Nf+9rXlMvldPfdd4dYNDLP817Xd7u7uyFeDBCeyRzEcJVKJX3v936vBgcH9eyzz+rMmTMaHR3VLbfcEhhUjmWirx3UUm6Mrxgoefyjj90YQB02t5gzziYfdm/MXPs7HZAcxhT5O9PmM//Husnr7OyUC9d5m/F5/C7KxHiOmeLYSOQ317huc8IhNiRfKnmutenFPvOoypEHYCxqkjr+jgcxLiYGMguzuyWlg6NteDbgCxfi8PBwx0HdDo5gKJhYuN/IuoxS9N1pcYZoTwzpisFdYjF17X9TV6f0D7MY+YyJCnNCPZxBjGO14ufxP65DWA9vWxhCXywdqPizPcCftvHdffQh/eoLGUG5lJ/rnHHxdojZr1hhuTJzpR4vDN72kjr6lzqxSDqoBKQvLCxoZWVFY2NjgR0BrBDgDOgFvGcymeD+gVEYGBjQ6OhoANS8I86R5r8PWwykg92R3nbxQpDmsmTckn2e7/r7+8N8KpVKuvPOO3XDDTdofHy8Iw1EuVzWuXPngtufHZTEsfmY8TGxtbUVzjbEhUfbkH/NmUlYx1wuFwCvL1YYZjDDJDddX18PmfzZZQroJbFzrVbrOH6sXq+rUqloamoqMKSUj40D+Xxeo6OjGhsbU6PRCPNqeXk5ZKi/4447NDw8rEcffVSNRiOMC45cAqRSfvqLeTI9Pa1bbrlFQ0NDKpfLeuihh9TX16dbbrklAF9cqAC3ra2tEMvKPMlkMuFAdca1GxgOWK4EBHwuOeh30BbP2zQwEj/TQRPtED8nnsuI76yMWSsfG/7+eO7w7jQj+jBD2gX9lGYsehvFhnAMFuP26sq1IUcagKUN4DTAEA88two8dYUDFQcDrVZLjUZDPT09Gh0dDcCCZ2EZQu+z6MCQAeA4EFbSZYuHB1A7UIzrBjiKxePeeF7sbuJ3DOZQ3nwWp5fw/GQe1B4rGhYlFnti6zyhaMzwAb7i2A8HKEi73Q6xPihFT7jrdaUOgBU/9SBWtO4682ekgTYfX7E139PTE9zJpBQAHNLWuI3i2JP+/v3zAOfn53Xq1KkQU+XuacCnAzDcV7A4bH7wuBxYTfrB63UYA0Z9nFEGDDrL4IaNB9ljcJDPicOoa7Wa+vv7NTs7q5mZGd18882amZkJc6NWq+mZZ57R+fPnQwLSQqHQAV55h48dX4z29vZCNvuxsTGVy+VQfxhGGCbc1O7Kpn+Z154mBXdeqVQKxw8RU0X/e26viYkJnTt3Lmzc2dzc7DgSiBAFUkksLCyEpKOlUkmNRiP0Y6lUUq1W08WLF5XNZnXbbbcpl8vp4Ycf1pNPPhncnH19fSqXy4EtJDcYdd7c3NTx48dVLBa1u7urS5cuhfi74eHhcIZokiQdaXTcIJMOvA4+/5mj9IXr1Vhi5svnfjwf054Rz2Ou83gr+jJ+p68Nz+WKPAy80B5pZYiNs7Tr0sTLleYujcEWutv1dxrLFLfDSyVdBqxTjjQAkzp3LsV+b8SVhDM+HoPlcTNMFhZ/TyFBnJgvAiz4DC4UgLNqMD3uQvFyx5aUu9NicOhWTcxM8b276NIUCNf39vYGBQ24arVawe3lYE1SBwiL25Z8Oz09PWHbv7toHSReSdxdmAbyAGGAFA/09z6n7WjbNLfbYTEmMdMXl8O/RwGyaJ48eVKlUqljMYeZw33q92UymQAQFxcXw3iD7UHZ8hwHZ8QXbW1tBTc5O/J4J/0CSPDNHQ6cfN4wNmEp6T9nuGJXu88pQDhgaHh4WHNzc5qYmNDp06d17Nix4MbPZPZdc+fPn9cjjzyi+fl5DQwM6NSpU+GQ6bS+cCCPUBfitvL5fIhrZI4DTgFgpHog4N53c9IWnGxALjWC3Wu1WgCVjEPYylKppEwmE45Mo/ybm5tqtVrq6ekJKWxopzNnzoQcZ8ePHw/9RwD98PCwnn32WT388MO6+eabddttt+nUqVP68pe/rM9+9rPh2cvLyyFFC6ki2u22crmcTp48qampKW1ubmp+fl7nz59XsVgMwNDHOHpEUmDDKG+a+y2OE/N+oS3TjOO0hfkwxjWWGOxwrzNHaWsCOsLnQfyuKzFHVwpR8HfE7DL3HFaXmAg4THxdcOOV71y8fl25duTIAzAHVA5qYoXtEwHmBGWLwnVfOgMZ9qCnpydsh+fZ5Nnxe3t6eoLVz/0cmQRbIx1kzgeQYWUeZulRx/izw/73escT2S383t797N8s+uyEhLFjR6i7GJw5gQFhYWHnmedJgj0AuHpqCvrCYz1Q5nEiUNg5393necIAJ142j19B3E0ds1mx8pUOFgJXYNQDkJXNZlUqlXTixImwy80VOrmnHPz4QoY1u7KyorW1tbADzePWent7O5g9xo9nBwfgAb58PAM4yFZO/1Med9k6UI3dNz6WfOx7G/IM4tKKxaJmZ2dVLBZVKBTC9zs7Ozpz5oweeughPfvss2HXHkApPrP1+Yxv6sYYGxkZCQfO9/T0aH19XWtrawGgFItFSQfJmJ0llNQxftl9OzAwoJmZGZ07d05ra2uamZkJ4wG3orS/c3J8fFxLS0thrpCouFgsBlBM2ZaWlrSwsKCbb745uDbz+XwAct/85jfV19en5eVlPfPMM2q1Wrr33nv1xje+UQMDA3rggQdCrBubCIaHh3Xp0iVtbm7qla98pe68807t7e2pWq2qXC5LUkhrwq5V6u1HovX07OcvA9D55oQYONAPMQCJJY3hikFPGriJr+N7H++uA2L2zo3VtPL4M+PPEY/limMh054Vx7M9l8QMHvcyXnw+HjYXrjV2qMuAdcqRBmA+qFFu/MRWs1spTBbATyaTCVSuXw9b4IlXPXaMZ3nQq3QQ+0PcBtYuip0YE6xq6iF1uiZj0OZKwxVPDK6ow5UsKW+PTOYg4SnuE3ISufssVpYs2pTPmT5vJxJS+kaD2HXkyoYF39NKSPtAiP7guZ7QNa1NHEA40/hckzZmxtKYF9qV3YenTp0KR83Qd1y3sbGhVqvV0Y6uRLmuUqmoVqsF8OQAMs3FzLs8xsl3QHpsHEYEuafcMIlBlivzeOHyMZS28HibDQ4OKpfLaWJiIrCifX19Wl9f1/Lysr7xjW/ooYceCoyZxxsRy5TP5y/b4BKLl1tScPERyL6xsaFmsxkMhWazGY4ho0y4LSWFeDn6IJM5cIUmyX7owvT0dIiLWl5e1uzsbEj1wZwpFArBdchB3TBu5DUjxgx3aLlcDuc4YvzhlpQUQFGxWNSFCxf0yU9+UjfddJPuvPNO9ff365FHHtGlS5e0uLgYdnf39/drcnJSd9xxh8bHxzU/P69nn302ZLWvVCrKZDLByCRpbbPZDCEYSbKfFobTQ+KYW2f8Y10R67A01upK7FBa/Jbf4/cBhNOMqvheH+NXckU+l77weZJm8B72/jRxosDv99+0p3tKkiTp0IPP9f6uvPRypAGYpA5AwxFBMbDxBZhcX7GLkEnL57AtPBv2gMHt5+rhKuI9gIKdnR2trq6GBKzk/GHhpPwsgoBBd/M5KPQz0Nz6cvqZOvtiFVuUCJ9jHVIPP8/S2TLYJ66ljQi0JycQQMszVKM0PHcaZZAOXBPENKFMuJYF2M//dECHGw6QzALoFrBvCPCt8w5YACMOoLwtnBWTFBaqycnJkKHd+2ZkZCQclRMDK8qJRQubtbq6GuJwHFzSL5yr6KklYCgBEwToE1ROXqdSqaSVlZWw+OCKc3d4vPAwnmhz2Ct38cBG0j6kdsnlcgEoZzIZra+vq1ar6YknntBjjz0WFviZmRlJCq663t5e1et1ra6u6vjx4xobG+tYaOg/WDZnvx1Ukt+PsQ1juba2FlyHAKpsNhtyeQFeuR4dAVgaHBzU6OioSqWS1tbWtLa21gEgnAWbmZkJ7kX0FCkpaNf19fXgbiXx6sTERADvMPUAMdyXx48f1zPPPKOHH35YmUxGt956q4aHh/XZz35Wq6urOnv2rCqViu6++269/vWv1y233KKdnR2trKyEA8ez2azy+XwAk+zaJG7MdQrgD4bR5wLjmfb2FCAxu58GUByc+fjju9hY8/hDZ5ec+fXnpAG9OD6M8e8uU9d3LvEzAUWHGWtxHWMDh2vTQlLSxEEm88vZdZ6FXOlZV0u+HWW4Fur1YuVIAzDiJuJcWW6J8L9PFBY/FhO3WBiwuEDireGxxQ8wwDUEiOjt7Q3nwPG5TxAHP/68uB6U2d1fabEKsaKLLc00q8wZQeLTWPSlzgOkPb7DrVGf+OwCc1bGj1qi7IexJpTf457iujkjCTu2s7MTGAPfJUlZuZf7KLezYyjzGND6uPHkrbTX6OioJiYmNDY2FtqF+1nAyXjOghTHytDW0j64WlhYULPZDO4eB/a+E5TPMAQ4GSKTOUiAS0wP49k3jcT9m8kcbPLwWEhvyzS3sI9JWGVPseAu0YsXL+r//b//p2q1qmw2q2w2G85KHB4elnQA8np7e1WpVLSysqJjx44FVtn7jMU2njNu4PT09KhQKGhtbU2VSiWMp62treBGA3DgIvQ6A5Db7f3cc75Tkqz2uJgZQ5SN5LAzMzNaWloKsWi7u7shmSz9Sj80m01tb28rl8upWCyGXYls4BgdHQ3ALJvN6tZbb1W1WtXq6qqy2axe8YpXSJK+9KUv6ezZs7p06ZJe+9rX6pWvfKX6+vr0zDPP6Omnnw51RccBRDneilg3SSE4f21tLeQmA6i5cRCDGvSAM8ox+HCJQdNh4nPT2aC0MI5YD8af+XVephhceXjDlcSNNH9m/LeXw/UU/3uZmA+0Z1zPGPDFHqDDwlu68tLKkQZgviCjKHzQMXidyYlpXI8NciuaBQ0QkcvlQlxXbGWjjP0wWengqBDKAyNDok4/CDiNffAyevyFW2OuRPiMxfRKk87f6cDDz2n0MvvElxQYBQTFTJ6lWBHQZtSfIGbKyTUeEwUY5J0AXRYKZ5Bwq/n5aOxYpT/7+vo0OjoaFkDO2mNnmh827mX3NqTtORpnYmIiHExM2R0EVatVLS4uqtlsdsTO8fxY4e7t7enixYuq1WqamZkJmyPoK/5nscHdCwgdHR0Nz/SFifgw4op8wbzSGPGx4gzTYWyZlxWQ4HFE1WpVSZKE3HP1el2SOlgeytzf36/19XWVy+WQYJYx4sDIx388/7e2tsKpCJyjSB8S38lYGx0d1c7OTjiXM051wbNGRkbCWCRGq1arBdDMQgkow21XKpUC0BwcHAzsl8+13t5era2t6eLFi5qYmAj5wnK5XDjMmySpuEz7+vp0/Phx7e7uqlaraXd3V8ViUWNjY3ryySd144036i1veUtwhT722GNaXl7W2NhYYOowDkZGRlQoFEK9vYx+KkCr1dLm5maYx876OgiLQ0TSmKArSRprFoON2ACP7017T5r+ROJ57/PVUx7F98Xv8vkTSwyOYiDocZXxPIzv8d9phrZ0YEzTny+VPJ8+fzHPPKpy5AEYixGKiEXNd3YBYmC9HIhJnYCKv0mpsLe3F2Io3J3p7AyuORgyGAZ2FfX29ob4F49fokyu6L1MMVgETMQxFmkTlfbhGn8uv1nEYOx4J//H9/I/7U6SzXZ7/4ggQBDWGkwGC4yzLXE96TcArYMMSQG0AsB47sDAgCYmJvTWt741uIl5N3WNXSfUzY8FYvMBrpnNzc2O/EqMD5ivfD6vqampcHi7s1SAiPX19QCm4i3rzjLym7oSHL21tRXi8Kgbea5oD5gK2gZ2hu8or7u1h4aGwkLtij6eW2kMhS9GPm/W19e1sbERUi94++LynJmZUalUUrFY1Pz8vDY2Njqy0mPswK4QGL60tBQC+OPQAsrIAo8ABAGfzH0SvNKnbBphh/Pw8HDor7jPvI8BlyMjI8rlcqH+uAcxlMi+j5u6XC6H8yXdYAAgj46Oam1tLZw3yb3OeJZKpZBjjL4gd1itVtPCwoKWlpb01FNP6dSpU/rRH/1R3XnnnVpYWND9998fYusQXJ/MK89N5qEBhHj44o/711lB1x2xMegskMeQubg+i8GMj0EHcfFYdf34XO64OFQjLfYzDUSlyZXKcxjD5rr3MEYwro+za4eVI60tXmrpArBOOdIADImDFn03lwMw/4HRYQFB6TsIiBUKrAG5cVDSLDTkvsKKrdfrHTslUWTunkmzmBC3eNzqjzcMpLWFP/uwieogzNm/tPMq+TuOWWMxIu0Bz4KBAYzEEietpT09J5k/izgmFgFYLQAGR89wHAxuLfoRkAxr51nhaRtcZ/l8XkmynzQTZo/6DQwMaHJyUnNzcx3nZdLGgM/t7W1dvHhRly5dCmyKM5f0Hwscn/X27qc6qFarajabHfmlGJeeE4xdg7itYGm4zmNYYPYA8ryffo3TUcTsLJ85QwgLTUwWQIYNHRgPXt/R0dEQMwiz7AAGdw9zpdlshoBy6ua7Ilnk4zgd5p0DTZKtki8NFodEqsSgMTaoJ4YJfS2pgz3jft9tx5iG9cLlCpPEWGR+O+sGO0e8WLvdDvGGfX19mpqaCps6hoeHA4Obz+d1+vRptdtt3Xbbbfqu7/ouveUtb9Hu7q4+9alP6Xd+53d000036e677w5AHlCXzWaD8cgcpL3Rm64zHVz5Yd3xIusGFnX1dkwTB9mxTot/O+Pq4ND1Fc+LXaE+79IMVf6OQd9hwCdmo3jXYWyZz7G058ZM85Vi6bzdHXS6O7Mr15YcaQCGxY8cZiURmI/LCtcBg55FmC3zxDrs7e2Fw32Hh4cDfYvFilXoII2FcmdnJ7hX/PxHP/ctjlOJJ6EzRg6ICHDFjenvj2PA0qx4Xwxh4aSDtBpY8W7lIjAwzi4SX8QuL3etAjBixs2Vj8dlAYKxxD3OJwZnPLvVaunrX/+6nnrqqY4F1C1sT63gDCbPJIhdUseGieHhYY2NjSmfzwf3bLFY1MjISGgHDsYmqe/a2pqefPJJLS8vpx5SHC9A7gLm/qWlJTUaDU1OToY2o98BL4xrZygAtriVqDfxPqQXod84uFvqNGQoU8wI9Pf3hzQSfq4lbitcVZ5yBDBG6gUMGB9XgI61tTUVi8XwTty4586d09TUVAAgMKEOAnxMAigZB7BfIyMj4cB5AFiSJB3JmHO5XAC0Ph89H5akAMC5H5APYwUwabVayufzGh4e1vj4eDj+iPaiPUgFMzw8rM3NTZXLZZVKpY7YN3TX8ePHValUgkuXvtjb29Po6KhOnTqlG2+8UXfffbey2awuXbqks2fPam1tTd/4xjc0NDSkO+64Q7lcLhgb0j44393dDZsnJIWYsExmfxOFn2xAW+fz+Y70FOgjABt6yNken3/ezr6zMmbiXSd6bKkDDr/GAVrMuKEL4ngrB0SIx+r6/a7XXGKWP2bZ/Bnxe2MmjP/xfMRAKgaIXn5vj2tBugxYpxxpAOYTFUmz3lmocEu5a8zBDtayW8IcqRNbf85aUI44JQMLHO919s2f44okjYbmcwc8/KQNaP6PGYG0gept5LFL7urBzeD30NZJcpBigbPjPLdZ7P51BQxD5nE2aeweLAMMhDMTsDwEDHvZaDN+xzubYhCYJElwHUsK2dPHxsY0Ozurubm5DvDmfYKbsNFo6JlnntHS0tJlSjteFPiMexlzPT09YWfd1tZWYCQdqNEOgHAAFgyhB3ZzPQwg5yACvJwpdjbM2QJcoJyZ6gDJ26/dboekoTx3cHAwAGeO+mLHJ305MDAQACJ1dgaUXYsEy/uORt/YwniLmQPmC2yX71oG2HqMD5/7d/49esTHMcYbjJF0YKx5/BjxXz4f6S+A28bGhqrVaigzdSFJMu+AWYM5LZfLqlQquummm3T77bfr2LFjKpfL+vKXv6xcLqfXv/71+vznP6+vfvWrSpJEt9xyi8bHx9Xf36/V1VUNDw93ZL/3UALcw/Q9YwTGzAETbHYcvO795T+0VazHDmOEAH7xPTwnZp/id7medQPWwVKaPNdiHzNg/O3u8TRWLH6vlzeNAXw+chiL15VrR440AEPYfi91unak/YFHwkUWY19wPF9Vf39/iMXY2dkJgblkq/YFW1I4ow9FzvO2t7eD24AFSjoIUkZ8wYgnfbxoSwfMBO4KX1ycQndl5xM5ZtZ4Js8FxMQMmMepxUfCABzY0UWqDpSjA11f2NPSGThIcivOgRGMBffhgsvn8yqVSiFNR7xTyJVtHOPhCrG39yChqx/lwg49tyhZnFiwFhYWwrZ/X6ipl8fB8O7Ygpf2wUi9Xle9Xg8xSowd6hynMWHs0VaeIJj294UbBs/nic8nFqXBwcGwyGcymZCmBDaY8ev5zXg/33GgNc8lKL1cLocTJghsbzabWltbU6FQCPXOZDJqNptaXl7W+Pi4crlcx5mE7hZ3oOCuRsbl0NBQ2HnJDlHiPZn/HgPpbrB4jsLW8nzujxP9El84PDwc0uA4U8u4guVot9shHhHGibZYW1sLzBoAB1YSN2i5XFZvb29gEldWVtRoNDo2I6ysrOiLX/yi5ufndc899+jkyZMaHh4ObBrlY5MBxo+7UBkz6EfGTj6fD5svAFw+71wnuYsxLa4pNqhdrgQqYgCepgtcDnvOlUBL/Jy4Hi6xK/WwdzkL7c9x4PtiJa3eV1u6DFinHGkA5hM6zV0CawCD4Is28SQAGpS5J2zM5/PB5eFAyy0/d9/gDiL+yEEO72c3nk+0NMqZ3zBsADkmptPRKLc0GtuVXEyf+/t9V6If2ZPJZALA8B1RLNDtdlvlcjm4MHwXG6ABYODACZDnismZQHcLADg9xiZmYdiV2Gg0OhZn7o/joWIQ5swPYyebzWpsbCwkEaVegB2YnVarpdXVVT3xxBNaX18P4Ch2t8RMAMJ4YtHH3d1oNMIxPpTfXe5eDwAtAIl8a/5eAIkbLM7aurt2dHQ0ZGrHQAAgOdvlLkDvS1zkPh78+B/cW7gh2bwA+MAYgOXa2NjQysqKTpw4EXYaet/5vHHXnrsGYeUAlQTIUy/Gvbsz47nCuHNQwZgkrAEW15nWjY2NAEyJeQOg8A5AKmA3SfZjKznSCCY6SZIA/JeWlpQkiSYmJtTf3x9cnS972cs0MjIS6nX69Gk98cQT4fDwbDarhYUFPfvssxoZGVFfX58mJiaC0QgQq9VqqlaroS1JPeFxYpSFMrsLmOu8zZwNQry9D9OFMXA7TOL5lbbop+lC7wvGrI+F2AiPdSj3xGxbGvMtqWMuSwcbcXwMxs+MvQPPR+J6deXakSMNwHAjOPuFAmBSYIVLCtY4uXuc9mfn3cbGhtbX14O7gCBoH7xu9cf5v7g23p0pHSx2WMuxpCkeqTNWKF4sqbdb6f5/POlilox3ASB95xMS18nrScbyRqMRdou6WyaOB3M3IYuwux297mnuQYCnAxIW1mw2q1wuF+6Nt1wfprQBZcQFwVKMjY2FIHGuoe+IY6tWq7p48WLIXu6AK3bbEuAcLwi0N4H67KxcX18PgIf0GX4sjyfkpV3dfU4f4Q6SDjLEY1gAiijj0NCQCoVCYH6dlSHPmy9QDuB4F8AD1x31c1alUCiEeCwWFphq2BN2DdMu5XJZ5XI5uMx8IwxjHp0wNjYWxhf1pHyDg4MqFAoBBFG2VqulXC4X6ksdqS/AxAE1Pz5/YKfdkPE8bdlsNgTN0/++y5q4L1hfmOT42lwup8XFRVUqlQBYC4WCbrrpJs3NzanZbOqhhx5Ss9lUPp/X3NycXvWqV6nRaGh+fj4cq7S8vKyHH35YN910k06ePKmJiYmg+xqNhsrlcng27yHma21trYPFpr19AwSbC2LwAHB1oyp23aWBIwckaaxObKA5s3SYuP50IJQG5PzzWL/yXgeMcbn8ff6dPzd+voO6FwukugzYtSdHHoA5c+CuGBZ/cu6ww01Sh1LknsHBwQAoABnuymDCsPBLB5YPCxoMmE9kwIJ/dpgVFDNClI17AJBSp6vVQY0rKBQ3DBSKLrbkqBdtwzU8j/r7QkfZ1tfX1Wq1QlkACcS6eL35HgDn8XJxm5BtnPezQBJcDvDFDeyuH/qONnUg5pMVZcAC39OzHwRfLBY1PT2tkZGRDguYY1t6enpCeoX5+fmQgLNYLAZW9DCGDVBEfb1dAAfspsX1RqJP+gcQz8YHz35PO3nOJgfrzWZTtVqtI3Fskuy75kZHRzU2NhYYmHa7Hd4RsxgxSE5T7p6Sw42WdrsdsuSTDR5DhaDw7e3tjk0d2Ww2sIKkfwBwMWbcDdZoNDoWuxgUwppyTJizHQ6U+cw3QPi8cZe5bxph7sFgw0yysxP2HZewJ4HlPY1GQ5VKJZTZWVrem81mtbW1pcXFRRUKBd1+++06deqUenp6tLCwoGeeeaZj1/Xo6KharVbYOdzT06NaraZnn31WjUYjjCN2RUoKZ7yurq6GviRWjPbGGHBXLGV1RpC2ZkymsU1pLBPlR/z7GIihl+kPj4VNY0wPE9epruvjZ/iPxyPG5YvdoLFXwscV98ZGY6xLvIxuDMX3e1t25dqRIw3APFDY2S7/YUBzJpqfJci9WNKtViu4kDgEGIYs3j3D5ygVKHoWD6h8Z7zimCdn6pA068qBAikEUMIoGlcKvjBwv09ElJ0rRg+Q9zLxHHcTrq2tBVBHrjMYAOKFSIIaAzDAcRx7x7P9/b7QswDiRmOhwjXjyg1ACVCirbwvnZXknkKhoNnZ2QCm6X9PHbK7u6tKpaLz58+HXF0ADfoaN7S3vwOEmCVjHNAPuHT29vbCc2I20119LHzu1vMdkPR/u70fIF+v10PCVo5v8pxmtI0fzO4LEP3mfUMdvFz0tfcLABVXFqCMMQAwIjGrH7tTr9dVqVRULpc7NhL4mOG3Jwn29ma8O8sL+GNseJoZ7mFO0Tbc63PQk5q6fuEZ7HDs7+8Pm3s8lQqAcGVlJcTI9ff3q9lsdrCZjHUMAljNQqGgsbEx9fb2qtFoqFqtBjaKDTLFYlG1Wk09PT2BLaau9XpdX/nKV1Qul7W8vKyXvexlGh8fVzabDcH/SL1e75i7vtsW8AmY5hxJ2po5EQMgBxb87/PHr0N8TsXP8HdcyWUZlyENZPn8i3OXObMVA0IHPnEbxIZL3B6uI1wX8lkMqryM8Try7WCeXow83354qZ95teRIAzCp06KJAQ2LOAqXNBO+0PMMt+KcgYif72CGScECDRggUBz2iXL5swh2x/JNkzgQGHHF7s+OLayYrpcO4oGczeCZMH7O3gFQsQIBlpnMvrurUqmEQHHyHHkAtrOGLO4sRrzTrUOYNWKPnNVwl5szjbASDmLidvCg50zm4OB0QMLo6KimpqY0OTnZwRhSH7bYz8/P6+LFi+E4FmdPWOwAF7wHpvUwBessJOAUFoaM494fXne38Gl3wCobB9xqBjTX63Xl83kVCoUA3ngHB0ezuxVw7UHxbuHH9aI9PNs+bQ0Ac6aUxKeAmP7+/pB3DSOBPqnX66pWq5qZmQmf8T5ndmOg6KwWSYQpkzPDgEMHdBgrPJdxCbNF27Tb+8cIccySb9yAGfPdiwCuJEmCW5QUOAA+ADCMJf1L+5GGplgs6uabb1Y+n9fa2pouXbqkjY0NFQoF7e7uBsA0MjKiyclJNRoNNRqNwL7BhLVaLT3++ON64okndPHiRd1zzz0aHR3VwMBAONkDA4P/mfsAZ+YWOnR1dTWw5N5HjM0YvPC3AwwHIDGYiO9LA2zx8w5zScbG72Hs7mESx2nG72Iseh39fS4xuIrZtphVY5wdBrReSD2+XfLtAILXArB8sXLkAZjUOcFixgUlRXB2oVAIh9wCzpwdyWQygc1yYJTmOgQssIur3W6rUqloaWlJW1tbHefLSQqKnzxTxKGlDUpnEygr7gGUc1osROz6gu2LKXF3U1IX39XFte7e5PnEQu3u7oZkkIBWNi6QjgJQ4qyXM5YAD0CrM1ZefwdfDu4cePEs+tXZPXcD4LYcGBhQsVhUsVhUqVQKMUeSwk693t79Q6HPnDmjCxcuqFqtdjCosGm0J2wRrkzyX8X5wBx8eX8DQHwBJm+Vsw3uIneQQuJM4o0AKS7k25qdnVWhUAgu33q9HsoL4KCtk+TgGJO0hTKNBQDwMHfItQV4wXBhPu3u7ob5gjuOZ9OPW1tbqtVqajQa4ZgezyXFHI51A2MZcEq52NUHSMb1DSB1Rs+ZcN4BsOc4qtHR0bDrEIaKsbu7uxs2VTAnPGluLpcLSYSZ37B+c3Nzgamjb3zHNe/f3NzUxYsXdfbs2RCUj4u+UqkEcDc+Ph76PZ/Pa3p6Wuvr61pdXdXy8rLK5bIefvhhra2t6a677gq7UmHbGG/ohEajEdrcU/D09vaGXauexy/Wo7HBFDNHaUxS3L+uK+P7Yt3NnPtWg5K4Pl7ew9yNsXEdg6w0Vs7F29QNNQ8V6bogr0050gDMmSjp8t1KrshRzAAChMHtqRRwfUDdx7S4x8NIB7Eue3t7qtVqqlQqQaF6nivuBxTGbJ2Lg0neD2vGgs/7DwNgTEBniRyAObPmICaOd+Dd5F8iMSMLlnRwCDNHy/gi5m3OM+O8XTzPQaGDLFceLADOStBmaRawu35h3lgsZ2ZmQiyLpLD5AldruVzWhQsXtLq6Gs7eczcdz3e3KeUHzANc1tfXO9zDvhDRR7g6fez6uXsOXgEUAAP6gD7ypLr0YT6fDztGs9msdnd3Q6b/tbW1wHgxbulDnwPORsSLivehj02MIYKyk+RgJx/P5x7GC4s6LsHBwcFwTFO9Xg+bPmgXB4j8dhbVWQPPYu+MGM/x+EWehZFG2QEXuKx5Xn9/v+r1uhqNhsbGxoIxgfvcmT4H/fzvOco4iummm24KRiGsF3OTFCyZTEZra2taXl7W/Py8stmsxsfH1dvbq0KhoFarFU4nwO0M2MNocBa3VqtpaWlJTz/9tGZnZzvq0G63w1zZ29sLoI15RX+iVzhWyvuHPvG4KfcUxIxVzPa4HouZJDcM/P/nAiF+XwwMX4j4XInfiWHoBmTMmnl53NCOQRhrmhMKh733WpAuA9YpRxqAubhrUercMs5RILAdDFomP4zI1tZWcKPBNribzt078eewH7BBTKo4Xgb3FIAEJeULmltHWLlc6wyVgybEB7gv9K6kAC++28sXab8OIMQmBlw50v6BvOyCAnB5ig0vB6wDlrEvnN627lrxOrs7yhdSD0hGCXlOKheAFwHnMHX0iwcQnzt3TouLi+FgZi8n/eSLBQrPy12pVMK4xCUJg8A1nvqD+CDakPgo0iX4YkV9+MwDoCWFpKaAKTcsAF99fX2qVqs6c+ZMYAQpP/3V09PTEc/l44x2OMx94mCYMeGAGabZDR1JHWODzO+9vQcbUKrVqqrVqkqlUkihABh3Zpc6+25bykD7e84vPgMkkd+K8ebgC0aNlBj+HayWgxt3v9L2DrZhUkdHR0Mam56ennAw9+Lioo4fPx76gTacnJzU1NRUOBS+2WyGBZkdtLDS1IP4OMbBwsKCKpVKGIOFQkGNRiOwtvPz82q1WpqamtLIyEhgfmGCkyQJKUvYCUx7O3uPnvQxnDae4s/SrnOJ9U2a+PN9nYjZsdjlSPljQOb3HWY8+3X+fgePMQPGPVzv4MuNGge3jE0vp5fLAWVXri058gAsXpT4zJUAweEoWhY9V4qAClyDDg78+SwKziyh3DY2NkK8DpY012BBs9DH8Wo8T7qcweMaB34sFGkT1a/hOxYoBykIgcDSAcDwLOosUli/PT09wRUEKxS7OV3RESsyPDys0dHREEBN2bjHd4C5Jcf3KH3ag6Bo2BraD5bL2SjqMjIyElIg8Hx2ym5vb+vSpUuan58PyVSdOfX4FVeCPIdr6du9vb2wg9BBN995G+OuZnzS3vEZlox3gOrw8HDYWML3SZKEnW60M+xpPp8P7BILPYu9g8ckScImh3g809ceyxK7XHxR8jby8QXLTDC95+ry9zo7Mjg4qEajoZWVFc3OzobDyn2BjBfHdrvdkZoDQ4H+pU5+vBEAjPow/3imG2itVqtjgwzzHVcvTKMfdQbDxn2kmyBZa7lcDn8Ta8mRWLTn3t6eSqVSiNHKZDKqVCpaXl5WLpcLmyiSJAn6D3AP6z88PKzjx4+HGFY/Voux1mq1wtgulUqBgYPRAkR7iIG76TFIMXLT3GnPBaJihsgNR9ehzmD5GAT8+XWuc33MxC7Bw5gn7o8ZM67zHfM8Px6rcb28Lq7TqQP6kLb17/nf2zPt2S+ldBmwTjnSAMxjMpwd8hghlDYuP6kzrxUMA0ACdoQF2Zkbj31gi7MDFJQs9LyfFeeB0n7UT/xD+RyAOevCfYCxtAHt/v+0iey0t1tObrl5LM3u7m5ggkjyiXsOV5fnBuMd8fFBXEMZWQScsfKgfPqw3W4HcEvuIt+FynOdpSHYmQUXZgLQAstJFvT5+XktLy+Henq8Ff3noMPbGmHR8fHgdeQsQgAHi248ZhkfpC6ArYyPr6GdnQ10BQ/byjtxAT799NNaWlrS5OSkTp48qUKhEMB8rLhjBZ8m9DtMrS8qsdKnv1ikYSE5OcIXbzLoM34BkeVyOQSMA9hJuOqHtfsYpx5suAAQcD9JdWlvZ5cdyDt4dvYR0DYyMqJSqaS5ubkActkNzFxDR2CMtdvt8DuTyYT5Rbk5hokEtTETyRioVCp68MEHdenSJU1MTGh0dDS0Ccc4ORja3t7W6uqqJicndfvttweABMOWyWRUrVbD+F1ZWdHW1lYAYXt7eyG4v1gsBkPOxx9jBnZvZ2f/jNw4R18saUxY2kIbAy1vkzSWynWig7LnYohiMBezVvH7HQzG16cBIR+v7tXwHIlp8XKxWzatna4F4NWVdDnSAIwFw4EKE9IXWo9twNL1iYH1nc/nQx4kX/Bi6pmFwBea3d3djkN2Y0DjjBQAy90y/I5ZMEACCz+Lsx9FFFtCsRXlbhnKktaW/l638HZ3d0O6Cdxpa2tr4cQAQCVxZrzTs5yTxJKFnvKzGHmgN+9l8dve3g6LLUCLNvc6u+Kin2hTgPXo6GhgKLa3t7W0tKTFxUXVajVJCvEqMZPC3856xW0X96+3BW0IGzYyMhLe4YwP48aD32kDwIWDauqHITE0NKRSqaS9vb1wbiN12NraUqPRUL1e7wADbIJw0O31jTegpI2dOE8Z4uwoMjQ0FDZd0C+VSiWMDZg3zwPHM4mdYvxxbiTjD0MqHg/Ey9H2gCeAOuCUNgGcwQhRF9cH9B2ACF2UzWZVKpWCiz6fz3fc70c5oUdoH1LgZLPZYAg0Gg1tb2937Kyk//38zKeeekqPPvpoiBeEjYPF6u3t1fj4ePiOd66srGhjY0NjY2MaGxsLc6Svr0/nzp3T6uqq1tfXQ8B/JpNRLpcLxglGGBuR2LnJGCPXG/UlhixO5+BjJv7/MHAUM6+up/2+mL3yMfp8GJQ0b4h0+Ukmsc53F2PMeKXVy5n/ONzCf9D5zr5dCRReKyCsy4B1ypEGYNLBgPcJhUIl2SPuH2Iu6DAUF65BUg7gaiMuxRksBr6fhYci7O/vV6lU6ojZwdVAWZkoaQtTvHD493yHYowns1tPMevlQEzqDND0SSx1ur/8HD1XmBsbG1pcXLwsCzaKwnd98b3HgHjsgisrz6FEnfmceBp3J1M3X/w9jQNuPZgn3Ii1Wk2rq6uqVqshuaszHh5s7YqQNvN2pt0cJEj74BDWDmaKxWl3dze4jXBhuwvHgRhlgP0jd5PHFjGGGcfj4+OhXAALAMXOzo6y2aw2NzdVq9XCtXH8pIuD8yuB93he0oZeL76jjUZGRpTL5QKDxZjxH3d/9vbunwpAItyJiYmO5LUs7r6RId4sw7hzNyJGAuOtXq+H2DPf5AETBtvj9cLl3Gq1wrhpNpthRyp1Jo+ds7YYFb67lYB53IjFYlH5fD4wop5Jf3NzU08//bS+/vWvBzC0u7urUqkUnk3IwPT0dDjfEfbw/Pnz2tnZ0Stf+coQuA9QBKC5G7HdbqtQKKinpye4IkmlQRtubGyEtBrEGGK8ck0Mwg5boK/0WcxuxUbElcSvAdgcdk8M9tKu83hAxNms+Dlp+t/1N/e7seW61g3ONOOwK9e2HHkA5oKi9Lgg6WCHGaAFJQyzsru7G/LyuEsMMMF9LLIO4gBjGxsbAcShiFFw0uUuOT5zAOIT0FkolL4vzO76cMURM1hpAIx3U/74N/Vj0nvA7cDAQHCJEO+WBhZdSfhi2tPTEwAUwfluKbrr0cudxiDG7CQLGu08NTUV3MDr6+taWVnRyspKSOvg2+JZsOINFr5Dy9vkMGbIQUOSJKGegAIWnna7HYLQiaEBAKT1PQtanNtLOkiRQl1htTi2iJxlHmTPmPM4r/i5/O/96r+RmIGN+9NjIQGusDDDw8MhXo128/HnBpDPo/X19eCWKxaLoW7sIvS28Tr39Bwk56VNHbQDFtl5Ojw83BGv52PR48doP8YVR54BKl0feQ46ykjfSQdxqJubm+HzZrOpxcXFDgaM5+7s7Gh1dVXz8/Mh8H55eTnMPZIiJ0kSmN5jx46FeMC+vj7dcMMNqlQqOnfunF7xilfo1KlTKhaLyuVyGhgY0LPPPqvz588H45J0FqOjo9rb2ws7VqemplQsFlPHB6dXUOfDwMLzYbvSxuOVgFcM0tI+v5JwXQzI4/e47vBr3NB1Q8d1mnSwDqArnOWPAWZs0MfhES7fDubpxUiXAeuUIw3APNUBCwwKl7w4WIeSOg41lhSUMLEoWGbEOjH43Q8PIGESEDuCywer1y0TJg2sXDyBACbxjj5f/FH27AYjdml7e7sjvk3qVHhY2h6o7hPf0ymwcAEIACRJcrDzi3gwssATk4IC8OvI4u6B4D09PSGNBX1A2ek7Z8BomxiASQcHOfM94G5ubi70S6PR0OrqqsrlcmAmADjOJHrmegdACPd5e3EN/cRCStJKT9HhoAyWrdVqaXBwUGNjYx2bPtzlDBNCnq7e3t7AghHA7a5fSSFf1qVLl7S7uxsACs9zN16lUtENN9wQQA1jx4OoAXjOVsTj2+cH8W9uIFBe5komc3BuJWPM+9t3XlIu77fe3l41m83AruC2GxwcDCkg6BNABi5HgD/MJP0MmwgAcj3QbrdDO9OOnk/M5x27AQGZrVYrMLDMBRghB2O8jzjHYrEYAvbJB3b8+PEwfjAchoeHNT8/37Fj9OLFiwFsue4htrVSqQT9SLD/4OCgzp49q6985Su6+eabdezYMd1yyy0aGRnR1NSUZmZmND8/r8XFRa2vrwdAjf7FtT02NhZyoKFXYP7izUC0EeOCMeUSgxM3ztCNLmmGgjNKfl2a0Xgl1sxZN8qfxgxTR8+n52MnDXy5h8Xfg45xL4TrRW+n2Hh7Lgbwaovrim/lM4+qHGkAhvgAd5AhXZ4okglPgDO7ywjs5hqUNc+HUfDF1N/ju4awTFHYlI/FLKbO0/6OmRSfUCxksHXSQbA418CmcH/ac1yxsSDFyTtjOpzDt4lHAnR4m7nL0YEMysEPMIdVQ/G4oHBYmByQAir7+vo6QDUL+6VLl7S8vKzNzc1QfrdG+YndBd4Hznz5Z7QrypcddfG48zY8zF3hQfNeB2cUUb6+c41r/Ngh3r21taVKpaKFhYVwvmMc80Lgu6cqABA5CI5ZxrR6+BjxnYt+vbOGnloFRiufzwejgrHojGZcBsYbB2r73GAuEkMFKOG5/sMC6a6/JDnYkUsqD8a11zV2/zBm3WVEOgrcw1wfbw7wzQkIYxzdxPmePB+Dg/LDwPf29obdrcvLy2GTAWA0SZIQXM/B5tlsVmNjY2q32zp37py+8Y1vqF6v68SJE5qeng79AFhDBxB3SNmr1aokhTNTMeboQ0Arz8Mt7mMpNn7ciKXtrgSU4s/j+Zt2n+vcmBWL9fBh7/F7YraK/nyuOK3DwISDQ2eaXWIX5HO5VLvy0suRBmA+KB3oeHyQKziugfni0GKCW50dchdD2mRyUAVThruHIFgvn+9yO4wOlzrdN/FkZsIRV4br0xOJSgeBnCw+KDksVn+/T2IUNSwVihG3Sl9fX0jW6cfJACxpi3iBSgNlLAZuQa+vr6vRaIT+cABCsD7tycLkZQBYz8/Pa2lpqQOY0S4e08UYulKbxxL3Jdc6EATQOvDz+/kNg8U9Doq5LgbOfu4lMWbUg+sxLLy96Rfcs4D0ZrOpcrms6enp0NeejoJne9wb493dz+6aQbgPcSDum2ZI6EkGfuIpndGjbLT/0NCQ1tbWQk4wmGXq3N/fH45TAljBStI/lIexR9u02/u7EgF36IaYEU1jGZxVZUz4+ZfMEUA1IMt1mIM9jisjoN8NEADd1taWFhYWQqA+5drZ2VG5XA7PAnAx3jgxgfeOjo6qUCioWCzqySef1LPPPqudnZ1wnFE2m9XU1FRgzS5evBji1DwWdmFhIbCS7H7MZDLhNBL6dGdnR9VqtaMNY2Drv3mHA7C0ORqzSPG8SxNvf59LcRnS7uP3YeAuLhvPdrDk+igGfjFLh8SbZqTLUwp5ea4FpujbUYZroV4vVo40AGNAosxxWTjQYLH2a1Dy6+vrarfbwX2RyWQCWNnY2OjYkUeySN4H6+CsBRYtTJJPKsrlVrADoNjycmXAQsbnruyw1Hmus2zOFDgIiy0x3kNdcR/xDF9wscSZ6LBfzv7xHq5h0WYBBbC4MiI2ikWE/oMFczdfDE5cYfvmAe5HPHYjrju/01wVLq5kvf+8PrB2z/UMcmDRR76Nn3YD/HrdiGEDLJFN3Xel0s8Ea7uBQV/j2qrVaioWixocHAyJYn0hSAOs3nYwsvE1h7EYPicARO4+cVDM+7ke9yzzi6OTOCuRDQ1DQ0OBHWTR510+JnzsZjKZ0B+AN1ySDibdiPJyomuc6WFnZqvVuqyvPa4TsMa7fdcgu47X1taCm9F3dbdaLVWr1eD+5ozH3t79VCarq6th/Pf394fwDN81CggmR9/CwoIajYbK5XKI92JODQ4OKpfL6cSJEyqXyx2peqR9QFgul7W9vR1OA0FPUHZ3o8agI834ceaR/680v/xZMUjhu1higOPj9jDWyZ8Vs3Lx+Kc8se5Ne44bK1wf3+tg3PUj4l6GrlybcqQBmC8ogCt2RPluJ/8tHey4I/i+WCwGS5VFDnod9wwZr1EWHg/DIhdPcAdFfu4j7JpTxPEkcfAVT1LcCYAaqRMAAPR8wqP0iRmTDpiymOnz3Wge+4RCp12IqXMl6ooK4EBONLfMAJAABo+zYes8rEWz2QxMjwMz3xgBUPS2OExJp7Fcz5em9zaNA4mdsfPgWcaAK1JnAQEWvtPWmVhnora3t7WyshKAKpsHAOKUgTEAgPMywmZSfg7nBrR5XB7ldfbxsDb1do0NCnf1UkfGMG5vZ4+Ym5QHRokyY1TBYpMKgZg4NtTwIx0waLzP2UXGKTtEYYIxRpi7LHIx08XfuKYJAQAIeQ49DDjeSR85C4OQOLjRaGhjY0Nra2uBUeK5Fy5cUCaT0dzcnM6fP69Go9FxDi0gDADm+rLd3j8phHbp7+9Xs9lUo9EI44TQC9g4khRL+3NtfX1dzWZT0n6crScOhsWTDo75Avg72HeDLy2eyj93XXMYYHKgz5w8jNHmOm93D984DHx5XzlouhJIjI3stM9jozGtLF5X/z/eje1M4fPVcd9O6TJgnXKkARjWIJOHOA7cISggH9RYlwRkY/ExwAmOrlar4by0iYmJsBBkMvsxRmx79wBugm8BBChWdi6h9JzFiWNzXAA+TCCUFZYuMSo8F1bAlRUxPQAi35HGddTbF28AEu+GTSmXy2o2mwFUEbCMknYXUKxU0rac+/fc7+UlCJ2s4ihrqfPYGAerlD8GEYi3jwM3/4zFzfuC79L+9xggvqdvXYFyD2OVRQ0WxhUu7i92wvFOwC/sgpfTd0Dihtva2tLo6GjHWGMMkLqh0WiElAWUAaXtO7sYj7E7nbguWLw49QSsEOX3uDkW/+Hh4ZAywxcNgGS8AAOyarWaJiYmNDw8HIAA88R3u9L+jFXc7L29vRobG1OxWAwsDoYc+e9oB4/b800gzn5SH08HQbymnybA+KY+xLJ5G7XbbeVyOY2Ojmp1dVVPPPFE6CfY6KWlJeVyOeVyOU1NTemb3/xmOKOR6zY3N7WwsBCMU9qd/nd20PMkOrAcGxvTsWPHtLy8HHKDOdjFWBscHAzGU8wOcnJBtVoNfYqx6SlofM7SPjGTdSVjADkM2PjcjK918c9iz0FcVtc5DvhiYB0zxA7e4jLzE18f6x+PgY7L91wxc1156eRIAzCUM9Y+iw4K0pkvdzWglLkeVwEKrd1uq1Qq6fjx45qdnQ337e7uhjxOKB5iLQBLTrfHipl3ugXnLFRsYbGAxbQ8bAEK3NsjjnlyhoTYIA+aja0wB3DOOhHQy24r6giQcPeql1vqTHfgbIa7mHBnAt6kA0VKDNju7m4HWHG2iDZxcElZDnMbHGY5ubslthyd3fFneMqR2DUaux74jvpSfmeKyBi+sbGhkydPhuBpSSHFAZ+5u92ZHQAEjC4GA3OBObOxsaF6vR6OtWEHoTNx7ualjs6WeRlit4d/5uPYWbDe3t4Qg8Qcjcdl/DzmLMfvEE/H3GRMuCtmc3NT5XI5jKH+/n5NTEyEHYfs5KOdYcNwA6axHM6Iw8x6zCJGBIAFBtpT1QAS4/ksSYVCQVNTU7p06ZIqlYparZZyuZySJAljhDAK2LHYEMAgXFhY6BhnznaS2qNUKml6ejqMZ1/cM5mMstmsjh07pr6+vpAjjJQbgC6AHTqVz0jWioEM8KSsMTiJ52ws8XUOWmh7JB6TzMnDXHTxmPP5y/yI9cBh96b9PuzdXrY08HXYO2JQ5t9dSd9dTekyYJ1ypAGYuy6IhYEFcreZJy9FIfjOMdw3HK8zODio48ePa25uTkNDQ0GBuJvSD1fGHYSr0TNdowRgJlDKsHdMwjT3TQwiASoAH/IZHRYXwP3sIPRAaXffSgduyEwm0xFb5vF0sF8odRYTd6N6cHWsCDwGy08M8Hd7XVkg3EUDw0k9GQNY9Q4EEYDCYRPV2zl2E/J3/Fms/GL3dNyvriBxU/n5pAARf14+n9f4+HjIRYWhAPsqqcNNxNj0d+DeBDS7MieYHRfe7u6uxsbGOhIJx6De68Jvb1sPzPd28t8Ojp3dyGazyufzIRjf+8/f633EfPCkwbi5iOt0Zk46cEUSfjA0NKTV1dWO3baktHCDhHJ78LMvbLRJzNzAQAE0mCMYJvzP8xyM8czR0VEVi8XwPAzHSqUSGHiS2g4MDITM95TDU/LMz88HfdBuH5w/2dPTE8617O/vDycqeBvUarUQmE8eNXKAcealpI5TRxiX6BJ226K7PT3MX1cYI4e5AeN5+UKeG4+/53M9f1OW2DCM6/18QCbyfMtCfZ/PtV25unKkARgD0N0e0oGF4jE2UNwE37t7g92LfDc6OhrOO1tfX1e9Xg9Hx5C3SDpYNH3hY7HkTMGYhvZJ55ao74Tz633hjtkWFj+PD4jjyuKYKRZWACXvZ+cVCxaAUTrYWceRKJ613RUK29k90aovJA4e3aVFOwIwuYc+YtHxTRFIDFIpC79jyy+N0Upb4NOu82f6Na5g3WXDM2MFSqyiM2AsdG5QTE5OduwU9I0MXEubsqC6G4fxD/vpZfT2J3lpq9XqiIe8Ursxznws+xjn/R7HRplpQ8CyJyslOai7VX1Rjce6u9qbzaZGR0eVJEnYyLG3txeOLHLmtbe3N7hwObPR5wtHJUkKLsiY/aOuHltGHYeHh8MmH9qP/+MYH0nB7c64B2zzN3m8ADwkO8Xlilt6ampKk5OTOn/+fHBx02+Ua21tTYuLi6HfORsTY3Rra0vj4+Oanp5Wu72fWoKzNznrktxyg4ODajabOnPmTIc70o0SNjRwggEMK65K3OhXYmmeL2A6DMDQzs6OvRDA54ZLrGv8nW5kxixpDNrjOZVW7rTYL8oTu0APA7HXEkPUZcA65UgDMOnADelMSOyGZMBDkfshvli8JAeFxmfhIvs4ViUTbGRkRNlsVmtrax1lgQnj/LN2ux0Uk++mcvB1pUkYL/DSAdhJo+t9IrKg4I7y3YQ8GyXJdZKCYiQwtt3eP0Kn0Wh0uPhidsfj2+LAeBcWIY/DoH+wkgHWLEpsIHDgICm4Mryv4+B4b5uYnaD83t5c62MnrV9cfEF28OV96OwFi7W3lycSdqBC39Afm5ubIZYGVpX2pz3pRxZEjASPyYrjjVqtVmAzHDh4+6UtMD52KCd9lMaAIZQfRrSvry9sLPD0CGnzw9ul3W6r1WqFlAgwprjgR0dHAwvFnCcHms9Nz4W3u7t/TNL4+Ljq9XoYDw4mvV/985jRpZzsYmR+eXtynY8X+gE2k5QyzG1379GHs7OzuuGGG3ThwoUOMMeZl/39+weGb21taXFxsSP5ck9PT0ccHkwXTGm9Xu/YoFMsFgPjv7a2pkuXLgVQRr1g3QCDtVotHOpNuT27v7ue4/5+PgttGvOVNr9fKNsWG2PPpbf9Oqkz1tef5+V0nRjXJZ6HPCMGYYeVQ3rhdf52SBeAdcqRBmAoJRIEwnZ5MlEWrSRJQtA6CQ1RlijFyclJDQwMaHZ2VkNDQ4Haz+fz6unpUa1WC5PEFbl0sIixsALgUJ4xY4RbiMN+ne3x3yysMcvibB8Sx3QhTGriwYjDAuy4K4j3EWOGFe5uAy+LA6SNjY2O9/oCE8emOVMC++Nl43mkXJAUdqJ6rEwMCFiYPY4sTXl5fEVsxfK8+O8rTXTaL1Z2fg8LIqwTZaRNnBGTFFgb2AEC8tmJSgwNCzb96Wyiz4OdnZ0QmB7HlwAQ6vV6WIT9me4ydqbI293b29OjeJ4rZw5pC9oOcOCxdPH1vlGBz0ndwLmb29vbWl1d7QCyvlmBMQJDTTt628H0MBY9tQmAJAax7oYmftJjVAl9cLe/A2PGIhuD2EDh7cyxQn19fSHnIHOL+ZrNZiUp9DNznbqzqWNra0vVajVk03f2rtVq6ezZs5qYmNDY2JgmJyc1PDzcER9I+54+fTrUa3l5OYxzxiDu9MHBQdVqteAdQE/Sn7R9bLjEfz+fBTeef7Qv3zkQi+f/YTFoh737SmyXP/MwVyPfxaEbriPjcsX66bna5LnAYldeGjnSAMwXA5QigAFL1uMsNjc3ValUVC6Xw2IGaCgWizp27FiwiNkNSTAvDAyLO+CAAFTfXURmb/Jlka/JLeM0Nob6+ITyiSkdMDPUk3bgO34DolC2vJtYFM8JFluFnnuK9qvVaiH7vbNrHsvAguJH6dBmaYwTihdF44wQ9WDjALEqLOZxcK27HVlw0+Lj3M3pcWbu7qGMcXm9rZzlAjxSrvgZcRkAWyzMnqeOhYh3XLhwQSsrKzp+/HhHXwLO/D7q7aCK7z0QX+o8mBrZ2zs4wxR3FOPImTOvC3UHmAPgqUPMyDpzlMlkOnYaSvuuuGKxqJWVlY7x4SEELNTe/tJBELmkEBROwLvHgfb09AQAFefH8+B94pfc1co4QLfE+d68rOiRtbW14Opkk4/3ibPE6DGAH/Owp6cnHKBN2XkWoBUw58Ht7Ox0o8X1x97eXgCrzGmMWI6z2tzc1OzsbDBy6S9iYQcHB3XTTTept7dXTz/9tL72ta9pa2tL+Xxe0r4BS3yYHwovKfRlmtvfQdRhACL+3HXgYffEYRoxAEsDUc8VP5Vm5HkdvEwxYKMePj/i96XVxcv4fMp3LTBFh5EEfx25Fur1YuVIA7AYtEidrilJwZ3A5G82m4HZItcPx7XgHqzVasEFwq4sqHdJYeFaX18PbhMUt3R53i+f0JSVRRSrVeqchH5PXEcmKArXlSsLritSBwbsegNkOXPjixnP7enpCWfr7e3thfgud/d6TJqXM81aS5NMJhPiRuK+bbfbHUHh8e5SB4L+WzrYkeY5uTzey8sYK00Uhfcfz0xj1Rxs8V4fi1zvqSfYXQdgYWFiYVxcXNSlS5dCfJ6nGeG9Dt7dBQy44D7yOnGPjylAE20DCwIAdhaWuqe5S7jfWTye7+0UM2fOsA0MDIQAeFz+Xqd4Pvk7iGMjGJ028DnkbmyArwNh6smz6DcHLDBiMIu0me8o9NMpqtVqGAPEo3E8kgMt2qjZbHaAT9zS0j4LDJPtOooY1eHh4eA2hOl3HcgYZA5tb2+r1WppdXU16I1MJhPOc+SQ70wmo1OnToVYtGq1GsB6b2+vSqVS6L/t7W09+uijmp+fV6FQ0NzcnEZHR4Pu9XZKc9fHBouPHZfYYEJiptXnLRKP31jS2DY3ptygTBOfZ1z/fJ7NOI2/i9+VprdcNxxWn65cW3KkAZh0+bEmKN29vb1A1xNcz24vjichLcT4+HjYft9sNoNimZub64hP8HgbgAGsA+wMDASAhInliw0KAlaNa71OMSBwYAZ1j5W+trYWGJU44zmTkoWGH2c0nFUjfsNdjZVKRfV6XdKBhc77iPmILT3q7K4Zfzfl83ZwNg0Qs7GxEY4+AtD4gorb18EJriQYSModL/4OPmPl7HVJW/Dj3zFzJ6ljV6N0sEj39u7nnZqenlY2mw2LsgdhO+B3lxVGAkYBcWNeN4/fot89WBzWir7DFcez/VBnYiQZo2lgyOtOWePEmsRBAaTpb/oQ9yus2MjISIjVvNJi53Um2FtSYEAxMmgHmEOfR+4Oxo3pfUH/eowXOxrdoof5clYSkE3bcpQX/eZGAu3tufpoS1KwYOgRzM7GBdI+sHsxm82GXcvZbDboLcYOuoE51Wq1dOnSpQ4mmxyItVpNq6urGh0dVT6f1/DwcGDpGCuSwu5xQkH+7M/+TPPz88pms4GJ83NgaTMHYTF7HP9/JZDhc9b/pv9iNvpK48nfnTb2Ypdi2jPiOeFssJft+dQjDZS6uMETt9u1BL6+HWxVlwF7icTTTLiVSICnx17s7u4f1cKOnrGxMQ0PD2tiYiLEfq2urmplZUW7u7uanZ3V+Pi4RkZG1Gg0wi7Jvb29kPwVoOVgBEuZVAEoHKfZPVgahejMkzMqMQPG5EKZlstlLS0thZ1MrjR4pzMbg4ODwUIGhMG6sEj7bkMYwbW1tY4dep72w1NV8G4WFC93vFjFQMHLBNtAvBOH/iJp7iwACy4YGBTGgjM5SKwsJV0GTr0f0tjCWBE7KKAtWOzGx8d1yy23aGZmRv39/WFzg+dCot8oB2PEyxXH8sXxLe6S9GN7nHGlvXGn8W42pDjD6n0Ut1/MqMWLAGWmLs5QwiYTBN9ut0Oes0qlcllQtrOfjBHGKoYWsYqAewCYz0f6kDHujBbhC7Fb2t/pu+p8/AGqAIBJkoQ5Vy6XgxuS3aYwn7gS2+120F8ALo9vY2zX6/Xg2oSxAwxNTExoenpa58+fV71eD23KOHG2DeaPNqhWq4EZ6+vr0/T0tEZHR1Wv17W0tBQAcj6fD2c7wsS12/vJlOfm5oI791Of+pSWlpZCehMfo96njHmXmG11ncjYi69Nk+cCOlcS1wGHxWC54Oa+0vMQNxgPq5O/Mw08psW18bnr3vjdXbk25EgDMHeFOTvCria208NWVSqVsIU7l8uFBIzEY5TLZW1sbGh8fFzHjx9XLpcLrAVxDx5LQoAs7Mzo6GgAGSjPtCOKYovI3VVS5+Thel9YWQyT5GBrO5YxChbwwwKN2wGmjknv2fRhjlj0PNaNeA/KAfB0heMuQBZ5ZwF7enoCGJEO8jFJCsAujs/iXE7cNrGb092vAFncac5ieFn8nE4+P0wBOrBzdsnFgaQ/w924uVwu7FA7duyYJIW4OtzBHtPlbUKfwFrR9w4+EGdGARK4O5kL9BntBrCmLs7COHPgoJDvfLexi4Nxbzd/lwNij9fc3d0N8UbOgOGyc0aQd3MKBSlUYvDrBhJAAPG4Rd80ELuEnEGKY1l8kfRwAP7O5/MaHR3V0tJSALi0De+PjQBny4m/hHlqNpuBYcNFL+3HvpVKJc3Ozqq3tzeESRB3GJeR9vfs9eVyWaVSSSsrK0qSRIVCIbQvSWwnJiaCvoF1hOHs7e3V+Pi47r77bg0MDOjxxx9Xq9UKZ5XGQMrHTDyXriQx4+zjzSUGIP47TQ/Qz/6etPLE709j6Pw58Xvin/gdDirRZ/6O2K0al4cx2mXArl050gCM3UIwMSgTlJdbLvV6PSRbnJ6e1qlTp5TP58N3uNkKhYJuuOGGoGCI04qZKCxtFCMZqJ2hGhkZUaVS0ebmpvL5fAAHPtlQ6H6fKyLeGdcJRofFknqzgGYymQ62TjpIXwEo4HmHWWBkGWenJ+WVOlmumBWhL7iWhZbrAGJYzB6b4gwD4Is6xtS6tx1xPyzK8Y5CPzWAWBX+5vuYyYrjvfzdDkgcPPu90v4CVyqVdNNNN+nkyZPK5XLa2dlRs9kM/ZbmkgBI805PyQFQ9qNrAFX0J2OD5+F+IzB7a2tLzWazI/WFpI7zUEk2zGHeXkbGsNfBf7zd4oXAWbDYHcXO5larFdJHpLGO3pc8g001HDBOShifE85sAa6dDYvb3ue95y3zOjnI9MWVtBEYLzfddFPYOU2bMo8cgDNHccFy/8TERGCrqA+sMAwxoOzYsWPK5XIql8tKkqRjk4MDw/h4KIw6SSqVSmEjUi6XU7vdVqPRCPMCoMquWcYi429iYkL33HOPJicn9dRTT+kLX/iCqtVq+J657qwmzGsMPmKh3dOMnxjMuHvOnxUzWt7XDu59fDjj5IZAWvmoT9p3GAUxyx7/7/rAy+PX8L2L6xR+H2Wg8p0qRxqAOdME0HA2jEHZarXCAcazs7Oam5vT7OxscAHgxtvZ2VEulwvuARYiXI9Yyc4eEJQOAPKB7rR/PNGdWYjjMmJLJv7MFc/u7q7W19e1tbUVWC6/jr9ZUHwnlFPcfg2yvb2tcrkc8it5sL0DNo8PQok7yENZ+d9eLl9k3V27vb0d2C8UojN8rgydOWIxhgH1+tLW/jwHw4grRO8DZ9RcYbNwsDjDON100006ceKExsbG1N/fr2q1etmOWkkdO0wdZHg52G1I+7I4uyL2dmbMwgiyI69QKIRrfZOG1Jk8VdoHdjAsLBjxoscYjxc+d387kxS7q7mXOvnmBG/r2M3Cd84Yttv72dorlUpIr8BZohhAjIsYIKaxHj5OYJo5NQP9433l5YJxHhgYULPZVKlU0tzcnFZXV7W6uhpYKlg7GEpnf3lOf3+/CoVCCKmgPyYmJgJg9Wtvvvlm3XHHHfrsZz+rtbW1jnQ39LnXn8/pLzLp+6HkU1NTIffhysqKCoWCRkdHOxjD3t7ekAaDuLXbb79dN9xwgyTpvvvuC4eFxztM05iaw1xscT/x9wsRB130YTyGD2O/nuu5aX/Hn6Wxdf7OmNl7vu8+rPxp7P3Vli4D1ilHGoD5DiZfCLEM2Zq9trYWjtDBqifOApZlb29PY2NjmpubUzabVZLs50Vi8XGLW9q3HEdGRlQoFIJid3DGZ34gsE8GgIgDgrRYIn7H/nxfaONM5yhxB6Iod+lg95cDQARLFkuYHVnEfWFBS/sLHrs9UcIe7B/XgfLhlsXdg4slVoZsMGA3mrMMlN0BnL+Lcro1ncba0D60B2WMWRHGWxoA4Rl8znE4Y2NjuuWWW4J7jbgdygmrFPdb7PJyYOoMou+cpHxxGwKycMvTv4ApPzLGn8+4iBlEPnPXo7dbrORjwB8zo84EePzh6OhoiImCKY37hmfEZd3c3AxxdbBCzhqREgZg7/fS7jFwxP0HkCEmi+sdjFMX5k0mk9Hi4mKokzN7gEFiHQHAPv4A32zMYCMRbtVcLqd8Ph8Ys0wmo7m5Ob361a/WyspKSMrKxgfGKPOHetDOuBsbjUYHUzk0NKSpqakOphFGEV1LG25ubnacLlAsFvU3/+bfVCaT0Z/8yZ+oVquFkAYf9y4vBFDFeub5SAyYPcbysOeksXFpz42NaL/P3xuzbPF90uWpGw5jvfz7mAFknL7U0gVgnXKkARgxMh5gDQuDoiS5JBQ9bgnAC27E0dFR3XDDDSEJK6AJVoNcQQANBrXnAUN5+s47jwMDQDjbFQ+ewyZhzDrB8uBOAGC5EpE6Jy/30U4oXhZlUnVQBs+MDqDib9IFZLPZjpg4j7WJAWNcz5hlQhxcocgBa3G7sLC4u9GZUNx18ft9gXNGMmbBXMmxMPJZzPokyX7A9fT0tObm5pTP50NWd3Zy0n68061dXFA+Brzv02JAHNwDHHy3n8dYDQ8Ph/HsCzE5mkhb4OxZvFsvzXp3UORANhZvJ3fxuOvKU71w4DgAPGYF4nbi/e12W81mM+zcZW54Qtv+/v7L4jNhVulbb3s37kj8vLm5eZnbkXbF+EG/tNvtsFmG0Amez1xER9GHuOTJ9+XMGC5MGDjeyfjNZrO6/fbbVavV9JnPfEbnzp3raD/qB+jz/oNNT5IknA3ZbrdVLpclHYR+7OzsqFKpaHd3Nxz9RP5Ezhbt7e1VpVJRf3+/xsbG9PrXv16ZTEaf+MQntLq6Ghg25oEbQ/73lZikeDy+EEkbq9738bOfi21znZZWPp/vjB3WEq9XPLeeL8sXv9uf9WLapyvfXjnSAAyr3oEPExprGhZnY2NDuVwu/Hhw/dbWlmZmZjQ5ORkUIPS7HyZLbJOkDjCAJef+fpRxbLn7gp5mCbnEi3D8HaCDxYWAe2dAnB1Je48v6pSRbOu0DUCXOiZJ0rFFnjP1pIPjb2KF4m3iiW1hIB1g0E4cHbW2thY+9yB+2s6D11ks4vQMLgCXKylRlL+3i/cZbeobD0qlkiYmJgKLSn6larUaFteRkZHL3pPmSvWAcwwMdzl6/zrwjt2x3sce04jyZ2Hk8729vY54M+kgvsw3iziwd9bOy8a99EG8qDAmYlaRI4NId0DuKOrnz3HwxtgETGIc+Ttxybq790qMhQMv39FMvis3Yty1urOzE3Jy7ezsaHV1VeVyWe12W6dOnVKpVAplciMK/eXvhK13IJ7J7B/QTYoJ70+PnywUCrrrrru0vr6uRqPRcfC2s56uKzDs6Efc47QNx7Wxg9c3GjC3cXu7LqrX6xocHNT3f//3a2hoSB//+MdVr9cvYxy53mOu6JvD5uyLARexbogB/ot9T6xPD3tX/Nw00BYDrys940rleSHXfzuly4B1ypEGYJxF5rEinlWaY4c2NjY0NDQUWAnO0YNdIb4CYAY4IP7LFZW7L5IkCcyC1BlcTLxYJpMJblCSukqX79yJ3Q4ORvyHxZbNB+6Cgb0AYPjB27lcrgMYAVSc2cCa5hn1er2DRZEOrMOtrS0NDg5qYmJC2Wy2Y9djJpMJLjLPou51wvUDoBoeHg4bGWBv6LudnZ2QVdvZmBhg4c6hzFzn7g1X6LHr0sVZjRiAuWUOAJ+dndX09HRo42azqd3dXV28eFGrq6uBrYDJ4zmkSHE3GVnccftks9kAuP3YHL+fhZe+dFeYs54AW1xzuH6Hh4e1srISxi19xTsJnia+jTZgDHm+K97LnPHFOEmSMJ/a7XbH3GGurK+vq1AoBBaIevkOZGea6ReP65KktbW1jl2/AKXt7W3lcrnL5mwaQy0plBe2yA+4jnO1cS1gll2JxEptbGzo4sWL4ZxJdiEzBng+YwvGjITPu7u7qtVqHUCKjQHO9JHMtlQq6dZbb1Vvb68+//nPa3NzM+QKJBwjduPSTrT/7u6uqtVqCM4fGBjQzMxM8CYA7pzZQ796iAXzvVgs6rWvfa3m5ub06U9/Wl/84hc7jB3Glc9r9K0bns468r/rFB8baZLGMjsD7LGprqO9HD5u/Dm8l2vdUPB68bcbUG78AMZj1t3jPL1+sVvSy92Va0+ONACLFTSCMmfH0e7urvL5fMgYzQLI4bL5fF7FYjEsbh73dVhCTE8REE94F4+hcUASs1EvdII44+b1d6DkO6uIiaMscT4sZ5Fog83NzaCQUPT8Zuepu2h4NsqS/4k58tgtSSGmDGbI3UJ7e3sh1sXdVc7GsOC4Be1sz2ExDw6qfBdWGuD1e4hXc7ZifHw8MBrDw8MBnDebTV28eFHlcjmwIYftiKI/XbHG7RorWkkd7UAaEkBI7HoASEn7hgnnanp8ZG/vftoCH5MAn7RFgL50F0rM8h7W/l6H+DMWP8Ya18UsAH2UFl8DYwh7RJmcaYUx9ZABdzt5Pby+gFoYJwfqHrPHZw6i9/b29PTTT2t3d1e33XZbB0NHyANAfXh4OGSYpz2azWZIoxHHTsXMN2UcGBjQy1/+cm1sbOiBBx7QwsKCpqamOtz6CADVxx7P3draUq1W69jRTD7FtbU1ra6uhk0BDoidDW2393d35nI53XXXXSoUChocHNRf/uVfBp3E9YyztJ2qLj4O43H2fPXqc8VHOVDl//gnjUFLY+Dj73zcpZU/bT45c851aaEWyJVCA66mxPFs3wq5Fur1YuVIAzAU4Pr6upIkCUpOUgjgJhEhWfFHR0fDeWarq6va2dlRsVhUqVQKQdse/0UsQ5xHiJxMbvFIlyfaw20JGHJLM743TdImuqQOIIcVzfMdXACA2DXFxAW84N5hMYKBYgMCYNOBQU9PTwBNAF3aA5bC3ZWUVzpIc9Dbe5D6wBd22hB2B6ubRd5ZL5SiK0APtPb0EGkUv7/XAY8rPV/sPV/U2NiYZmdndezYscBMsEW/1Wrp4sWLWlhYCAAf5hWrlTrx/jgukDIB2gC2uIR9cfHEuW51+/N7enoCY0qsF3On1WqFo2cqlUpH321sbAS2hfxVgAvGm3QAmuI5wPu93d3aR9zlx2LS29sbgCusKN+nxc64kCkeYOpMA21JPT1FCRIDPl+A0QcOSr0ePv4BUsViMaSdGBkZ0YULFzQ+Pq6XvexloXweb7q9vR2YJeL0isViMIwAJoctaMSN0Q6lUkm33367KpWKvvKVr6hSqahUKgVQ7jGhzlbxHTqMlDS0fSaTUalUCklct7a2dPLkybBhAWMMds9j9IaGhjQ5Oak3vOEN6u/v11/+5V8GnZ4kB+kwXI/EepAyPB+m56+78McMmEva5/EcjMXZMb8uLUzFr/fnO3N4GKPPNc9lGHXl6suRB2BxigKPiWo2m2o0GiEXU7FYDO4dkmCykGaz2aB0UBKtVktra2sdYAtXBIH7MEEOHnwCAEriXD/Px3qKJWY1sF6JlfKFJGYkUOpsw2dxJcM9ipjA91qtFnZWekwKCnJkZCS4Hj1PmnSwOYJ2QaG7exBl7Ds4Oc8ORcdi4wqEhdfjnPyYImfyYqvPlTVt6O4Ff6a7Abg3Sfbj4yYnJ3X8+HGNj4+rt7c3BLVnMhmtrKxoeXk5HC8EWGWzgi8Szla6EqY87uag/xifzjAyB+irdrsd2A0WaXcHMl4A0LAlg4ODoZ0Z73w3MjLSESfmgIR7uNbrFrOJcbyPf+6GAPUklQTjm3sYAzFw8rnibCDAgrZjzDiAd0Dpn9NH9IXXh7o7Q+txgbT/0NCQhoeHVavVAtDlkHXmEe5kB7Lsmp2ZmdHo6GhIqYGrD93kZWIxxhgiBKK/v1+nT5/Wk08+qZWVFfX19Smfz3cYkg7qfe573Tc2NtRsNpXJZEKW/2KxGMq7srKiycnJ0L6eFNvHKO7QG2+8MejbL3/5y5IO9Ca6IQZhLs9Xd/51BHCaxo4zX30cHMbWufhY83XDDWjvU2e2XW+5Uce1Lv6Ml1q+HWW4Fur1YuWl35f615AkScIhw/zPorW3t6dWqxXOXctms8GabrVaYUfP5OSkisViB5PDROAIEOnAQiQofG9vLwSrupKPLWen+eNJHNPTsWKPAZdfG7MjgBtnwViEYDn8bETK5mfwcX2tVlO9Xg+LoC8ouAx9EfX8Ya48YCzi+2HiOAiYRcbdFbB6WPvUFSYyDjJ3axCQ6LGBXt60H9rPn0Vdenp6lM1mNTU1pZe97GUhiS+MW6PRULVa1fz8vObn51Wv18MOQmIEvc2oZ5ql6jEiLIoOoD1XFGwMjK2zZridPWaKBZP4Oj+XkU0JPlZxr3HagrMkPBNAFo+/eJ7G7rw4VgYhhQftQYoF31zCM+J7XRwYeR40yudxnXGbO3j3ucT/Xl+fx/GclaT19fWQjqFQKIRxwOaCer2uTCYT2LHNzc0AVDBkRkZGNDQ0pLW1NS0vL4dNCjDYMLOcqgBTmiRJ2CxQLpe1u7urG264QW9+85t16tQp1et11ev1wPJ7W3nfUkd2fw4MDAQQtrq6qgsXLqjZbCqXy2lsbEy1Wk0LCwuq1+uBxWQuuD7AXS9Jt9xyi/7BP/gHetvb3qbBwUHV6/Wwe9RZRe93N3qfjxzWR89H/N2H6ZA4jMDn6fMpj9ctDjlIK6/3E3oqTQ6bl9e7fOhDH9KNN96ooaEh3XPPPfrc5z531ctwpBkwV5p+BAvWlaeeINlhf39/2CY9OTmpqampwLy4Be/KgR2TMDZs03bF7SyKdBBnxWT0JJ1MLK6PnxFPNnf9cZ90ELPChgGnoB0cJEkSFmSewSKD+8MzpdM+gASPu2Lx4rw92geQQHmJ5SI9B/3lyiUGq143BxyxAnYgA3DgmQ7COKMzBlf+Pt7pi22s3Pv79w84JgcSAe+UY2dnR/Pz86pUKh1usna73ZEqgMXbNybQdvwNs0FbMpac6XJlm8lkOoKdUcjMCWJ6Ypcm8X0EOOOyYsFmUc9kMtrY2AhjBXaFeElvT8CzGyU+n3i+s5XME28DjwMcGBhQPp/XyspKyH3FvT5mYmaTtiZVBOOcDTmUH7DucZ5uREjqSFzqY8PnpN/DeKVN0CMjIyPhkPFMJhPmGf3AvAEY04YwybBP9DfJUmkPZ0EYMzyzVqupv79fx44d0x133KGBgQHdd9994UgjNia4TgUk+Vyh3XZ3d8Muaeo3PT3dkSNMUmhryghooO2J8xwdHdXx48eDl+ITn/iE5ufnO8YG7eSeBCRmWV2cNUr7LI7NvBJA8+ekudAZe/FncRnTnu/vPcwVGYNNn38wrv4On6PXglwrDNj/+l//S+95z3v0oQ99SK997Wv1X/7Lf9Fb3vIWPf744zp58uS3vIyHyZFmwEh/wCDz2AEsu/7+/rBraW9vLxw71Nvbq6mpqQ72y90pDGQPNt/Z2VGj0QgLC8rJWZk0C413+64arnEwxmdI2mTz57prLqagPc0DTCCLoLMnrmA99oRF0BcWFmUAjXSwsFI+ftyFgJuRezkUGgud3Y8sGrQz1r3Uyfi5UnH3pnSwWPK3L6hexjjw1fuBOvX07B+ncvr0aR07diwsPrASu7u7Wl1d1dLSktbW1jpAE2V29ivudweUnK3p7iSYLZgNZ79c6A/az8EMYy9ODMozWdB2dnZCXjfaJm4TBwn+Hgcm7nL2tk3rAx+rAEsYY8IAAPEA09jNEy9u8SLbbDZDrirP+g9T5K5nX1xj1o7nORPihp8DITbw0F4s8uyEZNytr6+HEAcAbrvdDuxSo9EI7m3Kw87gWq2m5eVlLS4uanV1NRhMvjkFln5kZCSAMDZcHD9+XK961avU19entbW14Dqs1+tqtVqhDgBIfjwuTNr3ElSrVS0tLWl5eVkbGxsaGxsLyWY5io30NLRjvLMapnBwcFCve93r9JM/+ZN63eted5lrG7ey61qfv/H4ei6267Dv3QV92HvSxoZfE7O+afe4uE7g/7TNN4gbjG4wpIkbCy+1eJm/FT8vRn7jN35DP/VTP6V/+k//qV75ylfqN3/zN3XixAl9+MMf/hbX9spyJBkwGp1AcWnf0iJlQaPRCBOfrdo9PT1BsdVqNRUKBeXzee3u7gaXEXEK29vbqtfrqlarQZFxMHCtVgsAA9cZOYtQTqS/4L2wSn4osi80KF8SmfrOL5Qgrs9WqxUWIXZresCuM258T4ZsFlsAAXE1LCCtVkuVSkW1Wi20bW9vb3ADYtkz8eOFc2RkRAMDA8H9MTg4eBkDgxVP3X1DgqTgMl1dXVWlUtH29naITfJF3i1Ynked2+12x0Hh/uP3OeslHezY5ADrQqGg6enpkD6EOiVJolqtposXLwYGgYWB8YIypZ9hGVjQfMGlDVi8t7a2wvFSLHqApM3NzcCIAL6TJAkHHcPWAnBxP9PnvIOdaLiWGo2GcrlcB3Plrk3ax3fJsYmCMQLgxF1I/A5jOZPJhITGDrgBoB7/5YHyLLi0gTNxlC9WyPwmCXO73VaxWAzzk80UACX6h8We8gHYKAfvA1zFDJh0sAMToOttQv/39/erVqupXC6rWq2GfGHs+uVZuVwuxA9Wq9WOOQHLT9oJAA6bQUgV09fXF4AbfVUsFnXrrbeqXq/ra1/7Wnjn2tpa2JnpsavOxGHY0E7Ei8LOYaxg7DabTc3NzYXkrbQNQJG4U+Zxf3+/Xvayl6nVaunChQs6e/ZsAGc+j2OAcxhLFK8baZ87i+q6JjaaYoDuYCmNZUPvxQwt80Y6MHDQTW4ku3GbBjLT3pfGeqXNje80IWk4wnoby/b2th588EH9wi/8Qsfnb3rTm/SFL3zh21rGyyQ5gnLhwoVEUven+9P96f50f7o/3Z8X8HPhwoWrvmZvbGwkMzMz37Y6jY6OXvbZ+973vtSyzM/PJ5KSv/zLv+z4/Fd/9VeTm2+++Sq0xoEcSQZsbm5Ojz/+uG699VZduHAhJOm8HqXRaOjEiRPddui2g6RuOyDddtiXbjvsS7cdFJjyubm5q/7uoaEhnT17toMV/1ZKksL6pbFfLmks4dWOlzuSAKynp0fHjh2TpJBj6XqXbjvsS7cd9qXbDvvSbYd96bbDvlzv7VAoFF6yd3ss50spExMT6u3t1eLiYsfny8vLmp6evqpluTai8rrSla50pStd6UpXvs0yMDCge+65R/fff3/H5/fff79e85rXXNWyHEkGrCtd6UpXutKVrnTlxch73/te/eN//I9177336tWvfrU+8pGP6Pz583rnO995VctxZAHY4OCg3ve+9z2nn/c7XbrtsC/ddtiXbjvsS7cd9qXbDvvSbYeuuPzIj/yIyuWyfuVXfkULCwu6/fbb9YlPfEKnTp26quXIJMl36J7UrnSlK13pSle60pVrVLoxYF3pSle60pWudKUrV1m6AKwrXelKV7rSla505SpLF4B1pStd6UpXutKVrlxl6QKwrnSlK13pSle60pWrLEcSgH3oQx/SjTfeqKGhId1zzz363Oc+91IX6Vsqf/EXf6Ef/MEf1NzcnDKZjP7kT/6k4/skSfT+979fc3NzGh4e1t/+239bjz32WMc1W1tb+tmf/VlNTEwom83q7/29v6eLFy9exVr89eSDH/ygvuu7vku5XE5TU1P6oR/6IT3xxBMd11wP7fDhD39Yd955Z0gg+epXv1r/5//8n/D99dAGafLBD35QmUxG73nPe8Jn10NbvP/977/sMPKZmZnw/fXQBsj8/Lz+0T/6RxofH9fIyIj+xt/4G3rwwQfD99dTW3TliMpVPfjoWyAf+9jHkv7+/uS3f/u3k8cffzx597vfnWSz2eTcuXMvddG+ZfKJT3wi+eVf/uXk4x//eCIp+eM//uOO73/t134tyeVyycc//vHkkUceSX7kR34kmZ2dTRqNRrjmne98Z3Ls2LHk/vvvT7761a8mr3/965O77ror2d3dvcq1eXHy5je/OfnoRz+aPProo8lDDz2U/MAP/EBy8uTJZG1tLVxzPbTDfffdl/zv//2/kyeeeCJ54oknkl/6pV9K+vv7k0cffTRJkuujDWL50pe+lNxwww3JnXfembz73e8On18PbfG+970vue2225KFhYXws7y8HL6/HtogSZKkUqkkp06dSn7yJ38y+eIXv5icPXs2+cxnPpM8/fTT4ZrrpS26cnTlyAGw7/7u707e+c53dnx2yy23JL/wC7/wEpXo2ysxAGu328nMzEzya7/2a+Gzzc3NpFAoJP/5P//nJEmSpFarJf39/cnHPvaxcM38/HzS09OTfPKTn7xqZf9WyvLyciIpeeCBB5IkuX7bIUmSZGxsLPmd3/md67INms1mcvr06eT+++9Pvu/7vi8AsOulLd73vvcld911V+p310sbJEmS/Jt/82+S173udYd+fz21RVeOrhwpF+T29rYefPBBvelNb+r4/E1vepO+8IUvvESlurpy9uxZLS4udrTB4OCgvu/7vi+0wYMPPqidnZ2Oa+bm5nT77bcf2Xaq1+uSpFKpJOn6bIe9vT197GMfU6vV0qtf/errsg3+xb/4F/qBH/gBvfGNb+z4/Hpqi6eeekpzc3O68cYb9aM/+qM6c+aMpOurDe677z7de++9+uEf/mFNTU3p7rvv1m//9m+H76+ntujK0ZUjBcBWV1e1t7d32YGZ09PTlx2s+Z0q1PNKbbC4uKiBgQGNjY0des1RkiRJ9N73vleve93rdPvtt0u6vtrhkUce0ejoqAYHB/XOd75Tf/zHf6xbb731umoDSfrYxz6mr371q/rgBz942XfXS1t8z/d8j37/939fn/rUp/Tbv/3bWlxc1Gte8xqVy+Xrpg0k6cyZM/rwhz+s06dP61Of+pTe+c536ud+7uf0+7//+5Kun/HQlaMtR/Iookwm0/F/kiSXffadLi+mDY5qO73rXe/S17/+dX3+85+/7LvroR1e8YpX6KGHHlKtVtPHP/5x/cRP/IQeeOCB8P310AYXLlzQu9/9bn3605/W0NDQodd9p7fFW97ylvD3HXfcoVe/+tV62ctept/7vd/T937v90r6zm8DSWq327r33nv1gQ98QJJ0991367HHHtOHP/xhveMd7wjXXQ9t0ZWjK0eKAZuYmFBvb+9l1sny8vJlls53qrDj6UptMDMzo+3tbVWr1UOvOSrysz/7s7rvvvv02c9+VsePHw+fX0/tMDAwoJe//OW699579cEPflB33XWX/sN/+A/XVRs8+OCDWl5e1j333KO+vj719fXpgQce0H/8j/9RfX19oS7XQ1u4ZLNZ3XHHHXrqqaeuq/EwOzurW2+9teOzV77ylTp//ryk60s/dOXoypECYAMDA7rnnnt0//33d3x+//336zWvec1LVKqrKzfeeKNmZmY62mB7e1sPPPBAaIN77rlH/f39HdcsLCzo0UcfPTLtlCSJ3vWud+mP/uiP9H//7//VjTfe2PH99dIOaZIkiba2tq6rNnjDG96gRx55RA899FD4uffee/XjP/7jeuihh3TTTTddN23hsrW1pW984xuanZ29rsbDa1/72svS0jz55JPhMOXrqS26coTl6sf9//WENBS/+7u/mzz++OPJe97zniSbzSbPPvvsS120b5k0m83ka1/7WvK1r30tkZT8xm/8RvK1r30tpNr4tV/7taRQKCR/9Ed/lDzyyCPJP/yH/zB1e/Xx48eTz3zmM8lXv/rV5Pu///uP1Pbqf/7P/3lSKBSSP//zP+/Ycr++vh6uuR7a4Rd/8ReTv/iLv0jOnj2bfP3rX09+6Zd+Kenp6Uk+/elPJ0lyfbTBYeK7IJPk+miLn//5n0/+/M//PDlz5kzyV3/1V8lb3/rWJJfLBf13PbRBkuynIunr60t+9Vd/NXnqqaeS//E//kcyMjKS/MEf/EG45nppi64cXTlyACxJkuQ//af/lJw6dSoZGBhIXvWqV4XUBN8p8tnPfjaRdNnPT/zETyRJsr/F+n3ve18yMzOTDA4OJn/rb/2t5JFHHul4xsbGRvKud70rKZVKyfDwcPLWt741OX/+/EtQmxcnafWXlHz0ox8N11wP7fBP/sk/CWN9cnIyecMb3hDAV5JcH21wmMQA7HpoC3JZ9ff3J3Nzc8nb3va25LHHHgvfXw9tgPzpn/5pcvvttyeDg4PJLbfcknzkIx/p+P56aouuHE3JJEmSvDTcW1e60pWudKUrXenK9SlHKgasK13pSle60pWudOU7QboArCtd6UpXutKVrnTlKksXgHWlK13pSle60pWuXGXpArCudKUrXelKV7rSlassXQDWla50pStd6UpXunKVpQvAutKVrnSlK13pSleusnQBWFe60pWudKUrXenKVZYuAOtKV7rSla50pStducrSBWBd6UpXutKVrnSlK1dZugCsK13pSle60pWudOUqSxeAdaUrXelKV7rSla5cZekCsK50pStd6UpXutKVqyz/HwWStVecf4EvAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Display the original image\n", - "plot.display(image, cmap='Greys_r', title='Original Image')\n", - "\n", + "plot.display(image, cmap='Greys_r', title='Original Image')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Display the convolved image\n", "plot.display(image_conv, cmap='Greys_r', title='Convolved Image')" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "### Deconvolution\n", + "### Deconvolution\n", "\n", "Deconvolution is the process of reversing the effects of convolution.