diff --git a/.gitignore b/.gitignore index fdb048bc..b15fe8f1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,4 @@ *.swp lessons/.ipynb_checkpoints +.env +.DS_Store diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index 7cdb3b5e..00000000 --- a/.gitmodules +++ /dev/null @@ -1,3 +0,0 @@ -[submodule "JSAnimation"] - path = JSAnimation - url = https://github.com/jakevdp/JSAnimation.git diff --git a/JSAnimation b/JSAnimation deleted file mode 160000 index b14771b6..00000000 --- a/JSAnimation +++ /dev/null @@ -1 +0,0 @@ -Subproject commit b14771b6b6aa4429ca19c93aba8c315bf63ce227 diff --git a/LICENSE b/LICENSE index 4b4fbd76..96da8e97 100644 --- a/LICENSE +++ b/LICENSE @@ -1,4 +1,9 @@ + +Copyright (c) 2013 Lorena A. Barba, Gilbert F. Forsyth + + Instructional Material +====================== All instructional material is made available under the Creative Commons Attribution license. You are free: @@ -30,28 +35,35 @@ With the understanding that: For the full legal text of this license, please see: http://creativecommons.org/licenses/by/3.0/legalcode -Software -"Copyright (c) 2013 Barba group" + +Software +========= Except where otherwise noted, all software is made available under the -FSF-approved MIT license (http://directory.fsf.org/wiki/License:X11): - -Permission is hereby granted, free of charge, to any person obtaining -a copy of this software and associated documentation files (the -"Software"), to deal in the Software without restriction, including -without limitation the rights to use, copy, modify, merge, publish, -distribute, sublicense, and/or sell copies of the Software, and to -permit persons to whom the Software is furnished to do so, subject to -the following conditions: - -The above copyright notice and this permission notice shall be -included in all copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, -EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF -MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND -NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE -LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION -OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION -WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +OSI-approved BSD-3-Clause license (https://opensource.org/licenses/BSD-3-Clause): + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this +list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, this +list of conditions and the following disclaimer in the documentation and/or other +materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its contributors may +be used to endorse or promote products derived from this software without specific +prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/README.md b/README.md index 63a4cf01..0b6fda69 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,123 @@ -Welcome to the CFD Online Lesson Repository +# CFD Python + +> Please cite as: Barba, Lorena A., and Forsyth, Gilbert F. (2018). CFD Python: the 12 steps to Navier-Stokes equations. _Journal of Open Source Education_, **1**(9), 21, https://doi.org/10.21105/jose.00021 + +[![DOI](https://jose.theoj.org/papers/10.21105/jose.00021/status.svg)](https://doi.org/10.21105/jose.00021) + +**CFD Python**, a.k.a. the **12 steps to Navier-Stokes**, is a practical module for learning the foundations of Computational Fluid Dynamics (CFD) by coding solutions to the basic partial differential equations that describe the physics of fluid flow. +The module was part of a course taught by [Prof. Lorena Barba](http://lorenabarba.com) between 2009 and 2013 in the Mechanical Engineering department at Boston University (Prof. Barba since moved to the George Washington University). + +The module assumes only basic programming knowledge (in any language) and some background in partial differential equations and fluid mechanics. The "steps" were inspired by ideas of Dr. Rio Yokota, who was a post-doc in Prof. Barba's lab until 2011, and the lessons were refined by Prof. Barba and her students over several semesters teaching the CFD course. +We wrote this set of Jupyter notebooks in 2013 to teach an intensive two-day course in Mendoza, Argentina. + +Guiding students through these steps (without skipping any!), they learn many valuable lessons. The incremental nature of the exercises means they get a sense of achievement at the end of each assignment, and they feel they are learning with low effort. As they progress, they naturally practice code re-use and they incrementally learn programming and plotting techniques. As they analyze their results, they learn about numerical diffusion, accuracy and convergence. +In about four weeks of a regularly scheduled course, they become moderately proficient programmers and are motivated to start discussing more theoretical matters. + +## How to use this module + +In a regular-session university course, students can complete the **CFD Python** lessons in 4 to 5 weeks. +As an intensive tutorial, the module can be completed in two or three full days, depending on the learner's prior experience. +The lessons can also be used for self study. +In all cases, learners should follow along the worked examples in each lesson by re-typing the code in a fresh Jupyter notebook, maybe taking original notes as they try things out. Lessons ------- +> Launch an interactive session with this module using the Binder service: +[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/barbagroup/CFDPython/master) + +Steps 1–4 are in one spatial dimension. Steps 5–10 are in two dimensions (2D). Steps 11–12 solve the Navier-Stokes equation in 2D. Three "bonus" notebooks cover the CFL condition for numerical stability, array operations with NumPy, and defining functions in Python. + +* [Quick Python Intro](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/00_Quick_Python_Intro.ipynb) +—For Python novices, this lesson introduces the numerical libraries (NumPy and Matplotlib), Python variables, use of whitespace, and slicing arrays. +* [Step 1](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/01_Step_1.ipynb) +—Linear convection with a step-function initial condition (IC) and appropriate boundary conditions (BCs). +* [Step 2](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/02_Step_2.ipynb) +—With the same IC/BCs, _nonlinear_ convection. +* [CFL Condition](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/03_CFL_Condition.ipynb) +—Exploring numerical stability and the Courant-Friedrichs-Lewy (CFL) condition. +* [Step 3](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/04_Step_3.ipynb) +—With the same IC/BCs, _diffusion_ only. +* [Step 4](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/05_Step_4.ipynb) +—Burgers’ equation, with a saw-tooth IC and periodic BCs (with an introduction to Sympy). +* [Array Operations with NumPy](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/06_Array_Operations_with_NumPy.ipynb) +* [Step 5](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/07_Step_5.ipynb) +—Linear convection in 2D with a square-function IC and appropriate BCs. +* [Step 6](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/08_Step_6.ipynb) +—With the same IC/BCs, _nonlinear_ convection in 2D. +* [Step 7](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/09_Step_7.ipynb) +—With the same IC/BCs, _diffusion_ in 2D. +* [Step 8](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/10_Step_8.ipynb) +—Burgers’ equation in 2D +* [Defining Functions in Python](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/11_Defining_Function_in_Python.ipynb) +* [Step 9](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/12_Step_9.ipynb) +—Laplace equation with zero IC and both Neumann and Dirichlet BCs. +* [Step 10](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/13_Step_10.ipynb) +—Poisson equation in 2D. +* [Step 11](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/14_Step_11.ipynb) +—Solves the Navier-Stokes equation for 2D cavity flow. +* [Step 12](http://nbviewer.jupyter.org/github/barbagroup/CFDPython/blob/master/lessons/15_Step_12.ipynb) +—Solves the Navier-Stokes equation for 2D channel flow. + + + + +## Dependencies + +To use these lessons, you need Python 3, and the standard stack of scientific Python: NumPy, Matplotlib, SciPy, Sympy. And of course, you need [Jupyter](http://jupyter.org)—an interactive computational environment that runs on a web browser. + +This mini-course is built as a set of [Jupyter notebooks](https://jupyter-notebook.readthedocs.org/en/latest/notebook.html) containing the written materials and worked-out solutions on Python code. To work with the material, we recommend that you start each lesson with a fresh new notebook, and follow along, typing each line of code (don't copy-and-paste!), and exploring by changing parameters and seeing what happens. + + +
+ Installing via Anaconda +
+We *highly* recommend that you install the [Anaconda Python Distribution](http://docs.continuum.io/anaconda/install). It will make your life so much easier. +You can download and install Anaconda on Windows, OSX and Linux. + +After installing, to ensure that your packages are up to date, run the following commands in a terminal: + +```Bash +conda update conda +conda update jupyter numpy sympy scipy matplotlib +``` + +If you prefer Miniconda (a mini version of Anaconda that saves you disk space), install all the necessary libraries to follow this course by running the following commands in a terminal: + +```Bash +conda update conda +conda install jupyter +conda install numpy scipy sympy matplotlib +``` +
+ +
+ Without Anaconda +
+If you already have Python installed on your machine, you can install Jupyter using pip: + +```Bash +pip install jupyter +``` + +Please also make sure that you have the necessary libraries installed by running + +```Bash +pip install numpy scipy sympy matplotlib +``` +
+ + + +## How to contribute to CFD Python + +We accept contributions via pull request—in fact, several users have already submitted pull requests making corrections or small improvements. You can also open an issue if you find a bug, or have a suggestion. + +## Copyright and License + +(c) 2017 Lorena A. Barba, Gilbert F. Forsyth. All content is under Creative Commons Attribution [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode.txt), and all [code is under BSD-3 clause](https://github.com/engineersCode/EngComp/blob/master/LICENSE) (previously under MIT, and changed on March 8, 2018). + +We are happy if you re-use the content in any way! + +[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/) -* [Quick Python Intro](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/00_Quick_Python_Intro.ipynb) -* [Step 1](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/01_Step_1.ipynb) -* [Step 2](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/02_Step_2.ipynb) -* [CFL Condition](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/03_CFL_Condition.ipynb) -* [Step 3](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/04_Step_3.ipynb) -* [Step 4](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/05_Step_4.ipynb) -* [Array Operations with NumPy](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/06_Array_Operations_with_NumPy.ipynb) -* [Step 5](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/07_Step_5.ipynb) -* [Step 6](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/08_Step_6.ipynb) -* [Step 7](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/09_Step_7.ipynb) -* [Step 8](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/10_Step_8.ipynb) -* [Defining Function in Python](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/11_Defining_Function_in_Python.ipynb) -* [Step 9](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/12_Step_9.ipynb) -* [Step 10](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/13_Step_10.ipynb) -* [Optimizing Loops with Numba](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/14_Optimizing_Loops_with_Numba.ipynb) -* [Step 11](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/15_Step_11.ipynb) -* [Step 12](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/16_Step_12.ipynb) diff --git a/gen-readme.py b/gen-readme.py deleted file mode 100644 index c3e54787..00000000 --- a/gen-readme.py +++ /dev/null @@ -1,49 +0,0 @@ -#!/usr/bin/env python -#Original code written by https://bitbucket.org/hrojas/learn-pandas/ - - -from glob import glob -from urllib import quote -import re - -header = ''' -Welcome to the CFD Online Lesson Repository - -Lessons -------- -''' - - - -format_item = '* [{name}]({url})'.format - -bb_url = 'github.com/barbagroup/CFDPython/blob/master/{}'.format - -def notebooks(): - return glob('lessons/*.ipynb') - -def lesson_id(filename): - return int(re.search('[0-9]+', filename).group()) - -def lesson_name(filename): - filename = filename.split('/')[1].split('.')[0] - return filename[filename.find('_')+1:].replace('_',' ') - -def nb_url(filename): - raw_url = bb_url(quote(quote(filename))) - return 'http://nbviewer.ipython.org/urls/{}'.format(raw_url) - -def write_readme(nblist, fo): - fo.write('{}\n'.format(header)) - for nb in nblist: - name = lesson_name(nb) - url = nb_url(nb) - fo.write('{}\n'.format(format_item(name=name, url=url))) - -def main(): - nblist = sorted(notebooks(), key=lesson_id) - with open('README.md', 'w') as fo: - write_readme(nblist, fo) - -if __name__ == '__main__': - main() diff --git a/lessons/00_Quick_Python_Intro.ipynb b/lessons/00_Quick_Python_Intro.ipynb index cec770ad..b7f03235 100644 --- a/lessons/00_Quick_Python_Intro.ipynb +++ b/lessons/00_Quick_Python_Intro.ipynb @@ -1,945 +1,899 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Python Crash Course" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hello! This is a quick intro to programming in Python to help you hit the ground running with the _12 Steps to Navier-Stokes_. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Libraries" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python is a high-level open-source language. But the _Python world_ is inhabited by many packages or libraries that provide useful things like array operations, plotting functions, and much more. We can import libraries of functions to expand the capabilities of Python in our programs. \n", - "OK! We'll start by importing a few libraries to help us out." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#comments in python are denoted by the pound sign\n", - "import numpy as np #numpy is a library we're importing that provides a bunch of useful matrix operations akin to MATLAB\n", - "import matplotlib.pyplot as plt #matplotlib is 2D plotting library which we will use to plot our results" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So what's all of this import-as business? We are importing one library named `numpy` and we are importing a sub-library of a big package called `matplotlib`. Because the functions we want to use belong to these libraries, we have to tell Python to look at those libraries when we call a particular function. The two lines above have created shortcuts to those libraries named `np` and `plt`, respectively. So if we want to use the numpy function `linspace`, for instance, we can call it by writing:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "myarray = np.linspace(0, 5, 10)\n", - "myarray" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "array([ 0. , 0.55555556, 1.11111111, 1.66666667, 2.22222222,\n", - " 2.77777778, 3.33333333, 3.88888889, 4.44444444, 5. ])" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we don't preface the `linspace` function with `np`, Python will throw an error." + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python Crash Course" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hello! This is a quick intro to programming in Python to help you hit the ground running with the _12 Steps to Navier–Stokes_. \n", + "\n", + "There are two ways to enjoy these lessons with Python:\n", + "\n", + "1. You can download and install a Python distribution on your computer. One option is the free [Anaconda Scientific Python](https://store.continuum.io/cshop/anaconda/) distribution. Another is [Canopy](https://www.enthought.com/products/canopy/academic/), which is free for academic use. Our recommendation is Anaconda.\n", + "\n", + "2. You can run Python in the cloud using [Wakari](https://wakari.io/) web-based data analysis, for which you need to create a free account. (No software installation required!)\n", + "\n", + "In either case, you will probably want to download a copy of this notebook, or the whole AeroPython collection. We recommend that you then follow along each lesson, experimenting with the code in the notebooks, or typing the code into a separate Python interactive session.\n", + "\n", + "If you decided to work on your local Python installation, you will have to navigate in the terminal to the folder that contains the .ipynb files. Then, to launch the notebook server, just type:\n", + "ipython notebook\n", + "\n", + "You will get a new browser window or tab with a list of the notebooks available in that folder. Click on one and start working!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Libraries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python is a high-level open-source language. But the _Python world_ is inhabited by many packages or libraries that provide useful things like array operations, plotting functions, and much more. We can import libraries of functions to expand the capabilities of Python in our programs. \n", + "\n", + "OK! We'll start by importing a few libraries to help us out. First: our favorite library is **NumPy**, providing a bunch of useful array operations (similar to MATLAB). We will use it a lot! The second library we need is **Matplotlib**, a 2D plotting library which we will use to plot our results.\n", + "The following code will be at the top of most of your programs, so execute this cell first:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# <-- comments in python are denoted by the pound sign, like this one\n", + "\n", + "import numpy # we import the array library\n", + "from matplotlib import pyplot # import plotting library" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are importing one library named `numpy` and we are importing a module called `pyplot` of a big library called `matplotlib`.\n", + "To use a function belonging to one of these libraries, we have to tell Python where to look for it. For that, each function name is written following the library name, with a dot in between.\n", + "So if we want to use the NumPy function [linspace()](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html), which creates an array with equally spaced numbers between a start and end, we call it by writing:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.55555556, 1.11111111, 1.66666667, 2.22222222,\n", + " 2.77777778, 3.33333333, 3.88888889, 4.44444444, 5. ])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "myarray = numpy.linspace(0, 5, 10)\n", + "myarray" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we don't preface the `linspace()` function with `numpy`, Python will throw an error." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'linspace' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmyarray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'linspace' is not defined" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "myarray = linspace(0, 5, 10)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 39 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sometimes, you'll see people importing a whole library without assigning a shortcut for it (like `np` here for `numpy`). This saves typing but is sloppy and can get you in trouble. Best to get into good habits from the beginning!\n", + } + ], + "source": [ + "myarray = linspace(0, 5, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `linspace()` is very useful. Try it changing the input parameters!\n", + "\n", + "**Import style:**\n", + "\n", + "You will often see code snippets that use the following lines\n", + "```Python\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "```\n", + "What's all of this import-as business? It's a way of creating a 'shortcut' to the NumPy library and the pyplot module. You will see it frequently as it is in common usage, but we prefer to keep out imports explicit. We think it helps with code readability.\n", + "\n", + "**Pro tip:**\n", + "\n", + "Sometimes, you'll see people importing a whole library without assigning a shortcut for it (like `from numpy import *`). This saves typing but is sloppy and can get you in trouble. Best to get into good habits from the beginning!\n", + "\n", + "\n", + "To learn new functions available to you, visit the [NumPy Reference](http://docs.scipy.org/doc/numpy/reference/) page. If you are a proficient `Matlab` user, there is a wiki page that should prove helpful to you: [NumPy for Matlab Users](http://wiki.scipy.org/NumPy_for_Matlab_Users)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python doesn't require explicitly declared variable types like C and other languages. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = 5 #a is an integer 5\n", + "b = 'five' #b is a string of the word 'five'\n", + "c = 5.0 #c is a floating point 5 " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "float" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that if you divide an integer by an integer that yields a remainder, the result will be converted to a float. (This is *different* from the behavior in Python 2.7, beware!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Whitespace in Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python uses indents and whitespace to group statements together. To write a short loop in C, you might use:\n", + "\n", + " for (i = 0, i < 5, i++){\n", + " printf(\"Hi! \\n\");\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python does not use curly braces like C, so the same program as above is written in Python as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hi \n", "\n", - "To learn new functions available to you, visit the [NumPy Reference](http://docs.scipy.org/doc/numpy/reference/) page. If you are a proficient `Matlab` user, there is a wiki page that should prove helpful to you: [NumPy for Matlab Users](http://wiki.scipy.org/NumPy_for_Matlab_Users)" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python doesn't require explicitly declared variable types like C and other languages. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = 5 #a is an integer 5\n", - "b = 'five' #b is a string of the word 'five'\n", - "c = 5.0 #c is a floating point 5 " - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "type(a)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "int" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "type(b)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "str" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "type(c)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "float" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pay special attention to assigning floating point values to variables or you may get values you do not expect in your programs." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "14/a" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "2" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "14/c" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 14, - "text": [ - "2.8" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you divide an integer by an integer, it will return an answer rounded to the nearest integer. If you want a floating point answer, one of the numbers must be a float. Simply appending a decimal point will do the trick:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "14./a" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "2.8" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Whitespace in Python" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python uses indents and whitespace to group statements together. To write a short loop in C, you might use:\n", + "Hi \n", "\n", - " for (i = 0, i < 5, i++){\n", - " printf(\"Hi! \\n\");\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python does not use curly braces like C, so the same program as above is written in Python as follows:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for i in range(5):\n", - " print \"Hi \\n\"" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Hi \n", - "\n", - "Hi \n", - "\n", - "Hi \n", - "\n", - "Hi \n", - "\n", - "Hi \n", - "\n" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you have nested for-loops, there is a further indent for the inner loop." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for i in range(3):\n", - " for j in range(3):\n", - " print i, j\n", - " \n", - " print \"This statement is within the i-loop, but not the j-loop\"" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0 0\n", - "0 1\n", - "0 2\n", - "This statement is within the i-loop, but not the j-loop\n", - "1 0\n", - "1 1\n", - "1 2\n", - "This statement is within the i-loop, but not the j-loop\n", - "2 0\n", - "2 1\n", - "2 2\n", - "This statement is within the i-loop, but not the j-loop\n" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Slicing Arrays" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In NumPy, you can look at portions of arrays in the same way as in `Matlab`, with a few extra tricks thrown in. Let's take an array of values from 1 to 5." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "myvals = np.array([1, 2, 3, 4, 5])\n", - "myvals" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "array([1, 2, 3, 4, 5])" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python uses a **zero-based index**, so let's look at the first and last element in the array `myvals`" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "myvals[0], myvals[4]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 20, - "text": [ - "(1, 5)" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are 5 elements in the array `myvals`, but if we try to look at `myvals[5]`, Python will be unhappy, as `myvals[5]` is actually calling the non-existant 6th element of that array." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "myvals[5]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "index out of bounds", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmyvals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m: index out of bounds" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Arrays can also be 'sliced', grabbing a range of values. Let's look at the first three elements" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "myvals[0:3]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "array([1, 2, 3])" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note here, the slice is inclusive on the front end and exclusive on the back, so the above command gives us the values of `myvals[0]`, `myvals[1]` and `myvals[2]`, but not `myvals[3]`." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Assigning Array Variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the strange little quirks/features in Python that often confuses people comes up when assigning and comparing arrays of values. Here is a quick example. Let's start by defining a 1-D array called $a$:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = np.linspace(1,5,5)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "array([ 1., 2., 3., 4., 5.])" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK, so we have an array $a$, with the values 1 through 5. I want to make a copy of that array, called $b$, so I'll try the following:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "b = a" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 25 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "b" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 26, - "text": [ - "array([ 1., 2., 3., 4., 5.])" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great. So $a$ has the values 1 through 5 and now so does $b$. Now that I have a backup of $a$, I can change its values without worrying about losing data (or so I may think!)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a[2] = 17" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 28, - "text": [ - "array([ 1., 2., 17., 4., 5.])" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, the 3rd element of $a$ has been changed to 17. Now let's check on $b$." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "b" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 29, - "text": [ - "array([ 1., 2., 17., 4., 5.])" - ] - } - ], - "prompt_number": 29 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And that's how things go wrong! When you use a statement like $a = b$, rather than copying all the values of $a$ into a new array called $b$, Python just creates an alias (or a pointer) called $b$ and tells it to route us to $a$. So if we change a value in $a$ then $b$ will reflect that change (technically, this is called *assignment by reference*). If you want to make a true copy of the array, you have to tell Python to copy every element of $a$ into a new array. Let's call it $c$. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "c[:] = a[:]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 40 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unfortunately, if we want to make the true copy, the new array has to be defined first and has to have the correct number of elements. So it will be a two-step process. We can define an \"empty\" array that is the same size as $a$ by using the numpy function `empty_like`:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "c = np.empty_like(a)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 31 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "len(c) #shows us how long c is" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 32, - "text": [ - "5" - ] - } - ], - "prompt_number": 32 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "c[:]=a[:]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "c" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 34, - "text": [ - "array([ 1., 2., 17., 4., 5.])" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can try again to change a value in $a$ and see if the changes are also seen in $c$. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a[2] = 3" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 35 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 36, - "text": [ - "array([ 1., 2., 3., 4., 5.])" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "c" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 37, - "text": [ - "array([ 1., 2., 17., 4., 5.])" - ] - } - ], - "prompt_number": 37 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK, it worked! If the difference between `a = b` and `a[:]=b[:]` is unclear, you should read through this again. This issue will come back to haunt you otherwise." + "Hi \n", + "\n", + "Hi \n", + "\n", + "Hi \n", + "\n" ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Learn More" + } + ], + "source": [ + "for i in range(5):\n", + " print(\"Hi \\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you have nested for-loops, there is a further indent for the inner loop." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0\n", + "0 1\n", + "0 2\n", + "This statement is within the i-loop, but not the j-loop\n", + "1 0\n", + "1 1\n", + "1 2\n", + "This statement is within the i-loop, but not the j-loop\n", + "2 0\n", + "2 1\n", + "2 2\n", + "This statement is within the i-loop, but not the j-loop\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are a lot of resources online to learn more about using NumPy and other libraries. Just for kicks, here we use IPython's feature for embedding videos to point you to a short video on YouTube on using NumPy arrays." + } + ], + "source": [ + "for i in range(3):\n", + " for j in range(3):\n", + " print(i, j)\n", + " \n", + " print(\"This statement is within the i-loop, but not the j-loop\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Slicing Arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In NumPy, you can look at portions of arrays in the same way as in `Matlab`, with a few extra tricks thrown in. Let's take an array of values from 1 to 5." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "myvals = numpy.array([1, 2, 3, 4, 5])\n", + "myvals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python uses a **zero-based index**, so let's look at the first and last element in the array `myvals`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 5)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "myvals[0], myvals[4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are 5 elements in the array `myvals`, but if we try to look at `myvals[5]`, Python will be unhappy, as `myvals[5]` is actually calling the non-existant 6th element of that array." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "IndexError", + "evalue": "index 5 is out of bounds for axis 0 with size 5", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmyvals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m: index 5 is out of bounds for axis 0 with size 5" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "# a short video about using NumPy arrays, from Enthought\n", - "YouTubeVideo('vWkb7VahaXQ')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 41, - "text": [ - "" - ] - } - ], - "prompt_number": 41 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] } ], - "metadata": {} + "source": [ + "myvals[5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Arrays can also be 'sliced', grabbing a range of values. Let's look at the first three elements" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "myvals[0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note here, the slice is inclusive on the front end and exclusive on the back, so the above command gives us the values of `myvals[0]`, `myvals[1]` and `myvals[2]`, but not `myvals[3]`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assigning Array Variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the strange little quirks/features in Python that often confuses people comes up when assigning and comparing arrays of values. Here is a quick example. Let's start by defining a 1-D array called $a$:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = numpy.linspace(1,5,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 3., 4., 5.])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK, so we have an array $a$, with the values 1 through 5. I want to make a copy of that array, called $b$, so I'll try the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "b = a" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 3., 4., 5.])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great. So $a$ has the values 1 through 5 and now so does $b$. Now that I have a backup of $a$, I can change its values without worrying about losing data (or so I may think!)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a[2] = 17" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 17., 4., 5.])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, the 3rd element of $a$ has been changed to 17. Now let's check on $b$." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 17., 4., 5.])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And that's how things go wrong! When you use a statement like $a = b$, rather than copying all the values of $a$ into a new array called $b$, Python just creates an alias (or a pointer) called $b$ and tells it to route us to $a$. So if we change a value in $a$ then $b$ will reflect that change (technically, this is called *assignment by reference*). If you want to make a true copy of the array, you have to tell Python to copy every element of $a$ into a new array. Let's call it $c$. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "c = a.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can try again to change a value in $a$ and see if the changes are also seen in $c$. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a[2] = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 3., 4., 5.])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 17., 4., 5.])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK, it worked! If the difference between `a = b` and `a = b.copy()` is unclear, you should read through this again. This issue will come back to haunt you otherwise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a lot of resources online to learn more about using NumPy and other libraries. Just for kicks, here we use Jupyter's feature for embedding videos to point you to a short video on YouTube on using NumPy arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "# a short video about using NumPy arrays, from Enthought\n", + "YouTubeVideo('vWkb7VahaXQ')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + } + ], + "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": "3.4.3" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/lessons/01_Step_1.ipynb b/lessons/01_Step_1.ipynb index 1a495ba2..754a3150 100644 --- a/lessons/01_Step_1.ipynb +++ b/lessons/01_Step_1.ipynb @@ -1,530 +1,555 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "heading", - "level": 5, - "metadata": {}, - "source": [ - "Version 0.1 (July 2013)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "======\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hello! Welcome to the **12 steps to Navier-Stokes**. This is a practical module that is used in the beginning of an interactive Computational Fluid Dynamics (CFD) course taught by [Prof. Lorena Barba](http://lorenabarba.com) since Spring 2009 at Boston University. The course assumes only basic programming knowledge (in any language) and of course some foundation in partial differential equations and fluid mechanics. The practical module was inspired by the ideas of Dr. Rio Yokota, who was a post-doc in Barba's lab, and has been refined by Prof. Barba and her students over several semesters teaching the course. The course is taught entirely using Python and students who don't know Python just learn as we work through the module.\n", - "\n", - "This [iPython notebook](http://ipython.org/ipython-doc/stable/interactive/htmlnotebook.html) will lead you through the first step of programming your own Navier-Stokes solver in Python from the ground up. We're going to dive right in. Don't worry if you don't understand everything that's happening at first, we'll cover it in detail as we move forward and you can support your learning with the videos of [Prof. Barba's lectures on YouTube](http://www.youtube.com/playlist?list=PL30F4C5ABCE62CB61).\n", - "\n", - "For best results, after you follow this notebook, prepare your own code for Step 1, either as a Python script or in a clean IPython notebook.\n", - "\n", - "To execute this Notebook, we assume you have invoked the notebook server using: `ipython notebook --pylab inline`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 1: 1-D Linear Convection\n", - "-----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The 1-D Linear Convection equation is the simplest, most basic model that can be used to learn something about CFD. It is surprising that this little equation can teach us so much! Here it is:\n", - "\n", - "$$\\frac{\\partial u}{\\partial t} + c \\frac{\\partial u}{\\partial x} = 0$$\n", - "\n", - "With given initial conditions (understood as a *wave*), the equation represents the propagation of that initial *wave* with speed $c$, without change of shape. Let the initial condition be $u(x,0)=u_0(x)$. Then the exact solution of the equation is $u(x,t)=u_0(x-ct)$.\n", - "\n", - "We discretize this equation in both space and time, using the Forward Difference scheme for the time derivative and the Backward Difference scheme for the space derivative. Consider discretizing the spatial coordinate $x$ into points that we index from $i=0$ to $N$, and stepping in discrete time intervals of size $\\Delta t$.\n", - "\n", - "From the definition of a derivative (and simply removing the limit), we know that:\n", - "\n", - "$$\\frac{\\partial u}{\\partial x}\\approx \\frac{u(x+\\Delta x)-u(x)}{\\Delta x}$$\n", - "\n", - "Our discrete equation, then, is:\n", - "\n", - "$$\\frac{u_i^{n+1}-u_i^n}{\\Delta t} + c \\frac{u_i^n - u_{i-1}^n}{\\Delta x} = 0 $$\n", - "\n", - "Where $n$ and $n+1$ are two consecutive steps in time, while $i-1$ and $i$ are two neighboring points of the discretized $x$ coordinate. If there are given initial conditions, then the only unknown in this discretization is $u_i^{n+1}$. We can solve for our unknown to get an equation that allows us to advance in time, as follows:\n", - "\n", - "$$u_i^{n+1} = u_i^n - c \\frac{\\Delta t}{\\Delta x}(u_i^n-u_{i-1}^n)$$\n", - "\n", - "Now let's try implementing this in Python. \n", - "\n", - "We'll start by importing a few libraries to help us out.\n", - "\n", - "* `numpy` is a library that provides a bunch of useful matrix operations akin to MATLAB\n", - "* `matplotlib` is a 2D plotting library that we will use to plot our results\n", - "* `time` and `sys` provide basic timing functions that we'll use to slow down animations for viewing" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Remember: comments in python are denoted by the pound sign\n", - "import numpy as np #here we load numpy, calling it 'np' from now on\n", - "import matplotlib.pyplot as plt #here we load matplotlib, calling it 'plt'\n", - "import time, sys #and load some utilities\n", - "from IPython.core.display import clear_output #used for inline animation" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's define a few variables; we want to define an evenly spaced grid of points within a spatial domain that is 2 units of length wide, i.e., $x_i\\in(0,2)$. We'll define a variable `nx`, which will be the number of grid points we want and `dx` will be the distance between any pair of adjacent grid points. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nx = 41 # try changing this number from 41 to 81 and Run All ... what happens?\n", - "dx = 2./(nx-1)\n", - "nt = 25 #nt is the number of timesteps we want to calculate\n", - "dt = .025 #dt is the amount of time each timestep covers (delta t)\n", - "c = 1. #assume wavespeed of c = 1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also need to set up our initial conditions. The initial velocity $u_0$ is given as \n", - "$u = 2$ in the interval $0.5 \\leq x \\leq 1$ and $u = 1$ everywhere else in $(0,2)$ (i.e., a hat function).\n", - "\n", - "Here, we use the function `ones()` defining a `numpy` array which is `nx` elements long with every value equal to 1." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = np.ones(nx) #numpy function ones()\n", - "u[.5/dx : 1/dx+1]=2 #setting u = 2 between 0.5 and 1 as per our I.C.s\n", - "print u" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. 2.\n", - " 2. 2. 2. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", - " 1. 1. 1. 1. 1.]\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's take a look at those initial conditions using a Matplotlib plot. We've imported the `matplotlib` plotting library as `plt` and the plotting function is called `plot`, so we'll call `plt.plot`. To learn about the myriad possibilities of Matplotlib, explore the [Gallery](http://matplotlib.org/gallery.html) of example plots.\n", - "\n", - "Here, we use the syntax for a simple 2D plot: `plot(x,y)`, where the `x` values are evenly distributed grid points:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plt.plot(np.linspace(0,2,nx), u)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "[]" - ] - }, - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Why doesn't the hat function have perfectly straight sides? Think for a bit." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now it's time to implement the discretization of the convection equation using a finite-difference scheme. \n", - "\n", - "For every element of our array `u`, we need to perform the operation $u_i^{n+1} = u_i^n - c \\frac{\\Delta t}{\\Delta x}(u_i^n-u_{i-1}^n)$\n", - "\n", - "We'll store the result in a new (temporary) array `un`, which will be the solution $u$ for the next time-step. We will repeat this operation for as many time-steps as we specify and then we can see how far the wave has convected. \n", - "\n", - "We first initialize our placeholder array `un` to hold the values we calculate for the $n+1$ timestep, using once again the NumPy function `ones()`.\n", - "\n", - "Then, we may think we have two iterative operations: one in space and one in time (we'll learn differently later), so we'll start by nesting one loop inside the other. Note the use of the nifty `range()` function. When we write: `for i in range(1,nx)` we will iterate through the `u` array, but we'll be skipping the first element (the zero-th element). *Why?*" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "un = np.ones(nx) #initialize a temporary array\n", - "\n", - "for n in range(nt): #loop for values of n from 0 to nt, so it will run nt times\n", - " un = u.copy() ##copy the existing values of u into un\n", - " for i in range(1,nx): ## you can try commenting this line and...\n", - " #for i in range(nx): ## ... uncommenting this line and see what happens!\n", - " u[i] = un[i]-c*dt/dx*(un[i]-un[i-1])\n", - " \n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note**\u2014We will learn later that the code as written above is quite inefficient, and there are better ways to write this, Python-style. But let's carry on.\n", - "\n", - "Now let's try plotting our `u` array after advancing in time." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plt.plot(np.linspace(0,2,nx),u)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "[]" - ] - }, - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK! So our hat function has definitely moved to the right, but it's no longer a hat. **What's going on?**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learn More\n", - "-----\n", - "***" - ] - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "======\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hello! Welcome to the **12 steps to Navier–Stokes**. This is a practical module that is used in the beginning of an interactive Computational Fluid Dynamics (CFD) course taught by [Prof. Lorena Barba](http://lorenabarba.com) since Spring 2009 at Boston University. The course assumes only basic programming knowledge (in any language) and of course some foundation in partial differential equations and fluid mechanics. The practical module was inspired by the ideas of Dr. Rio Yokota, who was a post-doc in Barba's lab, and has been refined by Prof. Barba and her students over several semesters teaching the course. The course is taught entirely using Python and students who don't know Python just learn as we work through the module.\n", + "\n", + "This [Jupyter notebook](https://jupyter-notebook.readthedocs.io/en/stable/) will lead you through the first step of programming your own Navier–Stokes solver in Python from the ground up. We're going to dive right in. Don't worry if you don't understand everything that's happening at first, we'll cover it in detail as we move forward and you can support your learning with the videos of [Prof. Barba's lectures on YouTube](http://www.youtube.com/playlist?list=PL30F4C5ABCE62CB61).\n", + "\n", + "For best results, after you follow this notebook, prepare your own code for Step 1, either as a Python script or in a clean Jupyter notebook.\n", + "\n", + "To execute this Notebook, we assume you have invoked the notebook server using: `jupyter notebook`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 1: 1-D Linear Convection\n", + "-----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 1-D Linear Convection equation is the simplest, most basic model that can be used to learn something about CFD. It is surprising that this little equation can teach us so much! Here it is:\n", + "\n", + "$$\\frac{\\partial u}{\\partial t} + c \\frac{\\partial u}{\\partial x} = 0$$\n", + "\n", + "With given initial conditions (understood as a *wave*), the equation represents the propagation of that initial *wave* with speed $c$, without change of shape. Let the initial condition be $u(x,0)=u_0(x)$. Then the exact solution of the equation is $u(x,t)=u_0(x-ct)$.\n", + "\n", + "We discretize this equation in both space and time, using the Forward Difference scheme for the time derivative and the Backward Difference scheme for the space derivative. Consider discretizing the spatial coordinate $x$ into points that we index from $i=0$ to $N$, and stepping in discrete time intervals of size $\\Delta t$.\n", + "\n", + "From the definition of a derivative (and simply removing the limit), we know that:\n", + "\n", + "$$\\frac{\\partial u}{\\partial x}\\approx \\frac{u(x+\\Delta x)-u(x)}{\\Delta x}$$\n", + "\n", + "Our discrete equation, then, is:\n", + "\n", + "$$\\frac{u_i^{n+1}-u_i^n}{\\Delta t} + c \\frac{u_i^n - u_{i-1}^n}{\\Delta x} = 0 $$\n", + "\n", + "Where $n$ and $n+1$ are two consecutive steps in time, while $i-1$ and $i$ are two neighboring points of the discretized $x$ coordinate. If there are given initial conditions, then the only unknown in this discretization is $u_i^{n+1}$. We can solve for our unknown to get an equation that allows us to advance in time, as follows:\n", + "\n", + "$$u_i^{n+1} = u_i^n - c \\frac{\\Delta t}{\\Delta x}(u_i^n-u_{i-1}^n)$$\n", + "\n", + "Now let's try implementing this in Python. \n", + "\n", + "We'll start by importing a few libraries to help us out.\n", + "\n", + "* `numpy` is a library that provides a bunch of useful matrix operations akin to MATLAB\n", + "* `matplotlib` is a 2D plotting library that we will use to plot our results\n", + "* `time` and `sys` provide basic timing functions that we'll use to slow down animations for viewing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Remember: comments in python are denoted by the pound sign\n", + "import numpy #here we load numpy\n", + "from matplotlib import pyplot #here we load matplotlib\n", + "import time, sys #and load some utilities\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#this makes matplotlib plots appear in the notebook (instead of a separate window)\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's define a few variables; we want to define an evenly spaced grid of points within a spatial domain that is 2 units of length wide, i.e., $x_i\\in(0,2)$. We'll define a variable `nx`, which will be the number of grid points we want and `dx` will be the distance between any pair of adjacent grid points. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "nx = 41 # try changing this number from 41 to 81 and Run All ... what happens?\n", + "dx = 2 / (nx-1)\n", + "nt = 25 #nt is the number of timesteps we want to calculate\n", + "dt = .025 #dt is the amount of time each timestep covers (delta t)\n", + "c = 1 #assume wavespeed of c = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to set up our initial conditions. The initial velocity $u_0$ is given as \n", + "$u = 2$ in the interval $0.5 \\leq x \\leq 1$ and $u = 1$ everywhere else in $(0,2)$ (i.e., a hat function).\n", + "\n", + "Here, we use the function `ones()` defining a `numpy` array which is `nx` elements long with every value equal to 1." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a more thorough explanation of the finite-difference method, including topics like the truncation error, order of convergence and other details, watch **Video Lessons 2 and 3** by Prof. Barba on YouTube." + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. 2.\n", + " 2. 2. 2. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1.]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('iz22_37mMkk')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "YouTubeVideo('xq9YTcv-fQg')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, + } + ], + "source": [ + "u = numpy.ones(nx) #numpy function ones()\n", + "u[int(.5 / dx):int(1 / dx + 1)] = 2 #setting u = 2 between 0.5 and 1 as per our I.C.s\n", + "print(u)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's take a look at those initial conditions using a Matplotlib plot. We've imported the `matplotlib` plotting library `pyplot` and the plotting function is called `plot`, so we'll call `pyplot.plot`. To learn about the myriad possibilities of Matplotlib, explore the [Gallery](http://matplotlib.org/gallery.html) of example plots.\n", + "\n", + "Here, we use the syntax for a simple 2D plot: `plot(x,y)`, where the `x` values are evenly distributed grid points:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "For a careful walk-through of the discretization of the linear convection equation with finite differences (and also the following steps, up to Step 4), watch **Video Lesson 4** by Prof. Barba on YouTube." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.plot(numpy.linspace(0, 2, nx), u);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why doesn't the hat function have perfectly straight sides? Think for a bit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now it's time to implement the discretization of the convection equation using a finite-difference scheme. \n", + "\n", + "For every element of our array `u`, we need to perform the operation $u_i^{n+1} = u_i^n - c \\frac{\\Delta t}{\\Delta x}(u_i^n-u_{i-1}^n)$\n", + "\n", + "We'll store the result in a new (temporary) array `un`, which will be the solution $u$ for the next time-step. We will repeat this operation for as many time-steps as we specify and then we can see how far the wave has convected. \n", + "\n", + "We first initialize our placeholder array `un` to hold the values we calculate for the $n+1$ timestep, using once again the NumPy function `ones()`.\n", + "\n", + "Then, we may think we have two iterative operations: one in space and one in time (we'll learn differently later), so we'll start by nesting one loop inside the other. Note the use of the nifty `range()` function. When we write: `for i in range(1,nx)` we will iterate through the `u` array, but we'll be skipping the first element (the zero-th element). *Why?*" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "un = numpy.ones(nx) #initialize a temporary array\n", + "\n", + "for n in range(nt): #loop for values of n from 0 to nt, so it will run nt times\n", + " un = u.copy() ##copy the existing values of u into un\n", + " for i in range(1, nx): ## you can try commenting this line and...\n", + " #for i in range(nx): ## ... uncommenting this line and see what happens!\n", + " u[i] = un[i] - c * dt / dx * (un[i] - un[i-1])\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**—We will learn later that the code as written above is quite inefficient, and there are better ways to write this, Python-style. But let's carry on.\n", + "\n", + "Now let's try plotting our `u` array after advancing in time." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "YouTubeVideo('y2WaK7_iMRI')" - ], - "language": "python", + "data": { + "image/png": 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So our hat function has definitely moved to the right, but it's no longer a hat. **What's going on?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn More\n", + "-----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a more thorough explanation of the finite-difference method, including topics like the truncation error, order of convergence and other details, watch **Video Lessons 2 and 3** by Prof. Barba on YouTube." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, "metadata": {}, - "source": [ - "Last but not least" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('iz22_37mMkk')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, "metadata": {}, - "source": [ - "**Remember** to rewrite Step 1 as a fresh Python script or in *your own* IPython notebook and then experiment by changing the discretization parameters. Once you have done this, you will be ready for [Step 2](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/02_Step_2.ipynb).\n", - "\n", - "\n", - "***" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "YouTubeVideo('xq9YTcv-fQg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a careful walk-through of the discretization of the linear convection equation with finite differences (and also the following steps, up to Step 4), watch **Video Lesson 4** by Prof. Barba on YouTube." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "YouTubeVideo('y2WaK7_iMRI')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Last but not least" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Remember** to rewrite Step 1 as a fresh Python script or in *your own* Jupyter notebook and then experiment by changing the discretization parameters. Once you have done this, you will be ready for [Step 2](./02_Step_2.ipynb).\n", + "\n", + "\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook. We modified a style we found on the GitHub of [CamDavidsonPilon](https://github.com/CamDavidsonPilon), [@Cmrn_DP](https://twitter.com/cmrn_dp).)" - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook. We modified a style we found on the GitHub of [CamDavidsonPilon](https://github.com/CamDavidsonPilon), [@Cmrn_DP](https://twitter.com/cmrn_dp).)" + ] } - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/lessons/02_Step_2.ipynb b/lessons/02_Step_2.ipynb index e4fa19a7..f86b9d5d 100644 --- a/lessons/02_Step_2.ipynb +++ b/lessons/02_Step_2.ipynb @@ -1,306 +1,314 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "======\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This IPython notebook continues the presentation of the **12 steps to Navier-Stokes**, the practical module taught in the interactive CFD class of [Prof. Lorena Barba](http://lorenabarba.com). You should have completed [Step 1](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/01_Step_1.ipynb) before continuing, having written your own Python script or notebook and having experimented with varying the parameters of the discretization and observing what happens.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 2: Non-linear Convection\n", - "-----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we're going to implement non-linear convection using the same methods as in step 1. The 1D convection equation is:\n", - "\n", - "$$\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} = 0$$\n", - "\n", - "Instead of a constant factor $c$ multiplying the second term, now we have the solution $u$ multiplying it. Thus, the second term of the equation is now *non-linear* We're going to use the same discretization as in Step 1 \u2014 forward difference in time and backward difference in space. Here is the discretized equation.\n", - "\n", - "$$\\frac{u_i^{n+1}-u_i^n}{\\Delta t} + u_i^n \\frac{u_i^n-u_{i-1}^n}{\\Delta x} = 0$$\n", - "\n", - "Solving for the only unknown term, $u_i^{n+1}$, yields:\n", - "\n", - "$$u_i^{n+1} = u_i^n - u_i^n \\frac{\\Delta t}{\\Delta x} (u_i^n - u_{i-1}^n)$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As before, the Python code starts by loading the necessary libraries. Then, we declare some variables that determine the discretization in space and time (you should experiment by changing these parameters to see what happens). Then, we create the initial condition $u_0$ by initializing the array for the solution using $u = 2\\ @\\ 0.5 \\leq x \\leq 1$ and $u = 1$ everywhere else in $(0,2)$ (i.e., a hat function)." - ] - }, - { - "cell_type": "code", - "collapsed": true, - "input": [ - "import numpy as np #we're importing numpy and calling it np locally\n", - "import matplotlib.pyplot as plt #and our 2D plotting library, calling it plt\n", - "\n", - "\n", - "nx = 41\n", - "dx = 2./(nx-1)\n", - "nt = 20 #nt is the number of timesteps we want to calculate\n", - "dt = .025 #dt is the amount of time each timestep covers (delta t)\n", - "\n", - "u = np.ones(nx) #as before, we initialize u with every value equal to 1.\n", - "u[.5/dx : 1/dx+1]=2 #then set u = 2 between 0.5 and 1 as per our I.C.s\n", - "\n", - "un = np.ones(nx) #initialize our placeholder array un, to hold the time-stepped solution" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code snippet below is *unfinished*. We have copied over the line from [Step 1](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/01_Step_1.ipynb) that executes the time-stepping update. Can you edit this code to execute the non-linear convection instead?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for n in range(nt): #iterate through time\n", - " un = u.copy() ##copy the existing values of u into un\n", - " for i in range(1,nx): ##now we'll iterate through the u array\n", - " \n", - " ###This is the line from Step 1, copied exactly. Edit it for our new equation.\n", - " ###then uncomment it and run the cell to evaluate Step 2 \n", - " \n", - " ###u[i] = un[i]-c*dt/dx*(un[i]-un[i-1]) \n", - "\n", - " \n", - "plt.plot(np.linspace(0,2,nx),u) ##Plot the results" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "IndentationError", - "evalue": "expected an indented block (, line 11)", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What do you observe about the evolution of the hat function under the non-linear convection equation? What happens when you change the numerical parameters and run again?" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Learn More" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a careful walk-through of the discretization of the convection equation with finite differences (and all steps from 1 to 4), watch **Video Lesson 4** by Prof. Barba on YouTube." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('y2WaK7_iMRI')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "======\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This Jupyter notebook continues the presentation of the **12 steps to Navier–Stokes**, the practical module taught in the interactive CFD class of [Prof. Lorena Barba](http://lorenabarba.com). You should have completed [Step 1](./01_Step_1.ipynb) before continuing, having written your own Python script or notebook and having experimented with varying the parameters of the discretization and observing what happens.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 2: Nonlinear Convection\n", + "-----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we're going to implement nonlinear convection using the same methods as in step 1. The 1D convection equation is:\n", + "\n", + "$$\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} = 0$$\n", + "\n", + "Instead of a constant factor $c$ multiplying the second term, now we have the solution $u$ multiplying it. Thus, the second term of the equation is now *nonlinear*. We're going to use the same discretization as in Step 1 — forward difference in time and backward difference in space. Here is the discretized equation.\n", + "\n", + "$$\\frac{u_i^{n+1}-u_i^n}{\\Delta t} + u_i^n \\frac{u_i^n-u_{i-1}^n}{\\Delta x} = 0$$\n", + "\n", + "Solving for the only unknown term, $u_i^{n+1}$, yields:\n", + "\n", + "$$u_i^{n+1} = u_i^n - u_i^n \\frac{\\Delta t}{\\Delta x} (u_i^n - u_{i-1}^n)$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, the Python code starts by loading the necessary libraries. Then, we declare some variables that determine the discretization in space and time (you should experiment by changing these parameters to see what happens). Then, we create the initial condition $u_0$ by initializing the array for the solution using $u = 2\\ @\\ 0.5 \\leq x \\leq 1$ and $u = 1$ everywhere else in $(0,2)$ (i.e., a hat function)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy # we're importing numpy \n", + "from matplotlib import pyplot # and our 2D plotting library\n", + "%matplotlib inline\n", + "\n", + "\n", + "nx = 41\n", + "dx = 2 / (nx - 1)\n", + "nt = 20 #nt is the number of timesteps we want to calculate\n", + "dt = .025 #dt is the amount of time each timestep covers (delta t)\n", + "\n", + "u = numpy.ones(nx) #as before, we initialize u with every value equal to 1.\n", + "u[int(.5 / dx) : int(1 / dx + 1)] = 2 #then set u = 2 between 0.5 and 1 as per our I.C.s\n", + "\n", + "un = numpy.ones(nx) #initialize our placeholder array un, to hold the time-stepped solution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code snippet below is *unfinished*. We have copied over the line from [Step 1](./01_Step_1.ipynb) that executes the time-stepping update. Can you edit this code to execute the nonlinear convection instead?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for n in range(nt): #iterate through time\n", + " un = u.copy() ##copy the existing values of u into un\n", + " for i in range(1, nx): ##now we'll iterate through the u array\n", + " \n", + " ###This is the line from Step 1, copied exactly. Edit it for our new equation.\n", + " ###then uncomment it and run the cell to evaluate Step 2 \n", + " \n", + " ###u[i] = un[i] - c * dt / dx * (un[i] - un[i-1]) \n", + "\n", + " \n", + "pyplot.plot(numpy.linspace(0, 2, nx), u) ##Plot the results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What do you observe about the evolution of the hat function under the nonlinear convection equation? What happens when you change the numerical parameters and run again?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a careful walk-through of the discretization of the convection equation with finite differences (and all steps from 1 to 4), watch **Video Lesson 4** by Prof. Barba on YouTube." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('y2WaK7_iMRI')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] } - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/lessons/03_CFL_Condition.ipynb b/lessons/03_CFL_Condition.ipynb index 11f9b01b..f3b04994 100644 --- a/lessons/03_CFL_Condition.ipynb +++ b/lessons/03_CFL_Condition.ipynb @@ -1,505 +1,567 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Did you experiment in Steps [1](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/01_Step_1.ipynb) and [2](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/02_Step_2.ipynb) using different parameter choices? If you did, you probably ran into some unexpected behavior. Did your solution ever blow up? (In my experience, CFD students *love* to make things blow up.)\n", - "\n", - "You are probably wondering why changing the discretization parameters affects your solution in such a drastic way. This notebook complements our [interactive CFD lessons](https://bitbucket.org/cfdpython/cfd-python-class/overview) by discussing the CFL condition. And learn more by watching Prof. Barba's YouTube lectures (links below). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Convergence and the CFL Condition\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the first few steps, we've been using the same general initial and boundary conditions. With the parameters we initially suggested, the grid has 41 points and the timestep is 0.25 seconds. Now, we're going to experiment with increasing the size of our grid. The code below is identical to the code we used in [Step 1](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/01_Step_1.ipynb), but here it has been bundled up in a function so that we can easily examine what happens as we adjust just one variable: **the grid size**." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np #numpy is a library for array operations akin to MATLAB\n", - "import matplotlib.pyplot as plt #matplotlib is 2D plotting library\n", - "\n", - "def linearconv(nx):\n", - " dx = 2./(nx-1)\n", - " nt = 20 #nt is the number of timesteps we want to calculate\n", - " dt = .025 #dt is the amount of time each timestep covers (delta t)\n", - " c = 1\n", - "\n", - " u = np.ones(nx) #defining a numpy array which is nx elements long with every value equal to 1.\n", - " u[.5/dx : 1/dx+1]=2 #setting u = 2 between 0.5 and 1 as per our I.C.s\n", - "\n", - " un = np.ones(nx) #initializing our placeholder array, un, to hold the values we calculate for the n+1 timestep\n", - "\n", - " for n in range(nt): #iterate through time\n", - " un = u.copy() ##copy the existing values of u into un\n", - " for i in range(1,nx):\n", - " u[i] = un[i]-c*dt/dx*(un[i]-un[i-1])\n", - " \n", - " plt.plot(np.linspace(0,2,nx),u)\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's examine the results of our linear convection problem with an increasingly fine mesh. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "linearconv(41) #convection using 41 grid points" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the same result as our Step 1 calculation, reproduced here for reference." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "linearconv(61)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the same pattern is present -- the wave is more square than in the previous runs." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "linearconv(85)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This doesn't look anything like our original hat function. " - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "What happened?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To answer that question, we have to think a little bit about what we're actually implementing in code. \n", - "\n", - "In each iteration of our time loop, we use the existing data about our wave to estimate the speed of the wave in the subsequent time step. Initially, the increase in the number of grid points returned more accurate answers. There was less numerical diffusion and the square wave looked much more like a square wave than it did in our first example. \n", - "\n", - "Each iteration of our time loop covers a time-step of length $\\Delta t$, which we have been defining as 0.025\n", - "\n", - "During this iteration, we evaluate the speed of the wave at each of the $x$ points we've created. In the last plot, something has clearly gone wrong. \n", - "\n", - "What has happened is that over the time period $\\Delta t$, the wave is travelling a distance which is greater than `dx`. The length `dx` of each grid box is related to the number of total points `nx`, so stability can be enforced if the $\\Delta t$ step size is calculated with respect to the size of `dx`. \n", - "\n", - "$$\\sigma = \\frac{u \\Delta t}{\\Delta x} \\leq \\sigma_{max}$$\n", - "\n", - "where $u$ is the speed of the wave; $\\sigma$ is called the **Courant number** and the value of $\\sigma_{max}$ that will ensure stability depends on the discretization used. \n", - "\n", - "In a new version of our code, we'll use the CFL number to calculate the appropriate time-step `dt` depending on the size of `dx`. \n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def linearconv(nx):\n", - " dx = 2./(nx-1)\n", - " nt = 20 #nt is the number of timesteps we want to calculate\n", - " c = 1\n", - " sigma = .5\n", - " \n", - " dt = sigma*dx\n", - "\n", - " u = np.ones(nx) \n", - " u[.5/dx : 1/dx+1]=2\n", - "\n", - " un = np.ones(nx)\n", - "\n", - " for n in range(nt): #iterate through time\n", - " un = u.copy() ##copy the existing values of u into un\n", - " for i in range(1,nx):\n", - " u[i] = un[i]-c*dt/dx*(un[i]-un[i-1])\n", - " \n", - " plt.plot(np.linspace(0,2,nx),u)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Did you experiment in Steps [1](./01_Step_1.ipynb) and [2](./02_Step_2.ipynb) using different parameter choices? If you did, you probably ran into some unexpected behavior. Did your solution ever blow up? (In my experience, CFD students *love* to make things blow up.)\n", + "\n", + "You are probably wondering why changing the discretization parameters affects your solution in such a drastic way. This notebook complements our [interactive CFD lessons](https://github.com/barbagroup/CFDPython) by discussing the CFL condition. And learn more by watching Prof. Barba's YouTube lectures (links below). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convergence and the CFL Condition\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the first few steps, we've been using the same general initial and boundary conditions. With the parameters we initially suggested, the grid has 41 points and the timestep is 0.25 seconds. Now, we're going to experiment with increasing the size of our grid. The code below is identical to the code we used in [Step 1](./01_Step_1.ipynb), but here it has been bundled up in a function so that we can easily examine what happens as we adjust just one variable: **the grid size**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy #numpy is a library for array operations akin to MATLAB\n", + "from matplotlib import pyplot #matplotlib is 2D plotting library\n", + "%matplotlib inline\n", + "\n", + "def linearconv(nx):\n", + " dx = 2 / (nx - 1)\n", + " nt = 20 #nt is the number of timesteps we want to calculate\n", + " dt = .025 #dt is the amount of time each timestep covers (delta t)\n", + " c = 1\n", + "\n", + " u = numpy.ones(nx) #defining a numpy array which is nx elements long with every value equal to 1.\n", + " u[int(.5/dx):int(1 / dx + 1)] = 2 #setting u = 2 between 0.5 and 1 as per our I.C.s\n", + "\n", + " un = numpy.ones(nx) #initializing our placeholder array, un, to hold the values we calculate for the n+1 timestep\n", + "\n", + " for n in range(nt): #iterate through time\n", + " un = u.copy() ##copy the existing values of u into un\n", + " for i in range(1, nx):\n", + " u[i] = un[i] - c * dt / dx * (un[i] - un[i-1])\n", + " \n", + " pyplot.plot(numpy.linspace(0, 2, nx), u);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's examine the results of our linear convection problem with an increasingly fine mesh. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "linearconv(41)" - ], - "language": "python", + "data": { + "image/png": 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- } - ], - "prompt_number": 7 - }, + "output_type": "display_data" + } + ], + "source": [ + "linearconv(41) #convection using 41 grid points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the same result as our Step 1 calculation, reproduced here for reference." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "linearconv(61)" - ], - "language": "python", + "data": { + "image/png": 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- } - ], - "prompt_number": 9 - }, + "output_type": "display_data" + } + ], + "source": [ + "linearconv(71)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the same pattern is present -- the wave is more square than in the previous runs." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "linearconv(101)" - ], - "language": "python", + "data": { + "image/png": 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- } - ], - "prompt_number": 10 - }, + "output_type": "display_data" + } + ], + "source": [ + "linearconv(85)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This doesn't look anything like our original hat function. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What happened?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To answer that question, we have to think a little bit about what we're actually implementing in code. \n", + "\n", + "In each iteration of our time loop, we use the existing data about our wave to estimate the speed of the wave in the subsequent time step. Initially, the increase in the number of grid points returned more accurate answers. There was less numerical diffusion and the square wave looked much more like a square wave than it did in our first example. \n", + "\n", + "Each iteration of our time loop covers a time-step of length $\\Delta t$, which we have been defining as 0.025\n", + "\n", + "During this iteration, we evaluate the speed of the wave at each of the $x$ points we've created. In the last plot, something has clearly gone wrong. \n", + "\n", + "What has happened is that over the time period $\\Delta t$, the wave is travelling a distance which is greater than `dx`. The length `dx` of each grid box is related to the number of total points `nx`, so stability can be enforced if the $\\Delta t$ step size is calculated with respect to the size of `dx`. \n", + "\n", + "$$\\sigma = \\frac{u \\Delta t}{\\Delta x} \\leq \\sigma_{\\max}$$\n", + "\n", + "where $u$ is the speed of the wave; $\\sigma$ is called the **Courant number** and the value of $\\sigma_{\\max}$ that will ensure stability depends on the discretization used. \n", + "\n", + "In a new version of our code, we'll use the CFL number to calculate the appropriate time-step `dt` depending on the size of `dx`. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy\n", + "from matplotlib import pyplot\n", + "\n", + "def linearconv(nx):\n", + " dx = 2 / (nx - 1)\n", + " nt = 20 #nt is the number of timesteps we want to calculate\n", + " c = 1\n", + " sigma = .5\n", + " \n", + " dt = sigma * dx\n", + "\n", + " u = numpy.ones(nx) \n", + " u[int(.5/dx):int(1 / dx + 1)] = 2\n", + "\n", + " un = numpy.ones(nx)\n", + "\n", + " for n in range(nt): #iterate through time\n", + " un = u.copy() ##copy the existing values of u into un\n", + " for i in range(1, nx):\n", + " u[i] = un[i] - c * dt / dx * (un[i] - un[i-1])\n", + " \n", + " pyplot.plot(numpy.linspace(0, 2, nx), u)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "linearconv(121)" - ], - "language": "python", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "Notice that as the number of points `nx` increases, the wave convects a shorter and shorter distance. The number of time iterations we have advanced the solution at is held constant at `nt = 20`, but depending on the value of `nx` and the corresponding values of `dx` and `dt`, a shorter time window is being examined overall. 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "It's possible to do rigurous analysis of the stability of numerical schemes, in some cases. Watch Prof. Barba's presentation of this topic in **Video Lecture 9** on You Tube." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "linearconv(101)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('Yw1YPBupZxU')" - ], - "language": "python", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "" - ] - } - ], - "prompt_number": 12 - }, + "output_type": "display_data" + } + ], + "source": [ + "linearconv(121)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that as the number of points `nx` increases, the wave convects a shorter and shorter distance. The number of time iterations we have advanced the solution at is held constant at `nt = 20`, but depending on the value of `nx` and the corresponding values of `dx` and `dt`, a shorter time window is being examined overall. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn More\n", + "-----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's possible to do rigurous analysis of the stability of numerical schemes, in some cases. Watch Prof. Barba's presentation of this topic in **Video Lecture 9** on You Tube." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('Yw1YPBupZxU')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, "metadata": {}, - "outputs": [] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] } - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/lessons/04_Step_3.ipynb b/lessons/04_Step_3.ipynb index 7e266199..1454aa45 100644 --- a/lessons/04_Step_3.ipynb +++ b/lessons/04_Step_3.ipynb @@ -1,326 +1,339 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "======\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You should have completed Steps [1](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/01_Step_1.ipynb) and [2](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/02_Step_2.ipynb) before continuing. This IPython notebook continues the presentation of the **12 steps to Navier-Stokes**, the practical module taught in the interactive CFD class of [Prof. Lorena Barba](http://lorenabarba.com). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 3: Diffusion Equation in 1-D\n", - "-----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The one-dimensional diffusion equation is:\n", - "\n", - "$$\\frac{\\partial u}{\\partial t}= \\nu \\frac{\\partial^2 u}{\\partial x^2}$$\n", - "\n", - "The first thing you should notice is that \u2014unlike the previous two simple equations we have studied\u2014 this equation has a second-order derivative. We first need to learn what to do with it!" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Discretizing $\\frac{\\partial ^2 u}{\\partial x^2}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second-order derivative can be represented geometrically as the line tangent to the curve given by the first derivative. We will discretize the second-order derivative with a Central Difference scheme: a combination of Forward Difference and Backward Difference of the first derivative. Consider the Taylor expansion of $u_{i+1}$ and $u_{i-1}$ around $u_i$:\n", - "\n", - "$u_{i+1} = u_i + \\Delta x \\frac{\\partial u}{\\partial x}\\bigg|_i + \\frac{\\Delta x^2}{2} \\frac{\\partial ^2 u}{\\partial x^2}\\bigg|_i + \\frac{\\Delta x^3}{3} \\frac{\\partial ^3 u}{\\partial x^3}\\bigg|_i + O(\\Delta x^4)$\n", - "\n", - "$u_{i-1} = u_i - \\Delta x \\frac{\\partial u}{\\partial x}\\bigg|_i + \\frac{\\Delta x^2}{2} \\frac{\\partial ^2 u}{\\partial x^2}\\bigg|_i - \\frac{\\Delta x^3}{3} \\frac{\\partial ^3 u}{\\partial x^3}\\bigg|_i + O(\\Delta x^4)$\n", - "\n", - "If we add these two expansions, you can see that the odd-numbered derivative terms will cancel each other out. If we neglect any terms of $O(\\Delta x^4)$ or higher (and really, those are very small), then we can rearrange the sum of these two expansions to solve for our second-derivative. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$u_{i+1} + u_{i-1} = 2u_i+\\Delta x^2 \\frac{\\partial ^2 u}{\\partial x^2}\\bigg|_i + O(\\Delta x^4)$\n", - "\n", - "Then rearrange to solve for $\\frac{\\partial ^2 u}{\\partial x^2}\\bigg|_i$ and the result is:\n", - "\n", - "$$\\frac{\\partial ^2 u}{\\partial x^2}=\\frac{u_{i+1}-2u_{i}+u_{i-1}}{\\Delta x^2} + O(\\Delta x^2)$$\n" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Back to Step 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now write the discretized version of the diffusion equation in 1D:\n", - "\n", - "$$\\frac{u_{i}^{n+1}-u_{i}^{n}}{\\Delta t}=\\nu\\frac{u_{i+1}^{n}-2u_{i}^{n}+u_{i-1}^{n}}{\\Delta x^2}$$\n", - "\n", - "As before, we notice that once we have an initial condition, the only unknown is $u_{i}^{n+1}$, so we re-arrange the equation solving for our unknown:\n", - "\n", - "$$u_{i}^{n+1}=u_{i}^{n}+\\frac{\\nu\\Delta t}{\\Delta x^2}(u_{i+1}^{n}-2u_{i}^{n}+u_{i-1}^{n})$$\n", - "\n", - "The above discrete equation allows us to write a program to advance a solution in time. But we need an initial condition. Let's continue using our favorite: the hat function. So, at $t=0$, $u=2$ in the interval $0.5\\le x\\le 1$ and $u=1$ everywhere else. We are ready to number-crunch!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np #loading our favorite library\n", - "import matplotlib.pyplot as plt #and the useful plotting library\n", - "\n", - "\n", - "nx = 41\n", - "dx = 2./(nx-1)\n", - "nt = 20 #the number of timesteps we want to calculate\n", - "nu = 0.3 #the value of viscosity\n", - "sigma = .2 #sigma is a parameter, we'll learn more about it later\n", - "dt = sigma*dx**2/nu #dt is defined using sigma ... more later!\n", - "\n", - "\n", - "u = np.ones(nx) #a numpy array with nx elements all equal to 1.\n", - "u[.5/dx : 1/dx+1]=2 #setting u = 2 between 0.5 and 1 as per our I.C.s\n", - "\n", - "un = np.ones(nx) #our placeholder array, un, to advance the solution in time\n", - "\n", - "for n in range(nt): #iterate through time\n", - " un = u.copy() ##copy the existing values of u into un\n", - " for i in range(1,nx-1):\n", - " u[i] = un[i] + nu*dt/dx**2*(un[i+1]-2*un[i]+un[i-1])\n", - " \n", - "plt.plot(np.linspace(0,2,nx), u)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "[]" - ] - }, - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Learn More" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a careful walk-through of the discretization of the diffusion equation with finite differences (and all steps from 1 to 4), watch **Video Lesson 4** by Prof. Barba on YouTube." - ] - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "======\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should have completed Steps [1](./01_Step_1.ipynb) and [2](./02_Step_2.ipynb) before continuing. This Jupyter notebook continues the presentation of the **12 steps to Navier–Stokes**, the practical module taught in the interactive CFD class of [Prof. Lorena Barba](http://lorenabarba.com). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 3: Diffusion Equation in 1-D\n", + "-----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The one-dimensional diffusion equation is:\n", + "\n", + "$$\\frac{\\partial u}{\\partial t}= \\nu \\frac{\\partial^2 u}{\\partial x^2}$$\n", + "\n", + "The first thing you should notice is that —unlike the previous two simple equations we have studied— this equation has a second-order derivative. We first need to learn what to do with it!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discretizing $\\frac{\\partial ^2 u}{\\partial x^2}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second-order derivative can be represented geometrically as the line tangent to the curve given by the first derivative. We will discretize the second-order derivative with a Central Difference scheme: a combination of Forward Difference and Backward Difference of the first derivative. Consider the Taylor expansion of $u_{i+1}$ and $u_{i-1}$ around $u_i$:\n", + "\n", + "$u_{i+1} = u_i + \\Delta x \\frac{\\partial u}{\\partial x}\\bigg|_i + \\frac{\\Delta x^2}{2} \\frac{\\partial ^2 u}{\\partial x^2}\\bigg|_i + \\frac{\\Delta x^3}{3!} \\frac{\\partial ^3 u}{\\partial x^3}\\bigg|_i + O(\\Delta x^4)$\n", + "\n", + "$u_{i-1} = u_i - \\Delta x \\frac{\\partial u}{\\partial x}\\bigg|_i + \\frac{\\Delta x^2}{2} \\frac{\\partial ^2 u}{\\partial x^2}\\bigg|_i - \\frac{\\Delta x^3}{3!} \\frac{\\partial ^3 u}{\\partial x^3}\\bigg|_i + O(\\Delta x^4)$\n", + "\n", + "If we add these two expansions, you can see that the odd-numbered derivative terms will cancel each other out. If we neglect any terms of $O(\\Delta x^4)$ or higher (and really, those are very small), then we can rearrange the sum of these two expansions to solve for our second-derivative. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$u_{i+1} + u_{i-1} = 2u_i+\\Delta x^2 \\frac{\\partial ^2 u}{\\partial x^2}\\bigg|_i + O(\\Delta x^4)$\n", + "\n", + "Then rearrange to solve for $\\frac{\\partial ^2 u}{\\partial x^2}\\bigg|_i$ and the result is:\n", + "\n", + "$$\\frac{\\partial ^2 u}{\\partial x^2}=\\frac{u_{i+1}-2u_{i}+u_{i-1}}{\\Delta x^2} + O(\\Delta x^2)$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Back to Step 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now write the discretized version of the diffusion equation in 1D:\n", + "\n", + "$$\\frac{u_{i}^{n+1}-u_{i}^{n}}{\\Delta t}=\\nu\\frac{u_{i+1}^{n}-2u_{i}^{n}+u_{i-1}^{n}}{\\Delta x^2}$$\n", + "\n", + "As before, we notice that once we have an initial condition, the only unknown is $u_{i}^{n+1}$, so we re-arrange the equation solving for our unknown:\n", + "\n", + "$$u_{i}^{n+1}=u_{i}^{n}+\\frac{\\nu\\Delta t}{\\Delta x^2}(u_{i+1}^{n}-2u_{i}^{n}+u_{i-1}^{n})$$\n", + "\n", + "The above discrete equation allows us to write a program to advance a solution in time. But we need an initial condition. Let's continue using our favorite: the hat function. So, at $t=0$, $u=2$ in the interval $0.5\\le x\\le 1$ and $u=1$ everywhere else. We are ready to number-crunch!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('y2WaK7_iMRI')" - ], - "language": "python", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy #loading our favorite library\n", + "from matplotlib import pyplot #and the useful plotting library\n", + "%matplotlib inline\n", + "\n", + "nx = 41\n", + "dx = 2 / (nx - 1)\n", + "nt = 20 #the number of timesteps we want to calculate\n", + "nu = 0.3 #the value of viscosity\n", + "sigma = .2 #sigma is a parameter, we'll learn more about it later\n", + "dt = sigma * dx**2 / nu #dt is defined using sigma ... more later!\n", + "\n", + "\n", + "u = numpy.ones(nx) #a numpy array with nx elements all equal to 1.\n", + "u[int(.5 / dx):int(1 / dx + 1)] = 2 #setting u = 2 between 0.5 and 1 as per our I.C.s\n", + "\n", + "un = numpy.ones(nx) #our placeholder array, un, to advance the solution in time\n", + "\n", + "for n in range(nt): #iterate through time\n", + " un = u.copy() ##copy the existing values of u into un\n", + " for i in range(1, nx - 1):\n", + " u[i] = un[i] + nu * dt / dx**2 * (un[i+1] - 2 * un[i] + un[i-1])\n", + " \n", + "pyplot.plot(numpy.linspace(0, 2, nx), u);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a careful walk-through of the discretization of the diffusion equation with finite differences (and all steps from 1 to 4), watch **Video Lesson 4** by Prof. Barba on YouTube." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('y2WaK7_iMRI')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] } - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/lessons/05_Step_4.ipynb b/lessons/05_Step_4.ipynb index 492da4d4..ec8e076f 100644 --- a/lessons/05_Step_4.ipynb +++ b/lessons/05_Step_4.ipynb @@ -1,611 +1,591 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We continue our journey to solve the Navier-Stokes equation with Step 4. But don't continue unless you have completed the previous steps! In fact, this next step will be a combination of the two previous ones. The wonders of *code reuse*!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 4: Burgers' Equation\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can read about Burgers' Equation on its [wikipedia page](http://en.wikipedia.org/wiki/Burgers'_equation).\n", - "\n", - "Burgers' equation in one spatial dimension looks like this:\n", - "\n", - "$$\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} = \\nu \\frac{\\partial ^2u}{\\partial x^2}$$\n", - "\n", - "As you can see, it is a combination of non-linear convection and diffusion. It is surprising how much you learn from this neat little equation! \n", - "\n", - "We can discretize it using the methods we've already detailed in Steps [1](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/01_Step_1.ipynb) to [3](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/04_Step_3.ipynb). Using forward difference for time, backward difference for space and our 2nd-order method for the second derivatives yields:\n", - "\n", - "$$\\frac{u_i^{n+1}-u_i^n}{\\Delta t} + u_i^n \\frac{u_i^n - u_{i-1}^n}{\\Delta x} = \\nu \\frac{u_{i+1}^n - 2u_i^n + u_{i-1}^n}{\\Delta x^2}$$\n", - "\n", - "As before, once we have an initial condition, the only unknown is $u_i^{n+1}$. We will step in time as follows:\n", - "\n", - "$$u_i^{n+1} = u_i^n - u_i^n \\frac{\\Delta t}{\\Delta x} (u_i^n - u_{i-1}^n) + \\nu \\frac{\\Delta t}{\\Delta x^2}(u_{i+1}^n - 2u_i^n + u_{i-1}^n)$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Initial and Boundary Conditions\n", - "\n", - "To examine some interesting properties of Burgers' equation, it is helpful to use different initial and boundary conditions than we've been using for previous steps. \n", - "\n", - "Our initial condition for this problem is going to be:\n", - "\n", - "\\begin{eqnarray}\n", - "u &=& -\\frac{2 \\nu}{\\phi} \\frac{\\partial \\phi}{\\partial x} + 4 \\\\\\\n", - "\\phi &=& \\exp \\bigg(\\frac{-x^2}{4 \\nu} \\bigg) + \\exp \\bigg(\\frac{-(x-2 \\pi)^2}{4 \\nu} \\bigg)\n", - "\\end{eqnarray}\n", - "\n", - "This has an analytical solution, given by:\n", - "\n", - "\\begin{eqnarray}\n", - "u &=& -\\frac{2 \\nu}{\\phi} \\frac{\\partial \\phi}{\\partial x} + 4 \\\\\\\n", - "\\phi &=& \\exp \\bigg(\\frac{-(x-4t)^2}{4 \\nu (t+1)} \\bigg) + \\exp \\bigg(\\frac{-(x-4t -2 \\pi)^2}{4 \\nu(t+1)} \\bigg)\n", - "\\end{eqnarray}\n", - "\n", - "Our boundary condition will be:\n", - "\n", - "$$u(0) = u(2\\pi)$$\n", - "\n", - "This is called a *periodic* boundary condition. Pay attention! This will cause you a bit of headache if you don't tread carefully." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Saving Time with SymPy\n", - "\n", - "\n", - "The initial condition we're using for Burgers' Equation can be a bit of a pain to evaluate by hand. The derivative $\\frac{\\partial \\phi}{\\partial x}$ isn't too terribly difficult, but it would be easy to drop a sign or forget a factor of $x$ somewhere, so we're going to use SymPy to help us out. \n", - "\n", - "[SymPy](http://sympy.org/en/) is the symbolic math library for Python. It has a lot of the same symbolic math functionality as Mathematica with the added benefit that we can easily translate its results back into our Python calculations (it is also free and open source). \n", - "\n", - "Start by loading the SymPy library, together with our favorite library, NumPy." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import sympy" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We're also going to tell SymPy that we want all of its output to be rendered using $\\LaTeX$. This will make our Notebook beautiful!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sympy import init_printing\n", - "init_printing(use_latex=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Start by setting up symbolic variables for the three variables in our initial condition and then type out the full equation for $\\phi$. We should get a nicely rendered version of our $\\phi$ equation." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x, nu, t = sympy.symbols('x nu t')\n", - "phi = sympy.exp(-(x-4*t)**2/(4*nu*(t+1))) + sympy.exp(-(x-4*t-2*np.pi)**2/(4*nu*(t+1)))\n", - "phi" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "latex": [ - "$$e^{- \\frac{\\left(- 4 t + x - 6.28318530717959\\right)^{2}}{4 \\nu \\left(t + 1\\right)}} + e^{- \\frac{\\left(- 4 t + x\\right)^{2}}{4 \\nu \\left(t + 1\\right)}}$$" - ], - "metadata": {}, - "output_type": "pyout", - "png": "iVBORw0KGgoAAAANSUhEUgAAAUcAAAAfBAMAAACGzRJ2AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nCuzVkxBwE78wAAAAAElFTkSuQmCC\n", - "prompt_number": 9, - "text": [ - " 2 2 \n", - " -(-4\u22c5t + x - 6.28318530717959) -(-4\u22c5t + x) \n", - " \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 4\u22c5\u03bd\u22c5(t + 1) 4\u22c5\u03bd\u22c5(t + 1) \n", - "\u212f + \u212f " - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's maybe a little small, but that looks right. Now to evaluate our partial derivative $\\frac{\\partial \\phi}{\\partial x}$ is a trivial task. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "phiprime = phi.diff(x)\n", - "phiprime" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "latex": [ - "$$- \\frac{e^{- \\frac{\\left(- 4 t + x\\right)^{2}}{4 \\nu \\left(t + 1\\right)}}}{4 \\nu \\left(t + 1\\right)} \\left(- 8 t + 2 x\\right) - \\frac{1}{4 \\nu \\left(t + 1\\right)} \\left(- 8 t + 2 x - 12.5663706143592\\right) e^{- \\frac{\\left(- 4 t + x - 6.28318530717959\\right)^{2}}{4 \\nu \\left(t + 1\\right)}}$$" - ], - "metadata": {}, - "output_type": "pyout", - "png": "iVBORw0KGgoAAAANSUhEUgAAAzUAAAA/BAMAAAArsfskAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMol2mSJE71Sr\nZruYlGYbAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAM8UlEQVR4Ae1cC4xcVRn+7rwfO7M3bUoJqeyw\nYARFOoUgjRI6kVQSI3Z47FaR0qna3YaUdIWSrVDoVQNFAs1GFKKG7BYlKaI4CgGJmk4Fi8GFDiiP\nFNcdgkZQhN0W5VHo+v/nMfcx++js7MzsmvmTzjnn//9z/v+e795zz/nupkC1Euh6tdouLf8GzUAS\ndzQoUitMtTPgQ4dZbZ+Wf6Nm4MFGBWrFmXQGHtLal2UlK4uQBRinalurbMYMRIsqamI3IgWq96k2\n1Ze1ljQ1Gc0p2nTY765AIkcNjc3dMLYt0cZW2YwZ2KmCGsUVuOQGahA2kY1XFZCEf+JwMzJqxdQz\ncKeqhLACfmD78LnD+Xi0lIVPe7TKZs3AS8DS/v7+/OquezOLOAle00I5tLDhyWiu3F0OvxuPJKhB\n2ASTA3la01rS5BlYpuMvedu6bRM1CJu257szKG8StEOrbPQMVEDAaxrLo7Jo/TZvBiI5T2xTtTVG\nHnOr2cAZ+P7ksUIao8nNLe0czcA0ZH/7xPQyRxm0hplqBiYj++NvkCgKbap+ddKfVKdxF+SwM5L9\nCiNJewb5GveByc66yK7xugy7YAednuwnmpOFaM/bqAjTv+/tAQpU1kFO3N7CxjmtM5D9RHOy0Hb6\neSoYG6wE7FMpK+ZOQgsTm3hGTYFaZLLOGYl3F6m5+gor0rMVF26m06GJm3sz8fwPuWWsuwgHJj5g\nJRVG99fw62ieCz5PTk/2M8251/RhJ4LnUAiNTb2IgQWKjb5Vvd9SBESPRFJUlqLpMN3hr8VT0css\nOsQXEhMFbiXNE7Htlj5WUuGzdmH0fXBBT4OX7De6ey0xpPxhmjOGz4NozzTCw48N/1E8N/UiOxco\nNgU1Y95vKUJNrwCSlDGe3HcTzGAWfguvH59N0EGEWu2ImZmgyUoqOtCOZwAu6C3iJfvvsCLOJ5Jp\nzs/hVLwEI0UR9HPTwoYmoyyRPlmt+JbCauPIcxku42ljVQmBK8AwLD6KxMhWbt0Kf4ZfEaSkgkCx\nRl7IcIES93JK5H4szTgV2I2endw73tda01wTYzeiOVmv+JbCauMtDHG5yEqMPECbqhTDMHKwBJzP\nLYFNSmKTQltm0EIgLQpSuqXtZz30TDmEaM7f5+mthMh+Sz43n3k7Xzeyc2GuaYn85N9SDPrAcj3G\nwKuasY/mcLGVwzuEjVEwVvpo3aIWr2nGkMCGi5HPIkU9qKDSI+0Fj0I1Fe0ZVs16kZ0LE5vy31XQ\nIuP6liJm63EjjQyWfiObRNgYCgzxc1NCXxKf5hbvBehZYiUVIfNhlAIpLsjHI+3Ol43DpmhPQ6nU\nAutwmJvqwsTGKKir935LEeqlvZlIGqsm0tFrv4iHN2cCW67HXV0Dge5t3DJu+AKifWAlFSHab6/u\ntbioPEPGCJuciuQqXLRn3cjOhYlNxTTOxa0bH3DNPzVCKSy1vMrGtRcoNjd7Zsj0tGfTDFd26r2q\nWKmcVnPhtFaX8c+uVmUj/OThpyq1UhOoNq+pBqpRn8hUDhCtuMcrfarVbKi2wyT+kVJZafSsnSTJ\nSP8bGeXSpivlLsde2WW7Th5n541/s12ctZPoL7dUDr4Nga/T3zx0X0r2ro0Z3NT1L9rLPtHVlTWu\n7c1oi8/k7hdAmkUfMluACPE4G4Vovn76Tym1Wcd0sNmU8kJET3oMiTZYbLmHWYbgu1qT0pXqS5ph\nLZPGMQpYP8mdATC3rXOITUzkYJyGq+m8MRBO0aFxbxGjNHsFX95YqSyRNwU250GaRZ8TipE9kCFm\nde5OiiGhiLd60/l6ojbqCvBb4JvAXqUwLFnZB/xE+zicteoYy1DWdnTHOV4aiCyMDdk+5ZrgtnUO\nvss30V9wZfEG8GEkS6EjaE9jG1O5B4GLpcX4xwGeyNADkGbR50ngaeYjKYQzk3KUmSoHJTZEANxG\nrnRz1ZPOLyfDByslxEcsBt+TQuI5Wf4IOJCRVZygyuqLmGX3ccehowBLLI22t2TV/cv7C52DOKaN\nWuSQeId+ovehIwVC/VK8TjeVKSzAFp7I289QZtHnELDelCH4iFitRM+X2BQaQ+er9KJpO8/Bl7EO\nOFspNDbLif3OKJ0vb3tXV7vD4e6Oo7DxjU+Djc5BzPM/eajkkByQ1jTCKIf3gcG8sChssmewA5m5\nj3GUtAMqxMfYUJ18yG/Gx8FraEPofJVcsM/Osm3iuWL06aP7pUZjQ61VptHzhzu76V3r8Lb7HUtt\nrcPJHUdhQ3b/OI7b/O2NOYcrVdW+fJVJ9baR3gwOre4dQPsp3bQrAD7CP0kYbxM2OWGR2CQsgQ2Z\nRZ976LlhFCkERCoevp5HmUZyfhNDgRLdAw2h81UmSc5Yy45DJuJjqmVjE/0PluBPuTNpJUlp12rL\nfzs7uOLY2AxmjWziE35PDIkN5UCSNCPvGoeKuB8dH0csj8AlfaxehwiZ11wkLBKb40DYCDP3wXpg\neY48B7PAs9zFw9ezamoJdF12aaDgs4h4M1Lkxu8b/kQ5q20F9z028edtv8TW5fchnFIKGxt/CRfR\ncv0rJspt7+pqn3K4u+PY2JyHoBkcj1sOV6pKbCgHKa8YEyauMzvGEd5NmssytGKNKWyERWKTZWyk\nGXgFvlxcPDfnkY4/4lfy9ew9tcTM0Kat/E2mejrfOPkskjMzqG4zTrnEBmg7yp3POgV/QeI9059V\nGdrY3EhfkeSlBsbKzlWEGqMR76N/k8cpYxMuwYCvRI62K1UlNpSDlB3WYWB0gPZnCd7axwigUAoG\nPTeDOWER2NBmV2DDZmCHhee/uj5PN3yJWmvoXwVfT7ppxFi/re1LdBMUGkPnq0wYGy10HWvyHUIR\n7Ow8+Z7Ozj42hQr0g//yD2EzS2FstDjiXNzZ+cnOTnorkGzmn/Yc/zpFYBMqCNXv6LUxQO+O0WIs\nhcQHEQvJI/QSKQH0vtmbFxaBzRIQNtIs+lDvA6YKwdi0F+ineinILmJNo+qj1Y9QTQ/HmkZLA9qK\ng3TzCik/N4/wRQbeYr1zV1dNFMCxpnni6Ocm3odb6ObI0DPqEoGNyAGgyd9h0m5sdID2aYl328eR\n/IA+ARcAWqxGpUVg85vh4ff2S7PoQyOeTkuyCMFbxqn4elfkyoYi3tQMoa/SYy41yaI92h562VoX\nQ/4pGzQ2AaK+rV+ExrBhrvYC7jgamwuATXFzC30DsTPiGmMjcqA6TcaZdI7BdaAtbXgoVoKfzjlr\nSH0lH8yERb5vgAcgzaLPQStAjhxC7gVi2Sn4endsb8tNvHkz9XrX2g4W7BG+Y9IfSm03clKjsflW\n19rHokeSY5Eirdd0obOTyx3d3HEUNpGzu7anB/Nv4naHJ1cZG86Bj5k7kdiDZNa4H/g7Tsgnitib\no8WMsvLnjR8rizx7gj7pC7Po82VzUREiBNDPgzaXr+frAi6Uhfc3kVGaaNo2BZ4grnNXj1JobJZP\nTBw29m/YOEJ634Dt7anNEIgXEi3uOAobH21j0st6jltX1G6yFNw25wA/vYmvfcKkA8r2DB21+h8H\n7up/jbxuzdNrunszKYVl+8/PpTGumHhKmkWfIDuKEMBpPHD1fD33qlloAShLpFSu2hWmN+nCpKzU\nFW+psXHqb3U2xBqhFTMF8lva01vqNc2rr1c7MlSvkY9h3PJRgHwZhsWWu5PgaX1a94KueEvazFdI\nt1tDL2AtMwVKOG4Y3UeWxB82VIK5hoZzB7uyZLeZONbE8vFSLXnaMhubtGzvmWpGyuVBVG9ZZgz0\natm1yZVFTYwfebZkR99D1atVs7x4bKGHyRhS2kkXI2XzFh4cieoty4yBdpVdm1xp5k0SVEdrngLx\npj9bTYYLG5TZ2NuPfa5edLtKqlfoZg7E27z5IAmriVm8SNh8517gr5RDsA82sezGZm3NKTLVG/1o\nFolSnQPVnOl8GcDIETYGMRkED5J0s5aJZTc2go2tKWlB9XbkEcvWOVBNWc6nzkGmC+nUHKdzMJ3I\n6LyYUum5sXmw5qQF1UtH8b1WnQPVnOl8GeAhxiachq+PMmIms0wsu7FZU2vCcUH1XiM/b9UzUK2J\nzpv+Bv1PQiX4+/AVU2EjiWUn4cv7NMGU15S1oHpxDkIr6hyopiznU+f48PCBn+Y68tHdnBWvaWVi\n2f3cOEmUWV2AoHrpxfYDRrqegWaV3TztFCsRIftLi7PjvUCZWHZjU/tegKne+Hj81AYE4hD/F9Je\nwjVLsuJSmGUuE8tubJiNrVWOIryiSwxS50C1Jjpf+gfffL94+jMym2ia/i5SE8saG8nTCja2tpyJ\n6vU9xSuaPOTWL1Btac7X3k6WWWMjcp1rNrZhgebrTFefl5NldhG+c83GNixQ9XMwX3t42Ek7zblm\nYxsWyL6EhV6bkmWeaza2YYEWOiKO/KdgmeeejW1YIMfFLYTq/wARbVyAPegnlwAAAABJRU5ErkJg\ngg==\n", - "prompt_number": 10, - "text": [ - " 2 \n", - " -(-4\u22c5t + x) -(-4\u22c5t + x - \n", - " \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 4\u22c5\u03bd\u22c5(t + 1) 4\u22c5\u03bd\n", - " (-8\u22c5t + 2\u22c5x)\u22c5\u212f (-8\u22c5t + 2\u22c5x - 12.5663706143592)\u22c5\u212f \n", - "- \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " 4\u22c5\u03bd\u22c5(t + 1) 4\u22c5\u03bd\u22c5(t + 1) \n", - "\n", - " 2 \n", - "6.28318530717959) \n", - "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - "\u22c5(t + 1) \n", - " \n", - "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", - " " - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to see the unrendered version, just use the Python print command." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print phiprime" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "-(-8*t + 2*x)*exp(-(-4*t + x)**2/(4*nu*(t + 1)))/(4*nu*(t + 1)) - (-8*t + 2*x - 12.5663706143592)*exp(-(-4*t + x - 6.28318530717959)**2/(4*nu*(t + 1)))/(4*nu*(t + 1))\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Now what?\n", - "\n", - "\n", - "Now that we have the Pythonic version of our derivative, we can finish writing out the full initial condition equation and then translate it into a usable Python expression. For this, we'll use the *lambdify* function, which takes a SymPy symbolic equation and turns it into a callable function. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from sympy.utilities.lambdify import lambdify\n", - "\n", - "u = -2*nu*(phiprime/phi)+4\n", - "print u" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "-2*nu*(-(-8*t + 2*x)*exp(-(-4*t + x)**2/(4*nu*(t + 1)))/(4*nu*(t + 1)) - (-8*t + 2*x - 12.5663706143592)*exp(-(-4*t + x - 6.28318530717959)**2/(4*nu*(t + 1)))/(4*nu*(t + 1)))/(exp(-(-4*t + x - 6.28318530717959)**2/(4*nu*(t + 1))) + exp(-(-4*t + x)**2/(4*nu*(t + 1)))) + 4\n" - ] - } - ], - "prompt_number": 12 - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We continue our journey to solve the Navier–Stokes equation with Step 4. But don't continue unless you have completed the previous steps! In fact, this next step will be a combination of the two previous ones. The wonders of *code reuse*!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 4: Burgers' Equation\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can read about Burgers' Equation on its [wikipedia page](http://en.wikipedia.org/wiki/Burgers'_equation).\n", + "\n", + "Burgers' equation in one spatial dimension looks like this:\n", + "\n", + "$$\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} = \\nu \\frac{\\partial ^2u}{\\partial x^2}$$\n", + "\n", + "As you can see, it is a combination of non-linear convection and diffusion. It is surprising how much you learn from this neat little equation! \n", + "\n", + "We can discretize it using the methods we've already detailed in Steps [1](./01_Step_1.ipynb) to [3](./04_Step_3.ipynb). Using forward difference for time, backward difference for space and our 2nd-order method for the second derivatives yields:\n", + "\n", + "$$\\frac{u_i^{n+1}-u_i^n}{\\Delta t} + u_i^n \\frac{u_i^n - u_{i-1}^n}{\\Delta x} = \\nu \\frac{u_{i+1}^n - 2u_i^n + u_{i-1}^n}{\\Delta x^2}$$\n", + "\n", + "As before, once we have an initial condition, the only unknown is $u_i^{n+1}$. We will step in time as follows:\n", + "\n", + "$$u_i^{n+1} = u_i^n - u_i^n \\frac{\\Delta t}{\\Delta x} (u_i^n - u_{i-1}^n) + \\nu \\frac{\\Delta t}{\\Delta x^2}(u_{i+1}^n - 2u_i^n + u_{i-1}^n)$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initial and Boundary Conditions\n", + "\n", + "To examine some interesting properties of Burgers' equation, it is helpful to use different initial and boundary conditions than we've been using for previous steps. \n", + "\n", + "Our initial condition for this problem is going to be:\n", + "\n", + "\\begin{eqnarray}\n", + "u &=& -\\frac{2 \\nu}{\\phi} \\frac{\\partial \\phi}{\\partial x} + 4 \\\\\\\n", + "\\phi &=& \\exp \\bigg(\\frac{-x^2}{4 \\nu} \\bigg) + \\exp \\bigg(\\frac{-(x-2 \\pi)^2}{4 \\nu} \\bigg)\n", + "\\end{eqnarray}\n", + "\n", + "This has an analytical solution, given by:\n", + "\n", + "\\begin{eqnarray}\n", + "u &=& -\\frac{2 \\nu}{\\phi} \\frac{\\partial \\phi}{\\partial x} + 4 \\\\\\\n", + "\\phi &=& \\exp \\bigg(\\frac{-(x-4t)^2}{4 \\nu (t+1)} \\bigg) + \\exp \\bigg(\\frac{-(x-4t -2 \\pi)^2}{4 \\nu(t+1)} \\bigg)\n", + "\\end{eqnarray}\n", + "\n", + "Our boundary condition will be:\n", + "\n", + "$$u(0) = u(2\\pi)$$\n", + "\n", + "This is called a *periodic* boundary condition. Pay attention! This will cause you a bit of headache if you don't tread carefully." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving Time with SymPy\n", + "\n", + "\n", + "The initial condition we're using for Burgers' Equation can be a bit of a pain to evaluate by hand. The derivative $\\frac{\\partial \\phi}{\\partial x}$ isn't too terribly difficult, but it would be easy to drop a sign or forget a factor of $x$ somewhere, so we're going to use SymPy to help us out. \n", + "\n", + "[SymPy](http://sympy.org/en/) is the symbolic math library for Python. It has a lot of the same symbolic math functionality as Mathematica with the added benefit that we can easily translate its results back into our Python calculations (it is also free and open source). \n", + "\n", + "Start by loading the SymPy library, together with our favorite library, NumPy." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "import sympy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're also going to tell SymPy that we want all of its output to be rendered using $\\LaTeX$. This will make our Notebook beautiful!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sympy import init_printing\n", + "init_printing(use_latex=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start by setting up symbolic variables for the three variables in our initial condition and then type out the full equation for $\\phi$. We should get a nicely rendered version of our $\\phi$ equation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOgAAAAeBAMAAADUTE9PAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nv3I4tlFTpsYF/+MGM47H2fwF7Ab2ldJPuIkAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$e^{- \\frac{\\left(- 4 t + x - 2 \\pi\\right)^{2}}{4 \\nu \\left(t + 1\\right)}} + e^{- \\frac{\\left(- 4 t + x\\right)^{2}}{4 \\nu \\left(t + 1\\right)}}$$" + ], + "text/plain": [ + " 2 2 \n", + " -(-4⋅t + x - 2⋅π) -(-4⋅t + x) \n", + " ─────────────────── ─────────────\n", + " 4⋅ν⋅(t + 1) 4⋅ν⋅(t + 1) \n", + "ℯ + ℯ " + ] + }, + "execution_count": 3, "metadata": {}, - "source": [ - "###Lambdify\n", - "\n", - "To lambdify this expression into a useable function, we tell lambdify which variables to request and the function we want to plug them in to." - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "x, nu, t = sympy.symbols('x nu t')\n", + "phi = (sympy.exp(-(x - 4 * t)**2 / (4 * nu * (t + 1))) +\n", + " sympy.exp(-(x - 4 * t - 2 * sympy.pi)**2 / (4 * nu * (t + 1))))\n", + "phi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's maybe a little small, but that looks right. Now to evaluate our partial derivative $\\frac{\\partial \\phi}{\\partial x}$ is a trivial task. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "ufunc = lambdify((t, x, nu), u)\n", - "print ufunc(1,4,3)" - ], - "language": "python", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAA+BAMAAAD9moN2AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nshHEQRgjoRoNV/o/+aPcNTacEvIAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$- \\frac{e^{- \\frac{\\left(- 4 t + x\\right)^{2}}{4 \\nu \\left(t + 1\\right)}}}{4 \\nu \\left(t + 1\\right)} \\left(- 8 t + 2 x\\right) - \\frac{1}{4 \\nu \\left(t + 1\\right)} \\left(- 8 t + 2 x - 4 \\pi\\right) e^{- \\frac{\\left(- 4 t + x - 2 \\pi\\right)^{2}}{4 \\nu \\left(t + 1\\right)}}$$" + ], + "text/plain": [ + " 2 2 \n", + " -(-4⋅t + x) -(-4⋅t + x - 2⋅π) \n", + " ───────────── ───────────────────\n", + " 4⋅ν⋅(t + 1) 4⋅ν⋅(t + 1) \n", + " (-8⋅t + 2⋅x)⋅ℯ (-8⋅t + 2⋅x - 4⋅π)⋅ℯ \n", + "- ─────────────────────────── - ───────────────────────────────────────\n", + " 4⋅ν⋅(t + 1) 4⋅ν⋅(t + 1) " + ] + }, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "3.49170664206\n" - ] - } - ], - "prompt_number": 14 - }, + "output_type": "execute_result" + } + ], + "source": [ + "phiprime = phi.diff(x)\n", + "phiprime" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to see the unrendered version, just use the Python print command." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Back to Burgers' Equation\n", - "\n", - "Now that we have the initial conditions set up, we can proceed and finish setting up the problem. We can generate the plot of the initial condition using our lambdify-ed function." + "name": "stdout", + "output_type": "stream", + "text": [ + "-(-8*t + 2*x)*exp(-(-4*t + x)**2/(4*nu*(t + 1)))/(4*nu*(t + 1)) - (-8*t + 2*x - 4*pi)*exp(-(-4*t + x - 2*pi)**2/(4*nu*(t + 1)))/(4*nu*(t + 1))\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import matplotlib.pyplot as plt\n", - "\n", - "###variable declarations\n", - "nx = 101\n", - "nt = 100\n", - "dx = 2*np.pi/(nx-1)\n", - "nu = .07\n", - "dt = dx*nu\n", - "\n", - "x = np.linspace(0, 2*np.pi, nx)\n", - "#u = np.empty(nx)\n", - "un = np.empty(nx)\n", - "t = 0\n", - "\n", - "u = np.asarray([ufunc(t, x0, nu) for x0 in x])\n", - "u" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "array([ 4. , 4.06283185, 4.12566371, 4.18849556, 4.25132741,\n", - " 4.31415927, 4.37699112, 4.43982297, 4.50265482, 4.56548668,\n", - " 4.62831853, 4.69115038, 4.75398224, 4.81681409, 4.87964594,\n", - " 4.9424778 , 5.00530965, 5.0681415 , 5.13097336, 5.19380521,\n", - " 5.25663706, 5.31946891, 5.38230077, 5.44513262, 5.50796447,\n", - " 5.57079633, 5.63362818, 5.69646003, 5.75929189, 5.82212374,\n", - " 5.88495559, 5.94778745, 6.0106193 , 6.07345115, 6.136283 ,\n", - " 6.19911486, 6.26194671, 6.32477856, 6.38761042, 6.45044227,\n", - " 6.51327412, 6.57610598, 6.63893783, 6.70176967, 6.76460125,\n", - " 6.82742866, 6.89018589, 6.95176632, 6.99367964, 6.72527549,\n", - " 4. , 1.27472451, 1.00632036, 1.04823368, 1.10981411,\n", - " 1.17257134, 1.23539875, 1.29823033, 1.36106217, 1.42389402,\n", - " 1.48672588, 1.54955773, 1.61238958, 1.67522144, 1.73805329,\n", - " 1.80088514, 1.863717 , 1.92654885, 1.9893807 , 2.05221255,\n", - " 2.11504441, 2.17787626, 2.24070811, 2.30353997, 2.36637182,\n", - " 2.42920367, 2.49203553, 2.55486738, 2.61769923, 2.68053109,\n", - " 2.74336294, 2.80619479, 2.86902664, 2.9318585 , 2.99469035,\n", - " 3.0575222 , 3.12035406, 3.18318591, 3.24601776, 3.30884962,\n", - " 3.37168147, 3.43451332, 3.49734518, 3.56017703, 3.62300888,\n", - " 3.68584073, 3.74867259, 3.81150444, 3.87433629, 3.93716815, 4. ])" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plt.figure(figsize=(11,7), dpi=100)\n", - "plt.plot(x,u, marker='o', lw=2)\n", - "plt.xlim([0,2*np.pi])\n", - "plt.ylim([0,10])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "(0, 10)" - ] - }, - { - "output_type": "display_data", - "png": 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wiIxh2g6WnBtCiopoCMGlaCIBYCPCINLS0IaQu+7yats284SQ666rlsdjrgCyHYzBuHga\ngI0Ig0g7zlfCmBtCiotpCEFkuHgagI0Ig0g7TjOCR4+uUm9v6PM0hCBSbBMDsBFhEClr6FbwkiVe\n/f73FXrhBfO/trNn0xCCkSEMArARYRApyXkrWJL6jN9DQwhGijODAGxEGITrnKaDmLaCZ86s05Yt\nXj34IA0hiD/ODAKwEWEQrop2OsjkyVlau7ZCBQWiAoi4Y5sYgI0Ig3BVLNNBJFEBREIQBgHYiDCI\npGE6CFIdZwYB2IgwiKRgOgjSAWcGAdjIEwiYNuPi8MIejxL00khxTtNB/vzn7SHPBqeDFIZUABsb\nCX5IvmPHpJISacYM6fhxt1cDAPETLpdRGURcMR0E6YwzgwBsRBhEzIZWAGtqvHrkEaaDIH1xZhCA\njQiDiImpArh3r09dXeYKINNBkA5ycqTRo6Xz5yW/X8rNdXtFAJB4hEHExHQlTFdXg6Qq4/M0hCAd\neDzB6uC5c8GtYsIgABsQBjGsodvBn/60V6+8Yv5Xp7Q0X729TAdB+hocBidOdHs1AJB4hEGE5dwQ\nYr4SZupUKoBIb5wbBGAbwiAuimZG8DXXVCsnx6c336QCiMzCXYMAbEMYhKToZwTPns2VMMhMXC8D\nwDaEQUiKbUYwFUBkIsIgANsQBi0zdCv4n/7Jq1OnKpgRDHyEM4MAbEMYtIhzM4gk9Rm/hythYBsq\ngwBsQxjMUNFMByksrNNXv+rVf/0XV8IANJAAsA1hMANFOx1kzpwsPfBAhT75SVEBhPWoDAKwDWEw\nA0U7HSQ3t1+SqAAC4swgAPsQBtPc0O3gT30qtukgAIKoDAKwDWEwjTEdBIg/zgwCsI0nEDDdIheH\nF/Z4lKCXtpJpOkh9fbNeeWV7yLPB6SCFIdNBGhsJfsBwjhyR5s2TSkulo0fdXg0AxEe4XEZlMA0w\nHQRIHraJAdiGMJgGmA4CJA8NJABsQxhMMYO3g6U+TZniZToIkESXXy55PFJXl9TXJ2XzKQkgw/Ex\nl0JM28GSc0MI00GA+Bs1KthE0tYWrA5edZXbKwKAxKKBxCWmGcHbtjXr+PHQhpAZM6olFYZUAGkI\nARJj6lTprbekv/xFmj7d7dUAwMjRQJJinK+EMTeEFBXREAIkE+cGAdiEMOgCpxnBo0dXqbc39Hka\nQoDk4q5BADYhDCbY4O3gjo4+XXaZV/v3m/+xz55NQwiQCrheBoBNCIMJ1NTUqo0bd11y+TMNIUDq\nIwwCsAlhMA5M00Euu6xC69Y16513QreD582r1ocfmiuAbAcD7uPMIACbEAZHyHk6iOT0j/fqq2kI\nAVIZlUEANiEMRmFoBbCmxus4HSQ3t05FRQEdPx76OjSEAKmNBhIANiEMRshUATx82KeODvN1MIsW\nZem++5aptpaGECDdUBkEYBPCYIRMFcD332+QVGV8fqD6J4ntYCDNcGYQgE0IgwZDt4O/9CWvjh0z\n/6OaOjVf2dnO1T+2g4H0Q2UQgE0Ig0M4TwcxXwdTWsp1MECm4cwgAJtYPZvY1BDyyCPNeuGF0PnA\neXnVGju2UKdPMx8YyHS/+51UXi5df730+9+7vRoAGDlmExuYKoB79/rU1WVuCJk/n+tgAFtwZhCA\nTawNg6aGkK6u4RtCCH9A5uPMIACbWBEGB28HX3ZZnz71Ka9eecX8t15amq/eXq6DAWw2blzwj+3t\nUiAgeTzurgcAEinjw6BpO3j3bueGkKlTaQgBbDd6tDR2rNTdLXV2fhwOASATZVQDiWlGcH19s155\nJbQh5Nprq3XZZYV6800aQgCEuvZa6fRp6dQpafJkt1cDACNjRQOJ84xgc0PIrFk0hABwlpcXDINt\nbYRBAJktY8JgY6N5RvCoUVW6cCH0eRpCAITDXYMAbJGWYXDwdrDUp6Iir1pbzX8rc+bky++nIQRA\ndOgoBmCLtAuDpu1gybkhpKiIhhAA0eOuQQC2SNkwaJoOUl5eoc2bQ7eDpQbNnFmtQMBcAWQ7GEC0\nqAwCsEVKhkGn6SDd3VIgYF7y5Mk0hACIH8IgAFukZBh0ng5Sp4kTAzp7NvR7aAgBEE80kACwheth\ncOh0kJtv9urll83LuummLPl8y1RbS0MIgMSiMgjAFq6GwWing1x5Zf/Fyh/bwQASiQYSALZIWhg0\nTQd54AFzM0hwOogvZDrIQPWP7WAAiUZlEIAtkhIGTRXA3/zGp/5+poMASE2cGQRgi5jDYH9/vz75\nyU+qqKhIv/rVr8I+a5oO0t/PdBAAqYvKIABbjIr1GxsbG1VWViaPx+P4zC23bNGXvtQadjrIjBm+\nS74W3A6+NdZlAUBcvPpqq6QtOnq0XitWbFFTU6vbSwKAqDU1tWrFii1hn4mpMnjq1Ck9++yz8vl8\neuyxxxyfa23dLqaDAEg3TU2tevjhXZIa9OGHUnOzdOJE8H9c+XwCkC6amlpVU7NLf/lLg6ShPRof\n8wQCgUC0L37nnXfq/vvvV3t7ux599FHjNnGwYhh86eB0kMKQ62AaGwl+AFLPihVb1Ny83fD1Oj33\n3IMurAgAwhvaqPu5z3n13e826623Bj7LPHKKfFFXBp955hkVFhaqvLxcLS0twzxdL0nq7f2zvvKV\neWppoQIIIPX19Jg/Gv3+rCSvBACGZ2rUbW7+J0knFEnUizoM7tu3T08//bSeffZZ+f1+tbe3a82a\nNXriiScMT9dLkkpL6/Ttb9fq29+O9lcDgOTLyekzfj03tz/JKwGASw2tANbUePXQQ6ar+p5UTk6V\nenrqP/rzrY6vGdM28YAXXnhh2G1itoMBpBvT/2XzWQbAbabPppwcn3p6uiXtCHl+7tx75fcXfPR8\nHLeJhwrXTbxiRR3bwQDSzsBn1urVderoyNJNN/XL5+OzDIC7vve90ApgT0+DpCrj84MbdXftcn7d\nEVUGw/F4nBMoAKSDpUullhZpzx7p7/7O7dUAsMXQreB77vGqo6NCtbX1am+vD3m+rOxe9fQUhN3N\nCJfLXJ1NDACpjPnEAJLNtBW8e7dPwRxnPs9cXDyyq/oIgwDggCkkABLJ1AyyY0foVnAg0KC8vDqt\nXevVM8/4Pro3MCg4rGPliCa3EQYBwAHziQEkiqkCuH+/T52d3cbn58/PUmNjhbxexX1YB2EQABxQ\nGQSQKKZmkPZ252aQgautRlIBdEIYBAAHnBkEEA+m6SBHjpgj2MyZ+QoEfCHNIJs2rUzY+giDAOCA\nyiCAkTJPB/FJ+sD4/IwZI2sGiQVhEAAccGYQQDQinw7SoIKCauXm+nTqVHybQWJBGAQAB1QGAUTK\nVAF84YWB6SCh5s6drPvuW5bUCqATwiAAOCAMAohUtNNBcnP7k14BdEIYBAAHNJAAMBm8HZyd3ad5\n87zav98cqcrK8tXTk9yGkGgRBgHAAZVBAEOZtoN/8xvnhpCRTgdJBmYTA4CD9vZgILz8cqmz0+3V\nAEg204zg+vpm/eEP20OenTq1WllZhSHTQQbPB3YTs4kBIAZXXCF5PFJXl9TXJ2XziQlYw3lGsLkh\nZNq01GkIiRYfbQDgYNSo4PUybW1SR4c0frzbKwKQLE4zgrOyqtTfH/p8KjWERIswCABh5OUFw2Bb\nG2EQyFSDt4N7e/s0frxXLS3miFRami+/P7UbQqJFGASAMLh4GshsTU2tqqnZdclZP8m5IaSoKPUb\nQqJFGASAMOgoBjKDaTpIcXGFNmxo1smToRNCSkur1dtrrgCm63awE8IgAITBXYNA+nOeDiI5RaGJ\nE9O3ISRahEEACIPKIJD+nKaDZGXV6dprAzp5MvR70rkhJFqEQQAIgzODQHoxTQd56SVz3Ln55ix9\n61vLVFubWQ0h0SIMAkAYVAaB9BHtdJCxY/svVv5s2A52QhgEgDA4MwikJtN0kAceCN0Olho+mg7i\nC5kOMlD9s2U72AlhEADCoDIIpB6bpoMkA2EQAMLgzCDgLtOVMDZNB0kGwiAAhEFlEHCPqQK4f79P\nnZ3mCmAmTgdJBsIgAITBmUHAPaYrYdrbGyRVGZ/PxOkgyUAYBIAwqAwCyTF0O3jVKq/+8AdzTJk5\nM1+BgB3TQZKBMAgAYXBmEEg803Zwc7PzlTAzZlABjCdPIBAIJOSFPR4l6KUBIGnee08qKJDy86W/\n/c3t1QDpz9QQ0tDQrJde2h7y7IQJ1crNLbxkdvCMGfersZHgF61wuYzKIACEMVAZbG+XAgHJ43F3\nPUA6c54RbG4IKSvjSphkIAwCQBijR0tjx0rd3VJXl3TFFW6vCEhfTjOCnRpCuBImOQiDADCMK68M\nhsG2NsIgEKmhM4LLypxnBJeV5aunhyth3EIYBIBh5OVJZ84Ew+DkyW6vBkh90c4ILi6mIcRNNJAA\nwDAWLZIOHpT27ZNuusnt1QCpZWhDSHW1V/X1zTpyJLQhJDgjuDBkRjANIYlHAwkAjAAXTwNmzAjO\nDIRBABgGdw0CZk4zgrOzq9TXF/o8DSGpiTAIAMNgCglsN3Qr+O//3qs//rFCLS3mGFFSwozgdEIY\nBIBhEAZhM+fpIJJkKP+JGcHphjAIAMPgzCBs4TQdZOhWsNSgKVPq9M1verVjBzOC0x1hEACGQWUQ\nNoh2Osj06Vn653+u0LRpogKY5giDADAMGkhgg1img0iiApgBCIMAMAwqg8g0TAfBYIRBABgGZwaR\nSZgOgqGYQAIAwzh0SFq4ULrxRunwYbdXA0SO6SAYwAQSABgBzgwiHTEdBJEiDALAMDgziHTEdBBE\nijAIAMMgDCLVDd4OPn++T/n5XqaDIGKEQQAYRm6uNHq0dP681NMj5eS4vSLgY01Nraqp2XXJWT/J\nuSGE6SAYigYSAIhAQYH03nvS2bNSYaHbq4GthjaE3HabV4880qxTp0IbQubMqdb584UhFUAaQuxE\nAwkAjFBeXjAMtrURBuEO5xnB5oaQwkIaQhAZwiAARIBzg0gm04zg7dvNM4Jzc6vk94e+Bg0hiBRh\nEAAiwMXTSJZoZwTPmEFDCEaGMAgAEeCuQSRLtDOCaQjBSBEGASACbBMj3oZuBd99t1cffFAR04xg\ntoMxEoRBAIgAYRDx5DwdRJIMN0KLGcFIHMIgAESAM4OIlakZ5LHHzNNB8vPrtG6dV7/8pS9kRjAV\nQCQKYRAAIsCZQcTCVAHcv9+nzk5zM8gNN2Tp3/6tQsuWiQogkoYwCAARYJsYsTA1g7S3OzeD5Ob2\nSxIVQCQVYRAAIkAYxHCGbgd/5jNevf66+bfZWbPydeEC18EgNRAGASACnBlEOM7TQczzga+7jmYQ\npA7CIABEgDODGBDNdJAJE6qVm+vTyZM0gyB1EQYBIAJsE0MyVwBbWnw6f97cEFJWxnxgpD7CIABE\ngDAIydwQcv58+IYQKoBIdYRBAIgAYdA+g7eDs7L6NGeOV/v2RT8dBEh1hEEAiMAVV0gej9TVJfX3\nS1lZbq8IiWTaDn7+eeeGEKaDIJ15AoHg8Ju4v7DHowS9NAC4Ii8v2E38/vvS+PFurwbxYpoRXF/f\nrD/+cXvIs1OnVisrqzBkOkhjI8EPqS1cLqMyCAARGgiDbW2EwUzhPCPY3BAybRoNIcg8hEEAiFBe\nnnTyJOcGM4nTjODs7Cr19YU+T0MIMhFhEAAiNHDXIBdPp6fB28E9PX3Ky/OqpcX822BJSb78fhpC\nYIeYwuDJkye1Zs0avfPOO5owYYLuuece/eM//mO81wYAKYWO4vTV1NSqTZt26Y03BlcBnRtCiopo\nCIE9YmogOXPmjM6cOaP58+fr3LlzWrhwoV577TWNGzfu4xemgQRAhlm9WnrqKenJJ6Uvfcnt1cDE\nNB1k0qQK3X77Fr39dmhDSFlZtXp6CkMqgDSEINPEvYFk0qRJmjRpkiSpoKBAc+fO1eHDh7V06dLY\nVwkAKY7KYGpzng4iOf12N2ECDSHAiM8MHj9+XEeOHNHChQvjsR4ASFkDYZAzg6nJaTpIdnadJk8O\n6K9/Df0eGkKAEYbBjo4OVVVVaceOHbr88stD/np9ff3Fn1dWVqqysnIkvxwAuGqggYTKoPuGTgcp\nLXWeDnLTTVn61reWqbaWhhDYo6WlRS0tLRE9G3MY7O3t1R133KG77rpLt99+u/GZwWEQANId28Sp\nIdrpIGNSeuz+AAALF0lEQVTH9l+s/LEdDFsMLcJt3brV8dmYwmAgEND69es1b948bd68OZaXAIC0\nQxhMvqENIevXe7V1a+h2sNTw0XQQX8h0kIHqH9vBgFlMYXDv3r168skn9YlPfELl5eWSpIcfflgr\nV1JuB5C5ODOYXEwHAZIjpjD46U9/WhcuXIj3WgAgpXFmMHFMV8IwHQRIDiaQAECE2CZODFMF8KWX\nfOrsNFcAmQ4CxBdhEAAiRBhMDNOVMB0dDZKqjM8zHQSIL8IgAESIM4MjN3Q7eOVKr15/3fxb0axZ\n+bpwwVwBZDsYiB/CIABEaODMYHu7FAhIHo+760k3pu3g5mbnK2Guu44KIJAMMc0mjuiFmU0MIAON\nHSt9+KHU0SFdcYXbq0ldQyuAmzZ5tX17sw4cCJ0PPGFCtXJzC3XyJPOBgUSJ+2xiALBVXl4wDLa1\nEQadOM8INjeElJVxJQzgJsIgAEQhL086cyYYBidPdns1qclpRrBTQwhXwgDuIgwCQBQGnxtEdDOC\ny8ry1dPDlTBAqiEMAkAUuF7mY9HOCC4upiEESEWEQQCIgo1h0DQdZMmSCt13X/QzgtkOBlIPYRAA\nomBbGHSaDtLTI50/b/4thBnBQHohDAJAFGw7M+g8HaRO48cH9Le/hX4PDSFAeiEMAkAUMrkyaJoO\n8vvfm3+bWLgwS//6r8tUW0tDCJDuCIMAEIVMDYPRTgcZP77/YuWP7WAgvREGASAKmRAGnaaDmJpB\nJkyo1pgxPr31lrn6x3YwkP4IgwAQhXQ/M8h0EABDEQYBIArpXhlkOgiAoQiDABCFdAqDpukge/ea\nP/bnzs2X308zCGAjwiAARCFdwmC000GKipgOAtjKEwgEAgl5YY9HCXppAHDN6dPStddKhYXS2bNu\nryZoaEPI+vVe1dc36+jR7SHPBqeDFIZMB2lsJPgBmSxcLqMyCABR2LevVVKz3n03WytWBEezuRmi\nnK+EMTeEMB0EwFCEQQCIUFNTq771rV2SGhQISM3N0okTPklyLUw99pj5Spjs7Cr19YU+T0MIgKEI\ngwAQIVMn7okTDdq5sy4p4WrwdrDf36dx47xqaTF/jJeU0BACIDKEQQCIUE+P+SPT789K+K/d1NSq\nTZt26Y03BodRGkIAjBxhEAAilJNj2HdVcOs1noY2hHi9Xu3Y0ay33w7dDi4rq1ZPj7kCyHYwgEgQ\nBgEgQjU1Xp04cWnwGjPmfm3cGL+t12gbQiZMoCEEwMgQBgEgQgMBa+fOOnV0ZOngwX59+OFK9fXF\nFryimRGcm1slvz/0NWgIATBS3DMIADH6j/+QNm2Spk2T/vhHacyYyL/XVAG87LKBGcE7Qp6fO/de\n+f0FIdvB3A8IIBLhchlhEABi1NcnlZdLf/iD9OCD0pYtkX/vihVb1Nwceil0cEbwzw3P133UELJ7\n0HbwrQRBABEhDAJAgvz2t9KyZcGq4J/+JBUXX/rXh24Fr13r1dmzFdqypV7d3fUhr0cFEEAiMIEE\nABJk6VLpzjul//mfVi1Y0KzS0mDoq6nxSpJDM4gkmTuTuRIGQLJRGQSAEfrpT1u1bl1wMsmAKVN8\nCgQ+0MmT3w95vqCgTvfcc6t+/vNdVAABJAWVQQBIoKeeatbgIChJb73VIGmt8fm5c7PU0FChm28W\nFUAAriMMAsAIOU0mGT26R729oV8fuKSaK2EApIJRbi8AANKd02SS668fpxkzfJd8LTgd5NZkLAsA\nIsKZQQAYIdOdgQPn/yRxHQwA13G1DAAkWFNTK6EPQMoiDAIAAFgsXC7jzCAAAIDFCIMAAAAWIwwC\nAABYjDAIAABgMcIgAACAxQiDAAAAFiMMAgAAWIwwCAAAYDHCIAAAgMUIgwAAABYjDAIAAFiMMAgA\nAGAxwiAAAIDFCIMAAAAWIwwCAABYjDAIAABgMcIgAACAxQiDAAAAFiMMAgAAWIwwCAAAYDHCIAAA\ngMUIgwAAABYjDAIAAFiMMAgAAGAxwiAAAIDFCIMAAAAWIwwCAABYjDAIAABgMcIgAACAxQiDAAAA\nFiMMAgAAWCzmMNja2qo5c+Zo1qxZ2rlzZzzXhDhqaWlxewnW4z1IDbwPqYH3ITXwPrgvld6DmMNg\nbW2tfvSjH2nPnj36/ve/r3PnzsVzXYiTVPqXzVa8B6mB9yE18D6kBt4H96XSexBTGGxra5MkVVRU\naOrUqfJ6vTpw4EBcFwYAAIDEiykMHjp0SKWlpRf/vKysTPv374/bogAAAJAcnkAgEIj2m/bs2aOf\n/OQn+u///m9J0g9/+EO9/fbbevDBBz9+YY8nfqsEAADAiDhFvuxYXmzBggW67777Lv75kSNHtHLl\nyoh+QQAAAKSOmLaJ8/LyJAU7it98803t3r1bixYtiuvCAAAAkHgxVQYl6d///d917733qre3VzU1\nNSooKIjnugAAAJAEMV8tc8stt+jo0aM6fvy4ampqLn6d+wfdt27dOk2cOFHXX3+920ux2smTJ7V0\n6VLNnTtXlZWV+tnPfub2kqzj9/u1aNEizZ8/X4sXL9aOHTvcXpLV+vv7VV5erlWrVrm9FGtNmzZN\nn/jEJ1ReXq6FCxe6vRwrdXV1ae3atZo9e3bKNODG1EASTnl5uRobGzV16lStWLFC//u//0vVMMle\nfPFFXXHFFVqzZo1ef/11t5djrTNnzujMmTOaP3++zp07p4ULF+q1117TuHHj3F6aVbq7uzV27Fj1\n9PToxhtv1C9/+UvNnDnT7WVZ6bHHHtPLL7+sjo4OPf30024vx0rTp0/Xyy+/rKuuusrtpVjrG9/4\nhsaMGSOfz6fs7Gx1dXVdPH7nlriOo+P+wdSwZMkSjR8/3u1lWG/SpEmaP3++JKmgoEBz587V4cOH\nXV6VfcaOHStJ6uzsVF9fn3JyclxekZ1OnTqlZ599VnfffTcNhi7jn7+79uzZo/vvv1+5ubnKzs52\nPQhKcQ6D3D8ImB0/flxHjhxhW8YFFy5c0A033KCJEydq48aNKi4udntJVvra176mRx55RKNGxfW3\nHUTJ4/Fo2bJl+vznP0911gWnTp2S3+/Xhg0btGjRIn3nO9+R3+93e1nxDYMAQnV0dKiqqko7duzQ\n5Zdf7vZyrDNq1Ci99tprOn78uP7zP/9Tr776qttLss4zzzyjwsJClZeXU5Vy2d69e/Xaa6/p4Ycf\n1r/8y7/ozJkzbi/JKn6/X8eOHdMdd9yhlpYWHTlyRL/4xS/cXlZ8w+CCBQv0f//3fxf//MiRI1q8\neHE8fwkgrfT29uqOO+7QXXfdpdtvv93t5Vht2rRp+uxnP8vRFRfs27dPTz/9tKZPn67Vq1fr+eef\n15o1a9xelpWuueYaSdKcOXP0uc99Tr/61a9cXpFdZs6cqZKSEq1atUpjxozR6tWr9etf/9rtZcU3\nDHL/IPCxQCCg9evXa968edq8ebPby7HSuXPn9MEHH0iS3nvvPTU3NxPKXfDQQw/p5MmTeuONN/TU\nU09p2bJleuKJJ9xelnW6u7vV0dEhSXr33Xe1a9eukIERSLxZs2bpwIEDunDhgpqamrR8+XK3lxT7\nPYNOuH/QfatXr9YLL7yg9957T8XFxdq2bZu+/OUvu70s6+zdu1dPPvnkxWscJOnhhx/mwzeJTp8+\nrbVr16q/v1+TJk3SN77xjYuVEbiHcaXuOHv2rL7whS9Ikq6++mp9/etf5wytCx599FGtWbNGfr9f\ny5cv1xe/+EW3lxT/q2UAAACQPmggAQAAsBhhEAAAwGKEQQAAAIsRBgEAACxGGAQAALAYYRAAAMBi\n/w8ZIALzZ4aQUwAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 9 - }, + } + ], + "source": [ + "print(phiprime)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now what?\n", + "\n", + "\n", + "Now that we have the Pythonic version of our derivative, we can finish writing out the full initial condition equation and then translate it into a usable Python expression. For this, we'll use the *lambdify* function, which takes a SymPy symbolic equation and turns it into a callable function. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is definitely not the hat function we've been dealing with until now. We call it a \"saw-tooth function\". Let's proceed forward and see what happens. " + "name": "stdout", + "output_type": "stream", + "text": [ + "-2*nu*(-(-8*t + 2*x)*exp(-(-4*t + x)**2/(4*nu*(t + 1)))/(4*nu*(t + 1)) - (-8*t + 2*x - 4*pi)*exp(-(-4*t + x - 2*pi)**2/(4*nu*(t + 1)))/(4*nu*(t + 1)))/(exp(-(-4*t + x - 2*pi)**2/(4*nu*(t + 1))) + exp(-(-4*t + x)**2/(4*nu*(t + 1)))) + 4\n" ] - }, + } + ], + "source": [ + "from sympy.utilities.lambdify import lambdify\n", + "\n", + "u = -2 * nu * (phiprime / phi) + 4\n", + "print(u)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lambdify\n", + "\n", + "To lambdify this expression into a useable function, we tell lambdify which variables to request and the function we want to plug them in to." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Periodic Boundary Conditions\n", - "\n", - "One of the big differences between Step 4 and the previous lessons is the use of *periodic* boundary conditions. If you experiment with Steps 1 and 2 and make the simulation run longer (by increasing `nt`) you will notice that the wave will keep moving to the right until it no longer even shows up in the plot. \n", - "\n", - "With periodic boundary conditions, when a point gets to the right-hand side of the frame, it *wraps around* back to the front of the frame. \n", - "\n", - "Recall the discretization that we worked out at the beginning of this notebook:\n", - "\n", - "$$u_i^{n+1} = u_i^n - u_i^n \\frac{\\Delta t}{\\Delta x} (u_i^n - u_{i-1}^n) + \\nu \\frac{\\Delta t}{\\Delta x^2}(u_{i+1}^n - 2u_i^n + u_{i-1}^n)$$\n", - "\n", - "What does $u_{i+1}^n$ *mean* when $i$ is already at the end of the frame?\n", - "\n", - "Think about this for a minute before proceeding. \n", - "\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "3.49170664206\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for n in range(nt):\n", - " un = u.copy()\n", - " for i in range(nx-1):\n", - " u[i] = un[i] - un[i] * dt/dx *(un[i] - un[i-1]) + nu*dt/dx**2*\\\n", - " (un[i+1]-2*un[i]+un[i-1])\n", - " u[-1] = un[-1] - un[-1] * dt/dx * (un[-1] - un[-2]) + nu*dt/dx**2*\\\n", - " (un[0]-2*un[-1]+un[-2])\n", - " \n", - "u_analytical = np.asarray([ufunc(nt*dt, xi, nu) for xi in x])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, + } + ], + "source": [ + "ufunc = lambdify((t, x, nu), u)\n", + "print(ufunc(1, 4, 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Back to Burgers' Equation\n", + "\n", + "Now that we have the initial conditions set up, we can proceed and finish setting up the problem. We can generate the plot of the initial condition using our lambdify-ed function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "plt.figure(figsize=(11,7), dpi=100)\n", - "plt.plot(x,u, marker='o', lw=2, label='Computational')\n", - "plt.plot(x, u_analytical, label='Analytical')\n", - "plt.xlim([0,2*np.pi])\n", - "plt.ylim([0,10])\n", - "plt.legend()" - ], - "language": "python", + "data": { + "text/plain": [ + "array([ 4. , 4.06283185, 4.12566371, 4.18849556, 4.25132741,\n", + " 4.31415927, 4.37699112, 4.43982297, 4.50265482, 4.56548668,\n", + " 4.62831853, 4.69115038, 4.75398224, 4.81681409, 4.87964594,\n", + " 4.9424778 , 5.00530965, 5.0681415 , 5.13097336, 5.19380521,\n", + " 5.25663706, 5.31946891, 5.38230077, 5.44513262, 5.50796447,\n", + " 5.57079633, 5.63362818, 5.69646003, 5.75929189, 5.82212374,\n", + " 5.88495559, 5.94778745, 6.0106193 , 6.07345115, 6.136283 ,\n", + " 6.19911486, 6.26194671, 6.32477856, 6.38761042, 6.45044227,\n", + " 6.51327412, 6.57610598, 6.63893783, 6.70176967, 6.76460125,\n", + " 6.82742866, 6.89018589, 6.95176632, 6.99367964, 6.72527549,\n", + " 4. , 1.27472451, 1.00632036, 1.04823368, 1.10981411,\n", + " 1.17257134, 1.23539875, 1.29823033, 1.36106217, 1.42389402,\n", + " 1.48672588, 1.54955773, 1.61238958, 1.67522144, 1.73805329,\n", + " 1.80088514, 1.863717 , 1.92654885, 1.9893807 , 2.05221255,\n", + " 2.11504441, 2.17787626, 2.24070811, 2.30353997, 2.36637182,\n", + " 2.42920367, 2.49203553, 2.55486738, 2.61769923, 2.68053109,\n", + " 2.74336294, 2.80619479, 2.86902664, 2.9318585 , 2.99469035,\n", + " 3.0575222 , 3.12035406, 3.18318591, 3.24601776, 3.30884962,\n", + " 3.37168147, 3.43451332, 3.49734518, 3.56017703, 3.62300888,\n", + " 3.68584073, 3.74867259, 3.81150444, 3.87433629, 3.93716815, 4. ])" + ] + }, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 11, - 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5WLr+AWsFUNK55NHSrznSV1ulvEvKjdGtW/0677exyM93/Guzts8sDvYN1vqD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\n6N+tA1f2AAAAAElFTkSuQmCC\n" - } - ], - "prompt_number": 11 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from matplotlib import pyplot\n", + "%matplotlib inline\n", + "\n", + "###variable declarations\n", + "nx = 101\n", + "nt = 100\n", + "dx = 2 * numpy.pi / (nx - 1)\n", + "nu = .07\n", + "dt = dx * nu\n", + "\n", + "x = numpy.linspace(0, 2 * numpy.pi, nx)\n", + "un = numpy.empty(nx)\n", + "t = 0\n", + "\n", + "u = numpy.asarray([ufunc(t, x0, nu) for x0 in x])\n", + "u" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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nklGANeDkEIBeJxx2QKsVFAdvGs/0TFWrn/Bnvv+avPODjyRZumxUyShA581d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\nNRRsbMIhAKxCY53FU6fOpqq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+ "text/plain": [ + "
" + ] + }, "metadata": {}, - "source": [ - "***\n", - "\n", - "What next?\n", - "----\n", - "\n", - "The subsequent steps, from 5 to 12, will be in two dimensions. But it is easy to extend the 1D finite-difference formulas to the partial derivatives in 2D or 3D. Just apply the definition \u2014 a partial derivative with respect to $x$ is the variation in the $x$ direction *while keeping $y$ constant*.\n", - "\n", - "Before moving on to [Step 5](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/07_Step_5.ipynb), make sure you have completed your own code for steps 1 through 4 and you have experimented with the parameters and thought about what is happening. Also, we recommend that you take a slight break to learn about [array operations with NumPy](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/07_Step_5.ipynb)." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.figure(figsize=(11, 7), dpi=100)\n", + "pyplot.plot(x, u, marker='o', lw=2)\n", + "pyplot.xlim([0, 2 * numpy.pi])\n", + "pyplot.ylim([0, 10]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is definitely not the hat function we've been dealing with until now. We call it a \"saw-tooth function\". Let's proceed forward and see what happens. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Periodic Boundary Conditions\n", + "\n", + "One of the big differences between Step 4 and the previous lessons is the use of *periodic* boundary conditions. If you experiment with Steps 1 and 2 and make the simulation run longer (by increasing `nt`) you will notice that the wave will keep moving to the right until it no longer even shows up in the plot. \n", + "\n", + "With periodic boundary conditions, when a point gets to the right-hand side of the frame, it *wraps around* back to the front of the frame. \n", + "\n", + "Recall the discretization that we worked out at the beginning of this notebook:\n", + "\n", + "$$u_i^{n+1} = u_i^n - u_i^n \\frac{\\Delta t}{\\Delta x} (u_i^n - u_{i-1}^n) + \\nu \\frac{\\Delta t}{\\Delta x^2}(u_{i+1}^n - 2u_i^n + u_{i-1}^n)$$\n", + "\n", + "What does $u_{i+1}^n$ *mean* when $i$ is already at the end of the frame?\n", + "\n", + "Think about this for a minute before proceeding. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "for n in range(nt):\n", + " un = u.copy()\n", + " for i in range(1, nx-1):\n", + " u[i] = un[i] - un[i] * dt / dx *(un[i] - un[i-1]) + nu * dt / dx**2 *\\\n", + " (un[i+1] - 2 * un[i] + un[i-1])\n", + " u[0] = un[0] - un[0] * dt / dx * (un[0] - un[-2]) + nu * dt / dx**2 *\\\n", + " (un[1] - 2 * un[0] + un[-2])\n", + " u[-1] = u[0]\n", + " \n", + "u_analytical = numpy.asarray([ufunc(nt * dt, xi, nu) for xi in x])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + "data": { + "image/png": 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M3nkZRs1I94ikI4bhUJIkqb8IE7DuNcKVz1C/+M/kbXyFeLhnnxMCKD4Rxr8v\nGQhHnQYZ2Wkbbq/IKYSpH4bXfgmv/NxwKPUgw6EkSVIfkggj5q/awob6Rkbk5zBjcAPxVXOT1cFV\nz8LOrcSAlkYS70TDeCV+EqNP/RAnnXUJ5A5J4+gPk5M/lQyHi38H5/8n5BSke0TSEcFwKEmSdBgd\nEP7GDiEeS071fKJ6Hf/18N8Y17CA8lgVZbEq4rGalOvrogG8GJZQEZZREZaxOioiIIBn4Z5Ruzi/\ntONnHBFGnQbDJsGm5VD9v3DK1ekekXREMBxKkiQdJk9Ur+OWR5awrrax9bXjCjL57sw9DN3wPMOq\n/8yTwRtkZIWtx5uiGAujCQyY9H6+9+ZxzN0+iqb9foSLgAC45ZElhCHc+ljqM4oLc/jGRSWcX1rc\n25/i4REEMP1K+PPXklNLDYdSjwiiKOr8rMMoCIICoLa2tpaCAqcISJKk/qOzquC19y8gImJMUEN5\nrJozY4s4PbaEgiC1xcSqcCSVzZXBF8Kp1HNoLSZaaob3XDH9yAmI2zfBHZMh3AP/57nkWktJANTV\n1VFYWAhQGEVRXVevs3IoSZLUA9qqCrZU7M4dm03FH/4f/5HxCrPjVRwXbEq5dmuUx7xwKpVhGZVh\nGe9Ew3t0bPtWFs8tKToyppgOHAZTLoTFv4cFv4AP3ZHuEUn9nuFQkiSpC7pWFUzKpImTYyso315F\n8a+rCGKr+DZR609eu6M4r4STqAhLqQzLqI7GEhLr1fFHwLraRuav2sIZ44f26rMOm+lXJsPhot/A\nubdC1qFVWKWjneFQkiSpEx1WBUuKuOXhxUwI3mF2rIryWBWnx5aSG+xKucfy8DgqmiuDL4WT2UlO\nt8cxZGAWW7fv5lAWBW2ob+z8pP5i7Hth0GjY9hYs+SOc9PF0j0jq1wyHkiRJtF8Z3L8q2KKpdj1/\n+uVT5A1ewe93vUJR9taU4xujwmQYTCSrgxsYfNBjC4Ciwhxu+lAJX/jlAgJIGc/+H3dkRH73Q2mf\nFYvB9H+Av34rObXUcCgdEsOhJEk66rVXGbzpQ1O49bGlREAOu5gRW0Z5rJrZsSqmxNYkT9wOBNAY\nZfJSOKW1OrgsGsXerWCgcEAmdTv3tBniAqAwN5PaHcmG9vsHP6B1t9F7YtMPGGvRPmOtqW1sNyjG\nguRGn3AEtbs46ZPwzH/Amudh4woYPjHdI5L6LXcrlSRJR7X2KoMxQqYEb7VOFT01toLsYE/KOdXh\nmNZ+g6+EE9lFVrvPueGcidz51Aqg7fB3zxXTAdqdvrrvLqOdVTn3f8a+MuMBHz1lFE8v23DktLv4\n5cdgxZ/gjOvgvG+nezRS2h0AlXkYAAAgAElEQVTsbqWGQ0mSdMRrL0wlwojy2/7aGpKK2Ux5vIrZ\nsSpmxaoZGtSn3GdtNITKRDIMzgtL2ULyZ5X87AwadjW1WxUsKsyh8ivv4y9LajoNf4da0WurCjqy\nIJvBuZksq2lo97p+3e5iyR/h11fC8CnwhRfTPRop7WxlIUmS1IaONpMZyE5K6ufx2YzkVNEJsbUp\n1zZEObwYTqEinEZlWMrK6Bj2nSra4prZ47jzqRVtrgWE5JTQeCzg/NJizi0p6jD8xWPBIe0m2t4z\noijim48u4RcvvNXmdf263cWwScm39evSOw6pnzMcSpKkfq+rm8nESTAteJPyhioG/7qa6cHrzM5K\n7L1PFLAoGs9zYRmViTJejSbQ1MGPSy1VweveN4FJRXltrgXcf6rmoYa/rmj7GQEfLC1uNxxCP253\nkV+UfNu4DfbshMwB6R2P1E8ZDiVJUr/W0WYy33xsKccF6zmzed3gzNhiCoMdKde/FY5oXTf4QlhC\nHXltPqcnqoLp1tU2Fv2u3UVOIWQMgKadUF8DQ8ame0RSv2Q4lCRJfVp3ms8DFNDAifXz2frrH/JQ\nrIrR2RtS7lcb5fJ8OJWKcBoVYSkb4sXsagrbfPa+LSRufaxvVAUPRVfbWPS7dhdBAPkjYetqw6F0\nCAyHkiSpz+q0+fwjS4jTxHuCN5gdX8TsWDXTgpXEg71xcU8UZ0F0Qmu/wUXROBLEW49ffdrx3Dtv\nNdBxC4nzSvt2VbArZowdQnFhTrvtLlrC8IyxQw730A5dfnEyHDbUpHskUr9lOJQkSWnV3ebzNbU7\n+a8HHmHDiDe5dceLnJ69lLwgdRrk6+GxVIalVIRlvBROYTvtr0E7t6SIGWOHdLpesK9XBbsiHgv4\nxkUlXHv/ggOmybZomSbb77SsO6w3HEoHy3AoSZLSpivN5wGGUEd5rJryWBXl8SqOCbZALbQUADdF\nBcwLS5OBMFFGDZ2HuH2rZPFY0OfXC/aU80uLueeK6Qf8vQN8dMao/tfGokV+87jdsVQ6aIZDSZKU\nFu1XBhu54ZcvcUpsOZ/KSAbC0tjqlHN2RZnMDydR2byRzNLoeCJi7T6rs81k4MioDHbVvpvn/G31\nFu74ywoA5i7bSOOeBDmZ8U7u0AdZOZQOmeFQkiT1mo6az9/yyJJ9AlvElGAN5bFkA/oZsWXkBHtS\n7rUkHE1FWEpFOI2Xw0nsIov8nAwaGjtuPt/VzWSONi1h+IzxQ3ntnW08tXQDNXWN/Gr+Gj49qx9u\n6GLlUDpkhkNJktQrOtpMpnBAJonadVzaPE20PFbN8KA25fqaaHCyMpgoZV5YxiYKD3jGNeWdN58/\nUjaT6U03nDuRp5Ymd3X94TMr+dipxzMgq59VD/NGJt9aOZQOmuFQkiQdtO5sJjOARibVv8q7D/6E\n8fEq5ue8k3KvHVE2L4ZTqAzLeC4s443oWPbGvFTdbT5/NE0ZPRhTjynkg6VF/Km6hk0Nu7jvxdV8\n9szx6R5W97RWDtendxxSP2Y4lCRJB6WzzWQCQsqCVc1TRas5ObacrCDRem4YBSyKxjZXB6exIDqB\nPW38aHIkNJ/vD244dyJPLK4hiuB/nn2TT5w2mrzsfvSjYsuaw121sHs7ZA1M73ikfqgf/YuXJEmH\nU3ebzwPE697muYce52uxKmZlL2Zw0JBy/O1weOu6wfmUsjls+wf4I635fH8wcWQ+F007hodfW8uW\n7bv5+fOr+cLZE9I9rK7LzofMgbBne3Jq6dB+VvmU+gDDoSRJOkBXms9HQD47OCO2mPJYNbNjixgb\nS53SVxcN4IVwKhXNu4q+FY2kpfZ39awxR03z+f7in885gUcXrSWM4MfPvck/nDGagpzMdA+ra4Ig\nWT3cstJwKB0kw6EkSUrRUYuJ6++fz3UTt/Gx7XMpz6rixGAlGUHYek5TFOPVaAKViWQYfC0aT4K2\nNzY5mprP9xfjh+fx4fccx28XvEPtzj3c8vBizpw4vP+E8vzi5nDojqXSwTAcSpJ0lGpr2ihwQIuJ\nsUENs2OLmB2r5vTYEvLX7Ez5CWJlWExlWEplWMaLYQn15Hb43KO1+Xx/8c/vP4Hfv/oOYQS/XfAu\nv13wLrC3ctyn23/ku2OpdCgMh5IkHYXamzb6sVNH0Vi7gQtjiymPVVEer+a4YFPKtVuiPJ4PS6kI\ny6hMlPIuw9t9js3n+58l62oJ22gcWVPbyLX3L+CeK6b33YDYsmNpg+FQOhiGQ0mSjkDd2Uwmiz2c\nHFvB7O1VlD9XxfXZq4kFe9PBriiDV8KJVITTqAhLWRyNYUBWJjsTCZvPH2ESYcQtjyxp81hE8r/t\nLY8s4dySor5Z3W3ZsdTKoXRQDIeSJB1hOttM5uY/LuaE4G1mx6qYHatiRmwZucGulHssC0clK4Nh\nGfPDSewkJ+X4/zlzvM3nj0DzV21J+brZXwSsq21k/qotfbPa29rr0HAoHQzDoSRJ/VB3ms8DNNWu\n48lf/oVY7lL+uGchI7O3pRzfGBUmdxRNlFEZlrKRwW0+1+bzR7YN9e0Hw4M577BrrRy6IY10MAyH\nkiT1M501n4+AHHZxWmxZct1grIopsbeTJzYBAeyMspgfTm6tDi6LRrG37pdk8/mjz4j8nM5P6sZ5\nh52VQ+mQGA4lSepH2qsMrq/dwQ9/9TsuiVVRnlnFKbHlZAdNrcfDKGBxNJrKsIznwmksCE9gF1nt\nPueGcyby4MtrbD5/lJkxdgjFhTnU1DZ2uJ60ZWfbPievebfS3Q2wqx6y89M7HqmfMRxKktTHtDdl\ntGWzkJYf2ovZTHm8ijNji5gZW8zQoD7lPu9GQ1v7Dc4Lp7KVAgAGDchk9849bT5732mj171vglXB\no0w8FvCNi0q49v4FB1SOW+y702yfk50HWfmwux7q1xsOpW4yHEqS1Id0tJlMfHcDU+vn8X8ykhvJ\njI+lrquqjwbwYlhCRXPPwTejYvafKgrw6VljO91MpuWHf6uCR5/zS4u554rpB3wdZsQC/r9PvKfv\n7zSbXwSb65PrDodNSPdopH7FcChJ0mHW1c1k4iQ4MVhJeUM1Q35dxXuCNzg3K7H3PlHAwmgClWEZ\nFYlSFkYTaOrgf+3d3UxGR69915N+5bevsWbLTprCiJNH99HppPvKL4LNr7vuUDoIhkNJkg6jjjaT\n+eYjSzg+qGF2rIryWDUzY4spCHakXL8qHEll8yYyL4Ql1DGwzee4mYwOVct60gunHcPdc1cC8PzK\nTVx80rFpHlknWjelccdSqbsMh5Ik9aDuNJ8HKKSBk+pfYtuvf8BvYtWMyt6Ycr/aKJfK5mmiFWEZ\nmzKKaGwK23x2d5vPu5mMuqJ8wrDWcDjvjf4QDlvaWVg5lLrLcChJUg/prPn8LY8sIYMmpgevMzu+\niPJYFdOCVcSCvXFxdxRnQTSxtd9gVTSOkFjr8atnHM+981YDNp/X4TF99GCyM2LsagqpfH0TURQR\nBH34a6klHDYYDqXuMhxKktQN3W0+X1O7k+8+8DDrh7/Jt3e8yGnZSxkY7Eo5Z0V4bGtl8KVwCjto\nv4fcuSVFzBg7xObzOmxyMuOcOmYIlW9sYm1tI6s372DssLanM/cJVg6lg2Y4lCSpi7rSfB5gKLXM\nilUn1w7GqykOtkAdEE8e3xgVMK9lqmiijPV0vsnHvv3l4rHA9YI6rGZNGEblG5uA5NTSvh0OXXMo\nHSzDoSRJXdB+ZbCRG375EqfGlvOp5hYTU2NvpZzTGGUyP5xMZVhKRTiNZdEoon2miu6vKy0mrAzq\ncCqfMIzbmt+f98Ymrjh9dFrH06F9K4dRBH15CqzUxxgOJUlq1tXm8wEhU4I1zbuKVnFqbDk5QWpT\n+SXhaJ5r3lX05XASu8iiICeD+samNhuLd3czGelwKjmmgEG5mWzbsYfnV24mEUZ9t1Kd1xwO9+yA\nXXWQU5je8Uj9iOFQkiQ63kymcEAmYe1aLosnK4OzYtUMC+pSrl8XDaEyUUpFWMbzYSmbOPAH0n8s\nH9dp83k3k1FfFI8FzBw/lMeraqjduYfFa2uZdtygdA+rbVm5yUDYWJusHhoOpS4zHEqSjhrd2Uwm\nl0Ym17/K2gd/zIR4FS/lvJtyr+1RNi+GJc1TRct4IzqWvTEvVXebzztlVH3RzPHDeLwqucnLvDc2\n991wCMnqYUs4HD4p3aOR+g3DoSTpqNDZZjIBIdOCNymPVTM7XsX0YAVZQaL13EQUUBWNoyIsozJR\nxoLoBPa08b9Rm8/rSFU+YVjr+/Pe2MS17x2fxtF0Ir8INi13x1KpmwyHkqQjQnebzwPE69ZQ+dBj\n3BSrYmb2YgYF21OOrwmHUxFOoyIs4+VgKpsTbe/QaPN5HQ1GD83l2EEDeHfbTuav3kLjngQ5mfF0\nD6tt7lgqHRTDoSSp3+tK8/kIKGA7Z8QWN28kU82Y2PqU+9RFucwLp7b2HFwTjWw9dvWsMTaf11Et\nCALKJwzjob+9ze6mkFfe2sqsfaqJfYq9DqWDYjiUJPVrHbWYuP7++XzhhK18bPtcZmdVcWKwkniw\n98w9UZxXowlUJJK7ii6KxpGg7UqIzeclmDlhKA/97W0AKt/Y1IfDoZVD6WAYDiVJfV5XW0xAxPhg\nLeWxaspjVZwRW0Le240p/7dbGRZT0VwZfDEsYTsDOny2zeelvWaO3xsGn39jUxpH0omWymHD+o7P\nk5TCcChJ6tM6azGxq3YDF8Wqk4EwXsWxweaU67dEecwLS3kunMa8RClrab/SYfN5qWPD87OZXJTP\nspp6Fr1bS+2OPRTmZqZ7WAdqnVZq5VDqDsOhJCntutNiIpvdjK2vZtWD93JmvJoFOatS7rUryuDl\ncFLrusEl0WhyszLZkUjYfF7qAeUThrGspp4oghfe3NQ3/33su+YwiiCwui91heFQkpRWnbWYiIiY\nFLzN7FiyAf2M2DIGBLtT7rE0PD7ZYiIsZX44mUayU45/9szxNp+XesisCcP4f5XJX8pUvtFHw2Fe\nczhsaoTGbTBgcHrHI/UThkNJUq86mBYTTbXreOrBP/Nv8WrKs6sZEWxLOb4hGpRcN5go40XKqAkL\n23y2zeelnjdj7BAyYgFNYcTzb2zu/IJ0yMxJBsKdW5PVQ8Oh1CWGQ0lSr+lqi4kBNHJabBnlzS0m\nJsfeTrnPziiLl8IpVISlVITTWBEdR0vdrystJmw+L/WcgdkZTD9+MPNXb+HNTdt5d9tOjh3U8cZO\naZFf3BwO18GIKekejdQvGA4lSb2ioxYTn7//b3xhUgMfbniG8swqTo6tIDtoaj0njAKqorFUhqVU\nhmW8Ek5kN21vetHVFhNgZVDqKbMmDGP+6i0AzHtjEx85ZVSaR9SG/CLYsATq3bFU6irDoSTpkLQ1\nbRTYr8UEHMtGyuPVzI5VMTNWzZC3Gtg3770TDaMykawMzgunso38Dp9riwkpfWZNGMr3nkq+32fD\nYZ47lkrdZTiUJB209qaNfuzUUdTXbuHc2JLWqaLjY6k/oNVHA3ghLGneSKaMVVEReyeDprLFhNS3\nnDhqEAOz4mzfneCZZRv546vvMqKgj/1SZt8dSyV1ieFQktSu7mwmEyfBicFKZjdUMeu5Kr6Q/QYZ\nQdh6r6YoxmvR+NaNZF6LxpOVlc1OW0xI/U5mPMa44QOpereOusY9/PNDC4G9a4r7xL/N/OYxWDmU\nusxwKElqU2ebydz8x8WMDtZRHqvmzNgiTo8toSDYmXKPVeFIKsJpVIalvBBOpZ7clOPX22JC6pee\nqF5H1bt1B7xeU9vItfcv4J4rpqc/IFo5lLrNcChJR7HuNJ8H2FG7iYd/eTdNucv43z0LOC57U8rx\nrVEe88KpVDZPFX0nGt7mc20xIfVfiTDilkeWtHksIvnv+5ZHlnBuSVF6f4nTWjk0HEpdZTiUpKNU\n583nIZMmTo6taF43WMW0YBWxIIImIIDdUZxXwklUNO8qWh2NJSSW8pzO1gvaYkLqX+av2pLyfWN/\nEbCutpH5q7ak95c6LZXDhhqIIgj8niJ1xnAoSUeog2k+X1O7k+/96hE+GKuiPLOK02NLyQ12pZyz\nPDyOyrCMirCMl8LJ7CSn3THccM5EHnx5jS0mpCPIhvr2g+HBnNdr8kYm3yZ2J/sd5g5J73ikfsBw\nKElHoK42nwcYRi2zYlXMjldTHquiKNiacq+NUSGVYSkVieRU0Q0MBmDQgEwad+5p8/n7Thu97n0T\nrApKR5AR+e3/Quhgzus1GVmQOxR2bE5uSmM4lDplOJSkI0xHzeevvX8B184qZkL9fD6dUcXsWBVT\nYmtSzmuMMpkfTua5cBqVYRnLolG01WLi07PGdrqZTEsItCooHTlmjB1CcWEONbWNHe403NLzNK3y\ni/eGw5FT0z0aqc8zHEpSP9XV5vMBISXBW8xuXjd46t9WkJ2VWvGrDsdQGZbxXFjGK+FEdpHV7nO7\nu5mMpCNLPBbwjYtKuPb+BQcca+uXQ2mVXwTrq92URuoiw6Ek9UMdNZ9fV9tIMZspjycrg7Ni1QwN\n6lOuXxsNobJ5mui8cCqbKWzzOW4mI6kt55cWc88V07n5kSXU9OVfDtnOQuoWw6Ek9UHd3UxmIDuZ\nUr+AgrlVPJVVxYTY2pT7NUQ5vBhOae05+E78OHY1tTUhrPvN591MRjo6tfxy6EPfr2DZ+uQvoB67\nfjZD8tqfeXDY2c5C6hbDoST1MV3ZTCYg5MTgTcpjVcyOVzE9eJ3MINF6fiIKWBSNT7aYSJTxanQC\ne/b5ln/De0/gzqdWADafl3Tw4rGAGeOGtIbD1zfUc1peH/plUcuOpfXr0jsOqZ8wHEpSGnS3+XxN\nbSP/8cCfeHv4m3x9x4vMzF5MYbAj5Zy3whGt6wZfCEuoI++A59p8XlJPm1xU0Pr+spp6ThvXh75n\nWDmUusVwKEmHWVeazwMU0MDM2JLWjWRGxzZAHRBPHq+Ncnk+nNrac3BNNDLlOa4XlHQ4TC7Ob31/\nWU1dGkfSBsOh1C2GQ0k6jDqqDP7zL1/mPcHrfDyjitmxaqYFK4kHe8/cE8VZEJ1AZaKUinAaVdFY\nEi1JcT82n5d0uEwauTccLl1X38GZadCyIU1DDYQhxGLpHY/UxxkOJamHtTdlNBFG+7WZiBgfrG2t\nDJ4eW0pe0JhyrzfCY6horgy+FE5hOwMoyMmgvrGpw/5iNp+XdLgMzM5g9NBc3tq8g+U19YRhRKyv\nfK/JGwEEEDbBzi0wcFi6RyT1aYZDSepBHW0mUzggk121G7goVp0MhPEqjgm2pFy/KSpgXlhKZVhK\nRaKMGg6s6v1j+Tibz0vqUyYX5fPW5h3s3JNgzZYdjBk2MN1DSopnwsDhsH1DclMaw6HUIcOhJHVT\ndzaTyWY34+qrWP3gvZwZr2JBzuqUe+2KMpkfTqIyTPYcXBIdT0Tb055sPi+pr5pcVMCTi9cDyXWH\nfSYcAuSPbA6HNVBUlu7RSH2a4VCSuqHzzWQipgRrki0mYlXMiC0jJ9iTco8l4WgqwuS6wZfDSezi\nwJ5gbiYjqT+ZUpy67rBP/ZIqvxhqqmxnIXWB4VCS9tHd5vMAidp1PP3gn/lyvIry7CqGB6m79dVE\ng5M7iiZKeSkooyZR2OazbT4vqb9KbWfR13Ysbd6Uxh1LpU4ZDiWpWVeaz0fAABo5LbaUM5s3kpkY\nezflPjuibF4Mp7S2mHg9OpaW2t/Vs8Zw77zVgM3nJR05jh+Sy4DMODv3JFhW09d2LG1pZ2HlUOqM\n4VCS6LjFxBfu/xufm1jPhxue4cysKqYHK8gKEq3nhFHAomhs67rBBeEJ7CazzeecW1LEjLFDbD4v\n6YgSiwVMKspn4dvbeGvzDrbvamJgdh/5MbO1crg+veOQ+oE+8q9Wkg6PtqaNAvu1mIDjgo2UN1cG\nZ8UWM3hNA/vmvXeiYTyXSIbB58OpbCOfjrRMGW2pALpeUNKRZkpxMhwCLF9fz/TjB6d5RM2sHEpd\nZjiUdNRob9rox04dRUPtFj4QW0x5rJryWBXjYqlrU+qiAbwQTm3tOfhWNJK9k0FTdaXFhJVBSUea\nlHWH6/pQOMwbmXzrmkOpU4ZDSUeM7mwmk0ETJwVvMHt7NeXPVfGF7JVkBGHrvZqiGK9GE6hMJMPg\na9F4crKy2JFIdNh8vqubyUjSkWZy0d4ZFH1qU5qWymHDeggTEIundzxSH2Y4lHRE6GwzmZv/WM2Y\nYB2zY4uYHavm9NgS8oOdKfdYGRZTGZZSGZbxYlhCPbkpxz975vhOm8+7mYyko9X+lcM+Y+BwCGIQ\nJWD7pmTfQ0ltMhxK6je603weYGftRh755d00DVjC/zYt5LjsTSnHt0R5PB+WUhGWUZko5V2Gt/nc\n7jafd8qopKNRYW4mxxTmsLa2kaU1dURRRBD0gV+MxTNg4AhoqEn+MRxK7TIcSuoXOm8+D1ns4eTY\nitYG9KXBamJBBAkggF1RBq+EE6kMy3guLGNxNIaIWMpzbD4vSQdvcnEBa2sbqW9sYm1tI8cOGpDu\nISXlFyWDYX0NFJ+Y7tFIfZbhUFKf136biZ3c+auHuSBWxezMKmbElpEb7Eo5Z2k4qrXFxPxwEjvJ\nafc5N5wzkQdfXmPzeUk6SJOL8vnrsg0ALFtX14fCYTGsW+iOpVInDIeS+oT2powmwiilzcRwtjEr\nVs3s+CLKY9WMDLal3GdDNIiKsJTKRBmVYSkbSe6WN2hAJo0797T57H2njV73vglWBSXpIE0u3mfd\nYU0975/SR6Zw5rtjqdQVhkNJadfRZjKJxu1MrH+JqzOSPQenxN5OuXZnlMVL4ZRkIAzLWB6Noq0W\nE5+eNbbTzWRaQqBVQUk6OFP22bF06bo+uGOplUOpQ4ZDSYdFVzeTCQiZGqxmdkM1+Q9VcUpsOR/K\nakq5V1U4hopwGhVhGQvCE9hFVrvP7e5mMpKkgzd22ECy4jF2J0KW1fShHUvzi5JvrRxKHTIcSup1\nHW0m881Hl1LMJsrjyU1kZsWqGRI0pFz/bjSUikRy3eC8cCpbKdj/EYCbyUhSumXEY5wwMo/Fa+t4\nc2MDjXsS5GT2gb6CrZVDw6HUEcOhpEPWnebzAHnsYGr9K2z49f/wQKyK8Tmp03waohxeCKe2ThVd\nl3EcO5tC2tLd5vNuJiNJvWtyUQGL19YRRvDGhgZKjy1M95CsHEpd1OvhMAiCrwL/AXw/iqIv9vbz\nJB1enTWfv+WRJcRIcGKwkvJYNeXxKt4TvEFmkGg9PxEFLIwmUBmWUZEoZWE0gaZ9vj1dPeN47p23\nGrD5vCT1dVOKU9cd9o1w2PxLwu0bINGU7H0o6QC9+i8jCIJTgc8Ci3rzOZJ6V3ebz9fU7uQ/H3ic\nNcNW8o0d85mZvZiCYEfKOavDkcnm82EZL4Ql1DGw3eefW1LEjLFDbD4vSf3A5KLUHUv7hNxhEMQh\nSsD2jVDgOnOpLb0WDoMgyAMeAD4D/N/eeo6k3tWV5vMAhTQwM7aY2c0N6EfFNkI90LzUpDbKZV5Y\nSkVYRkVYxjvRiE6f3TJltCWMul5Qkvq+yftUDpfV9JEdS2MxyBsJ9WuTO5YaDqU29Wbl8IfAY1EU\nPRUEQbvhMAiCbCB7n5fy2ztX0uHVfmWwkX/+5ctMD17nExlVzI4tYlqwiliw98zdUZwF0cTmjWRK\nqYrGERJr91ldaTFhZVCS+r5hedkMy8tmU8Mulq6rJ4oigqAP/CIvv6g5HLruUGpPr4TDIPj/27v3\n8Lqv+s737/Xb1sUX+RZfZBJf4vtFcm4khFhOIJO0dHoohd4ozcCQFk5DMy3paYdhpucETk+nMGdK\n6YUDp3NmGCCQ0Pa0DLSFBwIFW06ci53Ekq9JfE98dyzJtiRL+7fmjy1ta1tbsuRI2tvW+/U8fmLt\n/du/tYw31v5orfX9hvcDtwK3D+HyTwKPjsY8JF3eUJvPQ2RpeI31SRMNSTN3JjuYHDoL7vVyen1+\nZfCZdBXnqWZq9QTaOrr7BUwYfjEZSdLVYdW8Gja+3Mnpcxc4cbaTOTXVpZ7SxXOHZw2H0kBGPByG\nEOYDfwb8VIyx43LXA38MfK7P1zXA4ZGel6T+BismU1NdwYWWY/xc0pwLhJlm5oXTBa8/Eaeyqaei\naGO2jqP0X9X79YbFl20+bzEZSbq2rKytYePLJwHYdaStTMKhFUulyxmNlcPbgDnAlj5bCDLA3SGE\nh4GqGGO+TGGMsRPILz+UxbYD6Rox3BYTVVxgSVsT+5/4b9ydaWJL9YGC+3XGCp5JV9LYEwh3xgXE\nAbaKDrf5vFtGJenaUViUppW7l88u4Wx65MPhkcGvk8ax0QiHPwTqL3nsy8Au4LN9g6Gk0TOUFhOQ\nsjocpKFnq+gdyS6qQ1fBfXakC9nQU1X0uXQFnVT2G8vm85KkvgqK0hwpk4qlrhxKlzXi4TDG2AY0\n930shHAOOBVjbC7+KklXYvgtJjp49LEn2TtnL584v5mGqmZmhcJKckfiTBqzuaqiz4U6jmSL96ey\n+bwkaSBL50zJn1/fWS7tLHrPHLpyKA3IDqDSVWqoLSYm0cHbkp09hWSaWJ68Bq3kW0yci1VsTlfT\n2NNm4pV4Pb1rfw+uW2TzeUnSsFVNyLBk9mT2HDvLK8fb6MqmVGQGrlg9Jlw5lC5rTMJhjPEdYzGO\nNF4MtjL48De2UB/28r5MM+szTdwa9lAZLu7mzsZAU1ycqyqareeFuIyuAf4psPm8JOlKraydyp5j\nZ+nKRvaeOMeK2hJ3K+tdOTx3ErJdkKko7XykMuTKoVSmht5iAm4Ix/Mrg+uS7UwP5wrudTCdTWNa\nz4Z0LU+lq2llSq7FRPfgLSZsPi9JulIr59Xw7Zdyv991tLX04XDiTEgqIO2Cs8dh2vWlnY9UhgyH\nUhkarJjMtIkVnGs5xU8nO1ifbKMhaWZRcqzg9a1xEk+la/I9Bw/Guf3GGEqLCZvPS5Ku1Ko+FUt3\nHmnjPTeXcDIASQJT5v/S+VwAACAASURBVELr4dzWUsOh1I/hUCqR4RSTmUA3N7S+yO4nvsY9mWZe\nqHqFTLh4RVfM8EJcSmM2Fwa3xcVkew8VXmK4LSYkSboSfSuW/mTPce5ZPrv0O09qanvCoUVppGIM\nh1IJXL6YTGRJeJ2GpJmGpIk7k53UhPaCe7yazmNjT4uJzekqzjKp3zi2mJAklcqLB8/kvw/tPNLG\nr/6XzfldMCX7AaS9DqVBGQ6lUTDc5vMAHS3H+e4TP+LjSTMNVU1cH04VPH86TmFTWseGdC3PUM/B\nbPFtnraYkCSV2veaj/CxrxcvnPbQY1v54gO3liYg5ttZWLFUKsZwKI2woTSfj0AlXdyW7OHuZBsN\nSRNrwgGSPltFO+MEnk9X9BSSqWdHXEgkVwbcFhOSpHJVrHBar0jue9Wnv7OD+1fXjv33pN6Vw7OG\nQ6kYw6E0ggZrMfHQY1v46Irz/OzZH7O+ook7kl1MDBcKrtuZzmdjupbGtI5n05V0UFV0HFtMSJLK\n1bP7Thd8b7pUBI60dPDsvtNj/z3KlUNpUIZD6QoU2zYK9PtJ6WzeyLWYyDTTkDQz58AZ6NNW6Xic\nzsa0jo3ZtWxK6zjB9EHHtcWEJKncHW8bOBheyXUjqqanerfhUCrKcCgN00DbRt9/+3zOtJzhHcku\nGpImGpJmViaHCl7bHit5Jl2VbzGxJ97Axc2ghWwxIUm6Gs2pqR7R60ZUfuXQgjRSMYZD6RLDKSYT\nSFkT9nP32SZu/0kTv1m1h6rQnb9XGgNN8UYa0zoa03q2pMupqKzmfDY7aPP5oRaTkSSp3Nxx40zm\nTavmaEvHoN/renfdjKnecHj+FHR3woTixzek8cpwKPVxuWIyn/r2dt7CCRoyzaxPmrgraWZmOFtw\nj8NxFhuzuRYTT6WreYOpBc//1t1LLtt83mIykqSrVSYJPPru1Tz02NZ+3+t69d0FM6YmzoBMJWQv\nwNljMH3B2M9BKmOGQ407w2k+D9DWcpr//xt/xflJO/n6hRdZUl24FaU1TmRzujrfc3BfrKXYVtHh\nNp93y6gk6Wr1rrp5fPGBW/t9r5s+qYLPvK++dLtgQshVLD1zENoMh9KlDIcaVy7ffB4yZLkpvNpT\nSKaJW8IrTAgpdAMJdMeEF+NSGtM6NmbreSkuofuS/yvZfF6SNN71fq/74o9f4T9/fw8AH7hjQemP\nR0zpDYeeO5QuZTjUNeVKms8fbWnnPz3+T/yLpJm7K7ZxZ7KDqaG94Jq9aS2NaT2NaR1Pp2toY9KA\nc3jkvuU88dxBm89Lksa9TBJ4z83X58PhnmNtJZ4RF3sdWrFU6sdwqGvGUJvPA0ynjbuS7axPmlif\naeKGcLLgXmfiZDala3p6DtZzOM7OvW5iBWfbu4qO33fb6MP3LnVVUJIk4IYZE6mpmkBbZzc7j5RD\nOLRiqTQQw6GuCYM3n9/Kb7z9LSxs28oDE7bRkDRTH/aRhItXX4gZtqQr2NhTVbQ53khK0m+cD6+7\n8bLFZHpDoKuCkiRBCIGV82p4bv8bvHamnZb2LqZNrLj8C0eLK4fSgAyHuqoMtfk8RJaF13Irg8k2\n3rZ1F5MqOwuu2J3eQGNPv8Fn0pW0M3C/peEWk5EkSRetrJ3Kc/vfAGD30bbStLHo1btyeNZwKF3K\ncKirxmDN54+0dDCLFtYlTazPNNOQNFEb3ih4/Yk4LddvMJs7O3iM4t+YLCYjSdLIWjmvJv/7nUda\nSxwOXTmUBmI4VNkYbjGZKi6wtG0bE3/8X/luZROrkoMF9+uIFTybrmRjWs/GdC37Jyyko6tYt6Xh\nN5+3mIwkSUO3at7Fnr+7jraWcCb0CYeeOZQuZThUWRhKMRlIWRMOsj7ZRkPSxO3JHqpCYXGY5nRR\nz1bROp5PV9BJZf65R+5ZxuefzFVLs/m8JEljZ8XcviuHJS5K0xsO29+Arg6oGPhYiTTeGA41Zobb\nfP5oSwefeuwHvDJnL588v5m7qrYzKxT+tPH1OLNnm2g9m9I1nGJav3FtPi9JUmlNrprAwusmceDU\neXYfbSObxtL94LV6Okyohu6O3LnDGYtKMw+pDBkONSaG0nweYBId3Jns6Ckk08TS5HVoBTK558/G\najanq/KFZF6Nb+Hi2p/nBSVJKleraqdy4NR52ruyHDx9nhtnTS7NRELIrR6+sT937tBwKOUZDjXq\nBlsZfPgbW1gb9vILmVy/wVvDy1SEbP6abAxsi0vYkNbTmK3nxbiUrgHetjaflySpfK2cV8P3tueK\nwOw80lq6cAi5iqW94VBSnuFQI2KgLaPZNPZrMzE/HOPupImGpIm7ku1MC+cL7nUgndNTRKaep9PV\ntDKFqdUTaOvu7hcwwebzkiRdDVbW9ilKc6SVf1lfwvZPViyVijIc6k0brJhMTXUF51pO8q6eraIN\nSRMLk+MFr2+Jk9jU03x+Y1rHoTi33xi/3rDY5vOSJF3FVvepWLrzaImL0kyxYqlUjOFQQzKcYjIV\ndDO/9QX2PPFV7k6aeaHqVTLh4hVdMcPWuIyNPYVktsXFpCRFx7X5vCRJ14YbZkxkcmWGcxey7DxS\nLu0sXDmU+jIc6rIuX0wmsiS8ni8ic2eyg8mhs+Aer6RvyW8VfSZdxTkm9hvHYjKSJF27kiSworaG\nrQfPcPiNdlo7uphaXVGaydT0/FDZlUOpgOFQw24+D9DZcpzvPfEjHkmaaKhq4i3hdMHzp2INjWk9\njWkdm1nLoezMomPbfF6SpPFj1bypbD14BoA9R9t466Linw9GnSuHUlGGw3FuKM3nI1DFBd6a7GZ9\n0kxD0kRdsr/gPp2xgmfTFT2BsJ4dcQGxZ6vog+sW8eVNuettPi9J0vi1su+5wyOtJQyHPT94Pms4\nlPoyHI5jg7WY+M3HtvAby87xv5z9Mesrmrgj2UV16Cq4bme6INdiIq3n2XQlnVQWHef+1bXcceNM\nm89LkjTOraqtyf++pEVpanqK33W0wIXzUDmpdHORyojhcBwotm0U6NdiYg5v5CqKZnJVRWcfaoU+\nRwGOxelsTNeyMVvHprSek0wbdNzeLaO9K4CeF5QkaXxb0Scc7iplUZqqqVAxCbrO51YPZy4u3Vyk\nMmI4vMYNtG30V946nzMtZ3hHsjO/VXRFcrjgtedjFZvTVT0tJup5OV7Pxc2ghYbSYsKVQUmSxrea\n6grmz5zIodPt7DraRppGklL8oDiE3LnD03tz5w4NhxJgOLzqDaeYTEJKXdhHw9km3rahmY9V7aYy\nZPP3SmNgW7wxf25wa7qMiqpqzndlB20+P9RiMpIkSStrp3LodDvnL2Q59MZ5Fl43uTQTqZnXEw6t\nWCr1MhxexS5XTObRb2/n+nCChp7m8+uS7cwIZwvucTjOYkNPv8Gn0jWcoabg+d9av+SyzectJiNJ\nkoZq1byp/GDHMQB2HmkrYTi0Yql0KcNhmRtO83mAsy2n+btv/L+cm7iTJ7pe4MaqYwXPt8aJPJ2u\nYWNPm4n9sZZiW0WH23zeLaOSJGkoCorSHGnlXXW1pZlIvteh4VDqZTgsY5dvPg8T6Oam8CrrM7kG\n9DeFV5kQUsgCCXTHhBfjUhrTOjZk1/JSXEKWTME4Np+XJEljpW87i11HS1iUZkpPxVLDoZRnOCyh\nK2k+f7Slnf/78X/ivqSJ9RVNvD3ZQU1oL7hmb1rbszJYz+Z0NW0MXJ75kfuW88RzB20+L0mSxsTC\nmZOYWJGhvSvLrpK2s+hdOfTModTLcFgiQ20+DzCdNtYl22lImlifaeKGcLLgXqfjFJ5K63KBMFvH\na8zOvW5iBWfbC3sT9uq7bfThe5e6KihJksZEkgRW1Nbw4qEzHDh1nrOd3UypKsFHUs8cSv0YDktg\nsObzDz22lQ+/bR6L2rbwwIQmGpJm6sM+knDx6s44gS3pchrTejak9WyPi4gk/cb58LobL1tMpjcE\nuiooSZLGyqp5uXAIsPtoG7ctnDH2k/DModSP4XCUDLRlNJvGfs3nIbI8HGZ9kjs3eMeLu5hU2Vlw\nxa50Po1pHRvTtTybrqCd6gHHHm4xGUmSpLG0qs+5w51HWksUDnvOHF5og86zUDVl7OcglRnD4SgY\nbMvotImVHGnpYDZnWJc0sz6zjYakmbnhTME9jsfpuTCYzVUVPUHxfzQtJiNJkq42K2vLoChNVQ1U\n1uTC4dljhkMJw+EVG26LiTdaWnj8G1/mXRN38N3KF1mVHCp4vj1W8my6kg09hWQOTFhIR1ex1vPD\nbz5vMRlJklROVvRpZ7HrSCmL0syFU225ojTXLSndPKQyYTi8AkNpMRFIWRP2sz5ppiFp4q3JbqpC\nd77FRBoD2+NCGtN6Nqb1bEmX00ll/n6P3LOMzz+5B7D5vCRJurZMm1jB9dMn8tqZdnYdbSNNI0kp\nPr/UzINTr3juUOphOCziylpMdPB/feMHuX6DFU3clWznulD4k7DX4nU0ZutpjPU0ZtfwBlO5lM3n\nJUnSeLBqXg2vnWnnbGc3r51pZ/7MgVtvjZp8xVLbWUhgOOxnOC0mJtPOncmOfCGZJUnhPyxnYzVP\np6vzPQf3xnlA4MF1izizab/nBSVJ0ri1snYqT+48DuSK0pQ2HLpyKIHhsMDlWkw8eNcNzGvdxi9l\nmmnINHFLeIWKkM1fl42Bl+ISNqZr2Zit48W4lO4i/xPfv7qWO26cOaQqoq4MSpKka1FhxdI2fmpN\n7dhPIt/OwpVDCcZpOCy2bRQo2mJiYTjG+iTXb/CuLduZWnW+4Ir96dx8i4mn09W0MnnAcXu3jPau\n/rkqKEmSxquV8/oUpSlVxdIpPe0s2o6VZnypzIy7cDjQttH33z6fIy0dTOMsdyXbewJhEwuSEwWv\nb4mT2JTW9RSSqeNQnFt0nKE0nndVUJIkjVeLrptMdUVCR1fKrqMlqljqyqFU4JoLh8MtJlNBNwta\nXyD58Vf5VmUT9WEvmXDxigsxwwtxGRuya2lM63h5wlLOdxUfe7gtJiRJksarTBJYMbeGlw63sP/U\nOc5f6GZS5Rh/NO175jBGCO7g0vh2TYXDoRWTiSwJr3N3kms+f2eyg8mhs+A+e9Lr8y0mnklXcZ7q\n/HOP3LPcFhOSJEkjYGXtVF463EKMsPtoG7csmDG2E+gNh13noLMNqvtXkpfGk6suHA63+fzRlg7+\nw2P/zM45+/jdc5tpqGpmXjhdcM2JODW/VbQxW8dR+m/1tMWEJEnSyOp77vCJ5w7R0ZWO7Q/UKydD\n1TTobMmtHhoONc5dVeFwKM3nAaq4wO3Jbhp6WkysSQ5AK/k/bUes4Nl0Zb6QzK44n0iSv6ctJiRJ\nkkZfW0d3/vfffO4Q33zuUH7X15gdxamp7QmHR2D28rEZUypTV004HGxl8Le+sYVV4SDvzuTC4O3J\nbqpD4cHA7enCfL/B59IVdFJZdJxH7lvOE88dtMWEJEnSKPpe8xH+9Ad7+j3e20Lsiw/cOjYBsWYu\nnNwNZ61YKpVtOHx272neubaGTBLIprFfm4m5nGZ9TxhclzQzKxSWQD4aZ7Axmzs3uCmt4xTTmFo9\ngbaO7n4BEwq3jT5871JXBSVJkkZJsc92vSK5z2Wf/s4O7l9dO/qfwaxYqmtMNo08u/f05S8somzD\n4YNfeY7r57zKo+9ezZSqCbS0nOGdyc58IZllyWsF15+LVWxOV/dsFa3nlXg9FzeE5vx6w2I+/+Se\nIbWZcFVQkiRpdDy773TBLq1LReBISwfP7js9+p/J+lYsla5yvcfwXjt+jYXDhJQ5rc00Pf4E92S2\n8WLVy1SGbP75bAw0xcW5raLZerbGZXQN8McZbjEZSZIkjZ7jbQMHwyu57k1x5VBXkeG27Ruusg2H\nG6p+hwVV7QWPHUxn51tMPJWuoYUp/V5nMRlJkqTyNqem+vIXDeO6N8WVQ10lLte271Pf3kElF3hr\nspu3TniB372CMco2HE4P52mNk3kqXUNjWsfT3MSr2TlFrx1u83mLyUiSJJXOHTfOZN60ao62dAxa\nC+KOG2eO/mRcOVQZGW7bviMt7fzZ1/+eg9P28J/an+eOql1Uhy5aY7y2wuGvdX6SHXENWTIAPLhu\nEXs37QdsPi9JknQ1yySBR9+9moce29pv11evvrUgRtWUubn/th2DGCH4uVGlMdS2fXN4g/VJEw2Z\nJhqSJmaHVuiAntjEsTid72dXAt8f9hxCjG9mV+rICyFMBVrmf/yvSaom5R9//CN30tJ+YcClVM8L\nSpIkXV2KfRieXJXhT37pprH7bNfVDn/Us7X0Ewdg4vSxGVfqY6CVwQBU08Hbkp2sT5ppSJpYkRwu\nuOZ8rGJzuip//O7leD1pZzuHPv/LANNijK0MUdmuHPbqu60gkwTPC0qSJF0jemtB/GjnMT7ytS0A\nXD9t4tj+0L9iIlRPh44zuXOHhkONkoG2jF7a2iUhpS7soyFpYn3SzG3J7oLCnGkMNMUb8z3ct6bL\nuEAF0yZW0NreVXzwISrrcFisxYTnBSVJkq4dmSRw/5pabpo/nZcOnWHP8bMcbemgdtoYFKPpVTOv\nJxwegTkrx25cjRuDFZOZkCRkWg/x/p5touuS7cwIZwtefzjOYkM2FwafStdwhpp+Yzy47sZ8274r\nVdbh0BYTkiRJ48M9y2fz0qEzAGzYc4Jfvn3+2A1eUwsndlqxVG/KcIrJ1HCe+rbnOP7EF1mfbKOx\n6ljBvVrjRDanq9mQrqUxrWN/rOXSHu69irXte+34+Sv6M5RtOPxvH7qdd65d6JZRSZKkceCe5bP5\n8x++DMBPShEOwYqlumKDFZP5P/9xJxm6uSm8yvpMEw1JMzeHV5gQ0vy13THhxbiUxrSODdm1bIuL\n6S4S1Ybatu+ftx3g/s8P/89RtuHwjsWeJZQkSRovbrphGlOrJ9Da0U3jKyfpzqZMyCRjM3hvODx7\nbPDrNG4Nv/l8pLp1H09/89v8YdLEnVU7qAmFPdxfTefRmNbRmNbzQlLHye7iW6mvpG3fHYuvrA1M\n2YZDSZIkjR8TMgnrl83mH5uO0NLexUuHW7ht4YyxGdxehxrE5ZrP9xaTmU4b65LtrE+20ZBp5oZw\nsuA+b8QpbErrcoVksnW8xuz8cw++fRFfLoO2fYZDSZIklYV7lufCIeS2lo5dOOzdVuqZw/FquM3n\nj7Z08NuPPcO/vuEo/+rcJhoqm6gL+0nCxSs74wS2pMvzLSa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+ "text/plain": [ + "