\n", "\n", @@ -209,8 +293,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# This function deconvolves an image with a kernel using FFT.\n", @@ -224,16 +312,45 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Using this function we can recover our original image given that we know the PSF." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/1_/d9gpvb451qlgcfrf4s5fbjth0000gq/T/ipykernel_95697/150898546.py:7: RuntimeWarning: divide by zero encountered in divide\n", + " return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x / y))))\n", + "/var/folders/1_/d9gpvb451qlgcfrf4s5fbjth0000gq/T/ipykernel_95697/150898546.py:7: RuntimeWarning: invalid value encountered in divide\n", + " return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x / y))))\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "image_deconv = fftdeconvolve(image_conv, psf_1)\n", "\n", @@ -243,25 +360,48 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "As you can see the all the blurring effects have been removed from the convolved image." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Convolution with Noise\n", + "### Convolution with Noise\n", "\n", "In practical applications all observations contain some degree of noise. We can imagine that our Raccoon was photographed with a very good instrument with only the tiniest amount of noise ($\\sigma=10^{-6}$) as follows." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Add Gaussian noise to convolved image\n", "image_conv_noise = image_conv + np.random.randn(*image_conv.shape) * 1e-6\n", @@ -272,20 +412,56 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "Given the amount of noise, the convolved image looks virtually the same.\n", - "\n", - "### Deconvolution with Noise\n", + "Given the amount of noise, the convolved image looks virtually the same." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Deconvolution with Noise\n", "\n", "Let's see what happens when we try to recoever the original image from the noisy convolved image." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/1_/d9gpvb451qlgcfrf4s5fbjth0000gq/T/ipykernel_95697/150898546.py:7: RuntimeWarning: divide by zero encountered in divide\n", + " return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x / y))))\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "image_deconv_noise = fftdeconvolve(image_conv_noise, psf_1)\n", "\n", @@ -295,17 +471,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "The image is completely destroyed! Why?" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Ill-Posed Problem\n", "\n", "A problem is considered well-posed if \n", @@ -314,9 +496,18 @@ "2. the solution is unique,\n", "3. the solution's behavior changes continuously with the initial conditions.\n", "\n", - "Any problem that does meet all three of this requirements is considered \"ill-posed\".\n", - "\n", - "### Ill-Conditioned Problem\n", + "Any problem that does meet all three of this requirements is considered \"ill-posed\"." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Ill-Conditioned Problem\n", "\n", "A problem (or matrix) with a large condition number is considered to be \"ill-coniditioned\". This relates to the 3rd point on our list of requirements for a well-posed problem.\n", "\n", @@ -331,8 +522,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 11, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "outputs": [], "source": [ "# Data for first problem\n", @@ -346,16 +541,33 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Two solve both problems we simply need to calculate $x = H^{-1}y$ as follows." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x1 = [2. 1.]\n", + "x2 = [1. 1.]\n" + ] + } + ], "source": [ "# Solution to the first problem\n", "x1 = np.dot(np.linalg.inv(H1), y1)\n", @@ -368,7 +580,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Now, let's see what happens if we add a small change to $y$ (e.g. mimicking the effects of noise).\n", "\n", @@ -379,9 +595,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x1 = [2.02 0.99]\n", + "x2 = [0.33333333 1.33333333]\n" + ] + } + ], "source": [ "# Noisy versions of y\n", "y1_noise = np.array([4.0, 7.01])\n", @@ -396,12 +625,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Notice that for the first problem the solution has changed only as much as the input data, while for the second problem the solution is completely different. \n", "\n", - "It is clear from these results that the first problem is well-conditioned well the second is ill-conditioned. This means that the first problem can be solved analytically, even with noise, well the second cannot.\n", - "\n", + "It is clear from these results that the first problem is well-conditioned well the second is ill-conditioned. This means that the first problem can be solved analytically, even with noise, well the second cannot." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ "We can relate this property back to our deconvolution problem, which can also be modelled as a linear inverse problem of the form\n", "\n", "$$y=Hx + n$$\n", @@ -411,26 +653,47 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", "\n", "## Deconvolution Exercise\n", "\n", - "### Deconvolution with Sparsity\n", + "### Deconvolution with Sparsity\n", "\n", "Given that we can model our observations as $y=Hx + n$, we aim to solve an optimisation problem of the form\n", "\n", "$$\\begin{aligned} & \\underset{\\alpha}{\\text{argmin}} & \\frac{1}{2}\\|y-H\\phi\\alpha\\|_2^2 + \\lambda\\|\\alpha\\|_1\\end{aligned}$$\n", "\n", - "where we assume we know that our data is sparse in the dictionary $\\phi$. Notice that this is exactly the same problem we needed to solve for the CS exercises, only now $H$ is a convolution operator rather than a masking operator.\n", - "\n", - "### Problem\n", + "where we assume we know that our data is sparse in the dictionary $\\phi$. Notice that this is exactly the same problem we needed to solve for the CS exercises, only now $H$ is a convolution operator rather than a masking operator." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Problem\n", "\n", "An observer with has obtained a very blurry and noisy image with instrument. Fortunately she is celever and knows the exact PSF of her instrument. She now provides you with her image and the PSF her instrument and asks for help in deconvoving the PSF effects.\n", "\n", - "You, being an expert in sparsity and having some experience with this type of data, know that this signal is sparse in Fourier space. You also know that $\\lambda=0.003$ would be an appropriate value for this problem.\n", - "\n", + "You, being an expert in sparsity and having some experience with this type of data, know that this signal is sparse in Fourier space. You also know that $\\lambda=0.003$ would be an appropriate value for this problem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "**Questions and tasks**\n", "1. Try to deconvolve the image analytically.\n", "1. Does it work?\n", @@ -440,45 +703,110 @@ "1. Deconvolve the image using the Forward-Backward algorithm.\n", "1. Display your deconvolved image $\\hat{x}$.\n", "1. Roughly how many iterations did it take to converge?\n", - "1. Finally, load the file `data/cs_true_data.npy` to determine the NMSE of your solution.\n", - "\n", - "### Data\n", + "1. Finally, load the file `data/cs_true_data.npy` to determine the NMSE of your solution." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Data\n", "\n", "For this exercise you are given a noisy convolved observation $y$ and a PSF. Your task is to deconvolve the psf effects from the observation to recover the original image $x$. " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Load the observation.\n", "y = np.load('data/deconv_obs_data.npy')\n", "\n", "# Load the PSF.\n", - "psf = np.load('data/deconv_psf.npy')\n", - "\n", + "psf = np.load('data/deconv_psf.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Display the observed image.\n", - "plot.display(y, r'$y$')\n", - "\n", + "plot.display(y, r'$y$')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Display the PSF.\n", "plot.display(psf, 'PSF')" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Functions\n", + "### Functions\n", "\n", "Some functions that may be useful for this exercise are provided below." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 18, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# H operator.\n", @@ -489,8 +817,19 @@ "# H transpose operator.\n", "def Ht(data, psf):\n", " \n", - " return fftconvolve(data, np.rot90(psf, 2))\n", - "\n", + " return fftconvolve(data, np.rot90(psf, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ "# Function that performs simple forward backward splitting.\n", "def forwardBackward(observation, first_guess, psf, grad, lambda_val, n_iter=300, gamma=1.0, return_cost=False):\n", " \n", @@ -511,29 +850,43 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Solutions\n", + "### Solutions\n", + "\n", + "### EXERCISE\n", "\n", - "EXERCISE: provide your solutions in the cell(s) below." + "Provide your solutions in the cell(s) below." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "---\n", "\n", "## Hints\n", "\n", - "** Deconvolution Exercise **\n", + "**Deconvolution Exercise**\n", "\n", "1. I wonder if the functions provided will help for defining the cost function and the gradient.\n", "1. Make sure you display your deconvolved image in the correct domain.\n", @@ -542,15 +895,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "[Open next notebook ->](./wavelets_1.ipynb)" ] } ], "metadata": { + "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -564,7 +922,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/sparsity_tutorial/plot.py b/sparsity_tutorial/plot.py index 6bfaeaf..f406ef4 100644 --- a/sparsity_tutorial/plot.py +++ b/sparsity_tutorial/plot.py @@ -4,7 +4,7 @@ # Set the default size of figures. -pylab.rcParams['figure.figsize'] = (10.0, 8.0) +pylab.rcParams['figure.figsize'] = (8.0, 6.0) def regression_plot(data, model): @@ -55,8 +55,8 @@ def grad_plot(x, x_norm, x_norm2, grad, tangent, index): """ - plt.plot(x, x_norm, 'b-', label='$||x||_2$') - plt.plot(x, x_norm2, 'g-', label='$||x||_2^2$') + plt.plot(x, x_norm, 'b-', label='$||x_i||_2$') + plt.plot(x, x_norm2, 'g-', label='$||x_i||_2^2$') plt.plot(x[index], x_norm2[index], 'ro') plt.plot(x, tangent, 'r--', label=rf'$\nabla ||x_i||_2^2={grad}$') plt.ylim(-0.1, 1.0) diff --git a/wavelets_1.ipynb b/wavelets_1.ipynb index 510967a..943a176 100644 --- a/wavelets_1.ipynb +++ b/wavelets_1.ipynb @@ -2,19 +2,30 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ "# Introduction to Wavelets\n", "\n", "---\n", "\n", "> Author: Samuel Farrens \n", - "> Year: 2018 \n", + "> Year: 2018 (updated in 2023) \n", "> Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) \n", - "> Website: https://sfarrens.github.io\n", - "\n", - "---\n", - "\n", + "> Website: https://sfarrens.github.io" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ "## Contents\n", " \n", "1. [Set-Up](#Set-Up)\n", @@ -38,10 +49,12 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Set-Up\n", "\n", "Here we will import a couple of packages that will be needed throughout the notebook. \n", @@ -51,20 +64,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ - "# Tell Jupyter to display plots in this notebook.\n", - "%matplotlib inline\n", - "\n", "# Import the numpy package with the alias np.\n", "import numpy as np \n", "\n", - "# Import the pyplot package from matplotlib with the alias plt.\n", - "import matplotlib.pyplot as plt \n", - "from matplotlib.pylab import rcParams\n", - "\n", "# Import some tools from scipy.\n", "from scipy.signal import gausspulse\n", "\n", @@ -72,71 +82,65 @@ "import time\n", "\n", "# Import interaction module\n", - "from ipywidgets.widgets import *\n", + "from ipywidgets.widgets import interact\n", "\n", "# Import tutorial functions.\n", "from sparsity_tutorial import plot\n", "from sparsity_tutorial.functions import *" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For Python 2 users only\n", - "# Uncomment the following commands\n", - "\n", - "# Import Python 3 behaviour\n", - "# from __future__ import print_function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set size of figures.\n", - "rcParams['figure.figsize'] = (14.0, 8.0)" - ] - }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", "\n", "## Introduction\n", "\n", - "### Objective\n", + "### Objective\n", "\n", "The objective of this notebook is to provide a very brief introduction to the concept of wavelets with some practical demonstrations of how they work. To keep things as simple and open a possible I have avoided using packages such as PyWavelets or PySAP to implement the wavelet transforms. However, after undertanding the basics, these packages can be very useful for practical applications. \n", - "\n", - "### Wavelets\n", - "\n", - "Wavelets are a class functions that are localised in both time and frequency (or space and scale). In this notebook we will explore some of the basic properties of wavelets and how they compare to other time-frequency analysis techniques.\n" + "\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "---\n", - "\n", "## Stationary vs Non-Stationary Signals\n", "\n", - "### Stationary Signal\n", + "### Stationary Signal\n", "\n", "Stationary signals are those that do not change over time. Take for example the following singal that combines 3 sine waves with different frequencies." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Set a range of t values\n", "t = np.arange(0, 1, 0.001)\n", @@ -151,16 +155,35 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "We can clearly see that the pattern produced by the combination of frequencies is periodic. By taking the Fourier transform of this signal we can easily pinpoint the exact frequencies it contains." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Calculate the FFT of the signal\n", "x_s_fft = np.abs(fft(x_s))[x_s.size // 2:x_s.size // 2 + 50]\n", @@ -172,20 +195,48 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "As expected we get three peaks at the corresponding frequencies. In fact, the frequency information tells us everything we need to know about this signal.\n", - "\n", - "### Non-Stationary Signal\n", + "As expected we get three peaks at the corresponding frequencies. In fact, the frequency information tells us everything we need to know about this signal.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Non-Stationary Signal\n", "\n", "Non-stationary signals are those that change over time. Let's combine the same 3 sine waves at different time intervals and see what happens." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Define a non-stationary signal\n", "x_n = np.hstack((np.sin(2 * np.pi * 5 * t[:400]), np.sin(2 * np.pi * 10 * t[400:800]), \n", @@ -198,16 +249,35 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "In this example the signal is clearly not periodic. As before we can take the Fourier transform to examine the frequency content." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Calculate the FFT of the signal\n", "x_n_fft = np.abs(fft(x_n))[x_n.size // 2:x_n.size // 2 + 50]\n", @@ -219,17 +289,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "The frequency space is not as clean as that of the stationary signal due to the sudden change of frequencies, but we can still see wider peaks centred on our original frequencies. For a more realistic signal we would expect something more like this for both cases. The main difference for the non-stationary signal is that even though we can work out the frequencies we don't know when they occurred." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Windowed Fourier Transform\n", "\n", "Most discrete signals, such as the non-stationary time series in the cell below, can be decomposed into a sum of sine and cosine wave with the Discrete Fourier Transform (DFT).\n", @@ -243,9 +319,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Define the signal\n", "n = np.linspace(0, 10, 1000, endpoint=False)\n", @@ -262,18 +353,36 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "In doing so, however, we lose all of the time information. We can try instead looking at the frequency information contained within smaller time intervals." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "subslide" + } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Slice the signal into four pieces\n", "slices = [slice(i * n.size // 4, (i + 1) * n.size // 4) for i in range(4)]\n", @@ -287,7 +396,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Notice that we can identify different frequencies of the signal contained within the time intervals, but what happens if we make the windows smaller?" ] @@ -295,10 +408,12 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true + "slideshow": { + "slide_type": "subslide" + } }, "source": [ - "### Uncertainty Principle\n", + "### Uncertainty Principle\n", "\n", "The **Heisenberg–Gabor limit**, \n", "\n", @@ -306,9 +421,19 @@ "\n", "an interpretation of the uncertainty principle, shows that it is not possible to simultaneously sharply localize a signal in both the time domain and frequency domain.\n", "\n", - "In other words we will always need to make a trade-off between time information and frequency information, but what is the best way to preserve as much of both as possible?\n", - "\n", - "### Gabor Transform\n", + "In other words we will always need to make a trade-off between time information and frequency information, but what is the best way to preserve as much of both as possible?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Gabor Transform\n", "\n", "The Gabor transform or Short Time Fourier Transform (STFT) can be represented as:\n", "\n", @@ -323,16 +448,46 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "outputs": [], "source": [ "# Define a Gaussian filter.\n", "def get_filter(m, a):\n", "\n", " return np.exp(-a * (n - m) ** 2)\n", - " return np.exp(-a * (n - m) ** 2)\n", - "\n", + " return np.exp(-a * (n - m) ** 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c614db3365044f2ba61196f69cce308b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=0.0, description='pos', max=10.0, step=0.5), IntSlider(value=1, descri…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "@interact(pos=(0, 10, 0.5), alpha=(0, 20, 1))\n", "def show_filter(pos=0.0, alpha=1):\n", " \n", @@ -351,7 +506,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Try adjusting the filter width and position and see what happens.\n", "\n", @@ -360,11 +519,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "subslide" + } }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7267f2867e4046779a8e1ea0732bd4c5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='alpha', step=10), Output()), _dom_classes=('widget-inter…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "@interact(alpha=(0, 100, 10))\n", "def show_tf(alpha=0):\n", @@ -384,7 +561,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Try changing the filter width to see what happens.\n", "\n", @@ -393,18 +574,24 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, "source": [ - "---\n", - "\n", "## Wavelets" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Multiresolution Analysis\n", + "### Multiresolution Analysis\n", "\n", "One way we can improve upon the Gabor method is using *Multiresolution Analysis (MRA)*. Rather than choosing a fixed window size that will be more localised in either the time or frequency domain, we can instead do the following:\n", "\n", @@ -416,26 +603,43 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Wavelet Types\n", + "### Wavelet Types\n", "\n", "The wavelet function is often represented as follows:\n", "\n", "$$\\psi_{a,b}(t) = \\frac{1}{\\sqrt{a}} \\psi \\Bigg(\\frac{t-b}{a} \\Bigg)$$\n", "\n", - "where $a$ defines the wavelet scale and $b$ the shift. $\\psi$ is known as the *Mother Wavelet* which must posses certain properties (see *e.g.* [Wikipedia](https://en.wikipedia.org/wiki/Wavelet#Mother_wavelet) for more details). Many different wavelets exist, but we will only discuss a few basic ones.\n", - "\n", + "where $a$ defines the wavelet scale and $b$ the shift. $\\psi$ is known as the *Mother Wavelet* which must posses certain properties (see *e.g.* [Wikipedia](https://en.wikipedia.org/wiki/Wavelet#Mother_wavelet) for more details). Many different wavelets exist, but we will only discuss a few basic ones.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "A couple of essential properties we want any given wavelet to have are the following:\n", "\n", "* $\\int_{-\\infty}^\\infty\\psi(t)dt = 0$ (*i.e.* equal positive and negative area under the curve)\n", "* $\\|\\psi(t)\\|_2^2 = 1$\n", - "* Compact support (*i.e.* signal does not last forever)\n" + "* Compact support (*i.e.* signal does not last forever)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "**Haar Wavelet**\n", "\n", @@ -446,8 +650,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "# Function to define Haar wavelet.\n", @@ -462,7 +670,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "**Mexican Hat Wavelet**\n", "\n", @@ -473,9 +685,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { - "scrolled": false + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } }, "outputs": [], "source": [ @@ -483,24 +698,54 @@ "def mex_hat_wt(t, sigma=1.0):\n", "\n", " return (2.0 * np.pi ** -0.25 * (3.0 * sigma) ** -0.5 *\n", - " (1 - (t / sigma) ** 2) * np.exp(-t ** 2 / (2.0 * sigma ** 2)))\n", - "\n", + " (1 - (t / sigma) ** 2) * np.exp(-t ** 2 / (2.0 * sigma ** 2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Display\n", "plot.wavelet_plot(n, Fn, haar_wt(2 * n / np.max(n) - 0.5), mex_hat_wt(n / np.max(n) - 0.5, 0.1))" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "In general one aims to use the wavelet most suited to the problem at hand." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "### Continuous Wavelet Transform\n", + "### Continuous Wavelet Transform\n", "\n", "The Continuous Wavelet Transform (CWT) is used to decompose a signal into wavelets and can be defined as follows:\n", "\n", @@ -511,8 +756,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "outputs": [], "source": [ "# Function to get wavelet scales\n", @@ -520,8 +769,19 @@ " \n", " J = 12.0 * np.log2(a_max / a_min)\n", " del_a = (a_max - a_min) / (J - 1)\n", - " return a_min + del_a * np.arange(J)\n", - "\n", + " return a_min + del_a * np.arange(J)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ "# Function to perform continuous wavelet transform\n", "def cwt(data, wavelet, scales):\n", " \n", @@ -531,8 +791,30 @@ " for scale in scales:\n", " w_ft = fft(wavelet(t / scale) * scale ** -0.5)\n", " out.append(ifft(w_ft * x_ft))\n", - " return np.abs(out)\n", - "\n", + " return np.abs(out)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Set wavelet scales\n", "scales = get_scales(1, 100)\n", "\n", @@ -545,11 +827,24 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ - "As you can see we are able to preserve detailed information regarding the scale and translation of the signal.\n", - "\n", - "### Discrete Wavelet Transform\n", + "As you can see we are able to preserve detailed information regarding the scale and translation of the signal." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Discrete Wavelet Transform\n", "\n", "The Discrete Wavelet Transform (DWT) uses a discrete set of wavelet scales and translations that removes certain redundancies introduced in the CWT.\n", "\n", @@ -558,15 +853,33 @@ "\n", "This corresponds to consecutively convolving the signal with high and low pass filters with a downsampling at each level. \n", "\n", - "\n", - "\n", - "### Undecimated Wavelet Transform\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Undecimated Wavelet Transform\n", "\n", "The Undecimated Wavelet Transform (UWT) (also called the Stationary Wavelet Transform) removes the downsampling factor to compensate for the translation-invariance in the DWT at the cost of added redundancy. \n", "\n", - "\n", - "\n", - "### Starlet Transform\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Starlet Transform\n", "\n", "Other specialised wavelet transforms exist such as the **Starlet** (or Isotropic Undecimated Wavelet Transform) that\n", "is particularly well suited to astrophysical images, which are generally isotropic (read more here).\n", @@ -575,16 +888,40 @@ "\n", "$$\\mathbf{x} = \\mathbf{x}^{J} + \\sum_{j=1}^J \\boldsymbol{w}^{j}$$\n", "\n", - "where the first level ($j = 1$) corresponds to the highest frequencies (*i.e.* the finest scale).\n", - "\n", + "where the first level ($j = 1$) corresponds to the highest frequencies (*i.e.* the finest scale)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "Have a look at the following starlet decomposition of Venus." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Load starlet decomposition of Venus.\n", "venus_scales = np.load('data/venus.npy')\n", @@ -595,18 +932,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "Reconstruct the image of venus from the decomposition.\n", "\n", - "
\n", - "EXERCISE CELL" + "### EXERCISE" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "outputs": [], "source": [ "##############################\n", @@ -623,28 +967,37 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "source": [ "This makes the starlet a useful transform for obtaining sparse representations of astronomical images." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, "source": [ "---\n", "\n", "## Hints\n", "\n", - "** Starlet Transform **\n", + "**Starlet Transform**\n", "\n", "1. You have just about reached the *sum(m)it* of this tutorial." ] } ], "metadata": { + "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -658,7 +1011,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.10.8" } }, "nbformat": 4, From 36dc27415dcd55dd2c0f525887c0041c18b3dbfc Mon Sep 17 00:00:00 2001 From: sfarrens Date: Mon, 9 Jan 2023 09:09:36 +0100 Subject: [PATCH 07/13] updated tutorial --- README.md | 20 +-- environment.yml | 10 +- exercise_solutions.ipynb | 10 +- images/cosmostat_logo.png | Bin 0 -> 19654 bytes inverse_problems_1.ipynb | 3 +- sparsity_1.ipynb | 36 +++-- sparsity_2_compressed_sensing.ipynb | 182 ++++++++------------- sparsity_3_deconvolution.ipynb | 203 +++++------------------ wavelets_1.ipynb | 240 ++++++++-------------------- 9 files changed, 220 insertions(+), 484 deletions(-) create mode 100644 images/cosmostat_logo.png diff --git a/README.md b/README.md index 231faac..76f4d8f 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ --- > Author: [Samuel Farrens](http://www.cosmostat.org/people/sfarrens) -> Year: 2018 +> Year: 2018 (updated in 2023) > Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/CosmoStat/Tutorials/ada) @@ -13,23 +13,19 @@ in 2018. The objective is to provide a beginner level introduction to the concep This tutorial is comprised of a series of Jupyter notebooks that demonstrate how the tools implemented in sparsity work as well as showing the applicability of these tools to various simple problems. -All code blocks are provided in Python (support for both 2.7 and 3.6+) and the number of external packages required to run the examples has be kept to a minimum. With the exception of plotting routines, all of the code has been clearly presented inside the notebooks at least once to avoid the use of any "black boxes" for solving the problems presented. +All code blocks are provided in Python and the number of external packages required to run the examples has be kept to a minimum. With the exception of plotting routines, all of the code has been clearly presented inside the notebooks at least once to avoid the use of any "black boxes" for solving the problems presented. ### Requirements In order to run the tutorial notebooks tutees will need to have the following installed: -* Python (2.7 or 3.6+, users are recommended to use Python 3.6) -* NumPy (recommend v1.14.1+) -* SciPy (recommend v1.0.0+) -* Matplotlib (recommend v2.1.0+) -* Jupyter (recommend v1.0.0+) +* Python +* NumPy (recommend v1.24+) +* SciPy (recommend v1.10+) +* Matplotlib (recommend v3.6+) +* Jupyter (recommend v1.0+) -Python 2.7 users should additonally install: - -* Future (recommend v0.16.0+) - -All of the packages listed above can easily be installed using either `pip` or Anaconda. +All of the packages listed above can easily be installed using either `pip` or Conda. *e.g.* diff --git a/environment.yml b/environment.yml index 9efe03d..1e0ef2c 100644 --- a/environment.yml +++ b/environment.yml @@ -1,15 +1,11 @@ -name: cstutorials +name: sparsity channels: - conda-forge - defaults dependencies: - - python>=3.5 - - astropy>=3.0 + - python + - astropy - jupyter - matplotlib - numpy - - pandas - - scikit-learn - scipy - - seaborn - - voila diff --git a/exercise_solutions.ipynb b/exercise_solutions.ipynb index acc1b31..54301c2 100644 --- a/exercise_solutions.ipynb +++ b/exercise_solutions.ipynb @@ -4,6 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "
\n", + "\n", "# Exercise Solutions\n", "\n", "---\n", @@ -27,7 +29,9 @@ " * [1D Example](#1D-Example)\n", " * [2D Exercise](#2D-Exercise)\n", "1. [Introduction to Sparsity III: Deconvolution](#Introduction-to-Sparsity-II:-Deconvolution)\n", - " * [Deconvolution Exercise](#Deconvolution-Exercise)" + " * [Deconvolution Exercise](#Deconvolution-Exercise)\n", + "1. [Introduction to Wavelets](#Introduction-to-Wavelets)\n", + " * [Starlet Transform](#Starlet-Transform)" ] }, { @@ -124,7 +128,7 @@ "The gradient and optimisation problem are the same as for the 1D example.\n", "\n", "```Python\n", - "sample_percentage = mask2d[mask2d == 1].size / float(mask2d.size) * 100)\n", + "sample_percentage = mask2d[mask2d == 1].size / float(mask2d.size) * 100\n", "```\n", "The percentage of samples used is: 5.0%\n", "\n", @@ -220,7 +224,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.11" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/images/cosmostat_logo.png b/images/cosmostat_logo.png new file mode 100644 index 0000000000000000000000000000000000000000..e9de5e22c4bc81821b6dfd4653bfd53bcb07678c GIT binary patch literal 19654 zcmeIa^;gu}`#wB`fPpwjC@mH|XsTMj!ow2n0d{kro$Ib)DIsR|wP5NM<~?rI;N)#b4jt7$j$S zm%TB4Nb_Wv=<_1}f+Ufs0ZeILEum0*r9@`*8skMtg=%mk>u2U?DT2mrgcW4ILHTkV 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\n", + "\n", "# Introduction to Sparsity I\n", "\n", "---\n", @@ -169,7 +171,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ @@ -332,7 +334,7 @@ "\n", "$$x = \\phi\\alpha = \\sum_{i=1}^n\\phi_i\\alpha_i$$\n", "\n", - "where $\\alpha$ are the sparse coefficients of $x$ in the dictionary $\\phi$ and $n$ is the sparsity of the signal. Each $\\phi_i$ corresponds to an *atom* of the dictionay $\\phi$. In the case of Fourier decomposition the atoms are sinusoids and when $\\phi$ is a matrix the atoms are the columns of the matrix." + "where $\\alpha$ are the sparse coefficients of $x$ in the dictionary $\\phi$ and $n$ is the sparsity of the signal. Each $\\phi_i$ corresponds to an *atom* of the dictionay $\\phi$. In the case of Fourier decomposition, the atoms are sinusoids and, when $\\phi$ is a matrix, the atoms are the columns of the matrix." ] }, { @@ -507,7 +509,7 @@ "execution_count": null, "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "skip" } }, "outputs": [], @@ -786,7 +788,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "fragment" } }, "source": [ @@ -798,14 +800,27 @@ "execution_count": null, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "outputs": [], "source": [ - "# Plot the signals\n", + "# Plot the x signal in the sparse domain\n", "if x_sparse is not None:\n", - " plot.line_plot(np.abs(x_sparse), title='True Signal in Sparse Domain')\n", + " plot.line_plot(np.abs(x_sparse), title='True Signal in Sparse Domain')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# Plot the y signal in the sparse domain\n", "if y_sparse is not None:\n", " plot.line_plot(np.abs(y_sparse), title='Noisy Observation in Sparse Domain')" ] @@ -814,7 +829,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ @@ -889,7 +904,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ @@ -930,7 +945,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ @@ -972,7 +987,6 @@ } ], "metadata": { - "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", diff --git a/sparsity_2_compressed_sensing.ipynb b/sparsity_2_compressed_sensing.ipynb index b60d898..cc9be51 100644 --- a/sparsity_2_compressed_sensing.ipynb +++ b/sparsity_2_compressed_sensing.ipynb @@ -4,6 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "
\n", + "\n", "# Introduction to Sparsity II: Compressed Sensing\n", "\n", "---\n", @@ -62,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -139,28 +141,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ca345eb582874b3ea665f2a24d200e56", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=9, description='sampling_rate', max=42, min=2), IntSlider(value=10, desc…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", @@ -216,24 +203,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# full sample size\n", "n_samples = 128\n", @@ -278,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" @@ -294,7 +270,7 @@ "# normalised inverse FFT\n", "def infft(data):\n", "\n", - " return np.sqrt(data.size) * np.fft.fftshift(np.fft.ifft(np.fft.ifftshift(data)))\n" + " return np.sqrt(data.size) * np.fft.fftshift(np.fft.ifft(np.fft.ifftshift(data)))" ] }, { @@ -305,14 +281,14 @@ } }, "source": [ - "Use the functions above to generate the singal $x$.\n", + "### EXERCISE\n", "\n", - "### EXERCISE" + "Use the functions above to generate the singal $x$." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -347,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -371,14 +347,14 @@ "source": [ "For this example we will keep only a quarter of the coefficients in $x$.\n", "\n", - "Define a mask that can be used with above function that will keep only $25\\%$ of the coefficients.\n", + "### EXERCISE \n", "\n", - "### EXERCISE " + "Define a mask that can be used with above function that will keep only $25\\%$ of the coefficients." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -407,16 +383,25 @@ "source": [ "Now, we can model $y$ as \n", "\n", - "$$y = H\\phi\\alpha$$\n", - "\n", - "Use the mask you defined to generate the observation $y$.\n", + "$$y = H\\phi\\alpha$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### EXERCISE\n", "\n", - "### EXERCISE" + "Use the mask you defined to generate the observation $y$." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { @@ -452,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -481,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -509,12 +494,12 @@ "\n", "$$\\begin{aligned} & \\underset{\\alpha}{\\text{argmin}} & \\frac{1}{2}\\|y-H\\phi\\alpha\\|_2^2 + \\lambda\\|\\alpha\\|_1\\end{aligned}$$\n", "\n", - "where we implement sparse regularisation given that we know a priori that $\\alpha$ is sparse. We can implement $l_1$ regularisation via soft thresholding of the sparse coefficients. For this example we also have to manage the complex coefficients of the signal, which we do using the following function." + "where we implement sparse regularisation given that we know a priori that $\\alpha$ is sparse. We can implement $l_1$ regularisation via soft thresholding of the sparse coefficients. For this example, we also have to manage the complex coefficients of the signal, which we do using the following function." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" @@ -542,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -576,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" @@ -590,7 +575,7 @@ " alpha_rec = first_guess\n", " cost = []\n", " \n", - " for i in range(n_iter):\n", + " for _ in range(n_iter):\n", " \n", " alpha_temp = alpha_rec - gamma * grad(observation, alpha_rec, mask)\n", " alpha_rec = soft_thresh(alpha_temp, lambda_val)\n", @@ -614,16 +599,25 @@ "\n", "The gradient of the above problem is given by\n", "\n", - "$$\\nabla F(\\alpha) = \\phi^{-1}H^T(H\\phi\\alpha-y)$$\n", - "\n", - "write a funciton that implements the gradient calculation.\n", + "$$\\nabla F(\\alpha) = \\phi^{-1}H^T(H\\phi\\alpha-y)$$" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### EXERCISE\n", "\n", - "### EXERCISE" + "Write a funciton that implements the gradient calculation." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { @@ -669,29 +663,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "af50446bb5e44b7fa908e8b5691e1fcb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(Output(),), _dom_classes=('widget-interact',))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "######################################\n", "# YOU SHOULD INTERACT WITH THIS CELL #\n", @@ -699,8 +678,8 @@ "\n", "# Interactive function to plot the recovered alpha after a given number of iterations\n", "@interact(n_iter=IntSlider(value=1, min=1, max=361, step=40))\n", - "def solve():\n", - " \n", + "def solve(n_iter):\n", + " \n", " if grad is not None:\n", " alpha_rec, cost = forwardBackward(observation=y, first_guess=np.ones(n_samples), mask=mask, grad=grad,\n", " lambda_val=0.01, n_iter=n_iter, gamma=1.0, return_cost=True)\n", @@ -713,7 +692,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ @@ -735,11 +714,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "outputs": [], @@ -770,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -794,14 +773,14 @@ "source": [ "We can use this function to test solutions to our minimisation problem.\n", "\n", - "Calculate the NMSE of $\\hat{\\alpha}$ after 10 and 200 iterations.\n", + "### EXERCISE\n", "\n", - "### EXERCISE" + "Calculate the NMSE of $\\hat{\\alpha}$ after 10 and 200 iterations." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -828,7 +807,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ @@ -903,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -923,24 +902,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Display the mask H\n", "plot.display(mask2d, 'Mask', shape=image_shape, cmap='bone')" @@ -948,24 +916,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Display the observation y\n", "plot.display(y2d, r'$y$', shape=(25, 20))" @@ -1007,7 +964,7 @@ "source": [ "---\n", "\n", - "## Hints\n", + "## Hints\n", "\n", "**1D Example Hints**\n", "\n", @@ -1037,7 +994,6 @@ } ], "metadata": { - "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", diff --git a/sparsity_3_deconvolution.ipynb b/sparsity_3_deconvolution.ipynb index d0c1a98..0c3f678 100644 --- a/sparsity_3_deconvolution.ipynb +++ b/sparsity_3_deconvolution.ipynb @@ -8,6 +8,8 @@ } }, "source": [ + "
\n", + "\n", "# Introduction to Sparsity III: Deconvolution\n", "\n", "---\n", @@ -60,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -125,30 +127,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Function to generate a Gaussian PSF.\n", "def make_psf(shape, sigma):\n", " \n", " psf = np.zeros(shape)\n", - " psf[list(zip(np.array(psf.shape) // 2))] = 1\n", + " psf[tuple(np.array(psf.shape) // 2)] = 1\n", " \n", " return gaussian_filter(psf, sigma)\n", "\n", @@ -176,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -206,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -226,24 +217,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Display the original image\n", "plot.display(image, cmap='Greys_r', title='Original Image')" @@ -251,24 +231,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Display the convolved image\n", "plot.display(image_conv, cmap='Greys_r', title='Convolved Image')" @@ -293,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -323,34 +292,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/1_/d9gpvb451qlgcfrf4s5fbjth0000gq/T/ipykernel_95697/150898546.py:7: RuntimeWarning: divide by zero encountered in divide\n", - " return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x / y))))\n", - "/var/folders/1_/d9gpvb451qlgcfrf4s5fbjth0000gq/T/ipykernel_95697/150898546.py:7: RuntimeWarning: invalid value encountered in divide\n", - " return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x / y))))\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "image_deconv = fftdeconvolve(image_conv, psf_1)\n", "\n", @@ -384,24 +332,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Add Gaussian noise to convolved image\n", "image_conv_noise = image_conv + np.random.randn(*image_conv.shape) * 1e-6\n", @@ -436,32 +373,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/1_/d9gpvb451qlgcfrf4s5fbjth0000gq/T/ipykernel_95697/150898546.py:7: RuntimeWarning: divide by zero encountered in divide\n", - " return np.real(np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(x / y))))\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "image_deconv_noise = fftdeconvolve(image_conv_noise, psf_1)\n", "\n", @@ -522,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" @@ -552,29 +470,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x1 = [2. 1.]\n", - "x2 = [1. 1.]\n" - ] - } - ], + "outputs": [], "source": [ "# Solution to the first problem\n", - "x1 = np.dot(np.linalg.inv(H1), y1)\n", + "x1 = np.linalg.inv(H1) @ y1\n", "print('x1 =', x1)\n", "\n", "# Solution to the second problem\n", - "x2 = np.dot(np.linalg.inv(H2), y2)\n", + "x2 = np.linalg.inv(H2) @ y2\n", "print('x2 =', x2)" ] }, @@ -595,31 +504,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x1 = [2.02 0.99]\n", - "x2 = [0.33333333 1.33333333]\n" - ] - } - ], + "outputs": [], "source": [ "# Noisy versions of y\n", "y1_noise = np.array([4.0, 7.01])\n", "y2_noise = np.array([3.0, 1.48])\n", "\n", - "x1_noise = np.dot(np.linalg.inv(H1), y1_noise)\n", + "x1_noise = np.linalg.inv(H1) @ y1_noise\n", "print('x1 =', x1_noise)\n", "\n", - "x2_noise = np.dot(np.linalg.inv(H2), y2_noise)\n", + "x2_noise = np.linalg.inv(H2) @ y2_noise\n", "print('x2 =', x2_noise)" ] }, @@ -633,7 +533,7 @@ "source": [ "Notice that for the first problem the solution has changed only as much as the input data, while for the second problem the solution is completely different. \n", "\n", - "It is clear from these results that the first problem is well-conditioned well the second is ill-conditioned. This means that the first problem can be solved analytically, even with noise, well the second cannot." + "It is clear from these results that the first problem is well-conditioned well the second is ill-conditioned. This means that the first problem can be solved analytically, even with noise, while the second cannot." ] }, { @@ -721,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -738,24 +638,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Display the observed image.\n", "plot.display(y, r'$y$')" @@ -763,24 +652,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Display the PSF.\n", "plot.display(psf, 'PSF')" @@ -801,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -822,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" @@ -836,7 +714,7 @@ " alpha_rec = first_guess\n", " cost = []\n", " \n", - " for i in range(n_iter):\n", + " for _ in range(n_iter):\n", " \n", " alpha_temp = alpha_rec - gamma * grad(observation, alpha_rec, psf)\n", " alpha_rec = soft_thresh(alpha_temp, lambda_val)\n", @@ -884,7 +762,7 @@ "source": [ "---\n", "\n", - "## Hints\n", + "## Hints\n", "\n", "**Deconvolution Exercise**\n", "\n", @@ -906,7 +784,6 @@ } ], "metadata": { - "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", diff --git a/wavelets_1.ipynb b/wavelets_1.ipynb index 943a176..a5219e6 100644 --- a/wavelets_1.ipynb +++ b/wavelets_1.ipynb @@ -8,6 +8,8 @@ } }, "source": [ + "
\n", + "\n", "# Introduction to Wavelets\n", "\n", "---\n", @@ -64,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -123,24 +125,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Set a range of t values\n", "t = np.arange(0, 1, 0.001)\n", @@ -166,24 +157,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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1r37Xv3TpUp09e1aS1Lt3b6/lWrZsqZYtWxaqzi5dunhd38qVK6tx48aSzl1YVJAzZ85o37592r59u9OP+/fvV40aNSSV7Gv8QgQHB+t//ud/vE7/5z//KUmqUKGCunfv7rOum266SdK599zevXud8V9//bXzt6+7bdxzzz0F3inhQv3yyy/asGGDpHP7A1+aNWummjVrSpJWr17ttVxh9wfnv44WLVrk3CVi2LBhvhvup6+++sq5AKyg9czttzNnzjjbRjp3v17p3D4q93WASx+3WkKxu/3223X77bfrt99+08qVK5WUlKQNGzZo+fLlOnbsmCQpKSlJHTt21IYNG5ydT1GtXLlSkydP1tdff61ff/3Va7mCru6+EFu2bHH+bteunc+yeadv2bJF7du391iuadOmXuuoXr2683dGRobHMps3b9bf//53ffHFFx6fJJXr7Nmz+u2337wGpBYtWnidN69+/fpp5MiR+vbbb7V7925FRUU50xYuXKgjR45Ikvr27Vuo+rypUaOG/vd//1fjx4/X5s2btXr1aqWkpGjlypXavHmzpHN3CXj66ad18uTJAq9EzitvP+b9QPckNja2UGHPVz9K/+1Lb/145swZTZs2Te+//76Sk5N93i2hJF/juZYsWZLvLhAFady4sUJDQ71OT0pKkiSdPHlSgYGF/1g6cOCAIiMjJcnp++DgYJ+v2aCgIF133XUl+qCL3PWRpAcffFAPPvhgoebz9T4t6v4gOTlZ0rn1vv766wvVjsLKu57+7Mfzruddd92lqlWr6ujRo7rnnnvUuXNn9ejRQzfddJNatWqlgICAYm0zLg4c+USJqVatmu68806NHTtW//rXv3Tw4EHNmDFD1apVkySlpqbqueeeu6BljB07VjfeeKM++eQTn8FTkk6dOnVBy/Il77LDw8N9lq1Tp47H+c7n6wbpeY8q5+Tk5Js+ffp0tW7dWjNnzvT5gZbL17bJ7a+C9O7dW0FBQTLG6L333nObNmPGDEnnPqC6detWqPoK4nK51KJFCw0YMEBTp07Vd999p++//16///3vnTLjx4/36znjv/32m/N3QUdnc4+oF6SgG93n9qWnfvz111/Vvn17DR48WGvXrvUZPKWSfY1fiIJeQ2lpaUWqN+/thXL7rnr16gUG2ILeoxeqONbnfEXdH+R+IalevbpCQkKK1C5vimM9a9SooYULF6pevXoyxmjJkiUaPny4YmNjVb16dd13333OPaRx6eDIJ6wJCQnRo48+qoiICN1+++2SpPnz52vatGn5TtEXxjfffKNx48ZJkho1aqQnn3xSN954oxo0aKBKlSo535iff/55vfjii8W3IgXwdt/HXLmnwErKf/7zH8XFxSk7O1u1a9fWU089pVtuuUUNGzZU5cqVFRQUJOlcIOzXr1+BbSrskYfatWvrrrvu0meffaZZs2bpueeek8vl0v79+7V48WJJUq9evfw6suWvJk2aaP78+brpppu0cuVKZWdna8GCBcV+utGWJ554wjlFeffdd6tv375q0aKFateurdDQUOe11qBBA+3du/eifWxoQa+h3MAUHR2thQsXFrre6Oho5+/cdS/o/Ze3bEnJGwDnzJlT6LMHhf2iVxSF2S7+yl3P4OBgt1PpBalfv77b/x07dtSPP/6ozz77TIsWLdKyZcu0b98+paena/78+Zo/f75uu+02zZ8/n6eWXSIIn7DutttuU2RkpPbu3avffvtNR44cKfRRpLzeeecdSVLVqlW1evVqr0eq8h7NKil5T3sVdKTx4MGDHucrLrNmzVJ2drYCAgK0dOlSrzd/L4nt0r9/f3322WfatWuXEhMT1blzZ82ePdv5kLrQU+6FUa5cOfXt21crV66UJP3444+Fnjfvh39aWlq+D8m8Dh06VPRGFkJ6errmzp0rSXrooYc0Z84cr2VtvMZLUu5vVg8ePKimTZsW6QtK7nvpyJEjysnJ8Rl4fR2xy/tFOPf3v56cOHHC67Tc9ZHOhb7SfBxp7u9Jjxw5oqysLAUHBxdb3bnrmZWVpRo1alzQT6hCQ0P18MMPO79t3blzp/79739rypQp+uGHH/TVV19p9OjR+vvf/14sbUfp4rQ7SkXei0XOP+pZ2G/oW7dulSTdcsstPk+R5v1dkifFcUQg74fL2rVrfZZdt26dx/mKS+52admypc+nDhW0XYqia9euatCggaT/Xng0a9YsSdINN9ygJk2aFPsyPfH1+vLlmmuucf4uaPuUxPbLa8eOHc6FYA888IDXct9//73Xp3iVFdddd52kc6djc780+Ovaa6+VdC4I+fotbnZ2tlJSUrxOz3uxoK9Q//3333udlrs+kpyj/qWldevWks79dtjXBU1FUZLr2ahRIw0ZMkTr1693vgR+8sknxboMlB7CJ6w7efKktm3bJkmqUqVKvqN/uRcmFPQotdzHwPn6nVRKSorWrFnjs57CLs+XiIgIJ+jNmzfP68UjOTk5ThirVq2a88FQnAqzXQ4cOFAiV5bmHnWUpE8//VRffPGF8yGde4q/qPw5VZo3GOY9NVuQm2++2Qmrs2fP9lpu06ZNJX5led7HHPrqy7feeqtE22FD3t/pFuUuBZL0u9/9zvn7/N8c57VgwQKfoTLv68XXF4zcuzd4cuWVV+rqq6+WJH388cfas2eP17IlrXv37s4X7OI+atitWzfnZzx///vffT7yt6iqVKmiNm3aSLJzQR3sIHyiWBw/flzt2rXT559/7vNU1dmzZzVkyBAnnN111135jjzmnrrZuXOnz8CRe4uaFStWeLxNzaFDh/THP/6xwLbnLu9Cb/00aNAgZ7lDhgzx2PZx48Y5wfuxxx4r9gsApP9ulx9++MFj8D558qQeeuihErs4pW/fvipXrpxOnjzpBNFKlSr5vNVOYYwbN04jR47U/v37fZbbtGmTXnvtNUnnwnCPHj0KvYx69eo5t/pZsGCBPv3003xlTp065fdz6YviyiuvdN4b3oLw559/rjfeeKPE21LS2rRpo65du0o6d2ugMWPG+Cz/888/66OPPnIb17ZtW+fL3NSpU7VixYp886WmpurJJ5/0WXfVqlWd32jOnDnT40WBy5Yt0+TJk33W85e//EXSuTsv3HvvvT5/ppGZman4+PhCPbfeX02aNNE999wj6dwtrV599VWvZU+cOOHXTzjq1avn3NZq06ZNGjBggM8AmpaWpnfffddt3FdffaXU1FSv8xw7dsw5W+TPF0lc5Erp/qK4xGRkZDg3Iq5Xr54ZNGiQ+eCDD8zy5ctNSkqKWbp0qfn73/9urr32WqdcWFiYxydmvPPOO06ZoUOHmqSkJLNjxw6zY8cOt0ezzZs3zylXv35988Ybb5hVq1aZlStXmldffdXUrVvXuFwut8fPeTJ69Ghn+oQJE0xKSoqzvPNvVp5brjCP1+zcubOZN2+e2bBhg/n888/Nvffe60wrzOM1ffF1I+x169Y506pVq2YmTJhgEhMTzdq1a018fLxp3LixkeT2LGZP/eDPzffPd/vtt7vdeLxv375+13G+ESNGGOncIy+7dOliXn75ZfPll1+aDRs2mKSkJPPZZ5+ZP/3pTyYkJMRZ7hNPPJGvHn8frzl48GDz7bffmqSkJDNr1izn8Zpt2rQp1E3mvT16MZevhyp0797dqee2224z8+fPN0lJSWbRokWmX79+JiAgwDRu3NjUqlXLa19dDI/XLMwDI3755RfnKTiSTLt27czbb79tVq1aZTZu3GgSEhLM3/72N9OlSxcTEBBg7rvvvnx15H28ZmhoqBk1apRZvny5WbdunXnjjTdM3bp1C3y8pjHuN22/+uqrzUcffeQ81nTo0KEmJCTE7b3urY9znyEvnXv4xOjRo83ixYtNcnKyWbFihXnvvfdM//79TfXq1Y2kfPuEwjxX3Zhzz23PLTdz5sx80z09XnP27NnOoz7nzZtnBg0aZGrUqOH34zUzMjKcJyzlbq9JkyaZ5cuXm+TkZLNkyRIzZcoUc/fdd5vg4GATExOTbxsFBQWZO+64w0yaNMl5hGxiYqJ588033R4nPGnSJK/bAGUL4RPF4tSpU26PPixoaNy4sUlKSvJYV0ZGhmnUqJHH+c7/sHj00Ue9LiMgIMBMmjSpwEC3b98+Z+d//nD+h2ZBHzZHjhxxC3WehmbNmuV7vnGu4gifxhgzbtw4n20YMWJEgR8qFxI+8z5aUJJZuXKl33Wc77XXXnOe5FPQUK5cOTNs2DCTk5OTr57ChLHFixc7j3H0NIwZM8Y899xzTsjxpDjC5549e0yDBg28tqNBgwZm69atPvuqrIRPY849hjFvqPc1PProox7r+PDDD52nRp0/BAYGmnfeeccJhd7CZ05OjtsjKc8fmjdvbvbv319gH2dnZ5uRI0cW6nVbsWLFfI+PLK7waYwxP/30k1tI9Db4Gz6NObffO/8Lp7fh5ptvdps3b0D3NQwaNMjj+xllE6fdUSxCQ0P1yy+/aOXKlRo3bpy6deumRo0aqWLFigoICFCVKlXUtGlT9ezZUx9++KG2bNni9SbelSpV0qpVq/TEE0+oWbNmPm+tMWPGDL3//vvq2LGjKleurJCQEEVFRemRRx5x6ihIvXr1tG7dOvXr109XXnmlz5thF6R69epatmyZ3n//fd1+++0KDw9XUFCQatSooc6dO2vKlClKSUlxuwF7SXj++ef173//W127dlW1atUUHBys+vXr695779XixYud09Il5a677lJYWJgk6aqrrlKHDh0uuM4RI0YoNTVV7733nvr166fY2FjVqFFDgYGBCgkJUXh4uG666SY9++yz2rZtmyZOnFikW3hJ555KtGXLFg0YMEBRUVEKDg5WeHi4unfvri+//FJjx45Venq6JDnrWRIiIyO1ceNGPfXUU2rSpIlCQkIUFhamli1basyYMUpJSXF+W3gpiIqK0tq1a7VgwQI98MADio6OVoUKFRQUFKRatWqpQ4cOGjFihBITEzV9+nSPdTz44INKTk7WI488ooiICAUHB6tevXq6//77tWLFCvXv37/AdpQrV06ffvqp3nzzTbVp00YVK1ZUxYoV1aJFC40fP15r164t1JXdAQEBeuWVV7Rt2zaNGDFC1113napVq6aAgABVrlxZ11xzjR5++GG99957Sk1NVfny5f3eZoXVqFEjpaSkaNasWerevbvq1q2r4OBg1axZUy1bttRjjz2mr7/+2nkSkT+qV6+uL774Qt98840effRRNW7cWJUqVVJgYKCqV6+uNm3aaNCgQVq0aJESEhLc5p00aZI+++wzxcXFKTY2VvXq1VNwcLDKly+vJk2aqE+fPlqxYoWmTJlS5PczLj4uYy7SG8MBKLN+/PFH57enr7zyikaOHFnKLSp+v/vd7/TNN9/oxhtv1PLly0u7OfBDnz599N577ykqKsqvhxAAKB58jQBQ7HJvsxQYGKhevXqVcmuK3/79+7Vs2TJJKvZHFgLApY7wCaBYpaena9q0aZLOPZUn7+NEywpfN6Y/deqU+vTp49yD81IM1wBQknjCEYALlpaWpvT0dKWmpmrcuHE6fPiwXC6Xnn766dJuWpH0799fJ06c0P3336+YmBhVr15dGRkZSkpKUnx8vBNO+/Xr59zcHABQOIRPABds5MiR+W7s/ec//1mxsbGl1KILl5SU5PMm4/fcc88lcY9NALCN8Amg2AQHB+uKK67QY489piFDhpR2c4ps4sSJWrBggb799lvt27dPhw4dkjFGtWvX1vXXX69evXo5N6MHAPiHq90BAABgTZk48nn27Fnt379flStXzvcoRgAAAJQ+Y4wyMjIUERHh876sZSJ87t+/X5GRkaXdDAAAABRg7969ql+/vtfpZSJ8Vq5cWdK5lalSpUoptwYAAADnS09PV2RkpJPbvCkT4TP3VHuVKlUInwAAABexgn4iyU3mAQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWBJZ2A1A6cozR2mNndfCMUXiQS+3CyinA5SrtZhWry2EdLxf0JQBcOvw+8rls2TL16NFDERERcrlc+sc//lHgPImJiYqJiVFoaKgaNWqkt956qyhtRTFZdDhbbdef1h+2ZGrQ91n6w5ZMtV1/WosOZ5d204rN5bCOlwv6EgAuLX6HzxMnTqhly5aaMmVKocrv2rVLd9xxhzp27Kjk5GQ9++yzevzxx/XZZ5/53VhcuEWHs/XYf7KUmmXcxh/IMnrsP1mXxAf65bCOlwv6EgAuPS5jjCm4mJeZXS4tWLBAd999t9cyTz/9tBYuXKjt27c74+Li4rRp0yatXr26UMtJT09XWFiYjh07pipVqhS1uZe9HGPUdv3pfB/kuVyS6ga7tLZNaJk9pXk5rOPlgr4EgLKlsHmtxC84Wr16tbp27eo27rbbblNSUpLOnDnjcZ7MzEylp6e7Dbhwa4+d9fpBLklG0v6sc7+tK6suh3W8XNCXAHBpKvHweeDAAYWHh7uNCw8PV3Z2tg4fPuxxngkTJigsLMwZIiMjS7qZl4WDZwp3kLuw5S5Gl8M6Xi7oSwC4NFm51ZLrvFNiuWf6zx+fa9SoUTp27Jgz7N27t8TbeDkIDyrcqcnClrsYXQ7reLmgLwHg0lTit1qqU6eODhw44DYuLS1NgYGBqlGjhsd5QkJCFBISUtJNu+y0CyunusEuHcgy8nSsKPc3dO3Cyu7tXy+Hdbxc0JcAcGkq8b12+/btlZCQ4DZu8eLFio2NVVBQUEkvHnkEuFx6sdG5bX7+saLc/19oFFSmL964HNbxckFfAsClye/wefz4caWkpCglJUXSuVsppaSkaM+ePZLOnTLv1auXUz4uLk67d+/W8OHDtX37ds2YMUPTp0/Xk08+WTxrAL/cUTNQ7zQNVp1g9w/susEuvdM0WHfULPvPHbgc1vFyQV8CwKXH71stLV26VDfffHO+8b1799asWbPUp08f/fzzz1q6dKkzLTExUcOGDdPWrVsVERGhp59+WnFxcYVeJrdaKn65T4xZkLhS93S64ZJ8YszlsI6XC/oSAC5+hc1rfh826Ny5s3zl1VmzZuUb16lTJ23cuNHfRaEEBbhc6lA1QLtP/qIOVQNKuzkl4nJYx8sFfQkAlw5+qQ8AAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrihQ+4+PjFR0drdDQUMXExGj58uU+y8+ZM0ctW7ZUhQoVVLduXT366KM6cuRIkRoMAACAssvv8Dl37lwNHTpUo0ePVnJysjp27Khu3bppz549HsuvWLFCvXr1Ur9+/bR161bNmzdP69evV//+/S+48QAAAChb/A6fEydOVL9+/dS/f381a9ZMkyZNUmRkpKZOneqx/Jo1a9SwYUM9/vjjio6O1o033qgBAwYoKSnpghsPAACAssWv8JmVlaUNGzaoa9eubuO7du2qVatWeZynQ4cO2rdvnxYtWiRjjA4ePKhPP/1U3bt397qczMxMpaenuw0AAAAo+/wKn4cPH1ZOTo7Cw8PdxoeHh+vAgQMe5+nQoYPmzJmjnj17Kjg4WHXq1FHVqlX1xhtveF3OhAkTFBYW5gyRkZH+NBMAAAAXqSJdcORyudz+N8bkG5dr27Ztevzxx/X8889rw4YN+vLLL7Vr1y7FxcV5rX/UqFE6duyYM+zdu7cozQQAAMBFJtCfwjVr1lRAQEC+o5xpaWn5jobmmjBhgm644QY99dRTkqQWLVqoYsWK6tixo1566SXVrVs33zwhISEKCQnxp2kAAAAoA/w68hkcHKyYmBglJCS4jU9ISFCHDh08znPy5EmVK+e+mICAAEnnjpgCAADg8uH3affhw4fr3Xff1YwZM7R9+3YNGzZMe/bscU6jjxo1Sr169XLK9+jRQ/Pnz9fUqVO1c+dOrVy5Uo8//rjatm2riIiI4lsTAAAAXPT8Ou0uST179tSRI0f0wgsvKDU1Vc2bN9eiRYsUFRUlSUpNTXW752efPn2UkZGhKVOmaMSIEapatapuueUWvfLKK8W3FgAAACgT/A6fkjRw4EANHDjQ47RZs2blGzdkyBANGTKkKIsCAADAJYRnuwMAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMAawicAAACsIXwCAADAGsInAAAArCF8AgAAwBrCJwAAAKwhfAIAAMCaIoXP+Ph4RUdHKzQ0VDExMVq+fLnP8pmZmRo9erSioqIUEhKiK664QjNmzChSgwEAAFB2Bfo7w9y5czV06FDFx8frhhtu0Ntvv61u3bpp27ZtatCggcd57r//fh08eFDTp0/XlVdeqbS0NGVnZ19w4wEAAFC2+B0+J06cqH79+ql///6SpEmTJumrr77S1KlTNWHChHzlv/zySyUmJmrnzp2qXr26JKlhw4YX1moAAACUSX6dds/KytKGDRvUtWtXt/Fdu3bVqlWrPM6zcOFCxcbG6q9//avq1aunJk2a6Mknn9SpU6e8LiczM1Pp6eluAwAAAMo+v458Hj58WDk5OQoPD3cbHx4ergMHDnicZ+fOnVqxYoVCQ0O1YMECHT58WAMHDtSvv/7q9XefEyZM0Lhx4/xpGgAAAMqAIl1w5HK53P43xuQbl+vs2bNyuVyaM2eO2rZtqzvuuEMTJ07UrFmzvB79HDVqlI4dO+YMe/fuLUozAQAAcJHx68hnzZo1FRAQkO8oZ1paWr6jobnq1q2revXqKSwszBnXrFkzGWO0b98+NW7cON88ISEhCgkJ8adpAAAAKAP8OvIZHBysmJgYJSQkuI1PSEhQhw4dPM5zww03aP/+/Tp+/Lgz7ocfflC5cuVUv379IjQZAAAAZZXfp92HDx+ud999VzNmzND27ds1bNgw7dmzR3FxcZLOnTLv1auXU/6hhx5SjRo19Oijj2rbtm1atmyZnnrqKfXt21fly5cvvjUBAADARc/vWy317NlTR44c0QsvvKDU1FQ1b95cixYtUlRUlCQpNTVVe/bsccpXqlRJCQkJGjJkiGJjY1WjRg3df//9eumll4pvLQAAAFAm+B0+JWngwIEaOHCgx2mzZs3KN65p06b5TtUDAADg8sOz3QEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGBNkcJnfHy8oqOjFRoaqpiYGC1fvrxQ861cuVKBgYFq1apVURYLAACAMs7v8Dl37lwNHTpUo0ePVnJysjp27Khu3bppz549Puc7duyYevXqpVtvvbXIjQUAAEDZ5nf4nDhxovr166f+/furWbNmmjRpkiIjIzV16lSf8w0YMEAPPfSQ2rdvX+TGAgAAoGzzK3xmZWVpw4YN6tq1q9v4rl27atWqVV7nmzlzpn766SeNGTOmUMvJzMxUenq62wAAAICyz6/wefjwYeXk5Cg8PNxtfHh4uA4cOOBxnh07duiZZ57RnDlzFBgYWKjlTJgwQWFhYc4QGRnpTzMBAABwkSrSBUcul8vtf2NMvnGSlJOTo4ceekjjxo1TkyZNCl3/qFGjdOzYMWfYu3dvUZoJAACAi0zhDkX+v5o1ayogICDfUc60tLR8R0MlKSMjQ0lJSUpOTtbgwYMlSWfPnpUxRoGBgVq8eLFuueWWfPOFhIQoJCTEn6YBAACgDPDryGdwcLBiYmKUkJDgNj4hIUEdOnTIV75KlSravHmzUlJSnCEuLk5XXXWVUlJS1K5duwtrPQAAAMoUv458StLw4cP1yCOPKDY2Vu3bt9e0adO0Z88excXFSTp3yvyXX37R7NmzVa5cOTVv3txt/tq1ays0NDTfeAAAAFz6/A6fPXv21JEjR/TCCy8oNTVVzZs316JFixQVFSVJSk1NLfCenwAAALg8+R0+JWngwIEaOHCgx2mzZs3yOe/YsWM1duzYoiwWAAAAZRzPdgcAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1RQqf8fHxio6OVmhoqGJiYrR8+XKvZefPn68uXbqoVq1aqlKlitq3b6+vvvqqyA0GAABA2eV3+Jw7d66GDh2q0aNHKzk5WR07dlS3bt20Z88ej+WXLVumLl26aNGiRdqwYYNuvvlm9ejRQ8nJyRfceAAAAJQtfofPiRMnql+/furfv7+aNWumSZMmKTIyUlOnTvVYftKkSRo5cqTatGmjxo0b63//93/VuHFj/etf/7rgxgMAAKBs8St8ZmVlacOGDeratavb+K5du2rVqlWFquPs2bPKyMhQ9erVvZbJzMxUenq62wAAAICyz6/wefjwYeXk5Cg8PNxtfHh4uA4cOFCoOv72t7/pxIkTuv/++72WmTBhgsLCwpwhMjLSn2YCAADgIlWkC45cLpfb/8aYfOM8+eijjzR27FjNnTtXtWvX9lpu1KhROnbsmDPs3bu3KM0EAADARSbQn8I1a9ZUQEBAvqOcaWlp+Y6Gnm/u3Lnq16+f5s2bp9/97nc+y4aEhCgkJMSfpgEAAKAM8OvIZ3BwsGJiYpSQkOA2PiEhQR06dPA630cffaQ+ffroww8/VPfu3YvWUgAAAJR5fh35lKThw4frkUceUWxsrNq3b69p06Zpz549iouLk3TulPkvv/yi2bNnSzoXPHv16qXXX39d119/vXPUtHz58goLCyvGVQEAAMDFzu/w2bNnTx05ckQvvPCCUlNT1bx5cy1atEhRUVGSpNTUVLd7fr799tvKzs7WoEGDNGjQIGd87969NWvWrAtfAwAAAJQZfodPSRo4cKAGDhzocdr5gXLp0qVFWQQAAAAuQTzbHQAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYA3hEwAAANYQPgEAAGAN4RMAAADWED4BAABgDeETAAAA1hA+AQAAYE1gaTfgYpNjjNYeO6uDZ4zCg1xqF1ZOAS5XsZS/mOr2x8XU7otlPcvqNimrdfvrYmk3dVM3dVN3WajbtiKFz/j4eL366qtKTU3VNddco0mTJqljx45eyycmJmr48OHaunWrIiIiNHLkSMXFxRW50SVl0eFsPbfzjFKzjDOubrBLLzYK0h01828qf8pfTHX742Jq98WynmV1m5TVuv11sbSbuqmbuqm7LNRdGlzGGFNwsf+aO3euHnnkEcXHx+uGG27Q22+/rXfffVfbtm1TgwYN8pXftWuXmjdvrscee0wDBgzQypUrNXDgQH300Ue67777CrXM9PR0hYWF6dixY6pSpYo/zS20RYez9dh/snT+xsj9jvBO02C3DvOn/MVU9/k++ugjPfjggx6nXUztvpD19LWOF1O7qdtz+bzoS+qmbuqm7uKru7gVNq/5HT7btWun1q1ba+rUqc64Zs2a6e6779aECRPylX/66ae1cOFCbd++3RkXFxenTZs2afXq1YVaZkmHzxxj1Hb9abdvCHm5dO4bw9o2oQpwufwqL+miqdsTbx/mZXWbeFpPX4HlYmk3dXsufz76krqpm7qpu3jqLolT8CUSPrOyslShQgXNmzdP99xzjzP+iSeeUEpKihITE/PNc9NNN+m6667T66+/7oxbsGCB7r//fp08eVJBQUH55snMzFRmZqbz/7Fjx9SgQQPt3bu3RMLn2qM5emR7ptu4a/Zu1DX7kt3GVQ9yKdglZRnp1zPnNtuRSrX1zbU93MrduvlfqnE8TdWDznVsbtnzba1/nbZGtpYkvd8sRK3KZ+vNae94LZ9Xpx73qkqtOk676x/ZpQ4/fOuxbG67JSkoKEgDBgxwps2bN0/Vq1d3+3Igua9jrn3VG2rVVbe6jXt861zp9AmP5fNKanSDdoY31fvNQiRJg5IO6Pbv5vtcx9x2t7j3j+q/O9gZf9X+LWq5e63P9ZSk6tWr66GHHtK8efP0P//zP5KkhQsXas+ePT7XU5J+qHuNUhpe7/z/frMQrXx/aqH65sbbu+v2qxs5r6s6R/fppu1f+VxP6Vw7Pmnfz21aq5/XqEnq1nzreH67D4bVU+LVt7vNe/um+Wp45qhTtzebotrp+4jmer9ZiNpVDdCy/Ue1+OP3C1zP6kEuterRU3GpFZ1xjQ7+R7E7V3osm7fdp4MraGGMe4js8P03qv/rz27lz3fq1Cm1bt1at97q/jp8++23dSLrTIH9s6rJLZpwY1NJ0iPbM1Uj46Bu3fK5z3XMbff0Vn/UmcD/vg697SMk9+3tbR/ROPOQx77M6/x9RPaZTH3x/nSf65jbjgf/cJ/2lK9d6H1EbrvPBARpQdtebtPb/LRC0WnfO2W9tdvbPiLr1IkC+yap0Q0a17ml2lUN0NqjOYXeR0jS7Gt76mTIf1+HnvYRntqdXqGavmx5r1u5Ttu+VPixXwrcf+fdR+S+dya+MaXQ+4hqEVFO3/jaR5zf7sLsIzy129M+YviOBcpO/63A/XfefYQkDUj5VXdu/NjnOua2++o7C7+PyNtuX/sIX69BSdpV+yqtv+JG5//3m4Vozcfv6tDJLJ9tlqT2t9ym7q2aOPvvwuwjctu9oM0jPvcR3trtax+Rd5t4kruPyH0Nrjx0Mt8+Iu9+JFdu+eKWnp6uyMhIHT16VGFhYd4LGj/88ssvRpJZuXKl2/jx48ebJk2aeJyncePGZvz48W7jVq5caSSZ/fv3e5xnzJgxRhIDAwMDAwMDA0MZG/bu3eszTxbppL/rvEO1xph84woq72l8rlGjRmn48OHO/2fPntWvv/6qGjVq+FxOcclN7iV1pBX20JeXDvry0kFfXjroy0tHcfSlMUYZGRmKiIjwWc6v8FmzZk0FBATowIEDbuPT0tIUHh7ucZ46dep4LB8YGKgaNWp4nCckJEQhISFu46pWrepPU4tFlSpVeDNdIujLSwd9eemgLy8d9OWl40L70ufp9v/n103mg4ODFRMTo4SEBLfxCQkJ6tChg8d52rdvn6/84sWLFRsb6/H3ngAAALh0+f2Eo+HDh+vdd9/VjBkztH37dg0bNkx79uxx7ts5atQo9er13x+rx8XFaffu3Ro+fLi2b9+uGTNmaPr06XryySeLby0AAABQJvj9m8+ePXvqyJEjeuGFF5SamqrmzZtr0aJFioqKkiSlpqa6XU0cHR2tRYsWadiwYXrzzTcVERGhyZMnF/oen6UhJCREY8aMyXfqH2UPfXnpoC8vHfTlpYO+vHTY7Eu/7/MJAAAAFJXfp90BAACAoiJ8AgAAwBrCJwAAAKwhfAIAAMAawud54uPjFR0drdDQUMXExGj58uWl3SQUwrJly9SjRw9FRETI5XLpH//4h9t0Y4zGjh2riIgIlS9fXp07d9bWrVs9V4ZSM2HCBLVp00aVK1dW7dq1dffdd+v77793K0Nflg1Tp05VixYtnBtWt2/fXl988YUznX4suyZMmCCXy6WhQ4c64+jPsmHs2LFyuVxuQ506dZzptvqR8JnH3LlzNXToUI0ePVrJycnq2LGjunXr5nbrKFycTpw4oZYtW2rKlCkep//1r3/VxIkTNWXKFK1fv1516tRRly5dlJGRYbml8CUxMVGDBg3SmjVrlJCQoOzsbHXt2lUnTpxwytCXZUP9+vX18ssvKykpSUlJSbrlllv0+9//3vkgox/LpvXr12vatGlq0aKF23j6s+y45pprlJqa6gybN292plnrR59Pfr/MtG3b1sTFxbmNa9q0qXnmmWdKqUUoCklmwYIFzv9nz541derUMS+//LIz7vTp0yYsLMy89dZbpdBCFFZaWpqRZBITE40x9GVZV61aNfPuu+/Sj2VURkaGady4sUlISDCdOnUyTzzxhDGG92VZMmbMGNOyZUuP02z2I0c+/19WVpY2bNigrl27uo3v2rWrVq1aVUqtQnHYtWuXDhw44Na3ISEh6tSpE317kTt27JgkqXr16pLoy7IqJydHH3/8sU6cOKH27dvTj2XUoEGD1L17d/3ud79zG09/li07duxQRESEoqOj9cADD2jnzp2S7Paj3084ulQdPnxYOTk5Cg8PdxsfHh6uAwcOlFKrUBxy+89T3+7evbs0moRCMMZo+PDhuvHGG9W8eXNJ9GVZs3nzZrVv316nT59WpUqVtGDBAl199dXOBxn9WHZ8/PHH2rhxo9avX59vGu/LsqNdu3aaPXu2mjRpooMHD+qll15Shw4dtHXrVqv9SPg8j8vlcvvfGJNvHMom+rZsGTx4sL777jutWLEi3zT6smy46qqrlJKSoqNHj+qzzz5T7969lZiY6EynH8uGvXv36oknntDixYsVGhrqtRz9efHr1q2b8/e1116r9u3b64orrtB7772n66+/XpKdfuS0+/+rWbOmAgIC8h3lTEtLy/ctAGVL7pV89G3ZMWTIEC1cuFBLlixR/fr1nfH0ZdkSHBysK6+8UrGxsZowYYJatmyp119/nX4sYzZs2KC0tDTFxMQoMDBQgYGBSkxM1OTJkxUYGOj0Gf1Z9lSsWFHXXnutduzYYfV9Sfj8f8HBwYqJiVFCQoLb+ISEBHXo0KGUWoXiEB0drTp16rj1bVZWlhITE+nbi4wxRoMHD9b8+fP17bffKjo62m06fVm2GWOUmZlJP5Yxt956qzZv3qyUlBRniI2N1cMPP6yUlBQ1atSI/iyjMjMztX37dtWtW9fu+7JYL18q4z7++GMTFBRkpk+fbrZt22aGDh1qKlasaH7++efSbhoKkJGRYZKTk01ycrKRZCZOnGiSk5PN7t27jTHGvPzyyyYsLMzMnz/fbN682Tz44IOmbt26Jj09vZRbjrz+/Oc/m7CwMLN06VKTmprqDCdPnnTK0Jdlw6hRo8yyZcvMrl27zHfffWeeffZZU65cObN48WJjDP1Y1uW92t0Y+rOsGDFihFm6dKnZuXOnWbNmjbnzzjtN5cqVnZxjqx8Jn+d58803TVRUlAkODjatW7d2bvGCi9uSJUuMpHxD7969jTHnbiExZswYU6dOHRMSEmJuuukms3nz5tJtNPLx1IeSzMyZM50y9GXZ0LdvX2dfWqtWLXPrrbc6wdMY+rGsOz980p9lQ8+ePU3dunVNUFCQiYiIMPfee6/ZunWrM91WP7qMMaZ4j6UCAAAAnvGbTwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+AQAAIA1hE8AAABYQ/gEAACANYRPAAAAWEP4BAAAgDWETwAAAFhD+ASAEhYfHy+Xy6WqVasqLS2twPJ9+/aVy+VSixYtLLQOAOwifAJACdq/f79GjRolSXrxxRdVu3btAudp27atJGnLli369ddfS7R9AGAb4RMAStBf/vIXpaenq1GjRvrzn/9cqHmuuuoqSZIxRlu2bCnJ5gGAdYRPACghP/zwg2bPni1JGj16tAIDAws1X/369Z2/f/rppxJpGwCUFsInAJSQyZMnKycnR9WqVdNDDz1U6PlCQkKcvzMyMkqiaQBQagr3NRwA4JecnBzNmTNHkvSHP/xBoaGhbtO3bdumRYsWqVGjRrr33nvdpmVlZTl/BwQElHxjAcAijnwCQAlYt26djh49Kknq0qVLvulz5szRU089pW+//TbftD179jh/R0ZGllgbAaA0ED4BoASkpKQ4f7du3Trf9OXLl0v678VFeSUnJzt/x8bGFn/jAKAUET4BoATs3LlTkuRyuRQVFeU27bffftPq1aslSXXr1s0379dffy1JatKkiSIiIkq4pQBgF+ETAErA8ePHJUlBQUH5rnL/8MMPlZ2dLUkKDg52m5aamqpvvvlGkvTAAw9YaCkA2EX4BIASUKVKFUnnLh5KTU11xp88eVKvvvqqc0Rz7969bvO9+OKLOnPmjMqXL6+4uDh7DQYASwifAFACYmJinL/HjRun7OxsHTt2TH/84x+1e/duPfvss5KkmTNnKiMjQ1lZWZowYYKmTp0qSXruuec8npIHgLLOZYwxpd0IALjUnD59Wtddd53+85//SJLKly+v7OxsnTlzRoMGDdKkSZPUpEkT7dq1y7mvZ2ZmpqRzz3Z/5513VK4cxwcAXHrYswFACQgNDdU333yjhx9+WLVq1ZIkNWvWTFOnTtXkyZMVGBioBQsW6KabblJwcLBCQ0N18803a968eZo+fTrBE8AliyOfAAAAsIav1gAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALCG8AkAAABrCJ8AAACwhvAJAAAAawifAAAAsIbwCQAAAGsInwAAALDm/wB7MGNNewhCcAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Calculate the FFT of the signal\n", "x_s_fft = np.abs(fft(x_s))[x_s.size // 2:x_s.size // 2 + 50]\n", @@ -219,24 +199,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Define a non-stationary signal\n", "x_n = np.hstack((np.sin(2 * np.pi * 5 * t[:400]), np.sin(2 * np.pi * 10 * t[400:800]), \n", @@ -260,24 +229,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Calculate the FFT of the signal\n", "x_n_fft = np.abs(fft(x_n))[x_n.size // 2:x_n.size // 2 + 50]\n", @@ -295,7 +253,7 @@ } }, "source": [ - "The frequency space is not as clean as that of the stationary signal due to the sudden change of frequencies, but we can still see wider peaks centred on our original frequencies. For a more realistic signal we would expect something more like this for both cases. The main difference for the non-stationary signal is that even though we can work out the frequencies we don't know when they occurred." + "The frequency space is not as clean as that of the stationary signal due to the sudden change of frequencies, but we can still see wider peaks centred on our original frequencies. For a more realistic signal, we would expect something more like this for both cases. The main difference for the non-stationary signal is that, even though we can work out the frequencies, we don't know when they occurred." ] }, { @@ -319,24 +277,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Define the signal\n", "n = np.linspace(0, 10, 1000, endpoint=False)\n", @@ -364,25 +311,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Slice the signal into four pieces\n", "slices = [slice(i * n.size // 4, (i + 1) * n.size // 4) for i in range(4)]\n", @@ -448,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" @@ -464,29 +400,25 @@ ] }, { - "cell_type": "code", - "execution_count": 11, + "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c614db3365044f2ba61196f69cce308b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='pos', max=10.0, step=0.5), IntSlider(value=1, descri…" - ] - }, - "metadata": {}, - "output_type": "display_data" + "source": [ + "#### INTERACTIVE CELL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" } - ], + }, + "outputs": [], "source": [ "@interact(pos=(0, 10, 0.5), alpha=(0, 20, 1))\n", "def show_filter(pos=0.0, alpha=1):\n", @@ -512,36 +444,41 @@ } }, "source": [ - "Try adjusting the filter width and position and see what happens.\n", - "\n", + "Try adjusting the filter width and position and see what happens." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ "Now, to try to preserve some frequency and time information simultaneously we will slide the filter across the whole signal and store the frequencies at a set of time intervals." ] }, { - "cell_type": "code", - "execution_count": 12, + "cell_type": "markdown", "metadata": { - "scrolled": false, "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7267f2867e4046779a8e1ea0732bd4c5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=0, description='alpha', step=10), Output()), _dom_classes=('widget-inter…" - ] - }, - "metadata": {}, - "output_type": "display_data" + "source": [ + "#### INTERACTIVE CELL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "fragment" } - ], + }, + "outputs": [], "source": [ "@interact(alpha=(0, 100, 10))\n", "def show_tf(alpha=0):\n", @@ -650,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -685,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "scrolled": false, "slideshow": { @@ -703,24 +640,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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uCxK+BEIHQ3DPcxd6SUSu8fu1f2e1pv6/u1t7W0vae7etzfi2e/YAgsYNR54btB86dAjBYBCzZs1CebnYb9Pr9aK9vR21tbWYNGkSAFEg7d+/HzabDQsXLgQgumwTJ05EfX39sP3u2LEDDocD8+bNy+v6s4U5hNdee63q7w8cOAAAmD9//qitKVeQICQIomQ59NVD6PlnD2b/YjYm3jyx0MuRsNgsKF9RjqHNQ/C96yNBOBbxeAq/rbtw7yu90LDyZ3v27EE8HseiRYtQVlYGQBRVym2UtLS0YNGiRbDZRkeeJBIJw9v6/X4cOXIEdrsdZ5xxhuo227dvBwDN51fMFNdtNUEQhAl8W3zgwzwcEx2FXsowKlZWABwQbgtn3pggSgwW0lW2l1ELFzOBtHz5cgBAOBzGgQMHdAVh+u8ee+wxXHjhhWhqaoLL5cL8+fPxxz/+0fQ2ADB58mTcdtttuOeeezBt2jTY7Xb09/cbes47d+6EIAhYuHChJG6VhEIhHDhwAG63G3PnzjW0z2KCHEKCIEqSuC+O4D6xkrJiVUWBVzOcabdPw4y7Z8BWSadZYuzRlgxts9AwoC4IX3nlFQDAWWedBQDYtWsXEokEZs2ahcHBwZR99vb24uTJk8ME4c6dO3HVVVfhv/7rv2C32/Hcc8/h5ptvxoIFC3DmmWca3qavrw8dHR3485//jDPOOAP33XcfeJ5HQ0MDAoFAxufMnh8Tt+ns3LkTiUQCixcvhiU9vF8C0JmKIIiSxLfVBwiAY6oDZY3D79YLTTGuiSByRTweB4AUd43l1zFBeOzYMTzyyCMoKyvDBz7wgZRtbr75Ztx8882q+04XhHfeeaf0fSKRwLp167B+/Xps2rRJEntGtmHHPu+887B+/XrTz5k9XksQZvp9sVN6EpYgCAJiuBgoTneQIMY6CxYsAAD8/ve/R3t7OwRBwO7du2G1WrFo0SIcOHAAV1xxBYLBIG655RY0NjYCEEWT2+3Gq6++OuzrhhtuAJDqMEajUfzmN7/BqaeeiurqathsNtjtdnR2dkrFLEa2YcfmOA733ntvVs85k0NYyvmDADmEBEGUKL53REFYuap42zt0/LoD3X/vxqQvTELjhxsLvRyCyBmf+tSn8LOf/Qytra1YvHgx1q5dC7/fD7fbjUsvvRSbN29GNBrFtddei+9///vS41paWrBkyRKcc845w/b5u9/9DlOmTEFtba30s6uvvhqbN2/GV77yFaxcuRK1tbXYvn07PvOZz0hVy0a2YcdesWJFSpjbKIIgYOfOnQAyC8JSdQhJEBIEUZL43ks6hCuL1yEMHQrBu8GL8uXlJAiJMUVTUxM2btyIW2+9Fc8//zyef/55AEAwGMSGDRtw+umn4/bbb8claY23d+zYodmyZfv27Snu2pYtW/DUU0/h2WefTdnPY489BkB04oxsw2hpacHq1auzer5tbW0YGhrCtGnTUF1dPez3TDCm92UsJShkTBBEyRH3xhE+Ilbvli8vz7B14Wj8aCPm/3k+Jn95cqGXQhA5Z968eXjmmWfQ2dmJ6667DgDw2c9+Fn19fdi4ceMwMdje3o7BwUFVBy0UCuHgwYMpAu7o0aPScRhHjhzBfffdh+nTp6O6utrQNgAQi8WwZ8+erN27GTNmQBAEqZgmHY7jMDQ0BJ7nUVFRvDepepBDSBBEyeHfITYFdkxxwF5nL/BqtKlcWYnKlcUb0iaIXNDY2Ihwcn7zZZddhqqqKtXtWNGFWo4dqz5W/m7FihWw2Wy49dZb8aUvfQkHDx7Ej3/8Y7hcLknYGdkGAPbt24doNIoVK1boPpeuri6sXbsWAHDjjTfixhtvNPw66HHDDTfg4MGD8Hq9OdlfPiCHkCCIksO/TRSE5SuK1x0kiPGEnthTbqMVUk2vUAaAmTNn4o9//CPee+89XHHFFXjkkUfwr3/9C4IgSGLPyDZs/xaLJeM85Ugkgk2bNmHTpk2S+5gLtm3bhk2bNmHXrl0522eu4QRBa+6NPkNDQ6iqqoLX6y3JmX0EQZQu+27ch64/dWHandMw47szCr0cXXzbfPC+4UXFaRWoOl3dOSGKh3A4jNbWVsyYMQNOp7PQyykJhoaGUF1djZqaGvT19RV6OeMCM+9To3qNHEKCIEoO//akQ1jE+YOMzvs7cegrh9D7WG+hl0IQeWHHjh0QBCGj+0YUN5RDSBBEScHHeWlCSSmEjMuXiWtkIpYgxhpr165FlsFGooggQUgQRElhsVmwpncNArsCcE4r/pCeJAhbSBASBFG8UMiYIIiSw+q2onJ1JTiOK/RSMuJZ4gEsQOxkDJGuSKGXQxAEoQoJQoIgiDxidVvhmuMCAARaAgVeDUEQhDokCAmCKCkOfukgDnz+AIIHgoVeimEobEwQRLFDgpAgiJJBEAScfOgkTvzmBPgQX+jlGIZVQ5MgJAiiWKGiEoIgSgYhIWDOb+bAv80P9wJ3oZdjGKo0Jgii2CFBSBBEyWCxWdB0XROarmsq9FJMwRzC4P4gEuEErE5rgVdEEASRCoWMCYIg8kzZhDLY6+1AAgjuLp3cR4Igxg8kCAmCKBl6n+pF/8v9iA/FC70UU3AcB88yDwAKGxMEUZyQICQIomQ4fOth7LhwB3xbfIVeimmosIQgiGKGBCFBECVBIphA6HAIAOBZ7CnwasxTeXolqtZWwTXbVeilEARBDIOKSgiCKAmCe4OAANjr7bA32gu9HNM0Xt2IxqsbC70MgiAIVcghJAiiJAjsEqd8eJZ4SmJkHUEQRClBgpAgiJLAv1PMvSvFcLGSRCBRckUxBEGMfUgQEgRREkgOYQkLwgOfP4AN5RvQeX9noZdCEEQO2LVrF6xWKz772c+afuzQ0BBqamqwdu3aPKzMPCQICYIoCZQh41KF5T6GW8MFXglBELngm9/8JqxWK771rW9pbnP33Xfju9/9Lng+ddxmZWUlbrnlFmzatAlPPPFEvpeaERKEBEEUPbGBGKIdUQCAZ1HpCsJJX5iEM7vPxJxfzin0UgiCGCEbNmzAs88+i+uvvx7Tpk1T3aa1tRV33nknHnzwQVgswyXXV77yFbjdbnzrW9+CIAj5XrIuJAgJgih6mDvomOqArbJ0myOUNZShrKGs0MsgCCIH/OpXvwIAfOITn9DcZtu2bQCA5cuXq/6+pqYGl19+Ofbu3YtXXnkl52s0AwlCgiCKnrEQLiYIYuzQ09ODxx9/HBMnTsS6des0t8skCAHguuuuAwDcf//9OV2jWUgQEgRR9IyFghLG8V8cx45Ld2DglYFCL4UgRsRrr70GjuNSiiL27dsHjuOwePFi6WfHjx8Hx3Fobm4uxDLzwmOPPYZoNIpLLrlENRS8fv16cByHe+65BwBw1113geM46eupp56Str344oths9nw+OOPIxKJjNpzSIcEIUEQRU9gpygIy5eUF3glI2fo7SH0P9ePoXeGCr0UghgRO3bsAAAsXbrU9M9KnTfeeAMAsHr1atXfh0IhrFmzBna7XdpuzZo10teyZcukbV0uF5YsWYJwOIwtW7bkf/EalG4yDkEQ4wZ7nR22WtuYcAjZc2CuJ0GUKkzoKcXNeBGEmzdvBgCceuqpqr+/6aabcP3116O8vBwulwubN2+G1WrV3N+qVauwbds2bNy4sWBtaEgQEgRR9Cx+bHHBK/ByBauSDu4OFnglxEhIBBKmH8M5OFhsYmCOj/MQIgJgAawuWShktd8yDha7uF8hIYAP8wAHWN3aAiQX6Im/TCKxlBEEAceOHQMATJgwQXO7Xbt2ged5LF68WFcMKvfT3t6eu4WahAQhQRAlwVgZVyc5hHsD4OO8JBCI0mJD+QbTj1n4z4VovEacZ937WC/2XLsHVWdXYcVrK6Rt3pr+FmK9MVP7nfOrOZj0hUkAgMENg2g5twXuhW6s3q0ezswFPM9j9+7d4DgOS5YskX5uxCH84Q9/mNK3r7KyEqeccgp+/OMfY+XKlSnHeeqpp3DFFVfg+PHjmDRpkqG1vfrqqzjvvPOwf/9+zJ07N7snqMPg4CDicXHaUG1treZ2LS0tAFLFsRZsPz09PTlYYXbQmYggiKJmrDiDDOd0JyxuC4SIgPBhalBNlCaHDh1CMBjEzJkzUV4u5vZ6vV60t7ejtrZWEm/RaBT79++HzWbDwoULAYhCafLkyXjzzTfx5ptv4r777sOePXtw6aWXYmgoNbf21FNPxZYtWwyLQUCs7K2srMScOfnp9xkOy5/bsjLtNlJmBKHL5QIg5h4WCnIICYIoag585gAGXhrA9Luno/ljpV+lyFk4eBZ64HvXh8DuANzz3IVeEpEFZ/nPMv0YziG73PUfqBf3kWbLnN52uvn9lsn7rT6rWtxvng11vdCw8md79uxBPB7HokWLJPHU0tKCJUuW4PTTxed6+umno6+vD1/5ylewdetWnHvuudLjJ06ciIkTJ5pa27Zt27BixYqcRxUSCTGcr3QFvV4vampqVLc3Iwj7+/sBAPX19SNdZtaQQ0gQRFET2B1AuC0MzjY2QsYAFZaMBaweq+kvZXqAxWYRf+6yjny/dnm/nJUTf57n/MHdu3cDQEp7GbVw8fbt2wHIffjC4TAOHDgguYWMxkYxlM6qchmTJ0/Gd77znZSfPfDAA1i9ejXcbjdqampwwQUX4MCBA9Lvt23bhlNOOUX6f2trK5YvX47TTjsNHR0dAERx97Of/QxLliyBy+XC3Llz8Zvf/GbYsW+77Tbcc889mDZtGux2O/r7++FwOFBZWQlAFnJqmMmdZPtpaGjIuG2+IEFIEERRs/jxxVj++nLUnK9+F16KkCAkSp22tjYASAnlqgkgNn3jrLNER3XXrl1IJBLDBOHrr7+OSZMmpeQQ9vX1oaOjI8Vhu+mmm/D5z38eF1xwAR599FH84Q9/QHV1NWw2MeAZDoexb98+qfr3hRdewMqVK7F8+XK88cYbmDRpEgRBwHXXXYe77roLH/vYx/D000/jwx/+ML7whS/gn//8Z8qx//znP+O9997Dfffdh8cff1wSbEzg7t27V/P18Xq9mDZtGqqqqjK+nnv27AGAFCE72lDImCCIomYsjntzLxDDxMG9VGlMlCasqELpkLEQKROEx44dwyOPPIKysjJ84AMfSNlm3rx5iMfjGBgYwPr16/Hkk0/i3//+N5xO57D9MfF1//33489//jP+85//pEwHufrqq6Xvd+zYgUQigVNOOQU/+MEP8N3vfhf33nsvvvzlL0vb/O53v8Njjz2GV199VRKq559/Pnbv3o0//vGPuPbaa6Vjn3feeVi/fv2w57927Vq88cYbePfdd3HZZZcN+z1zLOfNm5f5xQSk/oNsPYWAHEKCIIhRRhKEB4Lg43yBV0MQ5lmwYAEA4Pe//z3a29shCAJ2794Nq9WKRYsW4cCBA7jiiisQDAZxyy23SCFhJrTWrl0Lu92OxsZG3HbbbXjooYdw5plnphyjpaUF5eXlmDVrFgDgnnvuwcc//vGMo+JsNhtuu+02/OxnP8Pzzz+fIgYBscr5qquuwhlnnIF4PC59LVy4UHI+W1pawHEc7r33XtXjXHTRRQCAjRs3qv7e7RY/462trRmnjxw6dAgnT57E/PnzMWXKFN1t8wkJQoIgipa+Z/tw+OuH0f+ydp5OKeKc5oTFZYEQFRBuo0pjovT41Kc+hfr6erS2tmLx4sW49NJL4ff74XA4cOmll2LJkiXYvn07rr32Wnz/+9+XHtfS0oK5c+diy5YtePvtt/HAAw/Abrfji1/84rBjtLS0YOnSpeA4Dvv370d7ezuuueYa3XWxCuMnn3wSd955Z0qBCiA6d+3t7XjkkUdgt9tTvr7//e9L4d2WlhasWLFCs7p53bp1mD17Nl577TWcPHly2O+XL1+OCRMm4ODBg5gyZQrOPPNMrF27Fk8//fSwbf/xj38AAG688Ubd55ZvSBASBFG09D/Xj2M/OYaBF8fW3F/OwknVxRQ2JkqRpqYmbNy4EZdeeimCwSCef/55AEAwGMSGDRuwatUqPPvss/jHP/6R0pplx44dWL16NVauXInVq1fjhhtuwDe/+U3s3bsX7777bsoxWlpapPzBzs5OAPqNoAFREF533XW4/fbbcdttt+G9995L+f2JEycAAI8++ii2bNky7Ovhhx+Wjp3eE1EJx3G4+eabkUgkJEGnpLy8HM8++ywuueQSCIKAN998E5s2bVKtIv7b3/4Gu92OT3ziE7rPLd+QICQIomgJ7BWLLliIdSwx4eYJmPH9GdR2hihZ5s2bh2eeeQadnZ247rrrAACf/exn0dfXh40bN+KSSy5J2b69vR2Dg4PDCic+9rGPAQCefPJJ6WexWAx79uyR8geZEGTVzWokEgns3LkTy5Ytw913342LLroIV155Jbq6uqRtWAsbl8uFlStXDvuaPXv2sGNr8dnPfhb19fW47777wPPDUz+WL1+OZ599Fj09PRAEAYIgSK12GK+++ip2796Nm2++WQqrFwoShARBFC3MPRuLgnDS5ydh2renwT137D03YnzR2NgoNWu+7LLLNKtqWf5guiCcPn06lixZkhJO3bdvH6LRqOQQzpkzB6tWrcLXvvY1/P73v8drr72GP//5z7j22mslMbZv3z6EQiEsX74cHMfhoYceQm1tLa666ippfXPmzMGaNWtw880343e/+x1ef/11PPnkk7j33nvxta99LeXYK1asgB6VlZW44447cODAAfz97383+7IBAL73ve+hvLwcd955Z1aPzyUkCAmCKEri3jiiJ6IAAM8CT4FXQxCEHkaaMLNCDTWhdeWVV2Lbtm1Sn8CWlhZYLBZpLJ7FYsFjjz2G888/H3fddRfe97734Z577sGCBQtgsYhSZtu2bbBYLFJvRI/HgyeffBJHjhzBTTfdBEAM9T766KO45JJL8IMf/AAXXXQRPve5z+H111/H+eefn3JsI/0DP/e5z+F73/ueqkOYiaGhIZxzzjn4y1/+gqamJtOPzzWckOVcqKGhIVRVVcHr9UoNGgmCIHKF9y0vtp2xDWUTy3Bmx5mZH1BiCLyA0KEQgvuDqLusbszMai51wuEwWltbMWPGjJQWKIQ2Q0NDqK6uRk1NDfr6+gq9nHGBmfepUb1GfQgJgihKxnK4GACEmIB3FrwD8MAZJ86AY4Kj0EsiiKzYsWMHBEEw5KgRxQsJQoIgihImCMdquNjisMCzxAPOyiE+ECdBSJQsa9euRZbBRqKIIEFIEERRMtYdQgBYuW0lhYoJgigKqKiEIIiiJLAn2XJm4dgVhCQGCYIoFkgQEgRRdCRCCYRbxTYRYzVkrITCbQRBFBoShARBFB2hAyFAAGw1Ntgb7YVeTt4IHgzincXv4O3Zbxd6KQRBjHMoh5AgiKJDOaFkLIdV7Q12BHeLuZLxoThslXRKJgiiMJBDSBBE0eGY6EDj9Y2ou7Su0EvJK/ZqO8qaxTmvwX0007iYoDA+Uczk4/1Jt6MEQRQd1euqUb2uutDLGBXcC9yIdkUR3BtE5Wpq8l9orFYrAHGWrsvlKvBqCEKdSCQCALDZcifjyCEkCIIoIO75YhU1OYTFgd1uh8PhgNfrJZeQKEoSiQT6+/vh8XhyKgjJISQIoqgQeAGhIyE4pzthsY39e1bWZ5HlTRKFp76+Hh0dHTh+/Diqqqpgt9vHdC4rUfwIgoBEIoFQKASv1wue5zFhwoScHoMEIUEQRUX4aBjvzHkH1nIr1nrXgrOM7QsxE4SsETdReNi8197eXnR0dBR4NQQhY7Va4Xa70djYiLKyspzumwQhQRBFRbQjCovTAsdUx5gXg4DcZzF0OAQ+ysNSNvZd0VKgsrISlZWViMViSCQShV4OQcBiseTVrSZBSBBEUVG1pgpnBc5CrD9W6KWMCmUTy2AttyLhTyB0ODQuGnGXEna7HXb72O2FSRAMuhUlCKLo4CwcyupzGw4pVjiOg2uuWM0aOhAq8GoIghivkCAkCIIoMO55yTzC/ZRHSBBEYSBBSBBEUdHyvhbsvnY3wsfChV7KqMEcwuABEoQEQRQGEoQEQRQNiWACAy8MoOdfPbC4xs/pyT1XdAhD+ylkTBBEYaCiEoIgiobQQVEQ2WpssNeNn0T+qnVVWPDQAngWU0EJQRCFgQQhQRBFA8uhc89zj6tGwM7JTjivdxZ6GQRBjGPGT0yGIIiih+XQuebRDFmCIIjRhAQhQRBFA8uhY1W344mhLUM4/svjGHpnqNBLIQhiHEKCkCCIooE5hKzIYjzReX8nDt1yCH1P9xV6KQRBjEMoh5AgiKJAEAQph3A8hoyr1lQh1h2De/74E8MEQRQeEoQEQRQFse4YEt4EwAGu2eNPEDZ/vBnNH28u9DIIghinUMiYIIiigIWLndOcsDqtBV4NQRDE+IIEIUEQRcF4DhczBEFApDOCRDhR6KUQBDHOIEFIEERREDowfiuMGVtP3Yo3J76JoTep0pggiNGFBCFBEEWB1JR6HFYYM8omlgGQXwuCIIjRgopKCIIoCibcOAGexR5Ura0q9FIKhnueG/3P9EtuKUEQxGhBgpAgiKKg/sp61F9ZX+hlFBTmjpJDSBDEaEMhY4IgiCLBNVcsqCGHkCCI0YYEIUEQBSfUFsLAfwYQ6YwUeikFhRXUhFpD4KN8gVdDEMR4ggQhQRAFp/fRXrRc0IJDXz5U6KUUlLIJZbCWW4EEEDpCLiFBEKMHCUKCIAqOxWGBa64LnkWeQi+loHAcR2FjgiAKAhWVEARRcCZ9YRImfWFSoZdRFLjnuuF/z0+FJQRBjCrkEBIEQRQR5BASBFEISBASBFFQBEEo9BKKClZYQg4hQRCjCQlCgiAKSmBXABtrN6LlfS2FXkpRwBzC4AEShARBjB4kCAmCKCihgyHEB+KID8QLvZSigDWnjp2MIe6l14QgiNGBikoIgigooUNirpxrtqvAKykObJU2rNi8Aq6ZLlgrrYVeDkEQ4wQShARBFBQShMOpOmP8znMmCKIwUMiYIIiCEjqYFIRzSBASBEEUCnIICYIoKOQQDsf3ng/df+uGY4oDk2+ZXOjlEAQxDiCHkCCIgpEIJRA5Ls4vJkEoEzoYwrGfHEP3P7oLvRSCIMYJ5BASBFEwQodFd9BWbYO9zl7g1RQP5aeUY9KXJ6FiRUWhl0IQxDiBBCFBEAVDGS7mOK7Aqyke3HPcmPN/cwq9DIIgxhEUMiYIomBQQQlBEERxQIKQIIiCQQUl2sQGYhh6ewjh9nChl0IQxDiABCFBEAWDBKE2h756CO+d/h5OPnSy0EshCGIcQIKQIIiCQSFjbdxzxBF2wYM005ggiPxDRSUEQRSM+X+Zj9DBEDwLPYVeStHBXFMmmgmCIPIJCUKCIApGzTk1qDmnptDLKEqYa8rC6gRBEPmEQsYEQRBFCHMIY90xxIfiBV4NQRBjHRKEBEEUhMENg+j8UycCewOFXkpRYqu0wd4oNuumsDFBEPmGBCFBEAXh5PqT2H/jfnQ/TOPZtGBhYyosIQgi35AgJAiiIHgWeVBzYQ3KTy0v9FKKFlZpTHmEBEHkGyoqIQiiIEz+0mRM/tLkQi+jqKFKY4IgRgtyCAmCIIoUqdKYBCFBEHmGBCFBEKNOIpxA3EeVs5mg1jMEQYwWJAgJghh1Bl4cwMbKjWi5sKXQSylqpNYzPTHEvSSgCYLIHyQICYIYdVgI1FZHacx62CpssDfZYXFaED4WLvRyCIIYw9DZmCCIUYeFQJkDRmizaucq2Ovs4CxcoZdCEMQYhgQhQRCjDuurx9qqENqUNZQVegkEQYwDKGRMEMSoQw4hQRBEcUGCkCCIUSURTiByNAKABKER/Lv82H3Nbuz95N5CL4UgiDEMhYwJghhVwq1hQACsFVZpVi+hjRAT0PPvHtgb6LUiCCJ/kCAkCGJUUYaLOY4KJTLhnuvGrP+dBdccFwRBoNeMIIi8QIKQIIhRhbWcYU2XCX2sHiumfHVKoZdBEMQYh3IICYIYVaighCAIovggh5AgiFGFHELzhNpC8L3jQ9nEMlSvrS70cgiCGIOQQ0gQxKhCDqF5uh/uxp4P70Hn7zsLvRSCIMYoJAgJghg1+AiP8FFxBBs1pTYOc1OZmCYIgsg1FDImCGLU4OwcTjt4GkKHQtRyxgTMTWXhdoIgiFxDgpAgiFGDs3BwzXTBNZPCxWZggjDWG0NsMAZ7NYlpgiByC4WMCYIgihxbhQ1lzeJMYwobEwSRD0gQEgQxanT9uQutd7XCt81X6KWUHBQ2Jggin5AgJAhi1Dj58Em0f7cd/m3+Qi+l5KDCEoIg8gnlEBIEMWo0XNMA51QnKk6tKPRSSg5yCAmCyCckCAmCGDUm3jQRuKnQqyhNyCEkCCKfUMiYIAiiBJAcQhKEBEHkARKEBEGMCpGuCPy7/EiEEoVeSkkitZ7piSHujRd4NQRBjDVIEBIEMSr0/KMH7y55F3v/395CL6UksVXYYG8S+w+SS0gQRK6hHEKCIEaF4MEgABpZNxI8izyIVEeQ8JPLShBEbiFBSBDEqMBcLRb6JMyz7OVl4Diu0MsgCGIMQoKQGLscOgS88Ubqz845B5g5U/y+rQ145RXtx69dC8ydK35//Djw4ova2552GrBokfh9Vxfw7LPa265cCSxdKn7f2ws8+aT2tsuWAaeeKn4/OAg8+qj2tosWiesAAL8f+Oc/tbedNw9Ys0b8PhIB1q/X3NQ/aTpeazoN5y5wwWPlgb/8RXu/06YB558v//9PfwIEAQAQem8KADtcB14FHggDkyYBF18sb/vXvwKxmPp+m5qA979f/v/f/gaENMKmdXXAlVfK///XvwCfRiPsqirgQx+S///88+LrUlGcbXFIDBIEkTeELPF6vQIAwev1ZrsLgsgvM2cKgihH5K+//U3+/SOPDP+98uv+++Vtn3tOf9uf/1ze9o039Lf9n/+Rt926VX/bO+6Qt927V3/br35V3vboUf1tP/MZedu+Pt1tn1z8AcFx00Fh5V3tQngoqL/fD3wg9W9gsQgCICRgE17Fy8KreFUIo07c9uKLU7etqNDe79q1qds2NWlvu2JF5vcB+5o3T95ucFAQliwRhG98Y/h7iSAIokQxqtfIISTGLl1d4r/nnQe4k3lrEyfKv29uBi67TPvxU6bI3zc06G87Y4b8fW2t/razZ8vfV1Xpbztvnvx9ebn+tgsXyt87nfrbLlkif2+3a27b2hvDBmE+AGDn8Sj+tsWPT+rtd+XK1P9fdhnA8wj7a4DXrLBYYyh73xkAB+CUU1K3fd/7tF0/5XMDgAsvFB1TNZgDzDjvvOGPZ0yaJH//+98DO3fKjmwREm4PY/c1uxEfiuO0facVejkEQYwhOEFIxnNMMjQ0hKqqKni9XlRWVuZ6XQQxctxuUWC0tgLTpxd6NSXJmfccw3vtEcxosKG1J46z5jrx0tcnm95P37N92Pn+nfAs9WBVy6o8rDQH3Hsv8M1vAp/4BPDgg4VejSrxoTg2Vm0EAKz1roWtku7pCYLQx6heo7YzxNiF58V/Ke8qK3p9CWw7GgEAPHBjEwBg86EwBoPmK1xLoqCEvU+yu0ceFWyVNix+cjFW7lgJi5tO3wRB5A46oxBjl0OHgKNHU8OChGE2HwpBEICFE8twxmwXZjfakeCBtw6HTe+Lzd9l49eKkhK5cai/vB7lS8phsdHpmyCI3EFnFGLsMnmymAdoo7BaNjB3cNUMBwBg9UwnAGBLaxaCsBQcQkYRO4QEQRD5ggQhQRCqbG8XBeGKaaIgZMLw3daI6X2VRFPqEggZA0BgXwBHf3wUnQ92FnopBEGMIUgQEmOXr30NuPVWYGio0CspSXafiAIAlkwWheDSKeK/ezujpvbDx3iE20RXsagdQrtdrM622wu9El0CLQEc+cYRdN5PgpAgiNxBgpAYu/zsZ+KXVisTQpNITMCx/jgAYHZyfu685jIAwNG+OIIR3vi+jkcAAbC4LCibWJb7xeaKW24R3ysPPFDolejC8jBZXiZBEEQuIEFIjF1Y6K9EigWKibbeGAQBKHdwaKywAgDqK6yoKxdPGQdPakwUUcE1w4V1wXVYtXsVTdrIAa5ZoiCMdccQH4oXeDUEQYwVxr0g/MHT/Tj1rqN4aru/0EshcokyD4xEiGkOd4uCb1ajPUXEzWkSHb4DJ82FjS0OC1wzijhcXELYqmywN4iubegwuYQEQeSGcS0Io3EB33uiH7s7ovjtq95CL4fIJbwipEmC0DRHekRBOLMhNZ9uXrP4//2dxh3CkuHJJ8XJKj/+caFXkhEKGxMEkWvGtSDs8srhln6/8ZwoogRQOoSWcf02zwqlQ6hk3gTRIdzfZdwhPPyNw9h3wz74tvlyt8B80NoKPPMM8N57hV5JRlhxDmvnQxAEMVLG9ZXy5JA8cUEpDokxAIWMR8Rh5hCmC8JkYckBE4Kw9/FedD3YhfhgkX/GSuh9QoKQIIhcM6479ipF4MmhBOIJATZr6VwUCB0oZDwimEOYHjKenRSIR3piEATBUJHIjB/MQHBfEJ7FntwvNB8UeR9CgELGBEHknnEuCGWHkBdEUTipRv0lCcd43LK+B+vmuvCxM7WHQxNFgt0O7NsnXtwrKgq9mpKC5wUc7RMF4Yw0QTilVvx8+MICvCEe1W5rxv01Xt2Y+0XmgxJpTA2QQ0gQRO4Z1yHj9DCxXtj45d0h/GWTDzf9qRtvUWVf8WOxAPPmAfPnA9bMooWQ6fEnEEuI+mhiVeoNktthQUOyDc3RviIPAZulhJxkJgijXVHE/WPs70AQREEY14KwL62QxBvSLixp75OrKrMZ3UUQpULnoOicN1ZYYbcNF0nMJTQiCP0tfvQ904fwMfPzjwtGCTiE9mo77PXJ1jPkEhIEkQPGtSD0hVMFoE9HECovfl3UDLb4iUaBO+4AvvMdIDYGW6TkkRMD4vt7YrV6+sTUOiYIM7+unX/qxM7LdqLjFx25W2C+KCGHEKCwMUEQuWVc5xD60wThUFhbEB7rly9+XYMJze2IIiESAb7/ffH7b3+76OfTFhMnkqkTE6rVQ+1Tkw4hG22nByt6YEUQRc3nPy9+lQhVa6tgrbDCVjGuT+MEQeSIcX0mydYhVLarIYoUZZUx9SE0RSaHcEqdKK6NhIyZe8XcrKKmxBzCWT+eVeglEAQxhhjXV0omCFmSfLpAVNLto56FJQX1IcyazmT1/QStkDHLIezXDxnzcR7hI2LuYEk4hARBEOOYMSkI4wkBH/rVCfzX33t0t2MCcGJ1ZkE4pHAPT3rJISx6qA9h1hjNIcwUMo4cjUCIC7A4LXBMcuR2kfngpZeAa68FfvKTQq/EFFRlTBBELhiTgrDlWATPtATxq/94dVvE+MKii8ScEK0cQkEQUgThYIgEYdFDo+uypjM5UWRSjXoO4ZRaMWTc5U0gEtOuyGX5g85ZTnCWEhDlhw8D//oXsGlToVdiiLg/jk1Nm7CxYiMSATonEQQxMsbklfK4wrl4YVdQczu/5BDKzXZVt4sI4BW/isaheyEkigAKGWfNiUFWVKLuENaVW1CW/JVexX3woPjZK4n8QaDk3ie2chuEuPg+Dx2hSmOCIEbGmCwqOT4gX6R6NApABEFQhIyTglCjqMQbFH/OcbLOGArzaLBTw+OihULGWRGJCej1p34u0uE4DhOqbGjvi6NzMI5pdeoV3CVVUAKU1KQSxopNK+CY4ICtakyeygmCGEXG5FlE6RD2+tUFYSgqu36svYZWyNibDBHXeiwIxwQEIqKYZMUoRBFSUwNs3Spe3EkQGuZk0vErs4nvdy0mVDNBqB2qZCFj9xx3bheZb0pIEHrml8h8aIIgip6xKQgHMgtCpfhrTt5dBzQFofjzSpcFdquAQCSh26KGKALsduCUUwq9ipKjJ1lN31BhA6cjpCdUiTdDehX3JdWDEKAbB4IgxjVjMoewR9EiptenLggDEdEF8Dg4uMvEC0FIIy+QFZRUuayocIovmV5FMkGUKuyz05jB/Wb5hSc0HEI+ziPcWmItZ0owZBzYF8CBLxzA4a8fLvRSxhX/2RPEdb/pxB2P9g4bcECMX3hewJPb/Jq6o9gZkw6h0r1Ln1fMCMfEn7vsHFxJQRiM6ucQVrksYMWSmQThb18dxECAx23vr9F1WkaLnz4/gM2HQvjtJ5rGR6jb5wN++UvAZgO+8Y1Cr6Zk6E7m3DZUGhOErCI5nUh7ibWcAUrSIUx4Ezjx6xMom1RGjapHidf2BXH5/52QUo52Hovi8VsmFMV5nigsf3vbh0890I3p9TZsvWsqPI7S8txKa7UG8UWUgjABnh9+xx+Kij9zllngsosvQ1jLIQzLIWPmEA7phIy9wQS+8nAvvvtEP1qORbN7EjmE5wXc/kgfnmkJ4sY/niz0ckYHrxe4/XZxnjFhGDlkrC8Im5Mh406NkHGsL4aySWVwzXaVRssZAPj4x8UbiX/8o9ArMQxzX6MdUSSCxedKBCI8/rXFh1//ZxAv7gognigd91WNBC/glvU94AVg9QwHnHYOL+wK4qntgUIvjSgCntwmvg/aeuN4ebd2h5NiZcw7hLwADIZ41HpSL3BM/LnsipBxVP1kFUwKTHG7ZMg4oi0Itx2NSN+/sT+E5VML65C0KUaMHegqvEAdFViVMfUgNIXZkHGXRpP2ytWVOPP4meBjJRROs9tLbua1vdYOW40N8YE4QodDKF9SXuglARC7OPx1sw//9Y/elJvnGQ02/OXmZqya4Szg6rLn6e0BHOiKodZjwdNfnYSfPD+Ae58dwP+9OIgrVhTHa08UBkEQsOGA3P5pV0cUV5ZYGvuYvFqmh3PVcjxCCkHozBAyZtu6HRaUsxxCHYfwvXZZEG5J5lEVkt0d8npODMZVHdMxB8sDozCOKVjIuD6DIJzIHEKNkDHDYh+Tp5iigrX1YUU8xcCdj/Xj0w92YyjEY2aDHR84xYOGCitae+I4/97jeH1/6bknAPCPd3wAgE+urUSly4LPnFMFCwdsPhTGkR79UY7E2GYwyKM/IOsC5XW3VBhzZ2ueF+CPpAqeQGS4AGI5hE47J4WMYwkxJJBOMCqLxwqnKDD0cghbFSeGbo0+iCPlaF9Mci4zcfCkvJ5YQrsQYExBgjArDIeMkw5hf4CXPkslz8aNwA03AD/7WaFXYgoWNmZ9HwvNL18exI+fGwAA/PeVtdh1z1T87XMTsOueqbh0qRvROPDhX3fhcHdpCSh/mMdzO0Uhe80q0Q2cVGPDWXPF1/+FnRQ2Hs8c7Uu9Od7XWVrvb2AMCkKlGGRhL7+KcFLmELKQsfLnSsJROWTsLtPPNwTEvEWGVtubkdByNIIF327HJ+83lg+Y3py7va/03qimoZBxVkgh4wxFJTVuCxw28XOj1otw2znbsP287QiWkhN06BDw4IPiTOMSQnIIi0AQvnMkjG/9uxcA8IOr6/Cty2phSeaQVrmtWP+ZZpw204HBII8vPtQNoYQqup/dEUAoKmBWoz0lDejCRWKfzZf3lNB7ncg5R/vF66rHwc6LpTdjfMxdLZlzZ7MCdeVJQZgpZGyXBaFa2FhyCMss0rZBjXxDILWyOR/l53/ZPIQEDzy5PaDbB47Rk7aGjoHSe6OahhzCrOhJNqbO5BByHCc1dE9/D/JxHkObhjD46iAs7hI6xZToe0VyCAscMo7EBNz0p5OIJ4CrV5bjqxdVD9vGVWbBA59qhtPO4dW9Ifz9bf/oLzRLXkyOQb1qhSeloviCpCB8fV8I0XjpCFwitzCH8LSZYn7sQJBHSCMNrVgpobO1MZggrHBYpPCuWsiYzSJ2lXGwWDjJ7VDrRSgLQmUBivYfuj+Q6hDmOmfviCLU8tq+zBeBdJdyIFBab9KsYA5hiV7kC4EgCOg2GDIGFK1n0gpLOI7D8g3LMf8v80un5YySEnKtgOJxCP/vxQEc6IqhqdKKX/2/Bs02LLMa7bjt/TUAgLuf7C+ZyuM3kgUD5yxInbyzdHIZGiqs8EcEvH2k8DnjRGFgRsviSWWScZR+bix2xq4gdFngSRaAqIeM5RxCAJLQC6uFjGNyyNiVDBnrO4TymyDBi1XOuWRXh1wpbMTtYy4laxXSFyitN2lWTJ4MbNgAPP98oVdSMnhDPGLJt4YhQahRWMJZOVSdXoXm/9dcOi1ngJK9eWCjASPHIwVrPdM9FMePnhXzBn90bT2q3frvny+eX42GCiuO9MSw/i3faCxxRLT1xnC0Lw6bFThjVmqFtMXCYV0yj/CtwyQIxytS/nWlTTN6UuyMOUHIWhxUOC2ocGhPFWFOoDNZUKLXnFoZMmbbaeUQCoIwrBl2prDxicE4TvveUXzqgcw5gYIgpISAzYSM5zSJLTUGxoMgdLmAtWuBM88s9EpKBpZrWuGUb3z0kB3C0jrpZaTEHEJbrQ225N8idKQwLuFPnx9EMCpg5XQHPrw6c/uVcqcFX724GgDwq5cHiz6XkLUTWTndKXWaUHLqDNEJ39ZOgnC8wq7zdeUWTEiOwz1RYulZY04QMqHmKeMkhzCgU1TiSjqETBjqhoztnLS9Vsg4EBEQSeaRNCUT8/syFJbc+rcetByLYv2bPimUrYU/IqSIUa0+cEqYIJzXXAYAKaXxBMHo8RtrOcNgJ730opKex3rQ8euO0iooAUpydB0ghugL2XrmxGAcv3vNC0CsKjY6seOGtZVwl3HYeTyKTQeLW0htTArCtXPV+yeeMk0UhFvbSq/VCJEb2HW+ocIqXfvT8/eLnTEnCJlQc9g5lCerffxhtTCwnBcIQLc5tVxlbMkYMmb5g2U2OUSbadalslw9Uy+r9DY2JzJUMkVigrTW2ePJIezvB37+c+APfyj0SkoGdkKrLzcmCJs1Qsadf+jEwS8cxODrgzldX94p0ZAxUNjWM//7/ADCMQFnznZKBRZGqPFYcd1pFQAgCcpihQm902eqz+Vekaw6bu+Ll+wc21Jna1sYv3hpMKOpki96FOfP2nJmBpWW+TLmBGFYEQoud2g7hPJ24kXApSMIg1KLGrmoRCtkzMLTVS6r3MRapahFiVLUHTypP0mkx5d68c3kEHpD8u+n1omCcFw4hJ2dwFe+Anz724VeScnATl7pU320YL0IT6bdpDCXiomUkuFDHwK6ukpqdB2j5qIaTLhpAjxLPKN6XG8wgQc3DgEAvnWZcXeQcdPZlQCAp7YH4C3C0XuAOKlqzwnxvHzKdPUiqSq3VUrJobDx6HOkJ4Y13z+Ob/yzFz97caAga2Dnz/pyq3QO7S8x82XsCcK4LPTYYGnVopJkoQhz/JgwDKk02ZUmlZRxirYz6qKKCUKPgzM09zgaF1IuqMom0mowC7o62c4jkyUtFdk4Ocn5KbU3aVZQH0LTsPdFrVGHsHJ44jQf4xFuEy+IJScIXS6gqQmori70Skwz4ZMTMO8P81D3vrpRPe5fNvvgjwhYMKEMFyw0//deMdWB+RPsCMcEPL6tOBs7txyLgBfEIqqJ1drTXlckw8bKSVXE6PDcDvm987cRFCm1HI3gm//sxRv7zTntkZggXefrK6yoKxevO6V2rR1zV8uIwvkrT7adSZ9cAigaU0tVxpaUn6duqwgZ27W3AwBfMjxd4bRIglAvZNzpjaekLGWabMJ+P7vRnjwer9vWxpt8k1Y6LajxiOsZNOgQRuMC9ndGVae3FD3Uh9A0/cmQR53H2GmhOZlD2Ofnpf5r4fYwhLgAi8sCx8QSbDlDGCbBC/j1K4MAgC9cUGXaHQTE/MePJMPGI7mQ5xMm8LTcQcaSyeLvmZtIjB4bFTOE93fFMJil2/zpB0/i5y8N4qpfnDDVLm4geTyOA6pcFoVDWFrRuDEnCFko12GTR9KFVdy8bELGLjsHt0M/ZMzC0+VOxdxjHUGY3jYm0x3FYFDc14wGURAKAjCks39211LpskqOacBgs8xLf9aBZXcexS3rewxtX1SQIDRNX/LkZdQhrPVYYEtuejLZ0FoKF89ylVbLGQB4913gi18EfvGLQq8kKxLBBPy7/OBHqRnuszsCaO2Jo9ZjwUeToi4brl0tPvaN/aGMBXiFgOUPnjJNvaCEsWCCWLS3r5ME4WjTciz1Nc/mbzAYTEj7CUYFU8Lep+huYrFw0lCM/iJ8P+sxZgWhq4yDU2olo1NUIlUZ64SMFY2pXQZDxmLbG1bUon2CTk9AztQ0mgm8hgqr1EzbqxOSlgWhHMIORISMdz+hKI+NB8TQ36Pvls40AQkKGZtGdgiNCUKLhUNTZTKPMJnLWrL5gwBw8CBw333A448XeiVZ8eakN/HukndHrdL4vv+IhSA3nlUJtyP7z9mMBjuWTC4DL6SG/oqFXcdFQagcV6fGgoniTfr+rlhpRlVKlHhCkMbGzZ8g/g2ymSP8dloPyc2HjOeCsrQ0ds2vTUZZSq3n75i7WjJB57QrpoqouHnpOYRaIeMEL7eRUVYZq+0TkMVfuYNTFJVoC7Z0MZfJIVQKTpZH6A1mFoQVTotUdQ0AAZ3G2kBqLuNgqPRG8JBDaJ4+KYfQ+GlBziNMCsJDJSwIS/y94pzlhK3ahmhP/h2q9r4YXtsXAscBnz6nasT7u2y5WAzzdEtxCUKeF3DgZKrY0GJ6vR0OG4dwTEBbb2n1nytljvbHEU+IUcFz54tV7ge7zH8G0vP3MxV4KmGpYuyaLzuEpXXdHHOCkOUQOuycIt9PJWQc1QgZpwk9pUB0lXHSdvEEEFOZW8kEW7kih9Cn0vaGwcQcmwxh1CGsdFlQlRSEgwYEYZXLCqedA4viZWqFs1dhlwsCcKDL/B1XQRkHo+uO9cfwg6f7M7YqMkq/ySpjAGhmHfnTQ8alLAhLrA8hY8XrK7Cmfw1qzqnJ+7FYvt/Z81xS94KRcHlSEL60O1hUN59H++MIxwSU2YBpGZ6n1cJhXjNzqChsPFq0Js9/0+ttmFYvRiyOZ9EQuq1X3A+7xrebEPV+hVEDQLo2D2XI8S82xpwglHIDbRbdvMBQeg6hRsNppUBUNqZO/x1DWVRSbqCoRMoJTL6RBzIkwzLBWem0oNqVdAhD2o8ZUozy4zhl2Fj/pHs4TWSk/7/omTNHHFv3178WeiV5IRIT8L6fnsD3nujH+T86rpunahTmTtcZzCEEIIWMu5LNqYMHxWbUrFEyMXpYPdasCjvMIggCHk4Kwo+enn3uoJIVUx2YVGNDICLg1b2Fncms5EDSaZrdWAabNfNrO3/i6OQRtvfFcOXPT+Dy/zuB7qHx7Ua29zFBaJeqwDsy9OdVg7m6Z89zpezXCHJ3EfH6Wpm8zgqCelFrsVJSgpDnBXzpoW4svr1dUvPpKItFXDo5hMq8QPFf9ZBxUDHz2GKR284A6vmGUlGJQ+kQ6jl44oV0egPrEZjQHePkVTiElS7jIeOq5LZSs+4Mb9KetGrnkmu2WlUFXHwxcM45hV5JXnh5TxCHu8XPQKc3gcffG1mepzhyMRkyNlhlDMjNqU8OxVNazrD5uiVFiTuEo8W7bREc6IrBVcbhA6dmHlNnBI7jcNky0SV8bmfxhI33JyMjzPnLxPxkYcnePFYaC4KAG/94Ei/sCuKl3UF84a8lWPSXQ1i6yoRqGybVsJFx5q9XTACukwShcVHJ0sLYNd9p52BP3lfrGTbFRkkJwlf3hfCH14dwqDumeRepFIQsLzCsItwiLIcwQ8g4nDbijuMUQlNFVKmFjHUdwhBzCMUTTjQuFn1o4VMIQjZAXq+oRF6PuGaPAZEKDO9vWGojeMY6T25LFYAjFYT+iIBY8k9sxiFkrWe6vAlRDCYAi9uCsqRTUlKUuCAMtYWw47Id2H7e9rwe5+E3RXfwiuUe6RyXCy5MTjl5pYgcwv1Jp29us7H3M2sHlqs0DjXea4+kjPp7uiWgaZCMB1gf1GZFn8iOgbjp+dis48eZs8Vq8sEgnzG1iuFPu85yHCeZMHp9iIuNkhKESgv3pIZNLvUhTKkI1g4ZM2dQK2QclHoQys6g3rQSv6IRtJbIVMLcvYnVNim/T7eNjCJXQXIIdd5wTFwyK1tveouS7qQAnNEgfsBKziE8eVIcW/fPfxZ6JXnhnSNi5eM3LhXzxd4+HDZ9AlTC/r7KYiwjSA6hNy7nD8525Sx02TkYx69eHsTT2wMjen6GKPF8U6vbiv5n+jH42iAS4fx8XqNxAf/cIgrCj52Rm3Ax46y5TlgtwOHuWNEInP3JkPG8CcYE4cyG/AvCf20Rb/6uWVWOtXOdEATg+SJyVUcb1uFgQpVNEoSRuGCqB2BMsf2sxjLp2t1t8LrnS8shBMQ0LYAEYd5QihKtkW1SbqCNM5RDyFq3aIeMmcCUXyonK1ZRcR7Z9mITa/0WNYAs5qrcsqMY0BGEUg6hywKPgzmV2tszgetJrt9IXiMgh4wXJp2eknMIDx8GPv3pMTm6LhDhpQvVjWdVoswG9Pr5EV2EpPxBj8WUmGMOYac3Ac7GofLMSlSursx6HUpae2JY/b1j+K9/9OLq+zrxjX/25mS/mlxyCXDkCPD3v+f3OHnC3mCHtcIKCEC4NT/j017cFUSfn0dzlRXnLshtWkCV24pVM0R3pljyCA+YDBnPTDqEXd5ExpvubHllj5ine8UKDy5iruqe4ni9CoHSIXTYOan7hhkTgwk/q0U8B7Iiz/TUKS38itoBRpVL3AcJwjyRKgjVHcKwou0ME3mRuJDSF0oQhOE5hHZ1N085to4hCT2V0K7SUZRC1jotXphDWO22GGpTo6wylkfzae8/kPY8DecQJmcmLyhVQcjcpAL3IRQEAT99fgC3rO/Gsf7cuAa7jkfBC+IJcHq9HUsmif3RdhzLPm+J9SA02pSa0aTIIay5sAanbDoF8/4wL+t1KPniQ93o8SVQkzzB//JlLzYcyOOFz+MBZswAJkzI3zHyCMdxUnV3vnoRrn9TnFt83WkVhooszHLeAnH9r+wN5nzfZhkIJKSxokZDxrUeq/R+bc2DS9gfSGBnh/g5XzfPJeW7vTXCCEEp05k0h9jNKeuSYKYHICvMaaiwJvurivvo9hnLI1SmijGMRPCKjZIShD2Knj5aDqE8us6SIuKU4d2Iol3MsBzC9CpjnZCxan9DpUOoU9TCUIaAmVjTyu/jeUHal8chC0K9O1H2O7atJCJ1HMIEL0hTKxYmx4/1lljH9WJpO/P2kTBuf6QPv39tCHc93p+TfR5I9sdiCey5qGyUppSYaDkDAM3JKuNoHBjQKW4yy9uHw/jPnhDsVmDj7VPwqXWi6/jDZ3LzGo5VWHU36weZSwYCCTyTbBydq+ridM5Luo6v7QsVvF0HcwcnVVtN5UrmM2y8tTUMQRBzFZsqbVg62QELJzpcnRrXxLEOMysakyKOzRE2M/VG2kfSGWQOYaZRsgx/WlEJIBdykiDME8YcQmUfQlkMKEWZUhxKOYRaIeOILDDlx6iLR+VxlD0L1ULLDLWq5IBG30Llc/CUcfDoFLekr5+Fl6WQsY6IHAzyksE2p0k8uQ2UWMf1YnEIH1bMZ33iPb9qgZNZDiUbqLIEdjYya+8IBKE0pcREU2pA/JwxR6Qrh3lfD2wQp2Bcu7oCsxrt+PolNeA44D97QvnLL9u1C/jGN4Bf/zo/+x8F8ukQ/vtdP6JxYPGkMiydkp851afNdKLcwaHHl8CujsL28mM3XnMN5g8yWNiYdQHIJTuOi2tiU1PcDgvmJd3L7cmZy+OJYISXrtn1SREnOYQmmkIzJ5iJSilkbDAyJg+kUOQQGizgLCZKVhBqNWMOx+UqY4uFk3IEleKNvYEsHKTScFMhYw3xCChDxhYpZByNQ3OUkTz7mMs4+1iZi+gq46SKYT1xxx7DxKk0Ts/A9BR3GSd9uErpLgdA0TiErPgDEMP0O4+P/CLHLjSzk2KdOYUjaR4uTSkx6RACYqjGlhBwcs7beGvmW4iNMDQeTwh4YpvoRH1ijegMTq+3S/3BRlpRrcnBg8CPfwysX5+f/Y8C+XQI2c3N9TkuJlFit3E4I/kcNo3SCD4tWJ7u3CZzgnBGHh3CHcfE88mSyfKalkwRvzcze3eswCJXZTY5HYqlvWSa+pWyn6S2YKKyMRn5MOoQ+hTFpIwqA23hio2SEoTKnABfmFfNmQiltYlRKyxRNqVmCfRa4d2QQuAxtMSj8jhuR1oTaxXxKAiClMvnLrNIdxdaOYRywYq4buYQBnSKVlgOISsq8WRwIQHAm2yOXe22SG/qQERAPFFCOSpF4BBG4wJ2d4gncObmsRP6SGAXmlnJfbJK8PYROGfSlBKTOYSAmEfY5I+DiwmInozCluwFli1vHQ5jMMij1mPBmjlO6efvXyZPsyDUyZcgPNwdw5uHwrBwwIdPy58gBOS2H2ZmyeaDA53mCkoYs/LYeoa5pkqHlgnWAyZGrY0VWFi4vlxuyl6X7KPabyJkPCjl8jOXUdyH0TQYtaKSSqoyzi/KXLkEr972JZI2gUQt3y+c5poBsshLD+nJVcZKh1AvZCy7a86UkLXK+LyYIOmWcqdF6mGkVWUciqa6lSwfUD9knJpDKIlOA9NTqlxWSRAC+u1wio4icAj3d0URS4h3imxWay4cwmP9YroEG6XFRocNBPmswxN9iipjszRXWdFRacPO9Qux/PXlI245w1poXLTYDatF3herqNxwIJSfCs4S70MIyA3Bw0fD4HP4GrFRdectcEmtPfLFmjmyQ1jIQgnJITQbMs6TQxiJCdKaFiscwrlJwVpy40VzQG/yRlbZO5V932siZKws1gTk0XPeDJPDGOmTSlL2QYIwP6Q3bFa7+ClDxuK/SdGkEGTyNopWMhph4HQRBihCxiqCVM4htKRMNlErLFFW+rrLOIVY08ohZEI2rUBEN2ScXmWcuRBlSNEKx65o31NK1jeWLAEeeQT43/8t2BJYaHdesx2LJrGw7sgEYSjKS3ktk2vFC3OF0yLl8R01MW5JSbZVxkCyuo/j0GG3oHLlyFvOPLdTdAAvXepJ+fncZjum1tkQjQNv7M9DOHEMCEJ7ox3WcivAA6HW3LxGKaPqzshNSyE9Vk53wG4FTgwmpHFio00sLkjjOs06hEwQHu2Lq867z5a9nVHEE0CN24LJCheeVUAfHIcOoXTe8gwXhGYcQm/aRK+apFOolZqWjlpRCfueHMI8kOCFYY6gmnBiDp8j3SFUhozTwsrK70MxIeWulLmAqtumibxYXECCT93GSBNrVxkHq0WeM6wl8ILDHMKko6jhECpfM8khdGZuOzOY9uEoxY7raGwEPvhB4MILC7YEVvwwrd6O6clJNGbGIanBuum7y+RiDnYMQLwIZQNrylqXVQ6h+JjOLOaHptPrS2B3Mix2/sLUPnccx0nTLF7PhyBklLAg5Dgu52Hjtw6HcaQnBo+Dw5UrPJkfMELcDgtOmSaGRDfnIRfSCK29McQT4jl2kklHdEKVFa4yDgkeOJqjVlOAnCO4eHJZigvPBGif3/hkjbGCMmTMYOFeM21ntBxCo4JQre2MdN0sob9JyQhCpehhjSfT3/w8LyCavCYx908vh9ClEgYWBEj7SN1WfqncGiFjpQvpTpuAohYyZj8rl8SafksYFv51pzmEWo2p06uSlcfScxXTPxyl2E8pF4z07r496W5Mq7dhWp14UTnaF9MsMDICCxdPqbWlXBSmJt1CMwPZlUhzjE1WGQNAU6UNH902hAUPn0Rw/8jy+945IuaNzWu2q47QO22mM2W7nDIGHEIg95XGzB286pTylJBYPjkzKWoLlUeoLCixWMylQFgsnDSK9EgOK40Pd4trmpNW5FLpknO9jw8UxlEtFFIxnOK8JRWVmAkZh1NNkGoTIWOeFyR9olZUUkpGSgkJQvFFtXDy3UB6yFi9v+DwqSKsUbQzxfWTXwplHmG6Kwcowstprl9QUb1clryp1KtIZomobkms6fchTA//ejJUDKdXJQPKohK9HEK5qAQozTc2TpwAHn4YeOEF0w8VBAE3/+kk6r50GG8fzv6CxBzC6fV2TKqxwWYFYgmgczD7Fj7MIZyUVrgxVRKc2TqELIcwO4fwggMBnP7aAKIjDIm/mXy9WaVpOquTgnDb0UhOw3EACl6Rnity6RBGYgL+nRyVls/q4nTOTBYTFarSeH+yoGSuyXAxY3q9+HlsG2FEQAlrN8WKVpRMSd4QHsvyhrBUYWlMVYpoSZ3Udsa8QyhHxZIhYwPXPGW0Ta2oxBsqnZZtJScIPQ7t0KpSoDGxpzbPOKSYZsIos8nXA+V+0vP2tPap/D+rAmbfq20LyNXBzBl0O7TFo3It6U2mYwl1NysQGb4eSXTqtZ1J+5Cx17uU3thoaQGuvz6r0XWv7gvhr5t9iMaB7z+dfSNkqfij1garhZNO2tm6eIA8YqmpKl0QZh8yjsYFKf2iriKLKmOXBU3Jky9zp7LlzcOiADh9llP193Ob7KhyWRCKCrlvs3H22cDOneKNRAlT+75aTL97OhquaRjxvp7bGcBAkMekaqvU9mc0OGOWeKx9nbGCzFE3O8M4nZF8HrVgRSqzdQTh0f7x5RDKuX8qOYTBhOGiJHZtYyKuxiN318h048l0iNWSqimoyjiPMHFT7pCrcdOdNJYvZ7VAGqsk5fCpNKZWijyO41RzA8Nprpzye+2pJsObWKs1JJZEblnqWrXayAzLIVQcR+0x6RXGADI2vwZk+7zSWcIO4QjazihdiVf3BhHN0olizdMnJHOQ2GilkwZ7W6nB+mKx0UoMZUjaLOxO2sIB1S7zr1fNQAxWAQjbOPD12VegxuICtraJbXnO0BCEFguHU6eL+WXv5Hpeb2UlsHgxMGtWbvc7ylSvq8b0O6aj5pyaEe9r/ZtiuPi60ypSKr7zTX2FFfMniMLnrRG49NnCir/mGRxZl87UEXwetWBFaqoOYfJ4x8aZIEx39gC5uX48Yfyalb6fSoXTl8kl9CumjSnTeJSCsFTGCpaMIGQq3K2Y6KElCJUqnYV3U6qMVYpKAKV4G+4Qps4ytgzbTtxWTTxqt4ZhIpeFcaWcQA2HMH3+cpkNsCV1gV9F4KmFu41UJgfSeirJ001K400NYERtZ95tlXsFxhLZVQZH44LU9oAJQWk+5lD2J20mMtMFIbsAZVO0wsLFNR6L6XwpALAdEy/Yx6ts6PZlf9OwtzOKUFRApcsiTchRY+UMUSwy8Ujkh15fQmoB9NFRDBczWNrAm6NcWCIIAvZ3ZVdhzJiaY8euP5CQCr9YEYmSKbXiz8abIPSm5bsD4jWXXSPZSE49BEFQ7Ec8r9qsnJQPmCmPUK2gBJBdy1hCvai0GCkZQRhUuGly8UXqi5zegxBQ70MYUoy3U8IKUUIqOYRKN9GpUTkc1Glird52hj0nS8patYtEUvcvNqceLngZgcjw9ZQrRKdWcYM/zVk0MjO56BiBQ8iavzKxnU3vQCbc7Fb5jpWNRcqFQ9iYJghZTmG3L2G6gTgb8ZTNlBIACB0QL9jHq2zoGkF+5PajosBbNkU/kX9ZsinvruM5FoQHDwLf/S7w+9/ndr8FIHQkhL5n+hDtzj6s/u93fYglxDFpiyblZ1SdHquTwn/LKAv/bl8Cg0EeHCdPAzJLrkPGzB2cVG2VUouUSDmE40wQqjmEgHJ8XebzUSgqIJ7cTJmLWG2w9Yw0pSTt71Lu4CQ/olQKMktGEEoTN5wWzUpZFpZ12oa3iAmmjK4b3koGkIVkSCW8nOoQqredCatULzMxphYyVk41UW6r1UZGmRPIkFvPqFUxCynbpH+v5USy15UJb08GoVqUZOkQRuMCTiTbp1yenIyRzYzgLq+c68fCCE3JcUgnRzCEXhaEqaHZ+nIrrBZRB3ebzLmSCkqy6EEIyIKwo8qGkyNwP1uSU1zYnFYt2NiuPSei4EdQsT2MgweBu+4aE4Jwz0f2YOdlO+Hd6M16Hw+/mf9RdXqsnim+D95rC4+oMt8srKBker0tpVetGZhj3zkYzzrlRAkThDNVwsWAUhCWVlHJ957ow7k/Oo732rNLC1BzCAFILbmMCDG2jYWTc+wBuagy07QSZkyVO1OvNRZlK7kSaT1TMoKQvaCeMk61tyCgCBmn5PAlBZlq25nUp682ki59FjAgC0etqSZq+YZq4itdmDKxpjYBJWV7xbpl904tZCyH2ZVrZxpJzVVU7ot9ODylGDLO0iE81h+HIIh/NxaazOaum/Xkm1gli6xGKWSc+xxCi4WTfsbcSaNILWeymFICAMEDYquZjkqbJISzQXYI9QXhrEY7nHYOwaiA1hGM6xvGGGk7AwDly8vhWeYBskz7O9gVxTutEVgtwLWry3O7OIPMn1CGcgcHf0TAvixuyrJl/wjzBwGgscIKh40DL+SmFQwrKFELFwOyIDzeH8/tTVIe2Xsiih88PYA3D4XxqT92Z7UPLYewwkTeu7LNmjIH0Oi0Eq2QMaBoTk2CMLfIbpdFCtkOK+pQCRmrCTIWWtbKIYwow8tSvuHwMPCwkLFKiFatD2L6c3JLIeOkuNOsMtbOCVRzCNUcRY6TBbWWE+lPG8MjzUweBw4hqwCeVmeT7vKzuetmQ9frFVW7I80hTPCCtN/0kDEg5yqaDduy3KSsQ8bJJtHHq+2mxSiD5wVpzvOKafqC0GrhsCBZ/bkrB6MAhzEGBOG8383Dqu2r0PCB7CqN/5p0By9Y6Jac7dHGauFwynTWd3L0wsYjLSgBxBs0qfI3B4Ul7Yom92pMrLbBwon5amYjBIXi0a1+6fu9nVGpTZdRxNy/1OpgBhOIRqZrabuMxlrP+DVCxoBcnFIqBZklJAhlp86t0QdQyiFUhIz1cgiV84kBZchYGV423odQbVtprSpuXPr2bp3wLzC87YzysWoiMpiWo2hkTcrjs9C8O0OxS1GyciXw178C3/mOqYexZtJT6+yK3l7mRQ7LO6nxDHcIs80h7PUnwAuixq1XCe9KDqFJwclGPGUTMo4PxaXegyNxCFt74/CFBTjtnKELMRsFyPI9c8IY6UM4UhK8gPWbhwAAH19TWdC1rEpWlL/bNnqVxqygJNsehIyR9gZVwopTWLFKOjYrJ50TRpKSMppsTfubmp0+FI7JgyjSHUK5wjfzayEJwmFFIcZEpS+i7RBWuvR7CxcbJSMIla1itHr7hVWKRZizF1LJIVQKR7ZvwHjION31091Wde5xagg4U1/B9CpjQH4T6uUQuh2pzzOjQyi9wVnvwhIsKpk6FfjYx4CLLzb1MJYDN7HaJlXudQzGTecwDbDKXUWSMnNaun3G+2OlrM0rj2libZWUNCfb25gVZVK3/yxCxmwaRrzGhoDDknUO4e4O0QFaMKFM9bmlsziZR8gelxPGUMiYIWQRPnxlTxAdgwnUeiy4bFn+R9XpsSrZiHxLPibTaLC/c+QOIZDbwhK2D7ZPNZqqRt7FYDRhXQJWJkX/XpN9RZmQ47jUhtCAubFx6ZO5GHIOobGQcfoalD8jhzDHKOcPq4k8QL3tjP7oOvUcQpZvKAiC6raaIeO0ELD4vV4OYarAUzqL6qPuVELGOuHcgErVs/L/ascQBEHKFZSrjEswZJwlLEevocKK5iorLJzYz6rHZBiGJSKzSjVAdvBCUUF1DncmTmpUGDPYTGGzYVs24qk2C4eQjarjkvmW2VYZsybTCyYauwgvTG63r5NyCNXg4zy2LN+CDeUbEDMZsvzLZjFc/OHVFcM6MYw2K5Mh490noqNy/glGeMmNy7YpNUNyCEdY6MHzgpS2wvapRqMUISh+h3AgkJDOZ9euFouWzDaaZyKrwjm8XZaZ3rks7JzuMso5hPr7CEiCcPhnhYlMcghzDBtL57Rzmnl5eoLQSB/CdDcvlgASyYe5VQpFtIpKUrfVFl/pIWOHjQN7X6uFgNXayLj1ikokYZf6PNn/1XojhqKCdD1MDxlrOYpFSUcH8MQTwObNph6mLNqwWjjJNTM7LWEwwELGqX8rVqiTzV08e4ymIMyyirlvBFXGrKDEkewZl+3FiF0MFhoUhGye6+Hukc2GTmEMhYwtNgvi/XHwIV76GxlhIJDAk9vE3oP/b01hqouVTK61YWK1FQleLjrKJ4e6YxAE0dmvz2KutxKpF+EIHcKTQwlE42IV7KRqbUEoRSBKQBCyIpnmKitWzhAdQrOCUCv3D1BO18reIawx2naGFWGSQzh6MPHktHMK1y31RY7EWdWu0qEb3kQ6zMRlWg6hKy23LmUWsGKfcr/CdEE6vApYEo+qRSWp23Mcp1tprJajqBfOVeuLqFyTmkhVtvKRJ6KwPMXSeFMDADZsAK66CrjjDlMPO5kmulhRiHmHMHUeNIO1i8kmj1BqOaMxXi57h5AJQvOng/Il5Wj8SCNqk1MxTg5lV+W416QgnFpnQ5lNvFHMWe+11auBt94CHnooN/srMK65okhnLq4R/rXFj0hcwOJJZViRof3PaLGK9SPM9WQaFQ4oRtZxI7xByFXImD1+Yo0Ndpv2mlgE4mSWhV2jCROEMxrs0uSVjgFzLXq8GhXGgOzuGXMI+ZTHpO8jkyD064SMK02ErouBkhGEYcX8YVm4qRd1KMMcTpXG0Fp9CNPbzrD9WThxKkj6PuMJpDQBVms7oyVeU7ZPaaSduY1MatsZ7XxAqcp4mEOoXc3sV7iKzIZndz5qjmLRkmXbGWXIWPlvr4lB6YCiqMSdKt7kPB/zgpA5eQ0agpDtu9OsQ8gEYRZVxg0fasDChxdizs0TAIifCSPTAZTEE4LU6sNoyNhq4TCrQdz24MkchY2rqoDTThPH140B3HPdAOQ+kUb4yya5mGSkgihXsPZPyglC+SJXBSWAHN49PhAbUSsYZecDPdjnfySN70eLVkUbncYKK5x2DoLJFj1aLWcAeeLISBzCSo2JaOkYaTvjC5XGtbOEBCFzCOWxNMMduuEhY7Wehcp9KUkfXacMAStPjkoBlzrmTjtkrFZUwkSuUrDpVRqr7V8WkNoO4fAqY20XMr3CWHkMvXF3RUeWbWfS+/xJgjBLh7AmrVCjoTw7gQkAA1IYWsshlEPGRotWeF6Q285k2ZgaAOw2TgqxmXUojvTEEI2L78tMFz0lbLzdwSxGC44H3PNEQWjUIdx5PIJ32yKwWYGPnF74cDFj1Yw8za5WIRc9CBmTkq1govGR5fUZKSgBFI3vS0AQMld/ap3YuD+bFj0st0/VIWS5eyYEIRs1xzCa/6dXVEIOYZ4IK3oHarlu6jmEalXG6iFjR1qxiFoIOH3/Ke1sVEK0Wk20xfUPL1iRR9FpN7JWtp2RHEK9PofDqoy1Xcj0HoTKYwSjQskM6c7GIYwnBKkYhIWKsw4Za4g3JhD7sxKE+tXALGQciQsZwxwMb4gHMy/MVhknQgmEWkMQki45E6RmHUqpoGSC/si6dCRBmCuH8OhR4Cc/AR58MDf7KzAsZGzUIfztq14AwBXLPZoudCE4ZZoTHCcKo5FMwjHCAWmG8cgFod3GSSMlR9KLkBWlaLWcYeSi8f1owfKcJyTPGdm06BnS6EGo/JkZh1CrdU2mfbBrZrlj+LlLdghJEOYUZUsZuco4rQ9hXMchjA13CDVDxlIO4fAQMCA2HXXYhrtsattrtahRbu82G2JWaUyt5/YNqzJ2aB/DnzalRHkMQVB/HkVJFg6hUkSxJs31WTh6PC8PS0/PIWSFG5nGIanRn8EhdNot0vGMtp5h4eJyB2d6TJfvHR/envk2tizZAkARsjLpEO7uMBcuZsxpZiHjHDmEhw4BX/+6KArHAJJDeDAoiXYtBoMJ/O0tsbr4s+dW53tppqh0WaRG5PkMG/O8oGhKPfKQMZCbXoSyQ6gvCJtLqO0Ma57NRCxr8WUmZKyV+weYawitVZxSaVDM+aTRdeQQjhpy+5fMo+vUcgjV287o9yEMauQaKvcbVulZmOoQalcZh1X2r1XRKwiCRkhau62NZpUxW5Nq3mHSIXQqn4P8eCNh43hCyDjuJ+8wh9CEIGQzfStdFqkXXjYhY2+Ilw6fnkPIxBw7lhkyOYSA+cKS3hE0pY6ejIJzcHAmc7ykSSkmHUKpoGSSSUGYdAgP5MohHGM4pznBlXEQIgLCx/TDrQ9t9iEYFbBwYhnOmuscpRUah1Wi5rOw5PhAHMGoALsVmK4xEcQsuSgsMRoyZgVrvX5etY9tMZFevNeUhbuplfun/NlIHMIKhZjTi4zpFpWQQ5gfIipFJVohY7UijUhckNpTSAUqaRVbkshLiquwJMCGv0zp+YYAEFIZFWdmdB2gDM+mPjfl41O3H0mVsXbIWJlDaLXIrX6MtJ751AMnMf3rbaNSFagJcwhNhIz7VZpJs5CxmXFQzP1zlXHD+rjJIePcO4SAeVHWl1xHNiHCxmsbsS6wDgvWLxCPLU1iMXfxM9tyhsEE4bH+uObUHVOMoT6EAMBZObiS7YBCOlMgeF6QwsWfPbeqaIpJlKyazgpL8ndOYQUlsxrtutW8ZmBh3vYsQ8aCIEjh5kz5tXUeC6zJU1dPFikpowkLGbO8x2zGeno1cv8A2TUUp5nof54zOYSCoH/d86nk3TMqyCHMD1J+oE0WJ7G0Kl9pAoly7rBCnIXTqoed6UIprcpYK2QsHmO40GPbp+QwSvs0FgJ2a7h3SvGmVsWsJjgDKo4iYGz+cXo+hEcnlK3EH+bxj3f8CEUFfPbP2Q0szwlnngn89rfAF75g+CEs70/plkkhYxOCcDDpjtaqhDJq8+wQSuPrRsEhBETRYa8WhRmblNJpojl1LC5IIV+zIeOGCiuqXBYIgtzGYkSMMUEIyJXGer0I/7MniEPdMVQ4uaIqJlHCHMKtbZG85THnYoZxOtPqRxYyHgjyUhrPlAw5hBYLJ7WkKubWM/4wL13P2Pkqm7GeulXGCrcuU9hYaz+uMk4S2FqCLsEL0rWXHMJRRJ4/bEkROMrcQLUcQqVbyIoiQlo5hFLIOLUPoa4gVAg9tVnDWlXGCV6Q1qtWhJIu1ti+HTYOVstwB1LVIYwMX4/yGGoi0q/xGI/B5tQbFAnsx/vjhStCmTcP+MxngEsvNfwQySFUCK5sQsZMWFarOHlMzA2YbM0SjcsTZPQcwgnV5ioN+0YoCJWwBHozeUCHumOIJcQbkExJ8+lwHJf7whJgTAlC17zMvQh/+sIgALHVjNpFrRhYPMkBh43DQJDPjfhXgVUYz82hIBxpyJg9rrHCOqy4UY16qU1W8QoQFkHwODgp764xi6baeo2pbVa5p2+msLFXoziF47iMgs6vEIqqo+vIIcwPygpiZ4rI44dtowzTpReAxBLy+X5YDmFayJiJONWQsV22pBlqjaPZMdJ7Fiofp+oQavRYHOb26VQla1YZS30ItRtTpyfIMkGYKYdwv6IFiDfES2HOUkBqv6IQXOwE2xfgDU/EYE5eekGJct+Z5mMO22dye45TvyNmsBzCzkFjF6A+H5uPbO5UwMd4bF21FXs+sgdxv3gs5mCYaRStHFmXTahydi4F4Rh2CLUqjbe0hvHavhBsVuCWC6tHcWXmsNs4rJjG8gjzU1iyv5NVGOcmfxAAptfJIeNsbo5ZuDhTQQmD3dj1FXHIWG0E50hyCLXOh/L4Ou19hmM8ovHU7ZVkEnSsoMRuheqYRyYojYSui4HSEYSKJs4cpz6+ThlWVqLcVplr5EqrqnSm5QWmzxrW2xZQzA5WOoQaLWpSQsCKbbRyCIMq+wa0q5ITvCCtTasPoZqIDGiU0OuNu1OSLgYOdxco4f/4ceDll4EdOww/RM0hZM2aBQGGxe2ARlNq5b77/MZ7BQIK19FlSXGI0zHbiyzbkHG4NQzfuz70PtkLq5tVCrKQcTzl5kePPSfEi7vZcDFjRoN48W7rzaEgHENkmlbyk+cGAIhzi6dlKFooNCuni4IwX3mEyikluWJy8jMRjApZuXbsfJopXMxg56ts2lqNFun5g4AsDr0hfthIWC30HEJAMTZOx51TuodqVcKZZiIzg0TLWVf+vBTmGZeOIEwLB+s1nE4XcMrWM0yUcVzq9BFAbVKJdpWxKy2HUKsKWMvNDEr5jlxK7zUm+NLFmtq+jWyv9hi5yli77czwMLN23qGS48UiCJ9+GrjwQuC//9vwQ+RGp7I4sts4qcjEaNiYta/RcwhjCXOzoWWxqi/czE5CYUUldSaLSlhOmmuOC1zy/dtYYUWZDeAF4IRBh9LsyLp0WDVoTgTh0qXAK68Af/7zyPdVJJQvLcf8P8/Hon8tGva7nccjeHK7OLf4v95XM9pLM400saQt9w6hN5iQ+mfObcqdMHbaLZiQ/Exm04vQtCCsSJ6rilgQpjf/B8RzJbseGz13GXcIta9ZQ0Em6DjVG+1Ms4j1ppQAYuiaXX9LYZ5x6QjC5F0Ds2XVKo3VGlOL/5e3Vc5ETg9RyVXGqX0I1ULG6TmEkbggRZqU23Mcp1qAonQ8lWjmEEo9BTXEblRIGY/EHs9xau11dKqMNULG5c7kujIUlbATGLtr6yxUcnMWjanZhzv9jpNN8DBaCDKo4jQy3GWcdOIzU1gyIIWz9Z+P2UpfduGoN+kQsqpV1usOENMzWB6h0bBxthXGjBlJQdjak4P3WXU1cO654vi6MYKt0obmjzejcnXlsN9959E+CAJw9cryrB3a0YRNLNnWHsl5WxXWumhClRVVKs7+SJiWfI+2Z5FHKIeMjYlUuWiteMVHV1rLGUC8TjZUGM8jFARBM/ePIbWe0en5KruM6n/zTH0EfSpdObT2QQ5hjkjwghTnZ2HedIcOGC4aGUrRpNWUGhheAKIXMmbriKRVJCuPl/5/9Z6FxnoEqk01ER8v/18tJJ0+dg/IVGXMikrSn4OxohLmEK6YKp68zY58yxlZ9CGUBGGaGK42cGJRIoWMVdw8juOksI6ZwhLjDqF4Uu3z84ZyVrItKmEOIctRY7AGs0YEYTQu4FDSQc5WkExnVZz9McM5noRY/PX8ziBsVuCuq2oLvRxDzGywo8ZtQSQuYFdHbl3C/Z25LyhhZDOWjWHWIawvgRxC2SFMfU5NJiqNxZw88Xtth1Dcn54z583gMmaaNCL3INS+zmRyGYuJkhCEAPDgTU34zccbJbWtNo9XziFU77snCsLMzaYlQSiNrtPOIWTbMiFVZoPU1Fg6vn34WjUFnmYOoUbIWPF/pSgNakwpUT5GtcqYdV3XrDLWflMneEFynBYlmwybHfmWM7LoQyjb/6mvcXXSlRs0+IFmbWdqVELGgCzq+rJwCNVcx5R9uy2wJ7WdkTvtvmwdwmSRAqtiZZgpLNnfFUU8IZ6MJ9eYqzBmTKqxwW4Vi7bMVDer0tUF/PrXwPr1I9tPkeHf5UfHrzvQ/2I/APFzets/ewEAN6ytxOym4ncHAfFm6lQpjzDHgjDZg3BuDgtKGKz1THtvNg6hsSkljNoSEIQ9KkUlyv8biW4wIcdx2vl7lcnzr16VsV5za+XPMzqEOtX5UqVyCTiE2Z2FRxmrhcN1p6X2x3KpVNdGNELGynYyoZj4fXoPQnE7WSgJgiC5dLoh42iqm6i6bZkFQELTwVMiOXHDcgLVBZ7FIoakwzEBgQgvtUkJqDTJlo6hU2Uc0GiyKc1M1nEIB4PyXFzWy6tgJ6ZsHEKNk0N18k5z0KCAY+Gaao3QU00WrWf6pR6E+sKN9SLrGEzg5FBcSmpXQzm7uc5klTErUkh3CCdLIePMbggbWbdwUnYVxoB4bphaZ8fh7hjaemMjK4w4ckTsWzl7NnD99dnvxyTxhIDndgbwyp4QOgbicJZxWDSxDFesyE0ot++JPrTe0Yqmjzeh9qJa/PZVL7a2R1DpsuD2y0vDHWSsnOHEy3tC2NIWxs2oytl+93XK87RzzbRaFjI25xCGY7zkljHnPROsW0AxC0LWMSH95tbMLGYm5CqcFs3550aEWCaHMHPbGe0ehIwKA7mMxUJJCEI1XGkOHaCdQ8gcumBUSMkhHLZPRdVxJC5vq1tUIvUs1BZgeuHtdPdRruZNffPoha89DlEQpjbJzuwQsr6Myoux1IfQmb6uzA4hOwlVuiyYUG2+f19OyUIQDmmMIGJd7407hPpuXjbNqY06hIAYNu4YTEjVfJr7DMoj9jIJTSVxXxzR5AWUVbEypiSdjPTiIjVY2G+xyZF16UyvtyUFYRxnzxvRrkRGse3Ma/uC+PxfelR76/334/1YNcOB2y+vxcWL3VmL5opVFai7vA4VqypwrD+G/36sDwDw/Q/WSZNtSoVVrLAkxw4hK26an4dcymybU3ckHW9XGWf4ho19jou5D6FWMYgZQZipwlj5u5E4hJnazvh1ppRI6zBQ7VwslNbZQIFauxWpElkrhy9DDqEyby4QESQHzaOi/tkx5BxCbQGm1iKHuY9aOYHDq4ZZbp/a/i0A+LQq5mToVyW3ge1DEEQRrRSZfmlSiXqrmvQG20rkfDSL1L+vYCOUsgoZq9/tsdCvUYdQrw8hIBeGmGkNYdQhBBTTSjKEXtjfq8ZtGZbmoAdzB+1NdmlKCWNa0pFsMxAe231cvAgvGqEgFAtLQmgdacPiUe5D+PvXvPjywz0QBNHZuXZ1BeZPKIMvzGPTwRBe3B3EltYIrvpFJ86e58KvPtaAOVnkuNVeVIvai2oRiwu4+Kcd8EcEnD7LiU+tG15oUuywkPHezih8YT4njbQjMUES5PPz4BCyghDWi9CosGdFKFNrbYYfU2eyAK4QDKp0cwCABjYVysB5MZOzp/ydbpWxQYcwU5WxEYeQQsZ5hAknZdsXLffPqahIllxEFafNmhZ+1VP/zE1kx9QL0aZPQBHXotVGRt0h1A0Bq8wZZsmueg4hW4dLcQ4MaFRNGWk7w1qY1Jdb5Q93oRzCdeuAn/4UmD/f8EO0ikqydgg1Q8bWlO2MMKAyZ1kLVliSySFkfxvTBSV7RUHoWeAZ9ruZjcmq394YeF7QDOcAwO4TTBA6TB0/nZy1nhnFPoR/2TSEW9b3AAD+35kV+N+PNKRcVL72vhp0D8XxsxcG8ZtXvXh9fwgrv3sM37qsBrdeXIOyLGbt3vFoHzYfCqPSZcH9NzTq/m2KleYqG6bU2nCsP4732sM4e54784MycPBkFLwgigLWIiaXsAk8vrCAwSCfsTCMYbagBJAb6YeiAoIRfljf2mKAibn0G2aW/2gklSaTs6f8nb5DKJ4DNYtKMog5v4kcwlIIGRffu8Ug6Tl8UYUhkd6YOqUPodRbUP2plyty5QJSgYWBAhSNxtHisYY7hFrr8GjkEIY0qpKVj1EWrUjupsrarYrpLekCT2t0HROqaoUoDGWTY3bS84UFw02Kc8oppwC33mp4dJ0gCPLdXtrJgQm7QQMnqgQvSCcgrfAuOxEOmBKErO2McYcwU7WeVFBitgfhvmT+4PzhF+OptXZYLeL7pFNHkA6FeCmENmKHUGpOnaMWR3l2CN85EsYXHxLnfN96cTV+/8lGVYehsdKG/7mmHtu+OxUXLnIjEhdw1+P9OP3uY3jzkPrkES1+/fIA/vxEH9xRHvff0FgyhSRqsPYzuQob7+0c2bScTLgdFmnGsJnWM2ZbzgDitYoVlZkpWhsteF7QFHO1Jppqyz1jR+YQZgo9V2UQlT4jVcYl5BCWrCB0p/UhVHY3d6aJLDmHkIfWCDhpv4oRbVriSLnP9LYz6g7h8LYzIa0m2hmqjNXmWUrzjFVC0mprV65TKfCicQGx5GcxPdSc/nqroWxhovyglkLuRCAi95EcSQ6h8sSh6RC62Z2wiZCxRiK2Gmx8XaYB972KEL8ZmEOoJgjtNk4q7NCbOcsmlEystprKX1SDtZ7JmUOYR0EYivL41AMnEY0DV53iwT0frMsoQmY02PHklyfgwZuaUF9uwZ4TUZz7ow586aHujMUDgiDgB0/3Y/Cm/fj7w534UaUFV6woz+VTGnWkBtU5mlgi5Q/mIVzMYFXC7Sbeo9k4hBzHKVrPFN951x8RpMLDYQ4hS6UxMNZzKEMPQuXvDIWMNSIvmdvOZC4qIYdwFEjPywsreq4Nm0CSFDPhqCCJPC0rnYVK/WFerrjVySFkYeCATpsXp2rbGbYOdeEVjafOPtYTnEz0KcPMemJWPO7wSmPlnGIz4+4YA4p2K3abPF7QTGg0Z3R0AG++CRw+bGhzJlot3PDXmIVpvQZOVEzkeRwc7Bphvbw7hMmQcZdBh9B0yJg5hAvUw3WzkmFjvSk1uzpG1pBaCXMIu7wJ1ek7hhmFkPEPnxnAwZMxTKiy4refMB625Tix00LL3dPwiTVix4U/vD6E+d9qw52P9amK723tYbzvpyfwvSf60eMR3xPnjiw6XxSsnC4Kwi05mlgiVxjnb3Qfu0k6amLON3PQzQhCoLhbz7CWXGW24aldskNo/MZbzyE0EjIecWNqHY2Qvo9ScAhLNocwvcpYWWGcfsetnMwRiGiHgQHlLGH9bZ1pOYS6TazTGl6nbJ8eMk4pbOGlrvkhA1XDSrcvIDmEGk4oe01U8g4dtuFixiNtr/PhCqbebVW7LAhFE4W5M3roIeC224BPfAJ48MGMmytbzqS/f9jfwIiAG8jQcgaQ74SNFqkkeCFj5bISo4PilTmfZpj67akI7AzAs3R4DiEgCsKXdusLwj1JQbh48sgVSo3bgkqXBUMhHu198exbtcydCzz1FOByZd42C473x/HzlwYBAP/30Qbd94gWdeVW/O6TTfjoGRX45j/7sP1oBPc+O4B7nx3A4kllUh+93R1Rqbeeq4zDmRdXA3v8kpgvZU6Z5oCFE1/PzsE4JlSP7DK290T+CkoYI3EIzYSMAfkGrxgFIbsWVLusw86zTMh6QzziCUG30C1TMYjyd8xNzGY/mdrOGJlUQo2pR4H0EGZEp3pYmUMYyOCcsZ/7wryum5hedas1eUT5s6CKYEt3CB02TjIqUraPqm+vXF9AxYHM9DyVIWBJAKvkQ6j1fUxHThYWP9jyHdronZhODMax8Nvt+Nc7Q+IPDFYZ6zUYrZYcQgOCUCoo0T5utce4wARSHVatMLQS8yFjc8Kk+WPNmPWjWXA0q4s55hAe0RGEO46L7k4uHEKO4zAjF2Hj6mrgssuA888f8ZrUuOepfoRjAtbOdeKKFepi2ihnz3Nj8+2T8ffPNeOChS5YONF1fXRrAI9uDWB/Vww2K3DdaeXYetdUXHBFHQA53F/KlDstkujfOkKXMBYXcKg7fy1nGGZbz/C8ILVuMusQ1rHQaxHmELJzmVqoV3nOzHRulBxCnfMh+50vLGhOMWL70Qr5KtvOCCqpJIaKSkxOuiokpesQajSG1usvGIryCETF32s5Z0zp9/sTSPCpP1PiTnPMmFDSyzcMp4ivpGBLc/w4joOnjIM/IqSJtcwOoVKssTeqRyNXUm2esV6Y2e0Yvn063rQPu5Gk3lzzo2cGcKQnhh1HI7gGMBwGHNKoMAYUgjDEI8ELqkPQGSwkUq3j5EltbAyEoAE5DF3h1A5DK2EjofwRAf4wr3myYg7CSHP40pmVDOEe1sgh5HkB29vFCzlrIzJSptfb0XIsOvLWM3nixGAc698Ub1KM5A0awWLhcNUp5bjqlHKcHIrj7cNhHO+PQ4AYolwzxykVd0WTFeHh1jASoQSsGiGyUuHU6Q7s7ohiS2sYly3PXlwf6YkhlhCvB1OynJZjhGl15ppTd/sSiMQFcBxMT/Gpkzo8FJ8A0aowBsQJX1UuC7whHgOBhDRkQQ0zDiHbXq2626hDmOBFjZFuyBgpKpEGEZAgzB/DcgiTTl36HOP0bQOZii2Sf3Bl/pWaeEyf7SuPitMJL6uEjNX27XFY4I8kUtrIBHVCwOniFNAXqIAsFJVVxn4d+1tyZPVCxlI4ICkIWXXuKH4Q/rNHdEAsQvKYBh1C9tzV7lyVoT2tEwuDVSLX6ty5VivuXGNxIaPI65eaUhu7iJc7LfA4OAQiAk4OJTQFoTxT1Lg48G3zIe6No3xJOewaoazZTeLPD56MqraeOXgyBn9EgLuMkybajBS50ngEgrCvD3j6acDjAa6+OifrYvz6lUHEEsCaOU6cPiv3IemmSptusYi90Q5btQ3xwThCB0MoX1rahSWrpjvxl00+vNs2ssKSnUmnesGEsry24WEhY6MOIQsXT6yyGroJVFLMOYTeDEUcNR5REGZau5HG1GU2uY2cV+O8nWk/HocYsRME0TRIjxb6DfQhlLpUGDQACsmYCRmH4+K/qg6hoom11mg2Bvs5u1i6yjhVR8gjtWFJdQj1qoBTcvxY2xmd7YNqRSiqDuHwcG6m0LjaY2SH0FjYO51CO4ThGC8l13MwN6mErVEtX7TMxknPP9Nd3oABh1B5d2ykcnnARFNqBnMJ9eaCspByk4nea8d/fhwt57ag4zcdmtvMbLDDaRcFqVqxw9akO7h0isNUQ2w9WKVx60haz7S1AZ/8JPDVr+ZkTQx/mMf9r4vu4Fcuqs7pvo3CcZxUBDQWwsYrk61ntrZFwGuEA43QckwMFy+bmt9qG5YHOBDkDRWnSQUlWYxilKqMcxgy3n40gneOjLyqmz13LUeujqXTZGjxZcQhBORzrdo1KBqXB1VoCVSO43TzCKVBDnqCUOEQjuS9OhqUrCBMD3nKc4y1BZPYcFq/7QwTBOxiqSUc02cOB3UcPylkHFMTbNpVw0qHMKBTZexWc/syFZWoNMDWmtQhHkP8WSSunY/BknfZh9BIlVcu2d8Zk1oamBWEPh2HEFDmEWYYB8fcPB2H0GblpOMYKSzpNzG2jtEk5RGq75/nBXT7mENoPFBQ1lAG5ywnPAu1w3Q2KyeNo2MXXCXvJV2dXIWLAbk5tZmk/dFi/Zs+DAZ5zG604/0ahTijAROEgb2Bgq0hVyye5IDDxmEwyOsWL2VixzHx5mTZlPwKwgqnRSomM1JpzELLZvMHAbloLVcO4T/f8eH0u49h3f8cx0u7R3YzIU0pyTDnPZOYNeIQKn+vFqVSXpfUUoUYWuPr4glZUBpxCJnLGIry6A8kilIclqwglMbRqVQZp+NUzDI22o6FNfXV7leYKsL0qoDVJpVIIWOV7T1qDqHOuuUCEcHQ9sp1quUdqgtC+XXQak49mJboO9oO4YGToviY2WCHJZkAnIA5Qah1p1dtMPzNHEStsXXy/oznlUgOoYmq1GapObX6xWcgyEs9JxtNhIxn/XgWTj90Oho+2KC73ZLkBZZdcJUwh/CUabm7CLMcrba+uGrytyHy1IfwL5tEd/Az51YVdDrIWHII7TYOK5Lvn7dH4Fyx9+fSKflv1C21njEQNma5sDMbsnAIK4y3b8mEIAi4+8l+6f8/eLpfZ+vMDKWlFaVjdFqJUYdQ7xrEDAyPg9ONVGg1p1a2kanQqTJ22OUWbP0BHs/tDGLiV1px0U+1oyyFomQFoWtYY2ptQahsyxKUQsbqbwD2x2dd4rXytpjQiiXESrWAFDLWC1kPrzLWa1OjbAmjVZWs3IeZtjPs58rKZD1RpHxdAyrNqeMJ2X1lr6FH0dNxNDiRHAZ/6nQHNk0+DXed+hX0nmtsUoleUQmgaE6dQcANGmwgbaawJBuHsDHp+nVpOITMAa8rt2Q1Bi0Ty5IX2HRBGIry2Jp0CE+b6czZ8aYlc7SGQnz2Oat5EIR7T0SxtT2SrPityNl+s4GNGhwLghAATp8lvn82H8pOEJ4ciqPTmwDHiY5jvpHSGgwUPrFtZmQhCFlqiZGZwJk4dDKGgyfl9b59OGyqoX46gxmcPbZ2PXdTEASpc0Umh1AWc8P3Z6SXIaDdnNqn06YtnVrF+b7PZ/4Gf7QoXUFoTxVZIR1ByASO2GyaCSutpFb2YdK/CCudvWCU123zoja6LqCzfbpYS/ACInFtR1EOXytDxvoOoeRaKtakN6jbYuGg1ruQobTTmahi7WvSx/DlCzYqbVKNDW0Lz8QPV3wBh1deaOixvlDS+tcKGeuEHpQYCRkDsuNoZG6n1PDbTA4hCxlr9CLsGjIfLhYSgmH3bUmyv+CO46kh4zcPhxGNA5OqrVJ7mlygHA824oklORSED20W3cH3LfboVk2OBmyyTPBAEEIhxknmmDVzREG46aC5MX6MHcl0htmNdt0csFzB3u+HDIS4j/SIN2wzGsyHjNnkoZEIN8Zr+8XX9qy5TsyfYAcvAG/sz+71BuSUG60ISq2Ub6e99nBMkEbVZnYIxc+cmkOYnvOuhVZzar+BCmMGazXWH+Dldl8FPh+oUVJVxscu+ROEGA9wgCeSwK+PhFFmBY63uDAtUY7LAwuw1BdFR5kA7qWXgGgE1dMHMdXlxx1bvXDFnagPNeJomQvljpuk/fZ9+o9IDIbBcQJWDMTxv4eD4DkeAsej/kgthtZ+HFwZB4vdAu6fD8Pi7YXVGcN33uuFIADcd6vxkXd8WB52w132bXnBf/gD0NGBxd1R3LHVj+ZDViBcBQD4xKYw7p5/k+zgPfigmNQO4GNb/TilI4rFETfwHyfinA3AdQCSbuf69cDBg9JhTukS9z/piBUYqgLuuksSqM0vPgp0Hhj2Wl50IISyfSF0nv4N6WfTNjyNO7ZuxZlDTuDA8CkUddzVCMIphsefeQbYskX6nSWYwB1bvbBZAfs9tcBXviKJ0ZnbXgXu2qf9h/3854HGRvH7114Tv7S4+WZg0iTx+02bgJdekn512lY/7uiI4oKwCwu6YvhB3RU4MdCkvS8FemIYkD/QmRw9ww6hx5jABJRTSkzkELKQsUYvQpZbaKag5MTvTqD1O62YcNMEzPrRLN1tl0x2gOOAjoE4urxxNCenp7y2T7yYnD3fnfO5sdPrbej2JdDeF8eKaVnsIMfrSfAC/va2DwDwsTML6w4CgHO6E5yDgxAREG4Lw5WHaufR5MzZ4vr3d8XQPRSXXHGj5Cx/cOdO4NFHxRuJb35Tbmz+1FPA1q3SZh9qj8DZEsDMozbgQKU4a72yUvzl888Db70FQMzv/diLA+B5YPkDVYDLCnzpS0Cd2EsS//kPsGGD5nLq/p94bfNHBMReewP2117RXvuNNwJTp4rfv/WWuA4FTdv8uONYFGt9Trx0yoewD3XYfiyCK4X94vPT4sMfBhYskF+fRx4BALz/zSEs7IljreAB3ki+7h/8ILB0KQBgZt9h3LH1ISzsLAMOqlTCX345vLOXidv62lH5o79qfm6FCy5CrWUmKsIJcC3tCD/1G/AJCxzlEVjLeEzojOK7b8Uw1V4F/4yzUH7D2eIDu7rQ85V/Q0hwgAB8+nAE7x9IYNZeB7rq7RDmzAMWLMBAVwxX7+zGJT3voOOAG9XTB+FpEN330IATPXsbULZ0Cpp/chFqPBaUR/2o/vSfsGrQiv/inMAll2m/fgWipARh6/MTwEMOM81M/nvoqPjvFzAIvAkc/Ec3gOkAgHn4ESbgedwBoA+nYyf+BzNs7Smh1AMPVCGSkC9wC5UHPQS8d9p7ih/MATAHM/E73I6/AwCGts3FLPwO87huJBQh4P3f7keotxJWhPBBWGFFCIde64YdQ7jGFsdrZUFwbw/BP8sF2+8ehf2t52FFDB8E8EEA2Cbup8zlAq6/DhyXDA8//DDw7LPSceYBuIP95xUAd90lOaG1zz0KPPHIsNdybfLrptBX5P28/Sxu2PZP6bjp1H7hMhyDU3Rln30W+PWvpd9VK9fwLoBPfhLlDvEEtmD368Brv1XfKQBcc02qIPzud7W3vfRSWRBu3pyy7QeSX9gGnA5g/aWnomNgmfa+FGQUhEYdQimHUF9o1Uh3jMbH4ZlxCJkA0yoqYbmFjSbuUoN7g4j3xwED5lKly4Klk8vQciyKDQdCuGaVKIiYu3D2/NyLkWn1drzTGsneIcxxyHjjwRBODCZQ47bgkiWFKyZhcFYO7nluBHYEENwXLHlBWFduxcKJZdhzIorNh8K46hRzrXRakoJwyeQR5g9+6lPyzfGXv5wqCP/wB2mzFckvAMCLEG9umSB84QXg//4PgBi2k2wFdi7+6EdlQfjKK8APfqC5nMrLrwDHVUIQgPArb8B+t8759MILZUH49tvDzr1XJL+wDfAuPx1AHXYdjwLd7+mfp5ctSxWEyW2vT39eADBnjiQIp/Qewce2/RKJbQ7wz8RggXg+9WMWBrEErv7ZGPr2EgDA0sAJ7PheORJwgYcDCTjAw4kEHEjABeG7UXwI+/AhAHgIeAtnAACW4Fuow1uYD6AKF2M/bsOR/+nH0huS6zl5Env/MQs8xL9jOcRrbLQdkG2N/QAAUXovx8EjotbwQBTUQazGEfwI5dsH0PwT8TxbEQsgvqkGNkzEOtd+7M5x/9dcUFKCsHlJJ/iYeL6OJ4Aj3XFw4DC70Y6TvBsbhKmYXmfDKVMdwI5dEOIJOJecBmHSdPzh9SE4Qg2o7zmGPo+ARYo/RuXcOKKDPYAg5gT2+QRwggUcLCizO+CqrwMf5SFEBfC+EIQEwK1ejb8GrAhGBVwydRrwAuArc6FeEdIdsi1GABoXgjhw+yv96H6lH90AgFsB3Iopy/ehs/5t7O6IYvmEcjQOLQXvDIMTBLgdybFql14KTJMtkO6hBB57z49ypwXXn16BBC9XP/EXXARMbBx2+D0nxAu1Pyavt2X2Guzst2HNbKfqSDGL2wVEkqHps85KuXB2eeN4YlsAlU4LPnJ6BVBRgfJB8QK7fdIpuPpzn9P+w9bUyN+vWgXobduoeC7Ll6ds+/e3ffCGeFy+3IOjfXGc8DSiY8BYGxJfhhxC1kYmU8W0JN4yFJXUGMxJBGSRmY1D2JXBIWTC0QjSDOP56jOM01k3z4WWY1G8slcUhD2+BLa0ivle5+RFELLxYFm2npk2Dfj733M2uu6J98Rq3stXeFT7oxaChX9bCFutDWVN+S+iGA3WzHFiz4koNh0MmRaEkkM40pYzAwPiv1ddBTgU+zr7bMAmf74CER4PvekDB+BT6yph9SiuDWvWABFxPccH4nimJYBqtwUfXp10lquq5G1PO033HGlpbECNO4b+AI+BectQoXc+bW6Wv1+yJGW/vCDgTxuGEOeBD68ux8Ql04DDyd6N71+gf56eMUP+fs4cadu/v+3DUAC4eHYjylGF8JAH4eemIfzkbkS7ohAOTsB/LC/AypfhlA+9hMpG8bUdaJmLw5uXo3Evh0DyHOxtmIABTNFegwKBAyyWBCwWHsIFlwLTV2Dn8QhatjViUp8XjpkKB7+uDlVT9oBP+MBBgDfMwx9JoMLJideBhnpwE5rR5Uug5fAQlgaOYFKNFc5FpwGTxeft