" + ] + }, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.figure(figsize=(11, 7), dpi=100)\n", + "pyplot.plot(x,u, marker='o', lw=2, label='Computational')\n", + "pyplot.plot(x, u_analytical, label='Analytical')\n", + "pyplot.xlim([0, 2 * numpy.pi])\n", + "pyplot.ylim([0, 10])\n", + "pyplot.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "\n", + "What next?\n", + "----\n", + "\n", + "The subsequent steps, from 5 to 12, will be in two dimensions. But it is easy to extend the 1D finite-difference formulas to the partial derivatives in 2D or 3D. Just apply the definition — a partial derivative with respect to $x$ is the variation in the $x$ direction *while keeping $y$ constant*.\n", + "\n", + "Before moving on to [Step 5](./07_Step_5.ipynb), make sure you have completed your own code for steps 1 through 4 and you have experimented with the parameters and thought about what is happening. Also, we recommend that you take a slight break to learn about [array operations with NumPy](./06_Array_Operations_with_NumPy.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, "metadata": {}, - "outputs": [] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] } - ] + ], + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } diff --git a/lessons/06_Array_Operations_with_NumPy.ipynb b/lessons/06_Array_Operations_with_NumPy.ipynb index 2b988dcb..f9ba5b62 100644 --- a/lessons/06_Array_Operations_with_NumPy.ipynb +++ b/lessons/06_Array_Operations_with_NumPy.ipynb @@ -1,351 +1,361 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This lesson complements the first interactive module of the online [CFD Python](https://bitbucket.org/cfdpython/cfd-python-class) class, by Prof. Lorena A. Barba, called **12 Stps to Navier-Stokes.** It was written with BU graduate student Gilbert Forsyth." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Array Operations with NumPy\n", - "----------------\n", - "\n", - "For more computationally intensive programs, the use of built-in Numpy functions can provide an increase in execution speed many-times over. As a simple example, consider the following equation:\n", - "\n", - "$$u^{n+1}_i = u^n_i-u^n_{i-1}$$\n", - "\n", - "Now, given a vector $u^n = [0, 1, 2, 3, 4, 5]\\ \\ $ we can calculate the values of $u^{n+1}$ by iterating over the values of $u^n$ with a for loop. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = np.array((0, 1, 2, 3, 4, 5))\n", - "\n", - "for i in range(1,len(u)):\n", - " print u[i]-u[i-1]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1\n", - "1\n", - "1\n", - "1\n", - "1\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is the expected result and the execution time was nearly instantaneous. If we perform the same operation as an array operation, then rather than calculate $u^n_i-u^n_{i-1}\\ $ 5 separate times, we can slice the $u$ array and calculate each operation with one command:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u[1:]-u[0:-1]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "array([1, 1, 1, 1, 1])" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What this command says is subtract the 0th, 1st, 2nd, 3rd, 4th and 5th elements of $u$ from the 1st, 2nd, 3rd, 4th, 5th and 6th elements of $u$. \n", - "\n", - "###Speed Increases\n", - "\n", - "For a 6 element array, the benefits of array operations are pretty slim. There will be no appreciable difference in execution time because there are so few operations taking place. But if we revisit 2D linear convection, we can see some substantial speed increases. \n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "\n", - "nx = 81\n", - "ny = 81\n", - "nt = 100\n", - "c = 1\n", - "dx = 2.0/(nx-1)\n", - "dy = 2.0/(ny-1)\n", - "sigma = .2\n", - "dt = sigma*dx\n", - "\n", - "x = np.linspace(0,2,nx)\n", - "y = np.linspace(0,2,ny)\n", - "\n", - "u = np.ones((ny,nx)) ##create a 1xn vector of 1's\n", - "un = np.ones((ny,nx)) ##\n", - "\n", - "###Assign initial conditions\n", - "\n", - "u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This lesson complements the first interactive module of the online [CFD Python](https://github.com/barbagroup/CFDPython) class, by Prof. Lorena A. Barba, called **12 Steps to Navier–Stokes.** It was written with BU graduate student Gilbert Forsyth." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Array Operations with NumPy\n", + "----------------\n", + "\n", + "For more computationally intensive programs, the use of built-in Numpy functions can provide an increase in execution speed many-times over. As a simple example, consider the following equation:\n", + "\n", + "$$u^{n+1}_i = u^n_i-u^n_{i-1}$$\n", + "\n", + "Now, given a vector $u^n = [0, 1, 2, 3, 4, 5]\\ \\ $ we can calculate the values of $u^{n+1}$ by iterating over the values of $u^n$ with a for loop. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our initial conditions all set up, let's first try running our original nested loop code, making use of the iPython \"magic\" function `%%timeit`, which will help us evaluate the performance of our code. \n", - "\n", - "**Note**: The `%%timeit` magic function will run the code several times and then give an average execution time as a result. If you have any figures being plotted within a cell where you run `%%timeit`, it will plot those figures repeatedly which can be a bit messy. " + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "1\n", + "1\n", + "1\n", + "1\n" ] - }, + } + ], + "source": [ + "u = numpy.array((0, 1, 2, 3, 4, 5))\n", + "\n", + "for i in range(1, len(u)):\n", + " print(u[i] - u[i-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the expected result and the execution time was nearly instantaneous. If we perform the same operation as an array operation, then rather than calculate $u^n_i-u^n_{i-1}\\ $ 5 separate times, we can slice the $u$ array and calculate each operation with one command:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%timeit\n", - "u = np.ones((ny,nx))\n", - "u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2\n", - "\n", - "for n in range(nt+1): ##loop across number of time steps\n", - " un = u.copy()\n", - " for i in range(1, len(u)):\n", - " for j in range(1, len(u)):\n", - " u[i,j] = un[i, j] - (c*dt/dx*(un[i,j] - un[i-1,j]))-(c*dt/dy*(un[i,j]-un[i,j-1]))\n", - " u[0,:] = 1\n", - " u[-1,:] = 1\n", - " u[:,0] = 1\n", - " u[:,-1] = 1" - ], - "language": "python", + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1])" + ] + }, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1 loops, best of 3: 6.24 s per loop\n" - ] - } - ], - "prompt_number": 9 - }, + "output_type": "execute_result" + } + ], + "source": [ + "u[1:] - u[0:-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What this command says is subtract the 0th, 1st, 2nd, 3rd, 4th and 5th elements of $u$ from the 1st, 2nd, 3rd, 4th, 5th and 6th elements of $u$. \n", + "\n", + "### Speed Increases\n", + "\n", + "For a 6 element array, the benefits of array operations are pretty slim. There will be no appreciable difference in execution time because there are so few operations taking place. But if we revisit 2D linear convection, we can see some substantial speed increases. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "nx = 81\n", + "ny = 81\n", + "nt = 100\n", + "c = 1\n", + "dx = 2 / (nx - 1)\n", + "dy = 2 / (ny - 1)\n", + "sigma = .2\n", + "dt = sigma * dx\n", + "\n", + "x = numpy.linspace(0, 2, nx)\n", + "y = numpy.linspace(0, 2, ny)\n", + "\n", + "u = numpy.ones((ny, nx)) ##create a 1xn vector of 1's\n", + "un = numpy.ones((ny, nx)) \n", + "\n", + "###Assign initial conditions\n", + "\n", + "u[int(.5 / dy): int(1 / dy + 1), int(.5 / dx):int(1 / dx + 1)] = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our initial conditions all set up, let's first try running our original nested loop code, making use of the iPython \"magic\" function `%%timeit`, which will help us evaluate the performance of our code. \n", + "\n", + "**Note**: The `%%timeit` magic function will run the code several times and then give an average execution time as a result. If you have any figures being plotted within a cell where you run `%%timeit`, it will plot those figures repeatedly which can be a bit messy. \n", + "\n", + "The execution times below will vary from machine to machine. Don't expect your times to match these times, but you _should_ expect to see the same general trend in decreasing execution time as we switch to array operations." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the \"raw\" Python code above, the best execution time achieved was 6.24 seconds. Keep in mind that with these three nested loops, that the statements inside the **j** loop are being evaluated more than 650,000 times. Let's compare that with the performance of the same code implemented with array operations:" + "name": "stdout", + "output_type": "stream", + "text": [ + "3.07 s ± 15.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%timeit\n", - "u = np.ones((ny,nx))\n", - "u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2\n", - "\n", - "for n in range(nt+1): ##loop across number of time steps\n", - " un = u.copy()\n", - " u[1:,1:]=un[1:,1:]-(c*dt/dx*(un[1:,1:]-un[0:-1,1:]))-(c*dt/dy*(un[1:,1:]-un[1:,0:-1]))\n", - " u[0,:] = 1\n", - " u[-1,:] = 1\n", - " u[:,0] = 1\n", - " u[:,-1] = 1" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "100 loops, best of 3: 9.59 ms per loop\n" - ] - } - ], - "prompt_number": 11 - }, + } + ], + "source": [ + "%%timeit\n", + "u = numpy.ones((ny, nx))\n", + "u[int(.5 / dy): int(1 / dy + 1), int(.5 / dx):int(1 / dx + 1)] = 2\n", + "\n", + "for n in range(nt + 1): ##loop across number of time steps\n", + " un = u.copy()\n", + " row, col = u.shape\n", + " for j in range(1, row):\n", + " for i in range(1, col):\n", + " u[j, i] = (un[j, i] - (c * dt / dx * \n", + " (un[j, i] - un[j, i - 1])) - \n", + " (c * dt / dy * \n", + " (un[j, i] - un[j - 1, i])))\n", + " u[0, :] = 1\n", + " u[-1, :] = 1\n", + " u[:, 0] = 1\n", + " u[:, -1] = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the \"raw\" Python code above, the mean execution time achieved was 3.07 seconds (on a MacBook Pro Mid 2012). Keep in mind that with these three nested loops, that the statements inside the **j** loop are being evaluated more than 650,000 times. Let's compare that with the performance of the same code implemented with array operations:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, the speed increase is substantial. The same calculation goes from 6.24 seconds to 9.59 milliseconds. 6 seconds isn't a huge amount of time to wait, but these speed gains will increase exponentially with the size and complexity of the problem being evaluated. " + "name": "stdout", + "output_type": "stream", + "text": [ + "7.38 ms ± 105 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + } + ], + "source": [ + "%%timeit\n", + "u = numpy.ones((ny, nx))\n", + "u[int(.5 / dy): int(1 / dy + 1), int(.5 / dx):int(1 / dx + 1)] = 2\n", + "\n", + "for n in range(nt + 1): ##loop across number of time steps\n", + " un = u.copy()\n", + " u[1:, 1:] = (un[1:, 1:] - (c * dt / dx * (un[1:, 1:] - un[1:, 0:-1])) -\n", + " (c * dt / dy * (un[1:, 1:] - un[0:-1, 1:])))\n", + " u[0, :] = 1\n", + " u[-1, :] = 1\n", + " u[:, 0] = 1\n", + " u[:, -1] = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the speed increase is substantial. The same calculation goes from 3.07 seconds to 7.38 milliseconds. 3 seconds isn't a huge amount of time to wait, but these speed gains will increase exponentially with the size and complexity of the problem being evaluated. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, "metadata": {}, - "outputs": [] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] } - ] + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } diff --git a/lessons/07_Step_5.ipynb b/lessons/07_Step_5.ipynb index bbfc2857..1d3bfa8e 100644 --- a/lessons/07_Step_5.ipynb +++ b/lessons/07_Step_5.ipynb @@ -1,476 +1,515 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Up to now, all of our work has been in one spatial dimension (Steps [1](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/01_Step_1.ipynb) to [4](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/05_Step_4.ipynb)). We can learn a lot in just 1D, but let's grow up to flatland: two dimensions. \n", - "\n", - "In the following exercises, you will extend the first four steps to 2D. To extend the 1D finite-difference formulas to partial derivatives in 2D or 3D, just apply the definition: a partial derivative with respect to $x$ is the variation in the $x$ direction *at constant* $y$.\n", - "\n", - "In 2D space, a rectangular (uniform) grid is defined by the points with coordinates:\n", - "\n", - "$$x_i = x_0 +i \\Delta x$$\n", - "\n", - "$$y_i = y_0 +i \\Delta y$$\n", - "\n", - "Now, define $u_{i,j} = u(x_i,y_j)$ and apply the finite-difference formulas on either variable $x,y$ *acting separately* on the $i$ and $j$ indices. All derivatives are based on the 2D Taylor expansion of a mesh point value around $u_{i,j}$.\n", - "\n", - "Hence, for a first-order partial derivative in the $x$-direction, a finite-difference formula is:\n", - "\n", - "$$ \\frac{\\partial u}{\\partial x}\\biggr\\rvert_{i,j} = \\frac{u_{i+1,j}-u_{i,j}}{\\Delta x}+\\mathcal{O}(\\Delta x)$$\n", - "\n", - "and similarly in the $y$ direction. Thus, we can write backward-difference, forward-difference or central-difference formulas for Steps 5 to 12. Let's get started!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 5: 2-D Linear Convection\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The PDE governing 2-D Linear Convection is written as\n", - "\n", - "$$\\frac{\\partial u}{\\partial t}+c\\frac{\\partial u}{\\partial x} + c\\frac{\\partial u}{\\partial y} = 0$$\n", - "\n", - "This is the exact same form as with 1-D Linear Convection, except that we now have two spatial dimensions to account for as we step forward in time. \n", - "\n", - "Again, the timestep will be discretized as a forward difference and both spatial steps will be discretized as backward differences. \n", - "\n", - "With 1-D implementations, we used $i$ subscripts to denote movement in space (e.g. $u_{i}^n-u_{i-1}^n$). Now that we have two dimensions to account for, we need to add a second subscript, $j$, to account for all the information in the regime. \n", - "\n", - "Here, we'll again use $i$ as the index for our $x$ values, and we'll add the $j$ subscript to track our $y$ values. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With that in mind, our discretization of the PDE should be relatively straightforward. \n", - "\n", - "$$\\frac{u_{i,j}^{n+1}-u_{i,j}^n}{\\Delta t} + c\\frac{u_{i, j}^n-u_{i-1,j}^n}{\\Delta x} + c\\frac{u_{i,j}^n-u_{i,j-1}^n}{\\Delta y}=0$$\n", - "\n", - "As before, solve for the only unknown:\n", - "\n", - "$$u_{i,j}^{n+1} = u_{i,j}^n-c \\frac{\\Delta t}{\\Delta x}(u_{i,j}^n-u_{i-1,j}^n)-c \\frac{\\Delta t}{\\Delta y}(u_{i,j}^n-u_{i,j-1}^n)$$\n", - "\n", - "We will solve this equation with the following initial conditions:\n", - "\n", - "$$u(x) = \\begin{cases}\n", - "\\begin{matrix}\n", - "2\\ \\text{for} & 0.5 \\leq x \\leq 1 \\cr\n", - "1\\ \\text{for} & \\text{everywhere else}\\end{matrix}\\end{cases}$$\n", - "\n", - "and boundary conditions:\n", - "\n", - "$$u = 1\\ \\text{for } \\begin{cases}\n", - "\\begin{matrix}\n", - "x = 0,\\ 2 \\cr\n", - "y = 0,\\ 2 \\end{matrix}\\end{cases}$$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D ##New Library required for projected 3d plots\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "###variable declarations\n", - "nx = 81\n", - "ny = 81\n", - "nt = 100\n", - "c = 1\n", - "dx = 2.0/(nx-1)\n", - "dy = 2.0/(ny-1)\n", - "sigma = .2\n", - "dt = sigma*dx\n", - "\n", - "x = np.linspace(0,2,nx)\n", - "y = np.linspace(0,2,ny)\n", - "\n", - "u = np.ones((ny,nx)) ##create a 1xn vector of 1's\n", - "un = np.ones((ny,nx)) ##\n", - "\n", - "###Assign initial conditions\n", - "\n", - "u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2 ##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", - "\n", - "###Plot Initial Condition\n", - "fig = plt.figure(figsize=(11,7), dpi=100) ##the figsize parameter can be used to produce different sized images\n", - "ax = fig.gca(projection='3d') \n", - "X, Y = np.meshgrid(x,y) \n", - "surf = ax.plot_surface(X,Y,u[:])\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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5esSAo+Ye57meL2JZz7B//x7uv38X99//ODfccAswTiIxwtKlh7B+/UpOP/10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qoORNLYcOGmskYQiZ1oPQtjvkXkRkRG4Z17kdMjWgYWY8nJAh/J8xuBBhBfkud3gF4A/JvohN9G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\nyBtvvMGQIUNkKicItSCCTmg0K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- } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###3D Plotting Notes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To plot a projected 3D result, make sure that you have added the Axes3D library. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " from mpl_toolkits.mplot3d import Axes3D" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The actual plotting commands are a little more involved than with simple 2d plots." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " fig = plt.figure(figsize=(11,7), dpi=100)\n", - " ax = fig.gca(projection='3d')\n", - " surf2 = ax.plot_surface(X,Y,u[:])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first line here is initializing a figure window. The **figsize** and **dpi** commands are optional and simply specify the size and resolution of the figure being produced. You may omit them, but you will still require the \n", - " \n", - " fig = plt.figure()\n", - "\n", - "The next line assigns the plot window the axes label 'ax' and also specifies that it will be a 3d projection plot. The final line uses the command\n", - " \n", - " plot_surface()\n", - "\n", - "which is equivalent to the regular plot command, but it takes a grid of X and Y values for the data point positions. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#####Note\n", - "\n", - "\n", - "The `X` and `Y` values that you pass to `plot_surface` are not the 1-D vectors `x` and `y`. In order to use matplotlibs 3D plotting functions, you need to generate a grid of `x, y` values which correspond to each coordinate in the plotting frame. This coordinate grid is generated using the numpy function `meshgrid`.\n", - "\n", - " X, Y = np.meshgrid(x,y)\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Iterating in two dimensions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "To evaluate the wave in two dimensions requires the use of several nested for-loops to cover all of the `i`'s and `j`'s. Since Python is not a compiled language there can be noticeable slowdowns in the execution of code with multiple for-loops. First try evaluating the 2D convection code and see what results it produces. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = np.ones((ny,nx))\n", - "u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2\n", - "\n", - "for n in range(nt+1): ##loop across number of time steps\n", - " un = u.copy()\n", - " for i in range(1, len(u)):\n", - " for j in range(1, len(u)):\n", - " u[i,j] = un[i, j] - (c*dt/dx*(un[i,j] - un[i-1,j]))-(c*dt/dy*(un[i,j]-un[i,j-1]))\n", - " u[0,:] = 1\n", - " u[-1,:] = 1\n", - " u[:,0] = 1\n", - " u[:,-1] = 1\n", - "\n", - "fig = plt.figure(figsize=(11,7), dpi=100)\n", - "ax = fig.gca(projection='3d')\n", - "surf2 = ax.plot_surface(X,Y,u[:])\n", - "\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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LFvD4449zyimnsGjRIlc+pxt03pNZcA0nFatWIeKmR6kTUOuXzWaB8RW/XiLtADannAfB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wAAAg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\n" - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Array Operations\n", - "----------------\n", - "\n", - "Here the same 2D convection code is implemented, but instead of using nested for-loops, the same calculations are evaluated using array operations. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = np.ones((ny,nx))\n", - "u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2\n", - "\n", - "for n in range(nt+1): ##loop across number of time steps\n", - " un[:] = u[:]\n", - " u[1:,1:]=un[1:,1:]-(c*dt/dx*(un[1:,1:]-un[0:-1,1:]))-(c*dt/dy*(un[1:,1:]-un[1:,0:-1]))\n", - " u[0,:] = 1\n", - " u[-1,:] = 1\n", - " u[:,0] = 1\n", - " u[:,-1] = 1\n", - "\n", - "fig = plt.figure(figsize=(11,7), dpi=100)\n", - "ax = fig.gca(projection='3d')\n", - "surf2 = ax.plot_surface(X,Y,u[:])\n", - "\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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LFvD4449zyimnsGjRIlc+pxt03pNZcA0nFatWIeKmR6kTUOuXzWaB8RW/XiLtADannAfB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wAAAg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\n" - } - ], - "prompt_number": 4 - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Up to now, all of our work has been in one spatial dimension (Steps [1](./01_Step_1.ipynb) to [4](./05_Step_4.ipynb)). We can learn a lot in just 1D, but let's grow up to flatland: two dimensions. \n", + "\n", + "In the following exercises, you will extend the first four steps to 2D. To extend the 1D finite-difference formulas to partial derivatives in 2D or 3D, just apply the definition: a partial derivative with respect to $x$ is the variation in the $x$ direction *at constant* $y$.\n", + "\n", + "In 2D space, a rectangular (uniform) grid is defined by the points with coordinates:\n", + "\n", + "$$x_i = x_0 +i \\Delta x$$\n", + "\n", + "$$y_i = y_0 +i \\Delta y$$\n", + "\n", + "Now, define $u_{i,j} = u(x_i,y_j)$ and apply the finite-difference formulas on either variable $x,y$ *acting separately* on the $i$ and $j$ indices. All derivatives are based on the 2D Taylor expansion of a mesh point value around $u_{i,j}$.\n", + "\n", + "Hence, for a first-order partial derivative in the $x$-direction, a finite-difference formula is:\n", + "\n", + "$$ \\frac{\\partial u}{\\partial x}\\biggr\\rvert_{i,j} = \\frac{u_{i+1,j}-u_{i,j}}{\\Delta x}+\\mathcal{O}(\\Delta x)$$\n", + "\n", + "and similarly in the $y$ direction. Thus, we can write backward-difference, forward-difference or central-difference formulas for Steps 5 to 12. Let's get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 5: 2-D Linear Convection\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The PDE governing 2-D Linear Convection is written as\n", + "\n", + "$$\\frac{\\partial u}{\\partial t}+c\\frac{\\partial u}{\\partial x} + c\\frac{\\partial u}{\\partial y} = 0$$\n", + "\n", + "This is the exact same form as with 1-D Linear Convection, except that we now have two spatial dimensions to account for as we step forward in time. \n", + "\n", + "Again, the timestep will be discretized as a forward difference and both spatial steps will be discretized as backward differences. \n", + "\n", + "With 1-D implementations, we used $i$ subscripts to denote movement in space (e.g. $u_{i}^n-u_{i-1}^n$). Now that we have two dimensions to account for, we need to add a second subscript, $j$, to account for all the information in the regime. \n", + "\n", + "Here, we'll again use $i$ as the index for our $x$ values, and we'll add the $j$ subscript to track our $y$ values. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With that in mind, our discretization of the PDE should be relatively straightforward. \n", + "\n", + "$$\\frac{u_{i,j}^{n+1}-u_{i,j}^n}{\\Delta t} + c\\frac{u_{i, j}^n-u_{i-1,j}^n}{\\Delta x} + c\\frac{u_{i,j}^n-u_{i,j-1}^n}{\\Delta y}=0$$\n", + "\n", + "As before, solve for the only unknown:\n", + "\n", + "$$u_{i,j}^{n+1} = u_{i,j}^n-c \\frac{\\Delta t}{\\Delta x}(u_{i,j}^n-u_{i-1,j}^n)-c \\frac{\\Delta t}{\\Delta y}(u_{i,j}^n-u_{i,j-1}^n)$$\n", + "\n", + "We will solve this equation with the following initial conditions:\n", + "\n", + "$$u(x,y) = \\begin{cases}\n", + "\\begin{matrix}\n", + "2\\ \\text{for} & 0.5 \\leq x, y \\leq 1 \\cr\n", + "1\\ \\text{for} & \\text{everywhere else}\\end{matrix}\\end{cases}$$\n", + "\n", + "and boundary conditions:\n", + "\n", + "$$u = 1\\ \\text{for } \\begin{cases}\n", + "\\begin{matrix}\n", + "x = 0,\\ 2 \\cr\n", + "y = 0,\\ 2 \\end{matrix}\\end{cases}$$" + ] + }, + { + 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1dqzaOzRjsdgE65Fm8ZOgs6eaAUBVVWnGcx06dAjvf/81eGTrL3HaSRE8dN9x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WlYXaRHO46hUmxQO2mkLXsy25m+kjcu/YSEn3HNT3fiJRIsHck7n+ygj9O4KmrYsufdlNWUXkVW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\nCvfddx8ADz74INu3b8dqtbJ582ZycnJCv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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "Learn More" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D ##New Library required for projected 3d plots\n", + "\n", + "import numpy\n", + "from matplotlib import pyplot, cm\n", + "%matplotlib inline\n", + "\n", + "###variable declarations\n", + "nx = 81\n", + "ny = 81\n", + "nt = 100\n", + "c = 1\n", + "dx = 2 / (nx - 1)\n", + "dy = 2 / (ny - 1)\n", + "sigma = .2\n", + "dt = sigma * dx\n", + "\n", + "x = numpy.linspace(0, 2, nx)\n", + "y = numpy.linspace(0, 2, ny)\n", + "\n", + "u = numpy.ones((ny, nx)) ##create a 1xn vector of 1's\n", + "un = numpy.ones((ny, nx)) ##\n", + "\n", + "###Assign initial conditions\n", + "\n", + "##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", + "u[int(.5 / dy):int(1 / dy + 1),int(.5 / dx):int(1 / dx + 1)] = 2 \n", + "\n", + "###Plot Initial Condition\n", + "##the figsize parameter can be used to produce different sized images\n", + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "ax = fig.gca(projection='3d') \n", + "X, Y = numpy.meshgrid(x, y) \n", + "surf = ax.plot_surface(X, Y, u[:], cmap=cm.viridis)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3D Plotting Notes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To plot a projected 3D result, make sure that you have added the Axes3D library. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " from mpl_toolkits.mplot3d import Axes3D" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The actual plotting commands are a little more involved than with simple 2d plots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "ax = fig.gca(projection='3d')\n", + "surf2 = ax.plot_surface(X, Y, u[:])\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first line here is initializing a figure window. The **figsize** and **dpi** commands are optional and simply specify the size and resolution of the figure being produced. You may omit them, but you will still require the \n", + " \n", + " fig = pyplot.figure()\n", + "\n", + "The next line assigns the plot window the axes label 'ax' and also specifies that it will be a 3d projection plot. The final line uses the command\n", + " \n", + " plot_surface()\n", + "\n", + "which is equivalent to the regular plot command, but it takes a grid of X and Y values for the data point positions. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Note\n", + "\n", + "\n", + "The `X` and `Y` values that you pass to `plot_surface` are not the 1-D vectors `x` and `y`. In order to use matplotlibs 3D plotting functions, you need to generate a grid of `x, y` values which correspond to each coordinate in the plotting frame. This coordinate grid is generated using the numpy function `meshgrid`.\n", + "\n", + " X, Y = numpy.meshgrid(x, y)\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Iterating in two dimensions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To evaluate the wave in two dimensions requires the use of several nested for-loops to cover all of the `i`'s and `j`'s. Since Python is not a compiled language there can be noticeable slowdowns in the execution of code with multiple for-loops. First try evaluating the 2D convection code and see what results it produces. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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jzSToCE0oJHwTiQQ8nl3XutVqxdChQzF06FDVjnv++efjyCOPRN++fdHe3o4n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MAAAg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\nXbuG6upqzJo1CwBw5coVHDlyBK2trfjXv/7li6UT+RxDETSp7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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "The video lesson that walks you through the details for Step 5 (and onwards to Step 8) is **Video Lesson 6** on You Tube:" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "u = numpy.ones((ny, nx))\n", + "u[int(.5 / dy):int(1 / dy + 1), int(.5 / dx):int(1 / dx + 1)] = 2\n", + "\n", + "for n in range(nt + 1): ##loop across number of time steps\n", + " un = u.copy()\n", + " row, col = u.shape\n", + " for j in range(1, row):\n", + " for i in range(1, col):\n", + " u[j, i] = (un[j, i] - (c * dt / dx * (un[j, i] - un[j, i - 1])) -\n", + " (c * dt / dy * (un[j, i] - un[j - 1, i])))\n", + " u[0, :] = 1\n", + " u[-1, :] = 1\n", + " u[:, 0] = 1\n", + " u[:, -1] = 1\n", + "\n", + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "ax = fig.gca(projection='3d')\n", + "surf2 = ax.plot_surface(X, Y, u[:], cmap=cm.viridis)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Array Operations\n", + "----------------\n", + "\n", + "Here the same 2D convection code is implemented, but instead of using nested for-loops, the same calculations are evaluated using array operations. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('tUg_dE3NXoY')" - ], - "language": "python", + "data": { + "image/png": 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jzSToCE0oJHwTiQQ8nl3XutVqxdChQzF06FDVjnv++efjyCOPRN++fdHe3o4n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MAAAg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\nXbuG6upqzJo1CwBw5coVHDlyBK2trfjXv/7li6UT+RxDETSp7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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "display_data" + } + ], + "source": [ + "u = numpy.ones((ny, nx))\n", + "u[int(.5 / dy):int(1 / dy + 1), int(.5 / dx):int(1 / dx + 1)] = 2\n", + "\n", + "for n in range(nt + 1): ##loop across number of time steps\n", + " un = u.copy()\n", + " u[1:, 1:] = (un[1:, 1:] - (c * dt / dx * (un[1:, 1:] - un[1:, :-1])) -\n", + " (c * dt / dy * (un[1:, 1:] - un[:-1, 1:])))\n", + " u[0, :] = 1\n", + " u[-1, :] = 1\n", + " u[:, 0] = 1\n", + " u[:, -1] = 1\n", + "\n", + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "ax = fig.gca(projection='3d')\n", + "surf2 = ax.plot_surface(X, Y, u[:], cmap=cm.viridis)\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The video lesson that walks you through the details for Step 5 (and onwards to Step 8) is **Video Lesson 6** on You Tube:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('tUg_dE3NXoY')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" } - ] + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/lessons/08_Step_6.ipynb b/lessons/08_Step_6.ipynb index 04c650a7..59559a8b 100644 --- a/lessons/08_Step_6.ipynb +++ b/lessons/08_Step_6.ipynb @@ -1,396 +1,461 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You should have completed your own code for [Step 5](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/07_Step_5.ipynb) before continuing to this lesson. As with Steps 1 to 4, we will build incrementally, so it's important to complete the previous step!\n", - "\n", - "We continue ..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 6: 2-D Convection\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we solve 2D Convection, represented by the pair of coupled partial differential equations below: \n", - "\n", - "$$\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} + v \\frac{\\partial u}{\\partial y} = 0$$\n", - "\n", - "$$\\frac{\\partial v}{\\partial t} + u \\frac{\\partial v}{\\partial x} + v \\frac{\\partial v}{\\partial y} = 0$$\n", - "\n", - "Discretizing these equations using the methods we've applied previously yields:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\\frac{u_{i,j}^{n+1}-u_{i,j}^n}{\\Delta t} + u_{i,j}^n \\frac{u_{i,j}^n-u_{i-1,j}^n}{\\Delta x} + v_{i,j}^n \\frac{u_{i,j}^n-u_{i,j-1}^n}{\\Delta y} = 0$$\n", - "\n", - "$$\\frac{v_{i,j}^{n+1}-v_{i,j}^n}{\\Delta t} + u_{i,j}^n \\frac{v_{i,j}^n-v_{i-1,j}^n}{\\Delta x} + v_{i,j}^n \\frac{v_{i,j}^n-v_{i,j-1}^n}{\\Delta y} = 0$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rearranging both equations, we solve for $u_{i,j}^{n+1}$ and $v_{i,j}^{n+1}$, respectively. Note that these equations are also coupled. \n", - "\n", - "$$u_{i,j}^{n+1} = u_{i,j}^n - u_{i,j} \\frac{\\Delta t}{\\Delta x} (u_{i,j}^n-u_{i-1,j}^n) - v_{i,j}^n \\frac{\\Delta t}{\\Delta y} (u_{i,j}^n-u_{i,j-1}^n)$$\n", - "\n", - "$$v_{i,j}^{n+1} = v_{i,j}^n - u_{i,j} \\frac{\\Delta t}{\\Delta x} (v_{i,j}^n-v_{i-1,j}^n) - v_{i,j}^n \\frac{\\Delta t}{\\Delta y} (v_{i,j}^n-v_{i,j-1}^n)$$" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Initial Conditions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The initial conditions are the same that we used for 1D convection, applied in both the x and y directions. \n", - "\n", - "$$u,\\ v\\ = \\begin{cases}\\begin{matrix}\n", - "2 & \\text{for } x,y \\in (0.5, 1)\\times(0.5,1) \\cr\n", - "1 & \\text{everywhere else}\n", - "\\end{matrix}\\end{cases}$$" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Boundary Conditions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The boundary conditions hold u and v equal to 1 along the boundaries of the grid\n", - ".\n", - "\n", - "$$u = 1,\\ v = 1 \\text{ for } \\begin{cases} \\begin{matrix}x=0,2\\cr y=0,2 \\end{matrix}\\end{cases}$$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "###variable declarations\n", - "nx = 101\n", - "ny = 101\n", - "nt = 80\n", - "c = 1\n", - "dx = 2.0/(nx-1)\n", - "dy = 2.0/(ny-1)\n", - "sigma = .2\n", - "dt = sigma*dx\n", - "\n", - "x = np.linspace(0,2,nx)\n", - "y = np.linspace(0,2,ny)\n", - "\n", - "u = np.ones((ny,nx)) ##create a 1xn vector of 1's\n", - "v = np.ones((ny,nx))\n", - "un = np.ones((ny,nx))\n", - "vn = np.ones((ny,nx))\n", - "\n", - "###Assign initial conditions\n", - "\n", - "u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2 ##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", - "v[.5/dy:1/dy+1,.5/dx:1/dx+1]=2 ##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", - "\n", - "for n in range(nt+1): ##loop across number of time steps\n", - " un = u.copy()\n", - " vn = v.copy()\n", - "\n", - " u[1:,1:]=un[1:,1:]-(un[1:,1:]*dt/dx*(un[1:,1:]-un[0:-1,1:]))-vn[1:,1:]*dt/dy*(un[1:,1:]-un[1:,0:-1]) \n", - " v[1:,1:]=vn[1:,1:]-(un[1:,1:]*dt/dx*(vn[1:,1:]-vn[0:-1,1:]))-vn[1:,1:]*dt/dy*(vn[1:,1:]-vn[1:,0:-1])\n", - " \n", - " u[0,:] = 1\n", - " u[-1,:] = 1\n", - " u[:,0] = 1\n", - " u[:,-1] = 1\n", - " \n", - " v[0,:] = 1\n", - " v[-1,:] = 1\n", - " v[:,0] = 1\n", - " v[:,-1] = 1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from matplotlib import cm ##cm = \"colormap\" for changing the 3d plot color palette\n", - "fig = plt.figure(figsize=(11,7), dpi=100)\n", - "ax = fig.gca(projection='3d')\n", - "X,Y = np.meshgrid(x,y)\n", - "\n", - "ax.plot_surface(X,Y,u, cmap=cm.coolwarm)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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GuzMThqEnY1UrRIy0KDUCbP5SqRSAgRm/ZkLlAAaTz4KgqioSiQQCgQC5bomyIDuKfgq5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8ez\nb98+sSsmCD9i4shVEARhFJEkiQULFjB+/PheizkQR5yC8FMkdugEQRBGkR9++IGf/exnTJ482XaM\num7dOk6dOgVYjjgB27Bf6xFnUlLSiK1ZEIShJwo6QRAEQRCEUU4cuQqCIAiCIIxyoqATBEEQBEEY\n5URBJwiCIAiCMMqJgk4QBEEQBGGUEwWdIAiCIAjCKCcKOkEQBEEQhFHu/wOOZwhsT0jpggAAAABJ\nRU5ErkJggg==\n" - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from matplotlib import cm ##cm = \"colormap\" for changing the 3d plot color palette\n", - "fig = plt.figure(figsize=(11,7), dpi=100)\n", - "ax = fig.gca(projection='3d')\n", - "X,Y = np.meshgrid(x,y)\n", - "ax.plot_surface(X,Y,v, cmap=cm.coolwarm)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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GuzMThqEnY1UrRIy0KDUCbP5SqRSAgRm/ZkLlAAaTz4KgqioSiQQCgQC5bomyIDuKfgq5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\nRU5ErkJggg==\n" - } - ], - "prompt_number": 5 - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should have completed your own code for [Step 5](./07_Step_5.ipynb) before continuing to this lesson. As with Steps 1 to 4, we will build incrementally, so it's important to complete the previous step!\n", + "\n", + "We continue ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 6: 2-D Convection\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we solve 2D Convection, represented by the pair of coupled partial differential equations below: \n", + "\n", + "$$\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} + v \\frac{\\partial u}{\\partial y} = 0$$\n", + "\n", + "$$\\frac{\\partial v}{\\partial t} + u \\frac{\\partial v}{\\partial x} + v \\frac{\\partial v}{\\partial y} = 0$$\n", + "\n", + "Discretizing these equations using the methods we've applied previously yields:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\\frac{u_{i,j}^{n+1}-u_{i,j}^n}{\\Delta t} + u_{i,j}^n \\frac{u_{i,j}^n-u_{i-1,j}^n}{\\Delta x} + v_{i,j}^n \\frac{u_{i,j}^n-u_{i,j-1}^n}{\\Delta y} = 0$$\n", + "\n", + "$$\\frac{v_{i,j}^{n+1}-v_{i,j}^n}{\\Delta t} + u_{i,j}^n \\frac{v_{i,j}^n-v_{i-1,j}^n}{\\Delta x} + v_{i,j}^n \\frac{v_{i,j}^n-v_{i,j-1}^n}{\\Delta y} = 0$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rearranging both equations, we solve for $u_{i,j}^{n+1}$ and $v_{i,j}^{n+1}$, respectively. Note that these equations are also coupled. \n", + "\n", + "$$u_{i,j}^{n+1} = u_{i,j}^n - u_{i,j} \\frac{\\Delta t}{\\Delta x} (u_{i,j}^n-u_{i-1,j}^n) - v_{i,j}^n \\frac{\\Delta t}{\\Delta y} (u_{i,j}^n-u_{i,j-1}^n)$$\n", + "\n", + "$$v_{i,j}^{n+1} = v_{i,j}^n - u_{i,j} \\frac{\\Delta t}{\\Delta x} (v_{i,j}^n-v_{i-1,j}^n) - v_{i,j}^n \\frac{\\Delta t}{\\Delta y} (v_{i,j}^n-v_{i,j-1}^n)$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initial Conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The initial conditions are the same that we used for 1D convection, applied in both the x and y directions. \n", + "\n", + "$$u,\\ v\\ = \\begin{cases}\\begin{matrix}\n", + "2 & \\text{for } x,y \\in (0.5, 1)\\times(0.5,1) \\cr\n", + "1 & \\text{everywhere else}\n", + "\\end{matrix}\\end{cases}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Boundary Conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The boundary conditions hold u and v equal to 1 along the boundaries of the grid\n", + ".\n", + "\n", + "$$u = 1,\\ v = 1 \\text{ for } \\begin{cases} \\begin{matrix}x=0,2\\cr y=0,2 \\end{matrix}\\end{cases}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib import pyplot, cm\n", + "import numpy\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "###variable declarations\n", + "nx = 101\n", + "ny = 101\n", + "nt = 80\n", + "c = 1\n", + "dx = 2 / (nx - 1)\n", + "dy = 2 / (ny - 1)\n", + "sigma = .2\n", + "dt = sigma * dx\n", + "\n", + "x = numpy.linspace(0, 2, nx)\n", + "y = numpy.linspace(0, 2, ny)\n", + "\n", + "u = numpy.ones((ny, nx)) ##create a 1xn vector of 1's\n", + "v = numpy.ones((ny, nx))\n", + "un = numpy.ones((ny, nx))\n", + "vn = numpy.ones((ny, nx))\n", + "\n", + "###Assign initial conditions\n", + "##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", + "u[int(.5 / dy):int(1 / dy + 1), int(.5 / dx):int(1 / dx + 1)] = 2\n", + "##set hat function I.C. : v(.5<=x<=1 && .5<=y<=1 ) is 2\n", + "v[int(.5 / dy):int(1 / dy + 1), int(.5 / dx):int(1 / dx + 1)] = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, + "data": { + "image/png": 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dn1kzZ3PcccexcOFCRo0a1avPVgSdN9FolFAoVFLlHANJT0LP+enz+Ya0GaPU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\n5HK5Nd6jpaNdcqS1plqtkq/ZS6RSKRKJxJrcsFaD0EWJ3OjoKFJKHJ9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E4lJlmDH2cpgN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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "Learn More" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "ax = fig.gca(projection='3d')\n", + "X, Y = numpy.meshgrid(x, y)\n", + "\n", + "ax.plot_surface(X, Y, u, cmap=cm.viridis, rstride=2, cstride=2)\n", + "ax.set_xlabel('$x$')\n", + "ax.set_ylabel('$y$');" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for n in range(nt + 1): ##loop across number of time steps\n", + " un = u.copy()\n", + " vn = v.copy()\n", + " u[1:, 1:] = (un[1:, 1:] - \n", + " (un[1:, 1:] * c * dt / dx * (un[1:, 1:] - un[1:, :-1])) -\n", + " vn[1:, 1:] * c * dt / dy * (un[1:, 1:] - un[:-1, 1:]))\n", + " v[1:, 1:] = (vn[1:, 1:] -\n", + " (un[1:, 1:] * c * dt / dx * (vn[1:, 1:] - vn[1:, :-1])) -\n", + " vn[1:, 1:] * c * dt / dy * (vn[1:, 1:] - vn[:-1, 1:]))\n", + " \n", + " u[0, :] = 1\n", + " u[-1, :] = 1\n", + " u[:, 0] = 1\n", + " u[:, -1] = 1\n", + " \n", + " v[0, :] = 1\n", + " v[-1, :] = 1\n", + " v[:, 0] = 1\n", + " v[:, -1] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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kkRrqu1WFXDgcrll7MkAfc2GkkkvoFZOZ+Oabb+Le3/wPLvuWB8//3Y+9O0Vc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8lqTxSyqCZZYBzw4sGEEeTJ0KTbWbUX6Tn3E2xPPoVRLscf/2WinUcpJLdQlSLRnjsCgcy610oM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\ni0t5H+/curRzf5FZTD2p4z273Y7f7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CzeBue9IVkLCxYW08j73vc+Ojo6zvv1p59+mo0b\nNwJw5ZVXMjIyklGVZjGOpml86lOfoqmpiV//+tdcddVVgC7Wzp49S0NDA6+++io/+tGPCAaDVFVV\nmft4K1euxO12Z0yH0kVeMpkkHo/P6qaL2cIbqe3661//yre+9S2+/OUv8+ijj07Lcbemadx22228\n8MILlJeXc8UVV3D99ddTW1ub8bgNGzbwzDPPTPn1WFhMNZags7CYRXR1dTFnzhzzzxUVFXR1dVmC\nbhIURWHjxo386le/wu12m/9cCEFFRQU33HADN9xwA6D/cm9ra2P37t08/fTTfOc73yGRSFBbW5vR\ndOFwOLDZbGYP5rlNF7FYbILIS7lnL0eRlx4J4/f7Jwi14eFhtmzZQjgc5s9//vO0vo8bGhqorq42\nJ4E33XQTTz/99ARBd4FTKguLSwZL0FlYWFyyfOQjH3lDj1MUherqaqqrq/mHf/gHYNx00dDQwOOP\nP86RI0f+//buLaTJ/48D+HvqCOcks8xkWrYsDxUtlwfsAEUEdjA7h4EXBWJhBRpkkJCBUmlFBzGv\nhC60yIuM1BUURUX6aGIHMTpo80AHcDiQoKnb/yL2sLmZ9fvrnj36fl05/ZKf2c173+/z+X6cmi70\nej20Wi38/Pycui8dL0G2360GTK+mi/HGdtlsNtTV1aGkpASnTp3Cjh07PB54R384Cg8PhyAILute\nvnwJnU4HjUaDkpISxMXFebJMognDQEfkRTQaDXp6esTXvb290Gg0ElY0dSmVSuh0Ouh0OmRlZYlN\nF/ZJF+fOnUNXVxcCAwOh0+mwatUqcdLF6JDn+DyexWJx6ay1N15MhV08x7FdgYGBLu/px48fOHny\nJNRqNR4+fIhZs2ZJVOn49Ho9uru7oVKp0NDQgPT0dHz48EHqsoj+EwY6Ig+zd126k5aWhrKyMuzb\ntw+NjY0ICgricauHKBQKqFQqrF69GqtXrwbw+/9qYGBAnHRRVVWF79+/IyQkRJx0ER8fj5kzZ0Kp\nVIo7VaObLuzjruTcdOF4UbNKpXK5M85qteLOnTsoLy9HUVERNm7cKOl702g06O7uFl+7+3DkOCM2\nNTUVR44cgclkQnBwsMfqJJoo7HIl8qCMjAw8efIE/f39CA0NRWFhISwWCxQKBbKysgAAOTk5MBgM\nCAgIQGVlJeLj4yWumhzZbDZ8/foVgiBAEAS0trbCbDZjwYIF4vN49qaLf+mste/ieVvTxd+M7err\n60Nubi60Wi2KioqcgpJURkZGEB0djUePHiEsLAyJiYmorq5GbGysuMax4UgQBOzduxdfvnyRqGKi\nv8JrS4iIJou96cI+6aKtrc2p6cI+6cLds2buJl14S2fteGO7rFYrKisrUV1djZKSEqSkpHhVGDUY\nDDh+/Lh4bUl+fj4qKirED1BlZWUoLy+HUqmEv78/Ll++jKSkJKnLJvoTBjoiIk8aHh52mnTR0dEB\nX19fp0kXWq3WpXlidGftyMgIbDab0wXIk9104bgrN9bYrk+fPiE3NxfJyckoKCjAjBkzJq0eIhIx\n0BERScmx6cI+6aKrqwtqtRorV64Un8kLCwtzCU/ujmqByemsHW9s1/DwMMrKylBfX49r165xkgmR\nZzHQERF5G5vNBrPZjJaWFjQ1NaGlpQXfvn1DSEiIuItnb7oY/Tye4/UpEzHpYryxXQDQ3t6OvLw8\nbNmyBbm5uS5HyEQ06RjoiIjkwLHporm5Ga9evcLAwAAiIyPFrtoVK1bA39//n8eZ+fn5uX0eb7xd\nuV+/fqG0tBRNTU0oKytDdHT05P8hiMgdBjoiIrmyWq3o7OwUn8d7/fo1LBYLoqOjxZ28sZourFar\n0y6eY9OFj48PRkZGnC4IHh0SW1pakJ+fjwMHDuDw4cMuYY+IPIqBjsjTRkZGcPv2bXR2diIiIgKC\nIODEiRNYuHCh1KXRFGBvurBfnzK66UKv12PRokVjNl1YLBYMDQ2J3/f19UVTUxNMJhMSEhIwe/Zs\nFBcXo7OzE9evXxdHaBGRpBjoiDyttbUVy5YtQ01NDSwWCyIjI5GcnOw0d5RoooxuumhubkZXVxcC\nAgLESRd6vR5qtRpnzpyBzWbDhQsX4OfnJ4a82tpaVFVVobW1FT9//kRUVBS2b9+OxMREJCQkYO7c\nuVK/TaLpjoGOSCpHjx5Fbm4ud+b+wqFDh3D//n2EhobizZs3Lj9/+vQptm/fDq1WCwDYuXMnTp8+\n7ekyZcOx6UIQBNTV1eHt27fQ6/VYs2YNEhMTER8fj6CgICgUCpjNZhQUFMBsNiM/Px9Go1EMhy0t\nLQgNDUVHR8eUnlNL5OUY6Ig8rbm5GVqtFnv27MHjx4/x7NkzrF27VuqyvNrz58+hVquRmZk5ZqC7\nePEi7t27J0F18mUymZCXl4fHjx/jxo0bWLFihUvThVqtxtevX3H27Fns3LnT7dUpPT09PHolktaY\ngY6zXIkmicFgwLx585CSkoK7d+9izpw5Upfk9dasWQOj0fjHNeN8CCU3SktLoVar8e7dOwQGBgIA\n0tPTkZ6eDuB3WBMEAcHBwViyZInbf8PHx4dhjsiLcYeOiLyK0WjEtm3bxtyh27VrF8LDw6HRaFBS\nUoK4uDgJqpQXm83mVSO5iOg/4w4dEcmfXq9Hd3c3VCoVGhoakJ6ejg8fPkhdltdjmCOa+vhkKxHJ\nhlqthkqlAgCkpqZiaGgIJpNJ4qqIiKTHQEdEXsU+1sqd79+/i18LggCbzYbg4GBPlUZE5LV45EpE\nXiMjIwNPnjxBf38/5s+fj8LCQlgsFigUCmRlZaGmpgbl5eVQKpXw9/fH7du3pS6ZiMgrsCmCiIiI\nSB7GfCCWR65EREREMsdAR0RERCRzDHREREREMsdAR0RE/8xgMCAmJgZLlizB+fPn3a45duwYFi9e\nDJ1Oh7a2Ng9XSDS9MNAREdE/sVqtyMnJwYMHD9De3o7q6mq8f//eaU1DQwM+f/6Mjx8/oqKiAtnZ\n2RJVSzQ9MNAREdE/EQQBixcvxoIFC6BUKrF//37U1tY6ramtrUVmZiYAICkpCWaz2ekeQSKaWAx0\nREQy09vbiw0bNmDp0qVYvnw5rl696nbdZB159vX1ISIiQnwdHh6Ovr6+P67RaDQua4ho4vBiYSIi\nmfHz88OlS5eg0+kwODgIvV6PTZs2ISYmRlzjeOTZ1NSE7OxsNDY2Slg1EU0m7tAREcnMvHnzoNPp\nAPyebxsbG+uy+zWZR54ajQbd3d3i697eXmg0Gpc1PT09f1xDRBOHgY6ISMa+fPmCtrY2JCUlOX1/\nMo88ExIS8OnTJxiNRlgsFty6dQtpaWlOa9LS0nDz5k0AQGNjI4KCghAaGjohv5+IXPHIlYhIpgYH\nB7F7925cuXIFarXaY7/X19cX169fx6ZNm2C1WnHo0CHExsaioqJCnLu7efNm1NfXIyoqCgEBAais\nrPRYfUTTEWe5EhHJ0PDwMLZu3YrU1FQcP37c5efZ2dlYv3499u3bBwCIiYnB06dPuUtGJG+c5UpE\nNJUcPHgQcXFxbsMcwCNPoumGO3RERDLz4sULrFu3DsuXL4dCoYBCoUBxcTGMRqN45AkAOTk5MBgM\n4pFnfHy8xJUT0f9pzB06BjoiIiIieeCRKxEREdFUxUBHREREJHPjXVsy5tYeEREREXkH7tARERER\nyRwDHREREZHMMdARERERyRwDHREREZHMMdARERERyRwDHREREZHM/Q97KK07emsN+QAAAABJRU5E\nrkJggg==\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "The video lesson that walks you through the details for Steps 5 to 8 is **Video Lesson 6** on You Tube:" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "ax = fig.gca(projection='3d')\n", + "X, Y = numpy.meshgrid(x, y)\n", + "\n", + "ax.plot_surface(X, Y, u, cmap=cm.viridis, rstride=2, cstride=2)\n", + "ax.set_xlabel('$x$')\n", + "ax.set_ylabel('$y$');" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('tUg_dE3NXoY')" - ], - "language": "python", + "data": { + "image/png": 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kkRrqu1WFXDgcrll7MkAfc2GkkkvoFZOZ+Oabb+Le3/wPLvuWB8//3Y+9O0Vc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8lqTxSyqCZZYBzw4sGEEeTJ0KTbWbUX6Tn3E2xPPoVRLscf/2WinUcpJLdQlSLRnjsCgcy610oM\ntHTVfbelxBatb/ixurXi3oYXGdZa/9jExCWc5nakXa/MTk1eJtRx0Dg4NXWVYGf/wfXJKzitnYi7\njg2FrVuex99dxBAgv7ZAoKXefDXU3ku46wgbn/wYQohaLut+SGlhBcMUV5cI9A+hcjma+8549/kc\nSqm9GqHzN7Z5ZsGVcm2GT9o+3FwGPx3empToTAYZi2GFQuhqTms9oXOg6k2GEIBhfX29dmx4rbJE\nX0m81heb9zteiNC9Gp/b9evX+cAHPsCXv/zlHwp7lAeE7h5hPxGSUqKqB7CXSjq433AYGX252J9m\ncbfP2iuB16JC92JkVWvN5z73OX7zX/0muZ0CR4tjVKiQk0l22WBeT1QrYCCNpI1ummjniDpNkTw3\nxXNkTZouMYCWLikd50nzJWzjWYt4FiR+zvAmWkVXbZ5tTt1kl01sPFKRNUluyOdwCODTAXKkKJki\nw2KULjNAngwZlSQld5nR17x6nBY4wiFpdgFDm+7GIMiKNGHRSK8epiCyZGSCaX2FkinU5tkcgvRx\ntDbPliXNNfM0RVOgXXRTFHlu6XluMY9fOiitqFAmRJQzvImwiFIxZTI6yTwT7LCOrFbz0ibBuPU8\nARXCIUhCbQMwIs5W48G8lm2CHZbNDD5/DCktducv0DX6dqKdnihhc/xbRLqG64jX3uSzRHqH6/ZR\nt1TwLEXaeigld1DFAsZ12bvxNL5oI/n1RYS0kD4/ViBEZn6cSP9BD7j86jyR4dMH1gtr80SOHlzP\nLk0f6m+XmrpCqL330N9R7tYCbecOetb1v+sXmfzYhw9U7/bDibVRXFoEbbBD0VqmrWU7lNO7hDu9\niqBnLuzzzIWr8WXC9tWlRUi/QyUe97JxQyGoCiFkMHCH0O37vxASgyaXyxEOh180S3Q/yXslskRf\nabzetvfVxAsRukKhQCh0eC7xy3ntFzo3rKys8N73vpePf/zjHD16ULT0esQDQvcK4DYpKhQKrxsi\ndxs/CDl6tU2Qb+PVJHT7W+aHvcennnqKX/vg/8LE9Dg2PgJWCJcFYrQS1c3syW0sLI6IkwRNhKxI\n1vzZFK5nQWI0zXTQaFpoU11IbOa5wRqLhEWUdtHjpS/oZ5BGVitgJTSKPoYZ4Uy1ZVtkV28ww3U0\nKfzGj0GzzDQb1jKOCuJSJq2TtMluhvUoQC1twUucmMVgaikQCbZpMZ00qTam5RVcU2FInMQy1r4M\n2KvVeTbPULiHI3SbAcLGSyqY4SobeoUwURpkMxmT4FnzVfwigI1FyZRQuBzjDL0Meyd9k2FXbbLI\nJJ7VhZcCuiJn2FTLRIhRNHmS7CFtP5H2QVYufA4EtI48Vvt+0ptzdD5a7x+X3Vqg/dF3kL01T259\ngcLWKvmtVYxWTP3FR5C2HyEt73ftlhGWzdrnP44xGqM1Rrloo8mvL5GavoodjuJvbMXf3E45Hcdy\nArj5LFbwzoVNJZ083Gg4sUvLuTcfWM+vzhMZPOiYrytlyukEseGzB+6z/QECje3ofWKFuxEZOE5y\n/iqmXCYUvZMP6wtEKaX26h4rfX7cTLJG6KTfqctzlYEg7t4eDA1VhRBVQhcIgFJopRCBfYTO58e4\nLu/7mffx42//cc6/+TxjY2OMjY3R2uq1ffeTvBcypb3fq3kPKnTfH5LJJI2Njd/383/u536Ob33r\nW+zt7dHf38+HP/xhyuUyQgg+8IEP8JGPfIR4PM4HP/hBjDH4fD6ef/75e/gOXn08IHT3CPszXCuV\nCq7rHuo5dr/j9Wq58kofNPdXWg97j1/72tf4N7/9YWamZugrehYktytge2KDBeO1BoWWONJhV2/Q\nQLNXXVJZDIZW2UW3HiRPlqyVZEFPeP5s1VZkgCAdpo9200NQhEmxx03xPGVdoksMUJQ5NtQyt1jA\nj4NCU6FEhAbGqhUwZVwyJsmMuk6C7dprJ/QO161n8KsAfgIk9S4Gw3FxjhbTQYYUGZLE5Ra39LOe\nplVLgjJEWsdpop1BdZIlpiiQp0m2eobCMk2aOBf1UlWZK3FRNNDEICdo0R1YwiLJHjf0sxQp0iI6\nyJFmxlxnQUziFw6udilRpFm0cso8hkPAix5TSRaYYINldNXTTLtlUqsTRFuHcFUBJ+IpPN1ykUo+\nRUO/NxfnFnMk5q/h5jLc+tbfYjkhnGAj4aYeStJH49HHGDz/s3Xf8+W/+dcMv+2fEO24ozbV2uXS\nJ/4PTr371yjnMxSSmxRTWxTmplGVCrvPfI2tb38BhMDf2ILT1o1byKErJdxCDjsYrr6O9oQShwgi\nKqkE4UOsTApba1hOEDtweCVDlUtUMnHKmURd8sNtNJ54mK1nv4wq5Ok9cUdwEWxoo7RXb10ibYfK\nPnNhKxDEzdwhdHY4gptMevcFgzWRhLAssCQ6k8VqiIKr0K6LdBx0IU9DqYX5JzYY//pfUQrk2Cvs\nIKWgp6uXd/3k3+ehcw8xNjbG8PAwtm0fGjF1u5p3N8m7X6p598M23K94oQrdD0roPvnJT77o/R/9\n6Ef56Ec/+n2//v2IB4TuHsEYU6vI3T6oRCIv3Oq4X/FyCN3dJOe1Iq+v5MFyf6bsYZXWxcVFfvND\nv8UTX3oCUZEUTI5ZcZ0Vawa/ClCiSM5k6LGGqia8ystNJc4q86wxj8ZgY1OmyA7rNNOBowKkZBy/\ncTjCaQSCrJVky6wwq2/cqYAZQw9DdJg+GpR3wh7neXbMBo2iBZ/wkzKeBYkfB1k1DzYYz4IEjyTk\nybKlVlnitr2JwGBYFJPcYoGQaaBgsqRNnC7Zz6A+QYVybZ5tVl1njhsAXpVRawrk6NB9tNDJjLxK\nURcY4iRaqENatgaJYIgTdJpBAiKAQjFhLrJj1onQQFCGSOkEz/BlHCuAUBYlCmg0J3mYRWbJ4xGK\n/tGfYH322/Scu1ON2576rtdijG+wdPm/k9tc8lqmPofT7/x1ApE71h6X/+53aOobrSNz5UIGVSkS\nbq1PdkitTeFzQoRb+gi3QFPfKQB2Zp7FvflNzvzMh7yLntQW6Y1ZducuIqTFxlf+FlXMIf0OgY5e\nfNFGhGUfMCDWbhm3mCPY1X9g/8xvLOGvZrTeDe1WKKd3CURaSYxfoOP8/3DgMf6GJq/ylk7TPHiu\nth5q6mF76ULdYy0niJtJ1m7bkQbc9J3bVjiCSleVraFQnepV+vy4ySR2YwxsC53Nem3ZZIIUCQKE\naS13kirH2RFbBEUDgaVmvvSn3+Bz4S+RMUkyxQyRUIQffcuP8qNvfTOjo6OMjo7WZp/2k7z91bwf\nJDD+XuBBhe7F8UoRuv8/4gGhu0fIZrNorYlGowghyOy7cn094XshdPfbXOAr0XJ9qUzZ7e1t/vHP\n/wLfffI7NIt2HtVvx6mSkJSJM6EukCOLg3dy3lSrJKydmglvhgR+HI7zEA00eRUw41mQbJqVmglv\n0Aqzp7Zopo0edYSiyCPJ0Cn7aNYdZEWKtIxzRX0XjUJioVA00UqvOUqz6cAWNjusM2ku4VKhQ/SQ\nFSmm9VXmxA0cEaCiS5Qp0yq6OGkewREBSqZAWieY4RoZVpFVxWzcbJG1UgRVGB8Oe3oTHz6OcZYI\nMTIkycgkO+YWS6YaWaYlYRGhYLI0m0561TCzXGeLVZplO826nayVYsOsMK89oQiAwqWVLo5wioiO\nIRDE2WJcXUBRolG0kDUpxrlDPjqPvommzhMs3/giTQNnat/nzsxzlPMpFr/yMRq7TnL8XR9i7uKn\ncaItdWTOLRdxS7m6KhzA3sIlAg3tSKte+BBfvkakffDAPpRcHaeh05uHk1ISauoi1NRFZnOOcEsP\nQ29+P1q7ZDYXSK7cZHfmCmCY+uPfxGnpJDoySsPxsxS31/GFowfixwByK3OHRoQBFHZuYfuDdJ54\nG2vXv0r7Gx8/9MRphxpQMoe9z4Mv0jbI2o2v1D3OF2mknLjThvVFGyls38lvtSMRKre95+4idCLg\noJJedU/4/LjpFHYkShnP/HpHrLNq5qrtfQstFLts0KTb6cj0MS2uooyiKdvB7BdXufn1/0TRn2Mv\nv43jBOho7+Cn3/PTnD17hrGxMY4cOVJL7Pl+A+PvJR5U6F4YL0ToUqnUA0L3MvGA0N0jRKPRmpjg\n9sHj9YgXI0f7q1W2bR8gOa8V7iWh2x9FJoQ4kCmbTqf5oz/8I/7jn/xHGivttMseT7TAF/HhWZWU\nTRkbuypa6KzmpmZZVtNs7iNGLi7T8ip+EyBowmRMijyZmgnv7ZZiWib2mfAKfMJPUefJk6HD9BBQ\nYXIygzB+z4JElKoVsKt1ogULH0OcpN30ESCAS4Vr5mlSJk6jaMUIRULv8BRP4IgAwghKFBBIxjhP\nm+jCNS5Zk2RTrbLOUvUz80yOF61J/CpAA02kdYIcWbqtQfrUsPdeSJKxEtxQzyCreas2PoT2qo1D\n6jQFMkzJy5R1iQGOUZFl0sS5pL9dbdlauLj48HOCc3SYPhSKb/N5QOOEmjhy7r3MPP9JGrpGsHwB\nMlsLLD79X6gUMnSPvJW+sb9fS03IpzbpOF4/s7a3fAV/qAnbX0+gkmsTxLoPzrHldlfoPP1jB9YL\nyS2ah84dWM8nNuh5yKscSmkT6z5GrPsY6c052o+/iaaBs+zOPUdi6gZ7F76JUQqntQtVzGPd1Vot\nbK4cKogAyG8u4wvEaDv6GCtXP09+Y5nwIe3cQFM7xY3VurVQcze6UkZVSlg+LxnEaWglF7/zOF9j\nC5mFidptGWlArXqRYbLacq1VyAKBO8kRjh+VziCjXgcjTxZjNIPyRHXkIFM1n04wpS8jEWAEfumQ\n1HEaaaa12Eu5WCQp41TyLvZilP/2H57gb8N/R1onKJTzxBpivOGxx/jxx9/O2NgYp06dIhqNHhoY\nXy6XawKM/f8sy/qBydiDCt33hweE7uXjtT8b/5Bg/4/+fo6j+l5w9wHopapVPyy4TeSMMQeiyIrF\nIn/wB3/A//Vv/z1lVaKd3po6FWCRSVbNbNW2o4M0Ca7pp/bZdpRQVOhnmGHGEEjKFInrHaa5QoYk\nNl6W6Xo1VD6gwxg0Cb1Dg2yqmvD6PQsSmWRdL9VapFJLwqKBDClaTScdqp9JcYkKZbrlACEdJWul\nuGUWmdHXaj5zCpcu+uk3x4kSw2BYZ4lZfR2BpEm0kTFJrvM0fuHgw6FsSpQp0SOGGDajWNjkyZBW\nCea4SRKviiMQJNgmJzJETCO2sUnpBH4R4Jg5i0OgSvKSLKlppqmSVg1RmtBoOnUfRxllmitsskqT\naCNKE1mZYE7dYIKLnkRCCGwnxsPv+hAAye0Zes+9i9lv/CfSG7NEYr0oO0f/mXffESaU87jlHNH2\n+kpcfPX6ocStlNmlc/THDqy7xeyBCp3WmkohfWjlzi1mibQdXK8U0kTaBvGHGug+8zjdZx5Ha83V\nv/kwlXSSqT//ME2nH6Pt/OP4Ig1UMkm0WyHcfbhCL7c2R6S13xv/aO4nfv3pQwld8a5ZOfCIpuV3\nKKf3CLZ4nniB5nZSyzdqj/E3taDyudptOxLGFIre830+EAKdyyOjES/b9Xb1znFwM2nsaBTwqrAC\nwS2zwI61TkCFcXCI6x0CIsgJcw4/AbI6RVam2DEbLNZVfqOkSdCs2+nNjDDPOBkyWMkAs0+sMfGt\nPyNvZ4gX9og1xIg1xPiH/+hnOXPGq+b19fXVLgr3V/PK5XJNgHEYyXs5x/bX43ng1cJtIn03UqkU\nvb0Hc4sf4IXxw3dWfo1wN6G7fYB4vf2Q92/vfiInpTxQrbpf8INW6F4swUIpxSc+8Ql+60O/jb8Y\n5Jg6S05kyMgE4+oCZcrezBiaEBEGOU6b7sIWfjIkuWGeqdl2FESONb3ILZbwSwetFWXKhEWEUfNG\nIiKGaypkdJIFJmsmvAqXgskxZV0hoIKEiBLX21Qoc0Se9MQHpMngmfBe1XNYWGAEjghS0RUkNkfV\nGFussiQmsbDpM8MUZJ40e1zU3/AOrFi4VAgS5hhnaTGdSCEpmgLX9FPkyNAgmj09ZuAAACAASURB\nVPDhY90ssi3WcGQAraBEHh9+HuLNNNFGiSIZlWCLNdZZ8CQLxuAXDstyiqCO0kgLJVWkTIlua5Bu\n5YlCMtLLs13QE/tyaf04JkgDjQyq42RIcJnvoIVBSJu+U48jhCQTX8UtZlm58N8IRtp59PEPMXPx\nr2nuHq3bv7cXniMQbTtQiSumdg5U7bRbplI6SMTyiQ2MVgQbO+rWcztLCGnhD9cLETI7SyAETrSl\nbr2UTaDdMsHmekNhKSVol5Pv/DW0qrD83GeYnfw9ut/xswjLwg6EX1CAlNtcoutNPw9A70PvYvJr\nf0rPO95XF2+mSgXK6TgYg9G6Lj3DsgOUU/sIXWsPbu6OTYnT0okqFu5sazCMKe+L/3Ic8levUbg5\nSWlllcruLnZjIyIQQOVzWNEGAMI08AbeTtYkiavt2kWKwaCNZMa6il8FidJIQefIVUcOBvRxiuTJ\nmhQZK8mkuoTBIBCeWbeqoHDpKAxiY3NDPksiniCWaOfT/+6z/FXoUyQre7i6QltrOydPn+Qnf+rd\njI6OcurUKUKh0IFqnuu6h1bzbs9Nv5Cf2uvtPPBq4sVarmNjY6/BFr1+8YDQvUK4H9ILvh/c3u7b\nJshwMMLqfsP3652330vu7gQLYwxf+MIX+OAv/yrx+B628BEyGpsUbaabqGoiJ7MYbRgSJzHG1CtT\nq4P+AhjkON1mkAAhNJopLrOlVwkRJSJjpHWC5/k6fhHAMjZlCri4HOMsPXjVo5xJs6e2WGSCOJ4H\nm0CyLpbZY5MoTZRNkaTZo0m2clSPYjDeMLmVZFZdY5orCATCCCI0olH06iNIhpkQF0maPbrEAD78\npGWcSXWJMmVsfGhcDIYBjtNvjuEXfjSaVTPPoppA4FVKsibNNZ7GsQLYyk+FIkUK9IsRhoynLs2a\nFCkTZ4kpdlivtoNtUuxRpEAjLQR0mB25jkOAEc54ubTCa8PdVM+jcKuaVgMGpJC0DzyCcstMP/uX\nSMvH0Ol30zXkRXTl0ht0Hn9L3fcfv3WDpp5TdWuuW6ZSyhC5e35u+Tq+YAO2U9/y3Fu4RLilFyHq\nSVV88QrhtoPGwfGFK0RaDq7vLVwm2NiJlPWzqPnEBlorgrF2hJCc/ol/zs7CRZa/9rcIyyLQcjBR\nAqCSS6MrJRo6PW+8aEs/lj9IZmGC2LGHao/LrS9iOyF0pUwpu0eg4Y5xse2E66xLAk2d6ErJS3qw\nbKxwFIxBl0pIx6mlRQDoSgWEIP53n6czdppwZJhsfoPEZ7+AQaN7+/H1eXOLOdJckN9AaZcSBZpE\nG6fNY/hxKJAlo1KsMscaC3g0DxLskpMZQjpKmBh55RHNEXGGmGkiY1LkrBTbeo15c9O7KNAQIkLJ\nlGhx22lJd7LNLWbEVeLrSeZvrfFHT/4xOStDsrBHR2sn4WiYn3nfe3jkkUcYGxujs7PzQDVPKYVb\nbS/vr+bdJnkPCN2L44XOkw9ari8fDwjdPcL3k+d6v8EYL1cRvBbj3W3H+xkv57Pe75d3mPHxZz7z\nGX7v//x9Nle26MkNM8w5MiZJVibZNmusmBlAILUgJKLkTY5WOuhSg0xzmTJFWmUnTbqdrOWJAxb1\nFNIIbrc5W+hkmNNETRMI2DNbjOsLVMjTJFrJkmbGXGNJTOETDq6uUKJAs2jnhHmYACEv6F4lWWKK\nWyxgqie7Anlm5HUiOkaYMFmdwmA4Kk7RZNrJkCQrPQHCrLnhneyMd7KTxqpakJzgFossigls/HRz\njJyVZluvsWymsY0PjUFRoYFmTvEIkWrLNk+G6+oZsqQIE8WHYsXMsSVX8RMA7SU6+PAzyhtpos2r\nMKokO2KDRTOJRiO1hSMDrOl5ojTTZFrJqCQGTafsY0OvIqUPy3boOHqeUj7J5JP/L6pc5ORjv0hT\nh9c2Leb2cCt5GtrrDXuL2V36ut5dtxZfuoIvGMPnhOvWEyvXa+RoP9IbszT2jx5Yz24t0jz8yKHr\nTUMPHVhPr08f+vp7C5cJN/XUEca2I4/S1H2KK5/9CJVsElUuHsyb3VzGDkTqqnfRlgHS8+P1hG5l\njnCwlSJJ8snNOkLnRJopxe+0Y6VtI2wflWwaf6y5ZjasctkqoQtj3Aoqm2Xjz/4MUy4z2PYmRgYe\nZ2XjGYqlBKP9/4CLc3+FyueRQa8yKrAo6jwOAaKiiaTZ42mewLGCWMpHiQIVSoxwlj6O4uJVsdMk\nWGSSbW7VLgpuyXl2VIAYLfiUQ4kCURFj2JzxFOYiWauuaxQCC2EgSASJRW9+BD8BJrnE+uYaXTt9\nfPoPP8vHg58kUd5DWpK+3j56+rp57/vey9jYGMePHycQCNRV8/arbPev3Z2C8QAeHogi7g0eELpX\nCK83Qnd7fux2pSsSibxu/PO+1wPjS5kCX7t2jX/5G/+Sp59+Gle72MJPyRrHUSGixIjrba/aJEe8\nzFAyZEiSlvFqm/N2a9BBaAsLi2E1SpwdZuV1lHYZ4DhlWSQt4lxU38YYXWtzOgQ4xSO0mR6kkFSo\nMG6eI252CBPFFjZxs8Pz4us4MoBQkiIFNIoT4mE6TT8VSmR0kjhbrOBFWmEMPuFnS66SUnEaafU+\nC+H5xQ3pU54FifDey4qa9QbREQgjaaSFEBF61TAal5viOZImTrcYQAhBijjP668jjMASNhVTRiA5\nxlm6GfTeiymzpudZYroul3ZcPI8jvCSLEgXyJkufPMKgPonCJVO1eFljvkpaNTY+dvQGUtr0DryF\n1eXvEAi3cO3r/55IuJtyMUOs7Q55uzX/XSLN/Vj71KnF7B7KLRFprVeI7q1eJ9Z9jLtRSKzTc+4n\nDqyXc4kDaliAUj51YDYPoJxPEj1krq6U2aPz9NsOrGc254l1Hdwe6fN7lTJXs/jZP+fIz3ywLv0i\nt75AIFKfE9t65FEWnvubuopRZmmKno5H2dm4QT65TnP/nRZXqLGLxNZ0/d+1fbiZFP6YV10Ttg83\nm8HX3IIVCqNdl/U/+b8JlPzEoiOUKt7cnONvwFVFmqNDBGSUyt5ejdAZYQibRo5zlqhprMXCXVfP\nkCNNlEYEMMs1luUUfhFAKEGOLAGCnOYNRGi84/vIJstMo6tWOJawWBDjhHUDjaaNnMpg0HTLQTp0\nH1nSZK0kG2bJmy+t/o4dgtjKT6Nqo7HSSokCV8VTzEzPkJ9x+f1n/5AsaVLFBL1dfTS3NvPWH3sL\nb3vb2xgbG6OtzSPH+Xy+NnN8tznyD2PU2cvFi7VcfxjiuF5NPCB0rxDuRYTWq4HD5sdSqdRLP/E+\nwkuR55eyWbl+/Tq/+iu/ytWrVxnSp3iL+SnAkDVpEmqHBcaJs4UBJJIdsU6KODGacY1LysSrVQBP\n7JAhSdZKMqOuMcml6vA2NNKKQNCnh7GxGecie2zRKjoJEfWUqeoqN7mAjY3CRaPpZZhhTmHjtTm3\nzRpT6ioGTVhEyZk0U+Yyi9YEPuWpVwtkaZfdDOsx/DhkTIqMSrLKLLtsolFYxqYiyywzTYxWIqaR\nPbOFRHJEnCRiYmSEJ8CYUlcpU/SMiI0mRgthE6PNeDFft1hkTtzAMjZdDJC1UsypG8xwFT8OLhXc\nai7tKI/hFw7KKLImybi5SI5dHAIIYF0vsWdt4VcONn5S7GJhc5pzNNJKnB3G5QVa2k+Ry24gpMXC\nlf/K0WM/yd7uJC1dp+tal4mtabqO1bdbNxeeIdzch7TqD4GF5CbtI+fr1rTWlItZIh31xr6VYvZQ\nX7pyLol2S4Sa6/NTX8jHTrsulWKGcNtB+5FyNkG07aChcD6xju0L8NA7/zeufOXfsfT5v2Dop3+p\nlgGbXZ2hre/huuc0dp/yvOmSuzhNbahykXJyh/aHH6GQ2yW/W690DbcOsDX7dN2a9DlU9nnRSdtf\nS4uQjoNxXZyizWOnfon5ta+Tynqv6fgbUNprxzZF+9lcn0Dn78zfWVJwWX8Hg/Hm30wFMAxxin5G\nsIWNayps63VmuApAQATJmyyX+S6OFcCnvP0sR4YeMchRM4pGkdEpsiS5xRJbrKLQ2NikhNfij9FC\nm+ohI5PY+BgWY/iMr2oJlOCWWsTFxcZGG0ULnQRNmLZsN34RYMMsM716lZ21PeLjGT79F39LvLhL\nIBDg2MgxIg1h3v8/vp9z587VmSP/MEedvRw8sC25d3hA6O4R7t4hb89O3K+4TeRuh8rvnx97vVUX\nX2h777ZZuZvIbW5u8jv/5iN86q8/RciNIrXFrLnOspzBbxxc41IkT7No45g5S4ioN7Cvkqwwwwpz\nGDQCgStdFvUUUdPozfToLArFoDxOi+4kS4qMlWJdLzJj7lQBgkQImQhtdDGkTrLNGrPiOtoYBjlO\nwcqxpze4Zeaxja9qd1IhQgOneYwojRgMBXJMqIukSRAgiIWPbb1OSsZxCGBrH1lSKHS1mtdXq2gk\nxA6LxrOfEFrilw47esNLZjAdVFQFI7aJiRYG9XGKFMhaSW6Zeab1FSzjvRdpLLoYoI0eojpGmTI3\neIaUidMuujHSkNJxvmu+gB8HUY0nE0hGeQPtoreaS5tjU62wzDSeVlagKXkWLwRI6zjBQAsjJ9/D\ns9/5PYQQnH7of6KpdYSl+a9y7JF/VPuOtXYpF1M0dp6o2zdSm9O0DNZbimi3jFvKHqi4pdansGw/\nzl0Ch72FSzjRVqx9IgOA3flLBGOdB8hifOEygYa2Az52ybVxbCeML1BvRK7cMm45R7j1oKFwdncJ\nfyCGlDZnH/8Nrj7xe2xf/gYdb3gcrVxKiW1a3lpP6KSU+EMNZFdmcJrayK8vYjth/P4QsZaj7E1/\nse7xkdYB3GIWo1SNKFpOEDd954JP+jxCZ4xh54ufRUjJ2aPvR0pJ0GlkOz4FQGAfoQsH2jApQ/bq\nldrrnNNvJUea6+IZXFNhgBEKVo51vciimcA2fsBUs38jjPJGGmiqtvizLKhxdtnAh4ONzbpZYldu\n4sfB0UEypKhQ4rg4R4epiohUioxIsGDGvSA57VmjbJoVIjTSYtoJqihZmSJoQgyak5Sq+/6KnmXS\nXL6z72PRZrpoLXcRLVcNvsvPc/HSRdqtLn73wr/1LvSKGQYHhjhyZIih4SF+6qd+itHR0Rpx+WGI\nOns5eLHzjOu6+P3+F7z/AQ7iAaF7hXC/kqIXC5W/jft1279X3K3OvdtmJZVK8YFf+mW+8IXP4zdB\nTppHaBae/UiZEuP6Akl2CBAiIILEzTaXxLdxRBBLWxTIeaIFcZZuM0iZEhmVIMEOq3e1OXfZ9KKw\naMdWPkqiSFQ0csSc8lqKMuURKjWFREJVtNBJPw20MKhOolHc5AJxtmgTXdjCR5o4z+tvYGFhC5uy\nKWGAYcYYEN4sVtmU2NZrzHIDMPhwUJSY4zqr1iyOClKhTMYkabd6OKpGEQivoiESrJslbrHg+b8Z\nGyM029yimXZ61FFyMo3EYkAeI6jDZGWKuNhiSU3VFLMKRTs9dJkBmlQ7Ukg2WWXaXEEAHaKPjEhy\nQz+PzWX80qGsy1Qo0SF6OGEewSf8lE2RtE5wjWeR0ubk2Z9j/Op/BuDhN/4zQpE2UolllKrUtVt3\nb13D9ocIRO5SleYTB1qZeyvX8AUbDhCr+NKVQ9uqybVJGg5pz6ZuHb6eXJuk4ZD2aWL5+qGvn1iq\nCjH8Bw2F0xtzhFu8Sp9t+xk5/wtMPvlRGo897KVP+Pw4oYPVjVjHCJm5m7ScfTPZ5VmCQe9zaW47\nxtTVv0arSo1w2v4A0nYoZxM4Ma996wvFKO8TSkgngJvNknruKXJTHvHK5DcJBZtx/DGU8oRVfn8E\nrV1cVSYSbAelyI3ftkAxfJO/w8JGG00b3YSJMaiOYws/y0yzyCRh0UBUNJImwUX9TYSR+IWfiinj\n4tLLUY5xtppjXCKtE0xyiSwp/ATQKGbxUlwcFURikzA7hGngFI/iw1/d95PsiS1umQUALG2BJdhS\nqzTSypA6xTLTFCnQIXtp0m1kZYoku6zqeRQKC4lCEaOVVtVDa7YLW9jkTZar808yPz9P17f6+Own\nPsdeYZdYNMbJEydp727j7W9/O+fPn39Rc+T7Pers5eLubX49n39eSzwgdPcI9zspUkpRLBZfUAiw\nH/fbtr8U9ufouq5LPp8HOGCzUiwW+dM//VN+/3f/gFillV6GScs9rqmnwRgs4aNCGYNhiJMMcQJZ\nPTBvmVVmzDUMmgBhypSZ4war1hz+fa2eNtnFUT1abXMmyZgka8yzxRoahW18YBnW1RJNtNGmu0jJ\nXQSSfjlCg26utjkTjKvnqVDBxkKhaaSVVtNNm+nGFjbb3GKKKyij6RZD5ESKBT3OgrmJX3oVxgpl\nmmhjlDd6SRZGVbNcr1azXL2q35720h8cFcQhRNLsUKHCiDhLu+kmS5qMTpKSu4zrC1W/OEFABslo\nr1XVp4dxCJIXWfw49JtjFEWe9H6LF+NZvPjxIs06TDc2fhQu18zTJM0ejaIFLTS7eosn+SJ+EUAa\nQY4cQkha2k+xPP91sul1hkbeSSjizSqtLn2b5s6Tde3WreULNHWfrttf0ruLaFWhUsyxu3gZVSmg\ntWJn/nksf5DNye8iLRvL52A5ITLbS7QffxNaq7rXLqW36Tj5owf2x2Jml66xHz+4nt6m4+SbD6zn\n9lbpGn37gfXEyo1D5/AAsruLDD/287XbsbYjxFqPsPa1vyY2fAZfoOHQ57WP/AjjX/1jjNZkFifp\n6XwMANsOYPsCFFLbhPe1ii2fQym1WyN0/oZmSsnd2v12OEphcZbi2goPDb2fxfXvkMlv0tFyqq4q\nJ4WFbTlkC9tEQ13ek28LPYyhSbbTq4+SF1myMsmCHmfcPF9Ti/txaKvu+2HRgIvLNZ6qVX9dWWFb\nrXGLRRzhIJCUTBGJxVl+lBbRjjaanEmzqzZYZIrbR788WW7IZ/GbAGETpWiKZEjQbQ0wqE5QokBG\npTwVu5pknpveZyN8FHQOHw6tuoteRhiXz5HRSQbFCTBeXN+8vslN8xx29b1oDH0M010ZIOLGMMaw\nEV/h6aefxhY+nv3yRbImRbFS4OjQMGceGqOppYn3vOc9NXNkuH+jzl4OXkoBfD9u8/2MB4TuHmI/\nEbpfZuj2KzoPEwIchtcjodNak8lk0FoTCoXq1Lmu6/Lxj3+c3/j1f0GxXKCBZsLEaKcbvxhjgxXm\nxHWMgX6GyVlp1tQ8S0zhx0GjqFAmRjOjnCcoPH+qrEkxqS6TIu61EIE9vUnWStVC7tPEcalwTJyl\n0/SRq7Y5k2KHGXMNWTVHdWSAlE4gsDxRhJKkRJwIDQya45QoVm1R6k90trEZ5DgdppcAIVxcbvAs\nCb1Dk2hFCEFaJ3iSL+EIB8mdLNeTPEInXpUnZzLsqnUWmAT2almuK3KaTb1MxDRSpsie3qZFdtyx\nRdHevOCimmKOmwgE0kj8BCiQpdX00KOGmBCXSJgduuUgAR0iayVZ0pNMmUu1JAuFopshBs1xQkQw\nGI+06ssoVPXLhkJul2IhiTaKju47bdN0coWRh3+2bt/IpTeJtA6wfP0LpLbnPDFEpYi0/Sw8+2ls\ny49l2QhpU8zs4jgNJOcuYoxGGxelKrjlPOvX/ztrV76EZTs4kUacSAuVYpZSZo/M9iKBhjZ8gQjK\nLaNKeSJ3zcNp5eKWci9gKJwl0n7InFx8nd6z7zywXs6nUG6ZWHu9KnbkTb/I5S/+DqXkDk1dJw/9\nrYSbupGWTXZ1lnJql85HH63dZ/tCFJIbdYTO9gfrKnJOYzvZjbk79zc0krl4g47mUVpjI2zt3SRf\n8AhfwInVCB2A3xchU9ikMeKZxfplkBIl7z3pohf1ZdrpVP1Mi8uUKNInjhI0EXJWih2zzoKeAOO1\n4RUubfTQaQZorlZ/U2aPa/oZNGXaRTcZklw138XG513k6AolSrSIdk6bx/Dh99qoOsUKs2yxCtV9\nf1dvkLYSBFSICI1kVAKDZkScocm0kTUpsiJFSsRZ1tM1a5SACJI1GZpo45g6R5Ykk/IiRkN/tZWc\nMrus6Xm4y/tx2IzRku2szhGWmZ6+yqen/4YGq5HPfPzviBf2aG1uZXR0lJOjJzhy5AjveMc7XtAc\n+bWKOvte8UKE7oHVy/eHB4TuFcJrTYr2Kzr9fv/3RORu47Xe9pcDpRT5fB5jDH6/H8dx6rzkPve5\nz/G//4t/RTFeZrh8pkaMVvUcU7UZGINlbPoYoYNeQjpCmTI3eZak2aNVdCKkIKXjPG2ewL+PGAng\nFI/QUSVGRfJsqdUqMaoG1aNYElOsiyVCOkqZIkmzS5vVzVHlVY8yOkVWJqtKUG/uSBoLGx9pErTS\nRbvqYVJcpESRXnmEoI5U1XnLzOobSCNrJ7pO+hkwx4jSCAJ2zDoT+hIuLi2igwxJJswFZsU1/CKA\nqysUydMmumvO/F5lIskik2ywhAEMmhxppuRlQrqBMBFSag+N4qg8VZ0XTJOVSRLssKinarYoQUIY\nbQgTpU8Ns8sGM+Iaxhj6GaEoc6SI86z+KhiBRFbVv0EUGikttHaplHM0RLqRThCfz/OFy6Y3UG6J\nxrZjlEtZ9tZvsLH4DMotsr3wPMFgC23NJ2g+MsLU1H+ht/8tdPe+8c5+5JZ58tu/w7nzH8Tvv9Ny\nTSWWuHH5Y7z5x38HYxS5zCbp1Crb69fBQHz2ObbGv0mlVEBI6bVrhWBv8TLBRi+71XZCJFfH8Tlh\nfMFo3f6b21sDDIFYW9261voFEyWyO0v4/AcNhW3bz8DYu1m49Bmaeg9aqdyGE25m6+knsJ0Qtu+O\n3UkgECOfWK97rD/USCmxXbsdbO3Gzab3bahBWD5Gh97r3R9oZjc5622P5b12sZQi4MQIODFyBY8c\nOnaEkGighCewCBNDW4oJddEbPTDgCIesSWPjo1sdoZMBJuQFSrrEEU5SEWUyMsGEukCZUu3CQGJx\nhJO0m34CIoBGs2gmWTGzBAgRlBGSes+7yJEBfNpHEc/c+hhj9DGCRpE1KdIqyQIT7LHl5SsjWRGz\nbJlVojThNw45MgRFmBPmnCeKMrcvciaY4lKtmt1AEy4VutQAI5xhmRmWmSImmonRQlYmmdHXKJvn\n8OFHoVC4tNPLiBojmA17VcbtNE9+42m+/o2v0xJo47fFv0ajGDl6jEcefZiu3i7e+ta3cvbsWYLB\n4KsedXYvkMlkiEQiL/3AB6jDA0L3CuG1qtDtV3T6/f4DQoDvBa8HQrd/FtBxnNpM4G18+9vf5p/+\n0q+wvLKEhU1YREmwQwudNKhGMjKJZbz5r9sVox1ziwU9jjCiltDQRT+9ZoSoiiGFZJ1lZvU1DNAu\nesiQYNxcYFpcxV8LuS/RKXo5Zs7hF05tnmeW62yzVs1y1aRNnAnrAkEVIUCYXeOpT0fEGC3GE1Jk\nRZKk2GVFz9bSH4KEqOgKDfgYVmfYYoUFMYHEpt8M14jRRf1N70oXC0WFAGGO70t/cKlwwzxHwuwQ\noQFb+Ng1mzwjvoojg0hlUSTv2aLwMJ301zzAEuxURQtUlYk+NsUKSfZoog005EWWBtlUreZp0tVW\n8g31XPXEK8EIWujAxmZIn0RiM8lFdtioqX9XzRwaFzT4/Q288dw/46mLf8TosV+sfd8ri9/CCTUx\n+dxfktydw/E3IIVFQ0M/Dz/8gdrjtNaUimmamus96ba3rhMINNSROYCt9cs0tRyptqxsorFeorFe\nErtzdPU+zMjp99VeN5/dYuLqJ/DZIeJTz1GpZCkXc1i+AEJKLH+QxMpNwi29+EIxhBDsLVyioX3w\ngDGxlzQhce6a/QNIb80TjLYf+rto7DyJsHyUsvEX/O009Yxy68ZXaGyp/wyijX0kdpfq1oKNnWTj\nt2q3A63dqFIBozWVVJzk9ecQ+k50U9DfSMX1Rh6EEPjtMOncBgEnRtBpolD2tivgNBGym0gUVgDv\nQqisC/jxc5yHCRCsEqMUm3qVuao5sNBeskSeLC2ms+qXuMCCmCBEhA7TV424W2JW36hdsLm4xGjm\nGGeJ6iYEXk7xdf0sWVJERSMSyay5wZKY9rKMtUWBDBY+zvImYrR43o86SZIdVqlWKg34ZYB5MU5Q\nR2imDb8K4ooKraKTAX2cPDlvxpRtFvVUVeojsLBxTIgwUfrVMSSSGzzDntmmSwwgBKSI84z+ineB\nV52XBRjhLD3FoerMYJG9m1v85c3/DBj+n/Cfky4l6WrvZnR0lEfPP0J7ezuPP/74oebI9zrq7HvF\niylcY7HYPf97P+x4QOjuIfYToVdb5fpS1hwvB/czoTushSyEqKVafO1rX+N3P/K7TNyYojc/zI9w\nkiyeCWlcbLFq5qttTkFQRCjqPCGiDKnTLDNNQeQIEqbHHKEgsqRknMvKC4aXxiN5IaKMMFYjRhXK\nXDVPkTFJGkULrvDsFXbZqvrFCYrVkPtRHqONbs/kVCfZYYNVvIqGMQYbH7fkAntq0zuBmAJpkjTL\ndo7oUyhUbcZuYl/UkTCCGK0IJAP6OCCZFBfYM9t0yB4cE6w9x6WCDxu3aosywDGGOOXZMqDZMetM\nqctoFGHR4NmicJlFaxKf8qPQ5MnQLDo4Zs7gECJXtUVZZ5E5btZsUZR0WWKKGC3ETAsJtYPB0CeH\na+rfrJVkWc0wyeWa+jdAEL8JUKSAi0IIC9sOcv6RX2N7ZxxL+og1DmKMYW97gr2dSYQQRAJt/Mij\n/yuBQIznLv0HOtrP1O0/8fg0luUnEGyuW9/Zvklz60HBQja9QkfPGw+sF/ObtHXemZOTUhJp6EII\nw5HjP1FrBWvtkkosM3H14xgq3Hr+s5SKWYRl09AxRC6+TlP/GMboOlK3t3iZWMfQoSe77PY8bYMH\ntwkgs7uIFBbrE9+k4/ibDxBFgPbh89y68RWa2+vbso2tx9hYfb5uLdzSx97K1dpt2x9AWDZuLsP6\nFz9Fc3iAeGqBciWH3xcm4DTi6lLt8QF/A9n8Fu3NJwgFmshkNwAIOU0ow6nDuQAAIABJREFU10uT\nwBhS7CGMwCf8LErP+7GJVs9rUeRpkR0c0ae8CrtIkpYJbqhna/u/NJIAYSQWR9UoPvyMc4Ed1ukU\nffiEn7RIcEU9iUbjEz4qpoJGM8QJBsxxbOFZBcXNDhPGMx4OiQg5k+EKTxKQnl+iwZAhQavo5Lg5\nh4VNVqfIkGRb3GLaXKvt/wWZY4EJYrTQrrvJk0EgGJAjxHQLWZEiI5PM6hvcMM95tkAYIsQImTCt\nppsTIkqBHFfEdymZIj0coWBlWVQTzHANP54StEyJIGHO8iOE8w1eNW89zY31ab781S9jCx8+x0Za\nkhPHTvDIYw8zOjbK4OAg58+fv2dRZy8HDyxL7i0eELpXCK8WKbrbmuNuRef3g/tl/m8/7q487m8h\nG2NYXFzkn/7Sr/DEE1+qmgL7WLKmCahVGvDc59Mk6bL6GVDHKFOsBcNPqAsIvNeyjEWAMGDoNyNU\nVJkJeYmMTtInjiKxyMjEvlgsG43CAEOcpN+MYFcPyrdYYE55c2UR0UDGpBjn+ZpnVpkyRfJ0yX6O\n6tP4cGqJCUtMEWenqjCVlGSBecaJ0ULExNjSq4DhiDxBo26red+tqBmmbhMjY4jQQFBHaKeHo/o0\nu2wwJa6gjWKA4xSsLFt6jRUzi8/4MVVriDBRxjhfS38oUWBaXSXOFj4cLGz2zBaX5XfwE8DRAbJk\nKFFgWJymxxylSJ6MSpIWCZbNdPUz9tS/SbOHS4UWOvApH3G5Q9CEGTajGCAnUiTNLkmR9E4mwuL8\nI7+Gzw6ysv40XX1vJLE3x9zk5ygV02jt8rYf+S1s2wE8IpUvJGluOV63H21uXKal7fiBk0ghv0Vn\nz8Fkh0IhSay5fr7Nu6hIEWs6uF68a11Km8bmIxitOfPo/0wo7Jk6pxKL7GxeJ1mcYXfuAjuzz9HY\nfYzGwYdo7DtNfneF5oF62xHwWsOF9A6tfecO3AeQ/f/Ye9MoydKzzu/33nvj3hsRGXvkvldm1pa1\nqjchCQ1iGAYDAx7P2AI8ZlhsDrYBjecMhrFhPBwzGMOZ4zkg2cxh02EsEEcesBbUgATdjbqrq7r2\nrKqs3Pc9M/Z9ue/rD/dWZEVldrdouoXkU09/ijduRGRU3xv3/z7Pf0ktEYkMUShuktuaJdp/lEsn\nNA2Ehj/QPuaNxIZxGjWa9UpLWRvqHKVRLrQBTs0wydy+QnVvi/ef/We89uATZIvrdMVOY5sRHKfe\nek/bilKuemNWM0LTU70G7DgHmXksX4SmCXYFnnc+4m0McuyKdRbVtAeMdKpaiQXuESJGTHWSczIo\nFEPaGPHWxiDHupx3qRQtW6AglvKTVH2McY46Ve6IV6moMoOMUdXLbMtVVtQMhvIhhEZD1bCwucAH\niJFs2QJtyRXWmUfDwMDHgdolr72EhY0t3eSWosozqp1mSE54579LpdiUi6wx53WzTdJqr2ULFHd6\nmNFu0lQNxnGV5kUtxy4bLMj7LfqBVJJehkjQ0+IM7qkNHqpbGPjo0QbJqwxX1Zcw8OHTLBqeYryH\nIc6oZ9CqmvvbdzvLH9z+DAV+E8twRV2DfUNcuHiB597/LCdOnODy5cv09bnRcn+dqLO/TjfvzQBd\nJpN5air8DuopoHsX6/ET870GdI9bc+i6/q4AuUf19dShexywHtd5XFlZ4b/98f+O1159lSF1kg/K\n70JDo6Ty5Jw0C9wnzV6L/5InzRx33WggZZJX6ZaZaECFKJBpkfwfchsdHSUVEWL4lEkn/ZyQZ1lh\nhlUxh4VNN4OU9HzLM8un3C6WQ4MoSSZ5Dj9BFIoqZaac1ymQw08QHz625RppbRcLP7rnF6eQLdFC\nxfO+S7fGnAKhwBQWB2qHKjWS9NB0GjTFNiFcW5QGdQpalgO2WJIP0JRri4IS9DPijZ/dH81pbrLH\nBnHRiSVscqS5Jr/s3Rh81GQVB8kYZxnhNEIIGqpORu7xkFsUyWNiopCsMMu2vobtBBAIMmqfkBbl\nlLyETcBV/4os+2KLbbXq/r+ROn49wIazRJAwlvKTIY1QbvdrZPDDmL4g9XqZSiVFam+ajZW/or/z\nWZrBKpVGrgXmAHb3pzDNIH5/+02hVNpmuOfb29acZp1qNU803q4oLeQ2UVIS7OhuW88czHtdvvb3\nTh+4fnW2v72zkM+uIITAH3DHp5qmEUuM4Q92srNxnQ98689RyG2xvXGVrZtfYPm1T6NpxhH7FHAV\nsT4rgHnMcwDZ3XkGBz6MZUfZnv7LYwFdfncBzTAp5NdIdB8CXk0z0E2bSna7pa41AxGEptEs5fF1\nuN9L85kcvP5lxno+jKGZ+M0o+dIWXbHTWGYIRzZoNusYhknAjpMprAJut+5R9842ozScMlGrh73K\nIlXd4AYvYTl+qpQoqSKj2mmG5Um3K+fkKIgsm2qJDS/mzoePNPtUqRKji35n1DUHVj4mOI+GQVFz\nx6Orcq7Fr5NK0kU/EeKMOmdbivGH3ERXOp3aKAUy3JZ/hVAalrBoqAYN6nTRz1mea4kWijLHIg84\nYAcDd7y7rhbY1dexHD8BQqTlLo53PcfopKBco+O8luG+vNamGD+QO0RJMCDHSNDLjHYLTWmMqNNU\nRImilmXaueGafHucQQOTISbolH3Yws2LXlD32FRLdBBB08IcyG1e4bNYmo0ufdSo0qTOaZ6hrzmM\nRFJayzGztsoXP/9FmjTxGQaBQJAzp8/w7AvPcPHSRcbHxzl//nybOfJxUWdPgry/bjfvaYfundVT\nQPce1eNWGu8m9+BJj7UnrTnejfp6AHRvB1iz2Sy/+r//Kr/xf/0GViOAcmCJaTa1pdZYpEyBgOjg\nlLpEhETLFHiHNS8a6DAvdEuuECZGjC4yzj4N6nTr/fQ7J9ydtpZll43HeDmgK4NO+kjSw6g8Q1WV\nuS/eIK8y9IqhlpHuFfWn6MpA9/gvAsEZ3kevcJWQDVVnW66yyAMAfPio0WBBTLGuzWE5ARrUyZOh\nU+9j3DnvdgpVhoLIss0qWyy7eZbKQGiCPeX6xQ3KMWZEAYFGv3aCiExQEBnyWoYtZwUHp2XNEqeT\nPnWCpDdKzpNhitepyRrdYoCSKLAsZ1hh1lP/SurU6CDEOd5PUIS89Icc684Ce2x6psCSsiwyrd/A\ndGyChMmrNAVyDGsTDMoJV2no5CmQbt2whefLB4LBvm9CKcnUw0+hlMSHyTOX/zmmEeC1u/+OkaF2\n24/t3dt0dbXblTSbdSrVHLH4WNv63u5dbDuCz2zPb93Zukk0fpTftrd9m3iynX8GsL89dez67ubt\nFg+vff0GoXAvmmYQiQ0RibkGwqmDOe7f/F1W3/gjDhav0XfhOwh3u39zcW8JK3B856LZqFAtZejq\nuUBXzwWufOWXKGd3CER72o7Lb89iYFBILx15D58vSDmz3WaXohsWtXyqDdDpPpuxXjeqLGAnKFVd\n4YQmdHy6TaG0RSwygm1FaXjmwpYZxnFc1asL7urEgsPsFmZQpoZwBGn2MHGB+ZqcZ1dfx3RsbIJk\nlDuun+Q54riZxAXp0g8eyhttivE9uUWEBD1yiDAxytpdfMpkRJ2mKtzreU5OUVVXDxXjGPRzgi7Z\nT0C4Kus57rClVgiLOLqmk3UOeIXPYQkLAx81VaFJk9Ncpo9R93dHFcg7Lmc221KMSxa1+5jYBGUY\nHxZZmaJDhDmt3oeG1hJG7ag1FtQDdA/oBQlTIEtC9TDqnGGdeZaZIaoliMseSnqOTbXEnJzyNm3g\n0CRGJyeYJCLjaEKjqirck+5mMiLiVCkzo26yIO5haTaa43IGNQye4e8Qbsao5ssU38jx+Tf+jI/z\nCUChGwajw6NcunyJZ557H+fPn+f06dMkk661zeMg7+2izh7ZrDxZ+Xz+KaB7B/UU0L2L9WSH7t0s\npVQrbxVcjzXDMN4zRdLfFqB7/HsKIY4A1kqlwsc+9s/49B/8AT5pMem8QFjEQUBdVZmX99hlAx8+\nDAyKKsc97RoWNpb0U6JAhRIj2imG5UmaNMjLLHnSrLPgGekqfMJHVZbZZZ0EvSRlLxltDwMfY2IS\nn7Lc3b9IserMtzJZpXLoZpBuNUjUSaIJzY3F4h5CCfrFCHmR4aG8yay6g6lZNFTd3f2LPk6rZ7xY\nrKbnF3eXNLset0aSkftM6a9jO37vJrdLgzoT4jzdaqDlF5fX0od+cUpgCZuKLOMnyIAaI++kmdem\n0KXBKGdoiBoFLcusvMWUOtz9a8pgjEl61LDXgVPMM8WmWsYmQFALUZBZrvElLGGjK5M6FerUGROT\nDKsJBBplCuS8m1yOtGeiDNusktJ30B0fTZqUyHsAU2IaATTNoKvrPI1mmVv3fodS+YBTw9/JUK/L\nIavXi1SqOZLx9tFqubLP0MhH2tZ2dm7h90ePCB/2dqdIdLYnSQAUsst09R0de5aLW/QNHeMnV9yk\nf+ioL10xv0ZX33NH1rOpOaKJiSPrhewqkegg5y//CAuzn2f+5d8mPnSeoWf+IYXdBToSb+JNl1rD\ntDowDFccFIkMsTf7CiMvfLTtuMzmQ0b6v5nFtS8f4e4FO7qpZDbbjtdN27Uu6Rtz48LyGSwRaD3f\nYXeynb7XemybEfLlbWKRES/yyx2zut27OlI67mhWNkgE3U2NrNfIU2OS593NkHITILJOinnuts4Z\nicO8NoXlASOBICV3iWhxTsnLh8bYWpY9tc6yeuhy7KSGLXxkSZFQ3Qw44ywzzQZLJLUeYrKLop5l\nV623hFF4ivEYnYyqM63ruaaq3JavUqZATCQ9YHSbBXG/xZktU8KHj0t8iIiIU1PVlphozePM4unG\nZ/Xbh5xB6eYzd2m9jMgzVCl7PLsM951rSCTuAFZDkwYCwahzBhObh9xklw36xLCrjtcy3HVec/0v\nMWnSQOJSLYbVKUxhInHIqTT3nGs0aRAUIcqqwC1ewdJtTGmDggI5oiLBWfUsRtNHcTHH1OIcX/nc\nFXbqmygknfEuzp45y7Pvf4aLFy9y7tw5RkdH0XX9LaPOHjUQNE3j3r17nDp1ilwux+joUSufp/XW\n9RTQvYf1iIv2Nwm5f9Is1+/3t3msvRf1tyVbbzQab/o9m80mv/d7v8e/+rn/BbPsp1P2k1MprvMS\nBj4MfNSp4uAwxmRrLNhUDVJylxlueY7xFqDYVMsc6NvYThANQZp9bBHgpLpAiCgFlSUvshyIbbbU\nqtsnkhp+LUhOpknQzZA8yTIPKZAlrCXokyOURcEjbF+j6RnpOjj4CTLGORKqBwODBg3u8ip5mSUu\nOnGEQ0ru8SpfxNZshKNRowIIzvI8XfShUJ4x6i7LTAP74HWyNrRF9pxNwsRpUG/d5CbkhTYrhRVn\nhnmmvHxZ10qhQY1O1cewc4oZbrHLBgmtm6hMUtRdK5V5NdXmF5ekj1NcxK+CIFwfu7vyNcoUiIg4\nGiWW1AM2xAKmZiMdSYUSNjaX+AARkaCh6pRknhVmybCHD4sAQUoU8Ol+Jgf+AXfWPoPPF+DqzV+n\nw+oEJenvOuSPrWy/RjQy0LIvASiW9mg0KkSjI23n1/7ePZKdZ4+cd9XyPn1D33RkvVLJEn1CDevy\n57JE4kdB1ZP8ucP1LJH4yDHrKQZHP3JkPZteIBafwDBsTk/+54xU/z737/4uU5/7ZZr1CsMXvvfI\na8AVRNj2YfdubOK7uHX9Eww+85+ie+PoaiGF06wx2PN+ljdfolzcIxg67OCF4yPsbt9oe1/LH6Ge\n2wdg//ZL6EJHKaf1fMCK0Wge5rJaZoRSxT3etiI0va6crvnQNR+l6gEBO44jG/h9UTTTRtarBAgy\nzQ3muIOl20hHUaVEgBDneYGgCFNXNQoyyz6bbLKCwFVZV1SJh/oNLCdAhAQ1WaVCmV59iCFnwlWn\nkqOgZ7jvvNESU+gYKKlQKEac0xiY3BdXSat9BsUJl6bhCTCaNPHho0nDPZ7TDKoJDxhJMmqP+44r\npugQYYoqz01ewRK229FWiiI5OrVeTsnL3oYzT9HJsSc2DzmD6BTJM8ddwsSIqk6KTh6FZFAbIyl7\nW5zBVvxeizMYQFcmCXo44Uy6BsziVYoqxwAnaOg19uUma2oOAx+6cO2XDHxc4P0k6W1xZvedbXcj\nisDAR0btc138JaZmYzl+HBrk6mkGtFFOyEka6Rrp13L8v1df5LeNT5KvZdF1jVMTp7n8zCUuP3OZ\n8+fPMzk52bIkKZfLLRVtsVjkJ37iJ1hYWKC7u5vh4WEWFha4ePEiFy9epL+//6u6N/3oj/4oX/jC\nF+ju7mZqaurYY37qp36KF198kWAwyCc/+UkuXbr0tu/7jVBPAd17WH9TpeujTpWUEr/fj2maXxOw\n9bUeuT7KlXUc58j3VErxmc98hv/+x3+CQjlPJ330coII7hghzR4PuE5d1egSA5REnmX5kFXmWp2u\nOjXCIsakeo7AY2PBFWeWFDveWNABFPP6Pc9INEJGHVAgy6B+ggFn3PVlk66Q4qFz81BIgYEuDRrU\n6VOjdDkDPNRuUpRNBsUEutIp6K7HVE1dxVA+T0ihGOYUQ+pkq/u1yzqzzh0UirCIU1Q57nMNS7Mx\nlcvlqVKmS/Qzoc5j4XdvVl6+7Ppj+bJ1aiyIe3SoKBHiFJ08DRoMaRN0SS/PUsuRYodF+aB1UzCx\n8MsgIaIMOGOUKPBAe4OKLDHIBI7WIEuKK/LP0JSGJnQXvGIwyXN0qj404SqC19Q8K84sOjpBEaKk\nCtzkrx7z8qvg0CREjAgJNlhA10yeH/+n3F//PEpKltde4vzg97CZvktv5zl0/TDfMZWfo7+3HYyt\nb75OPD6GprX/vFWq+wzF2wFUo16mWi0cw5/bQEmnDewA5DLLCKHhDyTb19PLgMAfbBcalAq7OE6T\njlDvE+d8lVq1RDjabkAMUK1kiDzWnbDtMM++8DFmp/+YnZ1bVAt7BCO9R16X250lETvsNHaEejHt\nENmN6VZubX5nDr8d9bJWw+QyK23fMdF1huWZF9s6d1a4i3pmj2alyP6dV5js+Q4ebv1p6zV+M9am\nbA36k+TLrp+dmxZxKJIwfUGK5V1CgW4X3NVTaBIkMMwZehliR60x57im2yERo6hyXOMvXGCkLBo0\nqFCkX5xgXJ1DeJzZgpNlkyWWedjiy+XUAbMU3fNLJUg7u4BiTDvr0g88lXUbMFIQoAOhNJL0Muqc\npUKJKXGFmqoyxElqeplduc6KmkFXPlftrmqY2Jzn/cTpRKGoU2VLrrCCew1YWOzLLXIi1QJGNSoU\nVZ4R7RQj8pQbJSizFEWWLbXait8z8JFhnyoVYnQy6IxT0groGIyJSUxlewrgNJvOIg7NFmcwQTch\noiScXkxhUiDHHV7FUQ794gRFkeWevAoKfMJCKUmNGmFiXOQDXspMk6LKsekss8M6Gm6jYkuuktZ2\n8UmbDiIUnCwFJ8MJbZI+Z4TSTJ5rM1O8+sfX2GtuUW6UePnll7l8+XIro9YwDEzT5MqVK9TrdX7m\nZ36GoaEhstksv/Zrv8bdu3dpNBq8/vrrnDx5VI3+eP3wD/8wP/mTP8kP/uAPHvv8iy++yOLiIvPz\n81y7do0f//Ef5+rVq2/5nt8o9RTQvYv1bsV/vRXA+VrU1wrQPe4l5/f7j+TKvvTSS/yL/+Gn2Vvf\nZ6A83oqRuuu85uYlKo0mDpayOcuzLpBAQyJ5wBscyG2ChPFrHRRkhmt8GUvzozsGNSputBXnGcDl\nJx0KKabahBQpdt2RA0l0DLJqHx8mE1zET/Axhek8s9x1/eIkhIihKZ1O+hiRp1lVc6yKWXxY9Kph\nynqePbnBqprFUG4EV5MGIWKc4zmChEFATVV5IN8gS8pV7QmbPbVBVtt3x5yOSYUCDRqcFBfoU6M0\nvJtCjjQrzLLFCgqJT5ik2aNKlQRdBGWYPW0DW/gZVxdcLo/HsdtwljxwqKGkoos+wsSIy24MYbDK\nHMs8xMKmTwyT1zJMOzd4FNXUxM3YjNPNWZ7BJoBCkSfNPXnNVfiKYdCg4pTYYAFN+Lg4/I8oVlPk\ny9vYZoTnx/4pphFkevOLXBj8vtb50WzWKFcydCbaif/5wjL9A+2jz0olQ71WJvJE12576wbBjs7W\nmLK1vnHd472183t2N28QT44fuR53Nm8ST44ds36dSGzo6Pts3SYQTLQJOQDq9TL1WolwdIgnqyPU\ng7lvsfDGH4KCxODF1nPSaVDMbjE5+V+2vSYaGSO1crMF6HLbD4kE3KSGoN1DIbMMQ+9vHe8PJNB0\nnVoxjR1yQWswPsDu3F+xf+sv6LAT9EYnub/xBar1IrbZgd+K4jj1Fh/Kb8U4yM4BR82FLTNMseZ2\n7yxfB/nqDn4jSqG5w0NuMs9dmjToIMIkzxEi2uoYLcoH7LKBhZvCsqmW2Nc2sYSNz7GpUKBKtXUN\nVClT9LJZ19TCoZhCmOyyQYEcMbrocQbJaSlMLCa4iACKWo68SLHuLCBx0NBRStJJPx1E3G6eMNhn\ni2luYCiDbjFAXmS4K1/1vOksmtKN30vSyzmexxC+FjBadmZI49I4BLAu59nVN7AciwBhcuqABjVO\nikt0qX6K5Lxklhxzzh0EAqTwxFHbRIjTqfrocgZ4oF2nIeuMMUmTOkU9x5Kc5oG6jq50XHGUYoiT\ndKuBlpp9mzXm1B03ak2LU1AZXlV/gg/LpYfIOjUqDDLOBBcQCFfdK3OsMscWK62UmXUW2NM28Mug\n603Z1DBMnd/49d9odcSO45ibpkmlUuH7v//7mZg4pCTs7u4Sj7fbDR1XH/rQh1hdXX3T5z/72c+2\nwN4LL7xALpdjd3eX7u7uN33NN0o9BXTvYf11gdEjINdsNo8FOF+req8B3eNecsflyt6+fZsf/sEf\nYX5+Hg2NBD1uCLw6SdNpMi1uklUH9GhDWMpPXssw69zmPm9g4HpJSSRDTHCCyVaGYppdHjjXqVIh\nLKKUVIEF7rOuLeCTLsm/QpGQiHBSXSJElApF8m1CCoXmEa835KILcOgi4+xRp0a33s+AM9YSUhyw\nxaK83yakSNJHjCRDzgRNmjwQ18ioA7rFALrQvbSEL6M/MhKlhgJOcpFB4YJPhyb7cptZbuFQwMKP\npMoiD9jQl7Acfws8RbQ4J6UHPr182X2xybRyrU80qRPQg+w6Gy1hRN1xI8CSWi89cpiyyHtE8jtU\nVaU1frUJMKDG3Bg1aVOnzhRXyKs0XWIANDce7DX1IkJpnqmyQiIJE6WqytScGkUyCDTiHcNkyxss\n7X6FsL+XF8Z/BE3T2M48QAiNWOiwo7W28zodwU4s6zC3tCnrlCsZEsl2Tt3W5lWisSF0vV1AlDqY\nJn4Mf66YW6V74KjXW6mwQd+b8OR6Bz9wZD2XWaKz58KR9dTeA2LH8Of2tm4R7Ei2dSEP32uBROw0\n8dg409f/EDgEdcX0Oj4zgG23E8mHR/8ub7z+b2nWK+iGSXZrnucm/2sAuhKTzKx84cjnmFaQcmar\nBeg6ukZZvfHH1O5f4fnhf+J2KK0ImeIyvfHzGLqFrhkUK7uEg734zSgKd8zqmgsH2s2FqxnA5doV\nqwfEA0MUqjuAIiISBESQPBmuy79EKA1TmDSUuzl4BCQ0odFUrsn1Q25SZB8LVwy1oO6zri94ucR+\n0srloJ7nWcLEWtfAk2IKW/OzI9eIemKKDqKUtLuYKuiqTClT1DMsyHvcV9faxBR9jNKl+luRdYvc\nZ10uEBIxfMJHXmV4RX0OU1hu3JiqutxXLjCEex5UKJF3MixwjxwZb3OqWBbTbIolAjKETZC0s4dP\nWJxVz2ARoKiyFLQcGbXPsprxNpSCgOggp1LE6abfGSPDHjPiFj5MBtQYZa1Amj3W5JzLYWtFkHW4\n9BDZ0/LmW1D32VTLBAlhahabcoktVrykDZMqFerUXLEXwzRpeN58OTbEIrtqgw89+8385adeJJE4\nNMt+Kx+6J21L3i3Atbm5yeDgYOtxf38/m5ubTwHd02qvd9qhe7xTZdv23xqQe1TvlUL38Tiy43Jl\nZ2dn+bl/+fO8/NLLDFTHucQHW15xi859HnDdDZNXkjAxgjJCF/2MyjNeHus9UIoBz19tT26yrhbw\nYaGQNKjTQYRneT8BQq3u15y8yz5b+DDRMcipDFPaFUxsbOmn6AkpHvlLPRJSFEizdoyQYoc1EvTQ\nKfs8IYXBqDiLrQIURJa8lmLdmX/MRsHxMikHiSnXX2qfbR6KmzhK0idGKWpZ5p0pFtQ9TO3RKLlO\nVCQ4p5537Qq88PFlZ4YU2wgvdqwoczzQ38B03JFInjR5lW0pTB/5xRX1LPPOPRa4Bwj3x1w2KZFz\ng9GdPnfnrxqMiFMYyufFqHneX97O340eG6RbDRBxkhgYLDPNOot0aBF61QhNGtSosK7mUTy6RgT1\nZonVvWvomo9TfX+vdX6spq7R3/1MW6drLzNNd/dlms0axdIOlWqanb0plJLMzvxH6o0STrOOUg61\nWgEhdK5f+bfoug/DDGCaHZSKu/gDSfa272LZEWx/DNMKefy59jGslJJKOUvkCZUscOzx4I1Pjzm+\nWj6gb/Aoby+1P0MsfhToKaVIpxa5cPafEIuOAoLp63+IP9xFINJLfn8Rv31UFegPxLD9ETIbD7BD\nCXTDR6jDHdcmYydpzleo1wqY1mEkmc8MU8lswZALRO1QJyhF0E4SCbi+ZCF/J9niOr3x8+7n2FFy\nxQ3CwV5sK0KjeTiCDfhjLXNhvx0jlXFFAX4rTrmaoTfidViFRlQl6VYDBESHy//iNXIqTZfoo6k1\n2HHW2WAJS9hoSqOGG793kQ+QEN1IJT0xxQELTCE9rpxCMaPdwlQ2HSoMCNciREtyWl6mlUus5Vxh\nxGNiCl0YrpiCHoacCZZ5yDrzxLVuErKbopZjjw2W5IO2nNkoSU6os0RVsmVAPiVfJ0eaqEhSF1UW\n5H23y60/UpmWEGhc4oPERRd1VaMoc+RJe3GCygORPub1KSzHT4SaEVoBAAAgAElEQVQkPmlSokBY\ni3FSXsSh6dqj6Fnmnbvc5w1XNasEHQRo0qBfnuCkCLPGvGt+LOJESFLUMocRZMoVTjRp0kkvJ7mE\nXwVb3nwP5U1ypAkSQiF5yC2WtAeY2PhlB7qtEY2E+fRvfopv+ZZvOfa8Pu4ek8/nnyZFvIN6Cuje\nw3o7QOc4DtVq9U07VX9b9V4odN8qjmxra4t//a/+Nf/h//4PaOjYup89tUmUJEl6KTkFN4NU62ZQ\njlOlQlHPHPJeHstjHWKCLi+PtamaPOA6KbVDQnRjaD5yMsXr6s9b4K1ODYnDOBcYYvxYfzUfJqDY\nUEsc6Fv4nY7WKNYSNifVRcLEWzYiKW2HLemOHlwhRYCCdAUZw2qCdUenIHL4CTOoJqiIIgUtwwPn\nOnVPSCFxsJSfMc7RqfowpOGNkq+zL7cIiygdQicvM1zhT7GEH10ZLX+pk1yknxMIBGWK5B3Xfy9H\n2jMCEeyJDbLigLCKYWByIHfw4eMkF12rBJWloGXZU5ssqget8PEgIRqqQYQE/c4JNllmWUxjYjGg\nxql6flkz8jY1VXEjm7z/HNFkQyxQcvKexk+hCZe3hACkQ394knRtnVjQHTs2ZZNiZY/JcTcvtN4o\nc5Cdp1Q5YHX9FRaX/wzTF8A0/JQqOUJ2N2GtC7sjhKHbCKFzb+2zjPf9HXyGTdOpUm+UKRb3aTYq\nlLObFDIrNJ0ajWYV6TQBxfLcFwh09OAPdBHo6KJeK6JpRstP7lHl0suIN+PPNRuEQn1t681mjWo1\nTyQ2cuRaqVUOGDim01cpp1BKEgm7HcrurnOksrPMXvkkF7/9X5DfnSUSO2qZAhCLnSS9fINgcpiA\ndfi3a5qBbYfIZ1ZJ9hxmv4bC/ZTT663HTr2EQpH0HwLWoNlJprxx+NhOUCjvAG7nrenUDkew9qG5\nsG1GqHsCiqAdJ1/YIGglvcQIyb62ybKcbgNGvQwzqMbp8OL3CirLHfmaa8wruiiR5456FR+mJ6Zw\nBTghEeO8egFbBDyVaYZ9ttjGHce5FiNFHujXsT0xRV3WjxFTZCnorjDCPVE1L4ZMoIBheQbzsWQK\nV2VqejF3j4spmu0qU1wxRYEsU87rNCgTEhFKqsAdXnU5g8JGSI0iWUIiwlnlelq6nMEcGbHPspr2\n/ipBgzrz4h4dKkKCbhzH4UDs0CV6GZQTlClS1LLss8WifIDw8p91DAIqRJgYI47b3Z7jDlus0iX6\n0IWPPGmuyD9zJweaj4as49BkjHMMc9LjEdYpyCwL3GOPDb77276b3/rt38Lv9x85L9/q3iilfNd8\nVZ+s/v5+1tcPz++NjQ36+/vfk8/6WtdTQPcu1nEduuMSF46LrzrOi+dvsx6B0b8JuHsyxeI4IPeL\n/+u/4dN/8Gl65TDfzHfToEbeyZIXaZaV63n2aKwhpSRLii76CTtxCloOncM81oLugo8Fz0T38IYw\nxLA6RYd0d3xp9nig3qBBnU7RR0nkWJD3WBEPPSGFQ40qERHjjHrW81dzd7wbziJ7uLYOCumOV7T7\nWNK9IWTUvutDpw8z7JxyXdmle0OYcW7zCLloSsPydsp96gSaA/fFdbLqgD5tGMvLl12U95lW11tC\nCgeHfk4wps65kT8CcirNlHydGkUiIk6ZAnPqLivaLKayQEGZIkER4rS6TIiYK/Bwsuyx2eIWoQSW\nZrHKPB0yTIxOmtKhQpGk1sOIPO0StkWGgpbhjrPYAodCafjpoEGNuOqm0+llRruDo5qMiXOEiFGn\nxoGzzbbH5wPQhEHM6iVT26YvfJqzyW/nK+u/yXjPt7bOvZW9K5i+AKncIvcXP0OhtIehmRiayenu\nv0dneAJDc0eUL838H5zp/w6iwYHWebaXm8cwfAx3vtB2Ps9ufolEeIRnxtvJ03eW/iO1eoYOkaR4\nsElWzlJvVqjVSgghmLr+7wlFBgh09BIM9bC98QaxN+HPRePDCK1d5b63dRu/P96mzAVXKFGtFo5V\nyuayy/j97b8Tp8f/Iddu/ztW736WfHqD06f+i2Ovw+HRj3D1tV+hUjhgqLN9jGybcfKZlTZAF01O\nsPfgUB24M/2yaz3TOMyHDVpJ9gozrcd+M0G25AI8Q7fQhE6lliLo78TyRcnk3cxWvxVDUfc+O0JD\nVgmacfBu7n1ylFVtFkc6jHCKmlYhT5qb0o3fM5RBkwYGPs7wPrrUgCfAcdhWqyw499DQ6RARCirD\n6/w5trDxeWKKMkUGtBOMyUk3kcGLrNsWKyy1VKYGOVLMUiJMgrjqJOukUChGtVNEZafLZ9OzbKgF\nZuRhMkWADnzKJEkfo84ZmtS5I65QUnkGGaP+VahMH00OUnKbWe4C4MMkrzLcFC9jaTaW4xoIZ9UB\nPfog484FFNIdc3rq/G21gvI2uhWtzBoLxEgyKCdYY44iOfq0EWKyi4LItpsWezZJHUSIqiRJ1Y8t\nbBwc7vAqOZmmW/RT12qsOXMsMY0lLExhY/gMBkf7+c3f+RPOnTs8r96snrxu3g26z6MJ03H1Pd/z\nPXziE5/gox/9KFevXiUajf7/YtwKTwHde1qapuE4h/L+x0eOT8ZXfb3V34RH96T58ZOmwOVymY9/\n/BP88i/9MvVqHYnDlrZKSuwSUm5w9h4bWMJmQl1wlVO46QIpdlhRsy3hQYAwTdnAT5AeZ4h15qmK\nstctGqOilciJNNedv0QpdxfbpImfIGd5jpjqbBnrPlQ32Ffb+AliCou8ynCdv/CEFD5qHk9kXEwy\noMZbN4S8yrDIA09I4anrSDHLHaIk8XvpCALBuLhASEU9IUWGdWeBWR6p6xQhIljSTxf9jMhTHKht\nZrXbONJhmNNU9RJZuc9X1OfRlQ+BokETmwDP8GEiuB2YJg2W5DSbLKNjYArL7WyI17wbgp+qGxnO\nkD7GiHMGhaIoXbuWDRbYZR0HiYFBXVRZY54YXSRVLxm1jwBGtdNEZJIieUp6jj25yZKaBgRCCnSh\nsypmacqGZ2Is2sBcl3+c/coiE4kPMRJ9jt3iLE2nQU9k0s1pLS6zun8VRzXY3LlGT/A0z/d8lOub\nn6YrfIre6KF5cKq4ilSSSKB9t72ZvkV39GjcV6a0RG/sIk9WubbDcNcH6U+0Wxm8+uDX6YycQdcM\ncqkNMnsz1BolGvUqhs9m5u6nCIYH6Aj30xHu8/hzR9//YPf43NjdrdsEAnF8vqPdjFxmkaC/vdOn\naRoXz/4Q1279OoZh4Q8cTxi37DD+QJRKOUN/17NtzyWiE2yn7ratxRLjNKslnIbbrdydfY3B0Hky\n9cOOXIeVoN44tCoJWHEOCnOtx7YZJl/cIujvxG/F2XceAhDp6KdWL1NvlrHNKI6so2s+DM2mKavM\ncAtDGgRFmJqq0iUHmRAX2GGdOXEHnzIZYMwTBkzxgOv4lOWNBd2u8XleaAlwKpRYltPssYkPCwOD\nDemKKVxKRZASOSqqxIS44GY4e6rxYiuZwqUFGMLkgB0qlInTzZBzkmntups0w3k301XLkWGfVTnv\nGpejIZXjJVMkiDuuoChPhrtc8VSmoxRErqUyNTXX4qROlbCIckF90FOZOpRUrjV21rwovT1ni7ye\nxudRKsqqSIEsQ9oEQ3Li8PvoOVacGRa5D4AufBRlHg2dpOp1v494g5TaY1hM4FMWBT3LulpkVt5B\nUzqgcHDoZZgBdYKQE/O8+So8UNcpaBl+6L/6IX7lV3/lbe263q5h8E6bCT/wAz/Ayy+/TCqVYmho\niF/4hV+gXq8jhODHfuzH+M7v/E6++MUvMj4+TjAY5Hd/93ff0ed8PdZTQPcu1+NA6HEu2luNHL8e\n650AuuPMjx83BW42m/z8z/88v/Nbv0NHI8rF6ge9H27XDmSDRTZZ9rpFYGk2K2KWkIwQpZOiylGm\nQK8+yKDjpgu4isw0t5yFloxeUzpROrEJ0idHaVJnWtwgrfbpEgNY2OS1NPecqy07gCZ1JJIRTnOC\nsy3yfpYD7jvXqFAmSMgFSmqaTX0Z0/G7RqZksPBziheI0UmZAnknS0psH45DPCHFrlynQplOeqg6\nZRqiRkTEGZVnqFOjqOXYZ6utyyikoJ8TJOmhw3G7jHNMsc0KIREjKELkSXNDvoKudHzCpE4dhwZD\nTDDO+RZoLShXiZpmv+XIvyVXSem7WE4AG5uU2sdBclq8j07V55oVOy73b1beQfP+Jp/mRo+VKNJB\nhLpTo0SBLr2fE/I8BobrD6ZuUMX1F3y0vRHomJqfnfIMUX8fI9HnEEKwlH2doeSzbGbusrz3Ko1m\nFUc2+NCJ/4ag6ZKkpZQUaynOh9vFDGvpG8cCt0Jtl5Pxv9+2JqWkXMuSCI8ds54jETrKn6s2Cgx3\nPY9thtvW/+LO/8Zo5wcp1fZJbd5ha/VVr5unoWnTAIQig4Qig+i6j2rleN+7g71p4k+IOR5dV+nU\nPGcm/rMjzwX8cWKRUbL5VaRsHrFqeVSx2AT16u0jqtre5CUWVr+E06yjG26XU9N9+Owg5ewO+c1p\nglacvvAku9uzh59rJdzxtPeZfjNKo1FtPe+3Xd4cQDQ0xNzqiwDouklHIMnWwV2Gup7DkQ2K1X06\nrCTZygYGJufFN5ETB2S1fdaceYQ3ftWURvJRMotzBk1orgCCBSIijiX85FWa19SLGPgwhUVNVWnS\nYJTTnGASIYTnZZdhjikO2MaHqzBf4iGb+jK248dPBym1jUAwyfNESVBQuZaY4snIrpTcIUqSfjlK\nJ31Ma28gleKEOktNVClqGWblHZeC4IkpNKUxwil61FALgG6zxpy8gw+ThNZNQWV5lT/xxBQWTc+2\naFCMM67c67pCiaKTY4NFtlhxzxkUO6yR1vYIyBAR4pScgjsiFa4fZlHlPJuTDGvOvGdCDrbwU1Zl\n4gQZdy4AkilxlYLKMsJpHK1JXqS57bzqUkOEjWbovP9DL/Dx//PXv+rx5ZvdX94sPeKrrd///d9/\n22M+/vGPv+P3/3qup4DuPa5ms0k2mz02h/Trud6JQrdcLiOlJBAItJkCK6X4oz/6I372p/8l+f0i\ntWaDvFrjQNvFEn4Mx6IiCtRUlTFtkgE55pJ6PduNVWbYZg3lga+SKrDOAnG6Cak4u2qz5cXkVx0t\n240HjjtW1fEhlWufkVDdJOjFkIYXVH8LRzkMMk5ZL7DpLLsedp6Qok6dDkK8jw8TFGF3jEyFFWeW\nbVZdhRyCCkUeajcwlU1QhSmRJ6+y9OmjnHDOIJHkZcbb9S+3dv26cvllKXZJ0kNcdjMtXOXdgHaC\nsIxT1HLkRIoNZ7FlI+LQJE4XI+p0i3RdocQdXqOiSnSJPqqizIZcYpNlLGGjFNSo4sPHZT5ETHS6\nJHJVYN/ZYpnDEZpAsCwesqWWvXFplX25TVzrZEJecM1PZZ6iyLHFMntsoACJZN/Z4oAdHJroGDg4\nj50pj9h0gMefO5P8NoQQZMqbFKoHFGsZLF+QoeBl8rVdHKPRAnMAG7k7WL4OglY7l61Q2+FUvD2n\ntdYoUq0XSYTax5jpkusnF7Tb/eR2svexjGAbaHur9YP8Aj7DZqT7/W1AMlvc5PrcJwlpCVKbd9hc\n+Qr1eplAMEm1kkfJJtJpoD2muq2W94/lz1UraRynQTx2vP+W41QQCrY2Xmdg6JuPPabRKNBs1mg0\nK/iMww6gaQaxrCD57Cqx5MRj6x0U9pbYnvkKz3T/I0JWF/VmtZXRamgmPsMmW9ogHho5Yi4ctJMt\nTl3ATqCATGGNWGiIRGScg/wcIz3fRCIywlrmFtFAH9nKBk3q3FdXCaowDk0EggEvsq4osuS0NJvO\nktvtVe4mJUyMATVOXHW1VJl31RVXyCC6aIg6a3KeNea9NBODChVAcYFvIil6kMqhqPItlWkaN8pM\noLGg3cOUNmFi+DDJqgOPvvA+dHQKMkdBy7KtVlwDbo9fF8T9HUioHoadk+yxyZy4g4WfXjVCWc+z\nq9ZZlNOtzdsjy5aTXCIqE66alwaL8gFbLGMTJCjCbKgldsQqpubHdEwqVKhR4SQXGGCMJq4CuEjO\ntQ/BTXPQMdjSlkk5O0RIEFFJUtJVAZ8UF7GVvyWmWJEzTKsbj00P3GuwWw4xLs7ToM6c7zYlM8dP\n/+xP87GPfezYc++t6s0UruFw+Jijn9bb1VNA9y7XI97cI+4YcGTk+I1QXy2gezvPvE996lP8Tz/7\nP5NJp5lwLnGSQTfBgSb7cpNZ7iDJYyoXQK2qOXa8YHcQZNgjqIWZkF6CA1nyMsOB2GZauY72mic8\nyMoDNAz61AmazhxZcUBUJFwlpyhT0DLMy3vca1kOSHzKZIxzdDGAKV2S8kNusafWCYkYUWGRU2mu\nqi9hCtvNWfUsB8aYZJhTrTigvEwzwx0K5FrRVim1TVHLEJQRLALssoFEclJcdHfJ5MiTIaeluC0X\n3JuBEtgEaMoGGoIReZocB8xqd2jKBiOcpikarS5jk4aXSOGOe09ykT410uoybrLEgryPhk5Ei1GQ\nWW57pGuXW1SnSolebZgxeQ4Tyx3TyCxrzLPBEo/UdSXyTOs38DkmBqbnldVgggv0MgwotllhDjcK\nyjVfbgJ44K6JJnT6gicpNbLErAGCZpKpnS+wXZjFp/s5Ffsw/SF3lPryxr/nbKy9u7ZVuEdftJ2b\nU65nqdWLJDraO2trqRtEAj0YentnavPgFl3RiaO8t/Q9OqNHgZO7flR9upW+S2f0qC/dVuoOndET\nnB3+rtZavVnm/srnqIgUizOf5+G9PyQWH6Gr9xmi8TFq1QLR6FH+XCa9eIQ/96gcp0Euv8mZ/v+E\n2YUv0d37zBFunpQOB/uzWL4gB5k5ejvbx8C2GSOXXmoDdJadYOvelwmYYRKeOMX2hdgvzrfG3B12\nknRhlXhoBMsXQkqHeqOE6QsSCQ6zn3M7ekII4pFhdg6miIWGiIZG2E27Y79k+BQb+9cYij6PrXdQ\ndYo0qJFlH4GGgaswBehSAww4E0yL62TUPoNiDB3Di6y7TV1VMZQPBweJwxATjKhTmJ6dSZEcd+UV\nqh7XtKQK3OU1TGwszQapUSJHQHQwqZ4nSMhVgHuRXessuP84SoAmmBdTBGSIOF1Y0k9NVIlrXZyQ\nZ6lRbXW/Npyl1vhVKI0OIvjwMe6cwxAmK8yyzEMiIk6IKHktw5RzBYnj+Tk2aNKkhyFOcxkDn5uP\nrArMOnfIkcYmgI7OPPdY0+YxlYWlghRI0aDBOZ4nSS9lCu74VcuxKue8aYhyrUjUMh24YoqokySv\nXcdUNie56KrltRwH7LAiZ0CBrht8//d9H7/4S7/4jhSpb2VZ8lTh+s7qGwtlfANUrVajVCqh6zqB\nQIBqtfoNB+bgq1PovpXVyo0bN/jpf/4/8vDeDMF6BFP53VgfcQdT2DRUnTpVukQ/p9QlLOH3Ehyy\nLDkPSbHrjQmbVFSZOf0OthMkRIwMu+RUhkH9hDd6dRVsBT3LnHObOe4AuONHZVGnSrcapNsZ4IG4\nTp0a/WIUWwUp6hnW5ByzylXLSiQShz5GGVeTmLjGqEVy3JGvUaZETLhj1SU1zYZYxNJslOMKD2wC\nnOd5oiLZUnzts80Gi+4/jHKNTTfEEmm1T5QkUjXJkyGixRmX51FI8sLzyXJu0aTpAkQJCXrQ8dGj\nhjEcg0Wm2WSRDhFxAaKeYVk+ZFbdxoeFQxOHJgl6mORZTGWDgKqq8EC+QY60yxnEYluuktJ2sbAx\npEWJHE0anBKX6FMjODQpyhxp9ljBvVnbuOTsOe4wy22v9ybwYeLQwPHAnPAMnzVhMBn9CB1GgteL\nn6HXjPHK8icIGBGEEDzX848Jma5a9KCyStNpkHwMpEnZpFA9YLLvH7Sdj8sHV4mHhjGe8G87KMzR\nEzvqA1eobTMe/7tH1ku1AwaSz37169Udxnq+9ch6rrLOQKL9eNMI4Kgaw53PM9H7bZRrWVb3X2dt\n4UvMVP4fdN1HpZIi5GsfWWUzc4QCAxxXufwqphlgIPk+1jPXWV95iRMT39V2TD63iqGbJAMn2E/f\nPwLoEtEJ9g9m4OQhcI7ER0jt3udk5+F7RQO9pArLLUAXsrsoVNwunBAaltlBtrhGV+wMsdAw1Wq+\nNZKNhU6wk3Kvy2hoiGqtSLNZIxkZZ27jz+mwumiqBgIdhYMl/Eyq59GFQVrtkdX2uCFfaktyUEoQ\no4th5xQ1Kkxpr1OWRYaYoKHXSKtd1uUCBm6SQ13VjogPmqrBntpiTt1GARY2RZVrEx8oJDlSdOn9\nnHQuoqG7QieypMQOM+oWEomuDOpalRVmiJCkSw0gHVfI1aMN0iMHKZKnqGdZlg89k193/GphE1Od\ndNLHuDzvbSxvsqs26BS9CE2QkyleUZ/Dh4khTGqqgsThFJcZEO418mhjOettLH34cGgyI25haX5s\nJ4BNgH25iU+YnFXPEiBEUWY9S6UMU/L11jjZrwXYlRuub6acQKFYCUyjx+Df/PIv8r3fe3wM3VdT\nbwXootGjFjxP6+3rGw9pfAPUI+7YoyDib8R6M0D3dgrdubk5PvqPP8rDuYdESHCGZ+gQ7m6rQZ27\n6gp5lSEiYljCZl9ukxH7WJrrw1SljIPDKXGJXuVaNJRUnqyzzwIPSLHbGh2k2aNEkShJfF7WoI7B\nOBfoIEwBF+StywVm1Z3HRgdRLGWTpIchOc6u2mBem0JKyQinqOhlcuqAr8g/aY1EGzSw8fM+vpko\n7pjOocmymmHdcTtrlvBTVgXuiivYmh/Ti/UpUaBfH2HUOYuB0eLibLDAPltueLbyIYXjCQ86Sahu\nsvIAB4cB/QSdTp93M8iwKmd46I1CFAqbAF2qny76seVp6qrKlLhGXqXpEYMoTZGTKb6i/sS1axEG\nNeXync7wDL3CswdRDVJyh4fcRpLHxk+dKvNqilVtFkP6/j/23jxGsu2+7/ucc6tu7Xvv+z49+3tv\n3nvcKVFLRJoWJSFwAlgJEMgIoFgyHC+R40BSjASIICeSLUVBLFs0LTmyLUTURi2kSD7y8a2z9cz0\nvu97175v95z8cavvTE0PJUF6j6SQ+Q0agzpd1TU1favO9/x+34UG9RafMMg41/HhQwPrzJPmBI3G\nL4KUdRFov34Ekpfin6bDO8Q3Tn8DKQS56iE3Y5/kuLKOYbgdMAewmXuXgdh1pHhMU9jNPsDrDhF8\nalSaKW8z2tlu+KuUolRN0xlu77jVmyWqtQKJUDt/rlrPU60XiT81nq03y1TrxTZTY7BNjCu1PPHQ\nyIXnLVezF3h4AOVairEueyzq90S5PPApAN5d/TXq9QIP7/0q4Ug/I+OfJBIdtvlzyXVuXPmvLvws\ngEx2A7/bFkNc7f8Md9b/HX0DH8brezyiTieXCLo7GIl/gHe2P4dlNdoMlnu7XmRz72vt66qJkC7i\n/seJFWF3L8flx2N5v9lBpvJYKBEJ9JLMrtEVu4zpDuAxQ5ykF+jtuEk0NMzW4TcAcLt8+L1RjjPz\nDHTaHcVSPYXPDONSJpn6ATVd4ZF4C1N6cFsmFV0iIMJM6Gu2Z5ywu9r7TyQ5KKXoZpAIcRJWD1JI\nsiSZ0++igQEx9pT4wBYa1KkRFTGuazvi6tzP8cjaccQHGkhaRxRkpkWriFClRFYnGTDGGbGmHeV4\nwciyb22whS0EceGiokqkOCFBN/3WGCvMUKVCvxwloMKUjBxn+pDNpyxbEvTQp0eJWZ3O+PWBfoMC\nORKim5qosKoesqZn8Ro+hCWpUkYieImPExUJJ5nCHifPt9T5ILVkxZjBtHz2OFmb5EkTFBEu65cA\nWgr9HId6m1X1CEO6+Cd//6f4h//oH2KaF82v34t6Duj+8vUc0L3H5fV6HWXrXzXL9dtZTwO6p4Ud\nTwO5g4MD/uef+Wf87u/8Lt31QYblFFlS3FGvgQaXsLNOJQbTvEgfI9jaB93KbnyIaoGBks6zpmfZ\nNVYxLS8KRZGc08EKEmkJDzKcsM8Wi/YJWRl4pI9jtdPysOujYGWpUyMhuxlSU3ZuopG1QZyax9CS\nc0XmIBN00EtQRVBascIDjtklLBIERIgcaWbUN5B/hvBAoSjoLIvWXTJPCA9O1D5ZI9lyr/eT5ZQ6\ndabEDXr1MCUKFJSdw7imZhFIhLZjfcpWkRxpuugjZEXIyQxubTImriK1oGBkWx+4s87rUdpigHEG\n9DgBZZvGpjhmgbs0VYNu0U9B5FhUdhj6ee7tuV3LVf0BfCKA1poyRdbULBlO8REkLjuo6ipL+l6L\no+gCbM6QBso674xXH3PmJMPhGyiteP3kc9SaVa5Gv5s+/yW0htnsn3Kt45PO9dRUTXK1Ey73PD1u\nnaf/KWVqvVmmUsvRGW4fiR5l5zBd/gtcu92zO4QDvbifivvaPbtDLNh/ocu3e3qXsL8bt/FUPFhq\nFp8niukOtK0n8+sY0o3P0+50X6nnaDQqRAMXY71qjTzXB3+IiK+XpaMvMTvzWbp7b9Dd+wqgW2bC\nFyuZXqY/aqtxw/5eooE+dra+wqUrf8u5z+nJHOOxjxL0dmK6/aRzm3TGH4svvGYY0/RTyO4STYyj\nrAa7m69jaMlhYYGhqL25h73d7OTvOY8LeBI0mmXndjw4zn7q8fc7IuOcpGxAFwr0YDXrlKsp/N4E\niegEp5klBjpv0R2d5ig3z2jiw6yefhWXMGnqOkorKlaJBg2aNJDCYEevEqWDbj1IwIpQEo9wE2BE\nX6IiShRklmU1Q01XHVqFgZsxLtOtB5zx6z6bbKg5PPgIy5gdccUfYQovJh4auk6VCkNikgl9FYGk\nRoW8sg9ij73s4ETvk5XJJ8QHeZrUGZOX6VIDtqhIZCjKLI+sTaeL7cFDXdUIIBixLiMxmBPvktFn\njifmYy5ww/Gy0y3x1qCedDKgi+R4ZL1NnTJhEWtlJr/e8rLzIJWLAhn8Isg1/Sp+Qo76NUeSXdbt\nV6MFQkpWxSP8KkSMTnqsAer+EpenL/EvfvkXuXHjYsf7L9EP1coAACAASURBVFPPO3TvfT0HdN+C\neq8TF75Vda7QPecDPkvYkU6n+e///j/gd3/7d+xUA0ZI0GPnmArJFkvssGrnl7byPlesB6zoh3bW\noW7QoE6X6GNa32qJETQViqxYj8hwhgs3oMmpNPPGbTyWDz+2srNInhE5xZCaaoVaZyi0+CE7rKDB\n8bDLcEYnvUSsBHmZRSIZlJMEVZiCzJLhlB21Clo7woMOehnV04S0/XrKFHnEW1R0mU7RR+0J4YFX\n+lCWbTfgxu24vWutKekCSeuw5fZul0CwJ9c5sfaJkMCiyak6ICgiTOmbuPHYZsUyy7HeYVMvtEYh\nECRCTVfopJ9ea4RdVtkRa3jx0a/HKMsCOVLcVpstlaB9uvfrINPcIqoTDgBd0Q851rv4COCVAYoq\nxzt8CVN4MIRBVVWwsOhigD6GcSkTC4tlZlrpG1EMaZBWJ0gMQiJOSWeRGCTcfVjaomClSVZ32G3Y\n3LrJyKv0B+yEgO3iAwzhotP3GLRs5e4QMGOEPI87dk1Vp1hN0TPYnt26lXyXSKAX09XOHTvMPKI7\nduXCdf3NxrDp4sYzbUxShVW6opcvrJ9kF+iKXlSl2ry6i/y8/bP7RIP9GE8pUcu1LI1mhWhgEEO6\nuD74Q0zUczza+20eHMzg9z3bjqTRKFMqp+gffWyvMt3/ad5d/deMjP8AHk+YcumMRr1EX9jmHIbN\nHs4yC22ADsDniZJLbxJNjHO8fw+3NOkOj3FSXHMAXcjTRa1RclSIT1uXxEOjrB58xfl+JDhK+ug1\nAKQwiIT6ODx7wMTg9xENjpDM2DYnifAkJ5lFusPTLB9/mYjZRbGRxk+Qos4ypV+kS/ST0yky8oxT\nvc+uXnX8DwN4KFGgU/cxYk2zxzqbYpEQUTp1HyUjx77aYFU/ciL4bIuTONO8REhHnbVV9Yhj9vAT\nJCAM9vQaR6IVcWV5qFCmToUpcZN+PfZExFWWPdY548A+XOLiWOySIUmUBDHd2bL6EUyKGwR0yEnB\n2VUrTsf93LpIYtCp+xhTV6lSZVa8SUnb4+S6UeFU7bOtl3FhYghJTddw4eImHyJBDwio6xppdcoy\nM5znK9vj5NcxpRev5cPmKZ+RkF1Mq5eQGM7ryRkpFq17uF0mv/R//Et+9Ed/9D3dx77ZvpjJZC7E\nfj2vv1g9B3TvcT15gQoh3hOD3m9XNZtNcrkchmE800vul3/5/+QX/vdfIGH1MK1vtUjAtpz9sQqt\nSYQ44/oaUTqQSlKnyiPeoaAyJEQPlmiSVme8yR/jlV6EZVCjgkZzhZfpxuYPVSmTts5Y4yE5UnYX\nC8GJ2CPDGWHiSFwc613cws2kvmlnN5KlIDKkxRnbatlJPPATRCkLEy/j6hqnHLAu59BKM8Il6qJK\nTqaZsd5ocWQMx/X9Ki/Tqfsd4cEu62xZi0gMwiJKXmd5yFt4pR0cbo8qS/TIQSbUNUy8zgl5nw12\nWWspWAWWbLBhLRAmRowuyqpIlTJ9xgj9VssnS2TIyxQ71opj12Jogxid+AnSp0ZQKBa5SxLbKd6D\nj7xM88h6C43GjUkD2wdwkHHGuY5Luxyz4jn1DlUqdItBLNmgrIssqvvUqSIxkEh8IoASTQoqAwh8\nIkBBp3ELLy8Gvpd8M8lq9R4g6HD143KNs1OdYyjwGDjtVWYZi3ywTRV9WFog5htiK32HSjNNpZEn\nXz5G6SZvrf8aSjfR2qYziNbv4Ktz/xxDunC7fHjcAfLlY9yuAHvJe3jdYXyeGF53hFI1Q2ekvZun\nlKJUSdMx/Iz1apqO0EVBRKmaZLz3uy+sF6tHTPR+34X1dHGDrvBFgLmfvHcB6PnMCB8c/zt8beEX\nqVSy7B28zUDfh9o+R9KZdXyeMK4nOo1BbwchXxcHe28yNvE3SJ4tEPAknG76cPxVZvb/Xy6P/XBb\njFosPE46ucrg+Pewu/YVJoIfwOMKcFrZcO5jGj7c0ku6tEVHaBzTFUQgHPWq3xNHSoN0YYOOyCSx\n0DCL2495dPHwOKncWuv5hqnW7O/FQsPUG2WqjTyjHR9iL30PryvQCpe/wUrzAWviEQEVxtJNyhQZ\nNMbptVqdbZklyxnbagXZ6n4Z2kWQCH5CDFj2aH2NOQ7ZpEP02hYnpLmnvmaPX4Wn1XFvMsgkk090\n3Is6x7I144gPBII1PceusYZHefFqP1lSNKhzhVfopI9Sy+qnKHNsqdaYWtkec8dqhxAxEvQQt7pY\nkHdwa5NJ7PdESWYd8YFuHS6VtuhhiCgdxC07HrBInoe8iaWb9IsRiiLHrHrHNgg3vChLU6NMUES4\nqT+CV/gcNW/KOmaLZc5j0TIqyYzxDUzLQ4gYfkLUPGV++Ht/mF/8pV+ko6Od4vBelNb6mUKffD5P\nT0/Pe/58/3+o54Dufa73O+j+va5zL7lazc5iDAaDbV5yjUaDz33uc/yP//ifUqvX8OJrmc+6GdVX\nSFunVOQjGqrmqDEfR+A0MHRL6eioMUcdUHTMHqvWQzR1QiJKUedY5C4bch5TebCwKFMkLjqZ1Dfs\nDx2qFKwMh2yzz8YTiQde9lgjqGPE6aKqqxR1jg6jhxHrsp1IITIUZJqH1iaA4/nWzSA+gvTrMbDs\nCJwjdomIOGEdd1IfFriLiUmz9aebQS5zC1eL21alzLx1hwJZvPhx4eJE7ZGVyZYKzU+RLFUqTIrr\n9OsxO1nCypJv5cSepzi4hZuiynHAFh300KX7SWm7IzYmruDXwRapOc2std2yDDGwsIjTTaceoIMe\npJIUyDEn3qGmq/QzSs0oc6oO2debuLT9u27QwMDFMJcI6zg+K0CFkk2u1j4mxHW00JyqA1L6CImB\ngUFJF3ALD+PemyxW3qJsFenzTjDhexk3Xr6R/49cinwEQ9gfPSeVDWrNMqbhZzXzBtnaAdnKMQhB\ntrJLqXaKlyABI0xOHzEafIk+3zQew4cUJg1V5Rsnv85Huv82Gk3dqlCxCqSqOyhl0agUOKzeo6Fr\nNJo1GpbNHVzY/QNCvi4Cni4C3g7KtRQuw0vA2z6eTRU2EEIS9HW1refLxzSsOpHAYNt6vVlu8fOe\nwZ+rZkj0X1xPlzbpiVy9sN5s1mmqGi/2/whzO39IuXLG5PjfdDiFqcwyIU/vhcdN9fxn3N/+TYaG\nv4fT4wf0BR+DyHhgCEMaZAu7xMIjznp/1y22H77B8d5dpBAMhW9Qt6pU6yWaqu4kcUT9PZwVNugI\n2ckYXZFJ9pJ3iYWGEELQGZngMPmQjsgkXjOM2+UjmV2lK36FaGiY/VN7JOt2+XG5fCxs/wFut4mU\nbhYP/4Te6DUqjQJXYj/ITvUB2/UlRuVV9tQKOZJ2hxpBimP7s4Au+tUoFYpIBINygohKUGiJihbP\n4/QwUChCREnoHjp0H6awVe2PeIuMTtIl+rBkkxNrl3028AgPBi6q2j5cXuNVukS/Y12Us2zxQY6M\n815bk4/YYQW/CuHFjjA0hYcr+mV8BJwkh7xI81C9aR+MlMAr/aTViS3yUJfoosSCvIvSFqP6CjVR\neeL11JzkGKkNxrhCjx50xslJjliw7mJgkJDd5FWWt/hjZ5xsaTv9pVsMtKxXXK085xxpccKeXifg\nC/L//Oa/53u/96Jw6P2uXC73vEP3l6zngO49rmfFf/11AXSNRoNy2ebEmKaJUsoBc0opPv/5z/NP\nf+p/QuXhcv1lLJrkW55vs9bb2KFWAhR0M0iAMHHdBRZsMM8BWwREiAQ9FGSGTWuRFR7ZHzItaX4H\nvVzlFTs/taXGXFL3yXKGiRcXLlL6lKJ8Ew9evMpPkRwVSozIaYbUpG2gq2xQtMc6R2yhsOPD6rrO\nEdsk6KFbD5DRtt/UsJx0NoK8zLQicGqtjcCyc0v1GHG6cSn7A3BWvENR5+kRgzRlnYx1xuv8AR7h\nQWi7EymQtqpO2BtvXVdJqhNWeUiBLK6WqemOWOFY7BJQYQQGZ2LfNivWLxAiQkHbCQ5Zecasesfu\nTiq7Q1bUOTz4GNJTnFr7FGQOtzYZ1ZdtgGhkWFEPmHVMTZWzEfQyhKnsDs8mi+ywSlDYv7eKUeJM\nH7Kn1mnQQNAakyBY0vdR2iajjxhX6DIG2W2ucqRscLxRfYDSmg5PP1f8H0MIwVLxbdzSS493kv3S\nIkfVFTLVIxSK5cxX8YkwcVcvlrtO2Ozkaui7nWuz3MyxW5ljLHQLt3zckVrLzxDz9RIy2zsIJ5U1\nBkKXuRJtV6DeOf1tPK4AQaODXPGEfGGfuqpQrZdAwO3VXyPi7yPo7SHo62Lv7C7dsYtmxbtnd+iM\njCFFe4dh9/QuoUD3BX5eMmd3ukK+i52HSi17wW4FYD/zgIAnRldokg97fozbe/+eZrPClVbE11lq\nhReGL8Z9xUJD+M0IW5tfolxKMdh/q+37IbOLs9R8G6DzeqO43T42l77AZMT2wjMNL153iKP8IoPn\nPD2zj1R123lcR3CSjbPXndvx4Bhbp286txORMY6T83TFrxAJDlBvlFjb/VP2T++hleYkvUSH7CGo\nQ6TKO5TrSYSQ3M/8QUsZbbHOQwQG13iVTvqpUiZrJcnLDBtqgQ0WAHALD0WVw4WbHj3EgDXBvHiX\nnE4xIi5haBeFlr/akr6PoQ00trhpgDEG9aTNNxVQ1SVm1Jt2KoTookyBOf0urlZerLAkFYqYmLzI\nRwmJKA1dp6hy5EizySJgU1Zcws2q8Qiv5SNCh53AQI6gCDOtX7JFHsoev25bSyxxz5kiREjYbgC6\nnzF1hRTHLIp7uHDTp0coGQUO1Kbtf+cIuOr4CXGVV4joOAho6DoHapstFnHhxi+CnOh9UuLESY7x\nEyLrOeEnf+wn+emf/Wn8/nYKw3tdzzl07309B3Tvc32zPNfvpHqWl9x5l05rzVe+8hV+4sd/gr2D\nPefD4JhdOuilWw+S0adoYFCOE1OdFESOgkyzqO5R149BUZgYw3ra6RRVKTPHuxR0jm4xgCWb5FSK\nb+g/tEGRMqhjd1Su8Apd9DtO7zY35EEbKDpki5Q8JqDCdjdM7GPg4rK+RZQOe/SqM2Rlsk2a7xV+\nqqpCgCaDepK0dUxRZvEoD6NcsU06jaed3hVCS8a5Sr8ew6XsUeWRtp3eQdmnY53lkX4bU3gwtZcm\nTaqU6BC9TOmb+ETAsTc5ZJtj9jjfCIQQbMj5Fjm5g7IukNUpOg07J7JB3Xaub3UMFXdbQE8QoRuN\noo8xXJbL6TLGZScx1UXRyHGotljXc451gkWTEFH69AhhYgSsCEdssy7miYkOLuuX8YsQTdVgRr5O\nUeXokSNk9Rl79TUUTbrdQ/TJCXIqyZ61ylX/xxFCUGyk2a8t45Yevnr0b/AYPgLSNg/9rsSP4jOC\n9rWoGmylZrkWbgdia8XbdPlG2sAcQLK+zWjwlQvXdL5xymjoos1IoZliKvZRok91tl7b+1Umox+m\noWpkCodk8jvUrTKNZhWjZNJo/hYhXx8hXw8hXw+58h4jXR+58PNtvt2zxqr36XpGikWqsIXGtgB5\nuk7zy3QFbYWuz4zw4eEf463tX2Nj64t0dd5EwAWF7XmNdX0XszufJ+TrcLpr5zUS/wCPDn+fyZG/\n0TZ29bjDKKvJcOhFZ63TP8JJae0xoPN0c1icc76fCI4xv/8F6s0qpstLPDTK4t4foZSFlAax4Cg7\nJ29iWXV2jt8C4PD4HtfcHyJmdvNG5fN0u4bpdY2y3LxDyjriE67/nDUecNDY5AU+jld4OZAbzFt3\n8chZfDpIUEdJ6WNHZBUgRF5nKBpZzvQh62resTjxEaSpm0TpYMAap06VWfkOJVVgmCmaRp2cTnNb\nfRmhJS7hok4NicFlXqJbDyKFDS4z+ox56w4K5Qi47vF1PMKLR3sRrdi/sIhxRb+MFz8lbY9fsyLJ\nhrbBp9QSQxqs63nCrfGry3KREsdERaLFnatQlFmSHLGplhAaRyzVRT8BIgxak0ghOWaPZWbwCj+9\nYog8GWbU606UWFM37exq+rjKqzavuOVld2Lt2ePXuOKLf/DF90z08OfVc0D33tdzQPce19MX6Hey\n0vVJLzmfz9fmJSeE4LXXXuMX/vkvsr68QX9pghFu2qCIDBl5xiP19mO/IuGnoez4rEE90ZL45/Bo\nD6NcpimaFOTjTpFLu1DY3Z4JrtGnR21QBByzy4p6CFjEZRcFnWFe38aUXkxte6tVqRAXXUzpG/hF\n6JuCIlN42JbLLVDURUkXyOgknUYvo9YVu8uoMzZgsx6w6IAiiNODQNLHKC5rknXmOGSbsIiS0L0U\njSz7aoN1PYdbmy070wYxOrnKq3i17cjfoM6Suk+SY1t4IPwk9RE5kcIjfZiWlwo2T25UXm75PUFR\nZ8nrLLuscNoiW7twUaHEJkvE6SJGB0ltW58My0skVDcFchSMDLtqjSU949ib+AgQVZ100MuQmqSq\ny8zJ2xRVliEmEUJSlnl29TplVQTs7Fu0oCyK3BdfRynL9upTFgZuSiJLzbJd9z/g+zQhGSNnpdhu\nLDDkvcp86XWKVoqqVcYnQwx7rtLpGsRnhHin9HuMBm84YA5gvXSXkDtGyN0++kw3D7kWaR//lJt5\nKs0i3f6JtvVkxc50jXvas09PKxugIWK2d8lSlX1bFRy82gZwSvUsbxz9OtejnyBd2ydZWeZAzVBr\nlGylZOoe5VqKkK+XsL+3xc9L0xFu//cAFKvHTPZ+/4X1veRduiNTz9zYyvUUUx3f5dw2XX5eHfqv\neXfn35HKrON/ajz8ZPXErrK4/0d4jYuCio7QOFIaZPLbxCN2Z1BrRb1WBKs9dilmDnBWeePxbd8A\n1XqRaj1vq2NdPoK+TvaT9xjr+Whrzc9pZpGexHVioRGWdv6IN2Z+AVN6GHfdZLu5QFz24hIuJs1b\nrDbu0y2HmTReItn8Q1bUfa64XsXvDjHXeJtubYfPj4sbnKg9VnhIhmTL09HDjlghoMIk6KHD6iUp\nj/HgY6rFSTvn9u5bG47FiVaKTvoJEiZu9eASLgrkeCTeaiXHTFIycqxaj1jiPqbw2hnVVPET5CYf\nsb3wsPNik+rI6RRKJDmdZka+jgcvfhVGoUjqI7pkH1PqBTTKTnIQWdKcsKc3ENixhUooDtkhTicj\n6jLH7FAWthlyrxqhJPLkZdoZvxrabXv34WNAj9Op+zGFrX7dYZUttUSAEFGZIK/TvK5/H1N4bRCK\nl4qnwM/9zM/x4//dj39Lk4z+LED3fOT6l6vngO59ru/EkeuTXnJer5dAIND2xlpZWeGn/tE/4ctf\n/TJaKzzSy65cJaWOidNFXufaOkVNGuSxQdGCdRfdAmpCCTrow4VJjx5GWpJVHtpWIDJugw8jy65a\nY03P2ma02mr5L3VzlVcxtW370aDOgrpLhlO8+DGFh5Q+5r7M4MGLqX2UKVClzEgLFIG2/Zd0hh1W\n2hRoNSrssNICRV0k9SEWFsNyirjqptgCRU97vvkJ0qUH6aIPU01R11XmxG1yLc83JORUirf0H9ue\nb7ioU0WjucRL9DHc8piyyOoki9ZdSuTx4LPFFXqNU2Mfj+XHxEtanKC15iqv0EEvJXLknVzVBw6g\n9kgvBZXBhYtO+glYIbIyhVt7mMBWOBZllhP2WFdzoIVN+lYW3QwQIEJcd+KyTNaYpUqZHmOIIXUJ\njcLSTXb1GmccMO66xiBTGMLFmvWQfdZ5wfM95K0UG7WHpJRtNntUX6fLNUBIjrNjLfJy8JP4pG2h\nkmueUWzmeDnS3g04qq8zHWz3kzuqrKGUIuFpt/tYy79Lh28It2xPgdgqztAXmGoDZwA7xVn6QtMX\nNpHtwgw9ockL99/M36UrMEJvcIre4GMvu7XMuxyVl0nIfrLZXU6zi1TrJbRWKN3kJLNApZYlEhjA\n4w5Qqxep1J/NqytUj7jUcxHoFSonNJp1Ir52UBowY7zU/19wZ/c3GUzcuvC486rWcyjVoFA7fubG\nGfH2cJJ86AC64+QsYKG0IlM9IOa1zY3j3n6qZ0WaqolLunAbXuKBQTbP3uZKv20z0x2e5jS/zFiP\n/XvrjExwlJojGhxiefcLAIRUlJe99utM60MWG29zw/NxBowJdhuLrDZnmDZf5qb5ce5Uv4SPIKOu\nKwTdUbb0Am80v4BLu2jQaIGim7gxKegsOZ0mJ5OtfNUWFUEGOFH7xOliUE+Qt7KU5AyGNhjVl1vJ\nMVlW1SxV/W5bvuoo03QziFf5QcCZPmRR3cPAoFP2UdAZ3tFfwo2JKT1YyqJCmW7Rz2V9C5dwU9c1\niirLKQccOhYniqxO8ki+iUf5idJBVdcokKfXGGLEmnaSKYpGlnVrniVmkEikNkBjc9/0IGPqSstB\nYIW47CKqOigaWXbUKst6pk3NG6OTKV4gpCPO2p7aYIN5JsYmef0Pv8rAwLONq78dVSgUnkd//SXr\nOaB7n+s7CdAppahWq9RqtWeaAs/Pz/OzP/OzfP211xloTvBd+jOoFh/NzlRd4YQ9J9C+pivss0GC\nbuK6kzN9AGhG5CWiqjXiNDKsWbPMc7sFihRBovSqYTrpt/lousycuE1BZ+kVQyhpkVUp3tB/aHso\naUkdW6RxuaV6PTfeTKtTFrlHkfxjjym9TtI4wGsFceMmKY4RCK7oV+igFbdlZcnJJEtq5onRq5ei\nymPio5MBfFaArExhag9j2KT1opFhT6+zrOzNAUBpzRCTDOpxexMA0pyywB0auk6n6KcociyrGdbF\nLKbworRFjQpBEeUlbefEKm0r6k6sA/aw1YBaawwMtoxFDq1th1Nzqg+Jyg6m1A0E0uHgHKgt1rUd\n84WCMDFqlOmkn349ypZepiJK+AkyoMepiBJFmWVDzTOvS3b0GAKXcFFUeVb1Awzc5ElRpUyMLgpW\nlod8g5LO06SBQHC/9qf4ZICaqhIyYlw3P4ZPBlFK8Xbj9xnz3XTAHMBi7S2GA9cw5eNc0d2yHQfV\n7W0HPluVB4wEX7jAV0s39rgWbQdDSiny9RMmwh+4cP0XGqeMR159xvoJQ6GLoCrb2Gc89KEL62e1\nDQZD1xgNt4903zn6T1i6QTa7xXFmjmq9iOn24zb8eN0hmlatLYLM7nQVSQQvesztJO/QGRptM1U+\nL78ZQWvFQfohQ50fwO+52M04zi4QMGPUrBLJ4gadofau4WjiI9zf/Y9cGv0MCMHG7lcY8d2k5Mmw\nmbvHrRagMw0/HneAk8IS/ZHrAPQGr7KZedv5WR3BcbbP3nVux4PjLO39EW/P/wpRI8EV16usNO45\natcp18vcrX6JqruMV/q5bH6QB7WvMaAmCck4NzwfZ6H+NkfNLabFq7iUG4kgKCI0qXOmbINfr/YT\n1gk0ioxOkpDdTKobNFs50EUjy461wjIPMFrv8XP/tU7dx4ia5kjvsCZm8eKnRw9TNnIc6V3W1ULr\n/S1oUidImMvcIqLtrqhFkz29zpZexsRDUIQ51YekOHEsTpo0KFFgSEwwpq8AglLLVPxE7LGpF1FY\nGLjIkWKJ+4SJk6CHslXAosGAMUaPNWSrZmWWMw7ZUAs2wANcuPGqAEEiDFjjSCFZY5Z9NlpqXi85\n0txVryFaYjG3NJEB+I1f+Q1+6Id+6NvmwPDNOnRa6782meffafUc0L3H9Z0oivjzTIFTqRQ/97/9\nHJ/9N59FNWzi/q5c5UTuElARNIIkB3iEl0l9kygJJ1M1LU+YV3cc7pZXBKioEn5C9DPGiWWbbnq1\nnzF9zkfLsqkWn4i+UaAFY1xhQI/iUjbn54Q9ltUDFJpO0UeeDAv6jhMfZunz0Wsnl/SL+EXQiQ87\ntHY4YhsQaK3szES5xLHaJUYnRXIk1TFx2cWEsjcqm4OTYdtaZo1Hdg6pEkSJo7Doop8+a4RtltgT\nVXwE6dPDlGSeNCfsqjUH5DVpENRhXuBVQkRB28H163qOA72FiZeACFPUWe7ytdbo1Y4pK1NiyJhg\nxJrGhdu2ZrAy7LHONsv2JqANGrLGGnNE6SBBD0nriCoVx96k3LJ0SIsztqxlZxMwtIsQcQzcDOkp\nlNVkXtymTpUxcZmQjlHVFWqUyXBGkkP8BOkx7BxeiaTYzKGwuGy8QkhE8RFiQ81yJLZ5wfMJTGFz\n3VYb99FaM2Jed6630/oupWaeVyKPuVoA27VZxgMvtwG3cjNHsZHhVrxdBXpUXkUpRYe3vWu3X17A\nJU2iZjtH7qi0CghinvZYrVzthLpVI+FtV6tWm0Uq9SKd/pG2daWalOoZuhIXx6pVledG/FMkfIOt\n+ypOKxvMpf4Uw3Dz5tKv4DGDdEcu0x25wkl2mVig70LOLEC2sst4/KMX1gFOC2sEPTECrjiPtn+L\nD0z+t0jZvgEeph/S771EuZlnM/XWBUAX8w/gdnlJZddpNMtoZTHsf4FUfY/5/Gtt9+30DXNcXHYA\nXVdggoWTLzpj15C3G7CFHyF/DwfpGSzVZFhOM2G+gNaaXbXMUuMuVz0fImwk6DQHWGi8xS3P9xM3\nehg2p7ld+xOmXS/T757gw94f5E71i9zXXwU0PQxxSb+IS7ho0iCrkhyzyx7rdqScllRkiWUeECFO\nB73UrAoNanQbfQxak5SxDy8pjluctJbltZZE6SBAiAHLFrrssc4G84RElDAx8i37Ijv31I4ubNKg\nm0Gu8DIGBhpNmQI71gon7NsecUj29Aan8qCVMBG2u+w6y5i83DI7L9sWJyLHMXvs6XVH2Z7TaSya\nxOlmTF1hhUcUyDEgx54QcaU5snZsk2/9WM3boXvp1L24WuPXXVZZV/PcePU6n/v1f0s0GqVarSKl\nxDAMpJSO1db7Xd9sT/x275V/3es5oHsf6kkQ9+0URfx5psClUom/95N/j9/5/O/QLQZ5uf49eITP\nJturLDustvho9rhACoNNsUBQRYjSSZYkWZWk2xhgxJp+iqRv5xuej14T9CAQ9DGMtMZbKQx7RGWC\nhOqhaGQ5VFts6Hlc2kRhj14dPhp2J6dJk0V9l6S2RlsdkgAAIABJREFU+Wg+ESCtT7knvtYKm/dS\npUSFMqMt1atEUtR5CjrDNqukOcFCYWBQFzU2WSRGJzE6OFUHNKgzaIzTZQ22TsYZjtixo29abxm3\nNumkjzjdDOhxmrr5hOebzWE5Pxk/TpaoYdFklCuMcdnxgCqSZ9663fK58iEQ7FubJI0jTMuLlwA5\nkaSmq0yJG/TpUaqUyVsZCiLDjl5lB9uk1S3clK0iJ+zRQR9BFSUrky17k8v4dcixdFi2ZqhTfSIf\nM0xFV3DhIUKcbY7JkmJS3mBATyC0QFmKOfkOCM0rxvc7sW4H1ib7ap1XvD/ggLmiyrLfXOWS9wOc\n1Lep6hI1VeagsYZbmMzk/gQtFBpFQ9WoNsvsMcdedd5Jlyg1cnikn+PKOm7pxSN9eIwAW6X7DIdu\nXBiT7pfnGAxev7Ap7ZQeMBi6uL6We4e+0NSFTthm/h4xX+8FEcZecR6vK0jA3U7azlQPaVoNZ1QJ\nNn+2wzeKRvPBzv8Sr/RzVF7hIL/MQeoBTatOPDRCpZ7DZz4OI7eTL/J0BNujyc7ruLhE3BxiOvpx\n3jz5ddaOv8KlvseJGqVqiko9x3DiBRSKrx19llzliIivHeRGvQMcnc2Qze8x7nsJKSUJzwBNq0am\nekTMa9+/yzfJbPJPHMPg87HrxulbXB34FEIIuqNTrB2+RrWeISgjDLomOFU7TPACQgguuV5mpvYa\nk+oWpjSZMF7knfoXWOcRE56bTLheJESC+dqb7DZXqOgCpuHhkvUClmhyLHb5hvoCXunHa/mpU6NE\nnlF5iWF1iQZ15/1wygF7egPQju3IITt00M2ousImCxTI0S0H6FC9LY5dhmNr17Y4aYEiP0H69Cid\n9OBSNihaZZYDtUlUJDCkQcY64+v8nmMJUtMV6tSY5AZD2N6FdaoUVJZtVjhm185lRrPPBqfGAX4r\nSJgEWZ2iTpVxedXOfW1184pGjiXrvvN7M4WHsiriwUe/HmXUuswCdzjjiEExjlt7KLYOzYv6Hm5M\n3C6Tjp4EX/q3X+KDH/ygYxqvlMKyLBqNBkopxxvuHOCdf73XIO+8O/fNfu5fR9/W74R6Duje55JS\n0mg0vqXPqbWmXq9TqVSQUl4wBa7X63z2s5/lf/ln/yuUJNqCXb3BidzHxIepTYrkaVBnSlynT4+1\njV63WOaYPXRr9FrWRXZYJUEPYeIc6100mjF5majqIN8avW5YCyxw94nRa4QuNUgnfZgtkv68uE1e\nZ+kTwyipnuKjGdSoA5rLvEQPQwgEqsVHm9d3KVHEgxfQ7Ol1To0DvJYfEw9pcYrSisvcoosBSuTJ\nWxnyIs2anrVHr624raJVwM0pXQzgVyEy0k6sGBdXcWk3BZklySGbagGhpTMC7mWEYT1JEHuDLpJj\nVrxNTVfpEgNURJEdtcIuq08lS5i8yMeIiQ60tonWaeuENebIkUZoG/ztyjWOrT0ixBEYHLGLV9j2\nJgHCtsBDZMiIJDtqFQMDlMBHgIq2O6cjepoz64CCzOLXQSb0dSyalEWBksyzYe05EUMSgx1W2Jfr\nCC3srF1l4RdhltQdtNA0VYOqLmEIN7ON17HqTZqqiUajUazX7uEWHkzppWKVEFrQ4xrGhRtDu5FI\n1puP6HOPEzW6WtewoqGrrOkUfsIclhewaGLpJg3LjoUq1u9zWFrGb0YIuOJ4CFKopbkc6W8b5zRV\ng0I9yfVEe4yYUopc/Zix8N+88B5KVrcYf8bY9qi8RG/gYjrEduE+PcGJC2PhvcIj/O4wfpfNCeoP\nXqU/eJWG1eCrh/+Kei3Pmyv/N4nwMGMdHyMaGGDn7DYRfw+m4bvwPE2rRqa0z/W+70NKya2OH+Ht\n4/9AIjjuiDGOso8ImQmkdCGBhGeA7cy73PT9SNvPGuv4KG9v/hped5DhoM1llMKgJzDOdv4eMe8P\nApDwDiGQHOTnGIzaQoPe4FU2WmNXpVUrvzbJmOs6o+Y1mrrBQWWdg8Y6/e4JYkY3cVcXC423eNHz\nCfwyxEve7+NB7TXK9SzXXB9DozCEi4ouolCYyosPPwl6GNZT1KiyYN0lS9JJj9nX9sHHZwWJ0EFO\nZ6hQZkReok+N2AcybdMRFq37ziHThZumalCnSp8eZ8QyWWaGY/bolcP4VICCkWFTLbCo77Z83xQW\nTboZYFLfcDh2DW3ze9OcEiKKS7hZ13Nsi2U80mdn0VKiQY0r4hbdehCLppPIsMs6ZxxxnlF9JLZJ\nc0KEDmJ0kFYnAEyKa4R0jILOUTSyHJ3nq7a2cT8BhBZESTBoTSAQ7MpVtsUyn/6RT/Er/9ev4PXa\nB5RzMCWlbNsblFLO15NA70lw92Q3772uZrP5fNz6V6jngO59qKc7dN+qNvK5KXClYsfxBAKBNlNg\npRQ///M/zy/9i1/CqmimGy8RETYnpEGdY7XLOvMU0biwjSu3xQpHYhe/CqHRpDjGLwJM6huPUxhU\nlpQ8Zl7dsbMblMQr/RSVTfbvY4QTy0VGnuLTfkb1VazW6NURHbRIyWgYZZp+PY7ZGr2ecsCynrHN\ne0UfeZG1M0jFIzx4sbCoUiEq4kzrlxw+mh2wbZuEnv//GBhsG8scW3tESVClwikHRGSCKXUDA7cT\nt3Wod9hoxW2hIESUpm4Qo4tePcy+3qAqyrhwM6gnqcgiuSfyaw0kTZqY2ssNPkxMdzomyvtssGEt\ntKKyohR0loe80doAbCVvmSKdspdJdQMPPjtL0spwwh572GMZoQWGNNhiiZCOkaCbZiuMOyHtMU2D\nutOVW7DuorCcEXmcOFXKjgXNvHUbiyZj8jL9aow6NeqqSp40mywRFjE66ONcRVxWRY7ZZVheIiTi\nuHDhEiZr4hENUeOD7k853a+6qvKm9ftc93yMTuNxJ2u/sQYCpj2vOobDAHOVN4i7u3nZ9zjjFeBB\n5asIKZhyv0KumaTQTFGspTmwljCEwf2z38PSFn53hIink0q9iFt6kcLVBvSOK6tI5IUxbKGeovoM\n9WxTNSnUU1x7ChgC5OonXIldNGE9rqzQ57sIAPdLs4TMKB/p+tvUmiWW829wb+s/EA8OUaqlGIo+\nW/BwVtrA6w7iddl8xIA7ynj4VeZ2f4+PTv8ELsPDXvI+V8OfcB5zJfrdfOP4Nyh3ZPGbjzuLPjOC\nFAZeHWx7jm5zksX815zbQghGwi+ym7/nALquwCQLJ18kVz5i5fhPqdSyePBQ0SUAXMLNpHmL9cYD\neo0xpJRMum5xu/LHrIh7XDJfJmZ08UHvp7lb/RJfa/wWAhjhMiNympLOcyg3mbNugwaP8FKnhkI5\nBr/n9Iq8lWWX1TZQdCoOyJMhRgdx3UVanaJRjIurRHScAjYo2tcbLKsHbaDIo7zE6WZYTaG0Yp53\nSXJCnxgGIciR4i31RQxt4JYmNV1FoZjiBgOMtxImLAo6x7J130mY0GiW9AybxhIey4OPEFnOaFDj\naithokyBgpWlIHPsqlW2WQYNbmFyzB4liiToodPqZU7YvOQJcR23NinKLFmRYs/aQKFwG26uXb/K\nm7/6JhMT9rV83lw4B3TnoO68zkHbk3XeyTv/qtVqKKWcxz7ZzfuLjmy/GX8un88TiUSe8Yjn9Rep\n54Dufa5vFaA7B3JKKfx+P263uy1K6ctf/jL/+B/8D6QOM5h1P1krxb3z8GZlc7dqVBmQY4yqK5jC\nzlnNqwzbLHPKPhp79FoXNdbFnDN6TXNKViVbSq3W+ENlKBhZVqyHLDHzxOi1G9D0MkS/NcYKD6hR\nJSoT9vjDyHKsdtnUi22n4igdXOUVfARAY2eJ6hlOOcCDj4AIktVp7omvO6CoRoUKJYbkBCNqGgMX\nZQrkrQy7rLHJaYuP5sKSDTZZJEonCbo5VQdUKdNrDLfitooUZJaUOGbDWnS4coY26KKfCDEG9BhK\nK1ZaSt6ISBAkQk6mmbXeAWwbFZt/06SPYS7xoqOgrVFh2ZohzRkmHiSSM3VEXmbwaC++Vv5jiTwj\n8hLDagqrRQDPiwwHbHHIliNaadDgsGWi3K9HWVJpFIoBY4xOq58iOYpGjiPdPk428VFQWfbZJEoH\nOVJsscyQMcmYemztkVFnbItlJowbDItp51rcs9bI6xQfcn+6bZQ5q94g7uppA3NKKTbVLFOeW21g\nrqmanFq7vORrFys0VZNU84hX/D+AX4bwmyF6GUUpxdfL/4mbvk+QcPdTVUWSjQMytRPyjVNchsmb\nh7+BRhPyJIiaPZyUNunwjTjE9PNaz71DT3Dign/bdv4+fjNK0N1uBZKpHtKwanT42nl4SimK9QzX\nYxdjw46rK/T67P8zjyvAzfgnqasqc+kvU6uXKNZPsVQDQ7rbHneUnyfmbgegY5GXOamusXL4J/RE\nbyAR9AQeP6fXFSLm6WEz9QbXen/QWd9J38ElTUoq7XRhADo8gzSsGrnaCRGPzY/rD15lLfsO5boN\nCt2Gh5h/gNsbnyPm7uaj7s+Qd2W4X/syY+o6Xumn3xhnp7HAWmOGS56XCcoor3g/yUztK5TJcdn1\nIbasWZq6TkTEyek0h3ILrRSDTDCpX2CYKzwQX6ekC0REnILOssAd1oUPj/bhJ0iaMxrUHVB0HrtV\nMLJsWkvAogOKkvqIOnU66aXL6mNO2qBoXFzFrU0KMkfKid3C6bx3MUCn7rcPZcLORX7AGxRUji7R\nT01W2LAWWGcej/BiaIMq9sH6yUznCiXyVoY1ZsmRccDfmpxll1UCKkyAMEl1hERylVcIYUcJFoU9\n6XhkvWUfMrXAK3zkdIo4XQypSxhIds1VTly7/N2f/Lv89E//tAO0zkeq538/OXKFx2KE833j/HoQ\nQmAYRlvn7M8a2T49rn1WN++bAbpsNvvcg+6vUM8B3ftQT+e5vp+A7lmmwE8+/9e//nV+7L/5Oxwd\nHzLMFDf5qD0WElDRRR6qtyiSJ0gYhWZfbXEmjzDxYmqTAnksGkyJm/TqkSdGr6kLo9eSzrPNyjcd\nvZ6rXrfVMouOFYgi8MTodVBNUNVlFsRdcjpNrxgCAVlSvK2+iAvTTnxondYv8QL9jLZGr4q8TjNv\n3aGMzTEBONTbpIxjvJYfL35SnNCgzqXWa6pQtK1ARJpNvcgWS4C9AVStMilO6KKPUIuPZiAZEdP4\ndaCVI5lkV605uYtWK/FiVE8TIubk1z7kbYoqR5fooyHrnFqHHLOPR3iR2s6vBdui5NxEuaYr5FSa\nZWbIk23xb+CIHdLihKCO4CfICXtYNJkSL9Cpe20vOp2xo8DUOw5PziO81K06ZUp0M0TMqrAg7+DC\nzRQ3MVtB52WjwInaY/PcCBWDY71LUh5hWC40kCdNnC7COk5dV3FhkuaYNfWQm+6P439C1brdXCJv\nZfiI9zGgAFhu2M/d52rvhi3V3yHoihFzdbetr9TuEHJFiRidbevbjXncwkPcZdt8eGWQAc8lvDLE\nSXObjwX/FoZwUWxmOGvukSwdUrfKnFU2+cruEj53iIinh7C7m3R1n6vxZ3TbqisM+i+arm7kb9MX\nuoQU7R+nu8VHeFx+gu52FapSTYr1DDei7a/ZlF6iZg+lZopsaY83Nn+VG32fIe63hR8Nq0Ly/2Pv\nzYPsyu46z8859y33bbnvi3JTKrUvJan2sgvsYozbGMLNuE1jxnYzQQTgaRgwlB3zh6Mn3LiXCDcx\n44B2e2bohjZggxvvC1DlcpVKpZJUUmrJTVLu+/L2fbnnzB/n5s18UhkDxsYm6kRkRFUq38v38t3l\ne37fLbfAUz3ve+A1PNT607y08d9I5lZoDjwYQXGs+a1c2PjvDLU8QSTYQrmWZ37nFc5EfoKp4svM\n5q4w6lLMUlj0RA5xJ/Ui5zv/Z/ParDCd0RFmdp7nTM+7WM9OkSqsAoITvqeQ0kcT7XT6B7hVe5Hz\ngbchhDQO1tLzWPg5GDxFg9XCI/bbeaX4ZS5U/wcBbB7jbdgijEONNb3AmpxjTk3i1wGqlAlom/P8\nODEavY1PSsWZ4bqRI7ha1FnrFsvqLjFtukjjagMfPo5wljAxQ726dX+Lzl6nc4gIeZ2l1TUe1Khw\nS14irzIMiSMmq1ImmXAuU6WKHz81qmg0IxyjTx/Ep3yuKSLHDfUyBfI0iVbyOsN1XiIgggRFCEv5\nyJHBh5+zvJlG0UJFl8mqJBlSLDCNRrmmiABzchLbidBMG426lU29jJ8AhzmLH79HJ5tr6mv4hMXb\nn3k7X/6Pf05bWxv5vJmY7oKsXWC2H+SZY1I9APj2Z5LeP83721K2lUrF0+Xtp2y/k678jVDh7229\nAei+z+v7Bej2hwLbtl0XCgwwNTXFsx/6MJcuXqK51EWr1cWqM88Sd028h1ZUKBMmynl+jJgwJ1FV\nV1hTi8wxQd4VFTs4zItp1sQCEWWA3w7rhEWUQ/oUMZoecL3uUq8hGSavsthE6GYQn+MnIbexMa5X\nB6cu723XiYnWDHKYPn2QAGZSEmeTSa64USA95ESaGXWdWWF2xUorShSJigZO6ceJiSaUNmno284a\n80yDIXaxsFiS99hyVmmiHQeHTVYIiyhj+hQ2YTLa0JTbepU5PeHdAKI0onSNMFE6dT/beo278gY1\n5TDIYaqiREYmuO5cwMHBp33U3G5VL3leGVC9ozeYUFdQlOsnEDLkZvAJcqSJiAYO6zM0iGbvBhBn\nk2WXTsYNUV4Vc6R1nFY68esgaZH0PicLn2damXcm76sYaqFMiQZaaBPdLDl32RQrtFs9HHJOGVpb\n5SlRYIEZb6papsgN5wI1Kmj3eBEIptSrWNpnJgk1TZYUTaKDtdocIREjKs1ns+7Mcz78tnpnq8qy\nWV3k4cjb6475mqqyUZvnVOjpB86HldoMw4EzD+z6ZytXOWAf8aZ/UV8zUV8zOZWixeribPht1FSF\n7eoS8fIai8XrKF3jxs7XmUw+T5PdSYO/m5BsoFBO091eT5/u6vBeT2+3VpigP3Lige8vfQdjBcBG\n6Q4HwicYDJ9iJnuJ15Y/x3DbYwy3PM5GZoZQoIGQ78GMrqAvzEjsPHdSFzkae/MD/x72NdJi9zAX\nf5ETPT/D7M63ifmaaQ30MKzPcLd02QN0AEPhc1zY/mNylTjRgJFl9EdOcX37S9xLvMx84jJH/Y8S\nZ5Ub1W/xcNB8VqO+h7hQ+Au25DIdvn5arC5PK5evpDggjzBVewWf9NGuB9nQi1yVz9Omekz0jzxI\nTDUxIV9FKYduOUBKx7ms/9oLw5XKIkeKkIhyTJ8nQgNlimQdo/Fd4g4avJaEeaaIqAaTOak72NKr\nru70DALhNtuYthVjEvKjlelA9mk/3QwQUDZF8oyLlynrIgMcomQVWFXzzOoJ/ASRQlLWJfz4jB6W\nNhBQ08aVO8FVHGoECVEiz7i4QFDa2E4YP0HibBASYY7q80SIGSOXY65Bd9VNl+WQBKXNirpHI620\n0UOn08dSaIZSJMe//Xcf4+d+7ue8z3F3kuY4jjdJ26VM94M7y7LqWJ37wd0uAHMcxwNofx/K1nEc\narWa93zFYhHLsrh69So9PT0kk8k3AN33sMR3ARtveIj/HqtWq9WNsZPJJM3Nzf8gItL9ocDBYBDb\ntutOoOXlZf7le36eK69dJkojY5yhkRZTXaMVd7jBOouEiRKQQTIqaS4yMoRfGSdmmSL98iBD6gh+\nEaCqK2RIMs80WRIe9RqUNgFCxFQjzbSzwQpJNumw+vaoVzdweNtZ33O9Imijy2hB6DWieG6xJhaI\n0US77iVvpUmpHQo651V71agSo5mTPEpIRMzfQyvucpM1FggQxBI+8jqLT/i8Foayieuk1xpkyDlK\ngKChXkmxyjwZEi7tZmFbYYJOiCbaaaebFWbZYJkW2c6AGqNMiaxIkhYJUiruvR/j4B2kkz4zlROS\nBT3NIjPYIkKb7iJrJUk55nftxq5UqdAiOjmuHyYgTHxFRZdY4A6rzHpUYJWq6VzEJqRiVCiSIk6n\n1cuIc9yANVJkSbElVsjqlAewQjLi3dBa6WaFuyyLWRpFCwfUIROUYqVJ6wQZlTT6OgQWFm1000w7\nrRjH47i8QEkVOC2epEHsUY87aoNbXGREHqNJd1ClQpUyRZ1jgSkaZZurNypSVkXK2lS6CQR+GcAn\n/aYnkwhptUXYauRM8MfraM/x4reoUX5AU7dUnmK2Os6bYv+ijrYtqhwXMn/Okw0/S0ju6cSUUnw7\n9yecCL2ZNn/9NOti/i/o8g0z6D9Bwllju7pMVsfJVN1jRPppsjtoDHTT4O+gUE2zkL3G073/a935\nXamVeGHt07yp+/3YVr1G7eLWZ+iyDzF8XzVZRZV4Yf3/5U1t78W2zPGdrmxxLfM1GkPdlGo5Wv19\njDU/9brXhtnMZeaSVwj6QzzZ8b4HbqylWpaXNv6Q4z3v4Pb6V3k89i7CPqM3/XbqM4zFnqA3csT7\n+cnMC2RrcR7peo/5u2mH55f+M6A5F3yGBtlKVZe5UPoCI/4zHPAbsLtau8t05QpH/Y/S7TeZghkn\nzqXS1xAIojRynrcgpbkmbbHMhlwk7mwisXCoEaGBEzxKVBjwWtNVdlhnmutoV+NboURABr1zwsLH\nNqtEZIwj6iwBbO+cSMs4W2rVBA8jCVkRQk6EZtrpoJcieSblVdBwUB+nTJmclSKt4uR11o0lEYBm\ngEN0cYCIMBPonM4wzgVqVN2NZoqcMo8JWjbagRIFwkQ5yWOERdRofMmQZoe73Ha3meZ8sC2bgBOi\ngWb8BFkWd/ET4Kg+t3eeyzQZkSDpbCOR/PIv/zIf/TcfJRKJvO6xcf/aD/J2v3Zp9/uB3u5x9HqU\n7X788J0o29dbu7WSu9O6D37wg7zwwgsUi0U6Ojp4xzvewenTpzl9+jRHjhwhEAh8x+faXd/4xjf4\n9V//dZRS/OIv/iLPPvts3b9nMhne+973srS0hOM4/OZv/ibvf//7/1Z/rx+y9R2BxBuA7vuwdnch\nuyuRSNDU1PQ3HuDfbWmtKRaLlMtlAoEAoVCo7vl2dnb4nY/9Dv/tv/4hHbU+cARpGSftJACNJfzU\ntJmiDHOUAca8qUheZxjnFcrkidJIkbwH8oLaRIFkSVKjyqg4SbceMNotDPW6wIznaPQRICwjRFQj\nbXRhE2FGXiOn0gzKw/uo1xQptUNR571ewQgNHGCUdkx2UkVXmBCvktQ7dIo+pBSkVIKCzuITAfz4\nKOsyCodRTtLPQUMjaEWONBNcpkjBxAlQckGeTdAJEyFGgk2KFBiWR+lTw5QoksUYIlbUPKDQgA8f\nERpopo12egliMyGukNQ79Mlh855EkrRMkHaMTk261GsjrRzkOI20eqD6NpfZYZ1m0YaQgoxj/rZB\naeNTAcoUqVLhICfoxzgnq7pCliR3uUWBrEftBlyQF1ENNNLKJsukSTAgR+lTI0azg9Ezbjur7o3D\ngLUm2mihgw56kfiY4irbrNMnh0zfq6uxy6oUBZ3zwGWEGDFaaKKVVrpYZZ4FJhmTZ+hhLxS4pqpc\nln9Fo2jlGI/UAZ5xdYESeU7KJynrEiXylHSBTb1IXmdNzIsuYQkfARnE0n5yKkV/4DD9/iNELSOc\nVkrxYvFzjAbP0RvYa3MAuJr/GkErzAn76brvL5Rus1Sd4Knou+teU66W4pXcF3hzw3sI7IsrUUrx\nQu6POWk/jSUDbFUWSastyqJAsZpDCEFrqI+mQA+NgW6agp3cSV0kX4tzvu2f1/3ucq3Atzf+P97U\n9b4HgN5M6mVS1VUeaX5X3fdrqsIryc9TqKZ5vPtfEgu0PXB9UNrhWyufZiz0KPOl6zTbfRxveesD\nP/fazhfZLi7QFRzhVHSvL3ehdIvV6jRPtr3X+17JyfHS1n/n4a53E/E3cW37i+TKCaqqzKP224lJ\nQyVv1haZqF7iSftdBFwAvukscrv0Mr2+g8SsFmYqV4jIGG2ql2XuIgQ0qw76GaVRtrKoZlgQU8Ro\nooEWUmKbjEqaz1/YKOVQokC76OGIPotfBKjpKjnSbLO+F8SNxif8BGUI2wnTTBsODsviHjHRxCFl\nps1ZTPBwQm27x7aFAGI000w77fQQE00k9BaT8gpSWxzQoxSsHGkdJ6tSmPPIR5UKQYIc4SwtdCKF\nqXuMs8EEV9BAVMTI6jQaba5B2gYt3Ml1K0f0WXdyV3Cvq9ssMQfu+Rrcd5630EmYGMuRaZp6G/i/\nf+//4rHHHgzA/ruu/Zq4/V/7NXT3GyBej7K9H1O8HmVbLpcRQjwA1H7/93+fjY0Nurq6GB8fZ3x8\nnPn5eb70pS/xzDMPhn/vLqUUhw4d4rnnnqOnp4fz58/zp3/6pxw+vKft/fjHP04mk+HjH/84Ozs7\njI2Nsbm5WUcZ/4is7wjofuTeyY/i+l76XL9bllwul+PZ3/4wf/Rf/4iaqtHHCF0MmN2jgi1WmBHj\naK3pY4SclWLBmWGBafdCaajXqGjgpH4LMTdXrKxLrKsl5plEk8LCcl2v06yzQEw34+Cwzarret1H\nveokSbnJTXXJreIRhESEoioQoUIfI+w466TkDgFtM8JRHJQbE7A/cNhcHIY5TL8+6AUOp0lwS1+i\nTJk20UVeZLirbjIvprwokBIFbEI8xFM0uVEgeZ0h4WwyywRJtjzq1cQEbNFEG378bLGKD4tDnHXf\nkwF5cb3FnJ7al9sWQSqLIGHadDd5J8ukvEJB5RgUpjIrIxPccC56PaxVqoBmhBMc0Ac96jWvs4yr\nC+TJ0iCaKegc97jNsrxLgBA+7SdHGoBjPEw7PZ4hwkTJTLHJigHVIkCcDQrkaaGTBppZ16Z+6KA4\nQYNu8fSM9S4/TYQYAWUToYF20UPRyXNLXsKnjcbOwkdeZNx4k9tMcgWJxBI+VpkjrrZopIUIjUzy\nKg2ihaOcrwNOs85tknqLR30/SUhECIsY0E5B5ZhXE5y0nqBd9roUep68zjChLhEVTaScDVYqdxBA\nwAqhlENFl9HaTLh2gVi2liRZ3eJJux5QKaX1pGQRAAAgAElEQVRYrN5iJHj2gYn5TPlVuu3hOjAH\nsFiZwCcCtPqNrrHZZ6JViirHS9U/42Tox0jWNtiuzrKcu0W5VkAgaQx2slG4S0uwl4BlGkTuZS7R\nYvc8AOYAtsr3GAqdeeD7PhnAlmGqosTlzT/n4c5/TixQryHcLMxiSYtee5SYr4XLmS/RGz5Ks11f\nHdZhj7BTXCIq6o0dfcHDzBZeYyU/RZ87pbOtKAeix7kd/yYajVSCN/l+mnk9yfXK8zwZ+Gmk9NHp\nG2CLJV4pf5GHAm8lZjXTaQ2ggg63yheQNYsO+jiuHwEBA3qMHb3GlrXMVedbSGWMAQ26hRGOGee9\nG8S9rGeZ0xNmdisa2NbrJMU3DDvghFxwlqDHGnQn1ZZHVcbFBvf0hHG+aouyLDLDDRppoZ1uHKdG\nVazQKjsYUkcoUiAnkyTYZEEZd+luuHgPvSZI3BlCCmnAmjBxJr1ikKxMcdu5bKbvBF2tX5kG0cxp\n/bjXYlOmyLZa5x633Cm4j4Te5qr8FgFswioGCHZYo1V2cFg9hMQyTTAiRcZKcNt5FUtYfOz/+Bi/\n8qu/8g8GSP4m88MuuKtUKh7tuh/k/V0p291p4H4zDphIrSeffJJ3vWtvU1MoFL7rMOTy5cuMjo4y\nMDAAwHve8x6++MUv1gE6IQTZbBYw9WKtra0/imDub1z/tN7ND8n6h2iL2J8lZ1nW62bJffrTn+Zj\n/+bfEqs2M6pOkpVp0uywou4htDkBHGo06BaOctaEwCpzoZzmOptqmRARwjJCRqW4yvNeblLFJSoP\nyFEG1WH8wtjzsyrJAjOsMu/RBDVRY44JLzYjoxOkdIJOq3cvcJgkGSvJpHPVo15R0EEvfoJ00ol0\nht0WhllCROjU/eStNOtqiTk9hV+bcM8qFWI0cZY3ESLqXfwX9AyLzgwWPmwRpqBz3BAXzaTRCZmW\nClK0W92MOMcJEfFcr1tilTk9abLXlIUtQyZA1KVkCipPUeRokM2MqGPUqJEThspZcu4CxhChlaKH\nIVp0h2eI2GKNGXEd3IqwvJVh0ZlmjtsECKK0purqGR/hrUSIeflWm2qFu9wE8MwgM3KcBaaJqAZ2\nL/4RGWNMnSZKI1mdIqNTpOQOU+qq129pyxBJtY1A0kEPthMmKbcIEuIgJ9AoclaKLb3KrJrw+l61\nUvQy7E7mmumkj21njZTcoYlWRtUpKrpEXmfIWyYpv+pq6jI6yWXx1wSdEA20UtZF1lngrPXjHm0O\nxr16TT9Pj2+IdmFcnFJIwsRYVXNIYXHOegs+4UcLc2NMqA2m9FXarG7my+NMFi+Y6YwVplQrELGa\nqOoq+9PcFiqmx7bHX29IKKk8ydoGj9xn2gBYrU0zFHgwlHi6eIlOe4DOgPnaXRuVBW4Xvo3PEdzJ\nXKBUzRP2N9IZGmGrNMvhxjc98DvSlU1KtTxd9oOO2JqqkKps8kjsnayUZ7i08Tke6nin126htWY+\ne4VOn5mONvhaGQmfYTzxVZ7q+gA+6fZ6qjJ30i/T7RtmoTxOb+gQtjRA0yf8HIs8xUT2JTrsAQ+A\ndgRHmM/eICIaeNj/z5BSMqyPk1DrXK9+i7NBMzU5bj3BHLe4XP46B/2nqYgSS+UpOkU/jbSwyAwv\n6S/RqNvoY5g2utl0lhEIesUQAWxScsdtY2BfG0OFPg5yiJOuI9RoYpece2yw5G5EBOvOEgm5RVDZ\nRGmmSI6E3qRPDjOsjlKhTM4xoGibNZa0aQ6xtKF4N1imjS5G1AlWmCUvzMaqRw2RFxnPFLE/eDio\nbYY4Qrs2FYYI2NBLTOvrBAjSIVvJ6CQv8VUCIkhA7Pa+5umU/RxWZ/AJP1VdIafMpHGFe+YzRZPW\nSW5YLxN0J41hHSMZ3ODps0/z+//l9zlwoL4l5fux9k/W7o/A2q+J29Xl7ads90/09j+mXC7jOA5S\nSu/xu/fI9fV1Hn/88brXEA6Hv+vrXF1dpb9/z2Xe19fH5cuX637mgx/8IO985zvp6ekhl8vx2c9+\n9nv50/xQrjcA3Q9g/V0A3W6W3O6u5PWy5D772c/yr3/lX5MvFojSSJgGWumkWw+Q0xmm5FWyKk2f\nGAGh3Wy055Dawid9ZqIBjHGKXoa90NocGW46F8mSJkwUicWKmmXHWiPghLAJk2SbGlWvscC0SiRd\nMfJdD+j5hZ+yKrLGAm1006572dFGR3dAHqRFdZhpnpVkRo1T1kUsl3qN6gYGGaOFLnzKV9fC0Co6\n8Qk/aR3noluQ7SdAmTI1KoxwzNDJ7sU/p9PMOOMk2cbnxoMk1BZ56yK2EyFGM0k2yeiUF29SwZgO\nDPU6a7KgMBEllja9i+300a57mHGukyZJq+yiU/W7qfN7hghLmyy6sI4xxmmaafemcrN6giV9lxBh\nGmUzaZXgEn/lNl4EqblFYL1imGG9FyWTVUb7t8mKOWZQlHWRGXmdsGqgmXbyZEioDdplNyPquJlk\nuFEy884UM1x3O2yhiTaqlE21mRpkRc8xJyYIEqJfH6QgsmRk0ntP0r0JhlWEfkYJE3Pz6bq569yk\nRoWD8jidqp+CypInQ8HKsuhMoTA79Zv6JQLKxlYRYrqZTbFISEQ5xEN150NcbbCs7nDGejM+4ffO\nJ5swy+IO3b4Bjgoj5lfS6JIWnCmK5LAQXM59CRAErRB+bZNTKQ4EjuwyWd6aKF6gPdBPzKqfXG1U\nFig7Rboj9QBQqRqJ2jpngw9m0i1UbzBgH2U0YLLkasEaa9U7LOWmqaoKk6lvkaqu02UfoinQhRCC\nO5mL9IbH8N0XUwJwL3eVqK+JmNXCkfBj2CLCta0vcbLtbXSGR0iUVihWMzzSuBeQPBg8Sby6wtX4\n53m0/V8AcDf7CiErzAn7SW5VNOO5v+TRhp/xHtMVHGGztsC11Fd4tPXdpCubvJb4Cs2inYxOkNAb\ntNGDEJKTvjdxsfIVrlWe47TvzUjpY8R3imwtxb3KOBqTKTcsjgLQr0fZYZ1ta43rzgU3j1EZ2l93\n0EIng2oMjWaaa6yrJZpEK1oo1tUC68wTlCF8TpAKRcoUGXVz3wSCInn3vJhj1aMqYYd1MiJJVDfS\nQiclXaREnj5rhD5nxNPSZq2kO2EzcgmpJX5t8iD79EECKuBGHU3SKFpo0R1eheGkvopPB9AoalRp\npJXjPExImw1LjRqrapY5pvATICyibKpl4mLDmzQqHDIk6bGGOOgc32u3cczG7K66RTBg8+n//F94\n17ve9Y/epLBLu+4fMtxP2Var1TrKVgjhBQdHo9E6t2utVuMLX/gCX//61+tMHf+Q65vf/CZnzpzh\n+eefZ3Z2lmeeeYabN28SjT44Lf9RXW8Auu/D+vtO6HaBHPC6WXLf/OY3+dD//ltkt/IMFY9TpUzW\nSrGsZpnW1z2aUijBEEfp1UOeQ3TTpV4dpegWA+REmjvqhpeb5GiHMiVioolT+nGiotF0wFJg01lh\nnkkvIgBMY8Gms0IjrYBiXSxhE/JcrxmdJCNSpOQ2y+revsaCKI5y0EA/o6ScHXIyjaP9DIujaK3J\nWknuqBuU9CUvi06jGOIIB/QhfO5hmyfLDf0yRQq0iA4K5JjTkyyLewRlCOFYFMkhERznEdrpNtEC\nOkvKiXOPWyTY8sJI42KDPBmaaSdEjARbONQYFSdo0Z3kSJluVLaZU1OepsxoW2KEidBJHxWnwoS4\nTFJv0yUG8BMgKxPcci6ZwE8C1HQFB4cDHOQgJ5HagLyyLnFbXSJN0hhXCLKq59iRawQJEdRhcqQp\nkWdYHqNfHTRgXBuqe4VZtlj1KN6yKDLHFK2uISLhmMyuXmuILmfAZHZZyb3U+d3Sbx2gkz6aaKOH\nQZSjmGWSFe7RItpooJWcNGC8ok3ThaNrODj0MUKH6icojO4nppsZVxew8HGOp7AJk1cZcmTIihTz\nTILWFHWBi3yZgAgRVg1EdSOz3GLM9xDNoqPuXLnjXKekizxk7enApJCEVJg4axy2ztEjh9DCHMNZ\nneSOuo5EsFKdYbFym4AVIijChHSMuLPGY9GffuCcvFe5woh9xgOT3u8vXSVkRR+ITyk4WbLVJKeD\ne7EnPunjQPAoa849hgLHaBBtLJWmuJafxi+D9EdOkSytM9b6+jqozco9Dgb2goaHQicJSJubO1/n\nWOtbWM7dpMM3iJR7l3MhJCcjb+Vi+s+ZSr5IV/ggK9lJHgsb0HfY/zAv5f8Hi8XbDISOe487EnqC\nC+k/42byr9gszXFAjDIaOM2qmuVm7SXOi58gJpsJihCPBd7OzdoFXqp8gVHrDPPqNjVd5Zh4mILb\niLLGLDHdTI8eJkyMnDbyjd2JcNqKM+lcpUqVAAFqVHFwGOIIQ/qIF8RdpsiU8xpJdggRwYefe9xy\nJQk2IRUlS5oiOQ6JE/TqEXdjZqZy62KRdb3oRSyl9A5lSrTQSa8eYtbJo1H0ySHaVA850vURS+61\nNYhNq+6inR4GlDGBLHKHOSZpEM2ERZQ0CS6qb2Jpi4C0qekKFcr0MswYp73NZl5nWHMWWGXeXB+B\ndWeRhLVJ0LFpoMWcK8EUP/8z7+Xf/8d/R3NzfQTOD9P6TpTtrpGvVqt5U7l4PM4v/MIvcOzYMUZH\nR/nKV77C4cOHGR8fp6HhQRf3d1u9vb0sLS15/7+yskJvb31e4x/8wR/wkY98BICRkRGGhoaYnp7m\n3Ll6c9KP8nrDFPF9WLt06e7K5XL4/X6CwQdLuMHsTgqFAkqp182Se+WVV/hX/8u/Yml52XMettFF\nC10ATHKVOOu0yW6aVbtnONh1iO6G8zbTwTHOYwtDQimtmOYam6xgE8YSkqzOYAlDORqHaJECefqt\nYQadI/gJeMLdZe6RZs8EYMsQQRXy7PTrzLPBMs1WG0POEapUybg5UAln24BPd0zSRT8d9JkJlpDe\nlMiHn249QN7KkFJxyrqIXwRBaypUiBLjBI95jjNHO6yx4GlUAgQpkscvAm5EQAQQJNkiImMcUqeJ\n0eTt1JNiiw1tLgwSSVCGCKkoTbTRSR85MtyVN1DKGDEEkqxLvaadhNEaIXFw6OYAfQx7rte8znBL\nXKKoC3QzQMnKk3YSrgHFRihJmSISi+M8QqswGWxVXSHFDlO8Ro0qPtx8rn1C6RARNsUyFV1mVJyk\nU/eRI22mDzLBhlo2eiBMvl5MN9FCJ+30IoAJcZm0jnNAHCKko+RkkhRxcirtPc6hRjs9jHDM63A1\nJo9X2WGDLtGPkIK0jpNXOXzCh0/4KCvTF3uSx2gWewAorzNcly8RoYGT6jFDY5MmR4odsU5aJw0A\n3v3sVIQm0W50cExxzveWOqctwBX1V/jwc1q8ue4cSqs4rznPcd73E8REk4l+0Qmy2tQ7AUb/JG2C\nMkxUNJvsveo8Tzf+3AOhxy/m/oSToR97wCV7Lf+XWNLiVPDH6r6fd9JczH+RN0V/lqBLcyqlWKpO\nMVsZx9FVGgJtjEWeoCW4p3tbLc4wnXmZpxt/7oG+2a3KIjcLptHh6aZf8KjV/StT2+Fy5stI4afX\nGmHM3rt5bdWWuVl6kbOxn6TZv5f3d7dwhYXibTplPyf8eyBzXt1moTbNMd9jdFiG3nKUwyvVr1DW\nJkT3KA/TKfu8YyPOBttilTU17+k0m+mgE5M76RNmAn9DvOzlTlZkibST8JzdPuWnRAGNMhszYf4+\nFV0mS5I73KRIHguLGtU652sjLWyyTI4Mo+IEnbrfgDXXFLHtrLlTY4FP+InpJs8UYRN2O6eX6JFD\nRFUjOStFSsfJqYyXCbl7bR3lBFEaPfPTLBOsMEuEGFJK10gBQcvG79jGBU6eYXGUAT2GQHjX1h2x\nzppeoDHWyJ99/s944oknHvhsfxTWbuC93+/Htm3vnCyXy7zwwgt87Wtf4/r16+zs7LC1tcXhw4c9\nd+sHPvABYrHYd/kNZjmOw9jYGM899xzd3d08/PDD/Mmf/AlHjuy5tn/1V3+Vjo4OPvrRj7K5ucm5\nc+e4ceMGLS0tf8Mz/1CuN1yuP8h1P6DL5/NIKQmF6rsZ92fJhUIhgsFg3U1oYmKCZz/0LFdevUpv\n8SAhHfFiQBLOtpeZpNwLyq42ZRc8TLrUa68YQknHA3l+EcBil3rd14vqThIzJLnNq5Qpec6rXZBn\nO2FCREmILUq6wIg4Rq8eNgXUmMaCFT3rhmOChY8oMZpoo4NegoSYFFdJ6G165SCtqsuIfWWClJOg\nRsWNLnCI0sBBTtBCh3eRNC0Mi8REM7YIkdIG5AVkkIA2ztgyZQbEKEP6CD7hR2mHHBnmmSbOBhJh\n8uFEAFsYoNBMO2ni7LBBu9XNsHPMmBpcQ8S2WqVMyYv0aKCZZtchGhExFvUdFsUMQWxTAyZynuvV\nTAAtdwphc4SztLpuOIAtvcoUpny7QTST1SnvZhZQQTSQJ0ODaOawPkNUNBrqlRRxNlnkzu6Rh18E\nsYW5mbXSiQbmpUnKH9OnCRIyJg8rRVJtm4gXfCg0DTTRQR8d9GKLsDEjyCvkVZYBcQhH1EgTJ6NS\npg9T+KnqMiA4zBm6OOC9p7Iuc50XKZKjU/RTEFnPGbg/yqFFdHBSP4a1D6zs6HVui8scEKMMqjHy\nZN0bcJpNZ5kqFQSCoBU2GwjRSpvoZVXNkmCDR62f9CJgwACwV/RX6ZUjDIu9aRTAojPNvJ7kCesd\nJpNMJ8nqJGm5w1ZtFY0yAMEKE6aJVl8v8doaJTI8HP6puvO1qHK8nPk8j0Z+iqhVn6V1tfiX2DLE\n8WB95IhSim8XPstB32mKIstSeYawP8ZY9Cnagn28FP8M/f4jDATrX7f32MxnqOkaTYF2zkb+2euK\nx69n/4qdyjJnQ8/Q4uuqf//VSe6Vxznf8A4afK0slSe4k79MjxhmTc2ZFhCfEZZrrVnR97hTvU6j\n1UafOMQddQU/AQ7qU6SEmcb7pJ+obqRHD6PR3JHX8OsAB/VJU19nxUmoLUq6iA8/DjU0cJDj9DKM\nzwXPRV1gnAsU3ZDeAlnKei+qxKeC5EmjgWOcp1V0es7XDCkWmaZK1ZvKBa0952szHdwVN8noJAfF\ncRp1qznGZNo9xpP7JvAhOuilnW5imAnZDOOss0iX6CdAgLRMknGSros1SEVVcajSxwiHOOU5X8sU\nmWPCBIpjU6PmOtyDBAkRVjFCIsKWvcSv/m8f5NkP//Z3HAT8MK/dqdzukOJ+88H8/Dy/8Ru/wZkz\nZ/joRz9KKBSiUChw+/Ztrl+/zvXr1/nEJz7xt9LP7a5vfOMb/Nqv/ZoXW/LhD3+YT33qUwgh+KVf\n+iXW19d5//vfz/r6OgAf+chHvm/07vd5vQHoftCrXC57/10sFtFaewfn/iw527brdi4Ai4uL/NZv\n/BZf+epXkUgiNNBEKx30EqWJRaZZFrPYhBnQY5RFgYxMklI7lHXZdaQayu0gJ+ned6Pd0qtMcw2N\nplV0kiFJUefxiyBBEUQpU08TE02M6dM0iGbjECXDNutui8JeOK/tXSQ7EEiW5B3QmlF9igaaDSAS\nKVJim5SKexfJEBFTv0MvMWEKp6fkVQ88CIQH8hyq+ISfmq6i0AxzhEEO7wMPJa7zEgVyNIhmShQ8\nkBckhF8FyZOhSoVRcYIePQRAjjRpEiwwTY0Kjvs3C8kwtoqYIFLauStuktQ79MsRutQBQxXKJGni\npFXCe08WPvoYpoM+om6f7D1usco8TaKVBlrI7Is2CcogVVWjRoUOejnKOY/aq+gy80yxxjwW5ntV\nKt6F//Wy6PwEDKgmxY5YJaXj7sTQImxFCDkxWumgnT42WWJeTBEkxLB2+17dqVxWpd28LqOtHGCU\nbgYJC6M1Sek4t8WrOLpGFwdMhp2TBDQBaaOVpkKJCDFO8rhnftBak2KHW1xCo4nJJrIq5U0o/SqI\ng0OeDId5iD6xF4ECsKjvMMcER+UjxLRxVOdEkoxIkHC2kEh8IkBIRIjpFtpkDy10cpW/xsLijPix\nugDjospzSX2d4/Ix2mU9PXPTeZkSOR6SP05Op8nqBBmZIOFsUtUVpLAI+SJEaKbd10+7f4Dx4l8S\nlGFOBOtNDwWV5WLuC68L9O6Vxlmv3eMJ+2cQQlDVFZadaebLhhKuOEWebvr5ByhfgJnCq2xXFzkX\n/AmuVZ4DAQ/HfqZuUrddWeZG7jkGrMMsOlM8ZL/1AVA3Wx1nsTJFV2CY9fIsp+VTNMsOEmqDcecl\nuq1BDstzHljMqyyXqt8ATM7hKZ6kUZoph9IOO2ywKZbYUite7lsTbbTTTTt9BESAnE5zS16ioir0\nM0zBynkT+IAMIrUJ6fXh5zRPeJPYmq6RIcEEprUhiE2JQl1USYgI26yh0RzlLM107E3lZIo1teiy\nA+AXQUI6QhOttNGLTZgJ8SppHWdYHCOoQ+RkirRIkHGSdZFErXTRz8G6DeckV9lilTbRhZaqbtLo\n137KukSVCoc4TR/D3meeJcU6i6yzSG93H1/+6pcYG3uwA/iHfe1qwEulEoFA4IEhRa1W41Of+hRf\n/OIX+d3f/d1/UnTnD3C9Aeh+0Gu38gSgVCp51VylUolyufy6ocBbW1v8zsd+hz/6wz+i2xmio9pL\nniwZYSqc4s5mHXjoZpBOemkQzdR0jWleY5s1WmUXDaqFjJUgreJUdIWgCFDTZj94P3hwtMMM19lk\nGT8GXJZ03nVmGX3KLhXQbR1g2DlKANulKZNsiEVSOo5ytWi2DBNSe6Gda8yzIuYIiyjD6phxm3o0\nZRww8QAaTS9D9DDoNVfs6HWmxXWUduhmkIKV8UBeQNhmGkqZECFO8Jj3uJquEWedKa55urW9IFKj\n0QrgZ1OsIhCM6dO00uVd+DMyzqpa9IBNQASI6EY3t60fhwoT8qpbD3QYW4fJypQ3wTLvabcHspdh\njno0ZU3XuMUrJNmhVXSihENa7YK8+iy6Q5ykl+F9WXQp7nGLHBlv0riboB9xDRGbLJNkhz5rmAHn\nkPfZZa0kW84aDg4C42A1WXSddNKLjwDzTLIsZomJJrrUAUN16x2PehUIalSJEOMYD9MgzMRCa80a\nC9zlJhY+IjK2D6yFCLhgrUCWLnmAQ+rUPvBaYp0l5ph0myagso9SDqkoBbIUyHFKPEnLfXq6SX2F\nLb3CSfE4CmUc1TJBWiWo6rIBtDJKg26lTXbT4oYkv6K/SofsZUzU31TiaoMbzos84nsbEVGv57ms\nvklENNCnR8noBBkZJ+lsU9R5E+JsxWizeun2DdPgMw0Ll4tfIyIbOBZ8su65lFK8WPgch/zn6PYN\n1f1bVZd5qfAXONRo8XdyPPw0ttzvCq7w7cyfcirwFK1WDzVd5WbtRXIqxdno24n6mimrAi+n/owh\neZxB/1GW1R3uVK9xxn4Lrb7uvdehFS8XvkBR5RgWxxj27U0DszrJTfUyDlWGxQl8IsCMc5WIaGBA\nHWLbWmPdWSIobaKqkS4GSbPDKnN0yB761agJ0LXi3kR4t7tYIhnhGJ0cICCM1jejk9zgZRwc2kQX\nWVIeqxCUNtoRFMgSETGO60eIiJiXO5kh4Yb0Ot6GM2jZBB0jA4kQY05M4ugaRzhLiKgH8tJuSO/e\nhjNMEx2utKUDheKWuERKbzMgDNDKyqQH2PzCT03XUDgMcIghDuNz31NVV5jgCgm2iNFERZQo6YL7\nngwItX1h0sEtPvbxj/GBD3zge8os/cda+6dy4XC4TksHMDk5yYc+9CGeeeYZfvu3f7vO7PfG+jut\nNwDdD3rdD+h2bd2vFwqczWb5T5/4T/yHf/8f0AqCMkjYTfbvoJcsKe7Km1RVhYMcx0/QBUQ7pFxK\nbzdhvZN+BhnzgE1R57klXiWn03SJfmqyQsqJU6WKLU2NTpkSGu3RZkIIHF0jQ5JJXqNM0c1Qq3iU\nXlg1EKWJTZYpkGFAjtGvDhpKxaUp19QiuPoUiUUjLd578hFghmtssUab7KJd9ZoYEGHoDmNCFF5q\n/FHOmnwqd83rKRaYMeBMRsio3XBeM5GrUaVIni7Zx4g6gS1C1HSN3L6dMJhZ427PYtilKYvkWBFz\n2CLMIWWy1zyqW23tC9nVNNDqTRptESapt5mW16ioMoOMUZEVj8IB8AmfmfC4NWBdYi96IKtT3OAi\nFco0iCbyOutNr4I6hF8HyWB0emOcoYPefQHPCRaYQeF4FJMtw0RUI6100EAr0+IqaZ1kSI7RorqM\nycNKuYAt491obULG2OC+p/1Ud4voJCwipEl41GtQ2lRUmdp9FBOY6ek8k6yxiB/jBPToZFf/VyRH\nhgQDcowhdRgpLI9SXmWeLVa9qUhABAlKm5BjaPxV5qmKMg/x5jrwpZRiXLxIgRxjPESBDBkrSdqJ\nU9IFr0e4Q/TTJnuMnksGKKkCl9TXGbFO0i/qI0QWnRnm1W2e8P0UfhGo+10X9VdoEV2ECJMUWyRr\nO1jCIiCC5FWOh0Jvpf0+vd1U8RJxtcbjwZ9CiPob+GJ1ivnqLc77n2Fe32azukxnYJBjoSeR0sfV\n3NeQCM7490whWitm1Q0WKzOMhR9ltTKNT/s469szaKyou8xUXqPff5gx+xw1XWW8/C0KTpoBDnNX\n3aDJauMkT3mTPq0Vy+oeM+oaFhYhIpzmKWxh8vocXWOHDZbFPTLaPc7x0UAzLXTSQT+2sFnXi9wV\nN7EJ06UPkLPSpFXc0/oKBBUqRIhygseJurpYM/VbZ9KVJRg5QLYOsFn4SLJNVDZyRJ0lTJQCOVcG\nkmBZ33OvKHjnU0y30EYXfoJMyMtUVZkxziBdXewuYKtQwYeFg6KVzgf0fzfFRVI6Tg+DOFaVlIp7\ngM0nAlRUEQfFMc5557ujHXKkWeIOm6xw4thJvvClv6Crq356+qOwdiVGu4OK+zXglUqFT3ziE1y4\ncIFPfvKTHD169B/x1f6TWG8Auh/02rVs74YCAw+EApfLZT796f+Hj/2fH6Oh2kJf8aApmHcB0ZZa\ncfVCEhA0u8n8nfQREDYLepolcZcAQbyNQPQAACAASURBVFe3lSctdjxAZEBelQBBjroak921rdeY\n4jUUyusQ3QVEAbXXIRoWEcb0GZpEG4424CHBNgtugb1JZg9gi5ALHjrx4+OuvEVFlTkoju8F2cok\nSb1DTqc98BAmRg8DHngo6RKT4rK5QIpBfPhIywQZJwHupKyiKzjUGOIwwxzbE9rqIhNcIUUcmzBV\nKji74EGHCGvjhMuTYVAeYkAZIbKhKZOsiXkKOu/R1buVWa100kY3s0yywSJNso0DatRMvvbRlMap\nZvLoBhijmwPYwtDsG3qZO2IcqS06RC9ZV3MDgoA0Sfi7QO64fsSjKSu6xBZr3OUmpsrLouaBPJuY\nbjLifbGED7/pe6XFnVgkScs4m2rFRJQgCArbywtsp4cKJU8nNyyOmgBjD+Rl64Tf3QwwzFHvtdV0\njZtcJEWcFtGBI6oucDX1RZYToEzBo5h6GUII4YrZU8wxQZa0B9Z2QV5UNdJIGxsskSHOiDxOvzqI\nwvH0UatijoLOuaYJv6vvjNJMB020MSEvIbTkDG8mKPaCgmuqxhX511jaoosBt45tx9yACbiduz5G\n5SnaRR8BafRLSbXNdecFTlpP0Cbrw3qnaldIsMlj1k96xgWtNVmd5DXneWwRpqhzSOEjbMXosA7Q\nYQ1wqfhlHgq+hWars+75lKrxYvnzjFnn6bYGAcioONOO+ZxarF52aks8Yf+0lyO3f23WlrhVeQkQ\nPO3/WXxW/SQkpba5Uf02ARnCUQ6WlJzTb8EnfZR0gQleJatStNLNmDzLtl7hrh6nSbTSrnrZtJZJ\nOtuErAgRp5EO+lhljgxxhuQROlQfGZJGK6e33c2CZeKMCDLIIdrp8wDhhl5imuvYwughMyTIKVPf\nZ7LbapQo0SG6OaLP4RcBtNYUyLLDBrNMeO57wL2G2TTQjIWPFWYJyyhH1Fl8+M21yN08xtWmd26E\nRdQzC7XRTYUSt+QlSqrACMfN5slKklYJb1Ow68Af4BB9jHjne01XucZL5EjTKjopijx5tVtJaKJK\ngsEgKlbm9z71e7ztbfV1dj8qa1cHDhAKhR5wuF6/fp1nn32Wd7/73Xzwgx98YGr3xvp7rTcA3Q96\n5XI5zwwRCAQol8s0NjbW/cznPvc53ve+9xEJRGmnh1A5RgPNCART8jUyKsWAHKVd9ZB1bfTGYbUH\niILYDHCIDnpN84NWrjNrmSbRSpQmb+plpilBqqpClSrdYpAxfapOt3WPCTZZ9J5/rwIsRFQ3UiJP\ngm3aZKeZfBHyANG2WCOlDYUqkYRkhJhqopUuEyTKEnNyEqktRvRxk70kk6TZIasyWEhMT6wJ4O1n\n1LvoZ3WaW+IVyrpIp+inKHIeeAhaIYQjKFPAws9RznngtaLLpIm7DtGa50C1pU1AG3AToYFVMUdR\n5xkRx4yrloyXT7XprHhuXB8+mulwAVE3IJniKjus0yn7aFLtZGWKFDt1NzKHGk20cZRznhZNa80s\nEyxzD5swARlw6VpN0AoRcIJUqVIgR48cYEQdIyBsz923wTKbLLNb5xWUQa9bt41u8mRZEne8SSMI\n1xBhTDUlCt7nbAweexqntI4zKa9SUWUGOERZlkjvAldhmUgUXUYiOcZ5OtwwYK3dPEMuUqZEVDSQ\n11kz+XM7KoOESBOnRo0jPFQ3acyQZJEZatTQrsPVFmFvetpIC5PyNbIqxZg4TZfuN5pG1+ix7iyx\nGzJnuw5l41rsQ+EwLl8kTIxT+sn7el/zXOaviYpGQjJCWu9QUIbqCwjbACnRwXH5WF2TxKZaZsK5\n9LqO29vOK2RJ8oh8GwJDJybYYItlso45hzutAQYDR4nIvWvD9fLzVHWJ877/qW7SobVm3ZlnsvYq\nFhZjgfP0+Op1hgA3Ky+SdnYIihAFshyWD9PlG6j7mZXaPaZrVwDoEH0cEef2TeQ0SbaZ07dJK6PD\nbKeH4zziTV4rukycDWaZpEIJ0ASETUQbZqGTPgLYXnd0p+yjUbV4E+G8ynlRHTWqNNHGEc56bnWl\nFcvMMscEQWz8MkBWpREIbGnjV7bJmSRNl+xnVJ3Eh99jCBJsscr8vmNhb7PQShcKxV15g4C2GdOn\nUTjuVC5F0tmhTLHOldtGNx30EBA2JV3gprxIQeU5wCgVq0hKJzzAtuvsFvedG0orCmS5y03ibPHW\nt7yFz/zxZ34kc9B2G4x2O8Xvn8oVCgU+/vGPMzU1xSc/+UmGhx88Tt9Yf+/1BqD7Qa9sNosQAr/f\nj+M4ZLNZmpqaXvfnxsfHuXr1KhdfeoVrr11jbdN0bnb7BmipmeqmMFESbHFHjlNWJYbFMSwtyVpJ\nkipOXmc8t6LCoZ+DDHHU06aUdYkbvOzuGLuoiBJZlUK6tIXlmGgAk7t2kh49iBRGmGy0KbcoU3AP\nCG1AnjJZSc20s8wsKXbolUMcUKN71KuVZNtZ99ohBIJ2ummjm1a6kUjmmGRVzBEWMbpd3VZS75B3\nC64BT7dlCrvNzU9rzTqL3OGGu8M2OVdmQhTC75gbb44UUdnIIXWKBtHsAaI4W6xwzzvIAyKITYiY\nbvYy6+64VPchThIi6r2nZB0gUjTQQg+DdNDj9tCWuL1v0iiE8ACRT1j4RZCyKuLgMMoJDnDIcxkX\nyTPFNdLECWJTocweyLNNGwRJMhjAP6gOo9GuASXJBksUdN7L14vIqNut200zHSwyw4qYpVG0cECN\nmr5XK7mviHxP4zTEEbo5QMAF1jt6g0lhSsw7RC9ZkfSOo4AMopVx8kVEg6dx2nX3JdhihnHvuXc1\ng4b+aiJIiBUxa5zX+iGaaCfngryMTLKplt2JoZk0RnWTa/LoRfH/s/emQXJmd7nn75w398za91Wq\nTVWlXb0Ze8bGcIkbYwYufMDAHQIHfMAwd7BN4AbMYsAxgI1vDPZge+71+BImZsyMw2PMmBvGnoC4\nY2N3qyW19qW0Vkml2pfcq3J9z5kP57ynMiXZjbG71c3oODqi22qpM7PefPPJ//95fo/PZfkSO6pg\nhAEt5Mk4zERjarGNLivyhkjJNrJqi0viBbpkP7PqWSdalPbZ1Ctc4wwtop26qLGj8mYibSG3WTaZ\nlicY8Zp7ZNfUPa75p3ku9K/d9Rqcef8Ki+omU/IoW3KZrfoaUS9OG720yi7u1C7wlsiPEZcPf9Cf\nqn2VCFG69AC31WWiXoxJ7zh9VrDdqp7nfv0Gb/L+NXGSLDPPTf8CSa+VGe9Z2mUPt2rnWfSvMyue\nJU6S2+ISBZ2lk14mxBFSso1FdYs7+jKdopeUbmWV+1R1mYSXpMXvoJ0e7onrVHWVWZ6ihXazhve2\nyegtCirbMIVP2ffG3gr/NldY5g5dop+oMAK/qHLuOqqqoCWiOSVaYocl5lnmjpuQ7V1H5pqoUWHT\nfsGaUkfNfdaFs7bYVusEfcZxmSShWizCZ5ACGeak4c5N6MNUKLtraFcVkHj2fmGmcq5m0d5jz/EN\nypToE8Ps2GS3sBPriB8jFAvROdzOX/zlf+LEiYdr3t4Ip16vUyqVHLmh0T6kteaFF17ggx/8IL/8\ny7/ML/zCL7wh/YCv8/NE0L3Wp16v4/s+YDw2uVzunwyFTKfTXLx4kbNnz/Ktb7zAhQsXSGe2qdar\nBhHAEQu/TSKEYEnPsyCuIfEY0uOUvAIZW3wftmBhUyCd4Cg/0GRk3yRYvfokaGGHPNKmV6N+ghAh\nsmILiccBfcyuIirkSZNjm3vccuuOsIgQ1ynblTiER4jr8ixFlWNMztCmup0gCnwmrh2CVsaYMe0Q\nIoTSijnOss4SXaKXqIiTZdt9C44QpaYrVKmyjwOMcxBPhNxNf545NljCswiUvZuqMUjvUmCbdbq9\nfib8w3YVkyEvMoYurzM2OOCRECladSfdDNBJL+sscUdeRmjJhD5kEqIN7L9A5AGMMcsIE84gvaML\nXORFKrZofFcUzQeZxcIIX1Jmx3rsnqZbDDhBlCPNTS5Qo+YSqMFzaqWDNrpYFDcp6hxjcpYBtd9M\nGkWGgjTCOpg0eoSs4duslySSm1xglUW6ZB+dqo+ilyFjP8gCMdREwm9Ir97nNvNcJUSEqIhR0Dn3\nmod902NZIEu318+Uf5S4SFLRJYdeWXqwiNyuk7sZQOFzU15CaMGMfsr9rApeloy/yS5FJx4a05Qx\nEWNXF7ksX6KiSkxzwoQmZNZOhYPwikQiGWbSpK5t6fyWWuGKOM2wHGdCmeqvAIGzqu+yzDwRolQo\nmUmijJNQ7SR1K/Nc4UjozfSJ5nqmnNrirP//ctx7qwt3+LrOll5jidvklHmvdcthJkJHSMg9Dtdc\n7RQbaok3y/+GsDAg5/vcZsG/RsSLkVTtpPUqz3g/1DQtrOoyi+Im9+o3kIRQ+Dwt3t7kSc3oTVbk\nPGv+fbcCn+Qw+8VeF+auDlacV1BoOy0zU9BgKicJcVmcJK/TjIuDhHW0yafZuMLvY5gxDpKy3ket\nbUsEi7SJThB6b2JtU9BBJeGEOMyonrJfOksuJbrJKmbGr5t8mp30sUOeJe7Q5fUx4R+y97EsRTux\nDhiQAmHDQr1OhOb0NlfkabTS7OMAJa9ITqftlxmJFCFqukKIMId4hk76nQjdpciceJmCyPGud/08\nH/+fP/6G7BDVWlMul6nVasRisSbwPUA+n+f3f//3SafTfOITn2BgYOA7/GlPzvdwngi61/o0Cjqt\nNZlMho6Ojn92Zcva2hqnTp3i+vXrfOvrL3Dh4gWKxSJCC3ZrO3TTzwGOO5GntOIWl1jhLinRSkKk\nyGojoiIySoQYNVWhQpkhMca4PkRERJ035T63WeEeAmHTqwGiJEkbXVQosS7ukxApDqjjxIibSYow\n646cTrvpWoIWa44epIV2smxxQ56nqiqMiVnXDpFRW1R0iZCIoLQx9489AlFyAVNi3yG6DZpTFwiJ\nMDERx1MRShSpU3OTRlcNZH1bJQsplUhiXpyIH6fDpj3vcZNt1hn09jHiT1Fmh7wwcN5tf4MAhiwQ\n9DNCN4MOW7Cob3FXXCdKnH5tE6JWWIcceqRGkhRHebMz8Wut2WCJ65xDoUmIFDu6UVjHkYTIsklU\nxJnRJ+zPoEyBjBXWN51YC4socRJOhIaJMCfPsqMKTIrDpHTbI4W1RtFCOyNM0c2AE9YBiqFHDBAW\nkQeEtfE01qiynxnGOdg0TVlwwjqETx0n2FScFtu5uc06fd4wk/5hBNIK6yzbrJHTaQQ4YZ3S7U6I\nbrHKLXmJkA4zoYPmlIwDvwYrfAHsY4ZB9jmPU1HnuSxPUld1xpmlJHds0CPtEpI1qrTSwTQnaKHT\nTRrW1X2ucYZxOcs+Pe0M7nnSrIn7FHQWjTKgbZK008OA2A8Y8PGEPMyoaJ7o1VWdk3yVbjFAh+pl\nXS6y5a8S8xJ00E9ER7mvbvCs9yMPTfx8Xee8+keyeosQYXrEEFPiaNNquKx2eZn/AkoY1qP06FA9\n7GeWlF333lM3WOAanaIHpGDbX8MTIeIiSZvqIkqMRXGLqIg7P1qObfJe+oEpqKaFDnoYpJch4iJp\nkt3iJTJ6kxEmDdKDNAWVQSCJyAhVVbVC8gijTDWx225wgW3WiZOgSsUEZKzwT+hW03hC1vxM1AFT\ndUeWAlk2xTJ5nd3zxnpJ4n7ScSTXuc+CmKNVdLBfzbBLkaLMPoTwEcAwk/Qx7EJnBZ3jongBpX36\nxShFmSXvZ5umhkQ1R54+zKc/8x9defwb7QRTOc/zHqIzaK35+7//e/74j/+Y3/iN3+Cd73znY68m\n+xd+ngi61/oEpcPBSafT35Oge9TZ2Njgb//2b1laWuL0i2e4cPG88eqFu9gqblDXVaY5zqA1o4Px\nvlzgWxTJ00IbVVFxIi+KaXrYIU+ZEuPyICNqAolnPWUZFrnJLjsofItpSBDzk+7b7B2uscESXV4f\n+/xpKpQoiAw5mSbrb4G7NcIQ4/Qz4iaGq3qR2+IyQgsG2e/aIaq64sRmjQpxkhzlzU1tBduscY2X\njT+OOCV2GsDBKaJE2RJraK04wHF6GXIiz6Tg7rjXNSwiJHSKDrrpZZgQURfUGJWTdKge8haGnPO3\nTWG3nQS20MYEh5vYVHe4whLztIg24iJFTm9b9p/xZ9WUmTsMizEm9OEm0/cyCywxbydyyq3Ig8Lu\nGjVWxV2SooVpdYIIUcf+y4hNMmoTLzB9k6KNHnoYoJ1uCuSYk2fMCp+DZorWKKwJ4+OjUOxnmv0c\naEAx1LjAtyiQpVP0UBFliipPSHiuc7NMkSpVpsUxx/4LECp3uUGRnBPIgbBuo4MuBljmDlus0u+N\nss+fpsyumzSmfVPJFmBUeuz6PhChq/oet8VlwkQZ1uPsegXnPQ2mcTVqJEhyhB9wH84QfEB/C7Sg\nX4zYpGPGfUD7SlGlxAhTTHGkiWu3pu8zx1km5WH61aiZYottsnKLjL/thEG3GKRfjNJFP1JK6qrO\naf6eGHGO6be6P7Oua2yywjyXqegKIRGmhwH2i4MkGtaxN/wLLOs7HBdvpU6NJXmLjL9F0muhT40Q\nI8l1ztInh5n2jwOCNOuseHfZ8tccfNvH5zDP0idG3PsqT5oNVrjPbbT7Ypck4acclqhCiavyDL72\nOaCP4VNvaFTIufe8wmeIcYaZcFO5uq5zgW+RJ0OvGKIqy473FvVihGywpk6dQzxLnxh297ECGRa4\nTp5McwraJtbb6SbNOlussU9OMaKmKNnka8HLsu2vU6FsrwhJG520WQB6UrS4VG5cpBhSY+zIPDmR\npmABwp5N/seIM8PT7j0fvHevyFPUQ1U+8Lsf4P3vf/8bUuQ0TuXi8fhDqJHt7W0+8IEP4HkeH/vY\nx+jq6vo2f9KT8308TwTda30eFHSZTIa2trZX3U+wsrLCyZMn+dJff4mN1U0uXbmEX/PpjHRTKyo2\n9QpxEszqZ5yQMsy2Na5zDp+6ZbZVzDdMzConSpx1sUhNV5kURxjQo+xStFO5NCv6LkFFVEiEadUd\njm8GkmviDGm9waDcR4fqpSiyZOW2o6sHiJJWOpjhKffYAOb1Ne5xkxgJkjJFTqcNW0+aEntf1yix\nS58YZlIbREnAplrjPkvcBhoSuTJO3E/SSR8+PovyBiEd5oA2E869RO6mnTSa9UiClFvntYg2dnWR\nq/I0RZVjn5g20yWZdliYsIhYGLJhU01wuOHDus4lTpJlixRt1EXVibyoNOKmQpFddhiTs4yqKSSe\nRTFkWGKBPGkrrD1iXoKoH6eDHroZZI1FlpmnTXYyrg5Ro2J5hibV6eMT9GQOMEovQ3TYD6SM3uSa\nfBlf1RlmgpK3Q1aZJGhERA1ewq6XjvIDtNs6LyMAMlzhJcsGTFKy6+OoF9trGWHdQp6PMaBHrd8y\nS0FkWNbztjlAExJhErTQqjvoZoBWOh1rcVDubw4LPTBpjBJnjFnnaQRY0Qvc4jIxEaedHvIi3eT/\n81WdCmX6xAgH9TOuvUJrzTbrXOGUKVeXKfIq08TYq1NnlyIHeYaBB9asG3qFq5xmVEwS1Qly3hbb\n/jp16sRknIoqESbGc/yIS9UGZ0HNcZc5Donn8PHZkPfZ9k2pe1K1U6VKiQInxNua3jNlvcuaXuQO\nl5F4SDz6GGaESef5Aritr7DILdpFF2V2KevdhkaFHiqUWWORXm+ISf8IdWrkSZOXGdJ63XkuAdrp\nspN4M5Ur6hxX5CkqqsIYM1Rk6aGmkaoN1szwlFnZWkFUZpeLvMguRVpEOyVddLibKDFiKkGeDFUq\nzPI0fQy7YE2BbENi3SdE2MHCA+/kPa6zyj0G5X4G1H5TOeflyOkgQGbEt4fHIGOuJUIKaX2kZwgT\npVcPmuSrH1wPZs3rR2r8+L/5cT76P/3pG7FaCvj2tV1g3hNf+tKX+PM//3M+9KEP8Y53vOMNKVjf\noOeJoHutj1KKWq3m/jmbzZJKpV5z74TWmqWlJc6dO8cXvvAFrl+5weLSIijoCHcTLiYoqoytvBpk\nwj9sVyQ18mTYYIll7hJcCgZiG6dFtdPDABXKLMhrCC2Z0seIk9gLDjxULdXBAKMuSVnSO1yVZ8ir\nDCNiAoEwRHZlESUyQkVVUPiMc4gxZpomjVc5bcu6E9So2SaFAEZr/IBFcozKSfarGTxCDuexJhbJ\n64ybNJomhZRNrxoMw6K42QBDrtr0qmmHEAQwZMUQEwyx300MCzrDZXGaqi4zyH7K3o4TeVEZQzZ0\nth7hBxq8VD4FMlzlNBUqRIhQoexEXtxP0kIHGyyxS5FxcZBhPUGFXdvZmmFVLVKnhiCoXWuj3U5S\nEqS4ySVWuUuX7KNf7WNH5Fx7Rc1OGut2ynmAY65KDrABgbNIBB2yl7xOu5aRiIiCwrV1zOqnXSBi\nlwJpNrnNZectlEiidp3cTjcJWliQc1RVmWmO00GPq5ILoNrSrvCjROm0yekA+jrHy2yyypDcT1y1\nULQiL1h3azR1avQwwCzPuGowrTV3meOu/cIQluGGijKTNK5TY4cC+8QU4/qgQ5NULBB5gWsNaWa/\nwQNoWku2WWNWPM0Azeu2Db3MVc6QpNUK+l3zZUMn6dR95NgmwybHxX/tRLO5TuqsW9RHEHxJyBY6\nVR/DjBOTSXZUgYvym853uEOeLW+VtL9BSISJ6BhVy588wpvobEiFZ9lmhQXSbFhBZNaUMfslqI8h\n1llmXlwlJdoYU7OU2XW4m3zDmhI0oxygjxE3ldvVRS6Ib1HVFQYwPc35himoVJ4LHJmWiA732PJk\nuMF5KpgWCdNp3Njd2sEaS+yQ54A4Sr/ex45F3RS9LBv+MnXqVlCGSOl22ukyARnRyh19hUVu0yMH\n6VYDho0p91oigr7YJC1McZTOhgq/gs5yWb5ENBXh45/4GD/1Uz/1vd/AH8NRSjXB8B/8zFpdXeX5\n559nYGCAj3zkI7S2tn6bP+l7P0tLS7zrXe9ifX0dKSW/9Eu/xHvf+96H/r33vve9fPWrXyWZTPKX\nf/mXHD9+/FV7TK+D80TQvdbnQUGXy+VIJBKvCzq21pq7d+9y/vx5Tr10mi98/gtkcxnCXpR2r5NI\nMUFSt3BfzJPVWwx5Y4z5s5bZZnxye0lKw0ZLiBZadSc9DNBBD/e4yX1xy0xK9EHT9eqZerIdXUBa\nrw3AOLMMMu4SuUYQnaKiy/SLEXZEwWFXYl4cfOGwAgd5mi5hYJw1XbWIknPUXCdsvSlJ2Uo7y2KB\ngjZBjWE1YcG2e4gSbWHIHpJ2el1dVogQd7jCsligVXQwoPZRlPkmcLBEUqdGnBSHeIZ20e1e90AQ\ngaZNdJLXmb2pg46DNnVkSdFieHKi0/mzsmxzhysEhu8QYWJegrifpIs+4rRwS16kpIpMiiN06F43\nacwJs+4OJikRok5Yt4i2BtTNIt1ywHjspIHw1qgZFpiuW0E0yCGec32bvvZZ5BZ3uU6IEJ4INU0a\nY34ChSbLJp2yhyl1jDhJN2nMiE1W9F3ACOSoNNOXoPvXx2dOvmyTxscIEaFgRV4AfTXBmjqd9DHE\nON3WkF7Xda5ymm3W6RcjIHEg27CIECZCRVeoU30oaVxml3musc6S5dPVnNE+8P/tGFgP++U0Y2q2\nKRW+wRLrmC5YgSRmAbitdFtBdJ8l7jAuDzKqDrgKqCxbbIoV1vQiCkVERImLFO2qm35GaZEdrKn7\n3BBn6ZGDTPvH2aXItlhjS6zZKjp73ZPgWEN7SvDzusC3yJEmSQs7GNZgo3Vik1WybDEuZxlRk24S\nX/AybPmrVKm41XUbXU3hgVW9aOHBcYb0OLuyYL1oJoDiEaJOlQgxDvIMHfS4qVwBA9auU6NNdFLU\nuaapXEhFyJMhRIjDPEeb6HKwcLPGb+5ujcm4m8p10sctcYms3mRcHqJL9dn3R9Z5J4MvDGEi9DFK\nb8NU7r6+zR2u0iY6aaOrqSUiaj3JfqTKv/vVf8cHfvsDxGJ7/sU3ynml2i6lFJ/73Of47Gc/y0c/\n+lHe9ra3vepTubW1NdbW1jh+/DjFYpGnn36aL3/5y8zM7IV1vvrVr/LJT36Sr3zlK5w6dYr3ve99\nvPTSS6/q43rM54mge61PQM8OTqFQcLye18tp7JSNRqOsrKxw/vx5Tp86zdf+7mss3FsgLMP0RAaJ\n7CZo1e2m4UGep6hy7JczDKh9tp7MrGG2/DWXZPPwHFW9HSNsbnOZFe7SKjvoUYMUvaxL5O4FB4wZ\n/Qg/0JSkXGWRm1wABAmRpKhN/VXUJnIlgixpEjLJtDpOm+hyXpss29zjhvmzgIiIECPpECVholy3\nwYEJcciVdTeK0EYMwwgTbtLYKIg6RR8JUnbSGLD/Ypb9V2VQ7GNan3AMtKqucJ9bLHLbTTUaP8SS\nqg2NzyartMh2ptVxEqTcpDErt1lTiwSdmTEZd6m+XgYpU+KafJmy2mWSI4QJu0ljIEKDdXcfw+xn\nxgkApRVXOMUWa3SLfpCarEpT06ZTNqzMar5KpSl1qBwF/xYbrDh/k1l3x4j55kN2hyLr3KND9nJA\nHbNJWAPVTut1CjpnP2QFKVrduqwFE+oIhN44ZoIaoFeq1m/ma7O6neAww0w4EVrTVev/y9EheqiI\nXXZU0UJf44T9KCWK1KgyLY4zoM1kLVgNL3KTAhnL/8NyGmO06k666GeFu2yyYpEysyYFba0Jm6yy\nqwuAICzCJHWrFa+m/3dR32Kea/R5w0z4h9khT1ZskZWbZPwt+84VRInZlfJwQ5n9DhflC1RVhUHG\nTFjB38azCWrPj7BDnhBhjvAcraLT9TTnSHOPG5QpOUEU9xLE/RaXhL7NJTZYYsgbp88fMTV5Xsam\nPQOwtmE1jjBJL8OOubilV7kmXiasI/SIQfIWrK1QxLwYyteWW9jGMf0WYiLu3h9ZtpjjLD7+QxV+\ncZUiQYpVFtFCcVA/Qwfdlk1opnIr/j2CBHVERpy/rsdOra9yhi1WGJVTpFR701TOWBMMoL2NTsY4\n6LxyYNLBV+Qpevp6+N//j/+NpJbQNQAAIABJREFU55577vt0d35tzyvVdi0sLPD+97+fY8eO8Yd/\n+IfE4/HH8jh/8id/kve85z38q3+1137yK7/yK/zQD/0QP/MzPwPA7OwsX//61+nr6/t2f8wb/TwR\ndK/1eVDQFYtFwuEw0Wj0O/yu1+YERtfgm1gjS0hrjVLK/TU/P8+5c+c4feoML73wElfnrlBXdXqj\ng7RVu+3Uy0wr5uQ5ymqXcXGIhE7a4MA2OT/dAPWt00kvYxykjU4XHLjKGdNDK3oJyQg5tb23ziNC\nVRvhsF/MMKZnHKJklyL3uMkai5Zt5lvPVpyYH6eNbsqU2BD3aRUdHFDHiBBzk8aM2CStNp2YSpCy\nN/tBGxzIMCfPUlYlJsQhhBbOsxUgSgwJy2eEKcaYdZNGX/tc4kUybNEhuvFF3fmHDCcvQpkyFUpM\nykOMqEmk8FyTwiK3yLJpZ3I0sba66SfNBqvco112MamOGFCzFaFpf4Myu27q0EG3haMaAHVWbzMn\nz1JVZfYzQ01WDIDatlcEFWUgmOEEg2K/u34ak8Ytop2y3mnwXMaJqgRFcpTZZUocYUgbqKjpyc2w\nzDxFCnv+Ji9B1E/QaZlyJmE9T6fsZZ+aMUEKO5XL+mmCNg7QDDFOL8O02klKVm9zVZ7GV3WGGKfk\nFcmqLSq6TMStu8vW//dm2i26Q2lFgSyXOUWVEjESlNh1ZP+YnyBJG9usUqbkEtRVym41vKrvUqWC\nQhMWYeKkHHqlgx5uWcjukDfOkD9uXg+ZISvMe0TaRX6ECIOM09cgiDb0MjfEeeIkGdD7yNsausDX\niBZUKdMmujim3+KqybTW5EhziZPUbWtMhZLryU2pNlK0syLmqegyMzxFNwOO/1fwsmz4S+6DICwi\nzh/byyAhIi5N3yeHaFe9phvVTuUa+39baGeWp5uwSRusMMfLxh4gW8mrrGm3sY8PZZpc2kUXB/Uz\nxESCuq5RJEeONHe4BijnuYzJBDF7LbXQzg15gbLaZYanSNLSNJULppkAMeJ0M+SmcoB9Xgv0yRHb\n1dw4lYuZL3NRn9/7/d/l53/+5wmHw3ie5/56I/DXGmu7HjWV832fT3/60/zN3/wNH//4x3n22Wcf\n22O9e/cub3/727ly5UoTjPnHf/zH+e3f/m3e8pa3APAjP/IjfPSjH+Wpp556XA/11T5PBN1rfR4U\ndDs7Oy7y/TgfU1BFFg6Hm6paGoUcgBDioXF70Em7uLjIlStXOHXyFKdOnubm7RtU61Ukkv3M0Ekv\nKdoJiRBbeo2b8gJ1VWM/s9RFlbzcJuunzTduEaamjedrmuMMsL8BUVLiPN9ilyJtotOZtvcSuTGb\nyC0zKQ4zrI0PL1gRLXKTHSscgkSuEQ6BMdoIwU7Zy5iaNVwqkaEg06T9TZRFmwTBgT5GaKcbKSRp\nvcF1eZa6qjPCpAsOBOtGiUdVl/EINfnkTJNCjku8RIVd4qQoseNEXtSPk6SFNAZcPCkOMaQnqFOz\nlUUZlvUCVcoWwxAmIVOkrKexg143Be2SvQzblVnAlNtx7RU8Ehyc0VtcFafwtaJfjFCUOVu7ZiZR\nKChTJkqUw7zJ8MII1t1p5niZGjXX/fuQcGCBEjtMisMM6jF2KRgAsMyypu6j8DHrbtMF2kGPax24\nwxWWrdDrU6MUG/x/vg2H1KkRI8kMJ5omKWm9wRVOodF0yB4KOttwLRnhsEOBhEhxUD9Di2h3ZP+c\nBWubnlxjMWhMGrfSyby8SlHlmRJH6dGDRhDZEMq2v76HlCFKl20e6MJYBW5zmWUW6JNDdKo+8g2c\nvEZB1EE3B3nWTa0BtvUGV3gJD4+UbCOnMrbuLk5MxxFakiNNq+jgoH7GIUQC3M0Cc/YLw16nccpW\n+LXQwTV5hoLKMSWO0qLbHf8v666lR8ODYS/M1CF6SNJKXmy7dpcghFKlQi/DHOQZN2ms6gobLHGL\nyy6YUKXirqWEaiWExzpLJGULs+ppYiSdV64gM6youzZba9b4cZVya/wwYS7LU+RVhilxhLCOPmIq\nZ758dtHHKFMuMASwrpeYky8zOzPLF//miwwNDaGUwvd991dwH20UeJ7nPXRffZznO9V2AczNzfH8\n88/zwz/8w/zWb/3WY90uFYtF3v72t/PBD36Qn/iJn2j6tSeCruEXngi6V+9UKhX397u7uwghHsuo\nOvBG7O7uIqUkkUg4o2sg5LTWaK0fuuE0Vrw86hscGEbR+fPnmZub4+QLL3Hm1GnuLNwh4kUpVgok\naGEGw04LkoOB10ZqSR/DxlBtpy8RETOCmDIJWjjMcy5w4Os6ada5xlmbyI3aFcyecIiTYk3co6LL\nTImjDOh91idn1nnLasE9dk+EaNXNwmGOl9lghR45QK8aYcfe7M2ksea8eQlS1sC/d7Pf0MvMcRaJ\npEP0kscEByIPBAdSop1Z/RQp0eaYbRk2ucVFlPXwNYq8dnpI0cqCnKOkjCDq1UMm5WmFw5a/upc0\nJkw3A/QwQCdm9fAwODioYQqSioZR9qh197rl5AkkSdFCQe9VlEX9GAKPHNukRCsz+inTrGCDNTnS\nLHDN/FkWQB0TCduT20+SFq7LcxRVnklxmHbdvef/I93U8hAlZoWDmV4prZjnGve5TYfopkV32NSh\nEXmB/69GlS4GOMxzhG3Vna/rrLLIbS4BgjARyuw6SHDcTyEJs8UycZlkRplGhMD/lxNplvQdK9aE\n8/912HVemBhX5EsUVJZJcYiEbnU/q5zapqIreHgo2zSyj0kH1oZGQdRtcDdBm4LwiIoYNVWjSpkR\nMc6UPtYU1lhnyXouIWBJBnV3rbqTEGGWxB2ixJnVT++lu10rxzKebeWIyQQtqsN6SQdQwFV5iqza\nYkzMGkFk1/gP1t31MMgkR1yyVmvNAnPc4yZJWgjJMHllwkkPJoYN0PkwIRGirmsOQn2Pm8Hdyf2s\ngsBGiDC35WU87TGrnyFEyAQi7NTQTOXM65ukxfY0mzW+QnFVnGZbr7NPTCHwKLjAkJ3KyQiRljD/\n4X/9X/jRH/3R73jP1Vo3iTzf99FaPyTypJSvqch7pdquarXKxz/+cb7xjW/wyU9+kkOHDr1mj+1R\np16v82M/9mO84x3v4H3ve99Dv/7gynVmZoZvfOMbT1aujzhPBN33cKrVKsHrG/gTksnkK/yu7+8J\noudaa8cRCozfgLvJPErIBaP4UCj0EEzylU61WuWFF17g6tWrXLpwidMvneHu/bu0xzoolUsU63kG\n2Mc0J9wHWOPNPkqMiIxRcGXvxttkCtrzdHv9TPpHSIhUw81+jXvcCp6BqfISCVd7JRDckhepqxpT\nHHNerILMOON2AEaN08Ig+9zEoa7rLknZJ4aJk2yaDkVklLqqUafGAPuYYc8n52ufu9xgkZt4hPCE\n51ZlUfthpFGk2aBddnFAHSNBi6nkIkNWbLKsjQiVSCIyRrwBwaDwmfNepuSbFWdMJ51wCDh+Rjj4\ntNPNPg64dJ7SiuucZ936/yIiSo69XsqoiFFVZt09KqaY0IfxhOeAr8sssMhNm/g1TbyByGulE4Vm\nlbskZSsz6oQTDqbOK826WnJ0fiMc2m3SeIgaFa7IUxRV3rUOFF3rQM6JV586vQwxweEmJMcNfYEV\nTIDFE6GG6ZVpHahTo8QOI2KScX2IkAjZEEqWDZa5zx2CEEpj0riTXkCzIK8T1lFm9VOEiTr/X1Zv\nkdXbDbibFtf920IHdepcES+R09vsE9N42nNNIxVdJiyiDnczzDiTHG16j9zmsimcpwUhMQ0M1icX\n8eP4dpo77E0w7h8kJMJU9F7HaWMrR8z6/1osGgbgujwLNh3r4TVBqEt6p6GVo4s+humxHae+9rnK\nabZYY0iMNdTdZd11W1UV6tQYY5ZxDrp7TkWXuccNlpknjHmfB4GDKMYXWqfGFmuu0svDs2v8LDm5\nzZq6b98jXhOmpJch85rLU9RVlQMcBzQFmXM2g2Aa71OnnxEGGXPTeK01S+IOt7nM29/+dv7q//wr\nWlr2rrPv5jRO8oK/V0q9ZiLP9333xf7B2i6A8+fP85u/+Zu8853v5D3vec9DU7vHcd71rnfR3d3N\nn/3Znz3y1//u7/6OT33qU3zlK1/hpZde4td+7deehCK+zXki6L6H0yjoKpUKtVrtNStiDt64QfS8\n8VtY43o1+P8af61er1Mul5FSEovFvm9v6nK5zNWrV/n85z/P+so6F85fYHH5Ph3xTmLVFFu1Ncpq\nl2lxwjQ8NCQOr3OeDJuEiVCj2jS9aqOLPFmybNDnDTPuH7bfzPeqvPYaByQJ0eLCEKZxYI1b8iJK\nKyb1YWPOlxmybFGwEwfQ+ChGmGAfM8TsirKu61zkBXKk6RYD+KJGTqWd2TtkfXI1KkxxhGEmkELi\n6zoFctzjBtusO/BqWESJiqCuqNcgVlikXXYxpY4hEE6EpvUGBb1nRm+xH7EGjNpKXmdcIGKcg8Zj\nZz+Ya7pKWIStcNCMc5D9TLtJo699LnOSNJu0iQ5q1ExfsAhb4RCjTMngU+Qso+oAEuk6fNdYZIs1\nm/JsBAebWrgc29wTN4iLpAtEBMIhqGEKmHLmOQ3TwwAhEaGsd7ksT1FUWUY5gC/rDcLBIywjVFUZ\nH8UBjjEqJt01WNUVrnOeLVaJkaBOdW/6ok25fIkiOdKMWEEkkTaEkjVpUr3mVq9xmSCu9iC7BbLc\nkBeQWnJAHzNVYyJDTm47QLERDj4DjDLImPOS1nWdS+IkWb3FoNiHL+uOrxeRUULadATXqTHNUwyx\n9x7ZocA9brDOEiFCbpIctela4yXdYYNleuUQU+oIGlwrR9ZCqIV1k5r3SLsFNvezQ44r8jQ1VWOK\nI4b51lB3J+1UTqEYZZJRptzqVWnFZV5im3W6RB91WbNeTdMJHVJRqpSoUOYARxhm0qV+C2RYZZF1\nlsz9CeUsF6aDtZcKZRbFDVpFJ9PqxB6PTmbI6i37HjGC2Kzxe+llkJRos9eS6QAeYxZf1GzCO+0S\n8mEvQt9IL3/xl/+Jp59++vtyL2w8j5rkKaWQUj5yZfvP/W8EU7lH1XaVSiU+/OEPc+XKFT71qU8x\nMTHx/Xp639N54YUXeNvb3saRI0fc0OFP/uRPuHfvHkII3v3udwPwq7/6q3zta18jmUzy2c9+9l/y\nuhWeCLrHc2q1mvNSBNOuf+43u3/qaUyuxmKxJiDkK/nkAiEXTPNeC2ZeqVTi8uXLfPOb3+Rvvvh/\ns729zer6Kp3xbhLVFkQlxKpcQCnNAY7Ry5D5fZiqpnmuOj9ZUJUV8xO000M3A8xz1VV5jfoHXONA\nXqZJ+xsuRAGaPkaaALvr+j635CVQglGmKHlFMnrLeofCSOFR0xXChDnCm+loQJTs6DwXeIEKJVIW\nQhyIvKgfJ4ZZoVUoMSWOMKjH0CgKNr26LO5Q0ruurighkyRUq10RDbLANVZYoEP2sk8doEzJrigD\nTt4eB2yEKQYYdVVjeZ3hijxFVVVccMCkQ6sPcfKO8mY6GsDBBbJc42VK7FiDfdmlQ83r3skW6+TJ\nsF9Os08doEbNlqNnWGeJkt4FNJIQKVpopZMeBmmjixXuMi+uEhMJ9lu+WZA0bpwOgWCCWQYYcyGU\nii5znm+yS5E+MUxJFJtCKJ7vUbIpzoM8Sw8DCCFcCGWeaxTIuhXl3nSozeE8tlhmQO5jTBkUT54M\nRS/Llr/mQigC0YTziIukgzVrrRjTs1REyYU8FMqGUGpIBAc4zgD7nLiu6Srn+RZFcg4AHASGotJ0\n+Zasl/SAOMaQbeUIVsMr3CPLluWoSWJegogfp92K6y3WWBRmtTuhTCF9oQFCbRBARlz3Mkg3g66V\no6R3uCRPUlI77OMAVa/sqtdCIkRYhCmrMqA5yLMGHwNuwjvHOTJsEidJhTLKwpqjOkZSt7kE7pic\nYb+aRqFcwntbrpFWmxbj01zp1cMQedLckOeI6BiT+oiphrOr16AjVtgglWmtGXXhGqUV895Vlpjn\n3/53/5ZPfOLPX1PkVKPIa5zqNYq84O9faXPySrVdL774Ir/3e7/Hu9/9bn7xF3/xDRHm+P/5eSLo\nHsdpFHTB6vPVgjBqrSmVSlQqFaLRaNMb95WEnO/7DiT5qG9vr/XZ2dnh0qVLnD17li9+4UvcvnWL\nnd0infEe4pUU8WorPnXuyesILZnWx+miv6m5YlnPE/jQwiJMSrc7n5wk5JorhuUYnaqfQkOV1x7D\nzidJigMWdBt8wK7oe9ziIiHCdMqehhVlhCgxlFaU2aVNdDKjn3KA3QplMmxwgwsoO7nSaLP2UmbS\nmKSVe+I6ZV1iUhyhX4+46VDBy7DhL7vXKUSIDvrsOm8QiXSBiA7ZTa8apihzdp2csRbxoPYqxWHe\n1NQukNGbXOaUWc0Kg22pWkRJhDie8iiSJUyMgzxNu+hGaeU+dOe5ZnltBnYb8/YSh530cktcJqM3\n2S8PGKgxeZdeTVskh3mMnuXkGZEnhXQ1TBGiDOj9D/TkRuxEp0KMOEf5r2ixnkvDN8twkZPUqJLC\n9H4G4OCoHyNOigwbdvJ1gj6GXQgl70IoJRdCicuk8/910c8drrLKXXrlIMNqgh0KDWv8nPOiCQT7\nLGQ3SK8WdIZL4iV8XWfA1t2Z6ZCZGgorrkNEOMZb3M8rgFDPcZYSuy69GhYRoiJGTJnO5S1WKZBh\n3Kaog+q1gsiwwQplvQsYxFCSVtropJtB2uhklUXuiMvERYr9atoIxKZWjjCmjk4wwWH6GG0Q1xXO\n8Y+UrLjeFQU3QY16MTw/TJkdFJrDPGewOBhRXiDDXW6QJ9MkriPESdnXfZs11rnPoNy/J/zJUvQy\nbKsNSnrXVd4ZcW3sCQmRcl9olPYZ07OUxa6bygVwYy/kcfypo3z6Lz7N/v37X61b3Xd1gvv4g9M8\nIcS3neR9p9quQqHAH/zBH7C5ucknPvEJBgcHH8fTenK++/NE0D2O0yjo6vU6Ozs7tLW1vcLv+u7O\n95pcDVbBjzLHvp5OPp/nwoULnDt3jhf/8UW+8Y/foFgq0pcaJFFpJVFrpZUOsmwxL6/haY9pfZwo\nCbNW8jJk1WbD+kWTos1N5WIiQVVXuSpOk9GbDIkx04tqq7yCnsiaruFjkBgHOOZCHo3m/DARPOE5\ngG3gk1P4ZNl03DUzlTAryiwb3GceCAC7Ufeh3MsQAsE1+bINRBwhpVu/jbdJkaKdUabcFEVp5ZKU\nHcK0MuQtJy+ovaqrGlUq9IsRZvRThGxwoKarrHOfW1wGcP2VwfQqpdoIEWFV3CNEmFn9FG10ubL0\noBYuEDQRESGp25y4DhHhqjjFtt5gVE7SproaxLUJoZj0ap0UbRzgKO0N4npNL3KDC4SJ0Ca7yOu0\ne90jIoZWihK7tIsuY/4XSRdCybHFDS6i8F0KNWrFdStdtNLOXXGdkt7hgDhmQyhZ97pv+qsEXrQQ\nYTochHqIkAhxR181YQ3ZTY8a/rY4DyOuDRcuOFm9zSVOovBpE10UdLapCSWkwhayG3EAa8P/M6/7\nAnNUqTwkrk01nBGh26wxKqds9dWeuM40iOuAJRmgV4LqqzlxFg+PIT3Ojpdz16BJeEsqukKEGMd4\ncxOmZIc8FzlJhRJJWtjFMPlinpk0JmglxxYldpgRJ+jXo1Zcm9WwAZoX91h5MklC7XkUl5jnrrhB\np+hlVE3ZhLdJ5RbstDbwXo4yaRsszD25rmvcDF8g623xa7/+Pn7nd37ndXs/DE7jPf7B8AWAlJJo\nNEq5XCaZTOJ5xv/6D//wD/zRH/0Rzz//PD/90z/9un+eT07TeSLoHsep1+v4vg+YKVihUKC9vf0V\nftc/7QShhWCU3rgi/W6SqwEb7404Zs9kMly4cIGzZ8/y4jdPcvbcWbbSm3jCY9SbJFk3jLw4SZZZ\nYEFcI0yEcX3IfEg0+eTM81do9nGAUSYdyqOqy1wUJynoLH1i2DDbbBgi5sXwXIF4jQMcY4hxhBAo\n7VMgxwLXybCOsKbrQOSZgvNe2zm7SIfsYUodRSKNaBDGxJ7TGTflSVkXWi/DtIh2CjrHNXmGktph\nXBwEja1d26KiS4QI4+Oj8BllinEOOYO90oo5zrLOkoEJC900RYn4cWpU2KHAqDfJmD9LSISdt2mD\nFVZYaADsxogQp8WGUBSKW/KSrZ86QZS4SxpnLZYjENdJ2uhjmD6GXQjlGi+zxSqDYr8T14G3KSJi\n1HVjCOUpJ6597bPEHea5ZtsrwpR0sQlsLJCkWadVdjCtTpAg5aY8ObZZ5JYTeRGbXg38f1FiXJWn\nKagck+IwLbr924rrBK2MMElfQ6dsAA9OCTMRMxBqIzaiMoavfCqU6RJ9HNbPOaZcTVdJs84c55yg\nCfh/EZvwTpBkWdxD6TqzPE0nfXtoGAvZDZpQDNi4zeE84iRdErpfjtCtBiiIrOOvVW01nMInRpID\nHKGroRoup7e5KE6C1nSJfvIiw64qEBJhojKO9CU7FIiT5DBvIiVanUc2R5rbXLYtFHZy7cWIKJPK\nbaeH+9wmzzbj8hCDtn81gH9v+xu28s4gb/Z6ZU1gY0MvcV2cJ0aCYT3BjjRT5SB0FZUxRAh+7N/8\nt/z7P/v3b9iS+cBy4/u+w4z4vs+HP/xhPvOZz3Dw4EHA4FQ+/OEP85a3vOV10V705HxX54mgexyn\nUdAppchms9+XouYAQQI01Ym92snVN8LZ3t7mwoULvPzyy7z4jye5eOkC2VyWml8zvaz6EG10EiOB\nEIL7+g4L4hoRonbakG9AeYSR1pwdJc7RhmkDQEHnuMgLVKnQItrY0QXrkzOrvBgpsmxRtd6mQb2/\nyQO0xG3K7OIHPjkvRdxP0W17SheYc+vT/WrG+ORExiJUtoGgT1YzwgT97HOrxqLOc0W+REntMswE\nVa/kRF5ERvF0iIouA3CY5+gRZt0SwJqvc44caSIWC7NnsDfCpkCGNJsMyTHGlfmQCKYoW2LV9d1K\nPBIi1WSwz7DJdXkOpRVT+igK360oiypvpyimvcKEUKYfabDvFv0oqRpWlHHXXlHBgKBH9YGG9oo8\nq9xlmbsIsN61MDFhKqJML6zPPXGTuDCYkjARJ65zYpu02mxIr6Zs0tishk169RQ5vcU+MU1YR1xw\nIGhCMQjqOn2MMM0Jt6LUWrOIEXpR4kRkxHXKxrwYYT8GaPJk6Zb9TKvjREW8AQ0TMOXA4DyixETc\nTa+StDFnJ7zT4rjBu1hxbXyXGReuiRKjn1F6GXLTqwU95xAqbXQ59EpVV4mIKEoralRop5ujvNl1\n5SqtSLPOVc6gUMRF0vQ7C89MQ/0EMeJssYYUkkP6Wdrocv7EgsiypOdd9VrQ8JLSbXTTTwe93OQC\n69xnyBun1x82VWBNrDzDXQwTtr2yw+56KusSc5EziKTmT/70j/m5n/u57/Md6bU5jbVd4XC4yTsd\n/PrnP/95vvjFLzIyMsLOzg7nzp3j3r17HDx4kJ/92Z/l+eeff4zP4Mn5Ls4TQfc4ju/71Ot1wLyh\nMpkMHR0d/+zxdmBu/V6SqwEL7/UQR3+tzuLiIhcvXuTy5ct86xsvcPnyJcrlCijBbq1ID0Mc4KgT\nebA3RYkRp1V2ktPbTas8pXxK7NIlepnWJ9wqr0KJNOvc4JJLeD7ok0vRyj15g5LaZUocbfDJZWyf\n7J5PziNEJ7100e98cne4yjLztMsu+tUoxQb8QgAMNn2yCQ7xXFOfbEFnucgL1KjRKXookLMiz6zy\nwipMgTwCmOVpusWAE3kFMswzR5ld653yrHhNOGFzjxtssES/N8o+f5oyO+RF1sGag/QlQB/D1mDf\n71oerskz1FWNfUxT8XbtqixHSITwCFHVZQSSQzxLrxhyz6uiS1zmNHnbUVqhRL0hvZrSbRTIUyDD\nmJxhn5pGINyUZ1Mss6033IrSlNEnXLAhT4ab8gKeDjWlV83UMOOel8Knj+Em7EVjp+yAGEVL3dQp\nGxFRKqpMjSqTHGYf000J72XmWeS2Y9YZNEzMpYY12tXpzagTRIi7poe8zNhquD00TEq10mmvJ4Ar\n4iQ5nWZcHCKq407kFVTGLpSFbXjpY4LDtNDmpnJLep7bXCZOkoRMkdNpcz0J029q4MxFesUg0/oE\nERFtqBvb5qZ9n2Cv26jjLnbRQgfz8ppreuigx31pKFhgszmGIdhpO1sDq8GCvs49btApe+hSA02s\nvJDwiMgY2vP5lf/hv+d3f+933pD9q9Bc2/WoMNva2hrPP/88fX19fOQjH2my/RSLRS5duoTv+7z1\nrW99rR/6k/PPO08E3eM4jYIOzIqwra3tu56KKaXY3d115tZGuO93k1yNxWKEQqEnfglgdXWVL3/5\nyywvr3D6xdNcvnIJv+7TFuois7vNrioyyRH22cJ2MKs8EzpYIIL5BrwHDY6RUC3UqZJhi26vnynf\niMTAJ5dmg+Umn1xjEf0wANfkaUpq1/rkLCfPSz/CJ9fGKAfchxfAvL7KIrdoER200uHI98Eqr27J\n/D2in1n9jJui1HWNLVa5znkUqqEvcw/WHCPJqriLr31mOEEPg07k5WWWFRWsXk0FU0q30W59cjES\nbpXXIwfpVyMURZ6CNM+r0sDJixBliqP0MuxEQ0FnuShepK5r9Ilhswa03aExL47wJWV28AhziGdd\nKjfo8b3FJXbZcbgQU6EWp0W30Ukfq9xjm3VGvHH2+TPu5/Wd0qvBlCejN5mTL+Nrxbg+aNOrGXLW\ndxlqaEI5wDEGGXPPq67rXOYkGbZoE51UqbCrC25qGFEJyuxQosi4PMioOoBAuE7ZTZZZ5z4abX/G\ne52y3QxQo8pNeZGwjjCjT1jkjVlR5tR2Uz9xu/VqBtVwpo7vNJusMizG8Ag5uDZoIjJKzXIXh5ng\nAMcanleNZe4yz1VChJBN3MWYBTZ7bLJiRehTxEla+LcJoizp2/adugdsbqWLXgZIkOKKPE1WbTMh\nDlmxnn0gsBGsvFsYYcrSeZZQAAAgAElEQVSy8iLueroaPk1XXyf/8TP/gbe97W2v5q3mVTuvVNul\nlOJzn/scn/3sZ/nTP/1TfvAHf/DJvf9fxnki6B7HUUpRq9XcP2ezWVpaWv7J07HGuq03enL19X60\n1qysrHDmzBn+6nN/xfZGmmtzV0EL2kNdeMUIm2KFiiozzXH6GUUIYdOGBnmRY8v55IIapaRFjWQM\nHIJO2cuUOrrHk7M+ubzea0NI0UYX/Xbl1UpR57kmz7CrCoyJg65P9lE+uREmmeBwE4j2JhdZ4S4J\nUkjpWZ+cSXlG/Bg+dYrk6PdGmfAPE7X+tEeR+SMNYYhuBogQ5bo8516XFjpc28CDZP4YcfrZ5zpK\nlVbc4RpL3KZD9JiGCG8PhhwREbvKq9Iuujmq91Z5Wmu2Wecqp1EoEnaV1zg1DFZ5QsCMfopuMeBE\nXkFkua/vUKfqRGxMJp3/r5M+2726SJ83zJA/boIezv8XpFdNUdk+puhn1LVrFHWOS+IkVW3QMLs2\nvVp36dUQFXaRyIfQMEVy3OQieTJWXFcaOmWTlru4zTYbDMsJxtUsPnW3ojS+y22baTbNHi26k276\n6aSXAhmuypfxVZ0pjlCn7qrhdlXBYXz8b8OUC3yXnaIXLZTlLu41PdSoUmKHMTHLfj3juItFcmyy\nyqK9nhywWcSJKTMNDROxTQ8hDupnbO9y1k1Dt/0Ndz3FMWvybgZox0yhA0j2kBwjrlJNK++wiBCR\nUYhqPvQ/foh3v/uX3rCWk1eq7bp79y6//uu/ztGjR/nQhz70WBqKnpxX7TwRdI/jPCjocrkcyWTy\nFflujcnVSCTSRPT+l5Rcfb0frTWLi4ucP3+er331/+Hr/+XrbKe3CcswbbKTyG6CiIpyT96iqvZq\nxhS+S0MucdvytfZSeYEY6qCXu8yxzALtXhf7g+mQ7fLMqQymVCrwyU0yyH7XhrCri1wRL7Gjiwwx\nTtUr2YmXWaGGdYiyNv/tgzxDH8NulVeiyG2usMUaYcIO1bLnk+uixA6bLNPjDTDpHyFE2KE8smKT\nTbVmMbSChEi6toFuBihR5Ko8Yyad4jAhHW5q5Hiw5WGSIw7lAXu1VynRSlTGmzh5ER3F175BYsgR\nptTRhlVegTQb3OYywe2rUeS1002SVublVSqqxLTtfN0TDRm2/DXr4oMQEbroc/4/KSTz+hqL4hYd\nopseNUTBy5Kzq2Fp/1ejSoIUR3mz86GBEXrneYE6VYOG0TmXXo0QI6ri5ElbVt4zdIsB1ymbJ8N9\nbtt+YkUIz6VX223d2AoLLDFPrzfIfn+GErsN6dVtfGpIQoCijxF6GKTLPq+y3uWSPMmuKn4bplyE\niiqh0RzkGfrFqHteFV3iFpfZZJkocWpUHZg3ouOkdCsVKqQtE3LCP2ybHswaPCe3WVWL7ucV9xLE\n/L2mB43msjxJ2f7MPLyH0tABaqiLXkaYdG0oAFt6jWveGaanD/D5/+vzrxsUyXd7Gqdyj7q/+77P\nZz7zGf76r/+aj33sYzz33HOP8dE+Oa/SeSLoHsd5UNDl8/lH8oCC82ByNZFIPIQg+ZeeXH29H601\n8/PznD9/ntOnzvCfv/yfWV1bIRaO0xHqIbKToFW3A5Kb8gJl65Pr08NNyIsNf8WlKEOEnBDqcqLh\nKoviNq2i3TDbZN4lQ4UTDTViJDjCm2hrQF7s6ALn+SZVKnSIborkqeqyRV7EiagYRbLULHetHwN7\nDVaoi9xihzw+yq4190RDL0Osco8l7tAuu5hQh6lRdWIo429SpeJWeb0M0ssw3TYN+YotD8K0Ifj4\nHOAYI7Y1AP4/9t48uq66Xv9/ffaZh8zz0DYd0iSdJ4rT7dWr/FguGQS9iq5L/V25Kl9Fyi2iIv2q\nyFLBMqmtXq4oeuUqDnf5A+GWOyAghaZJmw5pOqVpk6Zp5pzknJycce/P74+9z845TUoZmqF0v9Zi\nLUoOsE/Oyc5z3p/38zy6yzOV+ebAjUQlnhah4tWyUFEZoJtCWymL1ZW48JhiaCQtn1AxjihTpe16\nPqFCs1JvdMouxyf95v6fLpSj2LCjoZJFHlXUmM8LxvfJvMJPjiggyJD5vJyKW/+wRYQ8UchSuR6X\n4aJOyDjDDHCEvSRJYsc+ofYqmzy6OEUEPUKlVM41ntcwo7ZhetVOEuhHuzbsZJFrHuX7RBZn5Ena\nxCH8Ips5WjVjIpTRKWvHjoqKDZsR4F1pTnkTMkETLxMmRJEoIyLCeqesIZT1NpQwCRLUsdb84JCa\nhnbRzgDd+s+PkfOWel6FlBFljFPiCNlCdxxrqOOVfHKQkBw2p3JZ6J2yqTaUpEzSbLRrVIlaQOj/\nnpEn6VJcOBUXjiw7P/mX7VxzzTVTfIeYOi5U23X06FHuvPNOPvCBD/D1r3/ddLlavOOwBN1MkBJo\nKUKhkLnrcC5vx7macje9U52rs5XU9z11XLt//34adzey69V6Wo62kFQTlHvm4ovmkiX1CJUoY7QY\nMSMLxTJ8MsucoJy7T5ZNHvNZQr7RXAHQIY9xiqN4RRY55BMUhmgwCtv1yIsI+aKYJXIdLqEftSRk\nnAB9HKYJjSR2o0JtPE8uFz85dImTRKXeCVsmq8y9ppASoEfrTIuGsJFFHnmGaPAKPx3yOO3iKD6R\nrWeAiVH9l6s2YIqGVDNHNSsy2hDiMsY+XkkTDWOMmhEqHhymaIhTI1ZTJuel1UMNc4Y2BugBJhcN\nY4zSIY7hFznUaKsRYD6vYTkw4ci7kFJTDCVlnGYjn3CeWIxd2s0KtbiM4hQukjKJSnLSfuIuTtJK\nMw50A8SoHMmo5VJQGGbQNDV4RZb5vEYYop2jRqOsppfRCy9+LYcCSsijkBaxl2HZz3yljkKt3Ky9\n0hsRAihG5I0dOxUsNI/yAQZlD4eNTLkyWaVntqWel9kaEsWJk5W8LyOwOUyQQzQyRggPXiLGnmLK\n2KDnQg5mBBunarmCIsCg6GFYGwQkNrMNJccMyg4yxGFlDzZpo1qu1D84GNPrVKYcCKPpYT4VLNDj\nd1Lfd3GSVg6yYcPf8pun/n3KQt2nmgvVdiUSCR599FFefPFFtm/fztKlS2fwai2mAUvQzQTnCrpw\nOGzWr6RIJpOMjY2haZop5NIND+lCDs7vXE0ZHiymntR+oqZp5zWaqKrK8ePHaWpqov61ehrqGzh2\n4jjJZAIQVFFDASX4ycUu7IzJUVqUBka1IFWiVhccRqhxggRO4SQpE6iozKOahSw3xZCUkjb0EFsX\nbmzCzqgMmntXLtWLRDLCAAVKCdXaSjzCZ+bJDTNoiAYdp3DhxkuWzKXQWEJvURoZ1UZYqCwlXysZ\nL6JngKA2bEZeOHAauWvj0RDjLQ9uSuQcQzQMGJEXToTURYMHLyt5r3mkLKVklGEOUm98XV+cTz9C\n9ZNLgD7CjLBQWUaltnC8y1ME6OMMIRk03as+xY8vbU9ugG5aDePAIrmcJAnzyDsVG5Lq2a1kIZUs\nNK9PkxqH2M0APRSLCqSiZYghp3SRkDGiRFmUFqGScg330kk7xwwntHaOy7MQB07alWPYpYMlci0+\nsjOmvL1qlyHWwCXc5MgCCozIGwXF3CcrU6rI1QrTRN4wMO6G9pPDEtZlRPIEZYD9vIpEI08pIigD\nZuSNCw92zU6IIHYcLOMKckSBGdgcJEA7RxljFIk09jW9uFQ3ORRQSDndtNNDB+W2KuapNUQIm8YG\nvcs3arynBIWUmK5cp3AyJkdpVuqJamPMp464EjXFq94AovevFlXk8/gTj3PFFVdc9HvAdJFKNjjf\nVG7//v189atf5WMf+xi33377ZZVecBljCbqZIhaLmX8/NjZmxoakllot5+qlw9vdT0wmk+zdu5dj\nx46x69V6Gnc30tZ+Aq/dTzAyrDctGGGwqZDcqBzjoNjFqAxSLuYRUyIMp+Wu2TUHUSKoJKlhFeUZ\nhe1B2jlGH12mwzPVu+pR9YXyKBG6xEmyRC612mocuMwJyogYYEDrGV9CFz5yZRFFRmtAKndtWA4w\nTyzGK/26qUEOmi5UiUQlSR7FLGGtaRoA6JVnOEqTfkSo5BKUQ+N7crhRNEGIEfwimzq5Fr/IMcRQ\niBECnOAgSZJIpOl41SNUCimkjDZxiEHZxzylerySyzgaHlL7UEma06tiKkyRZxd2AnJAd69qKlXU\nEFHGTJGnYMMu7MRlDBAs5QpKROX46ywTHKKBIfrwkU1CxDKiYTyanxgRRhgyApuXYMNmTkOHRB/d\nst2MoHGnibxUl3GL0kBci1GLnpWX7vJMd0Nnk0cFCyiizAw2bpdHOcVRckUhfnIIinExlMqUixGl\nUJSyVF5hBhsnZZJhBjhMI0mSZt1YunvVTy49dBAnRh1rKaJc708mwKgYpodOM//QLux4ZVZG3dhZ\nTtEmWsgWeczVFjNGyDQ2jMlR8yhfIFhAHWXMMwPANalxQjnIWdr5xCc+wU/+5SeXbGiulPJ1a7si\nkQj3338/zc3NbN++nYULF87QlVrMAJagmyni8bh5bJrKChJCmEutHo/nDQs5VVWJxWIkk0nLuTqN\npC8iX+z9xHg8TlNTEy+++CKn2k7RUN9Ie2c7ue48lJiNgUQvXpHFcvmuDNNAWAbZz6vEiJIlchmT\nIbO5wql68JHFMIPGztUKyo3C9jBBggToFu0E5TAaqmHW8OLWfBRQQjEV9HOWNtGCW3ip1vTw3+CE\n5Xr9aLiYSirOyV07TCMD9FAm5qIIG8MMmMv1TuEiocWIE6eKGhawNKOIvpsO2mghvXtWjxpx45e5\nOHBxVpzCgYM6uZYcCsw9uZAyTJd2itStyyFc+GS2eTTsxssx9tHDaUptc/UgWjGsF9EbhhJ9T04z\nI1TSo2HCMsR+sZOEjFMi5jAqRggZx5oumwebaidCGIFgKespECUApmv4FEcYYXDcDZ22J5dPCUEC\nnOUURbYyqtUVqIbBJqQECNCf4RrOIsfI/6sgW+QRl1EOKvWEtGEWiCUIqRgib4CoHMOBExUVlSTl\nVLGIFRnBxqmjYRceXIrbEHnSqENzAwojDJIniqiTa3ALL6pUGTWOhk9yGA0VaUTXuBUPbtVPAcXk\nU8IxsZ9h2c8CZSnFWgUhRs5bN1bKHAopN99TAdlPi9KIkII5chFjtqARHBwyG0AUh8LSlXU89vhj\nLFiw4KL8fM4Eqd5vu92e8fsB9Ndp165d3HPPPXzuc5/js5/9rLVic/lhCbqZIiXopJSMjo6a0523\n6lydLG/IYmpIP9ZWFAW32z0tRxqxWIyWlhaefvppDjQdpK2tjc6uTvI9BXgSWcSjcfo4Q65SyGJt\nlSn0YjLCMIMcpQmVpOmOTU14cigglwLaxVFCcpj5Sh2V2kLDDDFMyDZEv9pNgrhpvMiliEJKKKIS\np3DSJ89yXNmPkMIoNo8YTsNzc9eEkbtWZYo1Vao0U88QfeSIfJIizqgWMqeGLtVDzDh8q1IWM0+r\nxS7sJGScIAEG6Tb6blNVY3rumn40XI4NG0eVJhJaglpW4yPLFHkTI1S8lDHX3P+D8TaEXFFAniya\nsCcnpSROnGyRx0r5bnM/UUpJiAAH2EWCOD6RRViG9CBf43vvI4cheokRoUasolTONcRagBDDdIvT\nhGXQcK/a8Sr+tI7SCno5Q5vQDRcLtWXEiZmtIUF1yJhaKWiolDOfcuaRRZ7RkqFxiHoGzGBjLaN7\n1SncxLUYcaIsZClV1JpT3hgReunkJIdNZ3KqwN6luckmHyduTotWHDhZItfhJ4cwI2bmXY962nhn\n6xNAv9HlW0wFTty0ok/VypS5FGilk3T52kmSxIOXxaw0jUOgfwBosTUQtgfZ9M+3s2XLlkv23piK\nqUomk3i93gkrNKFQiG9/+9v09vby4x//mIqKivP8lyze4ViCbqaIx+NEo1EikQhCCBRFIStrfEfI\ncq7OTt7Intx0EolEaG5uZu/evfzm17/hbFc3g8OD5HsK8cazcMf0LtIzog2/yKZGW42PbDMkN8gQ\np2k1/msCp3Dikf60Hk8vh0QjQ7KPucoiirRyfYJiTHjSQ2gdOFnAEoqoMCc8w3KQFrEbVaqUUUXY\nNjLed6u4EZqNGGPYsLGMd2XkroUJcowDBBnCYfST2oUDl9CPJ1MNAX10mVl5gLknFxD9DGp9ZoSK\nT2SZ4br5FBMjQrNSz5gWYqFYjl3azf2/UW3EECoSFXXSCJUueYpWDuLGg1fJYkQOma5hJ26kpk8+\n80QhtXKN2RoSIcwIQ7RygCRJ4zsvMo5Q8ynmlDjCsBxgvrKECm2+OUXVO0p7DSOKLrALKDWL6O3C\nSUAOcFhpRErJfFlLVIwxogwyourTNYdwEJdxQFDLGkqZkyGwD7OHfs6SRQ5JkTDbK1yKB7fqJUGM\nEQIZR8OpWq5h+jlNGxh9Em4zsFmPr3HhoUVpMJsePPjGj4blIKPaiNnl68FHGVUZu5dnZTut4iA+\nsiigNM29msCtuHWnszPJ1R/+f3jo0YcoLBxvRLmUeCO1XS+88AL33Xcfd955J5/85CcvWdFqcVGw\nBN1MMTg4aFaypPYiUoLujThXUyYKa9l1eriUcvzC4TAHDhygqamJ1/76Gi/85S+EI6OUZJXjifnx\nxLP0xggCnBSHcOCiVq7GhSetn3SIQFo/qQcfxVSaTshUkGwfZyhR5pCt5RmTKyOsFScaGkkSE3o8\nQRd6B3kNFZVskUdIDht9t/rRsBsPAQaQaHrVGGVIpClqOjnBGKPjR8NGPlnqaPgMpzgt9KmaPrmK\nEjQmPMPqAAkSZmVWKXMoZQ55hms43b1aKRaC0IxqqBHTNRzTYiSJM58lLKDOfC8kZULf9+KwIbV0\noZMSeX4tBzsuekQ7LjzUybVkkWuaBkaVYc5oJ40qL0yBnUchRVTiI8s8Gi5T5lGizckQ2Kk9OT0U\n2cFCllJCpbknF5Vj7BM7ickIZcwzgo0DZtewTXUQJYxEsoz1FIoyAKPzdoQOjjNANwo2kiT0Y03h\nNjtv48ToFCfIE4WGa1gxarn099Rg2u6lV/jJkQWmwNbQaBENDMpeqkQtbunRjTJpu5cASRLkU0wt\nazIE9qgcodlWj91r46FHHuRTn/rUFP+kTR0Xqu0aGhri7rvvBuDhhx+mqKhoSq/nlltu4dlnn6Wk\npISDBw9O+PrLL7/M9ddfbx5p33jjjWzZsmVKr8liApagmylisZgp2pLJJOFwGJ/PZzlXZxlTuSc3\nnQSDQQ4cOMCePXvYtbOePXv20DvQg03YmOtYhDeeQzZ5ePEzSC/HlH1IKamWKwBpTK70JXnj3WnG\ncVRRm+HwPEGz3ikrCnEqLrN2yam4cEo3qkwyRphyZR7V2gocwmkc40UZoo/j7Ec7T99tFnm0iyNE\nZNjou52rNzUYE54+tYskSXPnKodCQ+SV4xRuuuVpTigHcUoX82QtETGaeYwnHCRlAgVhZPHNPedo\neBdD9JMnikiIWEbumlP1EGOMMcJmL6xN2EjKhHE03EMnJ4ybp8yoUCukFCdujih7SWgJ6lhjuFcD\n5vd+WBs0RY0LNyXMoZgKM5LjtGzllDhClsjVJ6npbQg4AUGCGF78rOS9phhKBUrv5zWijJElcgjL\nUMYRqo8cggwSZpTFYgUVcgESmdF5OyT70M7TeTvGKIeVPSjSRq1cjUQzdi/1OrQ4cTOWp5AyyqnK\nOEI9IQ/RSSuFogyXcDOSluWn99e6ibnG+PwXPseWb265ZBsQ0j+0T7ZGI6Xk6aef5pFHHuFb3/oW\nH/nIR6blg+XOnTvx+/1s3LjxvILuoYce4plnnpnya7E4L5agmymSyaQ5iUsmk4RCIRRFwWazZfyV\n+qRmOVenl5nak5tOAoEA+/fvp6mpiVdffpX9Bw4wFBgiocZx4mYRy8mlAA8+hBCEZIAWpZGYFqWK\nWuJK1Aw11sN/HcRkDImkjjWUiyrz/6XKJK00040+mZJCI5oWd5EK/x2ilwKlmGptZUbf7QiDnKbV\nzBhzKS7cml53VUIFLnwcErsZkQNUKbX6zpVxPBmQg4S1oHk07MRFFbUUG/t/oE8MD4ndSKlRThWj\ntpEM17Ci2YgaR8MreA+5ogBI5a6FOM4BhhnAgZMEMWxmJZeXHAoJE6KfLkpslSxSl6OgmFEjI8oA\nfdpZsxfWq/jJ0nKNSq5SNJIcVHYT0gIsFEtxSY8eNSIGCaoBw/UqSJIkjyIWs9IUeQADspsWGrHj\nIFcpZESO78m5FL3zNkwQn8hiqbwCn5FFF5P69/4EzUQYM2NUxncU9dqwbtrp4yxzbAupUmvH3au2\nYYa0Psbk6Hk7b9NbKBayVK8qMzplU/E147E8i5nPkowsv37Oclg0UlBQyO/++NQlHUVyodqunp4e\n7rrrLoqKinjggQfIycmZ7D8zZXR0dHDttdeeV9A9+OCD/PnPf57Wa7LIwBJ0M0UikSCRSJiGBxjP\nl1NVlWQyaX7NZrPhcDiw2+0oimIJuikmdWNNiehLNeLgrdDb28trr73G8ePHefWvr3Hw4AFGR0fx\n2PwEIoP4RBbL5Hp8ZJvvw4gMs5+dRBmjWFQQFiF9D0rY9AgV1UmEsBGhojdQCCFIyiQhApyhjf60\nxgCncOMSbrMKLUaEU+IIbuGlTluLA6e+/yeGGFGGCKjjR8Ne/BRSRjGVZIkc42i4iT46KVXmkqXl\nmceTY3IUOw4kkiQJcihgBe8yTQ0AQTnMAV7Vvy7yCckRQ+Tpe2Eu6WME3d2biuMACBvu2rOcIkgA\nDdWYXOntGnmGqBmgmzbRgl9kU62tHO9eNV2oEWw4kKgUUEYplRQY7lr9ue2hly7KxTzsOEwzBAic\nipukFidBnHLmU8vqjGnjID0cZg8SiQs3EcZFnkf14SWLHjqNurG1FIhSYjJqdN4G6KaDqIwa3asO\nvPjNPbk8iujiJCfFYbJFHlVaLRHC5hFqUBs2o2FAYx41lDHPnBpqUqOZegYNw0ZSSaQZUfT3hxsv\nY+4g92z5Brf80y04nU5sNtslN0G/UG2Xpmn8+7//Oz//+c954IEHeP/73z8jvwMuJOg+9rGPUVlZ\nSUVFBVu3bmXJkiXTfo2XOZagmyk+85nP0NfXx5o1a1i7di1r166lsLCQwcFBHnjgAW644QZWr16N\n3W5H0zRT6GmaNmGKZ4m8i0O6m2y278lNJ319ffz3f/83+/cf4EjzEQ4cPEAsFiPfWUh8VGVQdpOr\nFLJEu8KsrZJSEiRAM7uIE8eDlzHCpnPVrXrJJp9Behhl2GwMSO+77RdnCcohJGDDhk/x49dyzSq0\nYQY4ouxFSsliuRKJJCQCDJv5abrZQCVJKXOpoiajPzXVC5sj8nErHka0IXP53yn0Sq4IoxSLSmrk\nKnMHMCb1wNoj7DUy62xmCb1LesiWeeRSxGlxjJAcZpFYToWcz5iRJzdqC9CvdhMlYna85pBvijy3\n8BKQ/RxW9iClZKHUdwBTeXIxGcGOE5UkEo0FLGEO1RmTqxM0c4Y2skQuQigEtSFAdwA7NTcaGqOM\nUGabyyJ1OQ7hNKJGRhhhkBO0AFpa1IjXyCgsIY9ijom9jMgA1WI5RbLcyCjU69CG1H4kGgIFGzaz\n4i3VbDIsB2lRGpBSMk/WELGNMiwHzONrfdIbRaCwjPUUiXLzNUvKBKdp5SSHWbyolj8/9zQlJSXm\n/VFVVYAJ98hz95FnC+lTOa/XO0GMdnR0sHnzZpYtW8a9996L1+udics0r+V8gm50dBRFUfB6vezY\nsYNNmzZx/PjxGbjKyxpL0M0UUkoGBgZobGykoaGBhoYGWlpaGBkZYcOGDdx8881s2LABv98/YYci\nNcF7vRvYpfYpdSY51zV8rpvMYiLd3d3s27ePp556irZjJznVfpJkUiXPUYgz7CWpJTkrTpk9nD6R\nZZbJDzNIGy2mILFjZJMZeXcFlNJKMwN0M8e2kDnqIl0MiYApGFKl6wClzKGIClMwjMlRDim7GdNG\nqaKWpBJP2/8TOBUXcS2KisZiVjBXVJvPS5UqHRyng2M4cKIIhYgMp4XkZqFgo58uspQcarU1+ES2\nObkaYYhOThimBolDuPDgJVvmU0Q5WeRxlCb66aLCtoBSdS6jBBk1HJ6pyZV+sGyjisWUMtd0eOqB\n0q8RlqNUspCYbcyoUNNbKBzSSUzqgdJLuIJSoffxSimJMkYnbXTRhmLUrY3nyek7igoKnZzAp2RR\np63Fg49RI2pk1BbgrNqRcezt03LIp4QSKrDj5DgH6KadcqWKAq3MDGxO7cnp3bBJXHhYzMqMztu4\njNHEy0QYo0RUEhbBjD05l+rB7fQQ84XZ9pMfc9111014X6aSAdIF3mwUeReq7VJVlccff5w//OEP\nPPLII6xfv37G70mvJ+jOZf78+ezdu5f8/PwLPtbiomEJuplG0zR+85vfcM8997B69Wq+/OUvMzQ0\nRGNjI01NTYTDYRYtWmRO8pYvXz7pSP5CNzBr924iqT25SCRiuYbfJlJKurq62LdvH40Njfzxd3+k\nb6Afu2In116AM+wlS8slTow2pQW7tFMn15JNvuFcHSJkG6Zb7SB1X3IKJ1kyz4jjqMSOnTZa6KKN\nAqWEMq2KUTFiVqHpgsGmN1/gpJbVFFFuCoakTLCPVwgxQrGoICrCRqdparHeRZQIMSJUixVUyoUI\nIcyQ3F7OcIY2/fkicQoXTuHGp2VTQIkuaJT9SClZIteZpoZU8PKg2jdeyYWbQsooolwXUkKhQx7n\nlDhCjsjX3au2YUbSHJ6KsJGQMVx4WMG7Myq54jLGfnYySpAcUUCE0YwWCq+WRZggYYIsVJYxR1uE\nQBA1JXYPZ+kwqsZS7RpusimkmDIECi1KIwktTh1rcOExdhSHjena+I6ijyzKqaKIStzGxLZbdnBc\nHMBLFoWU6kYPbZCE0QAiNIgSxYufVbzXbA5J1aGdoJl+zrLhfX/L7/7w1JvqX32jIk9RlGk57bhQ\nbdexY8e48847+R7tu0MAACAASURBVNu//VvuvvtunE7nlF7PG6W9vZ1rr72W5ubmCV/r7e2lpEQP\ny25oaOATn/gE7e3t03yFlz2WoJtpmpqa+NKXvsTWrVt53/veN+Hrqe7P3bt309jYSHNzM5qmsXTp\nUlavXs26deuoqanJECKpG1j6FE9V1UlNF5eryEvfk5ssFsDi7SOl5PTp0zQ1NdHYsIddO3ex70AT\nCTVBpa8K95ifLJlLFnlEGeOw0khMi1HLKjz49T052xAj2mBa3p2Gj2zmsIhiys04Dj2brBk3Hoqo\nMLPJUqYGNIgRwYOPFbwnoxc2TIgWGgkTxI2HKGOGyPMYpoYCggwRoI9K20Lmq3UI9PqxIAGGlQH6\ntS4wUuG8it8QeXrPaJI4zUo9YS3IIrECt/QYIm/QMF6oRgVbklwKWcgycsg3heig7KVFNGCTdgpE\niT6p1EbMTl6hKowxihsvy7nSPFZOtVCc4BAh9F7dJAlzBy0lRIcZpJsO07ChoZrxNcNigIA2gA07\nAvCRTS5FFBvTRg2NZlHPsOxnvqjDIV1mnlxYC2VEjRRQSh1rcaftKKbiaySQqxRk1Ly50K9RepJ4\nil08/sTPuPLKKy/a+3OyD8JSyilbaTm3tuvcD9mJRIIf/vCH/OUvf2Hbtm0sW7bsbf8/Lxaf/vSn\neemllxgcHKSkpIR7772XeDyOEILPf/7zbN++nZ/+9Kc4HA48Hg+PPPLIRX2tLN4QlqCbDaRHlbyR\nx8bjcQ4ePGge1x4/fhy3283KlSvNSd7cuXMzPvmlworPvYGli7zLwXSRvidn1aRNL6lfaK2trbS0\ntNC0p4n613Zz+GgLsXgMgWAeNeRRSBZ5OIQzrbYqwHxRh006MjLX0murSphDHWuwi3ETS7/s5jCN\nCITeC6sFUFHNyjCHdDOCLliWcgW5otCcCgUJcJrjhAkZcRy2NFNDEcXMoYcOOsRx8kQhi7TlJIgb\nTQh6bVWUMexGLlwhpRQzx6wM06RGC430c5YKMR8HDmPaOGREhrhIakkSxCljLrWsNbt8NakRoJ9D\nNKCh4hE+wjJkijy36sOLnwF6SBCnjrUUi3JT5AUJ0CM6CcugeeztUXymEC2kjCF6OKbsx4WHam0F\nCeJmJVfqGlNCtJwqKphvtlAAnJDNdHKCAlGKU7gYYdBsANF3FJNEiFAm5lIjV5s7gKkGkOPsJyLC\nbNy4kUcefWRaJlVTJfLSa7vcbveEqdyBAwe46667uPHGG7n99tutD5gWbwVL0L0TSNWH7d27l8bG\nRhobG+ns7CQ3N9ec4qVMF5Pt46X/9U41XaTvrFg1adNL+tH2ZL/QNE3j0KFDHD58mIb6Bna9uovj\nJ47jsrsJjelVWXWsMYSQLtZ0MdRAP92UiApQmJB3l5BxYkSYJxYzXy4xxVBMRhmih6PsRyJRUDJC\njXPIN/LujhKVY9SIVZTIOUQMkZfKu4sTN/PussnLMDUMyB6OKk0o0sZCuZQYET1CRRswTA0OY4dQ\nsoClzGGRKWgAWmUzZzhBlsjDJmyMaEPmNTpUt5kBV6JUUK2txClc5o6i3kJx0OhP1Q0lbpsHt+oj\njyIKKOWEaCYg+5iv1FF+TgvFkNpHgoRh1xAUUm6KPLuwMyZHaVbqiWpjzGcJCRE1svz0gGKncJKQ\ncVRUqlnOHKpNkadJjTO00UYLTlzYhH2CEM0hn6BvgIV183ns54+xaNGi6XuzTsK5prQ3c59M/wDp\n8XgmOOaj0Sj3338/Bw4cYPv27TP+XC0uaSxB905FSsng4KA5xWtsbGRgYICKigpzird69erzmi7S\n41NSn1DtdvusWCh+M5zbrjHZzorF1PFWj7ZVVeXIkSM899xzdHV2Uf/ablpPtpLlysKt+umPdoOE\nFbybXDFe7ZSUSY7RRC9dePGTFImMXTKflk2SBIP0Umwrp1pdgUt4zMy1EQbpoNW8MzoVNx7Na+yS\nVeDFz2HRwKDso0qpGa9CUwJGJt+IuSdnx848ajJqqyIyzAHlNaLamGlqSIk83dTgMEwNKktZT4mo\nBDCDl8/QxmlazePnTCFagB0HnaIVFx5zj2/MiFAZtQ3TpXYAGqmaN5/Rn5q6xpOyhdO0Umgro0yt\n0oODDXdtVI6ZESoKdqpZTglzTCGqSY0DvEqAAUpEJXElyoiaKUQTRIkQYZFYxhy5yOyUHSNEgH5O\ncAhhg4cefohbbrll1t5j3siH4dRE2ul0TlrbVV9fzz333MNnP/tZ/umf/sm6L1m8XSxBdzkhpaSz\ns9Pcx0uZLhYuXMiaNWtYt27dWzZdzEZnbSoY2NqTm37S94UuVgRMMpnkyJEjvPTSS+x47nn6enpp\na28j25WLX8vBNuaiR+kgocWpZS1FlBl5d3pTwxlOMpCWd6fvaXnwazkUUU6cOG2iGSdu6uRanLjG\nq9CUIYbUPjPvzo2XYsqNpgbdnNAmD9FJG/lKMQVaKaNmL2zIMDUoJGQcN35W8W4zwBd0U8M+XiFM\n6LymhjGCjBJkgbKUuVo1AmEELw8zRC9dnDImjsJs10iZGuw4OaTsJqqNUcNq/GTrz824xvFqLYkL\nLxXMzxCiQ7KPFtGIDRvlcr6eJ2dEqDgVF4q0G5NHOyt5Lzki33wfxIjSSjP9dOHGQ5xYhhDVG0qy\n6Pac5INXf5CHH31oyquspoL0BIJ4PE7qd6jNZqOlpYWDBw+yZs0aqqqq+P73v093dzc//vGPqays\nnOErt3iHYAm6y52U6SIVnfJmTBfnxqcIITKmeDNlurD25GaO9Ino+faFLibxeJzDhw+zb98+/vz0\nn9m7p4mR0Ai57jx8ajbuiB8vWZwSRwjKITPvTiVpZKcFGBDdjGhDCEDBhk9kkSVzKaScfIp104TS\nQEyLUMMqFGxGN+kgQW3IqKDX8+4KKWMRyzLy7vplF4fZiwMXeUohIwwR1oJ6vpvwIDSFMCG8+FnK\nFRNMDa00M8qIua+WEqI+ozIswCBnOUmxrZxF6gq9Vss0NQxmdPL6yCbPqOPKQv//pPb45opqvNJv\nijzduTpuasihgGWsNx2oAGNylH28QpwY+aKYEMOmyHPhwaV5GWWEBDF9j48KhBDmRHRQ9HBGnsTv\n9fObp37DBz/4wSl7r0w1k9V2gX6P3bVrF7/4xS/Yv38/p06doqKigg996EOsW7eONWvWsHz5ctxu\n9ww/A4tLHEvQWWRyrumisbGR48eP43K5WLFihRmC/FZMF1Mt8qw9uZklPSR1JvuGo9Eohw4dYt++\nfezaWc+LL/2F/oF+stw55FOMO+onmzx8ZHOcA/TQQaltLvPUGiKE0/LuBvR+V2xIJGXMpYQ55FKI\nIhSSMskhUc+Q7GeOWIQQZOTduRQ3CaOpYS7VLGJ5xi7ZID200Kg3NQgPESPUONXU4CObXjpJoHe7\nFlE+3iQhAvTSyagMpZka/GQZ7RoFlDJEH0eVvdikncVylS5ijWnjiDpkhv/qnbxzqaSaLHLMa+yU\nJ2jjEFkiD5/IYoQhRrWgGQ6tqRpRwuSKIpbK8VDp1ET0KPsMU4iDBDFT5KWMFyoJOt2t/OMt/y/f\n/PY3ZzQ09+2iaRpjY2PA5LVdgUCAu+++G1VV+e53v0t3dzdNTU3s3buXvXv3MjIyYsV8WLxdLEFn\ncWGklITDYdN00dDQkGG6SIm8oqKiCXsimqZlTPGmwnQx3VMhi0w0TSMWi5FIJGbtRDQSidDc3Mze\nvXvZtXMXe/bspbPrNJrUyLUVUqbOM0WeIhS6ZDttohkPPubKxYyJkCGEBlFJYhcOEjKOgo1a1lBC\npSmEpJQcYS89dJIj8tGERsjIu3PbPDhVvakhSCCjqUEzmxqGaOMQ2oSmBj8FFFNAGcdEE4Oyj/lK\n7TmmhlTwctKwNAhKmEMRZeRTgiIUojJKs7KLUW2E+dShigQjyiAjagCQOIVuKEmSYAFLmE+d+Xpq\nUqOPLo7ShIKCU7jTTA16zIsbH/10YRMKS6TuHE5NG0MM06t0MqINUV5awX/86Y+sWLFiBt8Zb48L\n1XZJKXnmmWd4+OGH+eY3v8k111wz6c9GMpm0VkIs3i6WoLN4a5xrutizZw/9/f2Ul5eb+3irVq0i\nKyvrTTtrU/lMb0QUpPbkYGanQpcj6ULa4XDgcrkuKSE9NDTE4cOH2b9/P6/99TWamvbRO9CLUzgZ\njYfIp4TFrMjorQ3JYQ4qu1C1JGVUEbaNMKwOmjthiuogxhgSWMZ6CkUpMB6Qe5pWuunAhs2sDdND\nfPUoFAUbHeIoLuGhTluLjyyjqUEP8e1RTzMevOwiW+ZTQImZydcuj9IujpEnCqnQFhjBywEzxNcu\n7CRlEhs2allNcZoQTY9RKRAlJJUEQVWvUHMpbpyaG5UkowSZp1QzX6vDZsSvhAma7lqZJkRTIi+f\nYgopp99xhh5HO//32/+XW2+99ZIO8k6fSE82levp6eGuu+6ioKCAH/zgB+Tm5s7EZVpcPliCzuLi\nkTJdpPbx9u3bx+joqGm6SDVdnHsU+lZMF9ae3MySLqQn+2V2qRIMBvmf//kfWltbaWrcR1NTE0OB\nQfLdRSTGkgypfRSIUpbKKzLy7sIyxH52EiNKlshlVI4AEpfNg0v1kEWOfmTJCAuVZVRqCxEI04E6\nKProlaeRSGzYjbw7n+lA1dDMOrNaVpvByyHbEMPaIGNyFJuRd+cji3kspogK04E6IgdpFrtBSsqp\nYtQQogkSuBQ3ds1OhAgKghW8hzzDOZyqDOvgGN10YMdBkgQgjOfmJpdCBIIzog2vyGKJthY3PsIE\n9amcEqBHdpKQcd61/t386te/vKSNABeq7dI0jd/+9rf87Gc/4/777+cDH/iAdW+ymA4sQWcxtaSb\nLhobGzl48CBSSurq6sxJ3uLFiydM1s4VeclkEiGEGQegquqkcQAWU8vlKKSHh4fZv38/v//972k7\n1kbriROMBIfJdxXhivjQkpKz4hRZIpdabQ1e4Tfcnfri/wkOEUXfrxKA2+bFqXrIpYAiKujlNGc4\nSbFSkRZOHGDUFmBI62dUjpimhhzyKaSMYipwCy+a1DjCHnrpolJZgEfzETSCl6NyDIdw6pNU4uRS\nxArehVO4zOcWkWGaeIUYEXJEAWE5QtJo13BKNz6ZRYhhwoRYLFZQIRcAEGWMIAGG6M2oDPMYzy2P\nAr36Cw8d7qOEPEP8aNsPuf766y/p98uFartOnz7N5s2bqaur47777ruk9wItLjksQWcxvaR2Tpqb\nmzOaLlKmi5TIO9d0kUwmaWtro7S01PznmqZZdWbTRPqukMPhuOyF9MDAAPv372fv3r387re/p6e7\nh3g8Zoo8XzIHGzaOKwdRtQR1rKWAUlMIhUSAXnmGGPqU044dPzlGrVYFfpFthBPrpoZquYI4MTPv\nTo9CUdCQaKjMpZoqanCKcafkGXmSVg7iIxuv4mNEDpnByy48oAnCBMkWeSyR60z3alzGCBKgjUOE\nCaEgjOYKDy7pJkvmUUQ5Q/TSSRvFSgXV2opxw4ai5/kFtAEUFG6+eSPff+B75OTkTPq9vBRIj+GZ\n7IOMqqr8/Oc/5/e//z0PP/wwV1555WX982ExI1iCzmLmOdd00djYyOnTp8nJyWH16tXk5eXx5JNP\nUlFRwe9+9ztzmneuszaZTJoiLz0+5Z3QdDGTpKYSQoh31PHqxaavr4+mpiaa9jbx6l9fY3fjbiKx\nMUp9FfiiOfjUHLLJ0zPhRD0B2c98pY4irYJRhgkaQkiPUNENDQoKc6mmhDl4hR/AqEPbRUgbYS7V\nJGwxhuUAYS2EXThx4SYmIySIs5iVzGGR+f5PyiT9nOUY+wCw4yDGeN6dX8vBRzZnxSkSUq8MKxLl\nxGVU79YVw/TTRViGwDge9okssqU+OcylkBgR2r2HcRbZeOSHD3PVVVfN1EtyUUgmk4yNjZ3XcHX8\n+HE2b97Mhg0buPvuu824EguLacYSdFPF888/zx133IGmadxyyy187Wtfm/CY22+/nR07duDz+fjl\nL3/JqlWrZuBKZydSSpqamti0aRMtLS1cddVVdHR0TGi6eCumC0vkvTHOrS06t0zc4sJ0d3ezb98+\nGhoa2PVKPc2HDhIa1bthy8U8imQF2eThMgrrT8mjdIhj5IkiirUKQsowIwwS0oZRsGETNuIyhhMX\nK3iPGeALoEqVFhoYoIcckU+cGGMyZEahuFUfKgmGGaRCqWKhtgy7cJgO1BGGOMUR09TgFC7cwotP\ny6bQcMkeZS99nGWespgKbb7eJJEWhaKiuzXv+updfOWur0xL/+pUIaUkEomct7YrkUjwox/9iP/9\n3/9l27ZtLF++fIau1MICsATd1KBpGosXL+aFF16gvLycK664gqeeeora2lrzMTt27GDbtm0899xz\n7N69m02bNlFfXz+DVz272Lp1K/fffz+bN29m8+bNeDyeDNNFqulidHSUBQsWmNEpk5kuJgtBhtnf\ndDFTpB+vWnl+FxcpJa2trRw9epQ9jXt47ZVdHGppBinQEhrh5CiVLGQBdRnHpyNykIOiHiQUiXKC\nYohRbQSbsOFSPNhUJxGCCGwsYz15Qm9aSEWhnOEkPXQiEGiousgTHrxaFgWUYMPOcXEAB06WyHV4\n8RvBy0MElQADajemuxYX+UZ8SiGlKEIhKAO0+1qoWFjGg488yLve9a6Z+PZeNBKJBJFI5LzrBQcP\nHuQrX/kKN9xwA5s2bbLc9RazAUvQTQX19fXce++97NixA4D7778fIUTGlO7WW2/lAx/4AJ/85CcB\nqKur46WXXqKkpGRGrnm2sXPnThYsWEB5efnrPk7TtAlNF6qqsmTJkjdluphM5F2OE6lUDImiKLjd\nbut4dRqQUnL69Gn+9Kc/ceb0GRp37+Hw0cM4FAc5Sj5j4TEGZR8VynyqtRXYhM3894IEaGYXceJ4\n8DHGKHZhx21M5HIpZIAeggyaLRkSaUShDDGsDNCndYFxvOtT/Hi1bAoooYgKNDSaxWsE5TDVYjle\nmWUGLw9rg8RlHKfiwuG28YMHf8DGjRsv6Z8ZTdOIRCJomjZpXWA0GuWBBx5g3759bN++nerq6hm6\nUguLCZz3B8/6uPE26OrqYs6cOeafKysraWhoeN3HVFRU0NXVZQk6g/e9731v6HGKolBbW0ttbS0b\nN24EIBaLcejQIRoaGvjJT37CsWPHcDqdGU0X8+bNw+FwmMcoqTqz1BQvFosxNjZ22Zgu0n+RpZa+\nLaYHIQRz587lC1/4gtl963A4aG9vp6mpiSd//SRnO7s52d7GqG2IbJGPc8yLhkanaMUvclijrcUr\n/HomnAwSVAN0cJxBepFo2LDTQwfDDJq5dSMMMih7KVRKqdZWECdOSAsQtAU4qR2mRTZiw46UGvmU\nYpdOssknn2LQYFD2cMLdzIrVy3nsZ//C/PnzZ/pb+ZY5t7bL6/VOmPLv3r2bb3zjG/zjP/4j3//+\n962JvsUlgyXoLC5ZXC6XKdz+z//5P6bpoqmpicbGRr7zne9kmC5Sjy0uLs7Y+TnXdJFIJDLqzFLG\ni0t5H+/curRzf5FZTD2p4z273Y7f7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CzeBue9IVkLCxYW08j73vc+Ojo6zvv1p59+mo0b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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "ax = fig.gca(projection='3d')\n", + "X, Y = numpy.meshgrid(x, y)\n", + "ax.plot_surface(X, Y, v, cmap=cm.viridis, rstride=2, cstride=2)\n", + "ax.set_xlabel('$x$')\n", + "ax.set_ylabel('$y$');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The video lesson that walks you through the details for Steps 5 to 8 is **Video Lesson 6** on You Tube:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('tUg_dE3NXoY')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" } - ] + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/lessons/09_Step_7.ipynb b/lessons/09_Step_7.ipynb index bf54ea98..d929c392 100644 --- a/lessons/09_Step_7.ipynb +++ b/lessons/09_Step_7.ipynb @@ -1,384 +1,438 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You see where this is going ... we'll do 2D diffusion now and next we will combine steps 6 and 7 to solve Burgers' equation. So make sure your previous steps work well before continuing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 7: 2D Diffusion\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And here is the 2D-diffusion equation:\n", - "\n", - "$$\\frac{\\partial u}{\\partial t} = \\nu \\frac{\\partial ^2 u}{\\partial x^2} + \\nu \\frac{\\partial ^2 u}{\\partial y^2}$$\n", - "\n", - "You will recall that we came up with a method for discretizing second order derivatives in Step 3, when investigating 1-D diffusion. We are going to use the same scheme here, with our forward difference in time and two second-order derivatives. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\\frac{u_{i,j}^{n+1} - u_{i,j}^n}{\\Delta t} = \\nu \\frac{u_{i+1,j}^n - 2 u_{i,j}^n + u_{i-1,j}^n}{\\Delta x^2} + \\nu \\frac{u_{i,j+1}^n-2 u_{i,j}^n + u_{i,j-1}^n}{\\Delta y^2}$$\n", - "\n", - "Once again, we reorganize the discretized equation and solve for $u_{i,j}^{n+1}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$u_{i,j}^{n+1} = u_{i,j}^n + \\frac{\\nu \\Delta t}{\\Delta x^2}(u_{i+1,j}^n - 2 u_{i,j}^n + u_{i-1,j}^n) + \\frac{\\nu \\Delta t}{\\Delta y^2}(u_{i,j+1}^n-2 u_{i,j}^n + u_{i,j-1}^n)$$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.mplot3d import Axes3D ##library for 3d projection plots\n", - "from matplotlib import cm ##cm = \"colormap\" for changing the 3d plot color palette\n", - "\n", - "###variable declarations\n", - "nx = 31\n", - "ny = 31\n", - "nt = 17\n", - "nu=.05\n", - "dx = 2.0/(nx-1)\n", - "dy = 2.0/(ny-1)\n", - "sigma = .25\n", - "dt = sigma*dx*dy/nu\n", - "\n", - "x = np.linspace(0,2,nx)\n", - "y = np.linspace(0,2,ny)\n", - "\n", - "u = np.ones((ny,nx)) ##create a 1xn vector of 1's\n", - "un = np.ones((ny,nx)) ##\n", - "\n", - "###Assign initial conditions\n", - "\n", - "u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2 ##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.gca(projection='3d')\n", - "X,Y = np.meshgrid(x,y)\n", - "surf = ax.plot_surface(X,Y,u[:], rstride=1, cstride=1, cmap=cm.coolwarm,\n", - " linewidth=0, antialiased=False)\n", - "plt.show()\n", - "ax.set_xlim(1,2)\n", - "ax.set_ylim(1,2)\n", - "ax.set_zlim(1,2.5)\n", - "#ax.zaxis.set_major_locator(LinearLocator(5))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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75rzzzsOyZcvgcDjQ1NSEu+++G6NHj0743nnz5qGuri7l+3Mch/vvvx9XXXWV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\nZZclPM47HA7Vjv3FF19g/fr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- }, - { - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "(1, 2.5)" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$u_{i,j}^{n+1} = u_{i,j}^n + \\frac{\\nu \\Delta t}{\\Delta x^2}(u_{i+1,j}^n - 2 u_{i,j}^n + u_{i-1,j}^n) + \\frac{\\nu \\Delta t}{\\Delta y^2}(u_{i,j+1}^n-2 u_{i,j}^n + u_{i,j-1}^n)$$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "###Run through nt timesteps\n", - "def diffuse(nt):\n", - " u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2\n", - " \n", - " for n in range(nt+1): \n", - " un = u.copy()\n", - " u[1:-1,1:-1]=un[1:-1,1:-1]+nu*dt/dx**2*(un[2:,1:-1]-2*un[1:-1,1:-1]+un[0:-2,1:-1])+nu*dt/dy**2*(un[1:-1,2:]-2*un[1:-1,1:-1]+un[1:-1,0:-2])\n", - " u[0,:]=1\n", - " u[-1,:]=1\n", - " u[:,0]=1\n", - " u[:,-1]=1\n", - "\n", - " \n", - " fig = plt.figure()\n", - " ax = fig.gca(projection='3d')\n", - " surf = ax.plot_surface(X,Y,u[:], rstride=1, cstride=1, cmap=cm.coolwarm,\n", - " linewidth=0, antialiased=True)\n", - " ax.set_zlim(1,2.5)\n", - " plt.show()\n", - " \n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "diffuse(10)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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RiVQrbOXDID3C1z8x+NeTfy4OkhFQ/XHznQZb4Xd/93f53ve+x8rKCvv27ePP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t8EG4AjluMH7baznyv74H89D+Lfe9n+gkT9yqVHW3LUo1IiDWdkULO2EEFCbd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\nm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- } - ], - "prompt_number": 12 - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You see where this is going ... we'll do 2D diffusion now and next we will combine steps 6 and 7 to solve Burgers' equation. So make sure your previous steps work well before continuing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 7: 2D Diffusion\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here is the 2D-diffusion equation:\n", + "\n", + "$$\\frac{\\partial u}{\\partial t} = \\nu \\frac{\\partial ^2 u}{\\partial x^2} + \\nu \\frac{\\partial ^2 u}{\\partial y^2}$$\n", + "\n", + "You will recall that we came up with a method for discretizing second order derivatives in Step 3, when investigating 1-D diffusion. We are going to use the same scheme here, with our forward difference in time and two second-order derivatives. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\\frac{u_{i,j}^{n+1} - u_{i,j}^n}{\\Delta t} = \\nu \\frac{u_{i+1,j}^n - 2 u_{i,j}^n + u_{i-1,j}^n}{\\Delta x^2} + \\nu \\frac{u_{i,j+1}^n-2 u_{i,j}^n + u_{i,j-1}^n}{\\Delta y^2}$$\n", + "\n", + "Once again, we reorganize the discretized equation and solve for $u_{i,j}^{n+1}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{split}\n", + "u_{i,j}^{n+1} = u_{i,j}^n &+ \\frac{\\nu \\Delta t}{\\Delta x^2}(u_{i+1,j}^n - 2 u_{i,j}^n + u_{i-1,j}^n) \\\\\n", + "&+ \\frac{\\nu \\Delta t}{\\Delta y^2}(u_{i,j+1}^n-2 u_{i,j}^n + u_{i,j-1}^n)\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy\n", + "from matplotlib import pyplot, cm\n", + "from mpl_toolkits.mplot3d import Axes3D ##library for 3d projection plots\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "###variable declarations\n", + "nx = 31\n", + "ny = 31\n", + "nt = 17\n", + "nu = .05\n", + "dx = 2 / (nx - 1)\n", + "dy = 2 / (ny - 1)\n", + "sigma = .25\n", + "dt = sigma * dx * dy / nu\n", + "\n", + "x = numpy.linspace(0, 2, nx)\n", + "y = numpy.linspace(0, 2, ny)\n", + "\n", + "u = numpy.ones((ny, nx)) # create a 1xn vector of 1's\n", + "un = numpy.ones((ny, nx))\n", + "\n", + "###Assign initial conditions\n", + "# set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", + "u[int(.5 / dy):int(1 / dy + 1),int(.5 / dx):int(1 / dx + 1)] = 2 " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "diffuse(50)" - ], - "language": "python", + "data": { + "image/png": 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2m9hmQ86iVLoyNqPvQ6ZV1Y/Szyfbop1QaAOBgO6bGGiNqUUXgGZ3PGI8kq6L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\n8corr+Dbb79FXV0d7rnnHtx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g1o2aFrbDCChMuq2kQP7xH/9x3d+Pj4/z7//+75HMrRX0LekKITAMg1gsViv6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\n" - } - ], - "prompt_number": 14 - }, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure()\n", + "ax = fig.gca(projection='3d')\n", + "X, Y = numpy.meshgrid(x, y)\n", + "surf = ax.plot_surface(X, Y, u, rstride=1, cstride=1, cmap=cm.viridis,\n", + " linewidth=0, antialiased=False)\n", + "\n", + "ax.set_xlim(0, 2)\n", + "ax.set_ylim(0, 2)\n", + "ax.set_zlim(1, 2.5)\n", + "\n", + "ax.set_xlabel('$x$')\n", + "ax.set_ylabel('$y$');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{split}\n", + "u_{i,j}^{n+1} = u_{i,j}^n &+ \\frac{\\nu \\Delta t}{\\Delta x^2}(u_{i+1,j}^n - 2 u_{i,j}^n + u_{i-1,j}^n) \\\\\n", + "&+ \\frac{\\nu \\Delta t}{\\Delta y^2}(u_{i,j+1}^n-2 u_{i,j}^n + u_{i,j-1}^n)\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "###Run through nt timesteps\n", + "def diffuse(nt):\n", + " u[int(.5 / dy):int(1 / dy + 1),int(.5 / dx):int(1 / dx + 1)] = 2 \n", + " \n", + " for n in range(nt + 1): \n", + " un = u.copy()\n", + " u[1:-1, 1:-1] = (un[1:-1,1:-1] + \n", + " nu * dt / dx**2 * \n", + " (un[1:-1, 2:] - 2 * un[1:-1, 1:-1] + un[1:-1, 0:-2]) +\n", + " nu * dt / dy**2 * \n", + " (un[2:,1: -1] - 2 * un[1:-1, 1:-1] + un[0:-2, 1:-1]))\n", + " u[0, :] = 1\n", + " u[-1, :] = 1\n", + " u[:, 0] = 1\n", + " u[:, -1] = 1\n", + "\n", + " \n", + " fig = pyplot.figure()\n", + " ax = fig.gca(projection='3d')\n", + " surf = ax.plot_surface(X, Y, u[:], rstride=1, cstride=1, cmap=cm.viridis,\n", + " linewidth=0, antialiased=True)\n", + " ax.set_zlim(1, 2.5)\n", + " ax.set_xlabel('$x$')\n", + " ax.set_ylabel('$y$');\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "heading", - "level": 2, + "data": { + "image/png": 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a204LShXCIdbNiha2QwjITbrN6gLZCC1LuoIgoKoqHo8nF/RpBNxbb1EUCYfD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BpNlGRttks6/4Q/vEnXEO6IG+lr75c63xnZWSDpcZ3ZDhr3H8eRbSfBEY8owoBnCIPFQG/bwKwd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\nteFYnembb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NhCSWeQKofzuT3UazF6X2GsrprZ254p0en04a744lXQCv15svpWw2nFu2+3w+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mNzWultCRAamzptdmsw6/EUu8nw2KdXpCeXUwa7h3HmOWh6v5XJ0RBqjRpvmQebIw1R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\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "The video lesson that walks you through the details for Steps 5 to 8 is **Video Lesson 6** on You Tube:" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "diffuse(14)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('tUg_dE3NXoY')" - ], - "language": "python", + "data": { + "image/png": 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KZ3oNXfe6Z45huWMY47WcHLHjNOZMAjYykt4PoN1NtUZ2rea/26jvczsYV2uZWbp/reCl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\nWrKRApF+fu7YuAuNJLK9enLtbXYj4RE7rXKdTrXJ3npvsPIEbLh9Fo6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Z7z9XvKTkhWaz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\n/GNT0vcwO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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "display_data" + } + ], + "source": [ + "diffuse(50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The video lesson that walks you through the details for Steps 5 to 8 is **Video Lesson 6** on You Tube:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('tUg_dE3NXoY')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] } - ] + ], + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } diff --git a/lessons/10_Step_8.ipynb b/lessons/10_Step_8.ipynb index df958bfe..8cf72895 100644 --- a/lessons/10_Step_8.ipynb +++ b/lessons/10_Step_8.ipynb @@ -1,385 +1,444 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This will be a milestone! We now get to Step 8: Burgers' equation. We can learn so much more from this equation. It plays a very important role in fluid mechanics, because it contains the full convective nonlinearity of the flow equations, and at the same time there are many known analytical solutions.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 8: Burgers' Equation in 2D\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remember, Burgers' equation can generate discontinuous solutions from an initial condition that is smooth, i.e., can develop \"shocks.\" We want to see this in two dimensions now!\n", - "\n", - "Here is our coupled set of PDEs:\n", - "\n", - "$$\n", - "\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} + v \\frac{\\partial u}{\\partial y} = \\nu \\; \\left(\\frac{\\partial ^2 u}{\\partial x^2} + \\frac{\\partial ^2 u}{\\partial y^2}\\right)$$\n", - "\n", - "$$\n", - "\\frac{\\partial v}{\\partial t} + u \\frac{\\partial v}{\\partial x} + v \\frac{\\partial v}{\\partial y} = \\nu \\; \\left(\\frac{\\partial ^2 v}{\\partial x^2} + \\frac{\\partial ^2 v}{\\partial y^2}\\right)$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We know how to discretize each term: we've already done it before!\n", - "\n", - "$$\n", - "\\frac{u_{i,j}^{n+1} - u_{i,j}^n}{\\Delta t} + u_{i,j}^n \\frac{u_{i,j}^n-u_{i-1,j}^n}{\\Delta x} + v_{i,j}^n \\frac{u_{i,j}^n - u_{i,j-1}^n}{\\Delta y} = $$\n", - "$$\\qquad \\nu \\bigg( \\frac{u_{i+1,j}^n - 2u_{i,j}^n+u_{i-1,j}^n}{\\Delta x^2} + \\frac{u_{i,j+1}^n - 2u_{i,j}^n + u_{i,j-1}^n}{\\Delta y^2} \\bigg)$$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\n", - "\\frac{v_{i,j}^{n+1} - v_{i,j}^n}{\\Delta t} + u_{i,j}^n \\frac{v_{i,j}^n-v_{i-1,j}^n}{\\Delta x} + v_{i,j}^n \\frac{v_{i,j}^n - v_{i,j-1}^n}{\\Delta y} = $$\n", - "$$\\nu \\bigg( \\frac{v_{i+1,j}^n - 2v_{i,j}^n+v_{i-1,j}^n}{\\Delta x^2} + \\frac{v_{i,j+1}^n - 2v_{i,j}^n + v_{i,j-1}^n}{\\Delta y^2} \\bigg)$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And now, we will rearrange each of these equations for the only unknown: the two components $u,v$ of the solution at the next time step:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$u_{i,j}^{n+1} = u_{i,j}^n - \\frac{\\Delta t}{\\Delta x} u_{i,j}^n (u_{i,j}^n - u_{i-1,j}^n) - \\frac{\\Delta t}{\\Delta y} v_{i,j}^n (u_{i,j}^n - u_{i,j-1}^n)+ $$\n", - "$$ \\frac{\\nu \\Delta t}{\\Delta x^2}(u_{i+1,j}^n-2u_{i,j}^n+u_{i-1,j}^n) + \\frac{\\nu \\Delta t}{\\Delta y^2} (u_{i,j+1}^n - 2u_{i,j}^n + u_{i,j+1}^n)$$ " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$v_{i,j}^{n+1} = v_{i,j}^n - \\frac{\\Delta t}{\\Delta x} u_{i,j}^n (v_{i,j}^n - v_{i-1,j}^n) - \\frac{\\Delta t}{\\Delta y} v_{i,j}^n (v_{i,j}^n - v_{i,j-1}^n)+ $$\n", - "$$ \\frac{\\nu \\Delta t}{\\Delta x^2}(v_{i+1,j}^n-2v_{i,j}^n+v_{i-1,j}^n) + \\frac{\\nu \\Delta t}{\\Delta y^2} (v_{i,j+1}^n - 2v_{i,j}^n + v_{i,j+1}^n)$$ " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "###variable declarations\n", - "nx = 41\n", - "ny = 41\n", - "nt = 120\n", - "c = 1\n", - "dx = 2.0/(nx-1)\n", - "dy = 2.0/(ny-1)\n", - "sigma = .0009\n", - "nu = 0.01\n", - "dt = sigma*dx*dy/nu\n", - "\n", - "\n", - "x = np.linspace(0,2,nx)\n", - "y = np.linspace(0,2,ny)\n", - "\n", - "u = np.ones((ny,nx)) ##create a 1xn vector of 1's\n", - "v = np.ones((ny,nx))\n", - "un = np.ones((ny,nx)) ##\n", - "vn = np.ones((ny,nx))\n", - "comb = np.ones((ny,nx))\n", - "\n", - "###Assign initial conditions\n", - "\n", - "u[.5/dy:1/dy+1,.5/dx:1/dx+1]=2 ##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", - "v[.5/dy:1/dy+1,.5/dx:1/dx+1]=2 ##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", - "\n", - "###(plot ICs)\n", - "fig = plt.figure(figsize=(11,7), dpi=100)\n", - "ax = fig.gca(projection='3d')\n", - "X,Y = np.meshgrid(x,y)\n", - "wire1 = ax.plot_wireframe(X,Y,u[:], cmap=cm.coolwarm)\n", - "wire2 = ax.plot_wireframe(X,Y,v[:], cmap=cm.coolwarm)\n", - "#ax.set_xlim(1,2)\n", - "#ax.set_ylim(1,2)\n", - "#ax.set_zlim(1,5)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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QqODWfL2ZRCIB0zTh8XjS/r5nzx6ceeaZ+PWvf41PP/0Un376KdauXYuWlhbs2bOnKFG3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\ntXFRqOsSHn7YhZ//XMOcOTJ27+bC5Ka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atk+0suR5PZ5UpqnYCFVVhWVxN2dDg4y77jJh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\nnb71VohmzUz69JFl5eYqDptn7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05l8GDJ3P3rXyM8+KDOokUyl1d4Ns2usmUt+vUTkNao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RvFi5PIv2dPV5H/3LkGLFkionBhFbNn2/DoEY+zZ4VX5et6lZjdDrx8SQGqu3bZ8l1nnT7N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\n/v1ZklRqXiy5fp3t8K1b54bKSgU4j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- } - ], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for n in range(nt+1): ##loop across number of time steps\n", - " un = u.copy()\n", - " vn = v.copy()\n", - "\n", - " u[1:-1,1:-1] = un[1:-1,1:-1] - dt/dx*un[1:-1,1:-1]*(un[1:-1,1:-1]-un[0:-2,1:-1])-dt/dy*vn[1:-1,1:-1]* \\\n", - " (un[1:-1,1:-1]-un[1:-1,0:-2])+nu*dt/dx**2*(un[2:,1:-1]-2*un[1:-1,1:-1]+un[0:-2,1:-1])+ \\\n", - " nu*dt/dy**2*(un[1:-1,2:]-2*un[1:-1,1:-1]+un[1:-1,0:-2])\n", - " \n", - " v[1:-1,1:-1] = vn[1:-1,1:-1] - dt/dx*un[1:-1,1:-1]*(vn[1:-1,1:-1]-vn[0:-2,1:-1])-dt/dy*vn[1:-1,1:-1]* \\\n", - " (vn[1:-1,1:-1]-vn[1:-1,0:-2])+nu*dt/dx**2*(vn[2:,1:-1]-2*vn[1:-1,1:-1]+vn[0:-2,1:-1])+ \\\n", - " nu*dt/dy**2*(vn[1:-1,2:]-2*vn[1:-1,1:-1]+vn[1:-1,0:-2])\n", - " \n", - " u[0,:] = 1\n", - " u[-1,:] = 1\n", - " u[:,0] = 1\n", - " u[:,-1] = 1\n", - " \n", - " v[0,:] = 1\n", - " v[-1,:] = 1\n", - " v[:,0] = 1\n", - " v[:,-1] = 1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig = plt.figure(figsize=(11,7), dpi=100)\n", - "ax = fig.gca(projection='3d')\n", - "X,Y = np.meshgrid(x,y)\n", - "wire1 = ax.plot_wireframe(X,Y,u[:])\n", - "wire2 = ax.plot_wireframe(X,Y,v[:])\n", - "#ax.set_xlim(1,2)\n", - "#ax.set_ylim(1,2)\n", - "#ax.set_zlim(1,5)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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d0ymWOOJ9VpuamkpuYXKaoEs3XrsJOafNaT6sXi1g6FAm5nbfXcc334SwebOE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bMK1o167MbobDKlXIzsftJjBqNssB2d6vv06/pQUQDDRWDN8OGSIoiRfFxarjPwAlIWlL2BmN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\nzv8DuT9vU6DHaNEAAAAASUVORK5CYII=\n" - } - ], - "prompt_number": 19 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Learn More" - ] - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will be a milestone! We now get to Step 8: Burgers' equation. We can learn so much more from this equation. It plays a very important role in fluid mechanics, because it contains the full convective nonlinearity of the flow equations, and at the same time there are many known analytical solutions.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 8: Burgers' Equation in 2D\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember, Burgers' equation can generate discontinuous solutions from an initial condition that is smooth, i.e., can develop \"shocks.\" We want to see this in two dimensions now!\n", + "\n", + "Here is our coupled set of PDEs:\n", + "\n", + "$$\n", + "\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} + v \\frac{\\partial u}{\\partial y} = \\nu \\; \\left(\\frac{\\partial ^2 u}{\\partial x^2} + \\frac{\\partial ^2 u}{\\partial y^2}\\right)$$\n", + "\n", + "$$\n", + "\\frac{\\partial v}{\\partial t} + u \\frac{\\partial v}{\\partial x} + v \\frac{\\partial v}{\\partial y} = \\nu \\; \\left(\\frac{\\partial ^2 v}{\\partial x^2} + \\frac{\\partial ^2 v}{\\partial y^2}\\right)$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know how to discretize each term: we've already done it before!\n", + "\n", + "$$\n", + "\\begin{split}\n", + "& \\frac{u_{i,j}^{n+1} - u_{i,j}^n}{\\Delta t} + u_{i,j}^n \\frac{u_{i,j}^n-u_{i-1,j}^n}{\\Delta x} + v_{i,j}^n \\frac{u_{i,j}^n - u_{i,j-1}^n}{\\Delta y} = \\\\\n", + "& \\qquad \\nu \\left( \\frac{u_{i+1,j}^n - 2u_{i,j}^n+u_{i-1,j}^n}{\\Delta x^2} + \\frac{u_{i,j+1}^n - 2u_{i,j}^n + u_{i,j-1}^n}{\\Delta y^2} \\right)\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{split}\n", + "& \\frac{v_{i,j}^{n+1} - v_{i,j}^n}{\\Delta t} + u_{i,j}^n \\frac{v_{i,j}^n-v_{i-1,j}^n}{\\Delta x} + v_{i,j}^n \\frac{v_{i,j}^n - v_{i,j-1}^n}{\\Delta y} = \\\\\n", + "& \\qquad \\nu \\left( \\frac{v_{i+1,j}^n - 2v_{i,j}^n+v_{i-1,j}^n}{\\Delta x^2} + \\frac{v_{i,j+1}^n - 2v_{i,j}^n + v_{i,j-1}^n}{\\Delta y^2} \\right)\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now, we will rearrange each of these equations for the only unknown: the two components $u,v$ of the solution at the next time step:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{split}\n", + "u_{i,j}^{n+1} = & u_{i,j}^n - \\frac{\\Delta t}{\\Delta x} u_{i,j}^n (u_{i,j}^n - u_{i-1,j}^n) - \\frac{\\Delta t}{\\Delta y} v_{i,j}^n (u_{i,j}^n - u_{i,j-1}^n) \\\\\n", + "&+ \\frac{\\nu \\Delta t}{\\Delta x^2}(u_{i+1,j}^n-2u_{i,j}^n+u_{i-1,j}^n) + \\frac{\\nu \\Delta t}{\\Delta y^2} (u_{i,j+1}^n - 2u_{i,j}^n + u_{i,j-1}^n)\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{split}\n", + "v_{i,j}^{n+1} = & v_{i,j}^n - \\frac{\\Delta t}{\\Delta x} u_{i,j}^n (v_{i,j}^n - v_{i-1,j}^n) - \\frac{\\Delta t}{\\Delta y} v_{i,j}^n (v_{i,j}^n - v_{i,j-1}^n) \\\\\n", + "&+ \\frac{\\nu \\Delta t}{\\Delta x^2}(v_{i+1,j}^n-2v_{i,j}^n+v_{i-1,j}^n) + \\frac{\\nu \\Delta t}{\\Delta y^2} (v_{i,j+1}^n - 2v_{i,j}^n + v_{i,j-1}^n)\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy\n", + "from matplotlib import pyplot, cm\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "###variable declarations\n", + "nx = 41\n", + "ny = 41\n", + "nt = 120\n", + "c = 1\n", + "dx = 2 / (nx - 1)\n", + "dy = 2 / (ny - 1)\n", + "sigma = .0009\n", + "nu = 0.01\n", + "dt = sigma * dx * dy / nu\n", + "\n", + "\n", + "x = numpy.linspace(0, 2, nx)\n", + "y = numpy.linspace(0, 2, ny)\n", + "\n", + "u = numpy.ones((ny, nx)) # create a 1xn vector of 1's\n", + "v = numpy.ones((ny, nx))\n", + "un = numpy.ones((ny, nx)) \n", + "vn = numpy.ones((ny, nx))\n", + "comb = numpy.ones((ny, nx))\n", + "\n", + "###Assign initial conditions\n", + "\n", + "##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", + "u[int(.5 / dy):int(1 / dy + 1),int(.5 / dx):int(1 / dx + 1)] = 2 \n", + "##set hat function I.C. : u(.5<=x<=1 && .5<=y<=1 ) is 2\n", + "v[int(.5 / dy):int(1 / dy + 1),int(.5 / dx):int(1 / dx + 1)] = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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pU3r03oqgy0wwGMTn8xVVOUd/0p3Qc356PJ5BbcYoNjLVGoZCIc466yxeeuml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ElTjAindWpSYZxptrPXVYrOc4PLnOUdECATqytFd3IP21zxxGSdpsENcZkjvI6ExIyrFD3Iy3Wl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LBpqlyH+OICE2sec6F7Ikx3iFPDlZc5OUWhk7A9aBiSUF/B42st3BNqhVTpwlIWSmXHOjdC6q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\nQRAEQRCEAY4IOkEQBEEQhAHO/wcZcLwo2sSHawAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "The video lesson that walks you through the details for Steps 5 to 8 is **Video Lesson 6** on You Tube:" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "###(plot ICs)\n", + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "ax = fig.gca(projection='3d')\n", + "X, Y = numpy.meshgrid(x, y)\n", + "ax.plot_surface(X, Y, u[:], cmap=cm.viridis, rstride=1, cstride=1)\n", + "ax.plot_surface(X, Y, v[:], cmap=cm.viridis, rstride=1, cstride=1)\n", + "ax.set_xlabel('$x$')\n", + "ax.set_ylabel('$y$');" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "for n in range(nt + 1): ##loop across number of time steps\n", + " un = u.copy()\n", + " vn = v.copy()\n", + "\n", + " u[1:-1, 1:-1] = (un[1:-1, 1:-1] -\n", + " dt / dx * un[1:-1, 1:-1] * \n", + " (un[1:-1, 1:-1] - un[1:-1, 0:-2]) - \n", + " dt / dy * vn[1:-1, 1:-1] * \n", + " (un[1:-1, 1:-1] - un[0:-2, 1:-1]) + \n", + " nu * dt / dx**2 * \n", + " (un[1:-1,2:] - 2 * un[1:-1, 1:-1] + un[1:-1, 0:-2]) + \n", + " nu * dt / dy**2 * \n", + " (un[2:, 1:-1] - 2 * un[1:-1, 1:-1] + un[0:-2, 1:-1]))\n", + " \n", + " v[1:-1, 1:-1] = (vn[1:-1, 1:-1] - \n", + " dt / dx * un[1:-1, 1:-1] *\n", + " (vn[1:-1, 1:-1] - vn[1:-1, 0:-2]) -\n", + " dt / dy * vn[1:-1, 1:-1] * \n", + " (vn[1:-1, 1:-1] - vn[0:-2, 1:-1]) + \n", + " nu * dt / dx**2 * \n", + " (vn[1:-1, 2:] - 2 * vn[1:-1, 1:-1] + vn[1:-1, 0:-2]) +\n", + " nu * dt / dy**2 *\n", + " (vn[2:, 1:-1] - 2 * vn[1:-1, 1:-1] + vn[0:-2, 1:-1]))\n", + " \n", + " u[0, :] = 1\n", + " u[-1, :] = 1\n", + " u[:, 0] = 1\n", + " u[:, -1] = 1\n", + " \n", + " v[0, :] = 1\n", + " v[-1, :] = 1\n", + " v[:, 0] = 1\n", + " v[:, -1] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('tUg_dE3NXoY')" - ], - "language": "python", + "data": { + "image/png": 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25k709CbCES76Focbru1PNnnigQZceHUPZh+/ByuWjobLlXgw/fbOOgiCghde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\nwHNzc/zUT36SscdjOJCb6ApzbKtKrAR9ROlmmSnWWcRHgEt8iIgWt0ut9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Sa5g4EDgYWf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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "" - ] - } - ], - "prompt_number": 2 - }, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "ax = fig.gca(projection='3d')\n", + "X, Y = numpy.meshgrid(x, y)\n", + "ax.plot_surface(X, Y, u, cmap=cm.viridis, rstride=1, cstride=1)\n", + "ax.plot_surface(X, Y, v, cmap=cm.viridis, rstride=1, cstride=1)\n", + "ax.set_xlabel('$x$')\n", + "ax.set_ylabel('$y$');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The video lesson that walks you through the details for Steps 5 to 8 is **Video Lesson 6** on You Tube:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('tUg_dE3NXoY')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] } - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (CFDPython)", + "language": "python", + "name": "py36-cfdpython" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } diff --git a/lessons/11_Defining_Function_in_Python.ipynb b/lessons/11_Defining_Function_in_Python.ipynb index 7d6af4fa..ed3736b8 100644 --- a/lessons/11_Defining_Function_in_Python.ipynb +++ b/lessons/11_Defining_Function_in_Python.ipynb @@ -1,347 +1,365 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This lesson complements the first interactive module of the online [CFD Python](https://bitbucket.org/cfdpython/cfd-python-class) class, by Prof. Lorena A. Barba, called **12 Steps to Navier-Stokes.** The interactive module starts with simple exercises in 1D that at first use little of the power of Python. We now present some new ways of doing the same things that are more efficient and produce prettier code.\n", - "\n", - "This lesson was written with BU graduate student Gilbert Forsyth.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Defining Functions in Python \n", - "----" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In steps 1 through 8, we wrote Python code that is meant to run from top to bottom. We were able to reuse code (to great effect!) by copying and pasting, to incrementally build a solver for the Burgers' equation. But moving forward there are more efficient ways to write our Python codes. In this lesson, we are going to introduce *function definitions*, which will allow us more flexibility in reusing and also in organizing our code. \n", - "\n", - "We'll begin with a trivial example: a function which adds two numbers. \n", - "\n", - "To create a function in Python, we start with the following:\n", - "\n", - " def simpleadd(a,b):\n", - "\n", - "This statement creates a function called `simpleadd` which takes two inputs, `a` and `b`. Let's execute this definition code." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def simpleadd(a, b):\n", - " return a+b" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `return` statement tells Python what data to return in response to being called. Now we can try calling our `simpleadd` function:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "simpleadd(3, 4)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "7" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course, there can be much more happening between the `def` line and the `return` line. In this way, one can build code in a *modular* way. Let's try a function which returns the `n`-th number in the Fibonacci sequence. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def fibonacci(n):\n", - " a, b = 0, 1\n", - " for i in range(n):\n", - " a, b = b, a + b\n", - " return a\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fibonacci(7)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "13" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once defined, the function `fibonacci` can be called like any of the built-in Python functions that we've already used. For exmaple, we might want to print out the Fibonacci sequence up through the `n`-th value:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for n in range(10):\n", - " print fibonacci(n)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0\n", - "1\n", - "1\n", - "2\n", - "3\n", - "5\n", - "8\n", - "13\n", - "21\n", - "34\n" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use the capacity of defining our own functions in Python to help us build code that is easier to reuse, easier to maintain, easier to share!" - ] - }, - { - "cell_type": "heading", - "level": 5, - "metadata": {}, - "source": [ - "Exercise" - ] - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This lesson complements the first interactive module of the online [CFD Python](https://github.com/barbagroup/CFDPython) class, by Prof. Lorena A. Barba, called **12 Steps to Navier–Stokes.** The interactive module starts with simple exercises in 1D that at first use little of the power of Python. We now present some new ways of doing the same things that are more efficient and produce prettier code.\n", + "\n", + "This lesson was written with BU graduate student Gilbert Forsyth.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Defining Functions in Python \n", + "----" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In steps 1 through 8, we wrote Python code that is meant to run from top to bottom. We were able to reuse code (to great effect!) by copying and pasting, to incrementally build a solver for the Burgers' equation. But moving forward there are more efficient ways to write our Python codes. In this lesson, we are going to introduce *function definitions*, which will allow us more flexibility in reusing and also in organizing our code. \n", + "\n", + "We'll begin with a trivial example: a function which adds two numbers. \n", + "\n", + "To create a function in Python, we start with the following:\n", + "\n", + " def simpleadd(a,b):\n", + "\n", + "This statement creates a function called `simpleadd` which takes two inputs, `a` and `b`. Let's execute this definition code." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def simpleadd(a, b):\n", + " return a+b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `return` statement tells Python what data to return in response to being called. Now we can try calling our `simpleadd` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 2, "metadata": {}, - "source": [ - "(Pending.)" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "simpleadd(3, 4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, there can be much more happening between the `def` line and the `return` line. In this way, one can build code in a *modular* way. Let's try a function which returns the `n`-th number in the Fibonacci sequence. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def fibonacci(n):\n", + " a, b = 0, 1\n", + " for i in range(n):\n", + " a, b = b, a + b\n", + " return a\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 4, "metadata": {}, - "source": [ - "Learn more\n", - "-----\n", - "***" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "fibonacci(7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once defined, the function `fibonacci` can be called like any of the built-in Python functions that we've already used. For exmaple, we might want to print out the Fibonacci sequence up through the `n`-th value:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remember our short detour on using [array operations with NumPy](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/07_Step_5.ipynb)?\n", - "\n", - "Well, there are a few more ways to make your scientific codes in Python run faster. We recommend the article on the Technical Discovery blog about [Speeding Up Python](http://technicaldiscovery.blogspot.com/2011/06/speeding-up-python-numpy-cython-and.html) (June 20, 2011), which talks about NumPy, Cython and Weave. It uses as example the Laplace equation (which we will solve in [Step 9](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/12_Step_9.ipynb)) and makes neat use of defined functions.\n", - "\n", - "But a recent new way to get fast Python codes is [Numba](http://numba.pydata.org). We'll learn a bit about that after we finish the **12 steps to Navier-Stokes**.\n", - "\n", - "There are many exciting things happening in the world of high-performance Python right now!\n", - "\n", - "***" + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "1\n", + "2\n", + "3\n", + "5\n", + "8\n", + "13\n", + "21\n", + "34\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + } + ], + "source": [ + "for n in range(10):\n", + " print(fibonacci(n))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the capacity of defining our own functions in Python to help us build code that is easier to reuse, easier to maintain, easier to share!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Pending.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn more\n", + "-----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember our short detour on using [array operations with NumPy](./07_Step_5.ipynb)?\n", + "\n", + "Well, there are a few more ways to make your scientific codes in Python run faster. We recommend the article on the Technical Discovery blog about [Speeding Up Python](http://technicaldiscovery.blogspot.com/2011/06/speeding-up-python-numpy-cython-and.html) (June 20, 2011), which talks about NumPy, Cython and Weave. It uses as example the Laplace equation (which we will solve in [Step 9](./12_Step_9.ipynb)) and makes neat use of defined functions.\n", + "\n", + "But a recent new way to get fast Python codes is [Numba](http://numba.pydata.org). We'll learn a bit about that after we finish the **12 steps to Navier–Stokes**.\n", + "\n", + "There are many exciting things happening in the world of high-performance Python right now!\n", + "\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] } - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/lessons/12_Step_9.ipynb b/lessons/12_Step_9.ipynb index 81aca227..8855c39b 100644 --- a/lessons/12_Step_9.ipynb +++ b/lessons/12_Step_9.ipynb @@ -1,517 +1,559 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the previous step, we solved the [2D Burgers' equation](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/10_Step_8.ipynb): an important equation in the study of fluid mechanics because it contains the full convective nonlinearity of the flow equations. With that exercise, we also build the experience to incrementatlly code a Navier-Stokes solver.\n", - "\n", - "In the next two steps, we will solve Laplace and then Poisson equation. We will then put it all together!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 9: 2D Laplace Equation\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is Laplace's equation in 2D:\n", - "\n", - "$$\\frac{\\partial ^2 p}{\\partial x^2} + \\frac{\\partial ^2 p}{\\partial y^2} = 0$$\n", - "\n", - "We know how to discretize a 2nd order derivative. But think about this for a minute \u2014 Laplace's equation has the features typical of diffusion phenomena. For this reason, it has to be discretized with *central differences*, so that the discretization is consistent with the physics we want to simulate. \n", - "\n", - "The discretized equation is:\n", - "\n", - "$$\\frac{p_{i+1, j}^n - 2p_{i,j}^n + p_{i-1,j}^n}{\\Delta x^2} + \\frac{p_{i,j+1}^n - 2p_{i,j}^n + p_{i, j-1}^n}{\\Delta y^2} = 0$$\n", - "\n", - "Notice that the Laplace Equation does not have a time dependence \u2014 there is no $p^{n+1}$. Instead of tracking a wave through time (like in the previous steps), the Laplace equation calculates the equilibrium state of a system under the supplied boundary conditions. \n", - "\n", - "If you have taken coursework in Heat Transfer, you will recognize the Laplace Equation as the steady-state heat equation. \n", - "\n", - "Instead of calculating where the system will be at some time $t$, we will iteratively solve for $p_{i,j}^n$ until it meets a condition that we specify. The system will reach equilibrium only as the number of iterations tends to $\\infty$, but we can approximate the equilibrium state by iterating until the change between one iteration and the next is *very* small. \n", - "\n", - "Let's rearrange the discretized equation, solving for $p_{i,j}^n$:\n", - "\n", - "$$p_{i,j}^n = \\frac{\\Delta y^2(p_{i+1,j}^n+p_{i-1,j}^n)+\\Delta x^2(p_{i,j+1}^n + p_{i,j-1}^n)}{2(\\Delta x^2 + \\Delta y^2)}$$\n", - "\n", - "Using second-order central-difference schemes in both directions is the most widely applied method for the Laplace operator. It is also known as the **five-point difference operator**, alluding to its stencil." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are going to solve Laplace's equation numerically by assuming an initial state of $p=0$ everywhere. Then we add boundary conditions as follows:\n", - "\n", - "$p=0$ at $x=0$\n", - "\n", - "$p=y$ at $x=2$\n", - "\n", - "$\\frac{\\partial p}{\\partial y}=0$ at $y=0, \\ 1$\n", - "\n", - "Under these conditions, there is an analytical solution for Laplace's equation:\n", - "\n", - "$$p(x,y)=\\frac{x}{4}-4\\sum_{n=1,odd}^{\\infty}\\frac{1}{(n\\pi)^2\\sinh2n\\pi}\\sinh n\\pi x\\cos n\\pi y$$" - ] - }, - { - "cell_type": "heading", - "level": 5, - "metadata": {}, - "source": [ - "Exercise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Write your own code to solve Poisson's equation using loops, in the style of coding used in our first lessons. Then, consider the demonstration of how to write it using functions (below) and modify your code in that style. Can you think of reasons to abandon the old style and adopt modular coding?\n", - "\n", - "Other tips:\n", - "\n", - "+ Visualize each step of the iterative process\n", - "+ Think about what the boundary conditions are doing\n", - "+ Think about what the PDE is doing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Using functions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remember the lesson on writing [functions with Python](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/11_Defining_Function_in_Python.ipynb)? We will use that style of code in this exercise.\n", - "\n", - "We're going to define two functions: one that plots our data in a 3D projection plot and the other that iterates to solve for $p$ until the change in the [L1 Norm](http://en.wikipedia.org/wiki/Norm_(mathematics)#Taxicab_norm_or_Manhattan_norm) of $p$ is less than a specified value. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def plot2D(x, y, p):\n", - " fig = plt.figure(figsize=(11,7), dpi=100)\n", - " ax = fig.gca(projection='3d')\n", - " X,Y = np.meshgrid(x,y)\n", - " surf = ax.plot_surface(X,Y,p[:], rstride=1, cstride=1, cmap=cm.coolwarm,\n", - " linewidth=0, antialiased=False)\n", - " ax.set_xlim(0,2)\n", - " ax.set_ylim(0,1)\n", - " ax.view_init(30,225)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The function `plot2D` takes three arguments, an x-vector, a y-vector and our p matrix. Given these three values, it produces a 3D projection plot, sets the plot limits and gives us a nice viewing angle. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def laplace2d(p, y, dx, dy, l1norm_target):\n", - " l1norm = 1\n", - " pn = np.empty_like(p)\n", - "\n", - " while l1norm > l1norm_target:\n", - " pn = p.copy()\n", - " p[1:-1,1:-1] = (dy**2*(pn[2:,1:-1]+pn[0:-2,1:-1])+dx**2*(pn[1:-1,2:]+pn[1:-1,0:-2]))/(2*(dx**2+dy**2)) \n", - " p[0,0] = (dy**2*(pn[1,0]+pn[-1,0])+dx**2*(pn[0,1]+pn[0,-1]))/(2*(dx**2+dy**2))\n", - " p[-1,-1] = (dy**2*(pn[0,-1]+pn[-2,-1])+dx**2*(pn[-1,0]+pn[-1,-2]))/(2*(dx**2+dy**2)) \n", - " \n", - " p[:,0] = 0\t\t##p = 0 @ x = 0\n", - " p[:,-1] = y\t\t##p = y @ x = 2\n", - " p[0,:] = p[1,:]\t\t##dp/dy = 0 @ y = 0\n", - " p[-1,:] = p[-2,:]\t##dp/dy = 0 @ y = 1\n", - " l1norm = (np.sum(np.abs(p[:])-np.abs(pn[:])))/np.sum(np.abs(pn[:]))\n", - " \n", - " return p" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 20 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`laplace2d` takes five arguments, the `p` matrix, the `y`-vector, `dx`, `dy` and the value `l1norm_target`. This last value defines how close the `p` matrix should be in two consecutive iterations before the loop breaks and returns the calculated `p` value. \n", - "\n", - "Note that when executing the cells above in your own notebook, there will be no output. You have *defined* the function but you have not yet *called* the function. It is now available for you to use, the same as `np.linspace` or any other function in our namespace. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "##variable declarations\n", - "nx = 31\n", - "ny = 31\n", - "c = 1\n", - "dx = 2.0/(nx-1)\n", - "dy = 2.0/(ny-1)\n", - "\n", - "\n", - "##initial conditions\n", - "p = np.zeros((ny,nx)) ##create a XxY vector of 0's\n", - "\n", - "\n", - "##plotting aids\n", - "x = np.linspace(0,2,nx)\n", - "y = np.linspace(0,1,ny)\n", - "\n", - "##boundary conditions\n", - "p[:,0] = 0\t\t##p = 0 @ x = 0\n", - "p[:,-1] = y\t\t##p = y @ x = 2\n", - "p[0,:] = p[1,:]\t\t##dp/dy = 0 @ y = 0\n", - "p[-1,:] = p[-2,:]\t##dp/dy = 0 @ y = 1\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's try using our `plot2D` function to look at our initial conditions. If the function has been correctly defined, you should be able to begin typing `plot2D` and hit the **tab** key for auto-complete options. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot2D(x, y, p)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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Un69VFzQYwW2PLZlH2vEUcNS0iu4rht6CwaDy+hMLK/r7+0tWbhrFqkGoWnY5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\nB8dAR0REROTgGOiIiIiIHNz/Aevt4IE71WmYAAAAAElFTkSuQmCC\n" - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It worked! This is the initial state of our problem, where the value of `p` is zero everywhere except for along $x=2$ where $p=y$. Now let's try to run our `laplace2d` function with a specified L1 target of .01\n", - "\n", - "[Hint: if you are having trouble remembering the order in which variables are sent to a function, you can just type `laplace2d(` and the iPython Notebook will put up a little popup box to remind you]" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p = laplace2d(p, y, dx, dy, .01)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 23 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now try plotting this new value of `p` with our plot function." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot2D(x, y, p)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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M+Lo941/+NtRdpv1cOslky27t3dTUFFRV9bX2LivpHJCtY/ELCZ0Hbk0RaT8R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\niO+iQEcIc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- } - ], - "prompt_number": 24 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Learn More" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The [next step](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/13_Step_10.ipynb) will be to solve Poisson's equation. Watch **Video Lesson 11** on You Tube to understand why we need Poisson's equation in CFD." - ] - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous step, we solved the [2D Burgers' equation](./10_Step_8.ipynb): an important equation in the study of fluid mechanics because it contains the full convective nonlinearity of the flow equations. With that exercise, we also build the experience to incrementatlly code a Navier–Stokes solver.\n", + "\n", + "In the next two steps, we will solve Laplace and then Poisson equation. We will then put it all together!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 9: 2D Laplace Equation\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is Laplace's equation in 2D:\n", + "\n", + "$$\\frac{\\partial ^2 p}{\\partial x^2} + \\frac{\\partial ^2 p}{\\partial y^2} = 0$$\n", + "\n", + "We know how to discretize a 2nd order derivative. But think about this for a minute — Laplace's equation has the features typical of diffusion phenomena. For this reason, it has to be discretized with *central differences*, so that the discretization is consistent with the physics we want to simulate. \n", + "\n", + "The discretized equation is:\n", + "\n", + "$$\\frac{p_{i+1, j}^n - 2p_{i,j}^n + p_{i-1,j}^n}{\\Delta x^2} + \\frac{p_{i,j+1}^n - 2p_{i,j}^n + p_{i, j-1}^n}{\\Delta y^2} = 0$$\n", + "\n", + "Notice that the Laplace Equation does not have a time dependence — there is no $p^{n+1}$. Instead of tracking a wave through time (like in the previous steps), the Laplace equation calculates the equilibrium state of a system under the supplied boundary conditions. \n", + "\n", + "If you have taken coursework in Heat Transfer, you will recognize the Laplace Equation as the steady-state heat equation. \n", + "\n", + "Instead of calculating where the system will be at some time $t$, we will iteratively solve for $p_{i,j}^n$ until it meets a condition that we specify. The system will reach equilibrium only as the number of iterations tends to $\\infty$, but we can approximate the equilibrium state by iterating until the change between one iteration and the next is *very* small. \n", + "\n", + "Let's rearrange the discretized equation, solving for $p_{i,j}^n$:\n", + "\n", + "$$p_{i,j}^n = \\frac{\\Delta y^2(p_{i+1,j}^n+p_{i-1,j}^n)+\\Delta x^2(p_{i,j+1}^n + p_{i,j-1}^n)}{2(\\Delta x^2 + \\Delta y^2)}$$\n", + "\n", + "Using second-order central-difference schemes in both directions is the most widely applied method for the Laplace operator. It is also known as the **five-point difference operator**, alluding to its stencil." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are going to solve Laplace's equation numerically by assuming an initial state of $p=0$ everywhere. Then we add boundary conditions as follows:\n", + "\n", + "$p=0$ at $x=0$\n", + "\n", + "$p=y$ at $x=2$\n", + "\n", + "$\\frac{\\partial p}{\\partial y}=0$ at $y=0, \\ 1$\n", + "\n", + "Under these conditions, there is an analytical solution for Laplace's equation:\n", + "\n", + "$$p(x,y)=\\frac{x}{4}-4\\sum_{n=1,odd}^{\\infty}\\frac{1}{(n\\pi)^2\\sinh2n\\pi}\\sinh n\\pi x\\cos n\\pi y$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Write your own code to solve Poisson's equation using loops, in the style of coding used in our first lessons. Then, consider the demonstration of how to write it using functions (below) and modify your code in that style. Can you think of reasons to abandon the old style and adopt modular coding?\n", + "\n", + "Other tips:\n", + "\n", + "+ Visualize each step of the iterative process\n", + "+ Think about what the boundary conditions are doing\n", + "+ Think about what the PDE is doing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember the lesson on writing [functions with Python](./11_Defining_Function_in_Python.ipynb)? We will use that style of code in this exercise.\n", + "\n", + "We're going to define two functions: one that plots our data in a 3D projection plot and the other that iterates to solve for $p$ until the change in the [L1 Norm][1] of $p$ is less than a specified value. \n", + "\n", + "[1]: http://en.wikipedia.org/wiki/Norm_(mathematics)#Taxicab_norm_or_Manhattan_norm " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy\n", + "from matplotlib import pyplot, cm\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def plot2D(x, y, p):\n", + " fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + " ax = fig.gca(projection='3d')\n", + " X, Y = numpy.meshgrid(x, y)\n", + " surf = ax.plot_surface(X, Y, p[:], rstride=1, cstride=1, cmap=cm.viridis,\n", + " linewidth=0, antialiased=False)\n", + " ax.set_xlim(0, 2)\n", + " ax.set_ylim(0, 1)\n", + " ax.view_init(30, 225)\n", + " ax.set_xlabel('$x$')\n", + " ax.set_ylabel('$y$')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `plot2D` takes three arguments, an x-vector, a y-vector and our p matrix. Given these three values, it produces a 3D projection plot, sets the plot limits and gives us a nice viewing angle. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$p_{i,j}^n = \\frac{\\Delta y^2(p_{i+1,j}^n+p_{i-1,j}^n)+\\Delta x^2(p_{i,j+1}^n + p_{i,j-1}^n)}{2(\\Delta x^2 + \\Delta y^2)}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def laplace2d(p, y, dx, dy, l1norm_target):\n", + " l1norm = 1\n", + " pn = numpy.empty_like(p)\n", + "\n", + " while l1norm > l1norm_target:\n", + " pn = p.copy()\n", + " p[1:-1, 1:-1] = ((dy**2 * (pn[1:-1, 2:] + pn[1:-1, 0:-2]) +\n", + " dx**2 * (pn[2:, 1:-1] + pn[0:-2, 1:-1])) /\n", + " (2 * (dx**2 + dy**2)))\n", + " \n", + " p[:, 0] = 0 # p = 0 @ x = 0\n", + " p[:, -1] = y # p = y @ x = 2\n", + " p[0, :] = p[1, :] # dp/dy = 0 @ y = 0\n", + " p[-1, :] = p[-2, :] # dp/dy = 0 @ y = 1\n", + " l1norm = (numpy.sum(numpy.abs(p[:]) - numpy.abs(pn[:])) /\n", + " numpy.sum(numpy.abs(pn[:])))\n", + " \n", + " return p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`laplace2d` takes five arguments, the `p` matrix, the `y`-vector, `dx`, `dy` and the value `l1norm_target`. This last value defines how close the `p` matrix should be in two consecutive iterations before the loop breaks and returns the calculated `p` value. \n", + "\n", + "Note that when executing the cells above in your own notebook, there will be no output. You have *defined* the function but you have not yet *called* the function. It is now available for you to use, the same as `numpy.linspace` or any other function in our namespace. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "##variable declarations\n", + "nx = 31\n", + "ny = 31\n", + "c = 1\n", + "dx = 2 / (nx - 1)\n", + "dy = 2 / (ny - 1)\n", + "\n", + "\n", + "##initial conditions\n", + "p = numpy.zeros((ny, nx)) # create a XxY vector of 0's\n", + "\n", + "\n", + "##plotting aids\n", + "x = numpy.linspace(0, 2, nx)\n", + "y = numpy.linspace(0, 1, ny)\n", + "\n", + "##boundary conditions\n", + "p[:, 0] = 0 # p = 0 @ x = 0\n", + "p[:, -1] = y # p = y @ x = 2\n", + "p[0, :] = p[1, :] # dp/dy = 0 @ y = 0\n", + "p[-1, :] = p[-2, :] # dp/dy = 0 @ y = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's try using our `plot2D` function to look at our initial conditions. If the function has been correctly defined, you should be able to begin typing `plot2D` and hit the **tab** key for auto-complete options. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('ZjfxA3qq2Lg')" - ], - "language": "python", + "data": { + "image/png": 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KpfDBBx8YbtNf4PWT2f1ShauG86yMuKCBqDGV3kckSUI8Hteqd6JyU76wIhqN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\nt2+j1+vRarU0NjaSmJhIYmIi999//0B+BCEGox4DnX2OxBaDXm1tLX5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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "" - ] - } - ], - "prompt_number": 2 - }, + "output_type": "display_data" + } + ], + "source": [ + "plot2D(x, y, p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It worked! This is the initial state of our problem, where the value of `p` is zero everywhere except for along $x=2$ where $p=y$. Now let's try to run our `laplace2d` function with a specified L1 target of .01\n", + "\n", + "[Hint: if you are having trouble remembering the order in which variables are sent to a function, you can just type `laplace2d(` and the iPython Notebook will put up a little popup box to remind you]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p = laplace2d(p, y, dx, dy, 1e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now try plotting this new value of `p` with our plot function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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96FaB67q48cYb8dd//dexx0ejEZrNJhqNyvhwYQRBgNFohH7frHdWXtJmZ1hU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\neRPz58/H/Pnz0b17d0PXJVjLS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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "And for a detailed walk-through of the discretization of Laplace and Poisson equations (steps 9 and 10), watch **Video Lesson 12** on You Tube:" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "plot2D(x, y, p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The [next step](./13_Step_10.ipynb) will be to solve Poisson's equation. Watch **Video Lesson 11** on You Tube to understand why we need Poisson's equation in CFD." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('iwL8ashXhWU')" - ], - "language": "python", + "data": { + "image/jpeg": 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bEqvqyDqzzi4QnqAwB/uZbvzK6LDWwPPQKgd2J30H4imq1ebZQ+85lRLAzL01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\n/Q6+5ntuTVVdXSzhbLN8AfXUrU5rPl4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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " " - ], - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('ZjfxA3qq2Lg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And for a detailed walk-through of the discretization of Laplace and Poisson equations (steps 9 and 10), watch **Video Lesson 12** on You Tube:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", + "data": { + "image/jpeg": 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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('iwL8ashXhWU')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] } - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/lessons/13_Step_10.ipynb b/lessons/13_Step_10.ipynb index a03fcb29..c5a9bb15 100644 --- a/lessons/13_Step_10.ipynb +++ b/lessons/13_Step_10.ipynb @@ -1,388 +1,467 @@ { - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a moment, recall the Navier-Stokes equations for an incompressible fluid, where $\\vec{v}$ represents the velocity field:\n", - "\n", - "\\begin{eqnarray}\n", - "\\nabla \\cdot\\vec{v} &=& 0\\\\\\\n", - "\\frac{\\partial \\vec{v}}{\\partial t}+(\\vec{v}\\cdot\\nabla)\\vec{v} &=& -\\frac{1}{\\rho}\\nabla p + \\nu \\nabla^2\\vec{v}\n", - "\\end{eqnarray}\n", - "\n", - "The first equation represents mass conservation at constant density. The second equation is the conservation of momentum. But a problem appears: the continuity equation for incompressble flow does not have a dominant variable and there is no obvious way to couple the velocity and the pressure. In the case of compressible flow, in contrast, mass continuity would provide an evolution equation for the density $\\rho$, which is coupled with an equation of state relating $\\rho$ and $p$.\n", - "\n", - "In incompressible flow, the continuity equation $\\nabla \\cdot\\vec{v}=0$ provides a *kinematic constraint* that requires the pressure field to evolve so that the rate of expansion $\\nabla \\cdot\\vec{v}$ should vanish everywhere. A way out of this difficulty is to *construct* a pressure field that guarantees continuity is satisfied; such a relation can be obtained by taking the divergence of the momentum equation. In that process, a Poisson equation for the pressure shows up!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 10: 2D Poisson Equation\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Poisson's equation is obtained from adding a source term to the right-hand-side of Laplace's equation:\n", - "\n", - "$$\\frac{\\partial ^2 p}{\\partial x^2} + \\frac{\\partial ^2 p}{\\partial y^2} = b$$\n", - "\n", - "So, unlinke the Laplace equation, there is some finite value inside the field that affects the solution. Poisson's equation acts to \"relax\" the initial sources in the field.