6KtC0/Y2OJeIxXdNlTYEbS7Ep/cjHvRjV7nN9MjQ0aCkBOHcHTdI3w+FeKy65QgAYPDXM/HvZwfwg6cH8Nlzq/DxjzYAWJzy2P/+8pGUis6A4oK9aM910vedg3Gs+3qb9P//uboOX71YIVgkLsD/fLsdR3piWPK1ifjcz0/AO5TAywqHcPb6MxHtjuLYsQh+8mgf6iwcbl1TgVB3FE++5kVlmMfKGhsS/THE++MQ4gLsH7kUz8+6Gj95fhC3zy7DWbcdAVdrgzCfk/IHD+y9ENHus+Ge50b58nKcnOTElx84iboKK67/2UzpAwMAtk/fBKjkHe5424cv338S5/ByHtezy6/GS/ZL8ftPNmLxmsphj+HuOQYMRMS8w+uuE7/Y/nYF8OWfd2L5VAc+8h3xQ+rpFgXEc7Mvxj133azyGqrw/veDv+RSY9WYF14ofiW55ZYjGArxOPfuqdjcEsChf/dJuaCZYAnDmRxCvbyceEKQWvdUZ7j7k6uMjRSVmHcIJyWnG3R6E6pzQaUcQhMhY0kQLjAmCC9a7MEvX/bi6e0B/OpjAh7b6keCF6uLp2XRXy0TrPVM1g5hdbUY6soBgiDgye2iILxSp2H0aKPXLqgUWTPHhT+8PoRNB80VlniDCWnW8/KRCkI+eY75xjcAt+Kzcf314lcStyDg9i8dgT8i4Oz/nop51QpRfvXVUjP0F97w4st/7cHFi9348JcnDj/eFVeIXzrUetrRH+BxdNUFmHr95caex3nniV9J2npi+MK32+G0c7jpVzMxxZcAHm/D0b44IqetgWPtWkO7jc9bgXZPLQK7ArAf9GK2L4HW/SlbAOiR/meF+LpEP/FV4PJ6AIDnhX403N+JyrOqcCJ5ru6eOhcLHnLA4rbA6ram/usR//3jWz587bF+fGBVBdZ/lolf8Zrx1NP9+N4T/bjxrEr8+uMKo2HyZCw7+hHpv/c82Y97nurHzWdX4pcfk7fbvnkId/ypGxcumo2nvpL6dyoHsEDx/8ZKK7yOSjx017XYeSyCre0RPEqCMHcoZxYHo7Ij5tCo9qlyW1JagmiNH0tv/qt3EZaKLHigy8Ih5LamFKvUnCcKSX93DI8fiKLcweG+e2ehx5fA925tFR/7u1mwWDgIgoD4YByclYMr2cTWZ7VgwmcmgBWlSaXrz/UjnNZq4RE7h9b6MhziePArylEVSsDntqoW2QByf8VASh9CfVGkVi0tP3b4HGQmYM1UGf9pwxA+/9durP9MMz54qvELaSwuSInDtR6r9Hc0Os+TJQxrFpV45MHrWijdPq22Cgype72RopIscwjtVtHxPjEYlxrjMrqTIWOjU0rivjgiyakORh3Cc+a5UOuxoMeXwJPbAvjNK14A+au2lceDjaA5dY54rz2C4/1xeBwczl9Y2qHZYoYVlmw/GoE3mNCdbavk3bYIBEGsTjfjkqvCIiUZclA5jsOsRnHE4qHuGOZNUHdpR1JhzDBSnJEJdmM1vd4Gq4VDU6VVmoDU3hfDXJUpQ0NbhtD5x044pzkx7VtiFMvisuD4z49DiAlIBr3BeSxwz3TBOcMpfk13wjHBgR4Hhyse7kW02oajl9VJ+629uBa1F9cCAN7Z6gcgpqU0Xa+fI15Rawdv4VSrjI22rqnUaF3Drp1GikoaFTndrKiktrz4anpLVhDabRxsVjF0HIwIUrWsWhsXgOV0iRcKvYkPDjsnvenFbbX/aCwP0RvkpeOrvbmktjBJ0creSE47JzlhHMfBXmNP2W93tQ3zfjsPHXuDwP+ekIo05tw3B8F9QQT3BOHf5od/px+eiIDFnREc/9lxAMA/AByvseGglUf1edWovagWNsWJjwk3ZS8lfwZRJI/rGy5iJDGpEMRSlbHOuDslkZiAz/1FDKB/8v4ufPDU2YYeBwD9yRMfx4kFG6zSrt9g6wV/ppCxgRxCVlBiZOZwtcETtiAIWTmEFgsnVlv3xnF8YLgglItKjJ0CgvtFd9DeZJfep5mw2zjcdHYV7n12AB/5bRcA8eT6iTX5EYQzkiHjtt4YBEEwX7Ti9YpFSg4HcLlBV0WDx5Ph4vct9qg2yy8UcW8c7f/Tjkh7BAseXpDzwp7RZkqtHbMb7TjUHcPr+0O64/uUsN6Fp83KQesjg4IQAGY3inm1h05GAY10on2dohCb25S9IKxRVLVmC0u9YDdaHMdhZoMdO49HcaQnhqkxHgMvDaDyzEp45ovPJXI0gs7fdaJ8ebksCO0WTL9rOlBtwweeG0RHlQ0Hfj9bVbzbAgl0POOV0rfKVE5PrDo4k5AD5Fxu1SrjkP71jqElCGUTJPM6WDeH7qGENBCg0F0H1ChZQQiIAmUoxCMc4xWj3fT7CwKZnZYptTb5Q6kza5U5acf75RCVWgk7czMTvOhkBXWmlACys8ZEZvoc47r31aHuffLdUziUwIrr92NWXww/nO9E/+uD4PeHMHkgjhO/PYETvz2BBesXoOmj4t2UIAiSQ+hXtJBRE3Up65LG9ak5hMM/XNLEFYNtZ3afkHvWReOiszWx2thblDVhrUm6v7Vp7YMyMZSpMbVUFawt4IwWlADyCTtTDqEvLCCR3CRTXmI6U2pFQXisLw6kaevjA/KsVCOw3nVG3UHGVy+qxr+3+KURdj+9rt6wi2OWKbV2cJzoYPf6efMn3OPHxQKnhgagu3tEa3k6GS6+YkVxhWg5B4djPz4G8MCsn82Cozn//ffyzYWL3DjU7cWLu4KGBSEbw5aTXpgsZGxgbvqcZlFcsXC1Gvs6xXY4Wg6iEViDZ6M3xGqwaSksN1cQBJwWTGDlFi+4y3rw1lHxfD39u9PhuVN8n1euqcSUr09B1ZqqlH1N+/Y0dHnj2LMtBAunHYWqclmk2cH9gYSqeztksAehchu1EaFDBoWlVtuZTGP4lLDUnF0dUamF3GSDN9ajSYkLQg5DIfECEJRGu2lc0BUX00xOi3hBFz8Mc3Tu0liPIzaI3ONQn3vsHtazUH+trrRRdHpzjAHA6bLieGMZWuvKUPU/0zDUH8eV3z2KC8IJ3D25DP0v9qP2fbXS9sf/7zgi60/izBoH9rjlC5Y/gwWe3gxciVq4mYnOUFQcd6cVpmccSDtJHu6OGRaErJEpE4IsYddIyDjBy021NUfXuTILuEGDBSXKbTLdwbP1O+2c6txrPSbX2gGEJfHHUDZvTncOtQjuEQUha25slBqPFW98azIee8+PRZPKpFYh+cBh5zCxyoqOwQTaemPZ34GPsMr4aF8MezujsFqAi5eYE9D5xuq0Ytrt01DWVAaLznSFUuLCRW785lUvXtodNOQMC4KQW0H4k58APh8wfXrGTRclRzru7hg+wQcQoyTs5mn+SAShx9j5RQ8WMp7ni+Pw1w+j+1/duLpdsW4LULm6Eg5F2x5HswOz7lVvSaXs+6eVI261cKhxW8SCmACP5qrh2xhtKK3cRt0hNCYstRpTGx2fB8iN85kYnFBlLZpCMyUlLQjlkXR8xpCxcjxXJofwgoVuvHVYPGHoiRg2vYJdcLXeoGU2SOHtQFSQGkhruZnpzlqm+cuAKL4GgmLj7UCYh89pxeHZLsz+76nDtu3+WzdiW/2oPdMGf4SHIAgQEgL8oQQATtMlc+vkEKqNvVPmEwYiAipd+h+AgydTmxgfOhnDWXONCQgmCFmomN0hDwZ51aIKtbUDmauM9RpJs5NvpoISQDkKL9NsZCZ0zV+82RzUY2mzU9kUnrpyi+GGvIHdouPlXmRe4NRXWHHz2Spn9jwwtd6OjsEE2ntjWDXD5MU+R21n2LzXVTOchtzi0WbG92Zk3qiEWDfPhTKbmDt66GQMc3SiOoDo0vQHeLjLOCzNxQzja64xvOmiiUwQRlXF66HuKHhBFBkTq7N/79RIje+zcwjjvjjqnuvDzzZ4seD+KI4lf55wWLBpogP8BTW4/d4ZsFcbd7kGDTpyNR4r+gO85rQSo7l/gHyNVsv9NpxD6NRwCKXHZ/47NVRYUemySMecWlec0qukbxHditBqJhdNGfqtz+AcfPPSGvz3lbV45079knZ5zJ14wdW6U+A4uUI4EOYVIWB94SWFjHWmmjA8ymkszIHU2H7xE4sx6UczsWGGC/GEeNdy/IEu3Pe3k/jATh/cGjl/8qQS7Q+XUhA6bBysyf/6DeQRKkckAcDhnqjGlsPpC6Q6hEqXLpNLyD7odis079rYCTYSF6S51emwE54hh9DD8jEFxOLaAkQeW2f+4jAlWWl8fJggFP8/tdb4yZwJQs+i4gqBpiNXGmdRWJKjfDomCC9aXFzu4Fil3GmRnOcXd2cey/fqXrEX5po5rryMbdRjTlMZ7FYxVUet+ImlKs1vto8ov1PKoTYpCMPHwjj4lYPYPGEzLn+0Bwt6ooCNQ/0H6rHokUXofWEZfnB+HTbNcZsSg4Ac6s00573Oo792M84cu0aHogKiaedZo/thv/elCcIhE+vgOA5zFJOZjEZmRpsSF4SKGcVRfRdtXrP8B8jkHNhtHL51WW3Gu0f2RmAOjJa7BMizk/0RRchYI4fQnVYBzP7V2h6Q8/78EV7aXitH0THBgZn/NQXe5J2NLyyg668nMXkojs+87cXeBe+g9TutiPWlCjS3FDLWySFUvAYcx8nVzAYqjVnPwNNmiq+7mX5yUiVu0hm0WTnpxJMpbOIP64eLATGMzsSt1v4GMoxlUqK8K9UrLJGbUmfhENYxhzD178hyg4zepSaCCYST/QOLXRCy0Ex7n3aOVkZG4BDG4gJeSQqOi7JwU0cDPsJj6N0h9L/QX+il5Awmvl/YZUQQituctyBH6Qsvvyw26h8ayrip3cZhXtKc2NMx/IY3F/mDQPZFJeEjYXT8vAN8gMfxKhvuX12FeftWY/Gji9HwwQZMTK7rxKB55zHTHGMGO4f3aeR/D5lw5pTHSncJ5e4N+vth17ShEJ8yupPtz8j5HpDzR4EctDrKEyUtCJ1lcsiYtZ3REoRKh9BoGDIT7KLe48uciyCLPEEWeBpuJhNyQSmHUD/nEJDDs/6wIAmcch1H0WrhpPC6P8yj7i8L8Is11ThaY0NiKIH2e9rx1vS3cPi2w4gNxJLH1wkZR1hRRurr71EI4UywylfWKFZrDq8a0t2aQpDWSSeWDA5h8rF6rxfHcYpm0ur7GwwaO8EA4uvP3j96eYnSSSuL0ONk5hAOaDiEBgVhcG8QEAB7vR1ljcXd0DgnDuEIBOHbR8IYCvGoL7fglGnFedL3t/jx3qr3sO+TOuMkS4xLl4o3Kq/uDeqGSWNxARsOiIL9XIP9NDPysY8B738/0NpqaHMpj/DEcEG4N/mzkeQPAoq2MxkcwuD+IHoekXsAVq2rwqQvTkL93xfipqub8OzKSjQr8ixZGPvEYNzwXHPGoMGiu7oM+d9mHEKrhZPMmCFF6xlBEAzfwLPjxBJyDiBgfBYy46Ony90VriyyYjNGSQtCKWQck0PGWon30+vtuP3yGtzzwTqpae9IqUp7I+ndsSj7/kkhYA3xmu7ESVXJOjmE7E3pDSWkZNn09aWjdBWDdg7PLijHHZ+YiEWPLEL58nIk/Akc+9ExvD37bRz/xXEwk0qtqERNkAFymFtZzawFG6e2ZJJ4IWW98owgFcQoPpyZQg8MFjLO9MGulvJyNBzCgHGHEJBdP7319Y/EIUzmEPb5+ZRwhywIjYUtRpI/ONowhzCr5tQ5CBm/nCy+OW+h21hz9QLgXij+HaNd0WFRgFJl/oQyLJpUhlgCeCpZ4a3GhoMh+CMC6svF0Yo5gQkjA1XGgCwI2aQUJe+2iU78SKensJvSPh2HcGjLEN5Z+A723bBPeh9wHIc5v5yD7qXlAMdhap0tJXTNivxCUcHQlKWU4xnM2WPn7VzkEAKQuhooHcJgVEAsuftM6TjlikibsjjFTHELIBY/3XllLX5wdR1mF+mkoOLMbDSIashYJ6z6nSvqNH+XDekCsFJHCLB1BaJyRatWjh97Xqz4JFNOICBXwXqDvHTnkqk5ssfJAT7RIWRyzeOyoOGDDaj/QD36nu7DkduOILgniENfPoS5kx04dVEFgsuG390wVzK9nQATwmp5h0qCEV7q67Q4eaLuNuEQqoWsWeih15cpZDz8sWrUSK1nMuQQGsz3q3Fb0Ya4vkNownVMp8ptRUOFFT2+BA53x6QwxdFkCHlqrbGPf90VdVj2n2UlcfvIHMKjfXHwvGBOlDU3Aw88AJRlf7J+PTmr+fyFxSuebeU2OKc7EW4LI7A7gOp11YVeUk740Mpy7O7ox7/f9ePjKpOWAODRd8WmxpevKM+dYDfRhxAAViZTlljhIqPHl5Cc7ZXTRyYI2TlIzyGsOLUC5UvL4ZjiQMKfgF1xg8jOvax/HsNVZkFNcshD52DcVG7zoGIwhB6ZIjtmHEJAFGwdA6nnbXZTb7OmCj41LBYOFU4OvrA4/KAx+dZijqNRQchxHL59WW3mDQtICZzitUmtMtYPw+aD9DdknW4Ta7noI1PPRLZtNC6OQ2NiVysnEJDFqDfEKz4wGXIjHHKyrD+tByHHcai/vB4rW1Zi7u/mwt5oR9nxCL7/Qi/Oe6AD0bSKYJ9GHz8PC2VncAhZeNhp56S7p/4Ar1twoUQWdfJrZHRayZBBQSg5hBoh436TDqGxyuXsHUIAmJVMZFZWcJsNGdur7ag5rwY156iNcCwuptTaYLWIoZ0uEzcUAMR5sTfckDJuzAyBCI93k7mW63KUlpIvPIvFm7rALm03rdS4ZqXYg/Dl3UGpkl5JPCHgiW3i8zUzBSkjJvoQAsDqGU5YLWLuubLgi7135jXbR1ydXqsoWosk06m8b3qx49IdSCTPKZyFw/INy7HkySVwTkvNq2eCsFFlktHEZIStw2QeodE2L5lyCL0mcrWVx1O6e+wcXuO2GireYddSdq1I8PKYUqPCtBQo6WeSWmUs/nG0RrXlg7q00TN6PfPYXUgwarzKWNxebmStJ3arXcMFYaYPjLIkn72507u2W2wWTPz0RJx26DRErm9CggMWbvOj/X/aU7bzaYRdyxVhaT1OKkap1XksUgEH6+qeCbU8wHqD00p8Bu84q6UcwpFXGSu30212PYIqYwCYnRSEh5IV3P4wL4lv5qaNJWxWTkoJac92pnGWvHk4jFhCFKXT64s7+MLC/ywdYCwwp7kMZ89zgReA+98YXuDxyt4genwJ1HosOGdeDgW7SYew3GnB0smiA/jm4ZD08y1JQbjSbLskFapcFjADtLcvhsPfOIxta7eh/7l+HPvJMWk7W7n6+1RvtCW7zp0YNJen6zWYQ8h6yPap3MiLuX/mzonVKq1n2HnVsKh0popKZQqOkeKWUqHEBaEcWg1lqDLOB1PS2nZM0BGEbmmub+Y+hE47J51bAoqqYb3nVqUQK1K/pwxv9mrFPN1Mc4xtFTZYbpuKL1/ZiH1zXOIoIgWSQ+hQF4SZqozZ3WB9uRUWCyc1FTaaR8gcSLWQsV4ejXLtmRzCTI6j+RMVcxwzO4TZhIwBYG6ysm1vsnqRVTE2V1kNrTPuj+PwbYfR9dcu00nkhSLrwpJAQKwWffHFrI77+j7x4n72PFfRj4Qbiw4hAHzuPLHf5QNveIe1Cfm/FwcBANefUZFxtKQpTApCADhjtij63jwkh41Zs+zVORCEFguHGo8Fc3qiOHL2Nmk6TdPHmzDpS5MyPv6knkPICksGTArCkLHrEpvxqxYyVk5uMtqbVcqvV5xn2U248X2kjnpl11innSvKBtPZUtKCkFUZK0Nuox0yVt5h6DqESbERMOAQin0LZUcxENXfHlDcBQUThnMbJMcrxKv2EUzH7bDgUH0ZfnVNs9SDShAE7PvsfsxsD6s+3miVsSR8ylkfQWOj3RhqIes6j34uSvpjM4eMk6+XhsAcNBneZSejQZ2QtuwQZve+Xpws0NmdbHGxx2QVY3BPEMd+dAyHv3646EUOI+vWM52dYrXo1VdndVxWvboul+5TnmDtgwK7AiUj9I1w2TIPZjbY0evnce+zA9LPt7WH8creEKwW4IsXVOf2oCZDxgBwZlIQvpa8iQhGeGw8KJ5D18wZuSAUBAFX7fbjf5/qRuJACPYmOxY/vhgL/rwAdgP9R+WQ8fBrGjM+Or3mBKHRxtT1OmP32HXCYTM+ualKUXDJMDNmFEhtPaP812j+YKlQ0s+GiT/lG0drUkm+UFYsT9DpLM8EXiCcuU8gkFppHDTgELIcB2/IjEMoi0gjTpRcxCMLopN/OYmu33XiB8/3oDaYUBGExqqM0xOO9TrMq6Em6ozMH9Z6rBpMlKnlEMbigvQcjZ5k2HZ6vcJG6hAumSIKv/1dUURigiQIFxgUhNZKKyZ+fqI0B7sUGFHrmSzxh3mpQrQUBKF7vhuwAPH++LB84FLGZuXww2vE4sGfvzSA99rDCMd4fPGvYmuVa1aVY1qumwL/5CfAL38JNDYafsgFi9ywWcUbtANdUbyyN4RwTMDkWptUhZwt8aE49nx4D655qR92HoidV43Vu1ej/sp6w/uQi0qGn3eak3N5e0zm6BoVUbWKHoo8n3rdkFt7GZcu7FqSkkNo8uY9vTm12cKWUqG4E10ywAQKyzNz2tVnCeeTpkordneI309QGcTNYJNEfBFBatCpl3vgdogVwIEIb6gtSrXCFpdyCDPkNlQp5vOym9vacu1jqDWmrv9QPSpeaMT3j0bhrxw+n9GT1mRbi3ThkylfLx21SmEmuLwZ9mG043y1jmupFIlmi0q0nqMgCOhNG8lnlsk1NtR6xNmg249G8HYyLHWqwSpGz3wP5t43N6tjF4qsHcIR9CF881AI8YR47FLIzbS6rHDNdiF0IITArgAczcXZMzEbLl/uwaVL3Xh2RxAX/bgDDZVWtPbEUe224PsfMi6KDHPTTaYfUu224vwFbrywK4g/vjEkFX194BTPiJz4wL4Adl2xC6GDISQswO9XV+GSu2akVBAbgaXqqIWMmYNnNL+bYbQYhFUZ84IYvVLeDMuFe8ZvkNl1UBlJlCIv5BCmUNLPxmUXl88aGhfij/OpdVWY02THQ59u0s1LYWvzhXgMGbDOPYpG1kbefOwuaECRQ5hJ4CjbqBjp2u5Ka4cDiEnJ7p/MxrPzPdKHJrA3gFCbGAphxTSBDA5hemhUsvkzuHsMOWQs/w2qFa+J2ceqUaMjUpWvudGbkpoMVcu+sIB48ld1WTqEHMdhzRzRsXp5TxDvtYm9z1gO01hEdghHTxC+XkLhYoYybDyW4DgOD3yqCWfNdcIfEdDaE0eN24J/fWFCznrQ5oLPJ/Mdf/7SIJ7dEQTHATety37m98B/BvDe6e8hdDAEx1QHnv/mdDyxuCLj+S8dQRAksacmCBuSP+sxKQgHDXa/KLNxUreIvrRjyK29jF/ra1XG+EkRqSzzEMkhLEKYQOny6s8SzicfWlmOD63M3MJAnkqRkB1Cvb6FihxCI21kGpNFGEd65Iugmt2vtSbmzOtZ8entcGxW1gGeB5Jj6mIDMey8fCcS/gSWPrdUyunzZygqGRjmEA5vJqpFJCY3GVU2xlZWXushVUhnDBlr9/YyW7Wm3FbLIWTuoKuM0+1BmYmz57nw1PYA7n6yHwAwocqKmQ3GHAPfdh9cs12a1YjFyLR6NrIvjgQvmI8aZCEIN+wvQUG42IPex3oRNDD/t9Sodlvxwtcm4dV9IfT5Ezh/oVtynnLOhg1iHuFppwFO4zdaFy1247JlHjzdIgryL19YnfXIuhN/OIGDnz8IIS6gck0lFj+2GP/+jxf4j1e1WlePgSAvnU9ZcZ+SBqm/q/H9xuJy/10j58i6cit84Tj6AgnMUfw8mxSaOpUxfmb3I40CZILQZOubUqF0zvIqMAHIcrcyXdALCXtDeUO89GbSc/xYeLbXx0sukZ6AbEoLV1e6LBlFhFJ0SU6UzklTmcMYigmoYIIwLDuYfIiHtdyK8OEwtp+9HbXfmwEgc8h4IC2HsNKgmANSWwAoi0qUuSN6wsBoP6kanb6Bg1JfKxN3rhmKSvqlcPHILmQfOLUct/27V/obf/i0CkNhqfhQHFtXbAU4YO3AWth0UiKKiYnVNtit4qipjoG48UHyWYbqQlEe77WLzitzY0uBseoQMiwWbnQahF90ERAOi6Prpk83/DCO4/DwZ5vxry0+VLoseP/S7MaZ8TEeJ353AkJcQONHGzHvj/NgdVolsaPX51QNlhtY5bLAaR9+PmtIFpqwPrFGKraHUtq0ZD5H1pdb0dYbH9aLcDCLG+86FQFrtEm2cj2AfE422uu31CheBWWA9D+m3qSQQsM+BAMB3lD5PSs4YZVcFk6/o3qFk0sRbCzxV48aaUQQL91F6uVUKHs8KsfXSUUZLgscEx1Y8foKVJ9TjYQvgeavH8bZh4NSpbQWwx1C4zmE7GTjLkvNIVXmmQzpCEuph6HBKuOBYGJYZSZrbVNrQrxlajvDqqP1Gp4bYVKNDZ87VwxFNVZYpVBVJgJ7RKFQNqGsZMQgIM4vZS2h2vtMFJZkGTJ+rz2CWEL8zM0o8v6DSjyLPeDKOHC5bMEyHjE5uk5JmY3D9WdU4vLl2U9OsdgtWPLkEsz6ySwseGgBrE7xvGJ0nnE6ei1n2H7ZR8Wo+8jO4x4HJ0WW9KjVmFYiXyfMC8LUkHHm613KepLHY1EbyiEsQtLt3mL+4zAx0emNS3a8XmIscwhZOLzKZdF1dTiOQ7Piop0pXAzIorHLG1e4UdqvIcfJolOZE5g+x9hWZcPS55ei4ZoGcHEB33y1H/PeHNRdS3pltFRlbEAQao2eK7PJ69XLo/EbDRkn/16xRGphDSDfOdabEIRMkAej8jQBJb05cgjx/9t78zg56jr//1XV1/Q1PfeVmcyVmRwzCSQkgYREEBc5RAVk19Wvol8FRHFVQBCVBfFARGRdF1H8sbr+xF2VVRHvAxEQQUhCyH1MkplM5r66p7un76rvH9Wfqu5OH1U9M32+n49HHplOqqs/M9P9qVe9j9cbwIP/VIdn7lyBV+5tUx0xY6lEFkkqJpgxtKY6wpoaqVv04Yc1vRbzkrugu6JorHkAwLLWgp3endj4wsZ8L6W4ycKHcNEvGREx+6dZ+bGpxYS229vi3n/Vsu2WtghhuoYSQLrhYvuc2jpCZvmSaZwqI9X4OjmTpCVlbFNqySPR2iitdl6yqPTE+xCWWg1hUX83Z0UIC/iXwwQOE1IZI37R74WNGVPzvcVGBZtURHTYMQtBxTKlLknNSCysjjDWesYTEyFk8CYe6/5nHcR3NoAHcM3PpjD+/fGU500UZWzjcPoybzipxuYB8dY6qZhP8/xYpLtb6evENEw24q2yQrnTTtZYwmpelkIQsuYSNe8LBptiUYyCsD3aWDKkxXqmshL46EeBm2/W9FpsJu0F3cXVqMPxHHh94e6ZRUMWPoSLermwgEPvPoR9l+7D2PfGUh5Xm2WEMJ3lDEOzIJRv+NXtZWztiRHIbCKELHAkisq+PaexxIddF5lAZennZDWWxUxR7waJEcJCriFMjAZmivixuxrWJKIm+hnbQaemm85q4uNm/0pG2+nf4Ga52SUmQpgiwsbpOFjv78Qv11rBAzjyf4+kFIXzCaKSfb/zi4gQSudJb3AdCovwh9TVEHIchxrZeiZ+o5rNYqPieU4RvknWxzadZSuGzwAThGzMWTHRUZtFhDALRFHEy9HxY9u6i6d+kFhCchwh5HgOxiYjOAMHfZphCImNEGqZSGNKzZA7jVV6EWrtyq1NMc9Yq6E0IHlTssDAtCeCiCAqrhoq91amNWa8UrnQlIcEYcERG7EBCjtCaDNx8nxeILNpNOvkOjEpXdDU1EfGOtzvUFncHjtur60ms4hUUsYxNYRpPuw2E49vbq/CnzbYAFEShRM/nIg7JiIoHWhM1LE7SaeGppJktjHMViBVc4o75vuwq+jkZedLNJOecUuPtaSMgfSdy0xkZutBuFi8+0sgQqjFizAQAJ57Dnj+edVPGZgIYdojwKTncO7K4vPym3pqCrs27sLRDx3N91KKlzwIwlVfX4XNezej/pr6lMfJgjDDpKZE0lnOMOROY5Xn1trEUZtiWkk2xtSA8r1MzIcx5ZZcNXhOvaBje3BEkK4lTAiTICwgOI6LKwot5BpCaa3K+tLNPQaUD4Q8t1HFHdGb+5UL90Vr1AnC2BSiGkFoi/FHZMynidBZTDzAcXhkexVaPtwiicL3H8Hs75X6l1hLGjllrKGGkHUJJxN0VTHNPMlg9Y8VBk5Vt5ziRRi/UbGNUUtTSez5kq1PaSrJ/aYTnAoiGJ17bF1ffIJQqSHUkDKemgIuvhi49FLVT3kpmi7e1GEqzpmmIuDZ64F7lzvfKylecpAyFkUR4z8YhxAt1eE4DtZ16T+XTMQsBEX4Q+qjhJlqCAElQjipMkKotQmjNoXgzMaYGlDS3+POiLzmertOtSVVhYGXAx6T8xE5VU6CsMCILQrNJLLyTWvMDMnWDCndxFq+pjRj8Rhd9QY89bFm/P6TLRnHsDG6G5Q1qRKESXwF081BZjWH/jDQ9Y1VaHhXA8SwiAPvOID5XfPS86PnMuohX1Rlyxj/2eOLEmFzku1JNhtlBF7yjUttQwkjVWewEs3TtkHU2lPfabN0ST5Sxiw6WNFdUVQehAxmTj0yF0YorNFXUEOXMROE24usfpDh2OFA/y/60fdkX76XUrx85SvAAw8Atsx+tNky/OAwjlx/BAeuOQAxw37IqKzgwfSOFusZLTWE6iOEUSsblRFCFgGcTbSdyTJCyAIf464wJqKNmmoaL2NpjtboD06H5IxTvcZzFDpFLwhjx8X1Ni1uBuRy0xFjSZFJECaKgHRj8WK5fL0VF61WX/O1s1eJJG7uzHxRs8mziZPbziRijfUuDANr/msNqi+thmW1BRVt0ut5/PHpYkC5kxTFeA+rZKQTpOnGzQFn1y5mQrbDSUjxzshdxto+UuwOM1lx9kyWUcelwLPPAwCwbVi+i9xy0lipQ4WBgyACw3Mqo4RZ2M68zDqMi3Tyi7HeiLq31cHcRfWPWXPnncCnPgVYlyeSPvnjSZy86yQAoPrSanAqo1o8z8kBk1kNjSUTKtKhDVnWEKYb1xpLYhMHINV7s71ea4SQibmJ+QjGmeDVaKXVEr1m7xuWMid6nfqu6WKh6L+bnkYlwrWqobBniMYOVW/NEI1LFIRaukO1cMlaM8xGDpVmHtepmLjC/BGTRQiTRdnMRk6+znoDAngjj76f9uHcZ8+FsdEY9/xYQVdh4GGKpnAzmVOnayrJlHp2pxGTyZDNXmPOJ4qiHM3TKt4aVAhCrSJzKfBGpyfYzilOQchxnDLTWG1jicYasDlvBIejafULqKGEWAZcL7pw+H2HAQCtn2hF2yfaND2/JsmUjnSIoijvRemaSphgm1IZIXRlWUM4443IGSIWjdTxWUQIo9nDMVdEHnWbLiWeDBaU2T0o3QQ22HVZe0cWKsWXC0pgU4cJiNaAZ7INyTexEcL2DF5wieHsZhUp42xocujx18+0wmzk5fRuOphw8sTUEKaLEDLvQm9AaRzR2+Pfdu4/z0EfEc8SlFUWHhPzEWkzqU29JrmpJFkNYYyZdLrn2jPMMWYku+P2BkQEomlJrU0l9ZXKnWssoijKlgv5jBBas5yeUAi01xlwdDykrY4QUB0h/PtJ6cKwqsFQ1LVE7r1uzPxqBpYeCxre2ZDv5RQXogi8+qp0M7FxI6BfukvqwvEF7H/7fogBEbVvr0X3Q92az6HVnNoTEOGLOkikS6my+sREn8BUaO0yrrfpwHFSDf2MV0C9XRdX+6dViLGAyuhcWM4+ab2mtlRLx/8tmhVgZSmlRNELwvddWImBiRDO7yr8lM0b11pQY+VxYY8Zl69Pn9a1mnh01utxakq6mC1XhBAA+lao745koju2yzhdhBAArEYe3kAkbroJY+iBIeg/fQofXmPF7t6muP9zRAVhpmkl6UQdS1GkihDKljkqN6rG6F1zbDE1E24mffy0GDXIEcL5eNHiXBAQDLPXzK3YEMKCbDlTrCljQLkBU91prDFlHGtIXcy4nndh8F8HUfu2WhKEWgmFpBnGADA7C1RXL8lpw64w9l+1H+GZMOxb7Fj3w3XgVEz4SESrOTVrKLGauLQBgroUxtGpcGqc/WvQS+bXU+4Ixl1hSRAuopGD7QWnpkPyxK1OjYKOlXmxm3cShAWIjudw/3V1+V6GKtY0GzHyb52qpxksxEThVjcVxptPThknqSFMJaqsFRzgTj7P2LbeBpEDfAbu7Aih7NGXftNha0lnTJ3KvobVLyaLLiajUZ7uEiMIY1K7WidVyLU4CSljdv4qS/J5ostJYDgAjuPA23hUqKgrLVRYFF61F6HG3x1rKNlWpPWDDFYW4Hndk+eVFCGxNw9L1GUsCiIOX38YvmM+mFpN6H+6H7osnQaUEhd1wi3T2DqGMg4u/Zx4hlz7p6HmrrEyKgidYaxvNanqfk4Fa54cng2DrbSrXts1dU1zfI9CRxGNqVRLYedYSxAtguHTV0l3mze8oRJmY2H8qljKmFm9iKKY1nYGkCKEQLxVDaP2LbUY+M5qPH5+FewJhcIsBZ1uDnHsWrKpIZzXmMqI9bNizCzCQDpVU8lYlp1wS4G504ydnp3YcmCL6gL2QoTdwatOGdvtUrfoAw9kPDQUFrHrVFQQFnmEkJUFBIYCCDmX18i75IgVhEvkQzh0/xBmnp4BZ+LQ97M+mJqy97es0dhUonQYZ6hxTzL9Ix1OjZNKAGXy1lj05ngxVi8Ndh1sJg6iqMw379QoCPsTMmlaBWUxUHoSt4S48SIH+leYCiolxdII3qgIDIRFhKN7TcoIoelsM+tYpptMALywV/AQwgIinggMVQY5YuhJ8TyGmqaSVFFGt0bbGbZRTrgkx3qO4zCj0fU+lvpoPeWkWyqeZrUxLEK4nKUC6eB0HCraC+d9lw0sKsDM3TNitUrdoirYPxLAQlBElYU/K3JQbBiqDTCtNCFwOgDvPi+q3lCV7yUVD0LM3rQEgnDmtzMYvGcQAND7aC8qt1Qu6nyK8b3alHE0QphBdBn0HBxmHi6fgBlPJOPIU5dGH0JA2ftY1FLt2pLBcRy6Gwx4PdohbNRndvpIpM6ug14H+Xp38Zrim+CUicIIOxFJ0fEcdvSaoc+idmS5sCeINBZh47h4i5lYLEnMrGNhoqzeF8Hrl7yOg+84CDEiyilgFgFMRbpZxo4MtjPpnpsMFrGLnf+8GANpFnEMR+LT2swrK3Y+NaEN5jow5Y5onueaCVY/eH5XRUl0GlLaOEuWMGXsG/Th8LsPAyLQ/KFmNH+geZGLi/XzU5syVp+WTWUenYggKHYxan0IAcVXeDRqGzXqjO6JWfoNb1ulOAFc0J3ddfW+q6XuxvO7TBmdQooREoSEJmQfwqiQUjp8uZQXRjllnKSpJPYclWER7j1uOP/sxOB9g7L4zJwyTh3lYxHChaCIYBKD4kz1j4nYKnjYohFPJtpYeleNeXgiRr0yZzPW04tFCPNhtv76m1/HofccQmA0kPPXXkpsFTxaor+TATVRwlBI6hh99dWMjSUvnyiNhhIGCcIsWcKU8dEPHkXYGYb9fDt6/r1nkQuT0Go7M6myhhBQ7LAyNZZ4AiKYl7baphIAWBkVXKdnpc/umagw1BrZY7w5Zib7JWuzs4m67bIq/OrWFvz3zYsX64UICUJCE2xeMIvaZeowBgBLVEAtpIgQsnMYei1Y/Z3VAIChLw5h5RGp09WTwZg6bYQwRugl8zNMZ2qdikbZ9V7aCMeci0vvspqYWOuZsTxFCEOzIcz9cQ6TP5zMupC9kFgV9bo8PqFCEDqdwNat0p8MMOuJYm8oYVjPkeoImf8koZLYlPEiI4Q93+iBY4cD6360DrzKJrdMaLWd0SII1UYIWbmOUQ9NDXIroz6ip6M1f2dmo4Iwy8jcZestuO2yKnz4jQ7cdLEjq3NwHId/WGfBiixFaaFTmt8VsWw45EYP6UOuZtKHXHeYohbQHVMD2PjuRjifd2LssTGs+48zcFxZL/9/MgRB8TdMZjuj4yXT7XmfAKc3clZBsjtDQ0wyGit1ODEZkm0QxqPirTlL8VZv1+H4RCiusSRfNYS8mUf/0/3wn/RDn6f6xaWkp9GA54/6cHwiqO2Jopgy4jM8G8LIXBg6HthSxF3YsbAIofeAF0JYAK+nWIEqTCbgnnukrxfpQWjts2LjCxuXYFEKsu2Mygih0mWc+XtRpomomyRVpXJKCaM92hR2eiYMQRAxssgIYTE5kuSL4t/xiZxSGf1Qs3o3ty91hy+DpVgzpYyZoFv1b6vgesEFHFrAx/46h33b7CnPHWuQnWoNDiYIk0QI05lqp4JF7eSUMYsQZpnebZAHxSvdsON5ihDqzDrUvbV0Nk1WRzigJkIYKwDTpIxZ/eCGVpMqM/diwNxtBm/lIXgF+I77YF1bvIbkOcVkAu67L+unB6eC8A344NiWXcQqE6zRTW2EUJ5SoqJxQ60XodJhrO2z0haNBLp8Ao5PhBCKADwHtOShjKZcKI3djMgZrAbEGxARjoiyv1W62ZKWaA1hqpQxSz+zOj6dWYe1T6yFqOdw4ZAfHc87U56bCTodD9lwNNWakzWWyIJQw4WddRqzKB4Tby1Zijc2EmnEGSsII3H/R2RHTzRlrFkQpoEJwu09pREdBACO52BbT3WEuUQURRy98She2/kaRh8fXZbXYCnjhaAIfyhzlJDdlKqxu5JTxknGbsaSTYcxIGWWWNblV9FShpW1+oJqsiw1SBASmoitFXT5BNnOoDrNbMlMtjPJ6vjsG+0I3tICALjkqSn4TviSPjfWciaVx6PiRXj2xqW1qQSI9SKMYCEgyAXb2TaAtEVrZViNzEJAkH8muY4QjnxzBFNPTSHs0TjurUBhs86PTwQhqpxAAiB9hPBEafgPJkJ1hFkQDgMHDwKHDqmecMMQgyJ0Fh04nlu0vUwqKit4sF6/TI0lvqAg35xrqSHMFCF0ZdFhzFi3Qrqhe/JV6SZlXUtxWzwVOiQICU0Y9Jws8Fw+QTY8rUnTgMDSagvB5BtmKh9B040t2NdkhCko4vB7DkOMpO4STmcbw6KXcwkRQlEUs0pnyHMxnWF5CkaVhZfrdbTCamKGZ+O7ls1GTpNQXSxCQMDArQM4eM1BhNRO9yhwOusN4DmptCB2ukxSVKSM3X4B+4al7utYG4tSgDqNs2BuDujvB/r6ND+VN/FY99/rsGX/Fvlnv9TwPKe6sYTVD5r06vadOru6LmN2I661hhAA+qICcO9p6TNHgnB5IUFIaIaF/l0LypzhdHYCLGWcqls4VWOK3arDQxfVYMHIYf7leZz5+pmznqsm5ausN/71vQERkeg/aRmp1FkvCbgTkyGcik7B0DoXMxZWK8MihOycHbUGzaPwFoP3oBdiSIS+Wl/0ptQMo55De3TEVMZOYxU/67+f9EMQgfZafcl1GlZurUTNlTWoemNVvpdSPGRhOyOKYly02rJ6eQ2Oq1Vaz8R2GKvZd5Qu4/TnZdeIyiwihOcm7EPrW7Of2kJkhgQhoRlH9E5PfYSQNZWcHXUJhUX4Q8kbU+wVPCbtevxghzTC79Tdp7AwsBB3jJLyTb2BsXS20xd/J8s2KoNOisapZVWDdJc6OB3CsXGpe3Uxcy2ZjcKIM4yIIOLUlCRcmPDMFe49bgCAbZMtp0J0uWF1hMcydRpXVEgdo/fck9JC5KUBqXShVOxmYrGfZ8eGX2/AyjtW5nspxQMTdho+L2P/3xgOvuMggpMaO9+zRK05tTK2Tl0kT21TSTZzjBmXr48Xy5etL73pIIUECUJCM46Y+cCshjBdhFBOGSepIXTH/FtilI8JxF90mVH1pioIfkEe6yQ/X0XKWBawCRFCV1QgOsyp6w+T0VKlg9nIISIAfzokCVStczHjz6eHSc8hHJHmbCqCMLezMj17pFShfVPqru5iZHWT9HM8OpbhAmw2Sx2j990H6JJfFJn/4PYSSxcTWcJ8CFXuH/5hP07cfgLTP5/G5I8ml3FhCmrNqbVMKQGUCKHLJyCUxPSf4VxEDWGNVYePXOIAxwEfucSRtnmRWDwkCAnNsBSs0yfIpqOqIoRJuozZ3aPZyMGgj99UmSAUwGHlIz1Y8bEV6H2sN+4Yjz+z7U2qLmOl2FnbJsPzHHqidibPHJIiRue0ZZ/K0PEcVrHmh/EgTjJBuIg0dDbERghLiXUt0u/m4MjiIjLhiIhXTjJBWHoRQkZgPJCyiYtIQGOE8Pi/HEfEE0HltkqsuGXFMi5MQZ5nnGKeO4NFCBO9WlNRZVEaVtKZUzu9Efn4bHj4XfXwfrsbD7+rPqvnE+ohQUhoJtZuYFZDhDCZD6EnTQ2g1cTJ+2ygyYSef++B3h6fRlVG56WJEKYShAvZpzIuSBAEmzsXV9vSGxWER8dD2H9GEi5rc1hALYQFePdJ3aWlFiHsb5V+jodGMwjCSETqFj10KH4CRZT9ZwLwBkQ4zHzJFrePfHsELzW/hIHbB/K9lOJAgyCc+vkUZn4xA07Pofc7veByZJ+iNWWsxpQakG5k2bnTpY3n5Drz7KN7pTAvvBggQUhohqUUptwR2bcq3V2l1chG1yUZHZfGGJrjODnyF9uQIooiZv80C0DdpJFqC0sZJ68hzCaVsbNXSRnW2Xh0LTK9u7pZEhh/P+HHiejc3Q2LiDpqxXfUB8EnQGfTwVxi6dC10Z/tuCuS3jNtYUHqFu3rA4Jni0eWLr6gu6JkL1DWPivAAZEM4oGIwm4cMoytC8+HcfyjxwEAbXe2wdafuyg8M6fOlDJmptRqawgBdePr2D5bnWWEkMgdpdUmR+QEJv4Gp0Oyb1U6vzyLHCFMYhuTYRay3cRh3qfUGoqCiH2X7cPcn+bQ/3S/PKkkbQ1hdCNKtJ1hNYTZpDKu3GDF6iYDjo6HcMcV1Ytuwji/S4o4/nS3VMfXUqVTnbpZCuR08UYbuBITO7YKHh11egxOh3FwNICL1HR1JrGdeanE5hcno/KCSuyY3wG9jS4NqrDbgU9+MmXNKePkZ04iOBqEeZUZ7Xe352hxEuptZ7TVEAJMEIbSjq+bk1PGVP9X6NCnntAMEyostZnJL4+ljH1BERFBhC5GcChNIclFiN3MA86IXGvI8Rxsm2xw/c2F4HgQboO0lmRzjBlVMU0wsSgRQu0bldXE49m7WvHSgB9XLEHn27ZVFeA4RYdctCa33XSsocS2sbTqBxn9K0ySIDwTTC0I0/gQiqKIv5VwhzGDN/DgDRTJUU1VFfDVr6Y9xPWyC6OPSpNIer/dC10WfnyLgWVIMs0zjrWdUYuaTmN2I55ueAFRGNBviNAM2zBYarPJkd63yhpj6ZJoTj2fYVKIkjJWntdxTwe2HtqKlhtb5AhjupSx0gQTifP/WkwNISA10rzlHOuSpA+rLDq8uU8RKtdtzq0wYxHCUqsfZLCJBwfT1RGmeQ8PTocx6oxArwO2dJSuICSWFiEs4NiHjgEi0Hh9I6rfVJ3zNaidZ6zYzqiPE9Xa06eMI4Ko2M5Qyrjgod8QoZnEVGZThnm7ZqPSHJI4vi6ToGOpYHdMDaHOqpONk1XVEEa77IJhyJ6HgGKHkMtpIOl4+F112NFbgU9fVY0rN+QuQigKIjyvRSOEJdZhzGATD1R3GidECJ87KkUHN3dUyCUQpcrcs3PYc+EeHH7v4XwvpfAJhYDBQWB4OOl/j35rFN59Xuhr9Oj+Wndu1xal1ia9X9PVzwbDohzJ05YyTt9UEtvIV00p44KHUsaEZloSZvY2Z5i3y3EcLEYO3oB4lvWMO4NtTGUSQRhL7eEFfOyvTthvbkz5+jYTB54DBFFKX5iN8bONC+XOtbvBiD/d0Zrz1/Wd8CHijoCv4GFZW5rGr7GdxqIoJo9op4kQMkF48ZrSarhJBsdzmP/bPAJnAvleSuEzNAT09Ei1hPPzcf8VnArKvqmdX+qEsS4/nekNUWeGSXcEgiAmzWiw6KBep635oy7GcSIZzJbMZjrbVowoPArjSkgUFU0OnZyGBYDepswbnU2eZ5wQIfSnbypJFiFkhGZCePd/jeLKI17Y/+xM+docxyWtI8zWh7DUEIMiat9ei5rLa8DrS3NL6Gk0Qq+TfC9PR0cEpiUmQiiKIp47IhmQX7S69AUhqyMNnA4gOJ2baRpFSxrbmVOfOYWwMwzbRhtabmzJ8cIUWMQvIqTuNJ50R90ibDpNJTC1GWoI5cEFWc55J3JLae7+xLLCcVycR54aU2aLbE6dXBCm6hJOFyE01Brw2wscAADdw8OIpDFeVbwIlWMWW0NYKlj7rFj/1Hr0/7w/30tZNox6Tk4bvzaUIvJlMAC33y79MSg2QscnQhh1RmDSc7igu/TrB/WVeph7JOHriXa9EylggjDBdkaMiAhOSWK65z96cuY5mAyjnpOjfswmLJFxp7QvZir/SURpKkkuNOfIcqaooN8SkRX9KxRBqMYvzxpN056dMk5fx5cuQggAP99gw4RNB3E8iNMPnk75+rIXoU85z2J8CIni47xoM8iuU/7kBxgMwEMPSX9Mynv6L0ekdPH53RVyuUGpYz9Pai5y73bneSUFTorRdZyOw/qn1uO8186D40JHHhYWD4sSTswnv2kec0lCMZ19WDIy+RA6C6wsh0gP/ZaIrLjt8mr801Yb7npLNbrqM99VsvF1iSnj+QxNJcxOJpUgnA0Dj2+VNtzhB4fhH05+sZe9CL1np4zLebMSwgL8Q/647utS5bwOSeTtGtRWG/fckfKpH2TYzpPSxiQIM5BhUon93MLo2meCcDKFIBx3Sf/eXKUxQmhXlzKmhpLioHyvhMSi6Ko34P+/sQmfu7pWlSmzbE6dIkKYsqnEfPakEkYgJCIUAV7oNMN6YSUEn4CTnzqZ9DwsLczMqEVRlO9eC6XLOB94D3jxcsfLeGXNKyUvCrd0ShHCPUMBCEKS71UUpSaBoSE58hOOiPjzYal+sJwEoX1zNEL4KgnCtCQIwrAnjIFPDiA4UVi1l8xKZjJF88eYM7sIIUsZLwTFpJOoZFNq8iAsCui3ROQEljL2pKghTDa6DlCaUeZ9Z282zMMQHIeer68COGDyfybh+pvrrGNZ4whLE/uCkpgEyttB3zfgA6fnYGozLXraSqGzrsUIs5HDvE/AQNRDM45QCOjokP5EO0ZfPuHH3IKAGiuPrZ2lXz/IsG+2AzwQGA4gMELdxilJqCEc/uowznztDPZdvq+gbrCUlHGKGkIWIdRYQ2iv4BCdDYCZJD6Hyti68t1jiwkShEROYE0lCwkRwvlMo+uYMXUgydi7qCA0GzlUba5E0weaAAADHx+AmBABShzCzmpeDDrJEqFcabiuATvmd2DN99bkeynLjl7HYeNKKW3M5hKnJHox/91+LwDg0j4L9HlsDMg1epse1vVWAMD83+czHF3GVFcDH/4w8P73Sw8vrYZ9ix3td7cX1A2WPH8+Zco4GiGs0ibcOI5L21jCmkrKuSynmKDfEpETbHLKOH7TYBHDlDWE5tQRQk+CZU3Xl7qgs+vg3uXGxA8n4o5lZtpsgDsThPX29FNWygGdWYeKtvKIfu3sldK+z0d9BeNIMrruN/ukdPGVG6zLvrZCo/KCSgDA/MskCFPS0gI8+ijw4IMAgKodVdj08ibUXVuX54XFk6mGcMyVXZcxkL6xhJXl1JDtTFFAgpDICaypxBvTVCKKooqmktRdxomWNcZGI1Z+ZiUA4NTdpxDxKxvUWYIw+je7uyXKgzdEfQSfP+Y7O6WXcGMwOB3CodEgdDxwaX9pGnano/L8qCCkCGFGYt9LHM8V3E2mbE6dRBAKgiinkrWmjIEYQZikPlH2IaQIYVFAvyUiJ1iiNYSxKeNAWKnjS9XYUSnPMk4dIYwVk60fb4Wp1YTA6QBGHhmR/z0xZcKEYZ29fAWh8wUndm3ehcH7BvO9lJyxrbsCBh1wZjaMk1MJ9VQJEcLf7pPSxdtWVZRlhINFCN2vuiGEk3f5lz2hEDA5iYNXvYZT95xC2K3C9DwPyDWESUTbtCeCcER6+zdqGFvHqEtjTs2ihrVlvM8WEyQIiZxgTWJMHZsGtqao42PRv3m/cFZER4kQKs/VmXXo+EIHAKnAW4i+XsoIYRlvVPN/m4dntwfeg958LyVnWEy83G38bHT6SCqefFUyZX7rOeWXLgYAy2oLdA4dBJ8A7/7yeY9oYt8+zDZehenfzOP0A6cRmkrSrFQANDpYyjh81j7KGkrqbLqsxsvV2qOzkpMIQnYD3lDG+2wxQYKQyAlWeXSdshnJDSVmHroU45JY9C8iAP5QijnIpvi3cdN7m9B6eys2vrgRfPT/6qMpkylPBKIoYiqmhrBcYalA+9bC8ErLFZdF07+/fC1B5MRECE9PB/G3AT84DrhuS3n9fBgcz2H146ux8cWNsK4rT1GcCTEs4ARuBgCs+JcVMHcVpjVRs0MPjgOCYeWmmDGepSk1g0UIE9PREUGMq9UmCh/tBQMEkQUW49lNJcySIJ0PYGwHsNsvxE2KSDX2jtNxWPXQqrh/YxtSOCLN85x2S88t5xpCJghZrVi5cPUmG+59ahZ/PrwA10JEmWXNccCHPgRwHH66T/KRu2i1GSuqy3ebbLiuId9LKGjGfxmCF13Q8x60392e7+WkxKjn0OzQYdQZwfBsGA2Vynt6ODrbu0WjKTWDmVkzYcmY9QpgZg/lvM8WExQhJHKCkjJWonzypJA0gpDnOVkUJjaWZDK1ZviH/TAZONTapONGnWGMRo1YtTrzlwqBkQCCo0FAB9g3lVcEbHWzEWuaDQhFlC5iAJIg/Pa3EX7kUTy6S3qfvvuC8vrZEOqJLERw6jHpxqHd8TQM1YYMz8gvbTXSXscEIGNoRnrcUZfd+lkjCpuHzJiKNqrUWPmsUtFE7iFBSOQEaxLbGZcvc4QQiO00jk8ZJ2sqiUWMiDj8vsN4ufNleF73oDUa6TkzG8aZ6KbYWlOegpBZiVj7rdCVYcPENdHRbN994WwT86f3ejE8G0adjcc/brHlemkFhSiKGH9iHMc+egyhucKsj8sXI4+MIDgNVGAUKxzP5ns5GWmrkQTf6ZkEQTgt/V476rLbC1mqOTFCyKaiULq4eCBBSOSEpBFClaalzItQa4SQ03EQgyIQAWb/MCuLvzOzYYzMRQVhmaYDXX+VhJDjQkeeV5IfPrjTAb0OeOGYH3uGFJPqyOQUvvPkADhRwA0XOeJKFMoRjuMwdN8QRr85CvcrNMaOEXaFcfqB0wCADvwXeL7wu7CVCGG8sGcRwvZsI4QsZTwfQSRmIACrVWzIonOZyA/lvdsROUOOEAZjI4TShuHIMNaINY24fakEYep0RNcDXdj06iasvGMlWqMpnSPjQTk6Wa71YbIg3FGegrC1Ro/rNkvRvy//ak7+d76pEb/7ykZ0w4mPXFKeP5tEGt/XiNZbW2FcYcz3UgqG4a8NIzwXhqWDQyOekUfXFTIpU8bRCGF7bXZ7YWOlDhwnNf7FdhorHcbluccWI/SbInKC1Xi27QxrKnFkShmniBCyKSeJTSWxVLRXoKJdshlhG+KLx6WIUKWZz1h/WIqEPWG4X5OiPeUqCAHgziuq8b+7PPjlXi/+7fdz6G814U0iwAH45OWOuML7cqbj7o58L6GgCE4GMfzwMACg85PV4F55D9DYmOdVZaYtKviGY1LG/pAgTylpr80uQqjXcWiw6zAxH8G4M4LG6OdGThlThLBooB2PyAmxI+hEUQTHcbLtTEZByGoIA4kRQjHu/zOxRhTQMxXE3ujj1U2FXQS+XLj/7gYigGmlqWxG1iVj3QoT7n5rDT731Cw+/b8zAAAvxwGiiPdsK6/Oa0I9px84DcErwL7ZjrqPrAdu+X6+l6QKVkMYGyFk9YQ2k9J0lw1NDkkQjrnCOAfSvHBmQ0M1hMVD+YVHiLzgMEubQiii+AnKEcIMNYQsApiYMs7UVBLL9C+nYbv6AG57fhZ8tM6lv9Wk4TsoHcq9fjCWT11Zjfuvq0W1hYdRL0UHAUBPO2McEW8Ec8/MwX/an/ngEsY/7MfIo9IEpM77OwtuRF06WIZk0h2BL1q6I6eL6wyL+l7YDOSxmE5juYaQBGHRQNsekRNsJk72/WX1e3INYYYIYWWKecYswpguZcxw7HBAb9Whcy6Mi076AAAbWsuzJqrc6wdj4TgOt11WjdGvd8L5zW7wzCA9cc5xmXPo3Yfw+j+8jqknp/K9lLwy8s0RiAERVRdXofofqoFIBPB6AX/hC+UaKy9beLFGksHpaENJlvWDjGRehFOUMi46SBASOYHnOVnYzcuCkHUZZ2gqqUjuQzjvU9elDACGagPa7mwDALx3tws6QcSlfRYN30FpIIQFuF4iQZgIx3GSGCyiiE8uYe8V5wvO/C4kz3R+oRO9j/Wi6ytdUkTtuecAmw3YvDnfS8sIx3FY3STdBB8Zk/wTj01If3c3LK58hlnPjMUIQkoZFx8kCImcweoI5QihikklQEzKOMGH0Kkywsho/Vgr+HoDWtwRfNPKYVVj+UUIOZ7Dxr9uRM8jPbD20TiylFCEMA7HTkkQul5wQRTK92fDG3i03NSCyq3RGlP2PimSG4nVzfGC8OCI9HffisWVz7ApJ6Nz0p4siqJs/s/SyUThQ4KQyBmOBEHoVOlDyCKLnpgIoT8kIBiOP28mdFYduqLjpXp/NomI7+xh7KUOx3Own2vHiltWgNMVx0Usp7z3vcD11wPmwpxJmy/sm+zgzTzCs2EsHF7I/IQSIzgZhBBM4jVYZIJwbYskCA+NBuP+7lukpRBLOQ/NSDWJU+4I/CERHKfULhKFDwlCImdUmuNTxvMqRtcBSoRw3n+2ZQ3Hqe8yBoCWD7XA1GZCcCSI0W+Nql88UR5897vA978P1NTkeyUFBW/kUXmBFBUrx7Tx0Q8exStrXoHzeWf8fwjRPakIfAgB4Nw2SfjtHgxgZC6McVcEPAesbV6cIGRj7wanQxBFUe5ebnboYKSxdUVDcbyLiZJAjhAuCBAEURZ4lZkihEl8CF0xHoZyI4AKeBOPjs91AABOf/k0wu5w+ieUEKIg4uiNRzH23TFE/OUXHSUWh+MNStq4nAhOB+He5Yb/tB/GpgThVGQRws2dks3UickQnn7NAwA4p82kqjEvHe3RsXduv4gZjyBb26zM0tuQyA8kCImcoUQII3D7BXkvzZTytSVJGas1tU5G4/WNMPeaEZoO4czXz2h+frHied2DscfHMPCJAXB0154cnw9YWFAiP4RM1c4qAIDzL06IZVRjaawz4vyB87H+6fWw9CY0orH3SZEIwmqrTo4G3vWk5L25vWfxXqQVBh4tVVLzyKnpkJw6pnRxcUGCkMgZrFbQ6RPgjKaLTXoOFYYMxtSm+FSz9LW2hpJYeD2Pzs93AgCGHxpGKGG2Z6liqDGg/e52tNzcAp6M9pJTUwNYrcDwcL5XUnBUbq8EZ+IQHA1i4Wh51RHqrDrUXll79n8wYVwkKWMAuHaz1EwWCEtrv3qTbUnO2xNt0js8GsTR8VD03yhCWEwUz7uYKHpqbdId5KxHkFO+aixjWGTREzv2jk05UfH8ZNT/Yz2s51gRmY/g9IOnszpHsVHRXoHOL3Si+8HufC+l8CmjCJhadGadbGbufMaZ38XkAFEUMfPrmfRd1U1NwHXXAW96U+4Wtkjed2GlvKee127CjiWIEAJKY8qh0SAOR5tVWBMLURyQICRyRl1UEM54IpiJDkGvtmZ+C9qS+BAqHcrZeVxxPIeuL3UBAEa+MYLAWCCr8xAlRpGk/vJF9ZuqAQBzf5rL80qWn+mfT2P/Vfux96K9qVPkmzYBTz4JfOUruV3cIlhZa8Czn1qB+6+rxa9ubVmyaStMEO4/E1AE4SKbVYjcQoKQyBk1Vkm8TXsiylijysw1JqyLOBgGAtGxd2rnIKddz5U1qNxWCctqC0JTpZ029p30YfrpaYScpf19LhqOJpWkgwlC51+cECOl+zMSIyJO3X0KAFB1cVVRjahTQ98KE267rBrV1qUzjd7ULnkZPnPIB5dPgNnIyWlkojggQUjkjLro8PTZWEGowsU+1laGRQnVzkFOB8dx6H+qH+ftPg+2DUtTR1OoTP54EgfefgBHP3A030spbEgQpsV2ng06hw5hZxjuPe58L2fZmPjhBBYOL0BfrUfr7a2pDxRFeq9EOafNFDeVZGevGSZDaQnpUocEIZEzau1KynhCw1gjHc/BYpQ2FmZVo3YOciaMDUZwGmxripW5Z6QUX9UlVfldCFHU8HoetVfUovofqiGGS1MICUEBg/cOAgBW3rUShqo0jRFPPSU1lFx4YU7WVsjwPIdrz1NurK/eRJOQig3qCSdyBmsqmfYIypxLlYPPqyw8FoIROVW82BrCRCLeCM584wzq3lZXciPdIt6I7B3HUn5ECihCmJG1/7225FKosYw9Pgb/oOQ5uOKjK9IfXGQ+hMvNF99Ri3m/gLYaPd5/YWW+l0NohAQhkTNYU0kgLOJktGavQaUgrLbqMOpUmlHmF9llnMjxjx3H+HfH4X7Vjf6f9S/JOQsF51+cEIMiTO0mWNZYMj+hnLn6asmL0FpaNwVLSSmLwYg3gqEvDAEA2v+1HbpMN5wkCOOwV/D43gcb870MIktIEBI5w2riUWPlMesV8OopPwB1NYQAUBPtRp7zSoJwMcbUyWj7ZBtcL7hQ/476JTlfITHzW8mAtvaK2pK+mC8JP/hBvldQNATGAhAjIipal8a2pBA48x9nEBwPoqKzAs03NGd+QpGNriOIdNC7mMgpbJSRLyjdWXfWqzMuZR3Ks95oDeESC0LrWiu2HtmKxv9TWne3oihi9rezAICaK2g+L7E0DH5+EC+1vIThB0vHwDs0F8LwV6Tvp+PzHeCNKvYWihASJQQJQiKntNfGB6W7VArCalkQShFC1lSixthaLaXYXOIb8MF/0g/OwKHqjVX5Xg5RItjOsQEcEBwP5nspS8bwV4cRdoZh7bei8V0qbwyLbHQdQaSDBCGRU2IFYbNDB6tJ3Vuw1qpY1gCAK1pDWGleOh8tQOowHPnWCA5ce6Ak5rWy6KBjpwN6O1WIZKSmRrq4HzuW75UUNNWXVWP7+Hb0/aQv30tZEgLjAZz5d2mueeeXOsHpVAq8IhxdRxCpoCsEkVPWxIwy6tYw57I6JmXsCwrwBqSNmHkbLhWh2RBOfPIEhAUBM7+cQd3b6pb0/LmG0sUaYRf4ErgZWE50FTroKpb2ZiyfDH1xCMKCgMptlah9a5KZxalobgauuAI455zlWxxB5Ai6rSFyylvPjfGp2qjeDLqGmVp7FQ9Do16Zc7xUmJpMaP24ZER76rOninoaQ2QhAudfnACkhhJCBZT600wkWr5RzOjMOnBGDp33d2prvLr4YuA3vwG+/OVlWxtB5AoShEROqbfr8KV31OKdW2344BvU+1QxA+vJ+fixd8vRNdt2Rxv0VXp4D3gx8T8TS37+XDH7h1kIfkGym1lHdjOaoAhhRsKeMPZeshcv1r2I8Hw438tZFN1f7cYFQxeg+mLy6STKFxKERM65/fJqfP/GJpjVdPFFaXJI1Q1jrohsat2o0rJGK4ZqA9rubAMADN47CCEoLMvrLDfTP5sGANRfW092M2ohY2rV6G16BEYCEBYEzP5+Nt/LWTSmJlO+l0AQeYUEIVEUNDkk8TcxH8a4S4pGqDW1zobWj7XC0GCA/6QfY/85tmyvs5zUXF6DmitqUH9d6XkrLhsknDVR93apxpbdfBQbg18chHv3ImYyP/EEYLEA1167dIsiiDxBgpAoChorpQhhOAIcGZOsLtSOvcsGnVWH9rvbAQBDXxhCxFt8dVKN727Eht9sgGO7I99LKT4oQqgKdrMx/cvpoqsldL3kwuC/DmL3+bsRGA1kd5JQSJpsE8jy+QRRQJAgJIoCo56TO4r3DEmbL0sjLxctN7WgoqMCwbEghh8uHQNeIg1vehNw+eU0uk4l9i12mFaaIHiLL21sajWh8T2NaHp/E0wtWaaLyZiaKCFIEBJFAxOALx6Xxt511qm3rckG3sSj88udAIDTXzmNwHhxRAFEQcTIt0bgH/bneynFx49/DPz2t0B7e75XUhRwHCdHCaf+dyrPq9FGRVsF1v5gLVZ/Z3X2J6HRdUQJQe9iomhYleBb2NWw/DaaDe9sgH2rHYJXwOA9g8v+ekvB/EvzOP6R49i1YReEcHE2xBDFAxOEM0/PIOIv/LRxouH8oiYUUYSQKCFIEBJFw5pmY9xjtWPvFgPHcej+WjcAYOw/x+A54Fn211wsoiDCscOB2rfVgtfTR5xYXirPr4RxhRERdwRzf5zL93IyMvKNERx850H4TvoWfzIaXUeUEHS1IIqGtTGC0GLksKIqN4N2qnZUoe7aOkAATt9/OievuRiqdlZh4wsbsfo/F5EKK1e6u6X6wf37872SooHjlbTx5I8m87ya9ITmQhj8/CCmfjKFuT8vgXil0XVECUGj64iiYWtXhfz1hT1m8ItJ9Wik64EuWNZYsPJTK3P2mouFooNZ4PUCCwvAPfdIc40Z114LvOUt0tdDQ8DnP5/6HFddBVxzjfT1+Djw2c+mPvbSS4F//mfp69lZ4I47Uh970UXA9ddLX3s8wMc/nvrYbduAG26Qvg6FgJtvTn3spk3ALbcoj2+4IXWXdV8fcNttyuNbbgH8fjRO1WIEV2L6x6MI89+A3hgCenqAu+5Sjr3tNsDlSn7elSuBe+9VHt91FzCVoiaxsRG4/37l8b33AmfOJD+2uhp46CH54dBbf4Lw7ApYq+fQ/OI9wEsx36fVCnzjG8rjhx4CDh9Ofl69HnjsMaClRfq99JXGTGeizBGzxOVyiQBEl8uV7SkIQjNf+MWMWHnzgPji8YV8L6Ugmfn9jBiYCuR7GcXL2rWiKMmh+D/3368cs3t38mPYn7vvVo49ciT9sbfeqhx7+nT6Y2+6STl2Zib9se95j3Ks35/+2Guuif8Z8HzqY9/85vhj7XZRBEQBEP+O74nP4llxBFdKx+7YEX9sU1Pq827cGH9sV1fqY3t7449dvz71sa2t8mELJxbEv3B/FJ/Fs+I0tpx9bHV1/HkvuST1eY1GkSCKBbV6jSKERFFx99tq8JmrqnMaHUxEFET4jvtgWV1Y4+DCrjAOXH0AYkjElkNbYOkprPUVBT/+sTSbNjFCdtFFytctLeln127frnxdV5f+2C1blK8djvTHnnuu8rXZnP7Y2IiVTpf+2N7e+Mf33586QtjZGf/4c58DgkFwAJqe43Dyd8B4+81ouXkn0Noaf+xnPiNFYJPR2Bj/+I47AKcz+bGxkVtAipSmiiba7fKXA58YgCjqUb3KiZoPXAtwCWbSFRXxj2+4QYrgJkO3fB6oBJEvOFHMzoF1fn4eDocDLpcLlZXqZ9ISRDETnAjiwNUHsHBkAVuPbYWx3pj5STli5FsjOP6R47Css2DLgS00ro7IKYGxAF7pfQW1b6/Fmu+tAW8onJKFmV/PYP9V+8HpOWzetxnWteQzSZQPavUaRQgJQgOGOgMEvwAxLMKzx4Oay2oyPykHiKKIse9II/ZabmohMUjkHFOzCdsnt0NnLqzoWcQfwfGPHQcAtN7aSmKQIFJAgpAgNMDpOKx9Yi30VXqYVmQ53WAZcO92w7PXA87EofG9jZmfQBDLQKGJQQAY/uow/Cf9MLYY0f6vZDhOEKkgQUgQGrH2FV6EYewxKTpYf109DDXL789IEOlw73VDDIioPD+/5US+QZ9sFdX9tW7o7XTJI4hUFE6RB0EUIbN/mMX4E+N5XUNwKoiJJyYAAC0fasnrWghi9PFR7N64GwO3DeR1HaIo4vhHj0PwC6h6YxUa3tmQ1/UQRKFDt0sEkSVzz8xh32X7wFt5OHY4YO4w52Udo98aheAXYN9sh2OHIy9rIAhG7VW14C08TG0mRPwR6Cryk0ae/O9JzP56FpyRQ88jPVRXSxAZoAghQWRJ1Rur4NjpgOAVcOymY2fNSM0FEX8EI98cAQC03t5KFz0i75iaTNg+uh19P+rLmxgEANdLkgl2xz0dsK4rvDIPgig0SBASRJZwPIfVj68GX8Fj7o9zGP+v3KeOJ74/gdBkCKY2E+rfUZ/z1yeIZOgd+U8+9T7Siw1/2IC2O9vyvRSCKApIEBLEIrD0WtDx+Q4AwMCtA/Cf8efstYWAgKEvDQEA2m5vKyjfN4IAAO8RL0YfH83b69dcWkOfC4JQCX1SCGKRtN7aCvtWOyKuCA6/5zDESG5Sx2OPjyEwHIBxhRHNH2rOyWsShFp8J314dd2rOHbzMSwMLOTkNf3Dfhz854MITgRz8noEUUqQICSIRcLreax9Yi14Kw/Xcy6cfuB0Tl53+qlpAED7Z9vzWqtFEMkwd5lRc3kNEAFO3nUyJ6957KZjmPrxFI588EhOXo8gSgkShASxBFh6LOj9pjQT9tS9p+B60bXsr7nhdxuw7ifr0PwBig4ShUnXg10AD0z/dBrOF5zL/nrdD3WjclslVn191bK/FkGUGiQICWKJaLy+EQ3vbgAiwIF3HFj2ekJOx6HhHxvAm+hjTBQmtn4bmm+UblgGPjEAISws6+tZ+6zY+OJGWFZZlvV1CKIUoSsJQSwRHMeh97FeWNdbEZoI4eA1BxHxRZb0NURRxJlHziDiXdrzEsRy0XlfJ3QOHTx7PDjz8JklP7/3sBdzz87Jj8l6iSCygwQhQSwhepse/b/oh75GD/cuN47ecHRJzz/66CgG/mUAe7bvWfZoC0EsBcZGI1b9m5TCPXXPKXgPe5fs3MGJIPa/dT9ev/R1TP10asnOSxDlCAlCglhizJ1m9D3ZB97Ko+6auiU9t2WtReoqvqEZvJ4+vkRx0PT+JtRcXgMxIOLQOw8tSYQ7NBvC65e+Dv8JPyraKuDYSVN6CGIxcGKW4xXm5+fhcDjgcrlQWZnfAeYEUYgEp4Iw1huX/LxhVxg6uw4cT6kxongIjASw67xdCE2EUP9P9Vj3o3VZp3dDzhD2Xb4P7r+7YWwy4twXzqW6QYJIgVq9RiEGglgmYsWg76QPA7cOQAhpT/OG58PwvO6RH+sdehKDRNFhWmFC3//2gdNzmPrJFAZuHchq3GNgJIC9b9gL99/d0NfoseGPG0gMEsQSQIKQIJYZISxg/1X7cebrZ3Dq7lOanhucDGLvG/diz7Y9mH56eplWSBC5oWpHFXofk+yZRv59BCc/pc2f0PmcE7u37oZ3vxfGJiPOeeYc2Ppty7FUgig7SBASxDLD63l0f7Ub5lVmtN7aqvp5c8/OYffm3fDs8UBn08G0wrSMqySI3ND8gWb0fkcShbaN6sRcaC6EgdsGsPeSvQiOBmFZa8HGlzbCfq59OZdKEGUF1RASRI4QwkJcI8ixW47BvMqM2qtqYe42g+M5RLwROP/ixOhjo5j55QwAwLzKjPW/WQ9LD6XFiNLBvccN+yZF0E39dAqmlSbYN9nB6aSSCPdeNyaemMD498YRng0DkBpUeh7pgc5K03kIQg1q9Zo+h2siiLImVgzO/G4Go4+OAgBO3HYCvJkHb+bli570BKDlphZ0fbULeht9VInSIlYMRhYiOPKBI4jMR7D59c2wbZAihyfvPIm5P0oeg5a1FnQ/3I3ay2vzsl6CKHXoKkMQecCxzYGeb/Vg4okJuHe5IfgECD6p4cTUZkLd1XVo+UgLrGuseV4pQSw/odkQqi+thmevB9Z+5T1fdUkVdDYdmj4g2daQ1RJBLB+UMiaIPCOEBPiH/BADIgwNBhjqDDRtgShLRFGk9z5BLDGUMiaIIoE38GSbQRCgsXMEkU8o/k4QBEEQBFHmkCAkCIIgCIIoc0gQEgRBEARBlDkkCAmCIAiCIMocEoQEQRAEQRBlDglCgiAIgiCIMocEIUEQBEEQRJlDgpAgCIIgCKLMIUFIEARBEARR5pAgJAiCIAiCKHNIEBIEQRAEQZQ5JAgJgiAIgiDKHBKEBEEQBEEQZQ4JQoIgCIIgiDKHBCFBEARBEESZQ4KQIAiCIAiizCFBSBAEQRAEUebos32iKIoAgPn5+SVbDEEQBEEQBLF0MJ3GdFsqshaEbrcbANDW1pbtKQiCIAiCIIgc4Ha74XA4Uv4/J2aSjCkQBAGjo6Ow2+3gOC7rBRIEQRAEQRDLgyiKcLvdaGlpAc+nrhTMWhASBEEQBEEQpQE1lRAEQRAEQZQ5JAgJgiAIgiDKHBKEBEEQBEEQZQ4JQoIgCIIgiDKHBCFBEARBEESZQ4KQIAiCIAiizCFBSBAEQRAEUeaQICQIgiAIgihzSBASBEEQBEGUOSQICYIgCIIgyhwShARBEARBEGUOCUKCIAiCIIgy5/8BG/+wlT/JSEIAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Display\n", "plot.wavelet_plot(n, Fn, haar_wt(2 * n / np.max(n) - 0.5), mex_hat_wt(n / np.max(n) - 0.5, 0.1))" @@ -756,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" @@ -774,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" @@ -796,24 +722,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "data": { - "image/png": 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dwQb24RVT2HNIIyKKd1s23We0bsNGP6ZlheJIRE7sFnHsGO6XsbBrrnjW/poVl1yka1ryuQ46cOO/52R3vD3+DcMUcLrmavVrVnrNx/YrOuvorGEkMsjAm+4bdvF7prADUVbtcz5p/xWfpC/orFnG/Lfulpf9X7MbvyWEd3TNMz7u/pIv05+xpqUXx5gSBxv5Q3vJnfuG4/A1m/4zLv0X/Gn6S9a0y5y0s4G/bxve8XtCPHDd/oaP+JKveMHaOYIZb8KRN/qGg39NiEcu+i/4F/Z/4Up6rrq8LtqHwH/0Dbf6W8wSHzd/wSfpC770l3gVxmj8Lm75vum55T9w2X7Jx/yaT+0jAok7eQWEn9276i+rlUqlUqlUKpUnS12sViqVSqVSqVSeLHWxWqlUKpVKpVJ5stTFaqVSqVQqlUrlyVIXq5VKpVKpVCqVJ8sv1g3gD/yvWIocTm/o209pdct9HJlOnvs0cArvgMitvuH70xWKcCN3iCjjdM/L5h2n05aBwC59j6UTR9lzH0duBsddGDnoHWaRU7zjjZ04xYb7ODLEO8wSJ9lzNyXaKOxjIKYBgKMc2YUN3aQc0sSgR8wSR04cw4q9E44hMXIEFCMyJWNMkMxIRQma0sQkA8EMIkwpq61FdFFSioAK6CO1adGiiuAVPFmlDOBEaBRaFZwILjkMQ8Wzcp5NI3iBU6PchwbvegSlsYaVU1Y+q1w764o6tGMrHc86x/NOMGAfPN2wxWmLl46LpuG6Uy4a4RSF18Mapy0Aa7vkSns+WSlOYB+MZ+MlN5pVo9fpOS/aFb/aKI3CITh20wVH+wzBs5IrrtMln6w8aydMBqf7LYO9ADQrNdNnfHXZcNkIaw+vTi0Xg+Ob8Vccm7cYiRf2MZ+0a361zs4Rh+CI9x8D8I22dP6SKz7lq27LZatcNsI+eHZTy/H4a5IP3PDvuXCf8Vn6it+strxYzgn2U0c6/Iab5vcM01taf8Glfsav+YRP1g1eISaKW0LDvn1NiCdCOvLMfcWL9BlftVesnND7rMQ8BmM9rnmjr9n5lwxToGue8Ql/wa/5hFXj2HhBRTjFxNvhmnsbsCZxJ47T+M0SDfu5/QUbW3GlPU6FhHGMgVfygp1/R+oCDDBaoG8+Yu2f84LfsLIVl7bCl7Y3WuSOAyc98Lr9B47uhhAPJBvxumbTfkKvV1zzORd2hUdpLLflSOLIyCAD981bbvVrxrQnnR6iU71b0/hNiSzs6HSLp1tUvpEJIzGkXYmjvWOYbos7QMhKZ/GLwrnxmxxhqx1eutKniiLbUlY6kyNk54jGkI5Z/QzYucPA4i6QIyVnBTWA6mNXADtTupslUsqq2VlZnV8/uA8Y2YEAk/eiYz9UnZ+fQ+YnfrOQx+XIuvlzJbr7Gar3x8f5cfX9z8QeVPeP3QlSOd77jgA/HXX7Q3Uxv/djLg0fFOlR5C653paI0nmrh2siuBwFfKbOn50hZpV+fv0QOfxDUb/n7SIuiv0R7FRqITsBqPbZKaNEiy5tWrviDFDaoShpjjW2QEhDdtyI+6zmt4DZWGKU1zhp6ZorOn/5QX8Dlj432o5Q5t28r1Munfas2ue0bsPWf8aVvaChpbcVvtT9RCAQuXfveKdfc4p3TPFASke8W7PtPmPlrnlhX3GdLmnE0aqiCEOK3NvAkRPfNx0HfcNpeoOIp2uuuG5+zcae8SJ9zEZbFCFY4tZOHORAbPJ4cRqFdfcJF/4zfpP+JRfacd039K7MaVPi5bjlrdyyb14xhR2tv+Bj/TOep4/5st2y8kLrHsbmdmx45Tre6e/wumLbfspX6dd82W/oneAUQoKLY8dB7hj9joO2dM0zvki/4c9XF1y0ysbnOeR+bJmOXxHdxCtg6z7h0/Rr/nS1ZdsIGy/cTZ67seFm/JSjv+Ggr/iIL/mCj/n1tmXjhZCgOSg6Cr/XFlXPpX7Or5oNz3vH8w4OAe4m5fX9p/xOWozAs/SCF27LlxuPVzhGuL97xlEOiPQ5VlyPHG0iEgk2AC1w+ll9rP6yWqlUKpVKpVJ5stTFaqVSqVQqlUrlyVIXq5VKpVKpVCqVJ0tdrFYqlUqlUqlUnix1sVqpVCqVSqVSebL8Yt0AXt39j4BHdcWzzV8gKN/LawKBvd5wGm8AeBN/x185UBzv+BqAMbzl79L/QO8uCWng9vBbAHb2PV+rshuec6OvuEsvgcTu9A3/cfPvaWzFIDt2p6yifmdf83fjJR7Pjb5imG4B43v+llGOvD19xGv9lnfxD0DiO/kdo514fbzI2cHxt9mdIO74xu64jyuiJd7qS8ZxxzDdctP+gd+dnuNRdrJnN73MzgJ2y3enE7upYbLEG/2OYbonxCP39orvTr9iN3lOKXIrb4BEiCdepx3puGE7eb6Pe97qSyj55/s48W5ocAp3U+KQirI63nHbvuHN8BHrqBxC4l7fcZreoeqZLHKKiX3ICtEx5pR4gMkOnGJkP2X3gSEaw5yzDkQmgiXGBI1kdeREINqUldUITsAJeIFGwavQhAbRlshE/IEM8UTCbKSTLRvLKsiLBjbeGJMwJmU9XOLdiinuaXC0KmyafBxBWDnHKqwQFCcdva25bJVnrfCsJSs6RdgeV3SyBWDFJVey5sVK+XxlNAL7KNyOypthTcclqj1eOtZ2yXXv+XQltArR4BiFY1ixStfs3Hckm7hKz/lYt/xq7dg2sPF5u32A6W6DBuFrv2EIt7S64YVd88U2ux88a7NbxCk6Xh6V26HhbvqS6Cfe4Wjdlo085xOuuGoaPuqVToUE7KaG9ui4SWt2/ntCPDLFO1b+GVfyGV/Yx1w0nuvO0bmswh6icTN07MIFAO98x15fkmKg8Ruu3K+4sI/4lTznumvoXD53yOe/D4lTMN6GS166FTv3hhCPGIkYdzR+Q+cuuHRf0LHmIj2jI6t8AULRJx9kx6BH7vUVIo4p7Ikl91zE07gN3vWsmo/w0tPJdskOP1fsByYiE5MdGeyemAbGuCfEEyMUh4EEoqi0qLY0boOqx+vqB9XYj9rpmTLbLBJsJJV+F+N4lrs+PrgCWMDOVPHZCcAXNflZ7vzPULwb6SF/Xs4V7wlkdgjI2xgKP9DXHinxz1T48E9wJci1QdnRooaXM2eCHz7+mRsCnNVDPt55XciZM4C8Vz75AdeCRflfXhtpecYS6bw8ls6uhkO0BRTVFre4Q3SPXCJEFCcNKrNThHs4XhmTz90oQjyRbGICogUwW1wovK5pmwucdnTu4oM23Vi7HCPKRJLsRjPojsCJQ3hLiEdiOpE4gHhad0Hj12ybz9jIczpbsU5bVnRLf0sYAxM7uePk9ktdDtM7RFoav+Wi/YK1XPNp+pKP3ZpOlbVXvArJjDEZYzTeTmtQuHMNe3lJALrmio/cr7lKz/kTf83HqzxO96WqTrHhZmjZTWtiiohT3snvUPH0/hnP0xd8xAWf9j0XjeBKpv365LkLLe/0mkHvEGnYuk94YV/xVb/hqlU+6fNxDHg3Orhf40blG93kMcRveJ4+5lO/4cutY+Nh5WAfhN0E+7BlTM+zK47rWck1z92Kz9bKyuU5bUwwJc/6dImXDtWe3j3jY7fm87Vy1cLKGfeT0Dvl29OKNVcAdLJlayuuO+WyzAtOcn/ohzVO8tizshUb77lohI2HKeW5qy3tzmlHbxs2Xtl62HrDiRCSsKJFxRNsRNHsIKR5Ho4GG+d5Fq5Zd5/z9vDXjP2Ok98hKGM60DXPGaavf7i7v0f9ZbVSqVQqlUql8mSpi9VKpVKpVCqVypOlLlYrlUqlUqlUKk+WulitVCqVSqVSqTxZ6mK1UqlUKpVKpfJk+cW6AVxt/jVOO5w0tLpFcdzKK07pjjHtiDYCjtvj7xnbHSKOEI9ZBWqJm93/jojHiird+cusiNSBe33FEG4Z4x7VFeP0Pd/u/h0qnmgjIbzFactu/I4/NFkNOkx3THEHwLvj7zk0b3mlK4YhZ5ObRd4Of8e9e4nXjhAH7k9/BOAUbvlj/7e0rEkSuQm/Z5huGcM7bo6/5W/6CcER4oHd6Wu8rjnEN/ze/wNN6ogSeBt+y2l8S0gn7sI3/G1zSRdXTDLwJvwWMKa452v5G27kGU3ouJFvuB+/QaTFSHwvr3GHT3AIt3biVm5IKTDEW97qb/lrt6KLPQMnXsb/wO70Dev2Be/Y4QYlppYEvBqPnLgjWeAU3vE1NxyPF9yMniklvpdXTGGPIOzlHa/TlvXeF9V64pW+ZD+9QsVz5MQ+rHg3OhqFUzSOMTJJdgvYxe95o8/47rBi5ZWQjO/THe/0JWYTgpIwQsrKSxeFIWbXASOiKCEe2Tcjx9Cyn3TJPR5TYpBhaSMAMUGy/IiWlaKGEcnXSC0rJluFVqERIxoMTuido005b3vOGheysrJzuUzRoHWCS1ktrNKwomflHZdtVmpeeMtqThVeNco+tjSaXQu8dmy04XknPGuN523CS3YPAKVRx0fjFQd9jkiDoHS24qppuO4eK2DvvTClBh2Eb7nm6N4B0MqWi/SMj/uWy1b4pBc2PmuhT1FYec9ucpz2zwF47VbEdMK7FVf2nI95xuerls/WwsrB2hkiWaW6C45DgO1xjR4/40bWHJvcDo/hrqidr7iyF6xtzXNds/IOJ9n1YEowpcQ+rjmliUY68HAUzxBuMRtR8TR+Tecu2eondKzZpEs2rHAovrQZgIlIIHGQAwe9Y9Qje33N5I7YGEtOe2JWfntd0foLvHa0uqWTLY4Gh8fRoPaeG4Cm7DbAkN0HbGC0Q1Z965FkEyGeCChigWQjZoqlgZxFr0UBn1Xwcw69Ln/P6nfH+1hx7DBLj9TuWY0fShtNuf2LFheCWQFvZS965gJwrqw/U+LDmUvATzC7EpAQeXAlmMskgBlnxz9zApgV9eKzuv5M/b/UBW6pk/Oy5b9/oH7srH5KOVIKGJGYRjB9cINY+rMD8ai0WaXverxb5TanK1QaGl3ji1K7oceVaVrPrlEgO0JMnEgWGPSeUfbENJBSIHIAEqIrVHva5oJ18zGNrNnKcxrr2KZLVrQ4lEbc7JdAxIgpcWTiXm4Z5EhyiVF8cbQRVFva5oLePeOZfMHz9DErGra+oXeKlp3FBIcY6WLD3tYMuiPZBCS829I3H/ERX3KVrvmyveDTlaN3cNFkNXwy4RjhGMAfhcPwnKBTaUOJ3j/j4/QZH7s1X249n6+gd0avuf3to7D2yrtRuL2/ZGJAxeO0o9EVz2zLs6blRa9ctrnFnKIwJSVag7cOAKdrLuQFz9MVn/SO6w6+6CO9M6IJXpR3o3KKLU1aI6J4adlKx7ZRrhq4aHK5nAgqwso7VmP/4PxAQ6MPc4PXh1ZsRIyEkxYvHb3L9dRq3ufowAehEcWZB5REzHNPmYuWx5lhhpGYZGRMiUOZxqYEx5A42URMI6pNdodIxhCFYxQOAYZkBBKqnhRO7PSOu7DmbvRsvBAMGlW29Fz3f8Y3d/8DN/f/K+PqPrutaMe6e1HdACqVSqVSqVQq/8enLlYrlUqlUqlUKk+WulitVCqVSqVSqTxZ6mK1UqlUKpVKpfJkqYvVSqVSqVQqlcqT5RfrBnDZfEEj68c53jYQbSBZyIpY7RinN6Q0LmpZRHFuu2TTO13jtM+KtzQx2B1jelBdOl0j4gnxLqtVRXHuErPEFPfcpxGAEI9ZGYxwHL9jCDeL20B+P3IYvltUqDnve4d3zwjxyLvx96g0GIlxuiemEbOB0/CSKWSXgWQjKR1x2jNMt7zmbxFxRJs4jq+Z4o6URvanl7xME047kk2cxptSxh1vTn/DO81Z1afxhine4XRNSoE3/JFJRgTlJDtOdkeyiXF6xbs0MnZ7VBtiGtifviWlPdJ9yjt9xWQT9+OahHGjrzhN70gWOAyv+Lr5D7yRK3rbEmTiXfxDdk4Q5S58TfKRKeYM7FGOvJr+mt3pG7zr2esdr2NHs1caFYaUuLE9e32HSsv98DXWZfljN/YEIi/5a3anb/HuCkWZiLwbE5MprcLtaLwbYlbBkpjCjtf6EsJn6H6LL/nR2VXgu5yXbRMnPXA3ZeV3SMIhGndj4p3cc7JbRFuSJCZLHENxFBDhFIVTzCp1yOrjKR05uDuO8XP2ISs0k8EQsyNCYChKzZ/uwrqopOOieG4kn2evsHYJJ4YTZeNzWVbSsLINqg+uBE7Bl8ztWdmfTMp3HF1Y4ySrUFvWrOi4aIRnrfC8My59QjAOMSvBGxUuTi2neIF3PUPI6tlL2/KsbflkJXy5Sqxd4qqJqBjBhPvJsY9K55RgPe3guHGfEdqBw/D77HggHdu04UI6rjvPZas0ks8huwEod6NjiA06CZMOiFOO7nXpi9Doila3XNhzNrbmmazZeo8Twaugkq/HmIyYjH3suUtrBgacNhzlltQEpnjIOe2wZIB37pK2qLLXtsWZp6PFozj0Ua56MiOSmAgEIoMMnHRHJCy57aPuGNSTUmCKe1IaMQlFrZ5KO8iOAK70bZUGVb8o4nO7O8uef0/pDmSluyWMWFTviWQhuw9YKsrlVBT58aERLup/v5TlXI0PPHr9QywuBLP6HzAeHAlmVwBZfBrKoYsTwbkbgi6P4qihvtSPy++T1f9zmQSHvle2tJQhLnUUbSKmoZTD5TkCzdeCWdrtUW1x2mdFenGdUGno9RJHQ8+WzlY4c7Q0OByltpZ2MRFIGEc5Epk46Io9bwhyIMQTIoqZodrTuDW9f8ZWP2Ftl1ynj+jwXPqWtVe8CM1Zm56SEczYTy1tdOztxKhHjDKGSIMrGfUX+gnP08d86jesvHLZCr172Fc02E0KJ3BReSdbTnKLWaBrrlj75zy35zz3K361dny+NlYOrnxEBaIJd0HZB2FIyuuhZ2ebuVXQ6SXXsuJ55/lsBV/0gbVP9Jrbwd3kSeZwIlwdOnZxi3crkk1lLHS0Tugc9KX5RwMngghEm0g24bRnmy7ZuIZtAxfeuGgSrSaiCesgtJrHNUlnLheF2R3h0XsUrwpRYhoZOXCKiX1wJKO438AhJI6yZ0rH3F5xTClxio6m7HgfsovNZIkkCZGGo93wTu65GdZMSTkG4W4y7sbIQXekOJHSiRv5nia19IerPK4m49V05JW+Yop7nLYcueNmCERzDEkZItxPiZ3sS/8cubWXdLLi8tQyNK68b3hRrvmC3fpPOQzfsz/9Ae8u2fSf0er2R/v8D9VXpVKpVCqVSqXyJKmL1UqlUqlUKpXKk6UuViuVSqVSqVQqT5a6WK1UKpVKpVKpPFl+sQKrhjU9WwTNQgAiSeJyc33jNlnIYoEQ75ebxkU8oh4v6x8UIoi4LFYh35jf+guSTaS0PovI1HKDeyKkYz5+GouAoMHsRIwD7/9bIaXd2V8OkR4ki63GcI/gFnEDgOoGs0AsMa5Z3HUBQEwDh/FN3q9NhHgo0X8nQkzshywqwxLJsvArpSOH4dtS9oDZiODRJt8EfYo3BB1QUUIaCDZk8YFuiHHH3eG2lN1Q3dI2LxBxHOyGUQ7cS0dk4pTumEK+MTuEd7w9/C3erXDa5nOd7rM4SxoO4xumdGTvXhFtytGnw7fEeEfX/DkHbhFVCPlm9UjkRl8x2A7veo7D10xhx6l7h4ovoq7viPGOi/W/AGBg4maI7IPhBHZT5D5lIYtZIsR7Xoe/5ejvOIQvaaxhkIHX8nv206tcpzawl3d8P37M/dSwdp5DDOxt5LX8A/fjt6i0WQxhI+/GDq+Kkyya2gdjHwOBLI4Yp3v2/g2vxiPNYUWrsgh6XoUDO/ueMdzTN9dEImO5MR8EI0cGHkLe79Emgo1FolHiZQ0mgykJaBYyJMs366sIzhpUWkIamNzAlCAkY0pCLAqWZCXY0iz3L1KOGaShIQsX2iLiWrksmFDJEYhjEjpVmugXMYvTjpU0bLxw3RqfdIGLJvCsHXBqJBPuxoZD8CRrOYQsnPju9AmTO3IjfYlyhAZH7xzbRnjWlmhbzZG1wYROhUMUptRzH69JmrhzG6Z4AEuoNFkElbZspeNZ23DZKq1mMUaOQJwFW8Z+UvrRcUo9JGi0Y3IHBrdeRJROO7xb0eslPVuu0kdcssKLsnYep0KjWRQ0C1QMIyYYUiKacUqRQ7pgInCQHYMcOfo7VBqmdMx9v7RzK3VxLixy2uVoR21x2qHkMW7e7pyHGNF8bU1TFhCRSJLFVouIiFDaQngkcpI58rTEmjLHm3Iea/oQczozv17EVHYurMqipmRaxLJZ6CVCETLN4q45alYfCczO60Clwc31gsNLl2VukuVugi7Pj+pGUomzzHG4ZonAiSDD0gaNCAni8t0cU6rS0vhNbgvuGWu9xtOxtks661jR0UtDo0ojRbQjDwqdmIzJcns4pI6BgKJEnTgBqrdLXXntaf0FG/eCj9JnXLDmuV/Re+GqzaLKpvQNIYuLxiRMCW6dwqmniY57toxyKNfN43XFWq+5sI+41hXPe8fK577Wu4d9TSmf935yTMk/CJ5tonMXbOQ5H2kWSH2ygs+6yNonLnxABUISvDQ0orzxSquOxtpSE45Otmy8Z9solz5x3QZWLtK7iJXWdx+UMQkrr2xin+eFcU+wEwOBKTWMKYuZctwqHGPiGCMDO8ZwT+M3NCUOVQVk7p8mxPJ4CBh2qPosktWRQ/DcTUo0YXRZDLUPuU8HUo4NH99waN7wWg70hy1dEamNRey0k+8Zp3tElMCJXZh4O3iGKLQu7+9+TBxsZJIBpz278Ttetz1/OG3ZDO0yJx1tYq83BBtI6chN+D3BD4Tp17RTQyDyVr/jPn1PiDui23BIN7zklt1pxd3YMlk+1q2+WtYjh/iGt75jGzaEtMKJEC0HxTZ0bPwLAG7DHVO4YZw2+Lbj51J/Wa1UKpVKpVKpPFnqYrVSqVQqlUql8mSpi9VKpVKpVCqVypOlLlYrlUqlUqlUKk+WulitVCqVSqVSqTxZfrFuAIlpeZ0VndDQE2Sd1c9+QKKS0kiI95gNJFNcUaq+rxid1alKlk2mEu9nmtWyD8rZs0dRreZCKJguCnzDeIgkFMAV7SKLglXkQT2ZUkAkLeek4hG3Xo47bzfHtZolog1LJGJWByfAYTYSY1yiCLEcEQpZpWmWoxedu3wU0RhsJKUzdS4J51p6nmdHhBJjm+utK3XnCXYi2sTAjmQTUzpillWQUZTT+F1WhJc4RiuRl1hiCDeM8Z4Dr0qc7AgW8O4ZKp7JjuwFTNPi/HBiR2Si9ReE+JyYTtwffgvkGDrvLum6X7NpPsXRMMnIbRzwMdfBjhNHOTHZsdRpw7vj33Nwb9i33+cI25Rjasdwi0if3RfSDS/1Ja2t8NFxkB0HueXt6e84jq9o3JaRIwc58Pq0Yko+Kz6jcYqJG9sRbABgCDfcj9/wdfeC4fQZLTm+bmDiW/0994evmaa3rLtPGGTgkFa8OTkOXrh3wikax2C8GU+8lVuG6R1Os/JyTIn9BF6EVh1essp/H7ISdrJEKirVKe45+HfcTxONNmwmR7Q54g/2k3GIkRM7Qjw+brOWFfNZKSsks+wgUN7PbgJGShNOWxpZ07usKt5647KZuGgmnq1PeJdjPFvXcZgaDlF5O7ZEU66OFxz0Od5tCfG01KGSVf8rB70zWgUrLggg+CAcW8fFcc1kU3akkJZgB1QaPB0bWjYuR7Zet9kJYOXAiRU3AGFKws7nGNZDUOJwgU+eg95ycK8J8UQqLg9eOnq2bO2KS1Y8a1o6p2yaHHnZKjh5iGdMlh0MhqREg2Pw7KeGyRL3sWOwwL2sEKdMmttrSL7EMc+PhxjfOVq00RVe16goSpMV7/I4JnIez2bVe7ATSTvMIkFHUppKVGQeO0nFHYLEeebpMpadKfPPo07PnVbeV93P5chj4FTqJI9lYookJRHQ8v45gpy5IPS5/t0K71ao+Kz8L64PXjoEpaHD0eDN42hyHKZpmUEeSCSiZJeESQZMEgMHBt2RyNHQc3kkaR5TcajkyN3WX9Doio0+58Kes7IVW3pacWy9Z+WzU0jrcruYvQCM7AZwisqUoA3KmBospRwPLaHMVQnB0/gNnbvkyl7wXC7ZOM/HK8faCdddjg31Co3kCxYNDsWtQ0UYYh53+rThIN1yLfO4v2Vta7at47LN/eyyMXo1VGBIwpjgGOeIYinz0QQovbtma8+47PL3nzUPav5tk+tuSsopKcGya8EcOWuW8hxFs7gZNApejEYTjSYMKa+NVo1WhV4aev+Mu8PfcZhec9Pe4Ueldz2nmMu4nxJvhok3ds8+vmIMd1ys/gRFziKvhX1QTpLrbB+FUzCmZMv8N0y3vGne4CZlfVizaXIU7SladoCJgYEB1YYQ3nE/fM0f179lGv+ErrS9kcj3+g27IUe0t/6SkAbeyZ7vjw29U9plvI/sZE8guwHsh28xEn/fedZc0ccNkYlBjxzTTR6vcdwf/8jY3HNsbvDakSxxHN8whntSOpBsYoh3vGr+yD1bWlsxycAoR+7iN8R0yvNKuOcgb3jjrknpOSW4mJFIlKlEYbc4tyaGO6a4J6Qat1qpVCqVSqVS+QVQF6uVSqVSqVQqlSdLXaxWKpVKpVKpVJ4sdbFaqVQqlUqlUnmy1MVqpVKpVCqVSuXJ8ot1AxjtSCNrHDnjGbIrgJMGo1+U0SH2xHQqKviwKE/PVateuwfFatkX8Ejtn2xaHAJmZbxZQnBLRjSSFfQoYAEzyI4ARTorCuiiml2ez3K0gSXL2xY3gcec52nPSNl3dgPIxzUEzIpq1n1wbCctqjk3W+VxU5nLpS7nROfyPLgSOPGP1L3JpoeccZsWp4XktqR0KvnpAzCXWTGMFA9nzgkuq/n9M7zrUW2IllWGB7nF0eRjlHJ07hLt83uzkli1odUNXjtWco23fF4nRgQlEjnKkVGOYPk8Wv+McXrNIbzlNL0q9ZhIJYvd+2uSBYZ4x1v+gIqSLDGEO0I6chi+JaUjffOcYAM7ueVNWnE6tYgIk0UGArf6hhAGBEeM9xwG5Tv9K3b+TW63JX/8fviW0/ASitJ54MS9HXkzOLpJcSKcYuKQAi/le274hjHcsWpfYCQOaeJuaklklb6TnAV+M+Z86ZNNjHpERBlDVom+lnvisEXo2DRZkXsMxuth5Mb2HOwNw3SL1x41JWGMyRiicIiCK233FIVdEPYTHGLgIAemuMe7Fa2s6VSLKj7RuUTvA10T8O1DW1cxrsaGq6ZhTMKV69jFZ3TNFWO4Z0w7JolEy71ZJV+xRgwEnMHosrK3d1kh3FmX1eHqsZhQUTwNXpROc4b6RZNdBTbOaDT32SkJkwmdE0DonDLGFonCHdfcuQ3BHQnhXe4v0tBbUVH7hqs2ux9cteAF1t5oSvb4zJQe8tqPUbifhDE6+skxxkQbHYLjJDvwMKYdMY5ZpWtp6VPzmOa0w+uaVtbZGYA+K3VpPnADSBKJBBKRKKucJ86EpoEkE1O5rmLZgUSBaIHscGIPYxr52OdOIbk82RXAycOx53HufDwzEibtorI3i8RUtksQS5/PY5vl45f+odrmY7oWrytat8FJQyNrPB0d6+zgQc6dV4QGjytqZie6qNBTGavNjMkiCWOwiUDAiUfFMXEi6kSQI4niwmIJ0ZbGbWj8mpW7ppMtV/aCa7ukF89V09A6YdsIay94gc7ldvFwTXKbO0ZhiNk5YojKaey4K204ly+iuqJzF6zcNRfpio/alm2jvOizcv95m7jwWTk/H2MyYReUUxSmpKy9EJLSxIZG+nx9iltOays6fO5DjvxQoyt9I4jgyn6jGdGMSU5M6ZjLJlvWac3Ky/Ldud97ze4fgbOTZ77ctszTkB0z4vLI5XbF3SAV5xIV6JzQq2NjHyPSsj99zXft33HSzwjHz9kObR6jUuClvOJWX7E/vCTGOxpdkTAmS+xDGVMkt4powrsRjjFxSnGZ2/anr3nD74ka8MdfsRo9a+eYUt7PvR05yQEvLaItp+El3+v/zqG9yWMRjsjEYXrDYfiOGO8Rf02wgVv3hiY2tNHRime0wMDEQe8INuDdimF6yf6UXRVudUXnLnOdpKzuj2lEtGcKbwhxxzDdLn0vxANmIyItZokp7rnXl+zF46Qhpik74IxviHEHOFIKTOnIzr0BBW9+Wf8cZb+skbILkCemE1Pcf3B9f4z6y2qlUqlUKpVK5clSF6uVSqVSqVQqlSdLXaxWKpVKpVKpVJ4sdbFaqVQqlUqlUnmy1MVqpVKpVCqVSuXJ8ot1A5jswMgq5zxTFJKzKnZ2BZCUs7K1JaWsVMcSJg9KQ1cUpYJbFKt65giQJKuUExPJEkYkpoFYnAWiDI9U+YtLgPh8rPJ+Tn/WJcuaxY3ALy4E544AACJZvXi+/0U5OzsFGEVlqxiz28D8HQMSSJPLg6LaFrVnVtDOykYRl1WLReE/l+PcHUHPy/aea0KaM6Eluxk4zdneAAEtiv0RSNmVwSKz7jO7APQ47dGiqFX1+Pn8y3WdXQA8HU4avOswd+aIgOYsabLSsrUVjTUoWlS+kUlGIhOJmNuOrll1H6Oa89azc0QCFOcuFteEGHMGeygOATGOjPGelE6kNODcBaqewIkTO27lhhNZXRskMsnAkbusqFaP6ooQb7k9/JaD+x6RrLaONhLDHaI9jdvipGGQIzsUIriS5X2QA0c58Dr9luP0lhjvUPk8f8bAu6FniMoYBSn51ndjYl/ypQcOiCgh7DiMr3m1+iMn+4jp9Jx+KA4KFngtb7jVV+wOLxnDW7arX+fzJ3EKxk6Fd6Nwig4V4xCENwO8GxJvbc+Nfs8wveOjzV/Ss8WrlIxvsleBgKihkl+rGt5FOk30zuidsPLKNq7o/RX70x84hSt27Z5VbDhFzykKguBLdnjiQSEsknXrfu5fi3OIK1nWilehVVg7o3fG1idaze1qSko0ULSokIVdoyQ8q7im0RWjdhhxUb131tHh2ficib7x8Kw1WoWtS/Qu5bIx57ULp5iPs4vK1gtDgn50nILihhUShJOtSZrwriM0AyEdCUBKxzOXkzyOeeloJI+Pna1QlIZ26ZNL35VEIGKlb0wyEJkY9UBiKi4ikMwXJxQtY9wEZQwSUShjV+77D2OLL3nzeWx1H46vRJAPx9ckZ44nCikGHv/2UsZSbXHa0vg1Kg2du6TTLZ6Oni2NdaxsRUeDQ+nE40RoJLtSiOTRc3ZnsDJgR8s58NGMU/IE0uIsIqJMcjwbr3O5neRydO6SjTxnZRuu7ZKPfM/KK886pVW4bB5cIXpni6I+WR6xZ0eNwYGh6JTog6exrjgiJCCi2tK5KzZccyk9l61y2QpXjbH1xkdt4MLH4gaQT2xIiuDx4thHo3NC6yT3Dx7PP1rcEvTMuiIhJB4U+lMSxghjtOwywo4pHPBunV0YcDhhOcf5PGMSogljUsakDEkYE0yWSGdOAIEh7zsauyDcB0e0/F2AU1KiZecPr9A75XJ6wbb/FYfhO77f/Xvu22/Ytb+m5xJFGfXIfXzJcXrLOH2Pd89odEW0yJgSuykV14HsdhAN7iZjPyUGC9lxRDvMJm5Pf2Bqj+CgTxvWcY0iRCI7uWeQIypNdpwJd+yOf89pulnm/mSBEA+ktEd1ld1KLHHkjhtt8OZpaJc55MSOwCm7+LhLUjywP32Nas/JrRcnoWRTnmvEY9KBBabw+qz/OEQcqn2ed9LAqTia5Lk6ENPAGO7K2NYtffOUbkFZ1ktGYrQDY9wR08N6yGwkppGfS/1ltVKpVCqVSqXyZKmL1UqlUqlUKpXKk6UuViuVSqVSqVQqT5a6WK1UKpVKpVKpPFl+sQKrkE5MeiDSLCIoeBAgzchy47jPkaslMtUsMSe9zeIqL90i0jnHSCQiqcQSOtcQ0rAIfpJNy7EFXWJXTQJYiSXkLGZQHuIJ5xuiVR9Hrz5iufk/i8Ny+ZXEVD7O4irFL5Gw+d8psTzPMatZLHQeiajaLMIqLVFrglvqYIldPRNFnJOIpHLTeZQpx6NqiY09q8eUAihFXJWACTAED+Jx2uPLDeLe9UWokeMFnTQ48kPPro/aLAFwixhATPH4/JqHGMVcViNZPkcMOtnm2DtRvLREyzeVJwu5vDxE3hqREKciMgnEdCpxrAnnLvCaoy2jTUwc2cs7hjPhX2Qi2LAI2lr/jJjWJQbvVbmOAqI4t6X1V6UePCMHokz5Zn0cgYmD3TClA/enbxjDLSI5ntZKnOy70HOIjmPwqEAwYx8DJ5s46I7JjgiOlA6cpje89b9j716z00/oWJOIjJKFCEO44zh+C7BE7wayEMHmKy35DPbBeHOK3IWR7/QfeBf+gKUTvbtmbVsa5SyiMQslQnBIEViZCVbEE05y7GmOTPX07hoscRpvuO1e0VjD7dCx8Y5opSdL7m1DzI+QcvBwOItOnlFTnObv+FKuRkokZBF9NWpL5OrKGcmElc+Rj31Y4aUvUcW2iBMbGnr1rEuE68Ybz5rE2iUufGTtA04MneshCUMRiuyC4845jknpNMduOnXoqecQG0J6wUFWTP6wRBmOaV/ijMPDuWVpVfn/HmeehgbP3Del9IdZrhmZGJloCRJw0jBxKn02EmwkaYBEFm0s8rA8/sxRs1k8mCOsVTxe+kXwuowp7/2Gcj6+RiZQ0DLWaTmmyLiInyjx0ao5XtW5lkZXeF3T6yVrrmisze0Nz1paenU4EXqXRXKNUgRWfCCwSuR2M6UssvJRCCl/MNGDwYkGEVfG4lzv3mVxVa9XXNo1a1vzzHdcdzly96MuR5Ze+sTWG16MzqUlOnTuD4eQ54CmRK7GlAVhanl2yvOMx2tPJ1t627Bxnm0jbD1cNcamtLWLZqLRhJZjuOgZk2IIrerSHxt8FqbKQ3z1xMBEZIg5VtkJnCKElEVWh5CFgPuQx5Z7DpziHVPc07gNznKkrRXh2GTCITpSOc+piKt2QdkH4RRhssikQ7kWkdEO7GOgn4S7yfHOO04xxyPntp/FWUa+np0TPhqveNf9OZ2/5Gb/N9wd/prD8B2N3+ao6zQwxTtSOtL4j+nbaxpZ5whtC+wmn8sWs0gzGuynHG89EFAcXjq8v+IwfEuIR6yPtG7LSq9pyrg/MRDIY37rL1BpGOM9MR6KQHteFzQ0/nkRJub5OdjAUe7yfFFixiMTkx2JNuW+pWsiSoj3pHQgxgNO+2V9kXeuOO2Xdc/Sg5a1Rl4iphSYLI8nef4bSTaS0hGRDtV2+WwqIuNzkXVIB8a4J8QTybKQet7vz6X+slqpVCqVSqVSebLUxWqlUqlUKpVK5clSF6uVSqVSqVQqlSdLXaxWKpVKpVKpVJ4sdbFaqVQqlUqlUnmy/GLdAGIamNIRlWlR5UOO7YuWH+euABl75AIAFBW8Li4A87OeKeBTiRM1yarVwICoe1CKlxi+rBSfSOaBVBwIInMsYT7eg+L//LHErhZF8bztUvJS7jQ7GkiCxKLym2PPMkpW2+cYyrzfHO+m6pdIRO9yDKPTrLhXsiOCnrkBOPxSD+cq3vlYgWmpl7nugpxyvdnELBgUHJbmIL2zCLbiUqAlotFpVvaqNDSyppX1EqHalthIZw6Px+GKDnn2A8ivpUh7Z8Vzrr+sevYo3hyhxK1GJpw0NH6dlfx2yLGqNizRcTmGdX59KmrJY65bXZWY2KKqtFB01VnBP1+XWYnppUNVoZlVlxeLm8SsrnSuqJyLUnOyI4GBgR1GYkoHhnhHiCdO0xtS2uP9dXGmSJx0xzvLsZ+n4OeWz5GR4b3IPtU1Md6xO/6Ro/YcmtdF3Q4hnhjCLTHuEGlp/SWt25AkMdjAPvZMpjlqlaz/vJ8mXts99/qON+Pfsh++o2s/YSvP2aYNrcuq4mQ5ynQMjmFymGVF9jA5xuAIxRFAyCrf3jlW6QrvnzGFN9zHl6hzXE9bulPH0QtDIzRFYn8MMKQckXiwkZMcCClHlOZ45Q//HS8lnlWFRaHtxUCzS0BWkFPiI6X0jAbVB9cLxdHiaETpnLDxxsYZV01k7SLP2pFNM+FKrGxum8IYHNGU/dSwdg2npHTqOcQ57tLRTco0bOmsZdAjo99l5XDckdJIFE9KE1YcPxSX4xqtxeHo8DQlPnV2yUjY0jcG89kNwAIqmtufQNKEmCNpbqcxjSA+S+dtjnl9GMeyq8jsstEVh4QeV6Yj5WGMm50AIoEoE1q2yf1yIr7vjmKpRFNnZxPverxb0bkrGlmx4ZptuqShYSsdjSgb7+mdFLU4Z24AJYq37D7ZQ+TpGGFIWfnuRBiTMU2ezjqS2Htjc0SkwbsVvV6xlmdcpi0bbXnWOq47Ye3heZvjfK+ayMblGNROEyKGmTCZluO5JeJ354SxlEFMSZJdLaSMly0rVpbjXHuX3QbWLrFyibUP9D7iNJVRSGgsR682YksMaqN5/PTmcdqSYnaaOMmeva3ZhxV3kxBKROrcf/cBTtF4NyZu7cC93jAMt4S4o2+vl5jrKWUXgX0QenWcVGmiy1GtSXk3KfeT5DjT4gghxc1mTDvu5EQzKjejo1Vl7Y0hCVrKEVJ2UmgU1l649j2fh9+w0+e4bccQ7ziOr5nCbpm3vNvimxf07TWtblmVKFbDOMVEMCOkfI2j5fdONjEVBx6vHa274BjuFjeXxm8Y/a44w+RZaV4jeLdCtcG5lhgvStvPny0uGuKXuc+Ii3tMZMpzBTGvayzlWGXtljknu+yciDEsc+q81nhwHzqPcv9wLp+jUVMaMRvL2qU4CZWY35QCIR5JFh714ZgGQjwS07jEqYu47BT0M6m/rFYqlUqlUqlUnix1sVqpVCqVSqVSebLUxWqlUqlUKpVK5clSF6uVSqVSqVQqlSdLXaxWKpVKpVKpVJ4sv1g3gGSBkAacREzSom5LRYmWlpz36UyNLT+4r6yFdIua3dPgzKNna/1EIkpW5CkOEc1KN4VoU3YLsISYLvnxqSTU5xxte3T0WcO+KGi1WVwC5uOKPGTv2pmjgEnCLJ933k6zhHV+XihKwNkJQDxuzux2q6xMl+wA4KTBlyzx8/Ofdfb6Xt3NKvvIRJCAkbK6vijgc70oKaWsLleQ9OCEYGbkjO+5XG15dDjt8NLTypqWFY11dPR0lpXFHR5FaCTnfStFLSt8UM65rNEMMwjmGM2RMBpriEQaOgZZMcnAKB3JAqMdCGjJcJ5dHsKivIQI0i9KyVnJbxZJTKRyLWYnhfmae+2AfI6ze8QH7bE4QsxtOlh2V4hpIJGY4p5xus/OBGmHkJXRczlP7FB1jNZyol3qYGJgkoFgOXvbaUvjLonSEuI9Ib5jnF7ndgSYTYg0qPb0zfOsvJY+Z1TLxK0daKLHRS3J7olbueGt/JHD9Ia7w++AxPX6z7lKz9lIm5XHkp0DppSdBNqpIUSHiDHG7AYwJiVazvwWgUaVdbygb6+J6cT98DXWJV7qM/T0MSvnuAiOVvN3hmiEZLydRu7knnt5yzDdEuKBxm1zVr09qPiT5QzwOZ89mqBiJBPMSh8uavHHbSsW1awsfawxVxwMoFdYO2PjItsmcNmNbPoR7yNNExHJovoQlBSV9dDQu45T9DRiHKIWhbzQOyGkli44juk5J3eHiGNwt4zT96Sk2bmitC0kj21ucSjILgVzn5mJxQ3AmTKaEvBg4GhIkkgSCKKEktk+O5dgj/va7AjgpMkP8piSR9Xu0ZgyM49rQQITRflMAIEo7mFsozifEHNWuWQ3k9Zt8Lqml0s61mzTJZesacWx9Z7WCSunbJrc7tY+K+Db4uqg8nhcztceRgeHIISUr7kKnKLioiujupZ5ZgIiqhsat6GXS7Z2xYV2bLzjslWetbkNPGsivUtcNYGNDzSaaNyDG8AYHVNSDDg5JZjhRUo5z9qcTdkNQVsa6/KIrfmcOjWcGI0mnBiuPKsYJBDyaxFDMVzZfyOO1npat8EwxnDHffoeUeUmrPGnFXun7MND/z0G4xSNN+OJN/o99/aKMdxjdkLFF5+HxCkahwC3kyDiFieCyYQxwbtR2E2wD8aRExPDMmKOac+9v6VNjneDp1VlSMLkpZwXTElIBl6gd8Kz1hHtgqu0Zs2Wkz+wa94x2C6PzZaW9tnJloaOtW1p8AhCsERKsswZCeOUAkMZ9+bxvvFrxnhJjAfG8JaQDsQ00PqLPK8Wtb6QXTFMGry04PJ6AljmepWmqPezO5EWNx6ztDhmzO1/Vu+fO9AkG0kpYTaCxTzim6LaPnI+eeSsMffBcoys4k/FCWACDCnOIvN2iYmY9JHKP7vkDIR0yvtII9kBqfngWD9F/WW1UqlUKpVKpfJkqYvVSqVSqVQqlcqTpS5WK5VKpVKpVCpPlrpYrVQqlUqlUqk8WepitVKpVCqVSqXyZPnlugGkSJSBJBMq4QM3gJhGUjpTb1uAorB+9OBBoa5F+e7M58zvs7W+A5zlzN5JSoauRLCs3jZJmGY3gJSyUk7FE0Wz3HdxJEiPTAnmcp87Aag0jz7LWcBaFPcQLatll+/KrLBPc+0AhiDM2b5ZqdwsjgBeOhpdZ8WuZBeAho7G2qx0tKa4ACjuTL07MysSAw2RsKjNYym7kZZ6SRqwlB4pezPzdchlc9rhtaORNV5y7vXaLnHmWNHT4XGidMUFICvLswuAk5wVLTwoZ+ecbyiKZ4OQlCk5ohmNOaIlGvM4c0RZcZKGINNSt2pTVt/yoNC39xThP6Sw/KHPPR4rCklf2kEqaui5Ph/qN5IslefcpqeUM5mnsGeKu6z8xCHanrX/icmOnMQxSYMrCfYAE8NyPIBGV1iTiGmF0764CYSc915cKryuUPW0/mJpJ5HASXZEppL/nRjkyMiBfXzF/elrxnCH056+fc6l+4LLtGbt/KLCNoMxKRodzeQZi2J5iNkJ4BQdU4JkWfXrBNa2Zd18TEqBw+kPxDTi1h1RJtZhy2VY04hDgGCJicg7uectf+QYbximG1La0XSf4nNrKnWdnQCmBJPAECXrvUWKS4DkzwzC4hpghOzNUHKyHU4aGnqcKL7k0PfOWPvEtglsmolNP7LZDmiTaHoDNUQgTUKKQncItD4yTB4niUPwJBOiZaeDKSntJJyOFxzkBeo8Q3PHFO5I6UCIR1Q9gVNRED9cb4fQiCICXgSR3FFiyjpjTXk0nCyS6FBTIiuiTGAs6mZVjySPSShOCO+1d+Yscr+4izTWljE1Z9wv7VyyMwfG4m4SZSJZfMgeL+4qeXwzVFtUW7zrad2WRtas7ZLe1mxZceFaWqdcNEqrsGmEjZ/V4oYXaNVoFPRsQEoIUzKiCacEQlarTyYQoBHNPiSmeVwr7iAiLU57OnfJ2i7Z2oZN49g2ykUDl42xdomrJtK7yFU7sW4mXHEDSAZmgojhojJExYnhi9pdBUQkzzGz68zZfOFQVD50QckOFtmJJpqQijPN7HAx79up0Imjs47OXeH9FSG8ZTd+S2omGtcRpo9ZTQ29epxIUchHRgu8kbfc2UsO4S0h3iHSopKdJCYLHENCRek0u2w0KqWP5j73bjSOwbifJk5yYLJjOQMlxCOH5p6Glrupo3cNY1H/Nyq0pSnlv/PrbZPn0CF6NsEzpksO6TkD2dklEJeJwVse13PrdDh5qMtotjhlRNLyv9yX8nzVuDUAIdwS4z3TmbrffFocYHR2jZEOXeZ8t/SXeYw+Xw/ka1jmhbN+dr5mmd2Ecl9KUOYMK3OW2cP+THhv/i37/8ANIJYNZel/s/uRoJxPVYtbgQVSGvM+CGSnH/1gXvsp6i+rlUqlUqlUKpUnS12sViqVSqVSqVSeLHWxWqlUKpVKpVJ5stTFaqVSqVQqlUrlyVIXq5VKpVKpVCqVJ8sv1w3AckatiJIkq9RmpeScVZssENMJS2PJlG4estxFcwZ7cQFYFKzFEUBLivW5clVFSOgilEtETLLiz3TOiS6ZvYl8rFmp9wOqWXhwAYCcEzyXKX82KwRdVvWW4wopuxCgj5wFPty5Luer0uBcuyjufXEEcDS0rHJOsq0WF4Am615LCvKH/+ZJGIbhcEQril7JjgkIS73EkqU854mfuxYIsjgVOG1pdIXXjlbWeDp627KyngZPLw29ZheAThWn0BY3AFcU5pBdAc4xIFlWw5rBlIwpZaVnGzW7AiSHN2W0mNXQjMs5BDkRrCNqaUPpQ0eAOYf9wY2htBfytXTFAeBc9Tlft3M3gPl1skBCEUkEy9nz0cLicJEzmHN+89ymAVKaiKoEOyHokn/9gbIU8hXWdW5bFon+ovSfh3aq0uDEI+Jw0uGK08PEiQk4yS6ra21gCLdM6chheMUUXqO6Zbv6krX/iGv7hAvXsvJKU65VMGEyQZNyCB4vuUIny2roY9Qlp12L88OKlo1+jLWJYbphnF5xc1DiaqDVLWu5pmUFZEX5xMDRbrgfv2Wc7gnxHc5d0vsrerbZ+aJch2i5TGOCU9LipWFLGXZROQThFOdM9MRJjkzpSIhHRFucdDR0tKq0KnjJWe2tJnoX6Hyg6yfaTcR1hq4E8YJoNiuxZPh+wjWJcXCIGKupKWVoaVUZk9KocAwt++k5XjzH5oZhumUM75jiDhFl8kcmN+CkIVrM49Zy7R9cNABUpbROW8xKkhmKEOlzVrvAKAdMIk67ZWwzG5dxF3g0ps5j6TyKOHsYW5dxxBJCIpXx4vyz8/E8K5VHBI/TnsZtaN2WVrb0bNnaJStaLlzLtnH0TrhohNbB1sPWG15hpQlX3AC8GlLk0Vkln3PmJxMkKsmyWv4UIWpWzUsppVnKbjNpRKTF64pOt2zSmrW0bBpl00g+tkusfWLjA72LrJuJVZvdAFQNMyEmIabiGiKGE+N8KLMPLEjO68lIZhhS3Cqya8SUhCkqsVzrPP5pcQbIOKHUh7JJLWu5Zt2+4IiyP33NFPbYKnFyOzrW9LZBLRds4MSgR3b2PffjtwzTLWYR7y5w4olMDDJwiIGEwwkMSRbnluy8AHdj4hQT9zZw0DvGdFiy52MaGGzHQTr28ZLd5IgmOMlOD8k9uAA4AXX5mnVOmJKwiUJMMKaGMWZ1fzDDLM9h533inETeJpZtHm/rslOKdDjtclvQvM6I6QBRcZIdWpy2mHkQj+Dw0i3zwjwfzOPzMjcsZchjcXZdcSTKmkDAiSeJBwW1vN4w8cQy3+d+GbJyX8FKP/yh3y/nul7cij5Q8Ge1v5L3M2+fv3TmJJBGbHER+Kf/Tlp/Wa1UKpVKpVKpPFnqYrVSqVQqlUql8mSpi9VKpVKpVCqVypOlLlYrlUqlUqlUKk+WX67AKoUlvnMWIuWYsSKwshz9ldJ4Fv/V5thRneNH803P51GrP0cMgJTXojjKjeAlfo0Ecb7xOeWboBfhjQUoEWSz2MV4iM6Dh5g14cOI08eRqo8xK7Fp5ZkiXprj2JxrS3xmh5MOT0/LGoens1lg1dHgS0CnFiGGlgjTxzegx3LDf7TEhBKK6CsQMCJBcjSjKyKdWXx2djYwx8CqfxB+0dOyorMVK1vR09KIY62e3s0xloLXHLfXCIvAao5aPY9bzZGrWSQzCyjGZMQkNJpvuHdBkAgtHjVhos3nVsRhQQaiDgAEOeU2kGUoS9zceazl47N8uJH+PPr0nIcb6UN+XWLxYmkjiYeYxWRTvr4levL836Nzu0oWSCUiMwvy9NFxVTyKL+KbrhxjKnWWyjYfRgLOZY025WucBpJNjHHPabohphMx3tM1n9E1V1w1X7Lhmqt0wcoray854rJcnymVOGBAxRByBOtkwpjyNcttKF/rlTRc2gvEKeNqzzBtOQ7f8jYdctxlc5XFPyjJcgzqON0zhreYjbTNJ7T+gt5ds7I1HQ1+iRyFIeYzlZhjIEEeBFZB2AU4BGM/JfYxsJNbhnDHFA84XdPpltZWdE7pXI5bbTXRaaJ1ka4JtF3ErQy3EnSjiNecA5oMC4a2EfERf8zXoTtFQlSmpDTqOCXBi3AMjkNcoUnY6Sec2ncADNN3jAHGeMWgO1Qck6xREyINCcOdiUmKtopkWUDki6g0JgcGjbU0dCRJeOkwjcvYaYt4NC1Rqe8LSRWH2oNkdRauPrT9ebsP27FZLJGmUxFYTahu8LrKUau6ZUO+llt6evVcNI7LVuhUuGyhVbjwxsYnGjF6l0rcasLJg2gmWpZaDUWYZAZBs9DOlUhSXcqcxZC5jY04XefysKanpVdXonZzvGvvEmuXo1Z7H2l9wLuE04SoYaUfOE1M6cd/X8q1Uuq3jPWJRCAxlfbbqiwiplN0uBLZOgvJhkW4mAVluW9B74XJPFfpOfv2U1Qb7o//wDB9x42NnNp3eF3Rug1OGpIlog1EmximW07TG2Lc49wG77JwM4cRjxxtIkZDR+EUZRHBhpTH3/sQGCxwJ/ec7I4x7XJEehnzAqcslmTiFLNwqY95cJsFYioPQqtWoS/jfTAhpCLoTOW9Iuya47izAHf+24gpt8spJVISMJZR3JdxPOJKRHhbzuWUpUnpQEojKLhU4omlwdE99AfxZT5oFlHhj80Lc7zrPI6XYGSShCWWXaUpIqp2ETstsauzyEoUs3mdoY+OAvP6IfABNq9VSkns/Q3S8t08BxrgEHHL938u9ZfVSqVSqVQqlcqTpS5WK5VKpVKpVCpPlrpYrVQqlUqlUqk8WepitVKpVCqVSqXyZKmL1UqlUqlUKpXKk+WX6wZg4YNUsFmZjSVScQMwmxBy3Jlqi9McOaqSY0VV9JFi+1y5Cu/HsJX3DZI0qMWs/Cer3hMNJgnVBlKOXc3xnB4zl6PIZtX+mYJ8djBI8k//18USR8iDas+IxeXAozKfb1bcO8kxq42saFnhzdNaj8eTwyI9IkIjsxvAWSwjssTOzW4AUxGlu7P6ikQ8zeJ0ADyoA/PVy1vOTgVnTgCNZCeA1npWtKy1oREtanLFK/ROSkQgS3znrAhVeUigzUrP/IhF+TklstI8lRhFk6KY9Uwpx02OpiRbEyQw4gg6ECy7AThtsZQQaYryOTy6BonsGPHYZcI9OAKYX1pSrom0OEHAg+IzEZCz1mAWFycIe7/hn7ehNGHSZlW/TKi5UiEPsX5NiX9NRHxxbbAz1Wk6czawM7VoVqdOTOlItIkQj8Q0MoRbQrhBcHTNp1z0X9C5K57Zp6xty4VrWXulLddNs8CWaJL7CYrMcavpQYF/HgnZKmy959l0SWMNsZk4uWc47TiNb5jCHeP0fTlPKbF/+To4f4nXnnX3Ca1u2cpz+rRa2roAwWBMc5t5UCznaF7YBXg3GMeYuAsjdxzZ8YZhuiWEW7arP2XFJRtb07l8rq3aErfa+pgV4G3CrQTpBVl7xAs0xXEhJqxVvAa0M1ZpwvvEFBxDdDhpOETFibIPwj54dFyxsxccmxtEHFO8I6Udw3RL67ag0EqOoO2soTEFEmq5Vc6dRUscpxbduEOwoll2NDSkZYxTbdA57hFX1Mf+LBo1Le0oERfXllnZPCufzyMsc+uaiKU/RZtKxPBQYoaH/E3tafyazl3SyyXrtGVFz8Y19M6xbTTHrGqOWO1djjvd+EijRq8Jr0arcXGlmNtdMilBkcqkOZ7VmeUxcOkPhknWucc0YmnE+dwOW1Z04mmd0qnQuxy326nhxWg00WjEqS1OACI5JpXS7nJdsMRDp7PHMkaIFpeEwCQDkwXGZAwxj4eHWPq5utJ/cnyrIUucbEiy9C0vsHJKTHAVLzjqV7TNFoBxuuc0veL+8FtEGpxbo3O8c1Gdx3TALKLa0fpLvFtltwgSQQJHGwmWsGC06WEemCwRLbHjxCADd/KKIdwyhntiOp2dayLKRCQyWaJJWdkvYvQlZ1uARm1xhFnalT2MNSE9vF6cASzX+xzBPW8XkuHKuBzNiNEx4kiWXVQ86VHcqpMWJJEYMBtJCZJOOQbV5T4xz4HzfDC7wwiK2kPk+twf8vzxMNYvbi8oKhNO8jjtNCGmxZWjzW4BSlboW3EEsBKBavIoqvdh56mMmflID8c0ZHYxspGHFUp673uzO02OJD7//s+l/rJaqVQqlUqlUnmy1MVqpVKpVCqVSuXJUherlUqlUqlUKpUnS12sViqVSqVSqVSeLHWxWqlUKpVKpVJ5svxi3QDMAik9VkWnszxhswmIgEO0zar4c2V8UaErDW5+mH+UXa3FJWBGFzVrzggOgCvKakdDKHnBKh4UJBZFPjmf3oglQzedlTMrZ2flfHYzoGQBZ/XgY9eA+Ej9v+xjUeKWfF7xxQ3Ao1oU9/LwaOhorMPj6OjwKB0eL5ofmj0SzjOxMyVL3SwrVLO4mIjhi6LRWVbCzwp4EbcIDOeyisxuBU25HtmhoGNNb2s6Wnpp6NXROmHtlZUXGoHOZdVrq9A5K0rQXBQR0HKwRFHUwpL9PCahKQpvp9kVIAu/FZ+MFBwuScmLXmdlsJwImtWps/ozic/qSJPcFmc15nI941mN6dKWZrXnfG0dDrWcAW1FLSrFKYH3ctZ/mDM3iEWFPTtNPL5yD64EsytA996eYnmeVaeRyLS8np0AxrgjWWCYbklpJMTbnNnu1qy7T9j4T1hxyUW6YkPL2jnacr2yc8PsKFEyws2KWpWH3PLS82YnACtt4Fnq6VMD6StOcqDrtxybj5nSkZCOxDieuWEoTltat8HrmrVe07LiKj1nQ0uvniImJppll4jiHAFze4GY4H4ybsfIIUbecs+93rCfXjGGt4g09P6KtV2ypqN3wspBX5wAOpfwLtE0EW0NaRXtHNI7pHHQuuWA0uRrIE2kiRH1kc04cDl5vBrH6PAC+9axD4og7E6X7PQTxDuG5h1j8IzhHaewwVyk9WtMEo21uKQ04rKzh+TWJsUJ4H2PCRFBrbQYU5w0RHJ/zf02FOVvKur0CTEl2URkQtCiXc5Kf5WcR3+eQB4lkDAmGYgEJk4ETkQbCOlIiCdiOuT2qytaf5GdAPSKrT3jgjW9NGy8Y+2VjRc2HnpnXPisxL9oIhsXaTTRu4gWZf7scmIGTpRomlX45L8Ve6RsLr2NQCTaRCp56LObSWMdjSiNkp0E5KG9ezVUPlRGmwkxKTHNzw/lCMUVw86+pqVdW3FKGDlylCOneJGdIlTYB8FM8aXoTrIbQe5rD24bKrmcnYPeZ/eHq9gxxY9ZyRptHIPfsW8uOI1viWkkphPRdtnVpuTMq/Y47XNf8xdlrumX8WOQgWCBSMSlByeIiYkogYPsGDiwT685FTcAsxMifXGwaZa2lMq8c16T83nM9d2ILf16dgbI43meE+Y+Hm12gcj9fO7/Q8yf+TiPSfkahJRQhMlWICxzqWnK8wIJSQ1m2bkiphGU7NCieUyex9jZEWN2ApjXETPn88LcBI1EkljWCT0meR41HtwA8rlqdiEQT0pljso+CLneZmeAD3h/BLBypc7novfnpNkFIJ9NXvjosv+5TD+H+stqpVKpVCqVSuXJUherlUqlUqlUKpUnS12sViqVSqVSqVSeLHWxWqlUKpVKpVJ5stTFaqVSqVQqlUrlyfKLdQNIFpGiqodZdTar4SM5o9ZnJwDtUfF4t8Jpi0qzqAydNCW/fVZsO8Qe/ppRhFTU+ooWrXVW8pnMWb8NJhEnDWYJVY8mTySB+OIEEHOWNtm9IKWAqscsnmWh5+/L+7nARR2YbCo58FmBO+dE5/0WBwTxOGmLE0CblYs6OwH0WblqDS0NbfFDaNXRiOIkuwDMOe6CnKkq87PMKtWUZfhqRjzLN1ZT9L0M4gcXA0OLW4Fz7eJU0NDR2oqOlg5Pr46Vz+raVVH5Ngqdzs9Gq1byrQ0pOddS1IlGVnrOWdtZ/QlDyqpYJ1npKUUW3KTsHjCJEqMRbIWgTDIQZACFoEcAYjrlHGhCychWUgolBzqS7EHFubQhe+wE8F+Cc4eIcyWpnLkRvJ9HPePwJElFd/ugXDVLBBtIlt0ApnAgpoEp3GE2oLqib57T+DVr/5wtz1nZmg0tK+fpnNA6oSmK3blakuXX0c7bV75mSs5kdyX/WwUuTDE8Y3S46YrRtmztkoPumNzIwIHA8OicPA/OF31a09KwpWfl/NLWtajhh5jb+xgfHCTGaAzR2E2Jt+HEgYG3+pJ9esN+eAkoXfMRG/8Jl2nLxjWsfM6F753RuUSrkcZHvE9oA9Jki4PFCaDxD7JlH5BkiFdcNEQS/TCxHQZUjMvJI2I8C8K+VVSEfejYhRd4bRjaW1Qa9qd/4Di+IfmA046oE048akJnDSRwImU0y3WQrLh8FO2wFRn63HJmdw8tbhWCgvjivJLHMsERLRAtuwEEydcju1uAiFvcKAAiE0ECEwMjB4INjHFHsJEp7InpQErHxW2i9Rf0ekUvl6zTlpU0rJ1nU9xCNh423ujV2PpEq4mNi6x9oNFE6xJOE04e1PkxzY0vuwDMn6k8zpmPZoTitPEw3loe58XjcaXNShk3mf1d8jhUXC7MhBAVKcN7iNkNICQlJGVKD4p9o+TXY2TvB0WlwWwippHBdpxkxTF+xH7KzjMrl7/vREjmaNRo9EE/PyXBSi/MTgHZvUIQptaRxhXr1NKklqMc6ZtLjv4jQhqY0pGYhgf3ESiOM9klotEVThq8dCiORGJiIIoyMaFldDEigxyJBI52y2QHjtNbhumWmA6ItDi3znN2cevxOPSsbvO4lh9OjFbzPNCq0ZTrdz4EzzWQnRby85SMaMJw5gZw0lxHWlxipmSYKcFy/5ksu6iMcsRrfh3cESMSxGN2wmxaXGLSWX8wyWNrXi/40r/0vWcpfizZFWB2B8rjcb84tJh2RJvw0pLk4XqInSnxFcw0uzcsrgCJD1X9P8a8/Q+9f46Uki+je3GxeG8S/AnqL6uVSqVSqVQqlSdLXaxWKpVKpVKpVJ4sdbFaqVQqlUqlUnmy1MVqpVKpVCqVSuXJUherlUqlUqlUKpUnyy/WDcBsJIewz9pVOHcBoKjNVVu89sUBoH3PBaApSkOPo0HKq9kJYNbAnpMTy2N2DJBU1I26ZDarNQgDTjxRfFb6pzlL3sH7bgA2QcpqWSnqvZlz1Wwq6stZ/W+WsirTEmYBs7E4IThEGlRnJ4Buya729NkJgI6Ono4Oj9KKxyEl1zqrSL2eKVoFtCjm52xmEEwsZy3HhzztZLZozj+8ZimrEon52pTH7FLQ2YrGmuIE4Omd0juhdbAqj0azyrqRorZWw8nDQ+Xxv9BCaRpTUgwYktAkYUpZmT6lB13pmLJi1yWI5olxzoLeMhVV8+SyG4DTnphOmFHqPmeipzQRVXElPxwBR5MVwZKye0TJrv/nIssZnisvH9SgP8b7TgA/dI3S4gSQ9ajJApGpZLUPTGHPGO9JaSz53S2Nu6RrrmjdhrVcs05bNqxYOU/vHF1xApjdAPQnBKIqhpGdGho1RLKC1ZWcbkEJyWhdy5SMi9ByShtiSgyEotZ+qAdBaXEIQiceJ0KrSqeKK2VSyb4boVhd5Nf572M0xpi4jQNv5B0HueM2/CFnmE9vWHWfs2o/5tJecKEdF40rinTo1ehdpPUR7yOui9kNoFWk1cUJQFqfw81TCWwvzzpmxW5zTKyPIyJwMTaoGPvguA+KCuwnxz5u8clx8J+h0jDFPeP0ipRGVD3RDYjP13ygJdkGZ0q07IrwcP2t5KgnwtIKLDtLzHUqLivgLY+xZicwIdqImBLTQNShtDlHkpj3It1Z283OFaEom0cODGlHtIEx7gnxRIgHUlGGN25L21zQuUvW8oy1XbKlZ+s9K69smqxo33jYOGPljLXLbgBrH1j7gFOjdRGnCZFzNbPDWT5vFXvoVcaSQx+XTPpEkKzuzuO4Q8Xn/Pr33DWykj+r/7PyXNFojOFx35+iEpMyRscQlSEpU4LJhJAe666VXPcQienEmHac3IodJy5DC+QxM1oZjzGalMdJLU4p0R58ZnI/g97lphdNoDhuNEEZ04qNrdnLc6JOnNyeiVOeu4hLX5uvq5NmccaZ34sSFu35PLYEBkY7kJg4xTtCPHKabgjxDrMR7z+idRc0uqKRVZ4b8GduNdCo4EsfbgQaya4H2SUmoZJdArSMK8lk6d+zI8LsvDAk4RQp9Zbnh9kJJ88LSjCHJmFKDRgMsmKUNSg47TBLOO2xdPrAJSbXVx777WyMOh+r8vWV5Tm7KRjQFCeNs/YlLO4a2Y0nYVrcGfAIDiMSky7OFWbZkQjSe+umuQzvK/z5R96fCzKX+P2BPWFW3QAqlUqlUqlUKr8A6mK1UqlUKpVKpfJkqYvVSqVSqVQqlcqTpS5WK5VKpVKpVCpPll+swCoLlYyHm3/PhVWKSpvjPLXFu1W+Cb4IeVQaPCWClS5HuZnPciqb5VUfrvPnGLQ5eHAOSFV7iB+chVZy9jwLiUxcEeTMMZaBmEas5O6JaBEi5M+TPNyIbxaXuNIYxyXyLz/GEnloiPSotjh5OG8vWVw1R5o21tJYS4vDoctN6/mGdSmRgZQb1OWRIGaJXk1ZCJPESmxjViK8L0h7KPv5jeWyXJsseMsRsK1l0VcrLotgnNB7oS0CgN5l0c3a5Ti93uUb6b1Ao2mJ3dMz8UQsN3hPKR+5ScoQhUkFF4VJH8RjLhaBVRRigsl8vqHeyo30AkGHfO3TSEwnYgKzgZSUKCMxjfnaaUeSCQyiTDgaIllwlcVNDyQSSX5aHPUBomAxC9bOqvwfE1kBH8SszmUAipQmZVEVE4HTo5jFKe6J8VBiVtc4XdM1V3Tukk63rO2CDSt6aeido9UcteolizmyEO5BaHUeaSlYiVM8v34GaI5Q1CzUmpKw8lkEMURliI5oWRA1C2ESZ1GhZf9OBClt+rx9A8QEAYq4CE7RmFJiHwOjBd7KLTfyDad0y254yRjucG7Dpv2Ejf+Eq3TNRePZNsrGw9oZax9pNdK6SNMknDekFaR14BS8Q3wRWomCIxcqlPFgiijgNkZziMDIdmgAuPCRq6bErbbKPrS4SdjbZzjXMHXH3D7jnuP4huCOWYzhoGWdhTfWMFlLY66MabMAxYhkgVUsgpgk6QMxyDy2pSLmmMezmEamdMzjmFJiqHOb+kBgZUNubXZgjHtiGnLkpo2EeP+4jflL1nrN1q5Y25qNa1h7ZV1iVntX6t0lepdYuUjnYo68dTELrHxExBCxRfwRU/5b5bGQJJXg5liipXPcasxxl0XQ+KGoZI6tfXieTHBJGVOJWxYWsQ+QI1ajcgqOoWw3FBFotFw+sxyjKaI47RBpSenEGHc46djLgX1ck3Csgyxx0ioQZBZc5Tjq+dxynwA0Rxo7yVdVJceQem0IybOKnou0YrLIYNcMDETJ9XDeNtKPRHgGplzPZUwxUrneOVZ3nO7P4psjqls6f0XjN3R6Sc+W3tZ0+GWOmscCX4SYvsRvN2r0LtGVSF0nD8Kyh3YnhJTfmZISTWhTFhhPlseILLDKdeSjlOuvRXDblNjVNZMMiChTieEO8UjUHtIJS6csrNUH4W20CWVCRIkUoTUpx6qe9y3L0axaYkyhQS3m+aNsmyQu/WleM4hkQRU8zAWSFBNfhFaa35dZ7Hwew5pL88DPEVbB/JtoXhFI2e/P2cdj6i+rlUqlUqlUKpUnS12sViqVSqVSqVSeLHWxWqlUKpVKpVJ5stTFaqVSqVQqlUrlyVIXq5VKpVKpVCqVJ8sv2g0gr8VzlCKiJcKzBVGcnMeNtqg0NLrCa4fS4Ev0ny9OAILD2UPUKvBIIQssTgCz5jGrMx2UwFXFYZKy+tUSKk2OxksgJf4V5njOREoKWiLXSuSqncWmzfFqs6pviVtN5y4AgZSGsn2bY1alfYhZlXaJM/V0tLYq4bKeBoeTBzeAWWUp5Cg7laxc/aF/8aSSCjlzriTOz6k8R+LiWpBj3x4cEvJ18NLRsqKlRK06Rze7ATgWN4C1s8UNwIuxKnGKToxGE14Mp/n4UrSJycBMCJqIJjRiOMkKUEWZyjmkop6doz6jCbGopKfUMtgGESWUGEnvjoTYAxDilB0BrCWWaxHSkNuXUlSzEcjbmzjSe7V6Hj1pc3zqz4xRfZ9z1XYi4sqx5muyxPadXb9Z0RuYCAxEJiY7MKXjErMa00CIhyVm1bstXle0bkN/ptrtpaHX7ATQOclRiEW5O7sCKA8RudkFIDsFQFZpO1dcJkxoNBEMOhV6l1XSY8oK5zHNkYks6ulkWT29hAiW85QfSP6bP8vfz24CYzQOMTKmyB1HjnLknbzkbvqGMdwzjN8DsFn/hq3/jCt7wZX0XLbKxgtbXxTpmuh9oGtCjlptLceseskuAMURAOdAH9qDtB5TQUrcqh4jzTqfzfqYldUXwXMZcnTuvhX2QXHScBqeZZW/PxK6I+N0zzC9JMZDjmBsI17XRJ1opKO3LZ11ZfQ6cx8hEYhMMjHJsLThWfEtZGV6HnddGc9G0KyIFlGSZLW4k4YgpxLF6cr+I2mJj56Y0pEpHIrjxB0pDYg0tP4Z3q1YNR/RyQUX9hGXtqWXho13bJrcJlYuxy8/OAEkOhdpNTsBeJdwmlBNi+o/kccGLc4AqTyyA8CsAJcHdf/ilTE7IzzulyaJaLkN5Taa2+cp5mvbRLe4AISki0J9VqQfomOa3UpMGFOOW52PDSUyWXxxA9gxhQNOOw7+jl26gNixD0o0LWN4dlNJlGjV0v9m5tet2uKMIeX8GxViMrogxXHDGFJiTD3REhMxO0ZYJBTXiChzVHMkSFhqzIrDyGRHok2EdGCI98Q4lvjmE2YD3l3h3ZrWX9C6DSu5Yp22rOjp1dPNEdya54WuzA85YjXPA53mNiDkv2eHGD2bs6IpyR7qvkmKE8uRuGS3GC2uDY1m94RoeZtYXGJGWzFwAoFJtzkK2h0JswtA2oGNhHTCpS4fVzzONSTLMcQAKg41nb2Flrl0ju9VSUD+XC2RBBwe6HN/lLJGUIq4P0fdJptdB7J7kKQcDW+SI9pFsiNRDk2f3QFccQZILJmu/yi5fLlf23vvVzeASqVSqVQqlcovgLpYrVQqlUqlUqk8WepitVKpVCqVSqXyZKmL1UqlUqlUKpXKk6UuViuVSqVSqVQqT5ZfsBuAK+p6LUr7/OykPctPVpy2NLpCxGVVPD0qWQ/v8LjiBOCZnQCyB8D7TgA/xZz0Lsu3HCoNIgMqHhQ0eUzarAK3VLK0J1ICkZLVK4pKQC1ftnM3gFkRPivq8/OIWQQSIl0+f+1x2uJdT6MrVDyNrGlZ4WhorMEXJwAvii9OAE7lLDNdcNmo4AMFtZAV1CpCsgelX+L8dc6LzlrRrHBPKZfb0gjicdrmchangsY6Oho68XSqtC4ryWfVZ++M3hmNGCsX8cKS/T27AbjiBrCofYuiN5kUdb8ypYRGRyyq3Cnl7GUzivqcxQ1gSoqIME0Na1tnlagMoBDdQPQjEpVkIykNpHQiiM9OEP9/9t4nRpZlu8v91oqIzKrqPufca2NzgWfEwBLIA8B3BNgTkCwGCHnACAYMEROGCBBDJsDAEhMEkgeWJwyQmAACgYyEBLJkkEAIWQIJ8cdgPT9sbN+7d3dVZcRab7Ais7J69z73mHOx+p0X31Heqq6u7q7KiIzM3be/309jTrqFpakUEDBK967vD83VmLXePR4d2u0uHWCbCy8t5DU5gIb7amXa7evdaLKgpP6VYZS79I5p2vbzF869rz0M7Wt7F13v7T1mV8yeETmQ9MCUPyGnI3P6rCcBPHJkZpZbmsOkMKVIAyi69pN7v+997DxyPeR+7Ga9jV3t5m7d7svWu74YvedbehpA3DpQ++fMb15qM6j942a+JQgs5lSLJID3duXCwrf01zjzjnf1/+bp8v+wtHeITkz5azyU38rX/Bt86p/w2VR4LMInBR6y85CNU08CmHIjZUcLSFlTABTJKcrbNT6OxSS6tUUFn1I0gx8r6RjjOB96GsB14X3NqCTeN+FpEpIo5zZTWuLKb6NOZ87pAfNKbe94vv4S5gslP2B5IevMVT5jklgbsse6eJuPYXQvPR3Cujkca876cjMmOdY0v2BmNBQqqOaeKqCollgLd3M2kgAqZpXanlnau76mLYjMTPlrHKbvoeiRB/0tHHjk0T7hQScOmnjIkb5wSHDKzkFjjVhTQiaNRICsRkmG9Pm1rWm7JJBIAYhtnwCw2fhOmP7Sdsdf6NfuRvOFRa4sblybsphHEoAJz00wV5Kkm32ut2P4Pg1AeGrKucW8XvrrWJdaXc9TOmGWqBapJJf8jid5Ql041oQ5TBpre0uxchd1UOnudtjxim+RL9nXVI44forGMTUl4dycZnA15dp8SwZwh4s3Fm84ztVbrGByBY95VKUnSXil+hnzyrW97wkjV1ofd9VHpvwZOR2Y06fM+sjJP+GBY0+JiSSAQ96lxKhzVKMozMko4hySMenuvKDruN/OUc1izY99r6TmkYKgjopu5wXriSMxD+L8WE0RgaUWFn9EUWo/L5T0EKkpKG5n3K+YXantuZ8XCskXkLheWK9DqlSyxwgbjoptaQDxqPT5VlCPBIAYVY3zhjSyH0DBejKAe6b6tScDRAqPeCQOWf86QTEqa9YFgHjlPhVgd7DcjmDWV9CPoo8874szfrM6GAwGg8FgMHizjIvVwWAwGAwGg8GbZVysDgaDwWAwGAzeLONidTAYDAaDwWDwZhkXq4PBYDAYDAaDN8tXOA2gIFK2FAAhzH/tjyWdUC1kmUg6k6SQOZBl7hbemghQyN2Su2UB3JIA9p33imB4TwsId1tRGq1bmmkz7FSiq9u0gkHSOWw71+ho94r7gvsCXmk90cAlR2KAvPh3xtYV3616b0ADBJGZlE5h/qcHVDM5HUkSpv3EieIzhZmJQiFTJG1JAEUF6Z3RSaRbol0S3cUB7O3/PWsSgLvjm9MeRnvzhWaXMH8tOoqTnmJMdO5JBSdmn5kkM/UkgEMS5sSd9XlQo2jYvrmnAkw9DWDKLfrm9d76XDu/mwvNlGKR3VDXNIC+n8PWBUdILczPapAaLJZYakFdWLiiolS9UNMlupftCihm72jtCdRYaswi0xrzSCIVwSXmRyN63fe0npzQPJIAmi+4t9i6nb2fDy9xt94THV9jgEvrs0RpPQVgNb57s/fdz65+2Rm777a+9tqecDsjCDk9RtpEemBKD8zSkwD8yEEKhxRjGEkA0a09KZStv9t7IkCkAcxqCJDVSXJ7X2uPurPa2ZHQYISl7AiLxbY+Fj3qMXZGmNSL3ezu5rAIJI9UAJO1Cx2qRSLA2SrvOXOWJ97xK5zt13lefpVr/V+4N07z/8VcPuNT/QZfs0/5RGcei/JJgccc/fTRUd+YSyNPjTQ7OguSBcnd/s8KKcWtaiQB1BaPAZSeCnJI6KmRMaanmE+ny8InS0GAz7JynhJJhKtlyqJcr99D1cr7/Kv4wbjWb/N8/SUu1/+HpZ1o7UpKE0t+ZtLHWAdl3ubGZhn3dIqw+s80X2L96khPY3Ex8Eg4aXaOddAzZrWv0ZGSss1VGmaV5tdIN7FzTwEo5Px1sh44TN/DQ/4+ihz51L+P2Wc+kSOf5MychIcinHKsEacUc+vQkwBmtc0ETxpJALJPAtitD95TQ6pJbLsECmedN/7q+ud4JBp4pWrlYpViyqUlVOCpwqTC4rGuZnEWF0pPJtinXZybsngkCFwNrgZLn5N1d7yLJFQyIqWb5mcu9o5nfUJcObUJ9/i5WQUn1nZzQfAwxXvv+5oIoICJIA7SPfDSj6erxfdq3YyPNAC4NKW5MzXlaonqRvJY29QFE2dNHgFuiQB2obYz1c67cT9Q0iNT+YSiRw76KQceOfljrCmaOGbdkgDW88LU15Q5reeHSAI4pEZSp2iLc5valjSyri3NlOxGMyWJouK0buAvKtu6k3s6TOuJMYsJyZRqhcVm1DXSDwRqOlPtOdZUO4BfcTvTehJGs4mlHy+qkRCzoCiJKhWcfpzolgggrvH8nghgEMkBsn4U6S+LnMl+wCTWdJcGBk0VpdDkglikAdxSAep2FrqlAmRiFQV8nQ37841/5P5tVQjW79xeec6HjN+sDgaDwWAwGAzeLONidTAYDAaDwWDwZhkXq4PBYDAYDAaDN8u4WB0MBoPBYDAYvFm+soKVdoEqZKaMakalkPRWt6rEbZbDJhBk4vHicZtJJM/9T5xDAHitanWVq+4fC7lKuiyzbkkKeEg1W92qR42qoCChZgEhWeFdTlBEKq//G8O2mtb4o+aEyAERRfVA1gMqhZzituiRSU5RZUrfvMtVJBLxx/e5V6tK/1glxKqXctUHr8bjz669VwE2dyoWyo40GnWraDSrISH5KhtN5HSk6JHcX9vMtNV0Tio3QUdhVo8t+VajWNQ45FWwMqbcELkJVqu84F3SqU0xF64tITiLxT7OW70nZBGMEK2qC0vu9Xoef0yfXFk8ZJRFLrS0kCRjuVJbZsEwe6K1eJ8iilqB3IUVNVxv80VJd5W6q+xkLFS7dNmqbnW7W63qK3IV/fNRYZlpXklA8wXZ/RE+QJOb9LB+r02G8wu1C3FLfeLavt1rVt8DSk6f9JrVqESc5MSRTzn6kSNRgxmCnN6NYchVcVvEmVNIMEliTHMfuySGblWC3kWQGJ9VRnEPCWYVrJrfpJj14710de2y1bVXJl4anBv9uJQuz4TMcrHGMwtP8o6zvOe5/SqX9i3Oy68AUPLXOU3fyyF9nU/963ymBx5L4pMiPGTnMTsPuXHKjUOulFIpxdAJdAKZ0k2sUoGcQq5apap1xa6KTC1WnJKQOcTAcjKgcjovPC4LIs5TU55Nu2AlZFUWO1Drb+EoJyhwTt9CRHm+/k+sPfFsZ1QP1PbMlD/Z1oyscxyjXbRaxT7rc2udf/5Csor6T+ty3xlrFUSjihXd5vltzq2y6AI4Igdy/jpJJubyGTkdOaav84l8H7Mf+dQ/4Ujhk1R4KMqc4CHDMfUq5lW06WvDWrGaxDbBSu/ESzCLOs1mUcV8N4+2iuZY69a63ju5THK8Vwtp6JqeuVApnniqIckcklCqsGjfqxI1rEVXxYmt3vXcZcHnJpz7HK1dsmo4LrFGbOcayZhfaXah+YUrz2TJnP2ErNWoFiZOFjAl1nj3XvcZGh2EgKU4uUu2UT8d77uodMkIikGREMKyhpSWxNHWpdUGDcXcuXrCZJWQY11bhdvW66nNLpsgXPIDkz4w6SMnPuPgDxyZOOmtavWY6OKt72q4Q7yNutUQG6e8ire1Nxp/KFi5C7VpCFaqqMS+hpuUaZ76OaDXrTZYsqBNWDIsy4S6cPEHRKJ2taVLCK12pZnS2rc36VBbRrtgJaI4cQ5fuEQFt4C6AYVVstqzl6ziedprvBtwYJHzJu46UbsqnkLI1ps4uRetrMtWH4hWXdhdRav4nvtroH3t8LoWrIu39scF9ytfhPGb1cFgMBgMBoPBm2VcrA4Gg8FgMBgM3izjYnUwGAwGg8Fg8GYZF6uDwWAwGAwGgzfLuFgdDAaDwWAwGLxZvrJpAFHXGeb/WrGqkkk6I6JRqypKlsNWI5i6Cx91qyXKVV+pWV2TADazjZsFt6YCrFbedutRsQqFxhLZAlIwqVHOqt30tm6Ke+0WXg7rziuOf445t76mhOgUP1On/p7DrlfJTOkRlcwsnzDLI4nM5Edmn0l9LyRRimokAYgghPUu9BSAj7yC1fy33dbcb9tWznihsUR152Z+XnFqWL/pQJaJrDMzJ2Y/Ukhb1eq0VelFEkDUKPpWozirMfWq1TlXkho57aoVdWf9mvT5EmkAKo7ilF6rVzQyHqpDEqV6jO2SwsSNej1lyZCasLSZ5ImFT2m6sEiheUU1R+WdG+4Xmj1BjdQKd9s+b77c6hJJPTVirXwN83qz+lnt/khUsJ4McDcmUQrYre1basBqbmNRz+h9ZFT0riFv/ZnmdTN1aztHGkB7orV3Ww2m6oGSHyn5gaJHZn1k5pGjnzgxM0vmkFIkAazj92Is5163Oqtx6Lb2pLZVreZkKN7TKO7tXQgjF6CZ9hQKjQpWJ8bJwuq+9lrWS1POPSFAWc3/XqVpt3m9zuHqxoULT/ItLv6Oc/01rvXbtPZtpvJ9zOUzTum3cJKv8ak98lASD1l5KPCQnFNyTqlxTJW5JwGk2aJqddLobpxyVK6uiQDrrWr0v65MKY76Q0ZPDaOSjmHSz88LD9crKs65Jc5NKaJcLdI0qmXcT7xvBcM562foHGvk0t5zWf4Xrb3nbGeW9kTqCR05Hbb5SV/hICz4NQXAbNnm4Vp3DYbIhIjhvVY5KqXX9Wx139f7ikgipU8QyZR0oqQHks4c89fIHHiU7+UT+xozE5/pgUmVx6KceuXm8UXNaiROxO06n5L6li6xjvU6nyIJQLc5tKYBrLW9++1lqaRsCQey2e1XnjjLM+rCqcb+m7SnNPSK0yxw1W7Q99dVe93rxeL+U4XnChdzLs2p5jQ3qrQtjWCtsDW70vxKtQtLOnORzMUXkivXlrm2eOWlz6vFwvyPsWWzvff7SHBcQPrryu70olZSi69vDtqEpuuZSUnmuMPixuJKJt+lRsR+r7fNLoCReppNSZEEEPXNDxz9wLHXN89JOPZxj/rtqFo97Oq3D6lF3WpP4kjq5HSrW5WeFOMuuEUNd9Jet9p6OkxPiUmypkFIvx/zQ/p4RXJAT5cxuPopzhvyKTWdEUlRLdtyrxN+wsxYWqQWqVbSWlMc0whbEzgk9QnXEwFeJBPdjssSa/d2lAIcolZbbvXbjQVjIRPVq2sl/JoKsFWvrqkAorhXRHbJHeu8uxvPxD26zc0tAaQfP1+E8ZvVwWAwGAwGg8GbZVysDgaDwWAwGAzeLONidTAYDAaDwWDwZhkXq4PBYDAYDAaDN8u4WB0MBoPBYDAYvFm+wmkA02b+q2RENBICpIQhLwWlkKSQmbv/P5M891SAhHRjMZHukgD2KQCr/S9b42481vp98ejv1f4TYIlbAfVCEsNYSHqzOON7ZMwyza/R6751vr/e/S6rnSsZkXyffJAO23ufNLrrixyZOJI9M/vccxASRRKppwAkEZL2Xuh1oycCyG0fWFdojX0CAN2eDoO6ubFQubKwyIWrP1H9HHa5nTG/ImS0j1vWE0VOFJ+YPUzyosKkwqxC2Uxy74kAPQVgSwLot7ltaQCaDE3RAb6+fO+d8toM72kAIk7r1ufSFPcwQ6/dcs07+zNbWJ+LCUmUxQvZlMUfaVK5SKGlJczOzcSfaO09rb3DfMK8opYxq7QUCQ5bgsUr/55sXvt8aDS79iSApX//3tuM89JRvtnaNWxhN6rGHBVZwiuVtHt+236WWaQH1PZMbWfcK82etiSAlB7JemDKn3BIXyPLzJFPmf3IyU8cJDNr4pCEOcGsO3M3OUc1ihLWtsR4HlKLNIAU/e1FjZzaq13et7kYnd6+s7lrUwzhWtMuDSDtesuVxcJAv5hQPPrZ692+6xYzjaucufg7LvYtrvXbLO0dIjNz+YxD/hon+Rqf2Nd50ImHojwU4SGHmX5MjUNqHHJjLo00N9JsyKRIUaQkSAo53TaVuAXQnWFbwg5mLjBXFEjPDXDmU+V4WRBxPqmJqymlJq4Wx1B1wTxxqArXr/HsD4gqOhWu9o6kM7U9c62/Rq3/i0piaUeSHu7WlnX7YJ653SUC4JF8gRuecp+nL+nfr/fa36eYPDClRzIHTvI1is882qd8ypFJE5/kzJyETyblkxLz6pSdU4p5NW9JAH0eqSEC8kqiBIBZdMI3U5amNJNt7qypAM0jEWRNQIl3EK8/SUZlwqTQ7ExtMxd7x5O+A+gpDDnSVgSKCtXoa1zcQiRetD6Xrxbrz1OFc3OuBpdmnJtxodJYaCz3hr0b9PSP6heaHLiykEhcrXDtKQCTrgb7+rOdJPG+1iQAXRNv1nr3/j+Lx/Pt9ihtS4MBS6vxLSQTLFb6O4s9klBiTYs18go0RKZIA0jHSBiRRw48cvQDByYOKTEn4dCTAA79nHBIcV44JtudGxqlnxdKbpEGkBsiTs7G7pS2nRdaVbLZti4uLeE9Gcb6eeEiwuLC4pHgUHsqwJJjzqQm1DqhLixcqfIZoomaLogoza54T8YwO7O0TPL1PFC3Y8Il9vB6/YCDoxhpO0+8TAYQEtkjrSMSBaxfh4BJi1OEROJMc0U84d6oGus/SpzYhV0qQMXXtICeBHBLBtiOoO2Y3l5LXycEhf6eEGgfXs68yvjN6mAwGAwGg8HgzTIuVgeDwWAwGAwGb5ZxsToYDAaDwWAweLOMi9XBYDAYDAaDwZtlXKwOBoPBYDAYDN4sX9k0gJwOm1GddEZRVApZZ4REoqcCUCg9DSB57veF5Lkb/Im8M+2kJwLsWT82oos9EgIUXf/XIUnB3EAKhqGkVZzERMFAVaNHXnJ0I0tF/WaR+0eSAIBbV7eu3cJ5s8qzzhQ9ohQmOZEoTByZ/UjyxMzETCZJ9IcnEXJPAhCkJwHcv+c1AQC6OUlYfWsSQPRVR291XS1qFhZZWLiEmbr2zNsVswuqx838nOTExInJD8wUJlWmJBQVsq598msSgO96v8MeLxpJACU3UjJS7j3gyRF10LUEvI9dE6wJ0pMCmnXzUhQn7N8kYbympj3lIDqilxyd9LkJzTOlKbUdMTOKTLgaV5m3/RU94WDtCbOn6IbuY55s2sZwTbKAm0m5jr91m9qs0uzS50b96BxxN1zC5BRXaPE9bw7v61a3ecXdaHbpfd1Xmp1xX2L/pE9QmZjyJySdmNOnHPRTJk48+mfMPnOSiVPKTEk49C1SAG793YcUY3joY3jKjUOK/u451y3RIadIAViTHWLu3xvdbtFZ7i60FmkA7sI1Ja4tRdd3dZoK2lIf57C8Ubia3PWhrwkXizeuNC7yzGJPLPZMbU+09p6Sv86cPmHWT3n0z3jgyEPOnLJwTHBM0U9/TJFyMKVGKZU8OVpAikAWyIrkBFlvERxrKsBLcgMKMjU4ZBzQhwzaKGfjeF5IybjWzLUlkjiLCZOG+WuuHJIDM08to+37yJJ5Tu/JOrPYM8/Lkcvy65hfae09Zs+IJESmu+SR11IBVta55d1I/thzVDNC2tau1NetrDNTt8CLz5zskZmJR5l56PPqkxLrw2OGhxzz6pRu8+qWBOAk6XOobzHX5dYL70IziSSJngBQ/dYF31y2BIB15vlqysc7QaWQdKLZROuG99Xe8aS/DgKzz9Q29XNHYk70nnl6GgB9bkcSgDlUjzX2qTnP1VnMIwnAGwuVRS6RCNCTQbbjmDiOracFNGk0b7EuWyJJn98Wx2W1mPzujolsKQDAnTGf92kj2veJOKaC9iQA9UhLiffjXFUwE0QkUkhctznhtHidvvS0iJhnOR1JOlH0xMyJ2Y+RBCCZQ9KeBCDbOeGQbukPWxJAT4Ypakw57muyLQVAkyN5t25arCNJnWaCaMyV1M8byTzSAly6SS99/ITFIkGnmlCzxnHnGTFh8UcWuUYKQE+JsVx7uopi9kxFY50mkdIUx5WC9RSALe1BwNBu+28DsbunkQKw3VcyBetZRYKiksDj+9zGQcmAiSGeIokA29IA1tv1nLLOtTWt4GOseUm3xA9FSCyf+1W7KfYFnzcYDAaDwWAwGPymMy5WB4PBYDAYDAZvlnGxOhgMBoPBYDB4s4yL1cFgMBgMBoPBm2VcrA4Gg8FgMBgM3ixf2TSApPNdCoCIkuVAoicEUPoWiQDZ4zZ59OxmwoJbO4z3KQAv0wD2GPEvgEz0kcdj0Y1tUlBvYWXTgEO3+0p0BfsZ9YJ5xjBM69bJvhl3r5jeW0d3N+z271ulkGUmy0yiMHPaUg9mn3suQqJIihQAjfeZNd5z6kkAws0Edb+/b/02jNIwY+O+s3j0VoetunCVZxZ/pq42tT3jdgYskgA0OqCLHCOxgJlCYlZlUmHq3fKTQpGwZufkzN32ndfu59TIqZFzQ5NTioXRWTyM//7PNO+l3tIEbdL7wiutap9HMY7mhEneUwGaQ3MlCzsTNGz0JErzCTfn4pH+kGWGLnRXvyIkrpJ7EsIz7lfcrpgeEFNUpi0N4NapHN/Au825Gpm2pgD0RIAYkft+cKfiroDSLMxNEcV82b7vHqfdpQGYR3+12QUwRApJT+R0IunMXD4jy8wxfZ0Hvh7Wtp+YyZw0c8zKnIRjFk65d7enSHJYTflZw5QvahxSZc4xjmuPdymVnCMFQFOoxiKwl9DXw8Nb2LzWhFoVMyEnIy1GM0XFWXpKQHMBlKKO2f3xbR6ed3WnYSxEmsVizyz1Pc2eACjpgTl9xlE+42BHTjJxTLe+8kg8MA5ptZH7eyk9DSArMuktBSD1BICUQKNLG1Uwi1VbtRe3N6gFrhVRgaWhQHpwpktFk/O4XKim5CXTXJhb6t3tyiHFez82QS9HtAkX/4SiM+f0jiQzOR1pduGy/Dq1PfUO83cYgkhB+lwVyTvLd2f+9gHaki36fNvWrT7Pb+v0jEoklxQ5kigc/YGTP1LInGSiiPKQMw851oSHHDb4Q77Nq1OfV0WcORlFYo1IPRFAdikScTxFEoA5tJ4C0ExorixrKoBJWPk9CWA19be12JXU1904Bx1Y6q/QLHOt3+as38I1UkKqn9AmwMSl9R55gSndr7ve52G1+Fnn5lyacTHjyRYWGs/yzJVnql9u60FPDLnNZaNJJAJUCtWNZs7SzXUV+roWy0cVSL5Vw4MICd9+w9WDVPr5zqlIrIf98dRvs8C1vx/taTl3r4s4H7obZhWzsONFCqpTTwLo5wQ/MvvMLJlZE5Pep4usSQCH1JMAknHIjazrecEouZGnWFNSMVBHEx+cF9wFaaBVt9QIVY90H9M+N2K/VRcWi3NnTYr2lJjFYwyvllCEpc0s/kgi03RhkUL1a6zDLVOb9fNBZSHW521+iiGaaCxbKkAig4D6mj/0ITFqDXXF5PYbSkFpLGSZMRrmCZGES6O5oh65RU21z59ImVAyZrUfM7adK9aPX2O/Dghpd83y8Wupl4zfrA4Gg8FgMBgM3izjYnUwGAwGg8Fg8GYZF6uDwWAwGAwGgzfLuFgdDAaDwWAwGLxZvrKCVdYDWQ9RWSYFIf7wPRH39xWriRLVqp7I5KhbJfXqPCHt61blQ8nK1qo9j8drKFWdhPTPey9ajXozi6ozwGib9GUSGoe5xR80q+HesM+pWhXR0MAkRU2hlLtK2cKBwoygW8XqRGGmIAiz5PuaVYk/Gk+97XFfPbmyF6vW2sHmt7rVi9lWUblQuRJy1YUnrr5WVZ6jupOGyExOB3I6UPS41erNZCZJTEmY0lqzGmLVtNYoSlQqzsnIal2u6vWcq5BT+m0GSS/FClADq13aUSelXreqtwrGtW710hIt36SqEC5CLnCiXs8drE4Uizq7CwdUFE2Jqz9tIkpUzkbVqtsZs3dAwnol3yqtsJNWtteNdXlqnRv99m6e2FYAHHWsigINA7+vwNv+SH73fc2u/fsugEfNpj6gMlHSA1P5hCwTcwrB6oGv9zrMmUeZmVR5KMqpizBRPcomVhV1jqlxSo2izilXihrHUplLJSejlLpJcq+No4Q3tjllbjGmmNAWIS2GNUGv8fxmttVrNhcWV8ydJN6P8dt8j/kdwuCVFnXB/kxtsblfUX2k5JCBDv7AkcJBE/NOsJo1JMBJ21b9mCdDJ5Cpd2xm7dWqepOqVslqrVvVBKYgBlMOC2ZqyKHgKsixgcZ7LktDc+NUr9SWyGo0Fya1TQp6agII5yaoCOly5GwTyRIXPqXozJOcuPoTSWeuy7cxX1jau024c38PaJet1vrVLgY6W43qS6FqXa+UkEKzHBCUSU4IyoFHZj9SvHBk5iCFIsopJbIKD1l5KFHl+ZBD5HnMzkN2itykvSxOkVgbkvSt12fKrm4V+lrmElWapv1WumQnIVb1te9OrCLWzew5NplJOpHTgVoTZu+51m9zSQ+YL+Q0s8gVDKw5xRKLZZJIF6wkXh83kWtdWxczzi3qUp+4cGXhLO9iXfWnqEa2ECJfWzds+y/mtff31cy7LBrPi/sOxGsx74fZ7lywa91GcaS/bucmbIm8/lsxx7aKzqiEXXZS50JKn5Bk2mTpwoHiZVe/HdLmpHTp1vtx5reaVb3JVVNufT1pu/NCr+DOflclG+/L0SpYspAXCcHKXajNt/mhXVBbUlSvLh7fa7GoXhVCvkqiLF5Y7BSClVTOkqjpjNmCSsG9Utu3cT/TTLtoFmNoGlKTSgHoYtQq8JYod++i1WvSrG6id4yHbsKu9XMMW/XqXrQST30+6Fb7rpI/qID/2PUJvBSs1nUg3U+g78D4zepgMBgMBoPB4M0yLlYHg8FgMBgMBm+WcbE6GAwGg8FgMHizjIvVwWAwGAwGg8GbZVysDgaDwWAwGAzeLF/ZNICiR7Ic0dVCJWz7fQqA9sf2Fav7FIC1YnVNAVgTAF4WhG2+vwiGk10xHMHRXoQJgE89BSCepx5WnGNUFppEtepap9Y8Poaov7RXbDuVMP+kv5s1VeBl4kFh3lIABGUmUyTeaxEl6y0JIClbIoDK/btdX8Eq8d1bqk41p7qzmNFwLlQucmHhyoWnrWq1tjNLe4+1J0RmUjpR0gM5HZnk8dVavVllSwKYNUzy2GyzfYvekgBSNlJ2JBuphPGpCWSd9XuDvDqSQsBuYr0GsVKrdiN8rVGNVIBIPxBSW0cZssQ8KCKsJYQXU6gwexicCFEhKYUkmepXljpHKoBNmJ3D2vcL7h6zS9Z6yv3henvxNwtztfgdeGFZ9spVUAwDf+3fqev3W6v0lv59EiIzIpmkB0p6QDX3itFPo2ZVPmPiyKN9yiNHiiQeUu4JANprMcPaXm3tUzLmFLenFNbusUQawFwqh3lB1ZnmhmQjT70uN8cYSu9zlBdvxQ08+kSR5FiK2tV1KreqWB+Xq3g3xNf53vdsT7kwdikXNBYuVM40u9L8itD3iR7DXufAQfO9qaxR/7nN1T4/JfU5lyW2pMTBl27JABrJAJ53Y28WYwK32tXaEBW8hbksVyNVR5Ix18pDu1CuLepWa9SumstWt7qmAQiJc1PSIlxtJluiyMxFn8kyc9Ej1S6kdc62p0j08AX3M+4JdEZZkzGm24zVSLZIOpN6vapKIfVK6ImwpIvPWx30kYmEctLCIcU6dcpKVjgl4aFECsApO0Vibp2SUdQ5aNTbJgkbPInHuqC9cvlF3eqaBHBftSp9DkQiiCNb/fL2vrZ5s2awTFFxnY5hTqcT1p6o7R2X5ddp6YJKYZFnUGheKT5xbVMUgDel6C2BJo7MMPfNnerGMwuNxnt5YpELZ95xtXc9ZeW51yM3VOdbosj+GBHD3O++b7xnaP2prc9/PGpXVQhv3D/cb3GsyLZW7vfPmp5wW2Uc960gmkakADSvPRGgEskjGdVM7kkRhXmr3y6ilB6gUXYJMUU+TIi51W8bOfckgGJogjRbVDbn3TrSl1Y3cFmTAnoaQPNeN562qmZtTjOh9mSAxaJEdElxf00LyCZUSzSfya60Xe2q5Uq1S7z/nsJi9oxL3QYk+QxAlgnXto3jeo2gKC7pLhXgNdbaVYBMobLcEo8k9erVCpQtFcD8lhjgGK3XwK6JAHG/vfrzIPbXPnlmTS/6vASBD173F37mYDAYDAaDwWDwm8y4WB0MBoPBYDAYvFnGxepgMBgMBoPB4M0yLlYHg8FgMBgMBm+WcbE6GAwGg8FgMHizfHXTAORIkm7DdwM+U+5SABQheSZvNr2Qew7AyxQA4d6M31uaK+YeyQAikQTQjUfp7eyVWwqAYT17oPX7GZN5SwaAbmzSNtsOAaPtXkO3xNckgP4eBUV9TQOQngYQ77ewJgCEUSkCRZSk4eplDXsx9SSAeN/x8/YSqHl44tUiCcCJJICLWfSoe6NhXLhy4cxFnrn4OxZ/YrFnlvY+LGIqWR/JeogkAH1k4hRJALsO6KKyMz+dvCYCiJPFP0gCuLc+u0WuINO9Rb7KiJK65anxPKvxZrX3z5sLSRVDSPVmghZRnDBBS+/ELtrNWJSpa5dXS2CPiCuLLJEGkAqVM1eZqH6ltmeW+h7z2m3e2rfVvLzGJNj9GzNm2t7OfWlXep+lCt5iBr5Sx+z9efH8+BnR9T5FwoQeUMnkdIzUBp2Z9JGDfEph5uSfUrzwyJFHnZhUeSjKpMInJaztSaO7/ZSiv/uUo7/7lCvHXCnJOJaFnIx5qsyHiiYnHwzJTppBivRxDG1fVO7jORzcHGqMrSSPMW2OG+TWrd5q5ARJc/RkSxynstuX3uf8mnixyJUmldouYVzbGdFDdMDrieJhsBeN910U5rQmAURPeVFD1dC0JlREEkDY/4rsEgDiQEx4TnF/1ZVT6ukOIFUjHcBnuCbEHBdBq0E1JAulNQ5tIZ+NZsK0tM3WPreEOzwn7bNJODchSebSnFQfSJa4+JWiM+/TzKLPW5LFdSksLWN+xdpTpJbYJcaGjPcEizWRRbWQdSbJvKUAhD9/ZPYjicLRDyQSRwoHzWQVDkk5JKGocMyxb48JThmyxJwqCqcUCQBFnENqTBopAEUjBSD1FAAV39a1dT1bj/M41rr570Lz6IA3bub7igphkxMJKhOJxTOTnJj0EYCSHqkotf0a1/rrNLsgolR9wlJjkUskLvgDyROzz+SmWyLNenxGnoexsLDIwsKFs7xn4czZvsXV3lPbM7WdcbsCDZUJlYxq6ckxt7XD8d38DuO/9vsqawpGrAqpv/+1Sf7DFUY269+5fc81UcN9PVf0n4lTqTSWbTNfaHbF1pQNmXpyxEyWmeITEynOXT29Zu7H2ZoQM72WELNLAkjZSCVSYjSDljisJMVgroeYG3EsbeeI/i6bUCySRdyFgwlXEs2VxeOqoOVIiVk8UR1SgyULyWAxpXpCGlSP9ItFLrgaV3nqaQhGlTOtGe4LrT3F3LRIaTFZyBgoGAXnAMKWeLSmAuC3y7s1GUB7Goz2NBiTSASI64rSR1e3VABBcTdMGi5lSyXSflx/kTSALc1mlwYgJFS0J958McZvVgeDwWAwGAwGb5ZxsToYDAaDwWAweLOMi9XBYDAYDAaDwZtlXKwOBoPBYDAYDN4s42J1MBgMBoPBYPBm+eqmAXAiM9/Mf7+Zcqshrz0rIPdr9oTe7nctcE0BWO1/uRch14Zu4D4hQDye3HAS0Nw2c9vQ3oncMM8YhlFo3fS3/lnrlp3Jx/tz1bd3BEAmkboFuKYcTCTS+ixRksi25fW+hgvcRfktCeAlqwkbfdJhfFYLH3AxZ7HIL7hQaTQucjNWtySA+kRtT7idETmQ04mSHyIJQE7MfmRmYiYzd7N6SsKcege09CSA1frsW07Ru57UUPWwPkvvdu598lpWi5ywP/ubchOkd0Gj0dm+tz/d65YCkLr1X13I4pslfJEwZstavIxwbTFLrk3RKogJizcyiSyFhQs5HXoqwDuSTpjXbptX2pYKYL0z27p1ubq5X8SmdOhz6/Vn3+x/YEsAUJlIGvdzOqKSmdIjkz6SKBzk0y0F4IEjhcRDKjzkxKTCQwlb96HAQ44Uh4fsPCRjTsZDasyp8ZArx1KZcuUwVXJuTHOjHFsYu3OMn86CTIIkQbLSNf5bXEVXtcXAkyEex5K4oxVSdWxxkocZ7u4ktQ874rk3mps7zY0qjYXo7zavuF1J+VOShq08c6CQe3pFGMqpJ2uUbqInjX56Teu87E8Qub2XrGH875MBUuZOXyeBKK56O05FoTZEBXdHmkFupOpMNY6FZkK+GO5huk8aq8yheayNopwb/dYjLeF65OIT2cLgXeRCSoWrP0VmiijNLiyA2RX3c5+r3HV/h5GeI1WipwHMPJIpHPwhkhTIMZNEOWjikJSswikLcwrzOxIA4JDoKQDOoa8Hh2Qc+now99skfZ8T45309ppEHN8nAPT9sk4n362C9uLgWYMo1hSVJJG2MjMx+5FJTqCw5PeIKOZXavs25le4Qk1nDKPqhSwzF3kkSaEwkz2HMb37fdJ6XljkysKFxsLF31H9zLW957p8m2YXmj3hVEQmVMOoV8koBV3PBL7/vutaHutacxCLablYnBMWj/PYuv7dHy/xNb6dD+5v91t1Z3GjYTRpVKmx3vk5jqm+ITmSAHSK1IjIhqEQKTZZYz5kjbmQJZJhIgUgbl8mxKRsPSHG0cnRFOeEWE9uYRuoRBLAek6o3j/nSA3rXTRSDZoJIk5z5WCK4DSPFIelJ0kIcpcK0DzOxa052ZXmn2EShr2lSAPQVri6RcqGPdEauFaooHq7bDOpMQE9LPvV5k+ULSVF0I8t/JFMJPRZYbcEAIzUUwJephI5u48FnPsUAOvHvMptjq3XJ2tyURx7+uo1xscYv1kdDAaDwWAwGLxZxsXqYDAYDAaDweDNMi5WB4PBYDAYDAZvlnGxOhgMBoPBYDB4s4yL1cFgMBgMBoPBm+UrmwYwcWD2E0BPARDElUzu7vxqz4cfCZEAoL2NeU0AELlZ/i+TAD5AwohMCCZhC2rX8BKRDADgHj3PRqKtxj+OuXfTrvfR9/8+avLt+n7jdcftPt1gnwAQ9+M9rSkAItFpvZmt8noSwGrChiXtW+9zNWexeD+LG1dvNIwL0aN+lndceWLxJ67tPdWeWdp73M44jZw+peQHSnqIJAB55GCnrRd8TsqchEkjCWDrWt/sz26A9hSAnNpmfUo3rjVFBzQa1idZolO+jxkutxLrLl97dsDRBqtJr90ATf0W4NJ0M8eLKaAUWb8xXDT28bXfpjpzsUZ2JXlmkYUi85YKcNFvY15ZdMYsurKbXXA3ml/BbbNm+8j0nvjorr7x2qRZR3Wd5ayxCEi3tBEl9TSApDM5HVApFD2iUrYxKsyc/JGTnygkTlooojwU5SErWeGTEvb2Y4aH7MwaSQCPuTGp8VgqRY1jqZzmKzkZh8NCyk4+NPLRkamnAGRBiiCTQtaw6NcxfJEGgAHX2B1ijvaUB1scydGLLb0jfjuWZN1rsvWl7+d6xbYO8+rRX+603l8+RXe5ha0c9n/vLO9JAFmcrE4SQ3sqgMhtLsqaAqDSEwAkxkYlkgG2x1/8fkFj/olZn7gG14SYRzJAqmBOqgsyOYdWKeWWBjDXWAfPmlhPB+ckqAjnJtsas6VZuHLxCyhkmbeXUS3HHJW8dZoDu3m6zjwNu1tmCgdmP5IozD5zYiajHDT3BADtaQBwSMIxRcLCIcU+PSY4qvXHwgI/pJhbWZwpNaa+LkQSQ08KEe7GXsRjDdhhu4/XKbWfK/v1MqmQ3HtfvVI8MXNglkdElJqeUMk0u7J4xeyZqxvNzpgvtHxBpXDVd2jfN1lmtKe5rKwWtnnl6k+RDGDPkcRQn7i2b2N2xewZkUNPAphIKYz6JIV+FkD3awCRKmIuNHOaCKI3gx9A+zGRJez/Pe5Q+/GyWJjvrT/WbE2JcZo7td9eI8uAhQuVc6QYtH5c+UJKn8Tr7kkbhZniE7knAdwnbdySN7LQ02HivJD0Nu5JHU3r9iIdJq8pMbckl3XgJYNdIeF4ctxAxCgmWGuoOM1qJAOQaC6RmmHKoo5CTwiAakLbUgMyyYTqJyqNLAXTimdDtURekF2p0BMB4pgS36UBaBxfJmHvO5EqsD/eFGgSKQHqL9YP2B7bpwIAWzLAfu4BuNw+v37uDnn54Xqdcp8IEKkD90kCn8f4zepgMBgMBoPB4M0yLlYHg8FgMBgMBm+WcbE6GAwGg8FgMHizjIvVwWAwGAwGg8Gb5SsrWM1+ZOIAsElVsq9T7X9ivgpIwCYhvZSq1vubk/MR08rdt7/LvklWvbpPnNz/WN1klalCrHJfhSrf5Crn9thL9iVl6x/Jr+9FRLYK2Cy6vf5NopL4c+e1XnUVBXJ/c5uCs3uLe7mqWryyW91qr89z52rtrmZ1kQtXnrnYO5pfWNp7anvudYAN1SMlnSjpgaLHkKv8gZmZSROT3stVe2FlWsUqsa1aL6eoWc056ixTcbT0Sr3cK/Wm/pf47P+Y/vYmJTt+Ndwk3vHWsdjQJe7nevuD8hS9rABc+nMXE0QUiNeuIlw16ueSCAdTclWSKYs3ipdNtMoyh3og7zBdqH6htmcco7Xr9kf35nWTraIT0PhQttrPHdkq75AQqkA3qWqtwVQppDTFsZKOFD1GBaScomKVGKOoWD1wlEJR5ZQSuVesPuSbWFU05KpPc0gvjznqVedkPJSFrMZpvnKYF3I2yrGRipOOTjr2MZs16lUnjfFTQZLeC1ZdrnKL3kcBvBpiUUHqJlFxujWbej9ufBv+tVrzZd2qu/d6yEpjwWzZxCHVXgsZahVJlKKyyR9JnNxvk6ySx1q32udgXuUqvdk7XabyVarK+SZdbQtBf98qIVnVCmYhZJlHZWReEHNSdWRqTGa0Z+fUrrjDVFOIVmldaTJzixLRSYW1UPTap47VmckT7saFmWhybCTJmFXEtFeuLrFhUbkq9GrWhEZpJoncxZnCTGGWTBHlmFOXppRjlqhXTlGzWhQO6mSFU7J+37eK1UmjyjeJMaWo21wlG9nEtvs11T3WwdfW2m1Xsxsa976mdsFHwDTW0FkTbnDwIxceumAVApWVmDPNzrT2ntrOmF9pdkUls/RK46QzSTIi94IVhMzSfNmky/geC7U9xbrqS+jDeiDpgZyO/futc3StHH/991R9FdnqUpOsS2DsI1+rxHc0l35uCKlq6TWrS5erar+9tjhXXLxyIc4PlQvVLlTvIqld+9S+yVWZmdKreItq32QTrYrey7b7Y22r3s5GKjfpVqZbzWqIt3J3XnBzJAm0EKrWIlPUSRbirXsjmyDVaabUFufb1ufTYkZd73ucB0I8i8eaK6lF7WrzE5lElQXThkrGPdb6qNoOadHaU5zQ1nlpBTK4NEwKrjPqBRe7q15dR9yEO8lqPw/UtVe896p5CSVqL1CtUpXtHvtAsOrs5+5NBr/NaXX9QMD8PMZvVgeDwWAwGAwGb5ZxsToYDAaDwWAweLOMi9XBYDAYDAaDwZtlXKwOBoPBYDAYDN4s42J1MBgMBoPBYPBm+cqmARx8psi8WfKRBnC7/7EEgPV2b/6vV/SvhQCo7ITx/gTfbEnZPDnv9jysCQCv34eoeFz53NJMuSUBrK/t5XvY2//r+12/x75aVV95b/Fabq8jqvLCCG3WkwDMWcxoOOF2Rs3qVZ5ZuHDxd1ztXa8EfB8Vg/aM6hHttmokAXzCxJGZA0cKkyrTmgSQhDnB1FMA5m59RoWl9brVRk5Gzi3q9IqF9Zm79TlFnR5JblWduzct6xutkeggDqJGyIp9XFKYkC3t0gDqWqErFL2lAayjUS08/KvGWCRZ0wIgVWFxo1jaUgGKTCxyJetM5cLiT1SZMV9Y5Bl3Q6VsNrD06lUXi1v6Y9soO1sSwItq1dttVKsmjZrVtVo168wkj1GHyYniEwc/cWTeKlZXc/uYlCnBKQsPOdIbHnJYuo/ZeciNWY2HXHnIlZKM09QrVueF+dDQYpSToRPoUdBDJDfIIcWYlRSpAPtq0j3VkGqRCpA9rNPquAqSvdcpvpjfu2SNra21m9Ctp13UXo9cWWi+RN2tXaO2UgpJMonSq5u7Jb5LBBCcJGsSQb9VpwdG3JBu+681qyqQ0+2+RCqAq0a9KoT93/eDp54Dsiz9jVjsLzNwR88KtiDFma3iDmVJuAvnmvt7FyYVrCdZWK9lvRqYK+aFsylmDxQmTIymC4s8Uf2KNA2D2c44fW52m3lFSZuZXrxQKMxkJlWKRApB0UgAOKQISzhlOCSnCBxT1Nce1DmmsL8PqZHEmZMx9QSAorEm6C4FIOmH5rL14zeG4MPq1S10ghjL9TjOvZI0KRRijbpaTLClFRaPY6fJQtKC5/jZrR25Su7VqO+52gWRQm2Hre44aVTZqt5O0es+NF8wq5jXXk9aMbvEbJZC0hM5ncKm1yN5b9Uzkzxt58H93HeJc9dasboa/UliJ6mAreec3XlvS80gnn+1qFm9tjhfXFokAVwtkgAWGossXHnm6k9bZWzrc0bkQE4Hskxk7VWrfc1JEnMj633N6pq8cbf1hJjUN3RXtZqiUnar3l7PC/3NyRrrsyVuOIrhNT726qQKpcT8ak2ZTZAK1bSnAUT1bBIN81+ibnWxNRlASRK1q63FuBiOi5Gk4MmoetmN+5XW3oOfab2lNMnUj/2GSu1zeME5bAkcAN6TYF5LBFiJEvpbuo36h1Wo9hHz38Rer3Ld/T5UttfQy35dI83mCzJ+szoYDAaDwWAweLOMi9XBYDAYDAaDwZtlXKwOBoPBYDAYDN4s42J1MBgMBoPBYPBmGRerg8FgMBgMBoM3y1c2DWCmdD93tf3XRIAw8T6WALDa83ITA/tX3T7eI/1/Xlr7tnvAu5G9mfV+6yCPj/2Dr9m+z4vvrK+8lpeJBdvj63vaPfel/f9awsH2undJAObxLtYkgMXCkF7cqG5UjIXKRS7Rbc8Tiz+z2BO1PVPbmWpPWLeow1g9UNIDs3zCLI8c/cTBj0ySOKSwgrdEgL5tXdA9EaCsxqc6KRk5G1qsJwLc+p/X7mfJgkxp3Zm3HbHu/AxUiVQAgOybT6kVoCG1j58LurOLl9UEtpsxe+n3127oJEI1UNF+v/dDeyaZkj1x9WVLBbjIzKLPNJboF/eFqgW16GJvdumW9mpVRid0DN762sIwl54AIJJJMm0pAGu/fZYJlULRU5jacuTAI9kzh95dfWLmoGuHu3LK2nvahSnBMUUKwKzwkK2nAUQKQFnTAKaFnBqneSFlYz7WSAEojh5jvPSoPQUgtHDJXQHOCqr3E30dP1Wg3pR+rI/xx+d4zHPZrPfVanbCcLaeCNBoNBaMMLGdhkjsO5VCppBQsmgf59txl7bwCUfFEQ27eGOfTLEV0Cuu/YXnDCnHYznmrqcCbmCKrM9TjdVC+yrhBtcUSRfmSKqRmJANb43ZKukcJnNOhrvQXJhaorlQmtKK4gjnFl3mzYn5ujjJlcVPLHJBRFn0GYDanmk64eZbQoVzbxYrSvZMJpH7fisSne9zErLCrLId85PCoR/7cz/+w/yPNIBZYw2YtDHlhuLkZJRkdykAayLDfuwVx8VRhMZtnEIE9y3NwYixdFbzHEp/jbCuUQIoB88sNpM94TQuMsdhmJWaLmjNkZDSJszOuF2p7dcAoUlBorQeEd0Z3dZfc6Qs4JFREe9rQvUBkUxJJ0p6QLUwpQeSFCY5kSl9n2cS6e5csh5CsTlCHAvaH3ONhJQm9wEWzi0NYE0PWCySY679PHE152yNxa0nxly4Sk8C8CeqPcecsTNCRnXq6SQzmQOzH3tSSqKo9kSAW9pGkUiHWFMAck9/iAQORzUSODSBZuJ8oOvtLgkgx36WNV1jPW6sn99zJAiIAeokc6wZokJpDbM43zYXpCYOKY6nNRlAgCVBdSG1eF6S9bgqJFfMncZCkoyrUTlvaRrNcr9/xf2MmeJys+k1TlAkyduaJ2i/PtmncXyYCLA39bl75j3pg0f6M18kaADI7vtr/y/ur4lEepfG8p0Yv1kdDAaDwWAwGLxZxsXqYDAYDAaDweDN8l3/M4D/8l/+C7/8y7/M4+Mjv/N3/k5Op9N3+0cMBoPBYDAYDP5/wnftYvVv/s2/yV/9q3+V//E//sfd4z/wAz/A7//9v58f/uEf3rYf+IEf+G792MFgMBgMBoPBV5jvysXqn//zf56f+ImfAG6y0Mp/+2//jV/4hV/g7//9v7899j3f8z13F68//MM/zO/+3b/7u/FSBoPBYDAYDAZfIb70xerP/dzP8RM/8RPbReo3v/lNvvGNb/CzP/uz/Nqv/Rrw4QXsr/zKr/AzP/Mz/MzP/Mz22MPDA9/61re+7MvZmCQzS7y9L2r/783/m1F/b9KvfCeH7f4dC/aK8e93n3/te/jdT5IXP3X/Gvcfr6/zNet/nxrw6ute+553r9Xcb1a09du+rYZnxbhIWJ4LFy7+juoXrvaepb2ntnNYr35F9ZGSHsjpwKSPzPLIwR84+JGZzCElJhUOOUzbOfUEAOkmsPT73faNRIBGyj0FIDk6eVjlE1Euvk8CyOtgy2Z+Qrc/vXe2h4wbJqgaqOEVMo423/ZfrRqGtzjXFq7kmgqgApMJirJ4jF6S6Flf+8UXE5L2Du4m5KZUJs5euHpYoRcpYYhq3Ko9saCYLIgozS6IafetK+4Km329zY67JIBbCsCaAHC8SwFIFI7+wOQHJgoHJookDpo4pURW4ZSFY46u7occu/UxO8cU43TKYWs/5sYxVebUOJbKab6SkzEfKqkY5WSkB0eSkB6j7FsOKbakMKUwdVNPBVD9MAnALMYvaxi8Nex2tI+xfniAuUskAXBLADDC4G3dim593teeBtB8wXwBHJGMSqQBJM9kwlROEsdZVlB6AgAxR6Sb5tvBuL2+F+kG0FMPdqb/+vFq/6OQwFtFmCDZ7bkQKQBp6fuj4TkhzSA3kjleDc0Nawt5aTQTmglFPdIAdH3/8b7C9l7nbSKZsNiJhSsILPoYMzAdqe0JF8N9wbyinjcjWURREkJC0C1FoaiQVXrve6RLrGkAszpTTwGYehLIrMacjCRGViOrb+uBSNxXje527eOvcj8P+ivaEgK0W//Sx0wFch+zDGR13OL1lW6Flz5uSxIOFsPYLNF8YvGEuVOYUEloylS/kKTQfCG399T23JM9zjiG2RW3a1//19m5J8W5QBSVx9ifMpHTIZIp0oGixziu5USWmcxM8YnCROoJDCLywXnAiXNAw1EX1GMFcbs/n+wPvTUJwD1ST64t5s25GYvBuTWu1rhQOcszC1fOvGPxJ5aeBLC0J9wvqD6Q9EBOR4qcKHLsr7v0tAi2JIA1Geb+fk8CUOtbpMSs5wXUeyoDt/OCRhJAHIe3NylGaPMAa+pLi3OLVwFzcnPawpYGIAJTUxTv64j2FADpx5DuUgDYPRbrvzen+SOJgolRmbEUCS/Npv4SrrT2DrNnhMSaB6Ae1zvWr3tQSFKoHre3OW9kSrw1Z2fp683Uf2Hyv+T1x/bXKvfm//5Wdvdfph19Hl9asPrJn/xJ3B0R4S/8hb/Av/7X/5p/8A/+AX/qT/2p7SL1b/yNv8Gf+BN/gq997WvxRvoR4u7b9v79+y/7UgaDwWAwGAwGXzG+9MXqv/yX/xKAw+HAX/7Lf/nV5/y5P/fn+Lt/9+/yi7/4i/ytv/W3+P7v/37cndPpxB/9o3+UH/iBH9guYAeDwWAwGAwGg5UvfbH63//7f0dE+JEf+REeHx8/97mHw4E/82f+DP/+3/97fvRHf5Tn52e+8Y1v8J//83/m13/917/sSxkMBoPBYDAYfMX40herT09PAPy23/bbvvDXfO/3fi//6B/9I37oh36In/7pn+av/bW/xsPDw5d9KYPBYDAYDAaDrxhfWrB6fHzkW9/61gd5qofDYbv/9PT0wecfHh7423/7b/OjP/qj/JW/8lf4k3/yT/K7ftfv+rIvZ2PWxCxpk6lgdWo+FKpeylRrJen6NXAvJL12hb//vL/6N8OyKzz7+PPuRavXharXfuZr4tRetPpOrD/W8LvXtdbp3Wr1nNrFqhCsGguVKwsXzlx4onJh8SeaXbY/nje74n5FZCKnEyWfKHpk1pCrZg4hV0lmVmVOwqTCnG5Vq7NGld7UxYqiXaxIjbyvWp0cTVG1SpaoXJ26lJMFKb00Lt+LOmstJUlx66KVxc5VwHsFq7RblWlabrJVaYbiLJa2/V5X+cIEQTfpKqoCo3o1qcZtn6vNHTVh7vWIicIiF1QSjWUbbOt/UO9uiCbcrMshBl63MRUkahtXIUgzKU2o5JuIoSdmiT/sP/hDF6yOHJkoJA6au1SlHFJUrD5k4dTFqofsFIVTch5SjM8pNSY1TrnyUBZyMk7zwnxYyNkox4ZOTjpCetAQ4I67itVDvhercnpdRDIHk5CItA+aCi8aPmNf9SZaW+Uqv0kPN7GKLhJCsy4TSsX8toFscpWKkiibsLJt3LfCahd24HNqjvWFQNbv+/qYatSvvvgal6jfFLPYTwC27ocGdUZyhdq22tV0dSQ7U63oOVFrpbZEUg+RqiWWLCx+kwGvKe5fTdEqXK3w7LHWX+UJ18ZVJ1SnkELaOWpB3XBvfbNtUbrVYcc+SS+2VZzJepMso2Y51oIiIdGULtPknVij/VYkJJvY7/dVq4mom1WJIzqpg7GJOlh8TepfVyQEnWLOpCAuLNFoyaLQspAMzMPSvJqCweIFcSVJZpELOc1Uv7DokSU9Y16p7bnXal4wr7HPsJi0e0TRLtEknRASqpmcjgj6qiw5+5HJD0TZamguqe/7vRgTUmGUb67L3P58ufdh1nPVKlg1cxaDaz9mzjXOE2drhHZ75VmeNgH32t5TbT0/nAENuUqPFD0yyYmZE7PPTCSKKFlWAS+21AW4tWY17lsfv5gLIiAa6/l2Xuhio6znhH57t7ZE327scrRXOq8TN2pYtYa0ZS0kKxEPwUriPLBYQ/B+DMWxtLhvtaxr7Wo1IYliZGqbSZ4wjEUumDacRpMJd0MtgxvNnkJgbE9UQH3qL/smU7kYWdmOOelqlNE2Beol4vtq1J10tXvuS3Hq5X158fm18n6735/X5GMFrh/ypX+z+jt+x+8A+MDk/+yzz7b7v/zLv/zq1/6hP/SH+KEf+iEulws//dM//WVfymAwGAwGg8HgK8aXvlj9Pb/n9+Du/Kf/9J/uHv/tv/23b/f/43/8jx/9+m9+85u4O//wH/7DL/tSBoPBYDAYDAZfMb70xeof+AN/AICf//mfp7Xb/+/2wz/8w9v9f/7P//nHX0DPDPyv//W/ftmXMhgMBoPBYDD4ivGlL1b/yB/5IwC8f/+ef/Wv/tX2+O/9vb+Xb3zjG7g7P/VTP8WyLK9+/b/9t/8WYKQBDAaDwWAwGAw+4EtfrH7zm9/k9/2+34e78/f+3t/bHk8p8af/9J8G4Bd/8Rf5s3/2z2J2/4fiP/mTP8m/+3f/DhG5+7OBwWAwGAwGg8EAvgtpAAD/5t/8G371V3+VX/iFX7h7/C/+xb/IT/3UT/E//+f/5Kd+6qf42Z/9WX78x3+cw+HAz/3cz/GP//E/3p77x//4H/9uvJSNWZQp6W/Y/N/Xkm73X1j1L2Xk9fkf42UKwN3nvnjb2Bf62R+zjNevu6t7faVadU8kAETpX+vVk4s5xpoGYCw0riwssnCRZxZ/pnK+1ejV95idMbsgciDpgZIewvZMj0ycmDlw8GNPAkhMSZjSh0kARX1LASi9YrH0JIBb1Wo3PqeoWpUkSNZuk/ckgDUFYG+Wq4Y93RMBpJufWyqAClIdtBH9dqHKenGkVznmGiOdm237PFIAuj0toKIs5n3eCdUjoWIxtrSAxUAaLGaICzgUD+u8Se2Tl6j+JEzrtdISwKXiorcB3icBSCbpTNKZLBNZTyQpUXnLI8kzBz9tFasnLSSRVytWjylqVos6pxQ1mKcU9apZnMdSKWqcysJxWnrF6sJ8qmhy8oOjBfSk6DEGe6tYPWQo+T4FIK1pAGscQj+yxKMO0bx3JN4fDG5RkUlPAnATzKRb/9KHPMZirY+sWyKA09yosq9brQip71dFKZs1q92ZjbH27djc13yux+h37EHZFiu93a5pAfvP221/+OSRCGCOtwqqt5SLa+qJCYI0Q6sj2fBqiDYOdaE2JVXj2hJJnKspV9NIAMhwNUGacEnhFF8tc7QDOExywsX6/Jq21ATzinS73T6yGiphS2tPyXiZChCm95oQ0JMA1jpNidrlJB8mASS1MMHFP9jfIpF8EkkBini0KiYFcyN5HGjFjeYCprdUAHWKC+pOS2ttZqwKSaQfekox6cdyIrmSPVO9UmSmSeUiTyzyTGOhpjPmdZtj7q3vw25zb6+7zzRRRMLpzzrHsS2FzIEsc6QBMJM8M0dhMokU/ysaa9BuCXQc8131d18DG/0Q2+2/W0JMr+Ttx8qWBmDO2VqkAfjCM2eucubMOyoXrvau13A/9/PDM6oPWw130eOuIjYzSWZKUckbW58LsqYC7KpWexKA9hQATY70jbXBWIl1dE0AWA/alxXc23FrCAmvFskjAmqON9AKeXK8OiJGrZEKcLBKM+lrupFFWExpybf5IvQkhSxki3nTvJBNcT9xIdFkwdVY5Anb1a5SwfyKWaQpuNS+LNTt9ev2WNnmjHM/l3RXrbomAaxpAIn0HStT97evGf+6f0z2z41a4i/Kd+ViFeDrX/86X//61+8e+9rXvsbf+Tt/hz/2x/4Yl8uF//Af/gN//a//9Q++9vu///v5S3/pL323XspgMBgMBoPB4CvCl/4zgO/EH/7Df5h/+k//KT/4gz+Iu3+w/eAP/iD/5J/8E37rb/2t/6dfymAwGAwGg8Hg/2N8136z+nn8yI/8CD//8z/PP/tn/4x/8S/+Bb/0S7/E4+Mjf/AP/kF+/Md/nPwy5HowGAwGg8FgMOA36WIVQrj6sR/7MX7sx37sN+tHDgaDwWAwGAz+P87/8T8DGAwGg8FgMBgM/nf5yv7/71NSDulm8e/N/8+z/tfPr5/j7vNs32/Pa4b+/2n+d3+k71IB1vur8PhqUoCH5Wm7nvS6SwK4cOUqZxa5cuWJqz/R/MLS3tPaldZNz633OZ0o+YEpPVLkxMEfOPiRmUgCOPRxW1MA1iSASZ1JbesGz2oUjSSAnIyUDC1OKo70FACyIJOG/T9pDGRWmFIY1TsVVlTwzar22AG1bakAkQLgqIJXJxHGpy29QJtKWxRRp9QUJrg4Swuv8mKruQtVb/Oyesy5xWL+bWkBJBbR6Ld3WMj9dsGkYdJIFFx717qF8eludx3Ot/miPW2gkHQiy0TSmUlOqGQmjhz8geSJB44UEgfNuxQAJSucknDMYeA+ZOeYoi/9MUdf+0NqnHJlUuNUKlmN03zlMC/kbJRjo5wMnUCPgkyCHhNyypAUOeQYo5KRqd9Pfbz26Q0A1u/XFka8WnzeX/k3+Fqx3o1bc6GZ4gjNYwsT92Z1RxJApF+sSQDu/RutyQpS0G7M7teW/fEp+0L1/13WFIR1P8juPa5CbU9H8FYRK/GxLLF/5tbjKQqiCtXQq+HaSNdItChLJAKoOMe6IGROuXExQVAOmjj0n3VOAMpZndkyxkxhxjGKHrnqjFmlScG94l63vnujYTReQ3siwG3zXSqAk9W3Lnjd9cCvtn9SQ9Xic2pounXDx/e/jYX1Y89aJAK0piQ1zAU1CfNfjWRC0TjOIzVCmPpcqR7HsOJ4jlGPmzCfazfHmydyVbIpFWP2mcUXJjmyyIXGst2aVxoLTsM8Zt/LNID4CQkVRbrjr5JRUlj0zKhrTwNITBQKmYSSRElr+sIrZ5Kw/Ncp5XfnwX2CjPvt/LD0NIBqzsWMxYyrNxYa73nmSd+xcOHi76h+4dresdT3r5wfDkzpgSInZj8y+SHODZIofekusbTfpwBsW0+DkEiEUI25LT0FYE0CkN0kE43zgqRdyoZKpML029gLFscOQHW8RZqJSyQBpNmQRSi1oeKYKYsp2pxmwsWUYzKahxHfUqRsrOtPspgvzRNJBGsxdoZjYiQpmFo/piIFplnu98+4N0BxyT0pQnExRJS0SwBIPRkgjuCEYdtH23G4s/zXbfX7M/obtv51n8QktzNU/Y6RKDfGb1YHg8FgMBgMBm+WcbE6GAwGg8FgMHizjIvVwWAwGAwGg8GbZVysDgaDwWAwGAzeLONidTAYDAaDwWDwZvnKpgFEr7y8av6v1j/caoH3Hcn7RID983hx//9kKsCX/VZ7//jO8u8fy+6+rpan0O1o71/n23Natz6rGw1niTZrFllY5MrCheoXqj1R/UptzzS70uwJUFRncjqR05GiRyZ5ZOLEwU/MZA69+3lKElsX+EtPAogUgJ4IoD0RoCcB5NxIed8BLegEUiIJQLKE6bkmAfSOeVk75yE+3hRYj571KAm/GaMluqWp3SxWQ+pqmxq6xP1Ub+Zl1tCns+k21y4mQOqd0THay/YlQu73swmQoUHuhnv2HJawGLXbw00uoNDsimqm+Vp+vRrXocGq5tikkHQmS3SIZ2YO/sjsM4XMQQpFlFNKHHP0cR+zUAQeCpEAoM5Dck4pxueUW08FqDyUhZyM43p7WJiPFS1OPhjpoY/PMcXYnDJyLLcUgKwwZSg57Pe8s+D39HFCNZTuDw6Crit3gd8d3ARzwfsW9u3N6m7rXLfoPI/5bjRZu9rD1t762deOdlNUJUxXuU8h6SPwQTe9O79xVG9JAPridw3bD1PcHFnTAdyQ1vvC5wmkwrXCXBEFXQww8tGYrrEf5xrz9mqJY7ekD6Yc+nyNpBXnkIWpJcyd2Y+4GFkOJJ0wrYhkzC6Yh7VsvmAsWM9Y+Biy2wAityGSFYQXFviLPvgwwftxufbD97SAPUk8xiDdEgFAEQdPPRXABU/xUwXZHjMXHGMxwd1Y1m+epSd7hAFdFVJP+EiSmJqyuDFbpvnExWcWr1Qai8R+2VIBMFwiPWHf5x7752ZzS+9yTxTU4zaTEFcmwvyeSBQSIkIRvVnecutzh/Wc4P2+IM4Ho7Q+5+68YE7tt2erVIwzVyqNszxx5h2VC1d7x2LPLPWJ2jvthYTogZIemNIDWU+7c8NMFiWrkEUoKhEQIpDFt0SAovv5cEsEEHXQSItAQVI/fFKs6fLaBcA+jmJd/y3OhagjBo4hpn39AW1OavFzSmuRrmHKwYQrieaKNN/WGhWlukcqQI79qAItC82VJJFOklxjX2Nc5RnTxuLPeIo5oa3gGK0Z7g23M03ytkYknW+pAKqYKPgtUcJoiCj6SoJKzKtbEkDucy6J9rVNNvM/5qJ81Py/v77q8w5ILi9/7EcZv1kdDAaDwWAwGLxZxsXqYDAYDAaDweDNMi5WB4PBYDAYDAZvlnGxOhgMBoPBYDB4s4yL1cFgMBgMBoPBm+UrnQYQxmqYg3Bv5K422nq7PvZaCsBriQCvPfbax3u+i2EBH/BSLN6L7WH598p7bu+7dmu1+f7rfPu6dbvZ0R5JAB5JANFgfeHCE5ULiz+xWKQA1Ham2Rn3K6qPJD1Q8gNZj0waSQCzH5mZOUhm1sQhCZNGEsCc1kSH6ICekzOrUdSYtDHlRulJAJocLYYWj875CcgSpvmkkHoKgAqkFJ3z62CnXna+Dpx5L5FPSLXohVaBnKC2sEerISq4NryGSQ2OlrBBdZcGUCx6oq8t7R5bbcnEIg4oi8I6Q4p2E7SnMxiJatp7uoXqB6qE3V25kHQGA9WMN0N2/wZdcy8ERaX0Pvu8JQFMHCk+M/vMkYlC4qCJIsqcNFIAVDj28TgmeMjezX/nmBrzlgZgPJSFY6lMuXGYFlI25mOlnPr4HIX0GDqvnjJMiswZDgWSxthkhVJuKQAvx2gdJw17O9787uA1u4/A2CazYC22Zn3rZnfrSQDV47hYLeeGU+l99lu/fVjua//2amPrbs+/tg6s9v/LFIAw9/2WbmD24Re/RPXDNID1p6+W8vp9aov9CbDEki+HApclTOe5oUC6NNJkTMC8hAM+1cyh7/9zMw6aMI/j0j2O11kTbjD7jGFkmUk6Y6midcJ4wr32JIUcffdiWM8EWLePvtVtn/pm+8fH+8dX49+7edzv75IA1nSA/XgIILaOA4DhFsdfSTFHcp8j0rMIWk+UMCCJAEpZv1fvrV+N52qQRaguFHUu6lQXri1s8INlLt5wj3XV8C0d4JYGEDb49rppCKnvmzUVQBCPWZhJJBKKkPq8TNJzA0RI3eJOIput/fLQWn/SnvX8EbdOs1j5FrOeBmBUN55ZaDSe5czChSf5Fmf/FtXPXNq3aHZlae+x9oRTSenTfn44kfXErI8c/IE5sgCYNTGrMieh9PPBuq1JAFliS+oojqqh2j9OoKsg39eJLQWgPyb7BICX0T99h0iOdBjPjqhuawoaARtaI3FAq1EErFWaxfdqrgje1xnp5931FpYkJKGvSXBp/WsMWis0P5JJmDSSFDzF/FCJdBjcML9i9gzeMFMaGkkgaxqAKOIKAuYJJNIklIRh/UxzO0+tSQDrHJLdvNmb/6kfVKnvNyVOuXBv/utrSUy/gUSU8ZvVwWAwGAwGg8GbZVysDgaDwWAwGAzeLONidTAYDAaDwWDwZhkXq4PBYDAYDAaDN8tXVrBaRZ2XItW+Ue2lRPVa3eqHwpXffbx/Dnx49f+y4u9jfKd/NXyecrEKG+tz7qUq+aBiVbjVu62Pf/A9t81x961qdfFGw7iy0KSxyIXaq1YXe+5i1SXkKjsjZJIeyOlA1qhaLXLk6A9Mftj+gH5KGmPWparYnLlva91qUaMk63WrjZSNnI3U5SopYThIUWRKIe3kXtm5ylVZb3LVWuO5F6zW2xSijmSF2utX11rW9Q/0q6JURA27QsbRBSAEqFZ7TWq7lRaWterOIUsIQhcTLMV4VY/bbOAo5nDFqZ5QFyoziy8gUGWmcsal/7G92gf9iJsI1LeoWo2a1eIzhZmZiUkyRZRDShSFU5YuvUF3oTj1itUiIVc95hbVq3mhqHEsldN8JSdjPlRSMcrJSA+OFkFPGjWrq1iVFY7TTqzKIbPlWy3uJhLdjdHufarE5F8lpe2gcbz6VrVqtd82pZnSXDfh4X5bpUJi7tNoVJovuIdQpxK1tdIFBUW3Y/2LHPKbZGUfMQzW95Je/zSA7wSr7WeahnimiucUj08FbzXul167es0wh3QlUwpHbTbyHPu2lIo7HHLl3EKqmpMztaihnVOsK0WFKSmGU1qmemGSE1lmTCuqE7SE+xJ1wFIwX3pNcKV5w0lRhYv3ty2fo1sFe7lqj+zEqptoBZr6ur0zOoSo30WdBHiXHV18OzGutZTuXSCp0ES2tba57G6jUnnpklU2CcEqlo++r4RmzjVBNedqzsHiGF/c8H7bvItV7rFfvqOEFtIL8IEQI8Q685oQ87IGeF3a4j3dP76OT/M4Lqr79nqvFnLYhcozZ5o0nuRbNCoXf8fV3lHt0mtWn6ntXYhiciBr1HBP6ZF5lW/p54ZdDXfW2K9rDXeR2O5qd3dVqylZnwdRucraVLwTqz66vZxs1s3L2HFRuzrRT7qGTo430Ap5cpoYuQq5f00zRcRZTFnU4hycQtprSakesl7rtc9JuKtdNXMunqiEYNVkwdRoUnA33I1mud9fMLvEXNYJabrVripr9WoCB5O2Va6m3XjLiysSEflA0AuZ8CbprVJV6hLVXqhaa+/38y1JVBd/UcZvVgeDwWAwGAwGb5ZxsToYDAaDwWAweLOMi9XBYDAYDAaDwZtlXKwOBoPBYDAYDN4s42J1MBgMBoPBYPBm+cqmAUTdatzf6lZfJAOsj91/7Pef6/e3mtYXH8dj9/V/K695bp9Xx/qdeCkOvxSKrf9E914A6mDSDU5Ww/D2utavb97fzy5VwHoCQDOoHiZqw2kYCzWSALjE5k9Uu/QUgAu1JwE4FdXHSAJIPQlAT0ycehJAVK3ukwBmlV6z6hSBWZ1JrW+NORm5JwHkFEkAa9WqFEEyPQkg1FGZ0maZi8rNOFe91XlukQ99dH1XUwlQE6QWqQAq+Fq/t/YpAmhDc9yXqdunVCx6VEk1jGiApa1pAMKijpNIEuPnOJcWyQBrFV+1tZYv0gAWSxQPk/sqBaWgYoikO+v/VuvZK0G7wZ6kkCgUehKAzxQSsyQmjbGYU2yHns5wTGHgHpNzSkbRuD2lRu4pAEWNw7Qwz5WcjXJqpOKkBycdo/pWjgk5ZSQpnEokNBzKLQVgrcJNPREAXqkV7TWrW+XqhweK7/qCvRKbC61p1Kua9mrDMHHvUgA8TG2LEaTSaEQSgFmN+dH3sW51qzGH9okAdxWWH77K+/gO837AvqhdNbvNx8/BVRGLFAAMyBlqxTPIIpBy1EROfcnPC5IV9wzTEqkXk6CzkQxyNtwb+RrJG1GtahRVmkfaijlMvSK5mTKTaX1eZZ0xX0g6IZJwv/a62mVLA6i9tLmSu/n+stKz3/+O7/6WAgC7xBa91ayuH7/sdxR1xASXXr8qYeRX4uS4X+ebKeR+Sw8DcUElajQFJ4nSVHhucd5pGvXKzeFqa42mcLW4XcypFvMt0imc5olmfc3txn1MF9/qsF/mAtzOWWvl5a0OE9juh5V9q8WUV+T39Xzi/adsU5MPUwDMnas3FhoV48KFszyxyIUz76h+4WrvuNRbzWrUcJ9RfUR1ihrufn6YeYwabp8jCaCvR0VlOzcUhSKxf8suKSaJ9VSAqFoVcTQ5ovE+JcUJPW53m7z4WPV+4L3Pwl63Kn2nePY4x6iCR82qSySQAHgzSou6bTNFmlOTbjWrS6/wbS7UdJtHi0GV+9rV6hl12WpXmyy4GtULLS2Y177mV8wVs2fczr0oWvsciMptgNbTZKKctwALmYxLAldi9UtRl9rnkPYUgK1yVcLoT3pfq5r1lgCwXnul3WP7eSdf5OBe5/YXf+pgMBgMBoPBYPCby7hYHQwGg8FgMBi8WcbF6mAwGAwGg8HgzTIuVgeDwWAwGAwGb5ZxsToYDAaDwWAweLN8ZdMA1n75fQLAa9b/S+N/312r+E0U377u9jngRSrA7v4HyQDfqe3687llFLyoP4edLdrNzV5lbL1XuPXv4Lvv44D4bb+sPdD+UkqGbqg6zSMJ4MoSKQByoXKh2oXqF2o7U+2M9SQAkYmkkQSQexJAkSNHf2Bmju5nTTf7vCcCTAqzRiJA7qZn0bCSsxqlJwGkngSQskcKQAaZunWeFSk9CSAnJN9SAcI41+/QPU983hyywVVYFX3pBdquC9LTAEQFvzaghUpM7MCWwilP/XnmQhLFkeiANsWTcOn2p7si+GaMNhNqCul9SQoNComJAg5FZq5SMBZuruZuJsrNBBVRkmSUQmaORAAv8dFq3ibhkISit0SNSeGQwro9JueQjFmNU64ccmNKjUOulNw4zAvT3NBi5IOhE5EEcEwxLqeMHAtkReYSxn/JcCgxDltSQ/owpQG6Ga981BFfJ693K3dVyw2sgZlgJt3+V2q/X52eCrDbLNIvrDu15suWiSH01AUSGq3ZmzX7MQzpx5WASbyV7fX5zfx32ynZdpuT0tMBUqQkiBm+S0pw3b2C1m7JACmDLvF10vdt1jgOzOM4sUjT0AJenZQdc6MkY0qGuVDEmTVM8KIxR4uEnV1VKJIonik+kTnQdEGlIDLh3jC70iRT/UrzJYxmMZq3vr7Efjd33G/7yt37vhPMfbv9ItwlAmz3X0yZ3h2/TwXIGE1AzLekgWSONKdJdM9HGMhqcce+z0LveVcufW5li/dVLFIU1mQAc7ha2N/m0hMohGax5lrfH96TEvZG/janXuyG/XlHdien/Xnt5fMgbG7v/+2/9/ozW/9BzX1LAahuVIyFxoUrTSoXzpzlPQtnrv6Oahcu7Vs9BeBKbe9wXxCZyOlE0omSHih6ZJKeBMCBAxOTKrMqc5I4n6fYv3E+WBMA1u2WBJDUScnQ1Md1PT9oH/t+0pOeBCCvJQHc7aCeGEC7PV4NkiJTX2dqpGmgoC3OtNqc0hqiTjFFxKlNaR4T8KCxaizqzO4o0BIsJiwSSRMiaxpAQk1oNpE8UWmYGCJK0wVPcd+sIp5xN9zPuDkiGTfraQAZV0N7mkyi0GQBSl/nHOlJAPt5tJ5ZpO+KpHGWSRqJAPsUgKS3+bamBqzPhUgIWK+95MU8/DzGb1YHg8FgMBgMBm+WcbE6GAwGg8FgMHizjIvVwWAwGAwGg8GbZVysDgaDwWAwGAzeLONidTAYDAaDwWDwZvlKpwEcutCWts7oW1ft1k/7wvjX/pjsnnf7/O37rM9fUbn/3MrLFICXn/8YLy3P1eJfHze/Wf3Ro91N2W6zmkgYpbtXattXhGn7MgXg9r17L/dqRPc0gOh/bjRpLHKJRAB/6kkAz9T2HEkAdkVkQvVAToeeBHCkyJGJE5MfIglAMlO6tz0nDdtz0jCP526ezz0NoGgkAeRk5BxJADo5WnoSQIkkgO0bltw/7ib01DvoNyt67Z/facJ7AxugtvicWzyv7zyBsDA1RllyGJJenUQLwzr1ebE4bpHOcK0JQ6gm5LVXXFYr3fv9MIaXFB3SEPvFXcimFI8+9eQ5LE/KZvy/ZH1cJCGSSFLIFIrPFAqFRBGl9A7uKUFR4dDTALI4h27fHpJxUGNOxiE1jnkhJ+MwVXJuTHMjHxo6OenYx+SY0FOOHu05w1xiTA4l9uuhQCmxb3PaJTW88m9pI6IR9mEAm03fjXrzsHUtTF1vkQTgVWlNaRbbmgSwbOkAsksDiDlfMZo0zCvuhlmNo3Hdpy/SF1ap+GO4S8wDEzDC1l1jPF68DzHDrfeRm+Eqt8QAA1Q/SAS4fyEaz1ftm8f+dYvblJBseFaoPUEjWz+ejNSEnBpFG02lH3+3FIC2HmIqtASlKrNnChNZZhoLSSdUJ9xr7y2vmC3dIV9YuJLINDeaOa5s6QxtDXPYr0l9RbVdWkAijgv3z9nxuyQAkfvFVVKMC8mxFittjK/hHs9XcVpPBnA3rjUh4mRTsjnVjCRKain2TzfAqwlFwq++mnCxWFcvFu9jsdhizsk2davH51tPCNhCLqAnIvT59OIw+Ojb/8iu2X/NmgiwPhbpJGsqgWP4lgYQyTCtpwEsXCTSYa48c/F3VM5c2zuqX1nqE0t7wuyK+xlIpPRISQ/kdGBKDxQ5ceCRg58iKUYyc08niaU8EkqKEgkUEukka1JMEqckI6mhapHeoI4mbgkQ/WQuaxKA8tEkAHmxw2KXpBg89UgzIfaPYBFT0OJrpMQ1RGqONSMDLUcmz5TblgbQPB47plh3FGHxWHdTT5VQWdMhIm2keSG50vxIY0FFMamYLogo7kazC+6V1ipOxeyMyESTjEqkAFS5xOuSBSWjJEwMc0NFIszmlQSj/Yq32v5J4nWmNSlglwKQ5HbdtV5frY8JfDTU5dU5/MWfOhgMBoPBYDAY/OYyLlYHg8FgMBgMBm+WcbE6GAwGg8FgMHizjIvVwWAwGAwGg8GbZVysDgaDwWAwGAzeLF/ZNIBDCptc5EPz/6X1v16xx+duz4nH9mkBN+N//Zq9Wbq/8teXxulv8PXfW54C+CbOuQvG3ggVqsUPaQ6te50K1HAWCdezV4x/zs9df8ZLA7StPdCyRAoAFyoXql1odqHZleZX3K44laSfojKRU08C0BMTp979PN+66PXe9pz0lghQNIzPLE5WY0qRBFByI+dGKvZhEkD/BpI0jM2sm/m82f978zzldcBiX3azWlarfP1cTmFW71MBiO5wP8e+5Rp251r8rWZICntYRCitYRb9yu7SjXSnuqCmLCrMKqhD1Zg1i8JVwBSyCKZQVElNSSRSuPwYlSQFk/p6IgAJlYxKRlCSZzKJiURGySrdvO2JABqC66TOpDD3hIaDRgrAnIxDbky5UXKjlEopRj4Y+ejIBHpSpAhySDCnLQFAphzd9KXsbtNtP8tqr7+Yqa/pzttBYLckAPObQm6OV/AqWBPMhLZu/loKwJqA0RMBaLRurptXbvka4cQqCUW33uyXL+2W1HGz1b13rruxJQJEKsDLRIMW+6HW2Eemt8+pbab/mggQc/aFXrsmBaiC9uetJekqsf/7ceJZokO9giYnlUjdSBpmdVGjiLOIx5e5k0TICsmiGzy5UrynAchC0hmVjEnG7Bmza3TE2wXVQpVKo1LdwjS3bj/3lIxqYQ+vtnxyj30pPQGg79svioizPzzWw3ld29dj06WnfJggYlQBdUMk4S5hm/dkicV0SwUo6jQTLqYUcZqG4d1cuLQ4vtxhMumJILD0VIPFYqVuHgkB1t/zmviyJrdEaoBvc4mYRtv9l4fKy6Nm/Vrz2Lf+8vlbQoxT3SMVxj3GCGfxRsO4sGAYF7lwlnc0Khd/x8W/jXnl2t7T7MrS3tPaE+7LlhJT0omSH8gyMcsnFDly8Mc4N9DPDUk4pF0iwG4dKus5op8fIhHgNlc1OaKR3iCZbv5HYgu7ZIC7bZ8E8CJhQzY3vs+1fhxK0ji7Tn0QxJAa51utTqoxOqU1RGJuLC0SaKqFVd9cmFPY89WdlsKur/1cvSRYTHp6T0IMZi9UP6EoTSpNF8QVS7H+N7tiPoEr7kt/yRnTCYw49gD1siV3NDKZjKGRNHP3/rklJG3Wf5z3VvM/6y0V4GUKQNb7RID1sd8I4zerg8FgMBgMBoM3y7hYHQwGg8FgMBi8WcbF6mAwGAwGg8HgzTIuVgeDwWAwGAwGb5avrGAVso7fVawm8e3vquVOtrqJU+vje4FKd8/Zi1evfbx3Ql5WrfLiucBH5YDPr1fdVQ4SfxAv2n++h6BjXbTa6lWJljgVcHyrVNteB7eK1ag1vK/Zq9hNNpEacpX3rZ1pdok6PSpC7nLVWrV6osixy1UHJhKTKrNqjNPuD+hL/yP6+EN6Y9pqVkP2KMnIuZGzoSXq9DbBKktURqZb1SpTDrlqyvFH86vM04Ur36ooV4sujIvtz8vNwRJS2626srbbjqu3vbxWsKJRtaoOrkby+IN/d8Pt9of2U2uoOLMpKs5iyqLxdReNP+kvGgKLEQKaE3/QnlFaV3uU9KpU1WdbyECyakA3KSt5JqFkUYqudats25zupYY5xXjEbWNKjblUcjKmuUVF52zo3Mdj1hiTQ0IOIVVJSTE225jITXxTCeFN5dUKUWFXs2qrocQmO3jvqvRtC7nKGlhbxapet/pCrqrrrd0kq+rG+p/TYvw8TMZbhe0X+/f+KgKtotVavevVkeLQrPdstrjNUasalasOrauSqYt+KNHTuu6bHXs58KVwBbf5/uI2ZMSQrDSDNUd7hWXRRpIctZZdbKkS8yXJOm9CGEyeKMxULmSdSTr1mtV3vXJ1iS2KOslkGiFY3eqd99KbdF/Od/dlc+i8C0rS9+1awXo3d9bKTW639DXzTriyLrNWcO11uCqIRl2yprhNVUmqmAu5i1a1KdkSzaISuZpSXVj6fJv0Vu07Wchhi0WdsrmzeMyJ2uej+U36u1Wt3sQreFGXzc435CZSrZ9b7/dVqjdH+1a9vX5vuH2v1gWrxeMoWLxxIUTDRa5UGld55sozjYWrP7HYM80uIVbZlWZPt5pVPaEa4u2kD2SdmeWRiSOzzxwpTJI4pBRy1W49Ktp9TKVLVSFW5V63GnKV9S3WXM1drEoSxuhmVwskvd3fnwNekzs1dQHSoipZFXLsMEHx7HHsoMgUcpc30D5Anh0wcjWmXNEW8wRgTsbighCC7dKF6qWfT9Zj4dpgSYoItFaobqgrVSqLnBEU0wpA1mM/5q64L7gvmIfc6GIkm0DBfKH5AgLGjGFIX/O01+9+cBx1oSqkKondyH3FatGbRBUS1r10peJxXeb33//zGL9ZHQwGg8FgMBi8WcbF6mAwGAwGg8HgzTIuVgeDwWAwGAwGb5ZxsToYDAaDwWAweLOMi9XBYDAYDAaDwZvlK5sGMGtUrsIqAa7JAPHYepvlVpmaxe/s/jUp4MO6VX+9bvXF8/a8rF99yT4V4GUSgHfzdX1emMV7I/Rmwq6Kv+xeRSQBOCY9KeCDn31/v5pH5aHfqlYbRqWyyJWFC80Xqp971WrUrbqdETKih0gCWKtW5RhVqxyYfWaSzNzr9Naa1bVqde6m8Va32u3zsiYBpEgCSNlJJWo9JQuSBJm6vj6FaS65JwKstnm6r1v13JMCPrA/0/0O6XWWmCFV+9d0vThHMoB0pdYh6vpWtVZAzXBxUjOsBwlYa9QWVujSFEjdtBdoUdWICte+b5yQV5NH7WoSJbmSPIWlTdkqQMNQf1EXKIpIQiXSA3LfkkQawGprJhWy7Izbvs3J+m2LJIC1arU0Uja0GHly0gx6EGTS21a6+Z80btea1bSrWO3j4Lnv+30awGtG+/5zmyJtez0cmvdEALlLBKhNaR7bal7f1a36WrXqUWUolWZLpAHE0XSXAqB3xYQvXh63lxc1q7ckAEyibnXVvc3xGgkAkQrQYo61ClbC/m8t6ldzX7q9VwDv99E+JeGLIKsRHXPX1UG7BS+Qem11Et+2qECWzfxtvbozqzBZInsmy0ySmaQzZpVKwn2JulW/onahpSVsZq8sbqR1LKxv+4QGj7ST5mtNpWJuNFfcW+zbNTXFXk9Z2ScBrPWR+zQAX1NdlF6FC54caTFmWhV3SMn6fSHVhHujtkRuRjOlmLI07ca/Rs2mK4tFWsFawdpc+mNsaQCLR9Wm9/dcfa1hvSUE7NMB1ts1GeB2X+5SAtaEgPWxZlGPCb597Tpnva/9kU5gtDUNgMay1qxypknlwhNXf8JYqPbU61Wv1HbG/IrZMyITIhM5nUg6U9LDlhITSQBHjsxMkpg1bTXcWW9JMZPe1qO1+jeJk9UpPbVCNRIsNDmS+jzuDc7yol717mPpH8PtsVdRJEdKxLaz+ybZcQyp/TyQQEtk62iLdSPnRm4x4bLFbfNes02kwcwe1xctxXl8UVh0nfOgKNUSi2cUYfEji1xQSVS5hOWfInGjobhf4/iwKyZXUGh2jZcvkcqhnmiyYDKj3l6+6Rd7IF6X0MMVuC0hSe7N//zi8TUFINIDInHjizJ+szoYDAaDwWAweLOMi9XBYDAYDAaDwZtlXKwOBoPBYDAYDN4s42J1MBgMBoPBYPBmGRerg8FgMBgMBoM3y1c4DSD65fdGf+oWv/Rb7Tbh6v1l9Q+Mf3mRBrBa/a8lBACbYbp/7IuwpgHsq3L35r/tzH93wQRS/7xYWHkujngYxipdFKabnyL9m79uOcbPiCZgX79mTQPAqBiLLNTug1a/pQA0u2J+xalo735OGhZw1pnCgcJM8YmZwiyJrEIW2azP6ID2rfv5zvrs/c85NXI3PbUYWhwtEj30/ZvIapynngqQ7xMAKCWMc9XbY6qv90Hv+9Wr3qcCrM+pu6/JGl+j2nukw+iXrtrq4qTsQCNVZWphXZYUNnnRsIhNjaIxjmHmxxwoKjSLDuYkQnYl/ktbCsCrXfX9sUgLSCQy4pEckJDb91MoWxLA2sd964Kf1OJ+6mOSIgkg50gC0OJI6UkAJZIAKCnGIWmkM6QU+2kdl491c39skvpq/+9TANottaEaVOspANHPbU3wqljvcG8edna11ci+TwKo5hiOu2PiVNYkgIZ77SZsQiT2O4DujqvVvN7SOFy227utgldHTPDqkA3x/n6qRhqA9tsand9SK77up5wBBd1Z/+ucrTX2kVvfV3Yf+wH38/22oCEKmsDEw3rvfetZfZcIsPazSzd9haKxTwuJwkSlkqSQdKLpBZGC+xmzK80uVMlULiwUriw0j470xWJvvkxpqC6I+5YGsCaiqDvNFekpCO4C4h9NBFiTAG7JALv7tu2GSGewWDZ13ZXJcANtgqb4GarekwKcnCTmmAm1979Pfb4tpiwWCQKLayQAmLJorOnVuU8I4NYLv6UAcNsfcX7YfW5LDdgdGn3I168RhNg93q3wSASw3YnH3ePr8UgCwKlEIkyj0aRSaSxyoVFZ/JnFnzCvXNt7anvu54Un3K6AonogaSTE5HRk0kdmeWTiyNFPzD4zkyMJIClzErIKcw94mdO6Nt0nxaznhiRGThbzNTmS+3kixdyWvM7tbv1vCRhyvwatx8LH0gBUoIJgMb+yQrVI0cgS60GOM6hOsf4ooNd4LFdhag0Vp/U0gGLG1I/hOQnVw7OfHMCpfW6oCEufn4sps2fUhcVnFo6oKFUuVFGqXkjajX8/YHaNY8+vYGA6gUHTmeYLIonMjGO7/3RbB13kg92wC1Lo5xBe3bKyrRlbKgDx8WvJSR9j/GZ1MBgMBoPBYPBmGRerg8FgMBgMBoM3y7hYHQwGg8FgMBi8WcbF6mAwGAwGg8HgzTIuVgeDwWAwGAwGb5avbBpA0TDs1hQACHt1tf/XhAAheq6hd2B34/9lAoDI3vh3lI/b//IbSAEAtj7rl4kARjf/e2e0uZAJ4zZSAhz3bjeabl6di2A4IcMK6rcMAOm9vHvZcTWXvScCRM16GNG2maCNtk8C8AvNK2a19z9fgYTqgawHkk5kmcgctu7nmUIh9b5nYU7SjfPYJo0Uh6mP3d72LD0JYO2hT8WRTCQBFEGm1JMAXprn97e+JQPsjHSAtDsUuvG/Vb63entsTQWg79PVIO2fkyl63X1KYYkCWhOuYaqnGqNUpkarGmPaTdDS4v0aShHHBXK3LMOojJCDpGGfiwjarf4Y6XR3G+Ot261IPFdJZDIZJYvuTE6J799/3i2RYdfB/SKZIffxkOzoBDrRv0F8E+nmv+QXKQCiNwM3ZVCJpIW9kbuz12Xtud8nAdQWmzleWy+RjyQAWk8DqGA9ESAs7W5q77rZV+N67aAPo3qd9wvbkeCxsduX34ntWO7JHc0FM4mEApcwbWv/4VX6ezKQtqUCSG3x/vp+kXo71tnvs5XV/t8nJ+z3435f7nlhQIuyrXsqt7Uxb4kAst3mzfQVkijJY4ZlmVEpJJ1RnbBWsXXd0ErzhSoXmjQWbyTXu1SGdWwWE5o66rH+ifotGWW3RjYTcl8rE+Am0RH/4j2JcksB2O0+TffjJn03rvtKmoMJbh5jaJHy4Q45G60pZkJrSm0Wln+L47w2ZbGE9xQAc6Ga3s1Fc2HZfRyJFeAI1ePnLB7z1923hIA1GcF5OY97KEnf1oQAoc+93WGmcksPCAt8PS/cEmGuLCxcqVJZuMT5gHOkO3gkw9R27ueEC9BQPZH1RNKZkh7IOjPJ6XZe8JmZwqSJQ9J+fmBLAoh0kliH5l06yZoIkNRI6iQ1VJ2kkQIgSpwjMjd1PccmL1IAZJ8AoPuoiP3xYbfPa4oEHve4ilrjF3qEi6B4cyQBODo5UsEWJ7W+3qe4rU0p/Xsvpj0ZQMP8V2FRWDTON0sCEJYUiRJiMHth4YC6ssgFHIoeaXoBoNk1kh/agtsV09tj5gvmNc7tPe1HRCMVwHcpIx8h1gfp11l9F+vN+i8a60fZ1gffnhfXZSMNYDAYDAaDwWDwFWBcrA4Gg8FgMBgM3izjYnUwGAwGg8Fg8GYZF6uDwWAwGAwGgzfLuFgdDAaDwWAwGLxZvsJpAGEO6osEgCS+u+1pANrTAMQ2+3U1+tfnw83y339+Sw34SBKAvpIMsFr/cEsCWO+vZuYtBWCXBkAYzdLd/sUUF0IHVaOtqQLi4LIJ1avo2KuGP8pqlXr3n5s7zSMJYJFr74QOA7T5QrMLzS69d3hBpJBkCutTj2Q9UeRI8ZniPQlAlFmVor2LvqcATKvhubM85y0R4JYEkLORsiO599Bn6ca5RBJA7kkAU7fOS75PBNg66buZnjKohom+H1DSzQBWja711L+u97Q7IHVNA3DQbqerRBLAFKkBTH3+TYZdQN3R2t+PCyU13CPBIm229Ycdywq9T1n+3/be59eSJbvqX3tH5K3XbUFLDLCxDGYARjIMjEAIAe4JE8OAAcICiSGykJgiT5AA0XNmiL8BAZYHzDxBAgwDxA9L0AgwPTAI2UJgUPPtV/dkxN7fwd4RGZn33Kp63f26zre+6/OUnXnynJMnMyIyIup2rrVQRKAuKChQV2jITqHv+fenoqQjgKSjwFBwrorua5Zz5nBLZHAP9a2WI39bN4TytR51IkVDGipDIpqOAKsTQJa3v1C032moa8b9VLr7dAQIRX2sVycAbyOrXVNlHRnte2awdz8y2GPJtj8zsjvcO8zbuNlelOeLU01FukHg8FR6X+7rDngDvCAdARxy6/BbC3XycAB4qsC+xzWrAtIgZvCaN3e5dOPpBCCtH64ArYerxVp2r5W3CqA+BdEi6YBy6ksj31sQ93HzcAIIJbBg8w0dTyjYUPUNzHdISrPdb+j2DLWK5s8osmHHM274KoqHYl4RdbVbqKB7quA1FfA61PIukOEE4Apzg7lA/FD0v8Z0BBgXhEMEPnoBz9+HhTq+GOAWEvzhDGClz/osLT5nXdCawl0OhwBTtN6nM4BhtElN1X+0i3153TXcAYba31zw5I49r7kNBwAXNHMY4p4d7W03wKM6IQb05fqLCsx8usNMJ4DFEaB7uABMRxiJsaBhj7HAdzR7xm6fw7yh9c/R7S3cbwAMIk/hEFO+gqJPOS68wSbhBPDkn4UTgFQ8aTgBvCkyHQG2MTbM9Rgjsi/K7Vp6OAFknyQSY8Tq+CB1dQA4nACiE1z6fzn6pfM9kp8Z6n9VoHj2bw4xjzGhxggqTaBPDrsJdIsxVTdDaVF3tURtPJWOPY/9pIY9B/U3qlBHugHE+WwqABy7CVqe89YL3vgbQIANsW76FrV8Jev5CTDA5C3cd8DDkUNMwwnAdwgU3Xe4WDo8dAh0OgKVpRhEkH1A9HyCw2FoHa+iL/C5f9PoOzaNNrcp3QAIIYQQQsgnAierhBBCCCHkYeFklRBCCCGEPCycrBJCCCGEkIflkxVYDXFOPNBrUxwwBFNDXFXUp2BqvCen9Vk8NaJXx2tgiVnVJdbvHZGrJ1GVHVGrY/+IVnUX9Hy/Wzysr3CoaIq0DD4eb54JigKVeNBeUn+B/F7H8bu2PExvGbPqQ2BiWMRVhoaeSwirmr9NcdUN3W+hEkGH6g+h6BsUDZFVlTfYMMRVG56k4I1G3GpErI6H6D1fnx+gj1jPYynFUDaDPjl0i2hPSYWWbCne2WpGrpYQpqSgyp+2I9qz1ilM8bE9lBWr0KcgolVHJJ8tcXtRefCxf8S2PlWgSYirepb43qOeazxoD0SsYykWoo29xvW1o41WDSHJiLMUZNSqR8TlfMg961Sg0HsRgfleLCnC8imvir15/HHMEZcXwqvjnER8xhoWTSFD8VjnE/Yy8vYybhWaArhrnOEo51fOORpnlLdcRVUjZrVFJKk3m2uf4iqHd4e3iDaNuNUQtEzxyohZXaJWuzm6YQoLukQUoeGIHpzRtXfO3fyQDDjOMZjdFd0MZoreFd40zrfOfFF4M0g34NaALYR8uDVIqXFc2WNfLTPu14eKcjkJMQsh4BBXjTI8leXYvipr1njSQ0h6jl49UnWnqEIjkbKIoCIiV4tsUKkRuSpPcG3o/f8JYUe/oddnNN+inL1jh6JZiAz3JW51RrBmvelpO0Rz4pYRthklmiKkq9BqRqwu4qpT/Or62VGXlrLWJX5VsoK1hejKPYRybkBvchJbrTGsQ2zVTdCLnsRWIaCSEM8uYitHRrQiome3FFu17L93j7qwFKL1IUQTifRexPVpJoKOalacRVeDGb0Nz/8MDS3FN/sU2TZ/jvhtu+WY8BZub+FoEPkMqk+o+hlq+SyFtyG6fYOvprjqDZ6k4o0UvFHFm3JErQ7x7ZYx3IcAdwhvHVtZ4laHuKoatEYfK9lIJTs0ORry2qgxG/kqvHrBKLhs6Hk8X74rVeHdQnRZow9SOLznV4qj1KiE1hwVHbUothHBqobNFMjrhjm2HC+BEFsB8bpqjAC7FexeAQd2fAUQYMdnqPIMV0PRGwCg2xPcd7jvMLtBVNH7DUXfwBARqx0htnJ0uBRY3jz2DiHUKMahU1zFVaN/GOO6SMR4a+5713Ff/M4Hf5IQQgghhJAfMJysEkIIIYSQh4WTVUIIIYQQ8rBwskoIIYQQQh4WTlYJIYQQQsjD8sm6AWxieEoXgIj58lQ2R6Rq0dC/ze1F7byq/ue2+uIKsKpkj30rqzPAYCj/5+spvhW4yXzt+dpcUHI9olNFFGLxXZVQlMY/OcIRwNXRu6CKo0soFcf/XgPkjmjXof4cUav5Gh4xezJUoKH8tIxrs1QVOhoEFSpPKGU4AXwWbgD+Bk/YIngxY1aHknEoPbdUCK5Rq5tGfW1q2Iqh1o5SHbod8Z6hOI94zxHnKWu06lynK4DosV81nABGhOqM27vGfqZqWsIVQMa+mgUm6QTg6SrQU1s740Ud2EK5LTeNmFj3jCn1iF5VQ3GgqGe9+XQBKDJiguWIuZRwAVARwCNidcSsihS8CxGdcvURt7qKY6se8XkRrTniGM+OADOWOBXUUgCpAEoqbtdl5Vq+HvGlYuGsMBwAABzuC60fqva5HQ4A53XGrXaH3yJute8Re9laCQW2K5rpdANoiytAjxTNU8ywocNhMG9wN7jbfRcA+HI/OxwynQFm9GquuwnM0qVgl4hnbNGWhhOAA5A93QDqHup/S+3sKKeMWxXR++22pwtAuiZIa1lWfbUBOVwBPhDBcAWQqQI+2ky4ARQoKgoKwhGgSIVqhXgF0GF2g0md7iK7PGOXG4oXNLcZg7ubQxGxq6srgPrh6FBc0EwgOEdUn5qZ40X/N69Hs+0C4+bK/WeHhazoiysA4Fu87x3wLRqRdj85A1Szw5UiHQF8RAAvzgCeTgBbOgNUU3SN69zUQ+0vmnGrRxRrMZkRrOrhGFAkyk9U4jyznsYw1E8qbpnK71lmOFwxGjpcDO0yFnR/nrHb3W4wj/EAKFB9Qsmo1U2/Em4A8hVs+AxP/hW8wRu8QTgBVBU8pRPAGBumU8xlbKjLdsQ/h0OJqs/4ZymOTPed/dGI5Z6OJWP/2let99KLyNVxj1k6AkTkqsDgo69vFrYYsPitlqNvAfDk0O6QZnF/1I7WCkq63gCYTgcwRU07n80cT4sLAADsJeo4vqPYevT7G55gbtjkK2j6NhT++gx3g+rTdGpwbzC/wfwJZju6RtyqSfZ3sHSCsBdxq/O+QY4dWIov949Y5vvR3ekGIB4WCR8I/7JKCCGEEEIeFk5WCSGEEELIw8LJKiGEEEIIeVg4WSWEEEIIIQ8LJ6uEEEIIIeRh+XTdANTwpth0ARBkjvDqBpDKZp1uALEfALTYVP1PZwB96QaAZd/gXty5X0RvU3hphxPAcAvouQ71aKhCTXy6Aqg4uqUiWBXW4/qKAq2Haru7IDSJodp9DUsFtOFQLrs7mht6KgEb+pGR7juaPUeut93gHspP0ScUTScAfRNOAPgMG95gQ8WTVGyiqCrTEWAsdW4fqs8ihqqGWnqo5atBt8x+fgIk857lKQ9QFfJUc7scS6nw4QigkvtrKM9LbJ/dAC5lNco5F899YgZUi/rrFTLUwqXMtVSFe4HUDnePc66AWCjAtTiKRRt09yPj2qMOa6omyyquRyh3pyMABJJazbm+1wAR7hHAcA+QucR3FlV3tvdD5fnSEUAWt4xQxcrR7q8/r3dC1+8gdrlJhlLdU61+dQVoBs81bh1oDm8WKuwO2A54T+V9Kq+bHQ4Azc65893DUMD8UEEbLF0AevpkGN7Vba7OGnMBpnK9+8iFDwW4dYHs4V4ABfxmkK1Hwd/SDeBWgLID1SBAtN1hDzJyzF9zsTAHegsngFlufToEnJ0BAPiicr9Ux2gHwBKnjqNtFhUUA6oIKhQFBQUbFBtUxlIBlOko0u0ZRTbseJvJ8zuaP6G6YjdPx4ZRP4LdHE0F4vGeZZlGDzjKVyByOKrIK4JjmfYax3o6AIz3xmfL1RlAIA64eR5fwtEhy03SDWA4A8AE1s7OAG6C0sIZYDgEmAtqVzQtMBdspti7whHXbp7qalM4gJLOFmNfd0DS+aB7XIN6+gAY0BEuAI7oV7oI/OICMNo+MNxh4j5o6OjYY/EYC7rv6BZjgdkNbjcABSIbin6Gok+oZTgBfDWcAPAVvPFwAtjSCeBNOsVUFbwp63iAZazwVJOnC4CEir6WjloMpRgkXVakIuszLlTqRbae943M+2c4AYyGfcfJ5LgT4gYZGnlb7sWqkGZwhPMLatxQUqMOtDhKdXQYtCgqOmpXbD3dACyccADDpgJYOuikU8+mwwEgxlAHsIlikwI4sPkTTBwFGyo+g4lBdUOBQa3C9Cnv8wZ3DZeTWeM97knZYWIwt6MdeLqd3CmSw1Hi7ApQT04AftquEnM06x/uQsK/rBJCCCGEkIeFk1VCCCGEEPKwcLJKCCGEEEIeFk5WCSGEEELIw8LJKiGEEEIIeVg+YTeAUA0OFwAZKkK1Jd/cZ66wqEPVp/pfS6jUtPhU/GuK/yQz0YFF5KwfqGqzQ07nFmpSd4ltj/c13QFMBF3i0KsrQKgtO4ACmMFU4HA0w8yW9zxPvSffA2Zu+TjrUDA7ugPN4/sNkY8eWdCHAvRYGtx3iGxQSScAeUKRN6h4E04AvmFDQcFwAZCp/i8S53vNfi5imZHcUdRD6TlynzcPF4CK6QQg21D/D4V/mWsfrgAqofwf7gCi8brmLTBU/Kuq2iwEn6nKnLn1YvE9z4zo1gFtsV3zOON3h0pUUiZZBWJLfnWP9tdNoKlpHqprgUNDV3rkLs+1pGI/6lfvpje/RO78+3QVRMdnjn/FKsbv+UkcqxL3iujZCeNVPNWzM4fe8l5QAFl217IfcvShWO89FPK57a2HfL8ZvBv81uGprLcb4E3Qd0VrBa0XdFe0VE/vLlNhfjgBOLrnMtOxDYZ+XINHOPw9xwXD0Qd43l/j+KFo1zi2hWK9N4V1QJvAdo/Lvxn8qUNU4LVH7ZalLWV9RNsebesVN4B0UZDWgH0/HBX64qZgnq4KudihaI9Lflm5KkN77ycl8FxLqPHVBdUrynAC0Aq1CpEC9xvMaqiPsy/Z5RkVBc0Nuxu6l4haT3eT3UKt392hw81B43e6CQSCp3QI8LE2gUuo8V2O+ln7b1EcTgB5ETJuCuC+KnzURfbhACCbw3u6Axjg7XAGgDm0nZ0BrAtKN/Rd4d6nI0DtitYN7uFgUbWgu6Cmg0SoqcObQhDtbLdQqFdISP4VUA+VuiLGDddwBBjGBn3pU+5hq05cOhwdHeHiYNjR/Dm2rU13B0eDyGfQ1R1GnrDJV1HlDZ7wFWz+Bhs2bFLwRgreaLjEjHGhRjeJTdIpJq935MrXHMtjXDCoOrSEU4yW7JNqOjjUUZdy9MM17xdZnAAWh4DT9qVElrsgKzndVFTCpQN2OAJku5puBBYuBVqjf6jV0KBzDmLp6rBJuPxMVwATFJEoj+wyh0OCj3HV4lw3rzA3bPIGN6moiPKHAkWf0K3CxeD+DGSdmTUIFKYNJh2GfrgDwOCXfm29JYbT0ByfMMZ1pPJ/DnvY0t1m0xjfqjhMX7HquAP/skoIIYQQQh4WTlYJIYQQQsjDwskqIYQQQgh5WDhZJYQQQgghD8snK7CqGnGdRS1ivnK7lohW1SmkygezU1Q1BFWSugURh45SWvZjeUB/8r6pvwGAHzGGKWRwTwFAPqxvPQRWIgoRgzum0Kq1AnWDuMDdAGhIQCxFOCmEMT//tIwn8XG8d4qETJHJiFXrbvk4fTuiVrGjz5jEiNcDOkS+ApEaAgrZUGRLcdXTfJB+0zVq9YhbrSmyKsuyLaKqEaNXNoPWfHB+FVeVjFstL8VVKHVZV6AUeK2HIKXWlwKV9aF6HVGrADTKerzvtUCsAmghduk1hC/7+rC+pFBGI461jng/n1GAIQjwGf1bNYRyKhGTK3I/zi6a2xBXxTmNANVZ56+I69bvvovX0gZV/P4bg+sz8zbEVdnQhjBtCK2gUb59+eKIWgUOgdUpJtSAvYU4aO8hTGoRcWm7w7ug73KImXJpLtgtYld3P8etRiwqFlFJtHsfkasvLizwIUNYoouPYw0xYwqCRELo1UsIanZFF0O5OVwcvhtwMwAt4noBSG3AWznEewDQe7SpIey4I7CSJW71RcTqRaB2ZMRiij2n6HNWyf0GcRZXpVgPggJFwRZLRq2qbBCpMDvEOV2e0cueQs6ODov+xxzNHEVCXHXUVUSIHtuSSbFRxyGuQghX39VUl2jNIa6SqudYTuAstjqV8SyYuZaW8ZTNIE8pvkqhlbez2MrNYbtAtxgTyh5tqDVFaRpxrBoCnG6CvRe4AsUKmjmaxd3eXGZP0EN/C/Q85YxbNUici0abH8mZKlFuIew8X567p7wmxIZNhuR2n6K4bs/o9hxRq34DIFB9moLbQ2SV8dseotsnlBm/Xed4cCeGe4wHGcU9YrhLRq8WdZRFXKU1hauS48QpTncRW4mcO9QxBlzFVadCGY3Ajtfm0XepAmWojwwjehXVITD4nufT4vzUsMRtx7wEAJopbuYwM2yicJEQHmfdbRptqQpQVWDwWYZmguoKwxblLJ/BxVD0Dcxs3nsxp3gbEdLeYL5DXNF9X8J1h8TUsArtCl7eU9eE2hlPn6/XsX0d76v6q33qPfiXVUIIIYQQ8rBwskoIIYQQQh4WTlYJIYQQQsjDwskqIYQQQgh5WDhZJYQQQgghD8sn7QZwxHX2EOeVDh3qwYxWHTGeoh4qvRnXtkSsLpF8QCrrVV51Ahj7/Sp0s8t+c/hIcmw+1beS6lETCzW5CYCMY3NLlWvEtCKdAIqGkrevkZiI5L17Gl6/bHf3xRXgHLPXsC8K0B1mDe6xAOUcrZfKz6ECHqrPTXTGrw0XgKn41IzHTeVnkXBwqKWHi0O1qJvN0w1Ajgi7mq4AM1JVj6jTqfKUcAgYas9aj+2St4BqxuVdUA1VdWuH6hM44kHHOlXZskasqhznMVSps+1E3C/ScULSySHal8/I0yPx0We03drchg74GqMqoniXENre+e67WVXhR0yww23xH1hsJtwc0ix6m6HqTyMFTHcEuf7IEc3ae2wPBwBz+C2cAHAbTgAG3w1+c3gD+i6htG+K1hV7L9hNsWfc6lCSt8UNoNvL9h/Rg32qVh0OXW78GcU6ysM9jqOHw0Z3hBOApxtBVxQxtK5oTSEC2B4uJHozyBa1I1u4ILikinmozt2iPVu0R7kXubrErZ6cAG7pDHAbTgAdPiJr000hyn6JLPWzu8ha/+lXsDhURP9YMm5VIZB0BRiRqyIVwHemEtm8ovuOLhG5untDgWbkqp4cG7oDuwPiMusvthWa0dPmgm4RYTrO1y3a2Ys++biRjnt0KMfrReYMnLNJfYm/He19jKimQLOIY90cYhHFujoDRPymQ1s6SZTo03V3dI192hTaHJZ9fDeB9OgPikis/aj37gKYRrc0DTVk9r1A9CMjRXqMEVdOLhDweQ+MGE53g3ufUasxHnRIKs6LPkGlphPAZyjYZvx2eMTkuKB6OAEIFjeAEavqc2woizNAEZsOP5rjuAwnnxwjJKNOY3yQcI65OLXg7qJLfS+D+eyjMlZ1tA0LN5M5Vo9xqOU+FUiR6HEV0ffXnG9YOBKVjNcdUePmmtdrKBaOAG5j/Ixo2iqAa7oFqMId2FDCIQAFBRG3WmSDSYNqRZEnQAyWlhHulnGrBe6G7jsEii4NKiWcAbI/fBdyWWLsOhwA1vV0/RHL+PgPg39ZJYQQQgghDwsnq4QQQggh5GHhZJUQQgghhDwsnKwSQgghhJCHhZNVQgghhBDysHyybgBb5u2O3F2VzJmvBlFHrZb5wQYtocCeucIaOb6h1Mah/J+uAEdWtKwq5ougOcWXE09JrWRguJuEWtTid72nUlR8ugKYAtYdBQrpgGtkCXfTqSIv6ujuGCLdNef5+q+Rc9Z3nJ6lE4DDpxK6w9AQ2egdkQMdWdC3VPA2wBtEysz8VqnQdAEYGdAFiiKSuc+hYhzK1FAE+kkhONSQJfOftdjh2FABSReA1QkAqocLwJLv7LUu+3KRO9vA/Xz1UWYARDVj7Ef+c7gHyOlY4z059iEdBEZe9FS/H0LTkxNA1l3kK/vJRGAuF2GyXhqeLGr1a6b9F8liXhXgI2/9eC+Ur+4SefLuS7vO7ZbXPGT2zZBS5lCkqwMuh3vC9cd76pRbqOKx76la7+EKcOvwvcNvPZTsN4ftyEVgXdBaQU8HgMMJQM6OABan2D3bv3sm1GdOtluqn++XnaVS2uDznopjHb8x1vs8l4LWw61AxdH3aBd2A+TJIAC8NsAKpDb4W4HU5fe1A71EIxqOE1dall9PN4Bm5zIcjgrpBIAeTgqjH7IOuA1HgHQFuHP9174m2mw6AbiiyIYmzxDRXHLY8RZqcglXgOhrKlqW/qiPZkc9FTm7LHQX1HRbEBU4om7Vl/N+X5Nf+/LVCWDJkZ/9/L1yng3Bjz7eHWhxbwynAGkOLwZxpOtCjDO2x7YWh3WHFsnxQGbGullsV8+xqhf07AB0uS/VQ3VuUGiWDxBK7AaB4SjDcSXDCKFfhNmHI8b4r59dYbzlfdHS3qBD5A2KPEFlQ9FQoh9OAE/pBFBRRXNMWNTtqXCfrjEXBfmmlk4xMSZMJ4ASY7oWj7F82Kik4v+l6j8dNMZYMceEpY7lMhZcLX5U4wbR0ceP4x19majAa7hhuEa9S81xzJDzDIMWhapDPa9LLJ1ywgFgU0fNecGmAjdHlSgzs3DeKOkOUUSxecHmT9jwBiYWY7IYir6B6ucwrxAps+7miO/7dH2wuAMvzdtfqPfHmDTb33HLoMyxK8axch3r1VG/wHjEv6wSQgghhJCHhZNVQgghhBDysHCySgghhBBCHhZOVgkhhBBCyMPCySohhBBCCHlYPlk3gKKOrRgkXQBUHKVauACoQ7dwAyhb5vQKMlM41dlVDsV2PdR9R7b7Hbn9PaXokFBbfNVHhjRkqkIlnQHQENuaalz4zNruoe9EcZuK/m6CokA3T2cASeWdpwr0YHUByCj3Y71sh6I59YHSUxe4hxLUd5iFI0CoCDtUIgNataLKm1jwBhUFFQWbFGwa51kU6QyQ+c+Cc/6z2lQJztznkirPCkhJl4aySA5V4sBDhlhKqKNLPRSa6QjgJ2cAmYr+qDt9qQAFzurP+Xr53Pp9t8MVYFWfLm1jKFMlBaexjjobi1xymNc48mtE+eoEoCgnJ4Dr9ZhHI7RUYI68Z/NDtDzbx/gODkOLkRPvizrc080CufbukJYtd4tt1FCfT1XuLS9kqNjVXwaUmx156y3l6bcOv7XMuB9OABZLX9wAboK+K1pTtH4s3QX7sjQX7BYq6LYqzKe2PzPQ76anX053uW/MfbonhBOAz98s6QhQxNFN0VqJPmo3iAjs2SHVIObQGm4ho9K9WzqMZDurw23hFTcAy3D4ZucyHG4KzaIMmwO3cATwdASwLvAWeeNmCkt1vaXa3LPd2QsninVboK7Q9AYosqFLhUABFDh8KsoPN4ANXRqab+kGkHViZ3cFdYchzqe5z3NqJnga52oajhXDXcVkac0Xlvt0ttOiZyX55XP3yltmf+9A0XDG8NGOPe1d0hmgGVAdpYaY3ovHmHALRxprmXWvDusCEYf1454e93r34z4eLggtVdtVYqzpqc4GwqRgVW0fLg4vy2d6AUishyuGITLlpysAOgCB5FgQOfQVJceEzd+gYEPJ/91UUVVSwY7pDBDbwyUmzn+ox+uiIh9uMaXEOK7p4gPNcUKPMXw4A5wv+qL+n9uXMWCtZ/Ps3+14b9T36NdVckw5u79IlXSVyXOscc5uEm43l+syD1cAE53Xb6OsZHXUuYyrpjA4ikdJVzQU2eCwxbWnhSOHWzhyeIOYzrp16Ue9p1eAX9qF+dGerkUIADpdbQ7nnzG+HXUZ1/qh8C+rhBBCCCHkYeFklRBCCCGEPCycrBJCCCGEkIeFk1VCCCGEEPKwcLJKCCGEEEIelk/WDaCWDlWZikFVR62GshmgjlJDPaibHy4ARWI91YOx71CI4qT6A/C6G4BdVG4WTgAy3hsSbLGZjy6argC3yI4PTaFDbORyG9Q14tWLoZiiG6BLXu/YHvnyqyvAekarE4At255q6DbSoKWlMjBUn0P9GVnQDpWnyIDWzIPGhoots4lrqj0l1YqH4rOk2nNs11XtmSpB1VBISjVICYW91LEopCpQStTFcAGYWc9L5rOk6n/NgB7bwNkJYFWD2lD357ZqqHnRj+Msau35/b6ox4f69No+3oPM9VqfeZhx6PyUpOJ6flc0FdejskPTCWDm208ngNCoxnsY7SJUwUeseSjnCwTd9XCh6IrqfYhKQ9Xf0uECBvQ8+i3Ky00gQ0mbbX46NlwZbgB+qNm9GfC8Rwb73uFvO9Acvl+cAJqgNUVvca67Feym2E0zY16WbPmxeN4DDvdzHvpQyeaJnU5zfHKWn5+z64cIPJTs4QjQ0xFguBSIFPQ9jqFPHXqLm9JLjzB4zRqqWZ6jLfYe5dfK0pYXhbLlufYeZWj+wk0BzdIJwOC34QQQiztgXeN6TBE+FcMVYHEEuHR1gnC7gAPRMks4VSCuVaUifEsaPPuU7i3cRqSjocd/buECIJhOAC3dG8pSxpplKgZ4kelaMdquuES/WvzkXvHibtRlfc2Ur0v5ru113Nur3criZCFAnDQALwbpWQ+pEJfm8BKuAJLdqku6AKSqvYtBe5RpLx5uGgiHBCCGkFEnMEVVx5bnM/LlR19rEAgOQxWVKMM7RgAv2vnIjHfYdHHwUZhwCGq4Acg2leclx4SCDcULKhRFdBkXgKIyz+8YH3Jc0KsLwDE+FA23Hy0OKenqU7OOqkznnpNzzNXZYXVMOY0Nlz7d7Nhn6fyi6fwCHVYMOZbYcqzjN0UBV4EU5Hg/XAwcWizaqoSzj04HgOP6RDq1YwAANc1JREFULd0RXARVo0l2P8rL0xWguKBCUVHQvR5uAFCIKFQrxCpcGtzDJcQl69d3mG+wxREg1nr0b683kzFtyrVPdyLFGPPzevQY6z8U/mWVEEIIIYQ8LJysEkIIIYSQh4WTVUIIIYQQ8rBwskoIIYQQQh6WT1ZgpZeY1aKOslkIqsShTw7N+E7ZJOPZANR8sL4soqqqx3Yc/CSckfVh7As+MywvUXxDcJXJbGhDPJFfbADg0IxdjUg5gTegFEPvmg9iI9bweDD/3jks2yGaOQQ1hhG36ujuaG5w95Q47GjY0X3PqNWM1vMGRzseqNfLw/ReUVBCViEhxtmWh+hLPv9exM+LxgP1M3auRNzqjCatWJ/eXsQP5RBJZZyqX2JVX8TpvU9cNV6nSMVVQ9gy6n+UrepLoQZwP7oVePHPw1M6qvg5WvX+EU6nsP66iEK8LAcejQun2FCTEZnoU1R3CFKiXfQhtkthSwhrRuSqhODGAW8aUYzdoSkQkZ7hfDeD1BRR3RqgGTloo26WSMIrlmoaC/EP3OMYtw7vBn8bUauYMashsOpNYHuIq0LAVNAWcdVuIRZbo1ZXkVXzEBe6RMQk5u1rWKUFZ9FViB+7WwjQLiKrEbnaLEQlu4UcbreC1jtEHPseta3PIbYQd8hzxMjOZtk14pifUkzYsl+q9noZAoc4bZShOfy5hbDq1uF7Rq22Q6TmLeJqu+ms67V9TJHVB6YlDsGfpMgDEhmjPiI8U8jRscPR0aIWMnL1niBOpnCtjHYpjp5i1O56xALbiAZ+vZ8effjsy1dBTl36kUUsea/f94xZPRVOtRTUyIxbHfvQQjAlBriG0Aq3EKd4jShmIKJYAUB6/uYtTsVdppC2mABqsS1xr/UU5igkoi8RY6On0Erwej8zozYXEUzUV48FfY4HgABSoTNudZuC24Ka8dt1Su3qGrMqZ1HVEbO6iHI0xDojjnT0lUOgNKOrx1ixiqmuYrmsv7me48SlPtfxYGyPMUCG0HY5TsdxHDvaiPv47RRLKoCCEIWZzDGuFJvCIx9r8RTEHXGrNstG5rqm5quIoIqiuMY4nHXQZZ/CN0PL+7DC/RZxq14jPrdkzPQUjb4/anreMmORYz2EfGW5jpNI7sPTVvmXVUIIIYQQ8rhwskoIIYQQQh4WTlYJIYQQQsjDwskqIYQQQgh5WDhZJYQQQgghD8sn6wYQanKZkZ0jkq1sEYumW6oGN4E+ASPKc7oAVDn2Ler/V1WFgzXuELg4AKSyM5Wi0gzQcrgC3Gz550PGYNaIKkMFrGX0aC8R0ZbRa8ARlzeE8lcNn0019xG1OpbuZ6XviFq1EcE2o1bbWQUtmrF6FUXqEbWKNxn3pqlOPDsBXJcRJycYKsGIx9WMWtXiMwZXNKJWh0L3qvo8Fr1TN0f8KhBK/tN7P0je4SChF1cHXSJR731LcYlbfUfU6hGj5/CpwV7V/0dbaJ7RmjiU7N3CCUDE0VpBbRZnuIVy1ZuHSj9jcd0E0B7nndfsXjKScI0tPqttfTZIB1rGgjYLFbshlOzPoWK354gJ7c+K/lzQFieA/eQEoFNFHrGdh8rcl7XD0VIRe5TQfTyjCo9gQk9nDeRvRhsfDgRq6QYgwK0rnnq4N9SScavP0VeppeK5pkocCKW4pauIduCpwFWAvR/q9ZXR9yxxq2gG7xmx+rZnzKpFzOoOeBNYj8ha60d9h8uBpguATCeRI5J3lMfaLgUKOZwAROcyWupwAjALx5Eqb9CkYUt3hXCscJj7/N21zgyHO0BxydcKcVtiV2WeWzgE+H0Hj3niq1J86U9kVZbr+bPzipZyH225DRl+P2JYm2TsqkGwOMO0UbIZo4poC3HuMXYVE1i2l66h59/U0FUBO2JKPYeTMvtXyQhMmUmkR10d6aRRruc+yNAzarVHS3eDWbg5wA2CMmOeBZpjQqjPTw4xoqFWF0FVmQr+s0tMbusa02n5Ou6LcAUItx8pea+sTjGSY8UaeQoAqi/H8FMFLs4Pd9tGOqyMqOi1a1gcJXy8D8x5gw93APVsR+OcIxLefTj8+Gld0vVnRLB2OSJzqx5l1jCU9xG3qhm5WhB1oLKFS4M3SLpxjNr1ZWwfi0k/9WvXNnEqljvToFmkGblac84S43zWob/et774jQ/+JCGEEEIIIT9gOFklhBBCCCEPCyerhBBCCCHkYeFklRBCCCGEPCycrBJCCCGEkIflk3UDGGrymS9fDWVL5WAFdAOg6QRQBVKGyvziAlD0dcV5/NDxoyM3epXEjizhRX4vuXYRoFvkQkNjbYtS1RyeMdrokS0d2ccO9cyNtlV0eFFw+uuaV1/W5qFePqmZpaFjj8X3zIMeqsEGoEBGDrRsM3e4eEXxEhnQoqiSmdRyqBdH5nNNheCa/zxyn1Uy81mWmPtxIM2yriNjfnUAuPz7S84OAN8XTm4Pr6gZX1M5jt12KPzfxV3B6itGFKHCPWewm/uh8MRwdzhUnu5At1Rbp2PEmmd/bEsqwkPNLuIwE/SuEDH0PZWwNfOvU+k8w6HzhGXkomfmugNZZ4t/hdnZrqJZqGv3Hk4DzWHPHX5z2O6wG2C7wHaJPPsWTgC3FsueCvzdJbdHrnwu5qkujyz6uAcO9fPIQb9fnfk5sVSsO7oBXc5Z9s2A3QCBYF9cAW7DDaBZ5LwXh9wMxeL+lu6AWvxV4WaAlUOJ3CydS/RUxsfJHWXozdJZIRwUcDP4nm4KN8B3h+1A3wW2K1pTWA9Xhe7DFUBmWxhGDZ4OIoeGPdwUomzO/ZGgLG1Y4R7K6qNt9uhrxNDQY4872myHjm6Hq8lom1WGK0C6V/ih/DcXSDoCRP86Ky7L5ipjzvM7SeUXJ4BajnJ+zRFmlv1QhytgFs4NPU8gnQGkhcOBWJSa6CgzAyxNM57C7QJb9Icwi/tWgOqCBqCooGg4cxQbiuvMlEf0wVUcntuergBDLP8ao5+IejXYpS8JtxGHyDIe6AaFhgIdBepDma4oCFeRohdHGJ2GO6ipfi+rEh6YefKhksdpfBhjxLGdX0oHmdXRZ44Rso4fr6n/L/fTur/jcAdQDdeN9f2xrN/L841+D4A6RA5XBE2VvLlE3eVYGNujHGKaMhwBRllWjbJtEscsXtBcoVLm2DDGBUknHxuOHBL33zpGDBcUWywPPsQRYJRkVEPUnyBdAYDpCjBcHj4U/mWVEEIIIYQ8LJysEkIIIYSQh4WTVUIIIYQQ8rBwskoIIYQQQh4WTlYJIYQQQsjD8sm6AZRiKEWmE4CWUCpLRS4prasC2VLx/6QvXQCGTPFORjRwUY3ewW1knvuhFmyZ/dxSW6cOgcFNDkeAKukcIKm2BFAd0g/lIHA4AKg4+h31/yo+N2Sm9xK7HgrmUEJHPvqRBdzRQhXohu4N5jvMG+Btqj8FBUXCEaBgSwVoKj9F5rLmP4cKMLYFnpnUoQyM3GeHlsx91qw3iTobys4jU/7lNfv7FJ4fyqL0F7N4PeTEqwvAqva8ugPY6+pJ4HXTgHvIaVsAhOpTXafSemZ0TwuFSHAPR4Aj29tgaDA0N1TISbneHScXgGaRQT5U9U+maHC0XlCzLeseenCpPl0qXC1kq6McVICndMRINXt8+VKHQ8E+GmkLZbTfwg3Au8PfOuw5VNL9ObLs202x7wWta7oA6GWR07Xttqr1Q3VucLRZQqF+HmpYXyprVUQ7Qr3uqaftmWMfLgPxO82Blsr03QSKOKeW/cOWfUNtiv5cADOIWrgBwON1jU00j36rhlsI9kXp/KKBDUeFLM90A/Dd4Lcov6McFbYrepPDCcBi2U3RRnvI8msuh6h+LMvPAvfVw9FW9VyOQ2UuQ4kcbgCW/dLsr7IIuuP02z3V04cjQCzucZ+4CTCU9iZAefd9Odvl6mRxdQDQYVWyfH603wJAFycYKKSFC4aPPiH7Cpmfwbm/6A598vhYOmwoBNYdWuJ+M4trtHRVgRqqKqoN5b+j5G+IABJmHa94W7zOqgg/1Vf2L8AWSnMM5fkGEZ1jQvjDCKponAfGmCA5DmAZE2KMGGry4QIQ497hGCPTMcZPjgAv6uged21W9P2feReSJasKWJ/HEJW4T5d5xHB9ONwMFvebdAA4luOyVAD17PezPmfdYrgpjFoQVFTIdGbYpguApHVC3ItLfXq4n0Sdd1hYFZ3uY/fXnWx0zEfgx/kC81pEwqliOB98kRLmX1YJIYQQQsjDwskqIYQQQgh5WDhZJYQQQgghDwsnq4QQQggh5GHhZJUQQgghhDwsn6wbgIz8YAG0AFo8HQEAKRLK2uEEUEP1P9dFzy4AtRzZ0KvK/H3Z0AgFZ7zObOiLOnwoQcMBQGNtiCzvoRq0zBO+SDiHE8BYz5++fyq5LSfl7sj2tszePquhO2y6AOwwa3BvcHSoPM0saJECTeVnRUFFiWzikQGtofocGcGruUIUeSg/JZ0C1rob2y+Ch4FXMp6/oIJzFozddw9we6nwXwt2vDfy7Nft8XrImfO1X9rA/CmX/OjLa7j3r0q5qL91OAJkFnQ4AjjcLeoPhu47uuzoki4PqV6P3HVHl7MzwO4CnevhDhBnU7uh9tjuJSTLXQGRVK7Dcx3lMdv3ULG/dv8sDXRVsKN7uAGkgr0/A94E/aawLtMJoPVUr/dQ2++m2D3dDDyX4QRgq/tBlIPDYHJ4YrzGqpod3hlbumvEsY6ybC7YPQTpuylUDLspnrP8bq1EHriGI8ZQ0+sWqm/AIS1cKcQcsgu8WDqW3Hcnme1suClYuACEs4LD9uEGILAG2C7Y9yjLKMcyy7C7TneIcV1DlT/6D8fR1KN83q24l6EFTpeFNYt8ZNLH1TuiVqLvagaUcvxmn24B6QCQHiPuxz21LuMn3Q9Nsw+HirsneqffH04Asw2vrgC5tmHZ4vGDVQETiMV1ndwbRpvX6P8xPlMF8gSghSsKEOOYp5uB9siS7xptx7um2tpRYKHMR6qxIdM0QtKcY3Std40kUgFus16infsyEIVDhkdfI0fuvIwcemj+J+EHIIjtVP6fjHbEp4o8lO0+XQEEhzpe5vpQ00PD5mA4ApzqcnXy+RBe+9x1/B7q/+tnEL/vl/2iOPat57o4+kiq/8e4pwIUMago1CXrVqCI8uuz7ARFHUUk9wmqKzoE1Su67FAUFNlg0qBaYb0BonDvEDkcAQ4/oFjm65wfAMC77uyTWxGGA8DqaOCzn9MXs5XX4V9WCSGEEELIw8LJKiGEEEIIeVg4WSWEEEIIIQ8LJ6uEEEIIIeRh+WQFViGoGlGrIeqAXsRVRV6Kq7aSyp98eL6Us7BqfSIceBnRNk9gjdrDfLAelo8ou0fpj/dLRLQJ1mg2X55KjgfvZ8TcEGDI/UedrxqeIX4Y2+MzM7IwH6ceUoaOPR+ujnjOc7RexAeq1GPJmNXiFQVlxr1JPkxfZY1bHZGrR/xa0XPE3IyfWzRUUw1xjT38LhGziGb1VLBdhVRrFuqIWx2iKrOIYB3vDSHWNT91ft6PCNGsi/FRtw+/hvEw/RFrd8gYZF1S4JAnkSKrNuvRRkToFFet0aNRR/sQWdk5InTPaxgiHHOBFoN51FkXgXYAcEgPgQSqw7tDmgB1NF6c63KUV669ZZlltuYQBcGA/pzxoB1oN0XvitYK9law93LErfohrupDZLVeq0fUah+xnvM+iLYfdWUvo1anKKjP8hzixFVctVs0292iXBVAUwG6YhPHlmK1IiGHKc0gAlg3AA3aPdqJOaQ4tKfQSkO85pp92NAq3StLx1GWLcVFu8NugHdB3+UoxxbleGslY1bLjKodQrURtdoWsVWInEZ08xHPOMQZr3OOsp3lKUPscY5bDYnREbFq7jAJYVVzP8VJD0HWiF01F8Ay7jRvDTeEoGk9nXf9CecqrrqKbU+fTRUYDEAJVRN6jCfAMRZkXcnoI6oDphBYiLQ0xgoZCZ4dEbmKaAdaDNo1hSu5mMM9+gm/nJ7iw7vNGTV8qcPzeBBHHf3OIacqU1y1LkXl6McwRESL+HaIqXAIrWZU5xAV6zFGAHcEYq9d5LW/+V5Eue9DR/zqcfwZvTrOceyfw9kxcGuOjcBaDtEcjrjSaEsnARMwx15Z6mIVvY1YbmT47lGfo27fH8j7bvkksErMXkStzjp+31EO+JdVQgghhBDysHCySgghhBBCHhZOVgkhhBBCyMPCySohhBBCCHlYOFklhBBCCCEPy6frBjAU5SncFMmo1Zqvl6y3uzGrGbEq9RLl+a6YvZU1ag8Id4AWUlOpSxRi9akEDSWvz2g2qMARivgjRu7Dy2Ao/T2lwkPHb4uqNhS2Q3E7olYdJuEIYNgzcrWlmrwDEKjURXUeMW4lHQEUgioacaup+pSl+IYG8VAwDpWgLzFzS91N9eRFuXkvI/A1PGW+Zhmbh6i7oei/V675ngwngKH4v26n2l/WmNX5mYvacVgwjNOa4vf71/JKMusLQuO5qD5nwQngIy6vw3xHRy5S0NxQXNHdsVuUeXOBGlI9H2WzWyh4b6bYLHZWKyjNUQuw7xWl21SzWvfoXDQcEHSL40hdnDmmo8blYjzvjxE7+YqCPdwAIma1d8U+VOyecatTxX4o2fdUkbdU6vdUkIczgKH7ETXsOLsAXAmHBZvq9e4djjKja8Nl4FDL7w6IS7gpaMS+3kzhEJTu4aqQcZM9o5a1T+8OiMbntEX5zTJdoyav55iC7eEo4D2b5Q7YTWBdYCNitWmUpYXLw7kMo/yaXR0AolsbZRiRzekMMB0B+nuja6M8O8xttodwAnCYHI4A4Tggs//qLihZh45Q/Mc+yajVXGxdALGMzhTH8oNfjA+J3QZeugJ49kHQkHa7xntV0zIi3Ge8athVVIG0ETuNcIZRgXsoqj3719F/rrGkQykuGEry43qH+n4Ist8nih/3w3TDmPeGnGNWM347/EMKxGNMkDEOIMcE5Li8nMvYN2I5x3kNlwDguM54c3GMmQY9F7X/F+W16O0P7Yw/gGNMG2NcxKjG2s+uAIiyiLjVoywEcpSNLGWZ23edYq5xuKLZxx2uD6srgEmHS9zg835+RzFEE11dAM5D69E2Mdvnh8K/rBJCCCGEkIeFk1VCCCGEEPKwcLJKCCGEEEIeFk5WCSGEEELIw8LJKiGEEEIIeVg+WTeAUE6mkr744QigiKD6dAGQmg4AIocTgApQNFSFujgErHnQ71IcmocCdGybAaZR2uZAAwQWmfCrkvS07UD+5DsEyctPyvy5l++dIteXfT7fG6royDdvU/lpHiryQ/2Z8vBUFKpsKLKF5tAVBQUFelL/r0rPoVocas+RAX0vJ3jkPk/7gFPlfgBD6W+pwrWlIFPNLwD85ApwbMv6/aHsX9T/MAN6X/Zf3/fL9lohR72+zxHgNWZ2NhTiGu1lyebO5G04+nRz6N7QfUeXhubhC6AuaF5CzW3nPPtdAZhgVwE6sIniuReYG4ooVEqo2BcFOwBocbgbtMhx/eqQDkiL8x8q9nt4i3JyH9uA7eEC4Ab0m2K/FZjJVLDfWsFuBc0UNwtHgOdc30xS/R/X1XK7DUeApf13aWdHgDsy8aFsd/T5OYOhwdJhIRXz5tgRv72bQOB4lpBgP3dFEYe5o0joe7U7ZAe2EscvGmWwmUSZmkEboBXw5mcngHeWZToq9Ci/cAGQ6QIQrgo13BRMcesFfbgAzEXQfJTjuCbMdtOzqXdz9CwZW8puKsnf4wqwlqfB01lgdS8531Lr69iW6Qxg6QZgLiG8d8A9yt8NkLJ8efS/H6L6vjoA3HOHGc4jx5eiIixdADTU3W6eY0ruKwqvHvdJFaBnnrxiZquHIj5dAKaCHPBFKT/62O5D/R/fufK+Xme08eP1uU7jGDrXpzz66Q9zvDN+c1Wyj9IZ6nFJD5tVFb9eE+68Nz/wRRh1fdcNxt99vA8ZmL9LVmcHWZ0BxCEebgrx+igzFckxdXUCyMWjrwYAHXU1LQni+NPlAYClE8BgOAG86844TYmur0+uBUf7lHce8XL8D/4kIYQQQgghP2A4WSWEEEIIIQ8LJ6uEEEIIIeRh4WSVEEIIIYQ8LJysEkIIIYSQh+XTdQPQRTWpgFRMOZrkckgP0xVABaglXq9OACWV/TXXJwnjnfm+4qw8P6lAX8mFnjL58ZUvnmns0xHgUMGO9F2DzHztkas9lLWeub99ZMiLoWFPVW5fVLyhvY3s5wqVbWYMhw/AdlIgikgqFC+OAPCZVR1Zxhe1Y2YHx44vkMecql4xg1sWpNvMmIcOpf4Ik15U/6Me13ob272dlf69xXfc5j5ZXQCmQ4Bnxv2y7cdpuQtg8qLuPDWSr2lNR5Z2lJdAp9p35D2XdGqoECnp5tBg1mCyw7BjxzMKKjo6OhTNHLv5dAFQCG4GPFk4U+wWbfPZJJXrQO0FKkg3AEfJNusm0GLYTGDqsG4oLTOwq0NLZnyvvY+eL9gtVOzhBrAq2M859mZyUrDfut5XsI/FBM0jcn3k23cDmkf776lAv7oA+En5u+Ro+3G/dGnoXuKu8Vh2E4jEereorz0rd1Mc5ZoOC4ICEUwFt+lQ6naoOmoPVwCp4bQw+7fMjL+LCaxFGxuOAKMMPd0U3AV7K2i9RLllOT6P8vThDIAsyzjHUYYtHUW6H+4K4awwPAFeOgB4+B+80sovn/XhCiBYbqm4PEeq/1cngEP5b7mO688yWJw43ABxzHtVVODmkKEG91fKdfDaWLD2KbM/z4busijRJU1W5LL42mlOZ5hR3+IxzsXHzv2mStTL4bpynJbIF+tWjzpYcuPTsWG6w2BxBJDz6+EHMM5rNSQYfdm6/zr0HT3i68iHNaPgNbeHUbgf8tn37PcxXnwBhpNKjIfnQrg6I8wy8zGGYo4tAjnKcpT7rKOSvzEnGsgTPurW+3TsGI4c3y0nE5/lOKO9Xh2APvRYhBBCCCGEPBScrBJCCCGEkIeFk1VCCCGEEPKwcLJKCCGEEEIelk9WYDWiStfnpWckYT6wvgquliwwnCJV9fIaOAutXmX8sC2vl6g9IB7kX3QH8fp4wH/+xjuehh9CqtPrRaAzIwnvRBN2x4wyNB+SEp8CE8M+YzrNd7i3WXgh5NF8eH6Iq3QRV70s6vGQ+DlZdole+6KasqEYK7j/MLsd74sZHHqIpMaPvusZ+DVmtbUprpqvWz9HrrrlPo/13LYp3nDzI7UWIUYa9bfW4z1eCA+Wh+gVR10IFKoVYkeDj+jcHeY141Z37PKMm+8QKHY3NFOoAM1CFLcbcDMAENwk2k6VjAhFrKU7WorENj0aczGdoqDSJeIj1SHlEFjpSWDlU2wW5RKCKgCwDsAEfdcQBTmw7xWtK7oJbq1GxGovuPWC3Y+41Zspnu2IBl3jQXdzNDvEUG3Ee46o1Rk1fEcctIgOfcaD+hQqNgiaaRzXBM1ChIQeCZpQwbMhxWoROezI+6M7SpZFxLEC3QRFHWYdpRq0pdBKRmzt/SjNOFfAm2ZZhtAyIlYV3XSWYxvl1xU3O+JWb6YpSjtEarud41ajHENcFZHNhiO0NvqR2busN8B7iL7ofC3AEfvoDpgIzD00S8AUVJ2Epj7utUNUhWVxS3GSZTs8MpCzPfpZe2P+vf2pRw6xrWhKTzpeZlTmWhzwIbjKGHH0ow8Ykasha8rx5XsQxgxcPlAkNERVV3GVH4V0iEIPbfMVvYhwrsKwe9tfiFWVV9b9BmjJjmdRe17jc9djjH1DTDVUe9ex6EPiez+Qdey8Et3KobQSiU57RnFDoXIIcMcCYN5lq5B0FZhehVZ2uQ/ffc6rMCxEVSNO9os0Uf5llRBCCCGEPCycrBJCCCGEkIeFk1VCCCGEEPKwcLJKCCGEEEIeFk5WCSGEEELIw/LpugHgiFrFsqzK/5MLwGv7Rc/OAIPx3mvoxQUAx+pl9p0un//u8IwavGKphJ3OAL5ErA5XAPN0DxhK6NDw2lRD21wAPSkJR/SqnJwAFkeAVPnPeDgcqtChVj0Xh5/Ug69e7On1osI0OUejjqhDMwgA7y0+qwo0HJGGfYna84vqczgBpPJTWjuiVqcjQLoDjP2eDgAz43YUOFJ9PCJXD5UygBmP+xqLQDgVtUecXsEGwXPE4EIzDrcC3pa41XB36NjRsaFLR/eO3Tt2KxCRiF0VTMU3ABQBDIJNfCrYa6p1ixi0O7pJxmEKitpUsNfa0Xu4ARRd4lZTxT5awwmTxQ0gjtmboneFDeV6C8X6rRd0j2jQ53QBeM6o1ala94hdbZ6uAI4lKjTcAByOPiKGxz0w1euvNcW4T0w6OnY0FHRYRNi6Z8Qq5u9CQ1UPc+wCPEvEgQoENmsT2NSBBhT1VLB3NIkyrt1CKV1sugHoGlH84hyB3tMNwCJydLgAuAv2dAUYcbWjLLtLuCr4qv6PqNXdjsjafXECiNdRcp5OAKMsu++n/gSwUCN/YFbmVCAvSuTV6STeyzaDuJdGv7guGK4AeT+6La0vHTtk/UEzoAwnkfzkdCDJc/8u4rFfoBrWF9Maxd953BFTfa/eo2/97s9puMMM7kUPj7Xk7xxOACGzHxGfY1yIz5zPae3P7u3/UOYtast1XzN5zY7o9PElS78azXFDl+/muHFiNkI7H//6/pXL/ntditsXu+g1mf06NZnlPUfmAh1Rq5fo1VleS7163hRjRjAvA+d28aG85kwhXyD3l39ZJYQQQgghDwsnq4QQQggh5GHhZJUQQgghhDwsnKwSQgghhJCHhZNVQgghhBDysHzSbgArchXvD/mcLFK6kQO8qv9PMrtF+SnvUIGax/tD8jdVnukMMKLGdXEKeA3z00fGIUemfGRgL+9DUqQoU/lvqYp15GsfalksLgCphpY21dC+qKE9Zeyh/FzcAFKHrlCIa6oP5TBTkPtFKZf1B3FP3Tn2nRaDmISi0SxU/6O+TIFr3rUh62LJgJ850Hao/YcTwHABSPW/tH78dutAs9NrN899SLUxZkb5cWky3RzeFyX9wkxilLkrVApUFKobxG5QqejpCGDeYL6j2TOaPkNEseMZKoLdN+xuEAN2KwAcmwo2izZSROBwfC4KEUsFe5RpkSPHPhTYgiKGboaihtYVtRhEHCXXq4r9ZTXLkuMe2+aC1hVmkWO/94KW28+9oLvgbS+hXrehZgeebeTZ47R0czQbCvZYWuZfN2mwvAcGVzX0WCzvk449NO/S0dxQ3OZxd4vq31Pt+9wBU0GVcFlwATQdFkYX1d0jvtwcXuLOUzi6GcyyLHucky7leFXXDoVxN8111E9PRwVzmeW4pyNASxeA7oJnEzx3mdvhBHC4KrR5bY5uwwkgyrKhw8SybPYoL2+zL1mDwYezyGust/1Yz+5ALmYbU/k/+r5lsbGEy4JMm5QlrH49+FCJp2OKjBNQwylc3tKbQL+8v/9MZ5sviVeF7Ke233M9B6LTZ0cd6sURABi59S+Zw+8XONerIczpPfOo17U+x4/4GDfG+J/1ZoIcCKJuRV8vkOkEsLjGLGOTX8YjX/YPB4rJ92YClPd9OsLIeTzVO6OrvHAEmIUU5/gO55Mr5mfHCTu//L7Dv6wSQgghhJCHhZNVQgghhBDysHCySgghhBBCHhZOVgkhhBBCyMPCySohhBBCCHlYPlk3gFUVe3IBEEDuyQ9zn9xT969S9vc5AYz9wxFgVZh/j6xOAKe1y1T8DyeAUPof6vJDJRuvu2f+sw9luk9XgKmGHqrn3I4Q7eECUEKDLiP/ORwBNP0CRCSdAV4Wzb3tlaGKjxdyXMDxgUOIO/K5zY+sZ08HgFoPlW6PehDLZGO3cAbQXK4Z0OPYZovi/3AFQOtxzPGZ4Q4wVKItf3MUsAHeLOpwqI/tULqvl/aiDO6gMn0ZMEo6fBnqrBvVCpFYzJ/h3mCWjgD+FgLFLs9QV+x4g903wIDNFIDg1h1bZtcXiXMq4qgi6CKz/ra814r4dKcootg8VOubGrr1uI0y117kyDZfHQHGdYd6O9tyLiPDvqczwG4vc+yfTVOtng4ALriNTHtDZtgPJbuj2XADSCcANBx3Ql+cMM447OQK4DB0aSi+TR+N7nF8lfj9aqO8okk+i6AI0NM1I+o+Vfspdw4PBomyhGPzcFmIMgwXgKK+5MRf3AAujiHddLoBdD9cACzLb58uAFGON1PcRnm64GYXZ4WlPPelLDs6ujR07Oi+w9xgfjgCHCpyPfcj8v6/n1xrY7we/d3o+2xeu8z2tC4wgYvndrh0oDmkjtd+qP/d7/xw9i1rtvz3Iu9e1eV35O5fQKj9hVh/Kbra+yp4e8dY9rKnfzevGAO8k9ccANwAKVmvEWl/OAKs9Zj146bxeh2ApvPD4gjwGi+caGxxCPClz1/da3B2FxiGGHn+YyyP6/z+S+r1nfUTHdLo5471Ud+WfkH3OLsRvZ/vpu7HWRJCCCGEEPKQcLJKCCGEEEIeFk5WCSGEEELIw8LJKiGEEEIIeVg4WSWEEEIIIQ/LJ+sGMNEP0aclcpGqv0uZepWyv6Yofxen/PlzhvAqsRtCfAAnVWu8PvZP1SvOyv8Uu6ZKdrxOJ4BF+xyLvdgz1M6hSz7Uu6srwLsQfJgLwMpd5ecQ2y4KTzeHNAO2VGNKKvUrIK3BVQ7V/zifWkOdXwH0dAW4exJxTDEDWiqYT64A6RLQ++EOcHIJ8Bme7lkB3gDvhwI06vJQK4+6XNdrOYYDwFGmIlNPDUGBoqBgQ5EKkwqVCpeaxXYDXNHtBvMGkx07nqFScPMdN98AVOypiN0MeDbAICg9iqla1HbVkT2t6B4uAEU82h3CGaB7OAZ0NRRTKEK5XlJpG2r2+0VvfrTpULBjqtfNM8d+UbEPBftzV7SRY284OQEcy1CtA81jOxT8HS42nTDsFSeAozn20z1i0kP9jo4Gwe6G5goxQRXHZlGLe5pWFAGeBajprHBUt4azQpajI9T7RaNMiyiK+uEIgHBX0IsjwGg/trSt7pplKugejgD7pUx7lt9Yj+3bUoa3pSz3dFXY3bCbYUdHQ8OOG3Y8w7AfTgAW91Fcncx+JEpGZxu+16dcuwRf9hlk9mnR9/niCLC4pHg4cIQThw8hdKjIT04dOBTdzdIRZvTx6T6io++4OgLgpdJ8rEe/sKjHr/39ixthveYPHGIcMt0R7h72cvjXlPb3nAGO/Phcj/r7ACcHAOkX8/o5recFvLyW1eUBJgD8fP52Z3uU8VpH1zqcivysz9e4OgGsLgD3xnQ/xvbVIWAd14HhZnC/7/9u0aXcxj0l76iv4QAw+j1Dz31bnleU9bh/B6813+8n/MsqIYQQQgh5WDhZJYQQQgghDwsnq4QQQggh5GHhZJUQQgghhDwsn77AKh/A/vJ/586T79en4a8PYwMvn2y/ZJetz7L78tryAfN1ccgRq5rrEBkcIqsRteq5r9tIEhwSqr5ErfZcxpPgFg9lD3GVFKjoFESMuFWVeJT7Q4RUL4psebDcTeZD6LF4Cp6y3JoBWz7UPsRNAGAltkudYiu0/iJCb/ySvyLCkxGlOo4/YlWzHkNc1V6Kq5rBU1g1o2KHyCpjHX2Uux8iK8M5evVDKaKorqgo6B6iKpUNKg2qFeYRwere4N7Q7RnNniFQtPIMRcEuO27eAY+4VXdgU0XpAndHkRDuaQ+BRHPHUKcUCUFL1Xgsfzx6v6mhiqOoYtOIC70Kg4CX8XurGAjAFFUNQZA5sC/RoHu+99wjGnSfAqFDXNV8iIIczY641SMe1NDQ0FIedQgLVkHJwOa+cY/0lBWpFDRvUAi6G1pGPG4aQibAceuCqiGwUhG4OsQES+JililQBXEcEdSshyIONaCIhcgOo0zvt4/ZzjBEl6MsozxbCqh2F+xTTKVZZpJxq8BzP4usdnPceiwhtDI0NzQYdtnRTnGre4j6ch0x1CXigC+Rq6u4Sl8R46x37NqFhhjnEJhG33ZE9k5RowmsC4pGv+Itv11l6WdCfipLlKo3iTjWhlQZXkRW95jiGruIre70/dcl9/vls9F/LBGyJoeg7iJ8WfuUVZz7LvJqj+PkvWDfS5zsPLaf+vnXtWXRr5z3HaXsGVcd4rgllnuWX9SluOdnhlhO4a1DasnXDiDHjCnEfeU63ymWyz5+xGxfY1eHyPYkmD7qcZbPIrC9jgfXZvPdjBffL+JczkIrYLS/S3vFuQ1+N/Avq4QQQggh5GHhZJUQQgghhDwsnKwSQgghhJCHhZNVQgghhBDysHCySgghhBBCHpZP1g3A7XWVnNsrQXR+ktmlerzcP8iIbnvtvVVB/EoM64sItpNycKjfMRXkWJSsQ/25RnXOSEFgxq62VMOu8asn0SSQUZMjbHX81yN61Y/Y1VD4LXGrOOJW1e//u+c1hfIsJrw0RhgqSE0Vp08rA5nKWBlOAH5Er06FZutALUBvEJVU+gKvxaqK3tk/6mwoPVtLK4VYy20/nADGshZwKkK9pzOARWXEteBQJtuIxFx+Gi/9K9ZyjGjNEb06olYF4nq4M4iiLK4AkAr3Z5gpzMIRQETR/BkQYJM3ePYNjg2bFZg4ti4z7lN6RH2OyNciQ/GpqBKto2Yb6xm9GnGrjs0de8a0FnUUibIdSvZ7rMr1sf3CDSAV7UPBvmc06J7RoD0dAMb2ngYNR9xqLA2hYHcZd0KfcYPurzkCIB0zLCOKwxGgoKJLR3MNhwE3iCt2A6qEcrakeHyXkf4YZdnlUHNHuSv2bA9N/OwGIFEHinNs7bU8R09n03Hi6gYgaEs5jrIcbgo3W7eB5zC7SAeAJXLV41p3dOzzf5/TAaChe4NZy3K0bMdl9iWq2ym6WT28RYAlWvhOOzEXQHx2D+f6OSKo1/Wpbxmns6rHBXGRVcaPHDeoOrxZOAKMH1QLc4PTmJB9ytr3r+PCNa5z7fvn58d3Lq+XrmmWwXLN4/DncjorsoHlsH68vucWYHm/rtHD0w3jTqirfmDs6vrbc437DgB2x9UB6oea3s71KY5jXGgWzg2rK4PI4QgAvHR1mPV9LzZ3qcPe5/aMzX11OY/pGGO6pTvFov5ft9cI9VO5vFKGH8KMWp119fLL5jZvOhd7US8vP38+t+NSv1/RsYQQQgghhDwonKwSQgghhJCHhZNVQgghhBDysHCySgghhBBCHpZPTmDl+RDwt/cd9dYjllABKfmQviugEg/zVwFagfQSD2BXhewlItdqfA6lhFhHJPYrjoeSP0RgZYgHst0PEc4Q39xaCoIMuHX43oFu8FvkQ9qtw58jCrDfAO/AfhPYXtCa4rYbWlfcmuHz1tFM8Z3W8dYKdlN8J+MnP++Ctz1iJ59tiE58xk/u3tD8OcMmb+iISMQ1bjXK1eHupyhW8xGpWPIYCvGC3TogIXSpFuqI0iOerQogMGxuEHQADbsbOhqe0LFLw63sqG6opUMRVaBFIF0gapAqISYRB1QjGrV7VPaWAqu9ArXDR32Kxv6azV7Hw/XvEVj5PYFVAywFVrcG3PZ4//kW0apvb8DbBt87/G2L+nxu6M+A71GPLevxeTfsveDzZvhOM9xM8Z3eZx1+3hW7I+rQRx2O+rOsv1F3NxhGtGWfIqB31Z9B47t+S7GRhLjHOopFKKpICBxGky+COB5GJGjUa3PD5o4ijk0i/HJLUdVVYCXvEVhdY0JXgVXzlwKrNgVWIfzpWWbPuT3KbYiCmjl272i+Z8zqtfzedw9cy3FHR0X3G8QdDYIdBriiWJmRtTNaVTDLz+Eokk3NkQK1EK90GIo4akbUHgIr+54FVoaXAis7xdVibj9n/9GWcpxtEIbm0YO0LMfoR9a+5LW2aEsZlkt/En31bh23bIvV4jpKD/3H6PObO1QMQEeBwzyic7cUflU3PKHhSRuqGxSG4lHuCkA6ICIQFYiFeBQqkFaAngNIVUAEUrIvUTnGAdFjW98jsJpRzAYfmde3HB+awVsPQeatw28R4znGArvFeNB3ge2K1hRtL3jeDd0Ub1vH29bRXfGdVtA8+pG3vWA34PMe8bnj3ng5HvQUzD2/uCc+vB73l/WY44ID2ExQLXr/0uP+VInW2sQh0gEYqhm6d2xq6BLtq5jhhobihk06ihq0OEo56lGzLsQUYgoUjXpUibosWY91rdesv3U8+FCB1agzd2DPerz12GcOf9sBw2lMtxtgO9Bugr4XdBPcdsOtxbj+eTN0E7xtHd/pRz3uJvi8K952pKj0uDdfjgvjftznuH6tx5TWncYG8wZBgfmO7jcogOY17k6PMcpEsNlxP8ocEaIf2sUBMWySMe7eUNXQ8l68oWGXW/RTH5DBKv4hn/r/EP/tv/03/M7f+Ts/9mkQQgghhJD38F//63/Fj/3Yj73zM5/cZNXM8N//+3/Hb/ktvwXyLt8kQgghhBDyUXB3fPvb38aP/uiPQu/9P5wLn9xklRBCCCGEfDpQYEUIIYQQQh4WTlYJIYQQQsjDwskqIYQQQgh5WDhZJYQQQgghDwsnq4QQQggh5GHhZJUQQgghhDwsnKwSQgghhJCHhZNVQgh5QNx9hpuUUvCd73znY58SIYR8FDhZJYSQB+RXf/VX8X//7/8FAPzET/wEvvrVr37kMyKEkI8DJ6uEEPKA/Jt/82/m9k/91E99vBMhhJCPDCerhBDygPzbf/tv5/Yf/IN/8OOdCCGEfGQ4WSWEkAeEf1klhJBA3N0/9kkQQgg58yM/8iP4jd/4DQDAb/zGb+C3//bf/pHPiBBCPg78yyohhDwYv/7rvz4nqj/6oz/KiSoh5P/XcLJKCCEPxr1HAFpr+If/8B/iz/yZP4Mf//Efx5s3b/A7fsfvwJ/7c38O/+yf/bOPdKaEEPLlw8kqIYQ8GKu46qd+6qfwH/7Df8Af+2N/DD/7sz+Lf/SP/hF+7dd+DbfbDb/+67+OX/iFX8BP//RP4xvf+MbHO2FCCPkSqR/7BAghhJxZ/7Jaa8Uf/+N/HL/5m7+Jr33ta/j617+OH/7hH8av/dqv4R//43+Mfd8BAH/zb/5N/MRP/AT+wl/4Cx/rtAkh5EuBAitCCHkwfu/v/b341V/9VQDA09MTzAx/42/8DfzVv/pXT+EA//E//kf86T/9p/Gtb30LAPDjP/7j+C//5b+glPJRzpsQQr4MOFklhJAH4tvf/ja+9rWvYXTNqop/8A/+Af7sn/2zdz//r/7Vv8If/sN/eL7+J//kn+Cnf/qnfyDnSgghPwj4zCohhDwQv/Irv4L1bwg///M//+pEFQD+0B/6Q/ijf/SPztf/8l/+yy/1/Agh5AcNJ6uEEPJArM+r/vAP/zD++l//6+/9zvqX1f/xP/7Hl3JehBDyseBklRBCHojVCeAv/aW/hB/6oR9673d+62/9rXN727Yv47QIIeSjwckqIYQ8EOtfVj9U2f+//tf/mts/8iM/8n0/J0II+ZhwskoIIQ/Cvu/45je/CQD42te+hj/wB/7AB33vP/2n/zS3f9fv+l1fyrkRQsjHgpNVQgh5EL75zW/i+fkZQNhXich7v/P8/Ix/8S/+BYB4BODrX//6l3qOhBDyg4aTVUIIeRDWRwB+22/7bR/0nV/8xV/E559/DgD4E3/iT5yeXyWEkE8BTlYJIeRBWMVVb9++/aDv/J2/83fm9s/93M99v0+JEEI+OpysEkLIg7D+ZfXf//t/DzN75+f//t//+/jlX/5lAMBP/uRP4s//+T//pZ4fIYR8DDhZJYSQB+FXfuVX5vb//J//E7/4i7/46me/+c1v4i//5b8MABAR/O2//behyi6dEPLpwZ6NEEIegG9961v4P//n/wAA3rx5AwD4K3/lr+Bf/+t/ffqcu+MXfuEX8PWvfx3/+3//bwDAX/trfw0/8zM/8wM9X0II+UFRP/YJEEIIOT8C8Bf/4l/Ef/7P/xn/9J/+U/yRP/JH8Cf/5J/E7/t9vw+/+Zu/iX/+z/85vvWtb83P/uzP/iy+8Y1vfIxTJoSQHwicrBJCyAOwiqt+/+///fhbf+tv4U/9qT+Ff/fv/h1+6Zd+Cb/0S790+vzT0xN+/ud/Ht/4xjf4f/8TQj5pOFklhJAHYP3L6k/+5E/ix37sx/DLv/zL+Lt/9+/i7/29v4dvfetbEBH87t/9u/EzP/Mz+Lmf+zn8nt/zez7iGRNCyA8GcXf/2CdBCCGEEELIPfj/HRFCCCGEkIeFk1VCCCGEEPKwcLJKCCGEEEIeFk5WCSGEEELIw8LJKiGEEEIIeVg4WSWEEEIIIQ8LJ6uEEEIIIeRh4WSVEEIIIYQ8LJysEkIIIYSQh4WTVUIIIYQQ8rBwskoIIYQQQh4WTlYJIYQQQsjDwskqIYQQQgh5WDhZJYQQQgghDwsnq4QQQggh5GHhZJUQQgghhDws/y/Zpdxt3Kn36wAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Set wavelet scales\n", "scales = get_scales(1, 100)\n", @@ -904,24 +819,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Load starlet decomposition of Venus.\n", "venus_scales = np.load('data/venus.npy')\n", @@ -965,17 +869,6 @@ " plot.display(venus, title='Venus', cmap='magma')" ] }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "This makes the starlet a useful transform for obtaining sparse representations of astronomical images." - ] - }, { "cell_type": "markdown", "metadata": { @@ -986,7 +879,7 @@ "source": [ "---\n", "\n", - "## Hints\n", + "## Hints\n", "\n", "**Starlet Transform**\n", "\n", @@ -995,7 +888,6 @@ } ], "metadata": { - "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", From b19106a1217e4f158b707b2222eb9c2a8d9b8072 Mon Sep 17 00:00:00 2001 From: sfarrens Date: Mon, 9 Jan 2023 09:33:02 +0100 Subject: [PATCH 08/13] added colab support --- README.md | 14 +++++++------- inverse_problems_1.ipynb | 12 ++++++++++++ sparsity_1.ipynb | 12 ++++++++++++ sparsity_2_compressed_sensing.ipynb | 12 ++++++++++++ sparsity_3_deconvolution.ipynb | 12 ++++++++++++ wavelets_1.ipynb | 12 ++++++++++++ 6 files changed, 67 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 76f4d8f..728237e 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ > Year: 2018 (updated in 2023) > Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/CosmoStat/Tutorials/ada) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/CosmoStat/Tutorials/blob/ada/])[![Open In Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/CosmoStat/Tutorials/ada) This tutorial was originally presented at the ninth edition of the [Astronomical Data Analysis (ADAIX)](http://ada.cosmostat.org/) summer school held in Valencia @@ -41,12 +41,12 @@ $ conda install numpy scipy matplotlib jupyter ### Notebooks -1. [Introduction to Inverse Problems](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Finverse_problems_1.ipynb) -1. [Introduction to Sparsity I](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fsparsity_1.ipynb) -1. [Introduction to Sparsity II: Compressed Sensing](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fsparsity_2_compressed_sensing.ipynb) -1. [Introduction to Sparsity III: Deconvolution](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fsparsity_3_deconvolution.ipynb) -1. [Introduction to Wavelets](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fwavelets_1.ipynb) -1. [Exercise Solutions](https://mybinder.org/v2/gh/CosmoStat/Tutorials/master?filepath=%2Fada%2Fexercise_solutions.ipynb) +1. [Introduction to Inverse Problems](inverse_problems_1.ipynb) +1. [Introduction to Sparsity I](sparsity_1.ipynb) +1. [Introduction to Sparsity II: Compressed Sensing](sparsity_2_compressed_sensing.ipynb) +1. [Introduction to Sparsity III: Deconvolution](sparsity_3_deconvolution.ipynb) +1. [Introduction to Wavelets](wavelets_1.ipynb) +1. [Exercise Solutions](exercise_solutions.ipynb) ### Acknowledgements diff --git a/inverse_problems_1.ipynb b/inverse_problems_1.ipynb index cce9393..a2bd884 100644 --- a/inverse_problems_1.ipynb +++ b/inverse_problems_1.ipynb @@ -61,6 +61,18 @@ "Users new to Jupyter notebooks should note that cells are executed by pressing SHIFT+ENTER (⇧+ ⏎). See here for more details" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment the following lines if you are running this on Google Colab\n", + "# !git clone -b ada https://github.com/CosmoStat/Tutorials.git\n", + "# import sys\n", + "# sys.path.insert(0,'/content/Tutorials')" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/sparsity_1.ipynb b/sparsity_1.ipynb index f92e058..5f3a3f1 100644 --- a/sparsity_1.ipynb +++ b/sparsity_1.ipynb @@ -66,6 +66,18 @@ "Users new to Jupyter notebooks should note that cells are executed by pressing SHIFT+ENTER (⇧+ ⏎). See here for more details." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment the following lines if you are running this on Google Colab\n", + "# !git clone -b ada https://github.com/CosmoStat/Tutorials.git\n", + "# import sys\n", + "# sys.path.insert(0,'/content/Tutorials')" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/sparsity_2_compressed_sensing.ipynb b/sparsity_2_compressed_sensing.ipynb index cc9be51..78c6ce1 100644 --- a/sparsity_2_compressed_sensing.ipynb +++ b/sparsity_2_compressed_sensing.ipynb @@ -62,6 +62,18 @@ "Users new to Jupyter notebooks should note that cells are executed by pressing SHIFT+ENTER (⇧+ ⏎). See here for more details." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment the following lines if you are running this on Google Colab\n", + "# !git clone -b ada https://github.com/CosmoStat/Tutorials.git\n", + "# import sys\n", + "# sys.path.insert(0,'/content/Tutorials')" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/sparsity_3_deconvolution.ipynb b/sparsity_3_deconvolution.ipynb index 0c3f678..1d906a1 100644 --- a/sparsity_3_deconvolution.ipynb +++ b/sparsity_3_deconvolution.ipynb @@ -60,6 +60,18 @@ "Users new to Jupyter notebooks should note that cells are executed by pressing SHIFT+ENTER (⇧+ ⏎). See here for more details." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment the following lines if you are running this on Google Colab\n", + "# !git clone -b ada https://github.com/CosmoStat/Tutorials.git\n", + "# import sys\n", + "# sys.path.insert(0,'/content/Tutorials')" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/wavelets_1.ipynb b/wavelets_1.ipynb index a5219e6..66eae95 100644 --- a/wavelets_1.ipynb +++ b/wavelets_1.ipynb @@ -64,6 +64,18 @@ "Users new to Jupyter notebooks should note that cells are executed by pressing SHIFT+ENTER (⇧+ ⏎). See here for more details." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment the following lines if you are running this on Google Colab\n", + "# !git clone -b ada https://github.com/CosmoStat/Tutorials.git\n", + "# import sys\n", + "# sys.path.insert(0,'/content/Tutorials')" + ] + }, { "cell_type": "code", "execution_count": null, From 5731a14e6995b729057e7247b2f734c437cbac32 Mon Sep 17 00:00:00 2001 From: sfarrens Date: Mon, 9 Jan 2023 10:11:02 +0100 Subject: [PATCH 09/13] added colab support cells --- sparsity_1.ipynb | 25 +++++++++++++++++++++++++ sparsity_2_compressed_sensing.ipynb | 1 + sparsity_3_deconvolution.ipynb | 2 ++ wavelets_1.ipynb | 2 ++ 4 files changed, 30 insertions(+) diff --git a/sparsity_1.ipynb b/sparsity_1.ipynb index 5f3a3f1..05308df 100644 --- a/sparsity_1.ipynb +++ b/sparsity_1.ipynb @@ -317,6 +317,31 @@ " return np.fft.fftshift(np.fft.ifft(np.fft.ifftshift(data)))" ] }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "For a more complete understanding of the Fourier transform, I recommend you checkout this YouTube video." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "from IPython.display import HTML, IFrame\n", + "IFrame(src=\"https://www.youtube.com/embed/spUNpyF58BY\", width=560, height=315)" + ] + }, { "cell_type": "markdown", "metadata": { diff --git a/sparsity_2_compressed_sensing.ipynb b/sparsity_2_compressed_sensing.ipynb index 78c6ce1..298af3a 100644 --- a/sparsity_2_compressed_sensing.ipynb +++ b/sparsity_2_compressed_sensing.ipynb @@ -70,6 +70,7 @@ "source": [ "# Uncomment the following lines if you are running this on Google Colab\n", "# !git clone -b ada https://github.com/CosmoStat/Tutorials.git\n", + "# !cp -r /content/Tutorials/data .\n", "# import sys\n", "# sys.path.insert(0,'/content/Tutorials')" ] diff --git a/sparsity_3_deconvolution.ipynb b/sparsity_3_deconvolution.ipynb index 1d906a1..1e7d12e 100644 --- a/sparsity_3_deconvolution.ipynb +++ b/sparsity_3_deconvolution.ipynb @@ -68,6 +68,8 @@ "source": [ "# Uncomment the following lines if you are running this on Google Colab\n", "# !git clone -b ada https://github.com/CosmoStat/Tutorials.git\n", + "# !cp -r /content/Tutorials/data .\n", + "# !pip install --upgrade scipy\n", "# import sys\n", "# sys.path.insert(0,'/content/Tutorials')" ] diff --git a/wavelets_1.ipynb b/wavelets_1.ipynb index 66eae95..4c05945 100644 --- a/wavelets_1.ipynb +++ b/wavelets_1.ipynb @@ -72,6 +72,8 @@ "source": [ "# Uncomment the following lines if you are running this on Google Colab\n", "# !git clone -b ada https://github.com/CosmoStat/Tutorials.git\n", + "# !cp -r /content/Tutorials/data .\n", + "# !pip install --upgrade scipy\n", "# import sys\n", "# sys.path.insert(0,'/content/Tutorials')" ] From 0e19e3c1376cef0164055b2c620c6c5ce3a2d54a Mon Sep 17 00:00:00 2001 From: sfarrens Date: Mon, 9 Jan 2023 10:28:08 +0100 Subject: [PATCH 10/13] updated readme --- README.md | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 728237e..b67999e 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,7 @@ -## Sparsity ---- +# ADA Sparsity Tutorial > Author: [Samuel Farrens](http://www.cosmostat.org/people/sfarrens) -> Year: 2018 (updated in 2023) +> Year: 2018 (updated in 2023) > Email: [samuel.farrens@cea.fr](mailto:samuel.farrens@cea.fr) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/CosmoStat/Tutorials/blob/ada/])[![Open In Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/CosmoStat/Tutorials/ada) @@ -15,9 +14,17 @@ This tutorial is comprised of a series of Python * NumPy (recommend v1.24+) @@ -39,7 +46,7 @@ or $ conda install numpy scipy matplotlib jupyter ``` -### Notebooks +## List of notebooks 1. [Introduction to Inverse Problems](inverse_problems_1.ipynb) 1. [Introduction to Sparsity I](sparsity_1.ipynb) @@ -48,6 +55,6 @@ $ conda install numpy scipy matplotlib jupyter 1. [Introduction to Wavelets](wavelets_1.ipynb) 1. [Exercise Solutions](exercise_solutions.ipynb) -### Acknowledgements +## Acknowledgements S. Farrens would like to acknowledge the following people for helpful feedback and ideas for producing this tutorial: Fred Ngolè-Mboula and Jean-Luc Starck. From 14330d6a3dcbd5903dc0047ac1e05288bf25a125 Mon Sep 17 00:00:00 2001 From: sfarrens Date: Mon, 12 Jan 2026 09:14:12 +0100 Subject: [PATCH 11/13] fix bug in interactive cell --- inverse_problems_1.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/inverse_problems_1.ipynb b/inverse_problems_1.ipynb index a2bd884..fb13166 100644 --- a/inverse_problems_1.ipynb +++ b/inverse_problems_1.ipynb @@ -917,7 +917,7 @@ " # Calculate the tangent line at this point\n", " tangent = x_val ** 2 + 2 * x_val * (values - x_val)\n", " # Display\n", - " plot.grad_plot(values, norm, norm2, grad, tangent, index=int(np.where(values == x_val)[0]))" + " plot.grad_plot(values, norm, norm2, grad, tangent, index=np.where(values == x_val)[0][0])" ] }, { From 3959961a0673291f86eaa452f6df202fb2542db2 Mon Sep 17 00:00:00 2001 From: sfarrens Date: Mon, 12 Jan 2026 09:53:10 +0100 Subject: [PATCH 12/13] fixed typo in solutions and string formatting --- exercise_solutions.ipynb | 2 +- sparsity_1.ipynb | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/exercise_solutions.ipynb b/exercise_solutions.ipynb index 54301c2..4c157f8 100644 --- a/exercise_solutions.ipynb +++ b/exercise_solutions.ipynb @@ -81,7 +81,7 @@ "lamdba_val = 3 * sigma_mad(y_sparse) \n", "\n", "# Perform thresholding.\n", - "x_rec = ifft(hard_thresh(y_sparse, lamda_val))\n", + "x_rec = ifft(hard_thresh(y_sparse, lambda_val))\n", "```" ] }, diff --git a/sparsity_1.ipynb b/sparsity_1.ipynb index 05308df..f2ad686 100644 --- a/sparsity_1.ipynb +++ b/sparsity_1.ipynb @@ -265,7 +265,7 @@ " title='Fourier Transform of Sine', \n", " imag=False, \n", " ylim=(-0.1, 0.6),\n", - " xlab='$\\omega$', \n", + " xlab=r'$\\omega$', \n", " ylab=r'$\\frac{|y(\\omega)|}{n}$',\n", ")" ] @@ -1025,7 +1025,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "sparsity", "language": "python", "name": "python3" }, @@ -1039,7 +1039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.14.2" } }, "nbformat": 4, From 718493f79084e4634ccea34aaa1d970f019c0b24 Mon Sep 17 00:00:00 2001 From: sfarrens Date: Mon, 12 Jan 2026 10:25:47 +0100 Subject: [PATCH 13/13] added pooch to environment --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 1e0ef2c..892142f 100644 --- a/environment.yml +++ b/environment.yml @@ -9,3 +9,4 @@ dependencies: - matplotlib - numpy - scipy + - pooch