\n", - "\n", - "In discretized form, this looks almost the same as [Step 9](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/12_Step_9.ipynb), except for the source term:\n", - "\n", - "$$\\frac{p_{i+1,j}^{n}-2p_{i,j}^{n}+p_{i-1,j}^{n}}{\\Delta x^2}+\\frac{p_{i,j+1}^{n}-2 p_{i,j}^{n}+p_{i,j-1}^{n}}{\\Delta y^2}=b_{i,j}^{n}$$\n", - "\n", - "As before, we rearrange this so that we obtain an equation for $p$ at point $i,j$. Thus, we obtain:\n", - "\n", - "$$p_{i,j}^{n}=\\frac{(p_{i+1,j}^{n}+p_{i-1,j}^{n})\\Delta y^2+(p_{i,j+1}^{n}+p_{i,j-1}^{n})\\Delta x^2-b_{i,j}^{n}\\Delta x^2\\Delta y^2}{2(\\Delta x^2+\\Delta y^2)}$$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will solve this equation by assuming an initial state of $p=0$ everywhere, and applying boundary conditions as follows:\n", - "\n", - "$p=0$ at $x=0, \\ 2$ and $y=0, \\ 1$\n", - "\n", - "and the source term consists of two initial spikes inside the domain, as follows:\n", - "\n", - "$b_{i,j}=100$ at $i=nx/4, j=ny/4$\n", - "\n", - "$b_{i,j}=-100$ at $i=nx*3/4, j=3/4 ny$\n", - "\n", - "$b_{i,j}=0$ everywhere else.\n", - "\n", - "The iterations will advance in pseudo-time to relax the initial spikes. The relaxation under Poisson's equation gets slower and slower as they progress. *Why?*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at one possible way to write the code for Poisson's equation. As always, we load our favorite Python libraries. We also want to make some lovely plots in 3D. Let's get our parameters defined and the initialization out of the way. What do you notice of the approach below?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Parameters\n", - "nx = 50\n", - "ny = 50\n", - "nt = 100\n", - "xmin = 0.\n", - "xmax = 2.\n", - "ymin = 0.\n", - "ymax = 1.\n", - "\n", - "dx = (xmax-xmin)/(nx-1)\n", - "dy = (ymax-ymin)/(ny-1)\n", - "\n", - "# Initialization\n", - "p = np.zeros((nx,ny))\n", - "pd = np.zeros((nx,ny))\n", - "b = np.zeros((nx,ny))\n", - "x = np.linspace(xmin,xmax,nx)\n", - "y = np.linspace(xmin,xmax,ny)\n", - "\n", - "# Source\n", - "b[nx/4][ny/4] = 100\n", - "b[3*nx/4][3*ny/4] = -100\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With that, we are ready to advance the initial guess in pseudo-time. How is the code below different from the function used in [Step 9](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/12_Step_9.ipynb) to solve Laplace's equation?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for it in range(nt):\n", - "\n", - " pd[:][:]=p[:][:]\n", - "\n", - " p[1:nx-1,1:ny-1] = ( dy**2/(2*(dx**2+dy**2))*(pd[2:nx,1:ny-1]+pd[0:nx-2,1:ny-1]) +\n", - " \t\t dx**2/(2*(dx**2+dy**2))*(pd[1:nx-1,2:ny]+pd[1:nx-1,0:ny-2]) -\n", - "\t\t\tb[1:nx-1,1:ny-1]*dx**2*dy**2/(2*(dx**2+dy**2)) )\n", - "\n", - " p[0,:] = p[nx-1,:] = p[:,0] = p[:,ny-1] = 0.0" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 24 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Maybe we could reuse our plotting function from [Step 9](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/12_Step_9.ipynb), don't you think?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def plot2D(x, y, p):\n", - " fig = plt.figure(figsize=(11,7), dpi=100)\n", - " ax = fig.gca(projection='3d')\n", - " X,Y = np.meshgrid(x,y)\n", - " surf = ax.plot_surface( X,Y,p[:], rstride=1, cstride=1, cmap=cm.coolwarm,\n", - " linewidth=0, antialiased=False )\n", - " ax.set_xlim(0,2)\n", - " ax.set_ylim(0,1)\n", - " ax.view_init(30,225)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 25 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot2D(x, y, p)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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FK1AjoaMTS16xMymGkG/HNKITwFppMZEU0QtP3JrpD+L10xQ0iNfzxMa3OeWO\nRE0nc7rtI387kDngsMdenNhR416V2M0Hxw6uH/M4Fyx17mBV3iz4CCC/tg7kSyF2uuhZHrEDSOLV\nkU/ddXTpWvE6quuJ0TuWu/pTRZRLFXERm9GuY71UHWhDRKjq+1BkcUUbGj1noTZCRxTdokQ8gaWd\n9GBSQKESNTvwjFOhvuUcptbENVcauUu6D57UhDit4M37R5a3Y3g9MVonipyMb3dTRevmklSR8Ggf\n14xS5/YVTZLpxCiLGg4icAqxSxOtW+4jWezEogjVvsPDNbyNuONKiu7JqVkWvHpStVSoRpLRYvi4\nE3LVMpGXph8/UL/7IBdXUIslcX6x7svCJvagA2oodEUTiU7E9JQzvV6c3EVSYwn95uTtZeQ1dQDQ\n8fSVnzJ0zAuhCrYTUzkKAL5zeJs0ncJE7LzOQLnWbGX/BlIni9zqPpzcUqeUOOWVghWpA9JH6+j2\no/s4lK4sRRJpbkPeX1qZU8HROyYJ02pHSrc1lSYfu0jdhE6Gonfil4XFYoHJZALP8yJfFnQp17ZT\nS6FbZ1FEXGWgyfYWAqNCikiPMd9LVXxBskBjpAgaL5V0jESc3IkyF7ncQOy8rlxJG7MmLiYFO+8d\nNjeOG+eVReoo8oi0H74xUgeoo3WAWuzmjnpEW5oUbRyq1KrJ9ePSriaw3DEm6KJ34/Gy3+VkMll7\nr7IiabpANClNKX5ZUKX6geXrzXXdSJudpj9HSdRS6EwjYiJZR33Jt2G6Bk+8nq75q0ykiawmVRu9\n7fj7LgteUvRLhOTOVCxVYieLXHh5p5eY6hWjdaLIEdQGRCd2sSnYg+dw3lOkgOkNnUbsNClYQB2t\nA6CMusWJU9oomw7dWk3VbRVxezKcmmVMkKN33/3ud9HpdMJ0muM4lU8aMKXukS1Tmnw/5C8LtOZO\nnjs7Go1a3WanlkInYrqmLo2ImexPt98kAVKdUE0rXFd61QXppj74wmJ5E7kLLBuecP9MGiiT2Ola\ndBC0li9O7GYHlaNxj2ncnFZAHa1ze0fC3ymStoJlFRatI3nTVTgrbz6hgEH1+MrXSXoOjI5DI3dZ\nZE4FR+/WR1NPyJSyHAwGK4vh9/b2EARBmJqty5zQNtKW1DG9B3q93sqIu7bcRx21F7qsmI7yMhXG\n8ESbomjjsNgheT2dipWpDymqYJPkTiUDVD0ZJ3Zy9WtSZa4uWjcXBtqLhSEqTKRu0VFPsqDomVLs\nckTr5op7yVfzAAAgAElEQVTbUxVHJGEqdiaRt7j9JVGEHCbBcseYIEbvRqNR2BalTnNCRZoq0ira\ncj/E9LHYJJsiwm2l9kK37ibDcaOUkranY02M4hUgeFnkzvbnRidundipWpn4dsdI6oBltE4Uuch+\nEtrA6FKwc6myVpemDmDnitapBE65K80ausTrSSKmaiacRrriUqcm+8mbek2CU7MMkSREcisLirbs\n7e0BKHckmQltEbq23A+gXfclDbUXuixtTKpYTydi2qMu3F4qmDAhi9yJQmbS8y7sd5bwxkgaX7bc\nVw9e76DfXFx1r2G0Tha58O8x0zrSROtMBU5HGrHTtSdZ3ac+HRtH3uhb2XIH1DN6t4knhLojjySj\nxfDiWimKxKxrXnFb5KEt9wPgtiWNwaRgIquIJd62Yr9petSlrYYFzAQvSe5UUmEiYeJ2gEnkcXWf\ncuHG8nicRKnT3Z4oWto5vVTRlDJaN3fURR55UImdqcDp9xkvdkWusZP3uQ7qKHdNoKkn5KzHrWpE\nS9E7eYxUVdG7JtHU14+KNt2XNDRO6LKQRvBMK2xVa+qSood5BS+t3JntXy12qj54ppFHuq6q+pMI\nDvZlGq1TRcySpkCYROtU1ahFs9BMdsgL3Xfd+rq4atYk1ilwcXBqljFFjt7R2juK3olyV2SlY1vk\noS33A2jXfUlD44SuKT3qTI7TtN1JuL2h3NHJO00Fq0rgtNsmiJ0XKchIms6hF7uF0LctKRqbJlq3\nDonz7E5pUmRaICFimq6ti8jp4Ohd+yjj5KsbAu+6LqbTaSh/RYwka4M8BEHQivsB6O+LZVmtuH9x\nNE7o6tKjzmTuq2ofOoqSO90J26SCNW0EURY71YzRxJFdB4hp2IWiAW/SrN8ksZt3+ol9/fIgVyQD\n2StOVWSROBVJUbt1rJcrijLlri0nN2aJPASeonfT6RSe561Uzm4qbXjN03u3DfclLY0TOpEqe9Rl\nTeMm7RfIJnd0G3bCfROli+ROJwumKVZaexY/yixZ7CitmvRYJj0v4vXFaFzS2rq0qCRORRaxK0rg\nRNKmXZsqd8BmRu+a1OlfZN29wVTRO3HKQNyMUBVtkP823AeiTfclLY0Wuqxk6VGXpgFxkdG7JLmT\nT7T+wXEkiR1wKDtJ0TOd2MkRORMBVImdnAJNTKMmROtoxJZe8LOJnanA6YgTuzIEDiiuMMJCUHup\nE0kTvfN9P/xpohC1gSpPwPKUAc/zwokVvu+vVM7KtEEg2nAfiE2tcAUaLnR519MBxTcgTrNPeb9p\n5C5pv75wDLLcyVWvpmlRuv0kSTAVO8/uxEpVGrFTFR4kp2mTxS6vxKmg48lb7aqi6AbBTZI4HbLc\nUbuL2WwGz/PCQd4XLlwIR06x3G0mYvQOQCR6N5lMUkfvmsImCN0m0Gihy9KjLnZ/JYhYWXKXZr8k\nd4FlG0XPALXcqeQmLhKoj+wd7sdEquLEzkS40oqdrio17+uLBa565NTszsmTsCwLi8UCW1tb4cJ5\n13VhWRaCIAhHTm3qSaJs6nwClqN3NJKMonfdbhdBEDR+3V2dn4O0cISuJWQpmNDuq4RWJ2XsU96v\nap/i381bjxxG7eKkySTF61tOYoPdNGLnZ5SYOLHzrA6SXCZJDFWwxNWb7549G/5+5MiRyMB4YHlC\nFxfOx6XeqqZNJ+U6Io4kAxBpizKfz7FYLGo1kiwNbXrtbHJ0vVVCJ1Kk3AHVrrvLus84TAovfMuB\n75ileXVip47qxbVc0YudvK+s1cu68Vppr68Vw4JhgVsPTz7xRPj79s4OLMvCcDiMLJyntVWUmm3i\nybtuNFUmqKmx53mwbRudTiccSRYEAbrdbhjdrbtgNPU5UMERupbTJLnLm5ZVnaxN1+bZgRcrOSbH\nKKZ3TW5Pf1vJ0mX6mKluuwjyimHsvlniKuXcd78b+f8VZ86spN5c163NPFGmOqi6mJ7/0WgURu9m\ns1k4kqzOXwA2Qeg2gY0QOpG6y12edih5bjuAFYkumRyjfGzaiQWafcVFCeVIV5bjCfd1sCauqBFw\nQPESxwJXb8To3RVnzoQnZ3kiwXg8rn1qtm40/QSsOn7dSDKK3tHroy5fANbdOqZMdO176vA4l83G\nCR1RhMzJrDtyp5OANLetO/GbrBej20+SB5N9+ZYTu580M3ytwFc2OU5TSKI7xiJhiWsmstzpeppR\nVSSdvMuOzLTppNwkkoRUHklGrxEaSUbRu16vB9u2KxOPtghP078g5GHjhK4MkVNRduTO5Lb147BI\nxLIfn8n8UN2+ythPZH8G4mWWPq53KpUFrnpEuQOqT8028US2SSdgy7K00bvd3V0A1aTv2/QccFHE\nBlF0ytXoNguQO93JO03LFJ1MpDm+xFmgBvsqcj/ytmmRxY6jcEwe4lKzvu/Ddd1IalaMzDDNJI8M\nydE7MX2/t7cXkbsyW6M0dcqICi6K2FCaInex+zMskgjEpsQp16NllRJ5X0XtB0i/Xi92/7Aij09e\nWOIYYFXuHMdZqZoVx02tKzVbN5oeHSrq+FUjySjCO51Ow7YpZfRFbPpzINKm+5KWjRY6kTrLnfH+\nUkpgkthl6bumui1TmUzCJHpmesxFSxKnUpk4TFKzeRfN8xq6dkGSL0fvxL6IRUXv2iRBHKFjIqQa\n81WA9ADFy53RbSokViUTWWQ3T7GFyX7SXC/uvuWBJY7Jii41Sy0vKDUrpt1MUrObcNKqG+uQIVX0\nTo7w5hlJ1hahC4JAeV/acN9MYKEzwKQytKwpFYl96TL0YlPup4DoXhopybJeMAvLSOPB7eR9jDiV\nypRA2tQsyV1bUrNNX79VhQzJI8noSwCNJEvbOqctQke06b6kgYUuJSYn4bLlTiUW647wiQUJ65JJ\n4/3lLP4w2VcWWOCYJPKkZpn1U4c0txi9A6BsnZMUvWuL0G1yhSvAQlc6Zc6XNdnOpLghrZDJ+8kq\nk3H3J+8xpdlevi2WOKYuJKVmxYpIy7Lgui5s227UsPg2yESdjl+O3tGXAIreiZWzJD9teA6AzV4/\nB7DQrZWqCy9MtknqXWe6r6L2U8S+0txWIftjiWNKQJWapX5mvu/jwoUL8H0fFy9ebGVqto7UXYSo\nMpYiuNQ6h9KzVHhRh0hjEdT9+SgbFrqK0MldnAysQwLrKEmiJBYtX0XAAsesG1Vq1rZtDIdDOI4T\nRmXG43EYlaH0bN1OeJt+El4ntm1jMBiETY2pLQoAXLhwQRm9axIcoWMqp4iec6p9rCsKuC7yylyR\njxFLHFMnSPD2dncjqVkAlTWr3QSaLKMUvXMcB67r4tixY5jP53BdN9L4ukm9EZv8fBQBC11DKbI4\no7DWK0WuFyzhmJL+vu7edQxTBnGpWRo1Rc1q87a7KIImn4SbfOwE3QfdSLI8vRHXje/7HKFj2k9Z\nrVeS+s2Z7quo/cTtK831yupdxzDrRJWaFZvVqhbM1zU1W0faInQy8kgyqpylKK88tq4uj0HTW+Dk\nhYVuA8kb3UsrOVnWC5Z9TGlui2Hagq5qFjhMzc5ms/CkLY4jK4s2SFHTiXv846J3u7u7ABBJ41f5\nXOqEblNeXyx0TCKFNvotaF8sXAyTD1X0rs6p2TrSBhlNex/k6F2d1mi24fnIAwsdwzAMsxK9k0/a\n4iSCIqshm3wSbvKxE3nug2okGVXOTqfTsPCi1+ut5YtAG56PPLDQMQzDMBFkuRMnEVBERqyGXEdq\nlimHIiWI+trJ0bvpdArP81YqZ4uG25YwDMMwjAaT1CydtCkdlyY12+Smtm2ICJV1H1TRO9Vc4iLT\n+Loq102BhY5hGIYxRpeapXFkWVKzTT0Js9CZI48kk18rYqQ3axqfiyIYhmEYJgNxqVlxzJTYqJbG\nkTH1oAopFaN3ACLROxpJliV6p7ovmyJzAAsdwzAMUwCq1Kw4ZopO2BcvXoykZpsc5WrysRN16N0m\nR+9U/RGTIr1NTt0XBQsdwzAMUzhJqVk6YQPA3t5e7nRbFdRBhvJSNymlyljqjyhGeil6Jxbh0LHT\n/eAIHcMwDMOUhC41OxgMcO7cOXS7XWVqtk5TCNpK3YROxrbtSKSXonc0kqxp82bLhIWOYRiGWRty\nanY4GilTs+IUgnX1MUtL3WXIhCbdBzF6J0Z6XdfFfD4HAEyn040VPBY6hmEYpjKmkwmePEi9qhoa\np11LtU6aJEM6mnwfxJFks9kM+/v78H0/jN71ej0Mh8Mwfdt2WOgYhjEmiJv5mGJRctx+0u6LaQ+6\n1KzYx4zWUjmOU3lqtskyRLThPhCO42BrawtBEISvF9/3qz6stcFCxzAtRBamPIKUJF+q7VS3Z7qf\nLPvKev9Ux8QyWQ9UVbNiJWSTUrN1pi1CJ94Py7LC6B21RtkENueeMkwNIaEoQiLihCmNAKURrzL3\nkWZfSQKYZn9p98UCuB6SZs3KI6bWUTXbBhlqS7uPNjwXeWGhY5g1oxKKNBIRt58sx2AFQaECVjWq\nxzLr/TN5nLI8d0w+sqRmy1go33SJIJlr8n0gNn2OK8BCxzBroah0Yxni1SaZk6kyUgiw4K2DpNTs\nYrGA67rY29sDwKlZFW14HKhoZpNhoWMYgaJSoEWJRJtlaxPg6N36kaN3FJ0rIzXbhghdk49fpE33\nJSssdMxGoxOmdadAmfbDcrd+TFKz4vxQ1QSCOJouEU0/fhFOubLQMRtIWvFadwqUaT9ZXlMsgfnI\nkpqlH5UUtKGYYBOEbpNgoWNqS5ERDU6BMnWlqLYwTDriUrM0P3R/fz8cR6ZLzTZZItokQRyhY6Fj\nakZRKVAWL6bNsNwViyx3juNgOBzGpmYdx6nwiIuhTULn+36rZDsLLHRMpWQRL93JjCWO2URY7orF\nJDVLcgcA4/E4NjVbZ9oidG1IfxcBCx2zdtbdSoJhNgWTtincWiUdutRsv9/HxYsXYds29vf3sbe3\nF/6NxpHVnTYJnWVZK/elDfctDSx0zFpg8WKY9VP0lI1NR5S7Sy+7DLZtK1Oz0+k0U9Xsummb0G06\nLHRMKbDAMUzz4OidOU9/5zsAgL2DWbK61Oze3h6CIAjlrk6p2baIUFvuR15Y6JjCYIljmHbB0Ttz\ndKnZ0WgUNjSWU7MUvauKIAgakRpOgitcl1iB4WrCbz72WNnHwqyJogaKs8AxzGbCcmfOFWfORP4v\npmbn8zls2w7X3a07NUty2e/313abZTCbzTCfz3HkyJHI5Y7joNPZnLiVsdCJsNw1iyTxSvPhzBLH\nMIwIy106RMETU7Ou6yIIglDu1pGa3d3dRa/Xa7zQ7e/vw/M8bG1tRS7vdDqtaC9jSiahE2G5qydZ\nxcukKo5hGEYHC545cvSOUrOu62KxWJSemt3d3UW/30ev1yt83+tkOp0iCAKMRqPI5Sx0OWC5qxYW\nL4Zh6gTLnTmy3AVBEMpdWanZixcvYjgcotvt5t5XlUwmE1iWheFwGLlcNdmjzRQqdCIsd+XDAseU\nSRBoZopafJJm0sNylw5danY+n8P3/UJSsxcuXMDW1lbj15mNx2M4joPBYBC5nIWuBFjuyoXFbrMR\nxSuvbOkkTkfc7dG+WAAZFSx45iSlZsVZs2lSjOfPn8fRo0cbn5bUrQVkoSsZlrtyYbnbDEzEy1Sk\n0kpc3O0l7YvljlHBcmeOaWq22+2i0+nERu/OnTuH48ePN156dKnjXq+3Ua1L1i50Iix35cJyVz1F\npi3ziJd8e0VJXB5Mj4klcLNguUuHnJr1PC+Uu7jUbBAEOHfuHLa3txsvPbrUMQtdRbDcFQeLXHVk\nESWTtCWzxPSxYglsHixy+dGlZulHTM3ato1z585hZ2enoqMtDl3quOntWNJSG6ETYblLBwtctRSZ\nsixyf23HJMVL2zH1hmWueHSpWUrPWpYF3/dx9OjRxNRs3VGlji3Lanw7lrTUUuhEWO7UsMRVS52l\nK0B0PYwFv5B95dlP0fvKikmalwWwWljuykFOzbquG1aHUmqWfpq0pk6XOmahqzmbLncscdVStMQF\nsAsTG1niVJjeVl33VQWc4q0eFrziueLMGSwWC4zHYxw/fhy+74fr7ig1K/a8qzO+7+P8+fMrqWMq\nDtkkGiV0xKaIHQtc9RQpcXFyk0VqTGTJ9PZ8rH5oWzD7aJD3VeRx6fbVVAlk8sFyVzwmqVlad1fH\n1Kznedjd3cWJEycil7PQNZC2yR1LXLWUEYVLS5yskHiZylYSAczur8ntBbAKO6406B6vIlPPeWDB\nKweWu3JQVc2S3NUxNStGGkVY6BpOE+WOBa566iBxOiz4yuhZdJt0b2FTiTO5Pd2+qhC7NLDctQuW\nu3KQo3d1TM3O53NMp1McO3YscrnjOI2fgJGWVgmdSJ3ljiWueuosccv9ZWh/EiNR/sHxFSFadGzm\nKdmYdWjC/axKAlnu2gFLXbnUNTXrui5msxmOHj0auZyFrqXUQe5Y4qqnjRKnw0IQSlzcNmmIO740\ncmdyP1nuGBNY4qqjLqnZ2WyG+XyOI0eORC7vdDq1L+gomo0QOpGq5Y7Fbr3UXeJ00pVHaPxIe5Dk\n/ZhE9orYV5ZonGo7lRCWIYAsd82C5a46VKlZcdas4zhhata27UKjd9PpFEEQYDQaRS5nodswWO7a\nRxn94fJUgKr3Zy6Fprdjsk9TISsqsldVNM5UKtPcNstds2C5q4641Ox8PgeAUO6KSM1OJhNYloXh\ncBi5vC5FG+tko4VOhOWuuaxL4lSkkZE0ImdyW1n3pzvmtNE4030BgG0oRPL+8hZdmEplun1W1zKF\nBS89LHfVkpSalceRpYWaIw8Gg8jlLHQMgPXJHUtcdoqWOFOBi0MlBJ6w3yILEoqAjqeIiJxJZA8w\nF7s06I4vazQuzbbrljsWOnNY5OqHaWq22+3CcRyj6N3e3h663e7K3FYWOmaFMuWOhc6cKqNw6fdb\nTPozaZ+50r6BOPMwZzQukPq9Ge5Pljvd45Y2uld0mrdquWOJSw/LXP1RpWYXi0XYFgUwS83u7u6i\n3++vjPnq9Xq1a4JcNix0KWC5Wy9tkzgVcbLgBQdNhBNO6KnSvkFCNM5AHsLIXgH7SkOS2GVtgdIU\nuWOxywbLXTPQpWbn8zk8z9OmZi9evIjhcLjSRJiFjjHi7W9/O/7Xt7yltP1vstw1ReLyroeTIVEg\niVNuk1HsFkG0F5NJxCvutugYjSNnmn2pnuu00b2qonHpty1W8FjussOCV3/SpGZ3d3dx7NixlYpW\nOQW7CWxW172C2N7expf/8R/xile8Ar7v4+xzz1V9SI2lKQK33G956zFIumIrNA8eK60gSREqWeQI\nuh9xQibflko0xcfDZF+0v7jnPOk+EuLx2FZxkbvythULTvLLnfwYsuAxbeLJJ54If7/izBnYto1+\nv49+vx9Jze7t7SEIAkyn0zB6Z1nWxkXmCI7QZeDXfu3X8PTTT+PLX/4yfuAHfgC/8Ru/Adu2sVgs\nMBmPM+1zk6JyLHFLYqNxJoUIipO4LHEm+4mTMdpfkjSZ7MtUAEXk+5j02jE5ThKxNAUaTYncASx3\nKjgq1x7E6J3v+zh//jyGw2EkNdvv91cmR2wCHKEzYD6f47Of/Sw++clP4hOf+ASee+45/MiP/Ah+\n/ud/Hi9/+csxHA7heR48z8Mlp0+H13v2mWeMb4M+cNoodmUIHNA+iRMxGa8lRrN00bgAVqJgqCJ2\n8v5ovVySMKn2pXo8TaKEQPrXTtxxyunZNILZlMgdsBoRZaKfqyx37YEiccPhEMPhMEzN+n51rYWq\npBE1vffffz+uu+46XHPNNXj/+9+v3OaXfumXcPXVV+Omm27Co48+muq6SbzxjW/EO9/5Thw/fhx/\n8id/gj/+4z/Gi1/8YvzkT/4ktra2ACxfWHKw85LTp8MfU6wgCH+aTBBY4U9h+4Qd+SmyIa0PO/wp\nGi9wwp+0BLBi14ktgg7mfjc+jZmwD8KHjUXQ0cohsBSmpGII2pcbdBMfz6THXfx7mueIjtMPbKP7\nn2bftD+Tx1TcNml7+fVdBOL7sKwvVk0jsKzwh2kOV5w5o6yMFdOrlJqVp0ZsCo2I0L3tbW/DBz/4\nQVx11VV45Stfide+9rU4depU+PeHHnoIf/d3f4cvfOEL+MxnPoN3vOMd+OQnP2l0XRP+8A//MDLk\n98EHH8T58+cj26iETiRL5E6UuiZ8+JRxwkg6sYlSl6ZHW5lROGA1wgXkazMiRuy00bjAio3IxEX9\n5tI+nYRoUVwkbCGIK/3esbzY/QHRqJ2JCNK2Ouj+iq9Lk5Rs1sgdUM/oHUfp9HDkrt7IAifj+/7G\n9ZqLo/aPxIULFwAAt956K6666ircfvvtePDBByPbPPjgg/ipn/op7Ozs4LWvfS0eeeQR4+uaIMoc\nsCyKkIWOMFmS2MbIXVEylydKQXEQHWVG4QCE0a249GeexsBu0MUs6MGLOX6TSIx4HPOgsyJzAODB\njr0dQozYLQInInMicX+Tt3ODrtG2gPo5jXucxchd1v3HkTV6l7xtxvcEy1widf5c3UQoEpckc8Bq\nhI7Y1KKI2gvd5z//eVx77bXh/6+//no88MADkW0eeughXH/99eH/L7nkEjz22GNG183C9vY2zp07\nF7mMXkBpa0zaIneWFYQ/aSk6zUSnybRpuiwkSZyKNGLnBt3wRyRJtpLEzvW7mPm9RLExEbt50MHM\n7xmllEnsVMImXxa3rfpYHXhwjJ/rusmdKWm+9HC6NZ46fYZuMmkkTkQndJtKI1KuSQRBsCJSZT7J\nJ06cCKN/Rd5mW9KyotSt+0Sim0da1MisNOKWRFwKdOYfdj3XSTKJVlx6VG4D4vrdlW1MCh5Ut6WM\n7B3Il2OQYjUVNXFbVepW9dymSZtaCFIXEtD+6blLk2pNc70i4VYnq3DKtTrSypsKjtBFqX2E7uab\nb44UOTz88MN4yUteEtnmlltuwVe/+tXw/88++yyuvvpqvPjFL068bha63S4Wi8XK5Unr6NKQJmpX\n5wW+JlE7C37mNUKmUbikdGwcWaJwaaAozszvhT+RvydIsUkUbboYKGVOxCRSRbelkrnIdgZFIGKx\niGnBiBi1S1PsoRudpnpNmES15OumicZFbivjFw16z+RZW8dRuyhisURdP0+bTtZInA6O0EWpvdAd\nP34cwLJa9Vvf+hbuu+8+3HLLLZFtbrnlFvz5n/85zp49i49+9KO47rrrACwjaUnXzYpqIWaRQkfo\nUrJ1/+DJUl1ncpKik2bWNKqp2JUtcYQocXEnd5PHUSV1+14f+96yY7rnO/D8eHFKSkHOvS7mXtdo\nX4Be7HSXpWnrkiYdC0Tl31Tu5dexyesnrrJVdf00lbCH18m+RCHP8ohNos6fr02iaIkT4QhdlEak\nXN/3vvfhzjvvxHw+x1133YVTp07hgx/8IADgzjvvxA//8A/jZS97GV784hdjZ2cHH/nIR2KvWxZl\nCJ2IKHXPPPtsabeTlSK/8ZPULU9cijcszAexq/cfvb4sEnn3H4ccgRNJ6j+XNEmBpG7u6aNxnu/A\nseNTonIaVrc/k30B5v335G3l1K3q+RClzqSaFliutxNxYHY9UXZNmy2npexK2KRqaIbTr3kpQ95U\n+L6/MvJrk+FJERl5+ctfjr/4i7+IvJj29/fDGXPrpGq5Kzp1k7VhcBr5kgUjNjpWgNTFSZyOpJO5\n6qS8vzi8HcfgpG0kYwaROJN9zYX9dA1uN7Jvy0v9PJjK3cptGcqdjCx4RtM+YrbJu77OVPBY7tSw\n1KVjXRInsre3h263uzK3tdvtbmQ7k0ZE6OoIFUbs7OyEl5UdodNx+pJLwt+rkDs6IWQVuyKrW5f7\nWz2OpAhR3HWzRuuySJxImmidKHKEd/D3OLEjWVPJmLzPJAnT7WuuEEK6zETsgsCKpL5NJBTIFrkD\nltE78TE3HRFmOk1DRI7GFVEkkXZdHRdLqOGCiWSqkDgRTrlGYaHLCLUuqYPQiVQpd6bVrabNgvOk\nU7OufcsrdnklToVufNd0cfitNLZCNbASo3Vi6lQlh8BSwkwETBQ7lczJ+wT0Yqd6HcVJqA55rV2c\n4MmPtWnFrPwcpEmdql5baeWuiNFhLHNqWO6iVC1yBBdFRGGhy4iqubBlWbWaIVcnucsShUvTdkRe\nE5W3dYqp2MmVo2WtvROjdaLIEX5gJ0odkBytyytghOt3AL+Djr1aDW6yX5PnTEwFp5E7QB29MxEo\nuRDHhm8UkcvStiT9FIpskyVY4hgT6iJxIhyhi8JClxGd0FUdodOxbrlbSeMIRQ5ZkEVJPrHGCliO\nlLCFILJPucGv6fWKYDxfipxOyoz6yWmidZOU6dU4sXP9w4+VxcHvacTOD2z0DLcn8sgdvS7o+TJN\nmVK0Lk+hRFJaXbWtyfby+yy2cjxlD75NZFOjcnWUOBHf91fkbVNlDmChy4xqWkRTqDRyl1HsTFuU\nFCl2sryl3WcR0TqSOJGkFKpJtE68vixzgHl6Vd5OlDkRU7EjORL3U6bcqZoyy21b5McyLu2qavlC\n188bjcs3C5gaISfMpuX1dCEscfUnCIKNLH7QwUKXkZMnT+Kpp56KXFbnCJ2OsuQuSZ7EE4ssd/LJ\nLS7tmfT3NBImrrvzM+xTf1/TR+tUIidShNTNYlqbAObp1bnvGN8/ndjF9b4rSu6AQ8GLm64hk6XY\nQUWWaFwREyWKaj68yXLXdpokcYTuXMsROiY129vbkSkUQDOFTqQMuUuzlk0nBUmRriyFDGLVqzKq\ncnA9ndhlidYlSU+SxMlklbo9Vyrxd+JP+EnRuuniUAwHHTPhEsXOdJYqcCh3acWO8HwHtuVjAf0o\nMRXi46iKFquidjoBzBKNS7uerogCCWCzJa7thRCXXnYZOp3mKgCtn9tkgZNp7rNZMU1bQ5eWMuVO\nVyRhGnVL+nucvKmuRydendgVFa2Tr5NW4FQkFTvI0SVZ5gBg7tmZpU6UOQDYXyw/UkzFbjpfpnv7\nhl+h4FAAACAASURBVNuLxyNikh5WCZZq0oQoeaZRObkStuzWJbr1dFzpWg5tkTuKxJ0/f77xIsQV\nrquw0GVEJXRE215oeeVOLTfxa+myROXkyIlK0OL2a1t+6dG6yWJwcGwrV8lFUrROJXIiplIHHMqT\nLHMiJmI39w4f69nB9iZipxKepKbFaQRrETih1KVJt4pRuri1dDJ5o3HR69qZpY5FzozAsholdap0\nahvOUVzhugoLXUZ0Ebq2k0Xu4lOR+rV0y7+ro3LySU+ZBouJvOnELilat7wt9QlX3he1F1Gf3Ncj\ndRf2zdOrJlIHxIuczP6io5Q6UeZESOwAtdyZyI0od31nbnKYEVRp2DhBS9t4mJDHmulI2/IkTYVr\n5Ho8EiyWpkucSBsySaoK102HhS4jNClChtKubX6hBUEAz/Nw7OhReN7ypDSeTBKvl7SeTid3JkUS\nYQuJNYmdKHX73mGVqO7EX4XUyTIHJEsbiZZumz1XXC9nJiRytE4nczJy1C5tYYBjBeFaPRVyYUba\nUWG6107ScdJrK2lZgfK6GaJvafrTcRFElCZJ3NaRIwCAXq+H+XyOTqejPA+RzDX9HKWrcG36/coD\nC11GOp1OKDMibVpHJxIEARaLBRaLBTzPg+M4cBwHvV4vvM9bW1uwLMsocmcqd6q5rnFr6SJpL7kJ\nbEaxm3oD9Z3A4Ro2cfuqpe7Z8RC9nJE41TaizAHA/mL53KQROydDpehs0QF9RptG3Ezm2MoVt7Se\nzkTs4qJrOlHL04A4i8hF95NBBDe4hUlTRE6MxNEXbdd1MZlM4Ps+ut0uer0eut1u6/q1tT1wkgUW\nuoJpk9D5vh9KnO/7cBwHnU4Hg8Eg9o2UNi0bl5K1hUHpcXKXJmoni50851VeKE8nb9U8WMcKlFIn\n7j/5chxcvrL7VHx3ciiermcXKnWyzInsLxwjqXMXNnDwXAy75kUQ4ktN1WpFljwTmSNUffFUhRLh\nbdmu8b4JXUQuTTQuTcuT6G1wkUQWAll+avSZrkunWpaFTqcTVq56nof5fI79/f1wiH2v14PjOK0Q\nIV5DtwoLHRMSBEEocZ7nIQiCMAqX9CGgSzWnkbukqB3JXVqxo32ZNiemCI2p2KmkDlhftE4UOZEi\npC5O5ESSpG4pc4dM54cfPXFyZ/LZTJJHxRB+YFb1ajq9Yrnv5bZxryFRwLJE44CDSF7cVAejlifc\nsqRoqq5yzdInjrIog8EAvu9jPp/DdV3M58svQNPpNPxsbyIcoVuFhS4HjuNgsVhEevk0LUJHYXqS\nOGCZTu73+7Btu9A3TFFyJ0ftdJWwsniZRuyIOLFTSd3yNteTgn1uPJSup75SHqk7NzmUuVE/WRJ0\nKVhZ5mRI7mSxS/PSkwUubibtsDNLsV9z6fNhw8HqLFoTMXKQbv0esdqAOGdqliUukXXJXZHNfm3b\nRr/fR7/fx3w+x3g8hu/7uHjxIizLQq/Xa1z0jqdErMJCl4Pt7W1cuHABJ0+eDC9rgtDRejgSOdu2\n0el00Ov1Mr9B0t7vLHLnK1Jh1LdLFTnRRdSyiF2eaF1cCjat1MkiR/iBVajUiTIHAJOZbSR1QDRa\nlyRzIqLY5ZG5OHrOQpk6X93nMorhHTw/SWv/4oRMuUb04LnKKnLhflZanmSrcBWPiYmnKRIXh23b\n2Nrawmg0CtfdkeTRmjvVurs6QWsEZep8zGXDQpcDal3SBKETU6lU1JBX4oqC5C5J7GxhIbosd3HF\nEEWIXVnRuiSpOzuOplOzjBED0kmdLHNEWqmzU673Orxu9GMpLiVrKnM9J016dbXwwlM8RwDQtbJN\nrKDXnq5BcBxp+9JFrxuTzuUKVy1tkDhCTFWq1t25rov9/X2Mx+PwHNHtdis/T8hwynUVFrocbG9v\n49y5c5HLLMuC7xezhiUPtB6OonC+76PT6aDb7SYWNWShCJFNE7Uzkbt1iV3aaJ1K6p4bj2CKTuri\nonSAmdTtzeKjV5PZ8tiTxG4ys3Gknz76pHpZiuvtiGF3UbjMqUROu60kcnGSTQKW1Hcuro1Jnlmu\nh/tKMb1igytciTZJnEicCDmOg+FwiOFwGFl3N5lMwvXU3W63FuvuuChiFRa6HMRNi6gCWg9HEgeU\ntx6ubIqQOzFqt4C4zvHwg1qVflNVOar6manWaakucz3FdaVebF3HV/Znc2zAS/H9II/Ufef8MjJ3\nZJh8g3HROpI+kkNTsUvz8gwCwPWSTyqDzhwLPzmy0Hfm2qpkkSwROVWVaxnROP0+8n/B3JSmw22V\nOBHTyJa47i4IglDuptMpbNuuvGpWdT+adI4rAxa6HNRhnqtuPdxwOFzr4OIy77dO7tL0r6O1Sp58\nuSK6porChb3KBLGjCJFq7JR4Wc9ZrEidSuDSSF1c6jVJ6lSQzAHA3tTOLHUkcyJ7MydR6tK8TE3n\nxA46yRG3uL52ouD1DqJ3SaPoRLL0qgPy95xb7osrXU3ZBIkTyZKqFAsn6Jwzn8+xt7cHAKHc6ZoZ\nlwGnXFdhocvBzs4Onnrqqchl6xA6MZVal/Vw6xJZUe6efuY57Xa6/nXiInRR7vKKnWpYvCx1QDRa\nl0bqVGSVOjFKJ4qcSBapU8lcuL+YaF3RMpdX5ER6hmlYeh5EiUvbO07VeidNhI4lzpxNkziRvCJk\nWVZYNEGpWYrceZ4X/q3MdXdBEHCETgELXQ62t7fx6KOPRi4rQ2zWvR6uKVx6+lT4ex65K0LsdNE6\nWfTkaJ2p1OlSr3mKJL67G5+yzBOp0+4zZRpWJI/Mic/DqOMqC1FE+s6ygbCplKWNxtE+k6JxSX3n\nWOLMKbt/XN1Fjiiy3YdlWSvr7lzXDatmKdBQdLCBZG5Tz306WOhyoFtDV4TQiU1+xfVwde0VVHV1\nb1q584SXPp1UF8HyMtvyU4ldXBrWJAVbltQlpV53jnqJUmdKXHROBYld0mf8Vm/5+MkyJ1fCLrd1\nY/vPjTrJUx5I5GR0UtbJWOWat++cnbPdCcASVwRNkTiRMlOVtm1jMBhgMBgo192JRRV5joHTrWpY\n6HKgq3IFsr3gxCa/4nq4wWDQuKKGKjGROweHJ2KSO/HkTHInRkRI7qgdh+tH05Xugdj17MVKdE6V\ngi1S6nTESd3jz9gAAmwN9a8rkyjduV0b20fTR4pMvrCPXQdbPU8pcCJbvXhZS5I5ncipWLfEESxx\nZpQtcSe2t8MJDE1kXV+8VevuXNctZN0dV7iqYaHLwc7OjrIoIg2q9XCO42A0GtWu708cdWnXIpNX\n7kjsgNV0LK2vIrHrHaRhXakilsSt78xLk7o06+mWImdOnNSd27Uj/2YRuzgoQhe/jV7GBgdrF3Up\n1t6ByOn6zEW2tQ4KIwLq4ZV8YlTNYyVM1seJ2wSwM6VYN0HiykaMxO3t7TVeHNZ9/OK6OwpczOdz\nTCaTsEEwRe9Mjo0jdGpY6HJw4sQJXLhwYeVy3VxTQh56T40dm7weruqUqwlp5C4uapdW7EjcxMHy\n1Fttq+cWVihhInUqmRtP46N0gPl6OtNoXRHfVUSRk1OtR7vx4716KSJyJHIyqiprIqnnHKCvdI0T\nvSyTINIIaBsoajRXE9OpJlQtQ2IzY3Hd3f7+Pvb29kLxi1t3xxE6NSx0OXAcRxmVkuVGtR7OdOg9\nUw5JcidG7RZYiph4kvYCZ+X/cWInV7kOuwtM5x2M3V64j7Hr4MRwbiRwWYok4iJzWaSOonIySdE6\nU5nTReemcwfHh/r1ciqZowbPg0602EGHKHEBLKNomonExZGlXYkoeElyt4mTINLKnYnEVS1Eeanb\n8Yvr7qiZ8Xw+x3Q6heM4Ebmj4/Z9v1EZrHXBQpcT3RuD1gxQKpWqgdq6Hq4JETodSXLXweHJXZY7\nitLR/92gC4cKIbwubMvXih1JHbHV83B+ehjFm+wvP7AuOaaWPFOp+6fH6bcAw0G+1x1JnU7mRFTR\nuiwyN51Hxe34UB1do3Vy8nQO4FDkktBG42IEMOuaOqC6nnObOAlCJ3dpI3F1E6K01Pn45WbGtO5u\nd3cXAMK0rO/7HKFTwEJXIPQC9H0f+/v74Xq4OsxLZcxIK3crUTpRCAQPmXtddCyqprXDNCzNKT1M\nwXoYu8srjgY+Jvs2nr14KHkntqJRoLgiiUORM8MkSgfoI3O6bdOuq9vqeSsSR8gy5/k2jvb2ldv6\ngY1RV/03EZ3ExdFEiVPuZwNETkVb06km1FnoRFTr7mgMGa03d13XeN3dJmAFTQ2r1ITbb78dL3vZ\ny/DpT38av/qrv4qbbroJQRCELUY2Bd/3MZ1OsbW1VfWhlEJcKxTgMHJHiO1O3CD6t/nBWrp9r4OO\n7YdpWDFaR1IHHEbqiN0J8LxLBJH0gceeULUtUR9rUpQuSermGV3m5HGDtXUxN00y5wmjvEjmxKIH\nlriE/WyoxJ254opC9nPhwgVsbW2FA+2bRBAEOHfuHLa3txstQXt7e2FzYc/zwvPt1tZWLebMVkXz\nXpEV4/s+vvjFL+JjH/sYPv7xj+Of//mfcerUKdx111248cYbMRwOMZvFL8hmmsfpS06G1cgXLu6t\n/D0ucidqvRt00ZWmFCwOInditE4VqSOOjoDHn5U/tNZXYdztZJO6i+PkyN6JI+r7caS/iIjcVncp\nd6LIDTvL911csULfNi+GAFjims7O9ja63W6h8tWUCFccTT9+YJl+7ff7kXV3m05j84C7u7t49atf\njSuvvBKvec1rwt42Mvfffz+uu+46XHPNNXj/+98fXn733Xfjuuuuw4033ohf+IVfwHQ6TbxN3/dx\n/fXX4/Wvfz08z8OHPvQh/MzP/Aze+c534o477sBoNALQ7PVkWWnjfabGmPv7+xiPx3BdF7Zt49TJ\nbWyNBjh9yUnl9TqYhz/Acn0d/fSsefiz1Zmg68xxtDfFwFnAtnzYlh+KnbiObDTI0OdN85k93Y9/\nnsbT+L8/+fQCz5xNJzpdg/OpSuYmMwtH+oe3tdV1Q5kDlhJHPzr6thv+mCA+X0FYd2p2ArThhz9Z\nseCHP3mxrGDjZO7MFVfgiu/5Hlx26aUAgMlkgvPnz2M8HmM+n7fucyoNbZBRAJE1dLTu7siRIxu/\ntKmx9/73f//3ceWVV+Ib3/gGzpw5gz/4gz9Qbve2t70NH/zgB/FXf/VX+L3f+z2cPXsWwDJV+vDD\nD+MLX/gCxuMxPvrRjybepm3b+Ju/+Rs8+uijeM973oOXvvSl2NnZWWkuvMk0/cOSJG46nWI8HmOx\nWIR9AUejUViZTFx6+lT4o0KUOwtBRBYAROTuaG+Ko70pjvfH2Oq5cOxAK3VHR9HbGQ7TvZWzSt2T\nTx/KVVqpi0OWORK508eWUiyKXJLEiQKXReJ0iHInSl5dJW6TRO7MFVeEP8Bha4zRaITjx4/j2LFj\nsG07lLu9vT24rpvp86rJUtTkYxfRjS9rw33LQ2OF7qGHHsLP/dzPod/v4w1veAMefPDBlW2oR9yt\nt96Kq666CrfffjseeOABAMBtt90G27Zh2zZe+cpX4m//9m+Nbvfyyy+P/F8ldG2MViXR5DeSOFya\nJK7T6WBrawvD4dB4yHSS3DlYROSuYy0iP33LDX9O9S/gstF5XHbkAq48vhvuo0ipKwITqUuKzoky\nN5lZmMwsnD42x2xhhyI37Mww6u5j1N2PCAv9pBU4wEziYq8PL3dalSUuO7LExUHzRo8fP47jx4+j\n0+lgf38f58+fx+7uLmazmXFj9CZLUZOPXUR1P9pwv/LS2DV0n//853HttdcCAK699lo89NBDsdsA\nwPXXX48HHngAd9xxR2S7D33oQ3jjG9+Y6ThU81w3UeiA5IbKdULV3Lnb7Ro3d066r2n63FETY7li\ntmMt0AHQ77i4/uQ4MrXiv3/7dOIxAsu0q644Yrof38ZErnoVo3Miz5xd4PTJ7B8lk1n0GH7gsrNG\n1yuisME0lUrkHd8F8Lq4PBRR2KDqe0bD5JOa2jb9c70pn89JtOV+FE2the62227Dd77znZXL3/3u\ndxf2xvr1X/91HD16FD/90z+d6fo7Ozt48sknI5dtqtDVGbG5s+d5CIJgbc2d08gdAPhwVsTDg4Ou\nIDA3n3kSc2mW7DzowPU6+NwjR4o4bACHUqeTOUIndUnRuRuf96zxsWQRuABWbGGDqmGwKHlFCNzy\ndljislJUdaoKue+ZOEyePh9omLxIU2WiDSJE1a0coVul1kJ33333af/24Q9/GI888ghuuOEGPPLI\nI7j55ptXtrn55ptx9913h/9/+OGH8apXvSr8/x/90R/hM5/5DP76r/868zFub2/jq1/9aubrt4m6\niaxqQken00G/38/d3DnrfTUZPyYOYfcPmtnJYuHBQdeOCo4d+OjbLl71oknk8kUQPRn95ZcPizmS\nonRpUEnddZdfRM9ZhFM0gPgqVJGsFaZ5JzawxFVPmRKnQx4mL04ssG07HCTfZNogdERb7keRNPbV\necstt+Dee+/FPffcg3vvvRcveclLVrY5fvw4gGWl65VXXon77rsP73rXuwAAn/70p/Hbv/3buP/+\n+zEYDDIfB6dco1R9v6kvkTihg+bk1mlCh+/7OLlzAr7v49z5i9rtVHIHqAVPJTLL1G10ndj/8ENP\naYfV03VkXvw8aRvF8tu4CJovjqhSCIpO3kzlr4qxWypY4rJzcmcnbMZe9WgnUe5Go1E4sYC6KUwm\nk0aObmyD0OmmRDANFro3v/nNeN3rXocXvvCFuPHGG/He974XAPDtb38bb3rTm/CpT30KAPC+970P\nd955J+bzOe666y6cOrWMkLz1rW+F67r4sR/7MQDAS1/6UnzgAx9IfRwsdIdU9SYTJW6xWMC27bDR\nZJ3K2H3fh+cdiodt2+h2u7j8stPhY/ftp57WXt+GFznR+4J4dYS0rShPthUVDPqbfPnhPm2tHNmW\nHwpW0geHvDZNFCbV/nVr2XRi48BLvf5NhiWuei679NLwtU/vY2otYts2HMcJi9eqQpxY0O/3wzFU\nJHe05q7T6dReNNpwXuIKVz08KSInZ8+exRve8AZ85CMfCS8LggDj8RhbW1sb9SKjcWfdbjd545zI\ns3Idx0Gn0wlPAGUznU6NGpbKEkczfcVv9pQWns/nYYVtt9vFc2fPGZ/kfUVULfxbQjF7RAAVcpJ0\nfSA+kiYLXBoRo21N06BJ+65Ts19gMyXu0tOnw3Sm53mhLIlCRHJH66XqIneLxQLj8TjM/tA4Ktd1\n4ft+KHd1HUc1nU4RBEHYM7WJUBr82LFjkcvpHLDJbPa9L4ATJ06E7VEIy7IaVfHZFEiOZIkzrUxd\nB77vh2v3CPFkJEqc67rhSY0kbjQahdt8z+WXhvt4SlEcJGIL0iTLnSwxsvTIshQXXQtvQ5I8EhNx\nX9qoW0Ihgu42TZD3rbqtbPtlicvD5ZddFvk/FSJQlelsNsNkMgnfB+KXJXEtLLD8fBXfU+tE/kyn\ndijD4TCMLlIjcrFiti6fT204J+nuQ9PvVxGw0OXEcZxIBGaTKSPVnLe9yDogiaM1e0CyxNF96ff7\nRqka8YSYRu5UkTPxEnFd3uHfo8+hSsxs+ImyZCJu4rZJ4mkKS1x9kEVORqwyFSPV0+k0fK9T5N1x\nnFDu6IsdyV2n06l8eQUdI7VDocgdyR1F7qo8Tl26skm0QUrLgoWuAHTfFjibnR75A3ud7UXSQikh\ngiKGFKGlbSi9FARBKonTkUbuZJGQBc+WonM6wRMlKc/aNXE/aYUw7nZZ4upDksTpoEpSVZUpLeWg\nFiKi3Pm+j9lstha5M5UJXa+7yWQSfp5Vsca3DTLEETo9LHQFwEK3JOt9LrO9SJHI6VRa7ybKJv1d\nlLhut4vhcFiKkKaROyAqGaronR1JmapPNlnlLq90FSVtq/tlictLVonTIbcQociduE5Xljt674ly\nR++5osQpixDF9boTJVbudVcGbRC6qiug6wwLXQF0Oh3M5/NIMcCmCl2a8Tme54WROAC1bS8ip1Pp\nhEIfzuPxODxp0P0vU+J00En1Tx8c4darvpm4fZLcyaKjEry4KJoc/UvaVxJx4pV2fyxx+Sla4nSI\nVaai3M1mszAiR3Jn23ZE7lzXLU3ust4XWVRd18XFixfDqvcysxFtEDquctXDQlcAVBhBLVGAzRS6\nJHTtRYbDYa2+cVEah54/6mUnLsKm+0J/p8vow4Z+qviQ+ZlbJvjTB68G3fS/uzK/3AFRCVLLnW8c\nr0val7xNmv2p9lmkwAEscVUhyx0taaAvVfQ3+mwR5W4+X/ZJzCN3RQqReLzU624+n4ftUGjNXZHt\nUNoidE2/D2XBQlcA1ItOFLpNRCWx9C2URI5SJKPRqBESJ7ZJENf10ILsbreLra2tMF2iiiCIJ5l1\n8TO3TPBfHlq2Jvi7f706vLxIuTPZzoSiZYv2WdTxhftkiasV9B6lyL4sd2LkTqyYFXvdiS2ETN6f\nZcmEKHdixexkMoHv+2FUL6/ctUGG2nAfyoKFrgC4uXAUXY+4fr9fqzciSRx1HhcjcWLUTZQ4EjRK\nDcvo0kP7+/uRlgzreByeOevj9MnoMZLcnT0f4DUv+ufEfZhKTNHylBXd8WY5vk0VOKC+EqdDljuK\nyFHfNXHNXV65K/u9K94Xkjtac0d9+7L2umuDDHFRhB4WugLY3t7GuXPnIpelWU/WBqgy1fM8jMfj\nyIdrnd5oumkNssS5rhvKGK2Z00mcDlnuxKq9dcjdz//4Pv7Tfx2EUnf2fFRQPvblF4S/m8idKYlr\n8xSilCvCl1K84o6PJa75iM27xV53otzR55NO7nSNjKsQIrHXHa0LpF53VJRlmgFoq9A1/T4VBQtd\nAWxqhE7uEUdSJDbHrQMm0xpI4uRpDWklToe8GFqWu7JGB5HUOU78ftchd6bbJcldkdK1yQIHtEfi\ndIjvdUrLLhYLzGazlV53stxR2yRR7qru46ZrhyLKna4dSlvOR1zlqoeFrgB2dnbw5JNPVn0YpaNr\nL0JVWUEQYDqdVi5zaac10P1RTWsoA1Hu6PGkHlWqMUjrpiy5M0Uld5suXkXSdomLQxW5oykV4ntP\nNaWCInhAPaQirh0KZRXEdigU2ar68zkvHKHTw0JXADs7O3j44Ycjl7UlQqeTOF17karus25ag9zo\nVx65RR96VUUVxT5U4glGjh7kOTbTKJ3MU09N8ftPLU/+137/CC9/QXJBRdGwyBXDJkucDtWUCvpi\nJRZUyI3BbdteWyNjU3QZAGqHsq4+d2XThnNqmbDQFUDbUq5iyoEEqc494uhfSq+opjXQeri0I7fW\niXyCEeVOXNSd5Zh//sf38ft/OTTe/qmnppH/P/r1CYDDatkq5I5JB0ucOeIXK8/zwvcdcLgWVo52\n6aZU0P6qQpQ7aodC6+6CIMBkMqnl5B0T2hJlLAsWugJog9DpesSlGU8jrkcr6w2nSqeK6+HipjXU\nUeJ0yHJHqRS5Yi/NffG8wChKJ8sc8ejXJ7j2+5etUP7ro4dp2R+/dv1pWUYNS1w2xJQlRe5HoxEc\nxwk/E/f29iqdUpEFsTCr1+thPB4DAPb29hAEQWHtUNYFV7jGw0JXACqhI+pcVaRrL1LFjME4dNMa\nVI1+KRJX9sitdSIuhKZ1PKLc0fOVdB+TonQ6kRN59OsT/MvXn8aP3vb88DKWu2phicuG3JJIF7lX\njSBT9ZiUp1QU0ci4aGzbxmg0wmg0CtuhUK+7PO1Q1gW1mGLUWEGTwkg1xfM8vOIVr8AnPvGJyOV7\ne3vY2tqq1QtQHHwvSlxR39DG43Ehkx9UjX7FwgYg2mpgPp+H30blXnJtRDxp0HgjMXIQh0rqTGSO\n+JevPx3+LordY99cfvt/9xuWz4/JbFkmGyxx2RClbD6fZ24fJH4ZFj97xPefKHf0OVal3Lmui9ls\nhqNHj678jT5HxQglCV6dPkd194E+8zcdjtAVgOM4yp5zlP6r+g0htxcRW3IUfWx5Us2m0xrED2Rx\nWkOd1veVjdxrSzcCSfUhJ6des8ocAPztfd/Cme+9RLmtKB0sd/lhicuG+Jlh0hzcBPE9pppSIY8g\no+PIM6UiL3HnIrG1i9wORZS7qiOMdTif1hkWuoLQ5fWrCIDSt0Kxl5LjOLVcCCtPa6BvsEnTGjqd\nTmTk1iYTNwJJNXpMTL2mkTkVssxRdE6GZITFLh1HtrbC548xR47eFyFxOnTvv8lkkmpKhTgDugxM\nZUhuh0JdAagdCqVlq/js5TV08bDQlcg6hU7XXqTf7681cmVyn02nNdCHcZ5pDZuG6uRCaQpR7v71\nX/bQ7a3/A5mjdsnQY5Q0fJ5PYlHoM5AERIzer0s+xPefuCzCZASZqpFxkZ91WaJblmVpe92JlcHr\nenw5QhcPC11BdDodzOfzlW/SZQqdqr0Ihc3r9oGfdVqD2C6FSYd8chHnygJHUu9PTrfK6KJzOlju\nDlGlU+Miryx3h4iROKrcrMMSDNWUClHudFMq6Is5yZ28djgreWVI7nVH7VAuXrwY+VuZWSCO0MXD\nQlcQ29vbuHDhAk6dOhVeVsaLrIj2ImUitg1RNfpVTWuQK82oOrUO96ctiH2zAOCXXzvFPX+eXurK\nYhPlLs2aOJa7KOI6r6ZUtKvWqammVIjtUMSlM6IgZvlsLHJsmfiaEytm9/b2ACBcc1d0OxSqxmXU\nsNAVxPb2Ns6dO7cidEVE6FTtRRzHwWg0qpX0kMRRyheAstGvPK1hXSO3Ng1RlqlyTWzLMHc947Sr\nKjonrp/7l69/B8D3FHLcaeTO0ry/ghq/jvIWN2yq3ImvZyruqrvE6dA1ERenVMi97mS5Szuloqx0\npfh6HA6HkTWEvu+Ha+6KaIfCKdd4WOgKoujmwrr2ImVUpuZBbvRrWVaYLqBvnLZtRySuztMamk6S\nxJXFR95TjMzJ6OROJ3Kqv5vInRUEpUpgWRWqbZc7uSCqaQ3CTVCNIKPUrJiWleUu7ZSKdciQPJfz\n1QAAIABJREFUSu5oSgVVzObpdccp13hY6AqCInQilmUp25noULUXaYLEAdFGv5Rqnc1m2N3dDbdp\n44dxHcgqcb/1Jgf/+4eT95+0dm5diFL0naeeMr6eTu5kKUwrgUmsu81IW+RO1fB3UyL4YqGB+DiQ\n3IkzZtNOqagiuuU4DobDIYbDYfilfn9/H3t7exG5yxNlbPtrIg0sdAURNy1CB70ZZYmra3sR02kN\n8sgt27YjJfp0eZ3uX9NQpZ+yyHKatKuOtMUQRXDZ5ZeHv2eVO9Pt0shdXXrFNU3udA1/N0HidKiK\nEKioSRxBZjqloup0pTjxRu51R+e9pLXgVd+HusNCVxA7Ozt4/PHHI5epUq51aS9igq7RryxxYtPO\nuAXK8jdOcSFwne53XSlK4tKgi86J6+cWi0WlX0Cyyp0pSXJXF4nTUVe5oy+ArusW1vC3rYjPk2oE\nmRi5o8eOPr/p80L8qfrxlXvdie1QSFZV7VBUo7/43HEIC11BbG9v4ytf+UrkMhI6ncTVtb2ITuLE\n9iKqaQ1UpKG7P+I3TnEhsCh3dYtMVk3ZEmeadtXxxGPPYjodrfTYarPcBZZVe4nTUbXcqcb19Xo9\nlrgUyHKnew4BhMJMbVI8z4sU1slp2aruj64dCqWgxbQsnx/0sNAVhJxyFReuTiaT8IN0OBxGKj7r\ngCxxcdMa5NmFWZt2it/Q5P5MVXYirwP0eFAavs5rDz/87ksBIPIcAqhFSq9ouRP31wZkuaMvD0XL\nnW7uME96yY/8HC4WC8xmM8xmMwBQClEVUypMkduh0PmG2qEACNdV1u2zsA6w0BUECd2f/dmf4Rvf\n+Abuuuuu8M1Rx55qqmkNJiO3yvggFmeS0sJZOqmkXTTbVFRrDweDwVqiXb/+s1BG6ZKKIZ547FkA\nS6GLew7j5squC5KxIAjwdIo+d22TOB1ijzOxlcZkMgGQTdBVr+k6NPxtG2JUi2SHAgfipJgqp1Rk\nQfzsGA6HodiJ7VB6vR76/X6lx1knWOhy8txzz+ETn/gE/vRP/xT3338/nnzySbzmNa8JF/PSSa0O\nkMTRwlLxQ1yUOPpgWPfILTqe4XCoHFtFjSqr/qApCtUJrwl9teT5rSKq51CO+qy7Cba8RODI0aPh\nieKZp1eldVMkTkceuZOXCDTlNd00VOsPe73eShEJRfdVI8gosreOKRV5Eat3jx8/Ht73yWQCz/Nw\n/PjxSo+vLrRC6HZ3d/G6170OX/rSl3DjjTfiIx/5CI4cWe2Cf//99+POO+/EYrHAXXfdhbe+9a2R\nv//O7/wO7r77bjz33HPY2dmJvc2HH34Yb33rW/HFL34Rt912G17/+tdjd3cXH/vYxwq9b3kwndag\nGrlV9eJkMZUgnpDF3kxNrJRtisSZRefi0a3X2tvbi8yVLeM1Jhfr6Bbcb7q8JZEkd/RepOeWe0yW\nh2pWrcn6Q/E5pPchpWaTet3laWRcNGKFq9gOhdP2h1jBuqbHl8g999yDxx9/HP/xP/5HvP3tb8fz\nn/98vOMd71jZ7oYbbsDv/u7v4qqrrsIrX/lKfPaznw0nOzz++ON405vehK997Wv44he/mCh0zz33\nHD73uc/htttuw2g0AgD86I/+KD75yU9GthuPx2tNuap6xNEbUVy7JzawFCWu7pKkajRa50pZcf2Q\nKHFVFw+oENOupr3naA1dGnSylVfudFWTm5CyXzckFrTgHkDYeqKu78WmIkpc0WuMVT0s5c9TcT04\nSVUVcjebzeC6Lo4ePRq5nESUaUmE7qGHHsKv/MqvoN/v4w1veAN+67d+a2WbCxcuAABuvfVWAMDt\nt9+OBx98EHfccQcA4Bd/8Rdxzz334NWvfrXRbZ46dcpoW1XrkqJJI3HiyC1KfzWp11MTKmV1ElfH\nSJyKMmUO0Ldg2N/fT/3FQq6a5NYX5aFaqzUYDMIoOj2Hdf7S0hTWlbpWjSCjVGbcCDKTRsZFo5tF\ny6+vQ1ohdJ///Odx7bXXAgCuvfZaPPTQQ7HbAMD111+PBx54AHfccQc+/vGP48yZM3jRi160tmPO\nS9K0BoJC5m1Mh9SpUrbpElcVKrmjflQ6uVO1vuh2uzhy5AhLXMHI6w8dx0Gv11NmHeS0rLhei+Uu\nmarHnGUZQSamgcuWO24qnExjhO62227DdxTVae9+97szR8Asy8J0OsVv/uZv4r777gsvz7q/TqeD\n+Xwe9gCi2ygqQpdnWkNbJE5HFZWybZO4X/9Z4Gd/uboxX7LciU2oqTKPLufWF+WRJ+qpW3PHcqem\nrhMyVCPIFotFpPFvmikVRXz28hzXZBojdKJwyXz4wx/GI488ghtuuAGPPPIIbr755pVtbr75Ztx9\n993h/x9++GG86lWvwmOPPYZvfetb+MEf/EEAwBNPPIGbbroJDz30EE6fPp3qGKl1ySWXHFYB5hU6\n02kNTVlsXzZlV8qKjzU1id7Ux7pMSNioeIcWcQO8VqsMyoh6stypadoyAVXjX3FKhSx3QHRKBVCM\n3OlSrswhjRG6OG655Rbce++9uOeee3DvvffiJS95yco2VNZ8//3348orr8R9992Hd73rXTh16hSe\nFloXvOAFLzAqilBRlNBlndZA/Yc27QNSR1GVsrqTXdJkDCY9ctQTQKR/GUULVBV6/DykR3xdAygt\n6slyt77Hukx0619pPZ34PMY1Ms4id5xyTaYVQvfmN78Zr3vd6/DCF74QN954I9773vcCAL797W/j\nTW96Ez71qU8BAN73vvfhzjvvxHw+x1133RVWuIrkecHI0yJofyZCZ9roVzVyi5t1JhOXztNVdqkk\njh/rJVkLIlSoUtdUrCM/1pZlrSzirltRTN2RqybX/eVkk+SuzX355M/UuDFyciNjeu2laWSsm0Hb\n9MexSFrRtqQuvOc978E111yD22+/PbyMyvpV3axVEieGpgF1iwdxEDOTD7lsnz54aJ1ilg75TeZ/\ness/4cj20cTtihA61TKBrCdysUKvbVJQBCqxqNvj06QWP3E0rbVS0ai+DIvnLPlLM62/S5K7Cxcu\nYGtrK/yMBg7TwcySVkTo6oIuQidWouoicXKjX3ozrHtaw6ZB0QJKc9NzI59MNoU//8D3lVoYUdZa\nz7iKZ3E6xSacUAmqVHRdN2xTVOfiqP+/vTsPa/LM9wb+BcJORSiLnbY6QqtglSpWsaVSB2WRTVmE\nhCVOnWlhppW2np5OPdNrZjpdzlxnpttrbes57fWWsCRBFtlUFC0uBQGXCtrBqVJErRuiOYhASMj7\nh2/SJCQYMOR5nuT3ua75A3jq3Nm/ue/f/bsNNcDlyswdWzc3MEG/ofh4M7ATOaWCllzvjQKdGXl7\ne6Onp2fM77W3dQOmndZAIW7qjLecqt1ryRbPlJ0Khpb4pnLZyZRjq6w1pDPd+sKc2B7uuLa5gQn3\nCukTOaXCUKDj2nN6qlGgMyMvLy+0t7fr9IhT/0+hUIwJBdrLC9qnNVjyZAlbca8Qp22qd8raAjbU\nDunXahmr8eF6uLOF2SE2hTv1/7/6Szr1QDSd9uOoXwNrKNypvwwODQ0BuHtahHpmj+7vsSjQmZGj\noyPa29s139bUH/4uLi6apY+hoSGdbyHW+ObLFhMJccaYa6esLdAPceolFzYskekvA2mHOy4eEWas\n4a8tzA4xEe4MzTJzbYcq2xg6pWJ4eBh37tzRrF6p65rV7yMANKdUDA0NobW1FQkJCVb/nDcVBToz\neuihh+Dk5IT4+HhERESAz+cjODgYP/74I3bs2IHk5GR4eXlpikDVMwhcXA5hK2ObSMzx5jvRnbLW\nSntDhKEQx/YlPkPhTnsGlq3hjpb4xprKcMeGWWZboV/LDPzc4P+7777DDz/8gISEBHh5eaG5uRli\nsRidnZ1ITExEXFyczT7/9dEu1ymgVCrx9ddfY8uWLbh8+TIUCgWio6Pxxz/+EbNnz9a0MtF/c1Yv\nydKbxcQYCnGW/FDmwg7CiRpvY8T/fcdXZ2ewtYRZQ7NeTIc77XpO7Vlm9SYPYthkd1Db+g5VS9N+\nbgNja1xVKhW+/fZbfPjhh2hqaoKTkxMWLFiAP/zhD4iOjqbXgB4KdGb23nvvQSwWo6+vDykpKVi9\nejWuXbsGqVQKR0dHpKenIy4uDq6urpr/xtCh19QJf3xMhzhj9D9ILH2mrLmMF+j+zxsuVv9BZ6wu\nzVJfuAw1oeXi84gN7hXumH6sbY06NMvl8nG/APf29qK8vByVlZWYMWMG0tPToVQqUVVVhV27dmHB\nggX43e9+B4FAwOCtYRcKdGb2xRdfICQkBMuWLRsTLC5evIji4mJUV1dj7ty54PP5eOaZZ3SuM/XJ\nbovYGuIMMTSzwqWdsuMFuq/f9bOp56L+rM1UfeAbqtOi1755aW9oULfHUJe/cOn1yTWGQrOhSYuh\noSHs2rULUqkUAwMDyMjIwLp16+Dl5aXz7w0NDWHfvn2Qy+VITk629M1hLQp0DFCpVPjuu+9QUFCA\n5uZmREZGgs/n4/HHH9e5ztB0tK0ttbBxGWyi1Mvr6hlYNu+UVYeXF96+afQac54SwTXmXpKzxuV6\nNtOfrbO3t9fUbNF9b17Gaj7137tHR0dx5MgRSCQSnDp1CnFxccjOztaUJxHTUaBjmEKhQH19PQoL\nC3HlyhWsXbsWqampePDBBzXX2Fq9nTWEOGPYuLxjaAbq9+/fNnitLYc5fYYa95oS7rRn4a2pBpGt\nTAnN5jy1xNbdqy4OuPsa6OrqglgsRkNDAxYvXoz169dj6dKlnH+PZxIFOhaRyWQoKyuDVCqFm5sb\nMjIyEBsbq3NsmLXW21lziDOGyQJsQ88j/WBpaNmVAp1hhnb7Ojk56dRp6Ydma3jdstX9vLYo3E2c\nqaVCfX19qKioQGVlJby9vZGVlYX4+HiDR2OSiaNAx1Lnz59HUVERamtrsWDBAvD5fISFhem8OLRP\nl+Dico0thjhjLLH0Zmx20NjSLwW6ydE+V1Z9oPjo6KimV5y1zqwzbSpmvyncGWdqXdzw8DD27NkD\niUSCW7duYd26dcjIyNBZhSLmQYGO5VQqFY4ePYqCggIcPXoUq1atgkAgwOzZs3Wu40q9naGZCvUb\nL9vGyhRz7pS9n9BMgW7i9DfuqI8tUp8RTIHAvEyt0zIHCndjN3uNVxd37NgxiMVinDhxAtHR0cjJ\nycHjjz9uM/cVEyjQcYhcLseuXbtQVFSEGzduIDk5GSkpKTo7gNhYb2dslyAbNwWwyWR3ypprNzAF\nOtOYGiqMBXXtM52JaQwdv2XJL7C2Fu70d2Ab+pKpUqlw/vx5SCQS1NfXIyQkBEKhEOHh4fQ+byEU\n6Djq5s2bKC0tRWlpKaZPnw4+n4+oqCg4OTlprtGvk7J0jRaFOPO5107ZqWjpoh/oKMz9zFjDX1Nm\nUtX/rX4goT5z49O+z9QhSh3imAxR1hru9Ovi9OtC1WQyGSorK1FeXg4PDw9kZWUhKSkJLi4uDI7e\nNlGgswJdXV0oLCzErl27EBoaCj6fj8WLF+u86CxVo0UhburpB3V10f1U1CBqhzoKdOafZTN23jAb\nyyWYYGqoYAulUqn5YmUNdc3G6uJGRkbQ0NAAiUSCa9euITU1FQKBAL6+vgyOnlCgsyIqlQpHjhxB\nQUEBTp48iZiYGPD5fMycOVPnOkNd6Cf7AWKppqvkLkMBQD1DNxUtMCjQGZ4ZmooPaUvWg7GZtezk\n50qPwYnUxZ08eRIlJSU4evQoIiMjIRQKERQUxKrbY8so0Fmp4eFh1NbWoqioCP39/UhLS8PatWsx\nbdo0zTWTrbdT99+iEGcZxmZx9JfopuIDxFYD3b3akEw1Lp2KYg7GSgqs5T2F6efTeGNSf1ExVhd3\n6dIlSKVS7Ny5E8HBwcjJyUFERASVB7AQBTobcOPGDUgkEpSVlcHX1xcCgQArV64Ej8fTXHOvejsK\ncZZlaogzxlw7ZW0p0LH1YPaJtpvhCq4fjzdZhsKdpWub77WE3d/fj6qqKpSVlcHJyQmZmZlYs2YN\n3N3dp3R85P5QoLMx//rXvyASidDQ0IAlS5ZAIBDgySefNFhvp37Rq2u0KMRNrfsptJ/IvzmRD011\noLPWMMe1kgFTGkKznSk7Jm2FJcKdqXVxCoUC33zzDcRiMS5duoS1a9ciKysL/v7+nHlu2ToKdDZq\ndHQUhw8fhkgkwj//+U+sXr0aGRkZ4PF4qKqqgkwmw4svvqiZxdNe9qGCbfPR3vGof1SOuXfvTfZM\n2fV/vGpVgY6Nx69NBpfCKFfqyZhk7nCnfq2PVxenUqlw6tQplJSUoLm5Gc899xyEQiHmz59PjwsH\nUaDTs2HDBtTV1cHPzw8dHR0Gr9m8eTOkUim8vLxQXFyMoKAgC4/SvLq7u/Huu++iqqoKg4ODCA8P\nh1AoxNq1azUvajb2t+MyQ60OLNmCYSKhxhoCnaEwa001aWxcLmbjmLhisuHO1Lq4q1evQiqVoqam\nBoGBgRAKhYiMjOTsLOmFCxcgFApx7do1+Pr64sUXX0RmZuaY66zts1sfBTo9hw4dgoeHB4RCocFA\n19raik2bNqG6uhr19fUoLi5GbW0tAyO9f0eOHMHmzZtx4sQJxMXFYd26dVi0aBGqqqpQUVGBhx9+\nGAKBACtWrBjzhsBUfzsuY2u/Kmv94DVUh0g1WpY5I5jrs59scq/HU/v1q1QqjdbFDQwMoLq6Gtu3\nb4ednR0EAgGSk5PxwAMPMHjrzOPKlSu4cuUKFi5ciN7eXixduhQnT57UuW3W9NltDAU6A7q7u5GY\nmGgw0G3ZsgVKpRKvvvoqACAwMBDnzp2z9BDN4ty5czh16hRiYmIMNoH8/vvvIRKJ8M033+CZZ56B\nQCDA/Pnzda4xtJTChmafbMHWEGcM15fGqGmvLkv1n6R2K5Yx0fpmpVKJQ4cOoaSkBD/++COSkpKQ\nlZWFhx9+mBOv58lKTEzEpk2b8Ktf/UrzO2v67DaGd+9LiLbW1lbk5ORofvb19cW5c+cQGBjI4Kgm\nJzAwcNxxz5s3D3/7298wOjqKxsZGfPbZZzh79iwSExORnp4Of39/2Nvbw9nZGc7Ozpo39YGBAZuZ\nDTHE0Ieoq6srJ0KRocdzcHCQ9cXrhoKzu7u7zX+x0H481c9L9eN5v+HO0PFb7u7urHx+WAv1FxaV\nSgV7e3vY29trOhD8+c9/RmRkJCIjI/Hjjz9CLBbj4MGDCA8Px7/9279h4cKFNvFaOHv2LE6fPo2l\nS5fq/N6aPruNoUA3QSqVCvqTmtb+IrG3t9e8UQwMDGDHjh14+eWXoVKpsG7dOiQmJsLNzQ0ODg5w\ncHDQhAG5XI7h4WGbqLczFOJcXFw4EeKM0X481TsT2RTWuRycmTBeWDd1Zt1Qk2UKzlPLUF2cfnAe\nHByEn58f3n33Xfz617+Gn58ffv3rX6OxsRFubm4Mjt6y+vv7kZGRgY8++mhMixVb+OymJVcD7rXk\nqlAo8NprrwGwzmlbU12+fBnFxcWoqqrC7NmzwefzERERMWYXlbXW2zFZq8SUye6UNRdbvM+n0ni7\nrNWBQbt3mfo0Eqab4lo7Q3Wthu7zwcFB1NbWorS0FCMjI8jIyEBYWBgaGhqwfft2dHZ2IikpCf/4\nxz/w4IMPMniLpt7IyAji4+MRFxenWVbVZguf3RToDBgv0KkLK6uqqlBfX4+SkhKrK6ycjPb2dohE\nIhw6dAgRERHg8/kIDg7WucYa6u0oUPzMUgXw1rppg220a+HUy6jqpuJcPX6LS7S/LI33elIqlWhu\nbkZJSQnOnDmDhIQEZGdnY+bMmWMem4sXL6KyshJ5eXlwdHS09E2yGJVKhfXr18PHxwcffvihwWts\n4bObAp0egUCAAwcOoLe3F/7+/nj77bc131xzc3MBAG+++SakUim8vb1RVFQ0JrjYMqVSiYaGBhQW\nFqKnpwdr1qxBamoq/Pz8xlyn/cHBhiU8Y9Q1KtozFBQodJk7dFlDA12u0Q8U6i9aSqVSp2yCja9R\nLtO+z429F6pUKvzwww+QSCTYv38/li1bhuzsbDz11FP0eAA4fPgwIiIiEBISonl/eP/999HT0wPA\ndj67KdCRKdPf34+KigqIxWI4OjoiPT0d8fHxOjtq9ZfwHBwcWPHBTbNCkzfZnZX6M34ODg4WXc61\nRaacJEK7WM3PlH5xANDb24vy8nLs2LED/v7+yM7ORmxsLJycnBgaOWEzCnTEIi5evIiioiLU1NRg\n7ty5EAgEePrpp8d8C2UyRHGp8z5X3OtMWQoLzJjs8VuGQjc9XqYxtS5uaGgIu3fvhlQqxe3bt5Ge\nno709HR4eXkxOHrCBRToiEWpVCqcOHECIpEIzc3NWLlyJfh8Ph577DGd64zV25m7JQKFOMvQnglS\nL7OrWy6oW17QkXJTy9w96aiJ8L2ZWhc3OjqKlpYWSCQSdHR0YPXq1cjOzkZAQADdl8RkFOgIY0ZG\nRrBnzx4UFhbiypUrSE5ORkpKypjdWOaut6MQxwztxxGApuCellanjqVmvek1pcuU9yyVSoWuri5I\nJBLs3bsXoaGhWL9+PcLCwuh1QCaFAh1hBZlMhrKyMkgkEri7uyMjIwOxsbFwdnbWXGPqt11DaDaB\nGfpLe/o7m+lxMT+m71OmSyeYYqhHn6FVhb6+PlRUVKCyshJeXl7Izs5GfHy8znsdIZNBgY6wTnd3\nN4qKilBXV4cFCxZAIBBg6dKlOh8GpnxoUJE9M+5nU4QtBgFzYGstorXvEDe1Lk4ul2PPnj2QSCS4\nefMm0tLSwOfzrb43HLEsCnSEtVQqFdra2iASiXD06FFERUWBz+dj9uzZOtfpn2/I4909AEXdboEN\nH2zWztwf3Fw/U9ZStEMcMLYpMJtYSw/HidTFHTt2DBKJBMePH0d0dDRycnLw+OOPc+r2Eu6gQEc4\nQS6XY9euXSgsLERfXx9SUlKQnJwMLy8vyOVy7N+/H3PmzMGDDz6oWcqzs7ODs7MzhbkpYqhuaiqa\nz95rp6ytsYawy8XbYGpdXE9PDyQSCerr6zF//nysX78e4eHh9B5EphwFOo47ePAgcnNzoVAokJ+f\nj40bN+r8fXBwEHl5eWhvb8e0adOwadMmrFmzhqHRmsfNmzdRUlKCL7/8EgqFAj/99BN++ctf4u9/\n/zuWLl0Ke3v7+6q3I8YxWZ9lSs80a2XNy9H6gZ1N4c7U3fYymQyVlZUoLy+Hu7s7srKykJSUBFdX\nV4ZGTmwRBTqOW7RoET755BPMmjULMTExOHz4MHx8fDR//+KLL9De3o7PPvsM58+fR2RkJM6ePcv4\nG+VkHTp0CGKxGOXl5Xj00UexatUq2NvbY//+/Vi8eDH4fD5CQ0PvWW9HZ1GaTjvEMXF+q7ExMXmm\nrCUwvbmBCcZmYy15PKCp4XlkZAT79u2DWCzG1atXkZqaCoFAAF9fX6t9fAi78ZgeAJk8mUwGAIiI\niAAAREdHo6WlBfHx8ZprPD090d/fj5GREfT19cHNzY3TbzbqINfU1ITAwEDN71UqFZqbm1FQUIA3\n3ngDsbGxyMjI0Jxv6OTkBCcnJ8037sHBwXF3otk6Y0X2Li4urAhMdnZ24PF44PF4OsFncHCQ08HH\nUFBl0/0+1RwcHODg4ABnZ2fNa3VgYEDTq3CqltqNnVij/345OjqKkydPQiwWo7W1FStXrsTbb7+N\n4OBgzj3XiPWhGToOa2howFdffQWxWAzg7mzcpUuX8M477+hcl5mZibq6OigUCjQ3NyMkJISJ4VrM\n8PAwamtrUVRUhNu3byM1NRVr167FtGnTdK7j0nmylmBoSZPNRfaGcHVpUv88T2q0/DP9LxfmfF6a\nWhd36dIlSKVS7Ny5E0FBQRAKhYiIiODM64LYBpqhs3KffvopeDweLl++jI6ODsTHx+P8+fNW/UHh\n7OyM1NRUpKamore3FxKJBHw+H35+fsjMzERkZCR4PJ7ObID6A3VoaIjTMzyTYWinpLu7Oyc/rAzN\nxg4PD2NwcJBVtVmA4eO3uHq/TyXt2VgXFxfN83VgYGBSu9gN1cUZut/7+/tRVVWFsrIyODo6IjMz\nEw0NDXB3d5+Km2kRGzZsQF1dHfz8/NDR0THm742NjVizZg0CAgIAAKmpqXjrrbcsPUwySTRDx2Ey\nmQwrVqzAiRMnAAAbN25EbGyszpJreno6fvOb3yAmJgYAEBYWhoKCAgQFBTEyZiadOXMGhYWF2Lt3\nL8LCwiAQCBASEmKT9XaGmqCyKeyYG1t2ynJxdydbGVueNhTuTJ25VSgUaGxsRElJCS5duoS1a9ci\nMzMTM2bMsIrH59ChQ/Dw8IBQKDQa6D788ENUV1czMDpyv2iGjsM8PT0B3N3pOnPmTOzduxd//vOf\nda5ZuXIlampqEBUVhe7ubvT19dlkmAOAuXPn4t1338Vf//pXHD58GF9++SU6OzsRFxeHjIwM/OIX\nvxi33o7r7TIMhQlXV1ebCBP6tVlyuVxTmzXVS+2GwoSzszPrl4HZzlgd5dDQkKYGzs7ODgqFYty6\nOJVKhVOnTkEsFqOpqQkRERH4j//4DyxYsMDqHp/ly5eju7t73Gtojoe7KNBx3Mcff4zc3FyMjIwg\nPz8fPj4+2LZtGwAgNzcXfD4f33//PZ566in4+vrik08+YXjEzLO3t0dERAQiIiIwODiIqqoqvPba\na5DL5Vi3bh2SkpLg4eEBe3t7ODs76yzJWioEmIs6TGg3/LXlMGFnZwcHBwe4urpqlu/kcjmGh4fN\nulPW2A5Vrm9KYivtujqFQoHh4WEMDQ0BuPt6d3BwwMjIiGa5VKVS4erVq5BKpaipqUFAQACEQiH+\n8Y9/aBqT2yI7Ozs0NTVh4cKFiIyMxEsvvaSz+YywGy25EvL/Xb16FWKxGBUVFXjkkUcgEAiwYsUK\nnRk5LvS3s1TDX2tijhYhbD1+yxYY6xennqE7duwYkpOT8eyzz2Lu3Lno6OgAj8eDQCDG7Vd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DAEAgGefvrpMbNOphwQby20Q5xKpdLcVv26uOHhYezevRsSiQT9/f1IT09Heno6vL29GRo5IYTN\nKNARYoIDBw4gLy8PIyMjyM/PR35+PrZt2wbg5/5UahToxlKpVDh+/DhEIhFaWloQGRkJPp+vU5en\nvk67v516xorr9Xam9osbHR1Fa2srxGIxOjo6EBsbi+zsbAQGBnL69hNCph4FOkKIRY2MjKC+vh6F\nhYW4du0akpOTkZKSMmbmaXR0VDNrN14IYquJ9Iv78ccfIZFIsGfPHoSGhkIoFGLZsmU2s/xMCLl/\nFOgIIYyRyWTYvn07pFIpPDw8kJGRgZiYGDg7O+tcp70kC7C33m4idXE3b95ERUUFKisr4enpiezs\nbCQkJIy57YQQYgoKdIQQVuju7kZhYSF27tyJkJAQCAQCLFmyhBP1durZRLlcPm5dnFwux969eyGR\nSHDjxg2kpaWBz+cbrcckhBBTUaAjhLCKSqVCa2srRCIRjh07hujoaPD5fPzyl78ccx2T9Xb6dXHq\nJVVDdXHHjx+HRCLBsWPHEBUVhZycHMyZM4cTS8eEEG6gQEcIYS25XI6dO3eisLAQt27dQkpKCpKT\nkzF9+nSd6yxVbzeRurienh5IpVLs3r0bTzzxBNavX49nn32WdcvEhBDrQIGOEMIJfX19kEqlKCsr\ng7e3NzIyMhAVFTXmjFJz19tNpC5OJpNhx44dqKiogKurKzIzM5GUlAQ3N7fJ3WgWOXjwIHJzc6FQ\nKJCfn4+NGzfq/L2xsRFr1qxBQEAAACA1NRVvvfUWE0MlxCZRoCOEcM7Zs2dRWFiI+vp6LF68GAKB\nAIsWLTJrvZ2pdXEjIyPYv38/xGIxrly5gpSUFAgEAvj5+VnVkqq6F+OsWbMQExMzphdjY2MjPvzw\nQ1RXVzM4SkJsFwU6QghnqVQqNDU1QSQSafq2ZWRk4NFHHx1znSn1duq6uJGRESiVynHr4trb2yEW\nizV99XJycjBv3jyrCnFq+qel5OfnIyYmRue0lMbGRnzwwQeoqalhapiE2DQ6y5UQwll2dnYIDw9H\neHg4hoaGUFtbiz/84Q8YGBhAWloa1qxZg2nTpumcJ6ueeVOfJ8vj8eDg4KCZzePxeHB0dISbm9uY\nsPfTTz9BKpVi586dmDNnDoRCIT7++OMxs3bWpq2tDUFBQZqf582bhyNHjugEOjs7OzQ1NWHhwoWI\njIzESy+9hMDAQCaGS4hNokBHCLEKLi4uSEtLQ1paGq5fvw6JRAI+nw9/f39kZmbiV7/6FXg8Huzt\n7eHs7IybN29i2rRpmhk5Ozs7ODk54X//93/h7++v+Xdv376N6upqlJaWwtHREQKBAHv27IGHhweD\nt5Z9QkNDceHCBTg6OqKgoACvvPIKamtrmR4WITaDllwJsXL3KmYvLi7Gf/3XfwEAnnjiCfzlL3/B\nnDlzmBjqlOjs7ERhYSEaGhowf/58TJ8+Hbt27YKLiwsaGho0mxuUSiWuX7+OJUuWYN68eQgPD0dX\nVxcuX76MtWvXIjMzEw899JBVLqnei/6S68aNGxEbG6szQ6dNpVJhxowZ6OnpoUbJhFgI7Z8nxMq9\n8sor2LZtGxoaGrB161b09vbq/D0gIAAHDx7EyZMnERMTg3feeYehkU6NRx55BEFBQfD09ERpaSkO\nHz6MgIAApKSk4ObNm5r6OAcHB9y4cQNCoRCenp6oq6tDfX09Zs6ciSeeeMLqNjlMhKenJ4C7Xw66\nu7uxd+9ehIWF6Vxz9epVqOcHampqEBISQmGOEAuiGTpCrJgpxezaent7ERoaip6eHksOc8q0tbUh\nKioK4eHhyMnJ0bQQuXPnDqqqqlBSUgK5XA4fHx+cP38es2bNglAoxMqVK8Hj8XD9+nVIpVIUFhbi\nwoULOHPmDB544AGmbxYjDhw4gLy8PIyMjCA/Px/5+fnYtm0bACA3Nxdbt27F559/Dh6Ph5CQELz+\n+usICQlheNSE2A4KdIRYsYaGBnz11VcQi8UAgC+++AKXLl0yOgv3/vvv49KlS9i6daslhzllhoeH\ncevWLZ2aOH2XL1/GZ599hjfeeGPcsHbhwoUxu2cJIYQtaFMEIQTA3fBXVFSEpqYmpodiNs7OzuOG\nOQB46KGHTFpmpjBHCGEzqqEjxIotWbIEnZ2dmp9Pnz6NZcuWjbmuvb0deXl5qK6uHnOsFiGEEPaj\nQEeIFTOlmL2npwepqakoLi7GY489xsQwCSGE3CdaciXEyn388cfIzc3VFLP7+PjoFLP/9a9/RV9f\nH/Ly8gDcPfu0tbWVySETQgiZINoUQQghhBDCcbTkSgghhBDCcRToCCGEEEI4jgIdIYQQQgjHUaAj\nhBBCCOE4CnSEEEIIIRxHgY4QQgghhOMo0BFCCCGEcBwFOkIIIYQQjqNARwhhrYMHDyI4OBiPP/44\ntmzZYvCazZs3IyAgAIsXL9Y5t9aWx0YIsT0U6AghrPXKK69g27ZtaGhowNatW9Hb26vz99bWVhw6\ndAhHjx7F66+/jtdff53GRgixSRToCCGsJJPJAAARERGYNWsWoqOj0dLSonNNS0sL0tLS4O3tDYFA\ngH/+8582PzZCiG2iQEcIYaW2tjYEBQVpfp43bx6OHDmic01rayvmzZun+dnX1xfnzp2z6bERQmwT\nBTpCCGepVCqoVCqd39nZ2TE0Gl1sHhshxPpQoCOEsNKSJUt0NhKcPn0ay5Yt07kmLCwM33//vebn\n69evIyAgwKbHRgixTRToCCGs5OnpCeDubtLu7m7s3bsXYWFhOteEhYWhvLwcN27cQElJCYKDg21+\nbIQQ28RjegCEEGLMxx9/jNzcXIyMjCA/Px8+Pj7Ytm0bACA3NxdLly7Fs88+i6eeegre3t4oKiqi\nsRFCbJKdSr/IgxBCCCGEcAotuRJCCCGEcBwFOkIIIYQQjqNARwghhBDCcRToCCGEEEI4jgIdIYQQ\nQgjH/T8uVRzAjHotQQAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ah! The wonders of code reuse! Now, you probably think: \"Well, if I've written this neat little function that does something so useful, I want to use it over and over again. How can I do this without copying and pasting it each time? \u2014If you are very curious about this, you'll have to learn about *packaging*. But this goes beyond the scope of our CFD lessons. You'll just have to Google it if you really want to know." - ] - }, + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a moment, recall the Navier–Stokes equations for an incompressible fluid, where $\\vec{v}$ represents the velocity field:\n", + "\n", + "$$\n", + "\\begin{eqnarray*}\n", + "\\nabla \\cdot\\vec{v} &=& 0 \\\\\n", + "\\frac{\\partial \\vec{v}}{\\partial t}+(\\vec{v}\\cdot\\nabla)\\vec{v} &=& -\\frac{1}{\\rho}\\nabla p + \\nu \\nabla^2\\vec{v}\n", + "\\end{eqnarray*}\n", + "$$\n", + "\n", + "The first equation represents mass conservation at constant density. The second equation is the conservation of momentum. But a problem appears: the continuity equation for incompressble flow does not have a dominant variable and there is no obvious way to couple the velocity and the pressure. In the case of compressible flow, in contrast, mass continuity would provide an evolution equation for the density $\\rho$, which is coupled with an equation of state relating $\\rho$ and $p$.\n", + "\n", + "In incompressible flow, the continuity equation $\\nabla \\cdot\\vec{v}=0$ provides a *kinematic constraint* that requires the pressure field to evolve so that the rate of expansion $\\nabla \\cdot\\vec{v}$ should vanish everywhere. A way out of this difficulty is to *construct* a pressure field that guarantees continuity is satisfied; such a relation can be obtained by taking the divergence of the momentum equation. In that process, a Poisson equation for the pressure shows up!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 10: 2D Poisson Equation\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Poisson's equation is obtained from adding a source term to the right-hand-side of Laplace's equation:\n", + "\n", + "$$\\frac{\\partial ^2 p}{\\partial x^2} + \\frac{\\partial ^2 p}{\\partial y^2} = b$$\n", + "\n", + "So, unlinke the Laplace equation, there is some finite value inside the field that affects the solution. Poisson's equation acts to \"relax\" the initial sources in the field.\n", + "\n", + "In discretized form, this looks almost the same as [Step 9](./12_Step_9.ipynb), except for the source term:\n", + "\n", + "$$\\frac{p_{i+1,j}^{n}-2p_{i,j}^{n}+p_{i-1,j}^{n}}{\\Delta x^2}+\\frac{p_{i,j+1}^{n}-2 p_{i,j}^{n}+p_{i,j-1}^{n}}{\\Delta y^2}=b_{i,j}^{n}$$\n", + "\n", + "As before, we rearrange this so that we obtain an equation for $p$ at point $i,j$. Thus, we obtain:\n", + "\n", + "$$p_{i,j}^{n}=\\frac{(p_{i+1,j}^{n}+p_{i-1,j}^{n})\\Delta y^2+(p_{i,j+1}^{n}+p_{i,j-1}^{n})\\Delta x^2-b_{i,j}^{n}\\Delta x^2\\Delta y^2}{2(\\Delta x^2+\\Delta y^2)}$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will solve this equation by assuming an initial state of $p=0$ everywhere, and applying boundary conditions as follows:\n", + "\n", + "$p=0$ at $x=0, \\ 2$ and $y=0, \\ 1$\n", + "\n", + "and the source term consists of two initial spikes inside the domain, as follows:\n", + "\n", + "$b_{i,j}=100$ at $i=\\frac{1}{4}nx, j=\\frac{1}{4}ny$\n", + "\n", + "$b_{i,j}=-100$ at $i=\\frac{3}{4}nx, j=\\frac{3}{4}ny$\n", + "\n", + "$b_{i,j}=0$ everywhere else.\n", + "\n", + "The iterations will advance in pseudo-time to relax the initial spikes. The relaxation under Poisson's equation gets slower and slower as they progress. *Why?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at one possible way to write the code for Poisson's equation. As always, we load our favorite Python libraries. We also want to make some lovely plots in 3D. Let's get our parameters defined and the initialization out of the way. What do you notice of the approach below?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy\n", + "from matplotlib import pyplot, cm\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters\n", + "nx = 50\n", + "ny = 50\n", + "nt = 100\n", + "xmin = 0\n", + "xmax = 2\n", + "ymin = 0\n", + "ymax = 1\n", + "\n", + "dx = (xmax - xmin) / (nx - 1)\n", + "dy = (ymax - ymin) / (ny - 1)\n", + "\n", + "# Initialization\n", + "p = numpy.zeros((ny, nx))\n", + "pd = numpy.zeros((ny, nx))\n", + "b = numpy.zeros((ny, nx))\n", + "x = numpy.linspace(xmin, xmax, nx)\n", + "y = numpy.linspace(xmin, xmax, ny)\n", + "\n", + "# Source\n", + "b[int(ny / 4), int(nx / 4)] = 100\n", + "b[int(3 * ny / 4), int(3 * nx / 4)] = -100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With that, we are ready to advance the initial guess in pseudo-time. How is the code below different from the function used in [Step 9](./12_Step_9.ipynb) to solve Laplace's equation?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "for it in range(nt):\n", + "\n", + " pd = p.copy()\n", + "\n", + " p[1:-1,1:-1] = (((pd[1:-1, 2:] + pd[1:-1, :-2]) * dy**2 +\n", + " (pd[2:, 1:-1] + pd[:-2, 1:-1]) * dx**2 -\n", + " b[1:-1, 1:-1] * dx**2 * dy**2) / \n", + " (2 * (dx**2 + dy**2)))\n", + "\n", + " p[0, :] = 0\n", + " p[ny-1, :] = 0\n", + " p[:, 0] = 0\n", + " p[:, nx-1] = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Maybe we could reuse our plotting function from [Step 9](./12_Step_9.ipynb), don't you think?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def plot2D(x, y, p):\n", + " fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + " ax = fig.gca(projection='3d')\n", + " X, Y = numpy.meshgrid(x, y)\n", + " surf = ax.plot_surface(X, Y, p[:], rstride=1, cstride=1, cmap=cm.viridis,\n", + " linewidth=0, antialiased=False)\n", + " ax.view_init(30, 225)\n", + " ax.set_xlabel('$x$')\n", + " ax.set_ylabel('$y$')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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Eo3Rc9FAULh38uHUxXr3mh0lvRi4RKy/56J1fai8qiphyZfscxX7z3ndAuGMopmf5oAWA\nkehd0Y5TlJCgSwDR1HdcTzg+jUoRnGzgVZBCx3A60Oc7ICmR4+WbllT0Lo/E+ePb7xiK6VmGLHrH\n7mnseUrPjg8Juikga60VNo3K3s9C4Um0ZipqhG7S/eaP/7gFKdOiiMd4bXt30ptQWMTonVfXg0mi\nd2zecdGYxrVFdQzZ/Dv+ddHehhd41JpsckjQxcC4rbXYe2V2FGkyhS1i+iIM/K9O9o/9omVRB/r8\nkoeda4wXXjwPzfVPUNuzBBEn5qsiP2m5FqaZpK7TquKKTqczVnGFqjUZP5YYQFeuiIiitVbaPeHS\nsh3TJkjUKktRuCJjWRYsy3Ii5aVSCd82DADA82YNtVXhwG4c7P95JO0/zPwiP2Gid2nf1zhIwz7L\nvO+CFsjIBB61JvOGBN2YTNJaKwpPuKRg4ibN2zgNvOYzTmILQ0SLrOgEGJxv9Xp9cIw6w/Hr16+H\nZVlotVowTRMLCwvOhHAyzE0WWeSHpWa95m0B6RA30yaN++wl0lmKnT+OQbzvGJSeJUEXGFk1KiNo\nFC5qTzhiusiicEnNZyTUyOx7+KKTTqfjebzYeCb62NxXsd1VlpvVZx0+8gOo522xtldFJI2CTsQr\nPTtOazI+Pcvuy3FVTqeRYn7TAyJLoxqG4UTTvL4kee2tWaRJ80yI82k6XojnORWXJWQ/mIK2QWu0\nLvZctspyQ7zhsO9DVs/rrONnistu7uz8LQJZEHQ8svRs0O4jMoG3vLwMXdedrFcRWpORoPPg7W9/\nO+6//34AGMnTi18I8hPLB7J0OLsZFOE4ZkWwi8eJXbAn/cH0wovn4eyznla+rmp3tbS0BACuG07a\nvytZu+EHRWaKu7i4CNM0sbi46BLpWThO45KF89gLTVN3HwnifQfAFdVj0Ts2Po/kc68igrWs4ZH5\n5xTJEy4rN/ygqI4jS6Xquu6k2PN4PLNCkOMUBc+bNZwdcCx/w5Gli3jRUJSoUBph1+LZ2VmUSiXP\n45Sn63beBHsY7ztd10eMldnfvGZWABJ0nmia5qpyE9NvUbfWIqaD6O1HxzGdZGnOoiwqNG5FJhEf\nsuPEhIHomZaH6F3Wt19FEO8727adH315FnE8JOg8WLduHb761a/iX//1X3HppZdiz549zi+4er1e\nmC8JTxYjdKqilKijO8TkiL1s4ywe8ps/Nwl+FZlp6IaQtwiOF6p95Vtazc7OBp6UnwWKZKYsnm8s\nzc6OJX8cmQjMIyToOGzbxve//338/d//Pf7u7/4OjzzyCE6ePIk3v/nNOO+889BoNJwTvignikhW\nBF2UUbis7HNUTHNfVXNPp2Xhwzzo4kRVkcnPBSJblHgJIl79JuUDNEcyC7D5tACwdu1aaJrm8r6b\nmZlJeAvjI9WC7tChQ7jttttgWRauu+467N27d2TMLbfcgoceegiNRgP79u3Drl27AAALCwu4/vrr\n8f3vfx+6ruPee+/Fnj17lOtqtVq44IILUKvV8Ku/+qv44Ac/iJe//OX4rd/6LVx00UWx7SMRDapK\nx7xXpEbNNG4AfrYiSd+EXnjxPKxtPB7b8lXdEERblCKlitKIOEeSCfGsRO+KKugY/Bw6PjKX53Mq\ntYLOsizcfPPNOHz4MM4991zs3r0bV1xxBXbs2OGMeeihh/DUU0/hiSeewKOPPoqbbroJR44cAQDc\neuutuOyyy/D5z38e/X4fKysrnutrNBr4t3/7N7z0pS91nnvsscdw5swZ17iiRWtE0rT/ebWGyRte\ntiJprBx+3qxh7ZTWxd9s+JRft9slW5QImVTc8LYXYoVzWoV4kQWd6h6VZ8sSIMWC7ujRo9i+fTu2\nbdsGALj66qtx4MABl6A7cOAArrnmGgDAnj17sLCwgJMnT6Jer+Nf/uVfsG/fPgCDidTr1q3zXScv\n5gCg2Wzi9OnTrufSJGiKhqrPbRC/sUmgYx4e3laET3mnRWyv76qj9UkyTVuUotzw4zh3VZYaohBP\nMnpXlOMrQ6xwLQqpFXQnTpzA1q1bncdbtmzB0aNHPcfMz8/jxIkTKJVKOOuss/D+978f3/3ud3HJ\nJZfg05/+NOr1eqht2LBhA0XoBFjl77SgKFw2mJatSJEgW5RoifNaESR6N+0imCLfp4oqZnN5le33\n+/jOd76D3/7t38Z3vvMdzM7O4o477gi9nGazOSLoGEU9WeIWtCyy0+120Wq10G63YZomyuUyGo0G\nZmdnUavVpvqrt2giPui+smPV6XSwsrKCTqcD27ZRrVbRaDRQr9cTreLME2zCfr1ex7p16zA3N4dq\ntYp+v4/FxUUsLCxgZWXF6WhDDJn258GEeKPRwNzcHNatW4dyuQzDMHDmzBksLCw4HSzi3rYiihpg\nEAiQ7XveP4/URujm5+fx7LPPOo+PHz+O+fn5kTHHjh2Tjtm6dSsuueQSAMA73/lO3HnnnaG3odls\nYmFhwfVc3r8QSSBaVaRtknyR8Pusp2krQqiJwhalSFGMJPdTZYjbarVitbAp0vEVKZJlC09qBd3u\n3bvx5JNP4plnnsHmzZuxf/9+pw0X4/LLL8fdd9+Nq666CkeOHMHc3Bw2bdoEYCDofvSjH+GCCy7A\n4cOHceGFF4beBlWEjkVsiniyRBGtojZp2SFpW5GkeGJxF85f+x9Jb0YgyBbFmzRdq1W9gYO0swoD\na1aflv2eNqp9z/vnkVpBVyqVcNddd+HSSy91bEt27tyJe+65B5qm4cYbb8Rll12GgwcP4vzzz0ej\n0cB9993nvP/P/uzP8J73vAe9Xg8vf/nLXa8FxU/QEcFgF5esRuGKdrzZHKC02opMylnW/wlgOh50\nSRDUFqVI3+m04tfOatLoXdbP1XEpqpjVfE7qQp/xnU4H73jHO/ClL33J9Xy73c51g18vLMtCu91G\no9HwHCdr28SicFmLEti2jVarhTVr1iS9KbHA24qweT3sOOWxTRXrEOEn6M5f+x+YnZ2dxiZNDRYR\nYv/SUI0ZN/1+H61WC+vXr096U0LBR1pZpXjQ6J1lWVhYWECz2ZziFqeHdrsN27ZHzl92Xcs4ygNf\nPEUSgpmZGXS73aQ3IxPwVY7i/Kq8NL3O068+la1IpVJBr9fLtZt6keEjQqdOncLMzExmOyHkHVWk\nVWxGL6siz9O1ahyKuv8k6HxQ5eGLmq7g910Vhcvb/Ko87EdQWxF+rlwe+cvj/wOvb3Swq0qCFYAr\n/ZpXW5Q83Nz9mtGL0bs87PMkMMErkvfPhATdGBRV0DFRAMD5lUhVjulFJriZgMtres2P1zf+d+Cx\nTy79Al49+8MYtyY5xOuX2MdUFAzMimPSyfpJkEdx41flXCqVnB9wWRbj40JVroQUZqTLfzmKJOhk\nogBAJi/sk5CVymYvW5EiXuAm4ZxS/qdbqL7PfoKB/TAgn8HkkVU5dzodmKaJxcVFJ7rH2pKl/RoW\nBVm4VscBCTof1q5di8XFRczNzTnPTbtbwjRRpeZ4UdBqtQob4UkbRbUVGYdBujXprcgeKlsUNp+L\npfvSGvkt2s2dVaWbpok1a9Y4qXRmZpyXVLoXZFtCSGHWJbygyxuipQhAqbk0I2uHlidbkWnwmNFB\nkEY5L7x4Hs4+6+nYtydLqCbrx22USwSH72Xql0rPYyFM0UQ8gwSdD3ns5zpp382s7/84JLnPvK2I\naZpOyisOI+a8Htu/PP4/kt6EVBHVDc/LKLfVaqXCFqWIN3fVPstS6apCmCz/OCziMQdI0PkiMxfO\n4k2PtxThbSrGjcJlbf+zhspWpFarZfpCmyR8QYTpE50rwvy5OPBqUg+QLcq0CCJoZNE7Fm3NcvRO\n1SWjCEV7JOh8yKqgk0V1wkThvMj7SSEj7mM+adSUiI9PP3Ulbn3Fl5PejMzBKmOZLYplWTAMY+q2\nKEWM1oyzz6xFXLVaxezs7Ej0TvS9S+tnyqebiwYJOh82btyI06dPu55Lq6CLOgqnIq37nzXIVmQ6\nTJpu/c31j0W0JcVF0zSUSiXU63VXNMgwjNhtUYoq6Cb90c5H7/ho69LSEoD0RluLeLwZJOh8aDab\n+MlPfiJ9LekvTpxROCJ6ZD1tyVaESIKkr118NIify0W2KNEQ9Q9uv2hrGuZKMopa4QqQoPOl2Wxi\nYWHB9VySXwxZheO051YVMUI37j5nzVYkb8eWRefCGApfXK3ihOmeQ0dp1/jwqsSMwhalqCazcV1b\nxGirOFfStm2X7920P/ukf6wkCQk6H5rN5kjKFZie0SwvCMQKRybiiHRBtiL5g9Ku04NsUSZnmqJG\nFr3r9XrodruJRO8oQkcokRVFAPFGMtIQhfMib1GcIHjt8zRtRYjgfOiHv4Ofi9BIeGlpKTdpwKxE\nMaKwRcnKvkZJUvvMoneqSmcWvYtTkIudnYoECTofVIIOiG6egiotl1ZBkOdOGUEhW5H0wQvrjzz9\n+/i5xn9Huvx7X3gvrj37r500IBN3Sc8ZKhLj2KIU7ccnkB4Ry0fvgFFBzuYPR3keUYSOUFKr1WAY\nxsjzk345xJ6blJZLN6ygwTAMshVJESphzSPOn/PzoAOA+dJanDCXRp5fu3ats07DMMhfLUGCTNRn\nrxXtmKR1n0VBHkc6Pa37Pg1I0I1J2LRj1qJwXhQl5crbirBjluaelVHARzXSun/ijyFRWA9SreNF\n5/5xeSf+kXvMz537zfWPYec9d+HxD9zs3HREt32WBkx7ajZv569qoj77AdZqtdDr9QrToD7N5y/D\nK53OF8OEtbJRFcGk/fOIAhJ0AVCFb70uijKLCorCpRsvWxF20anVaglvZfEIUyn8oR/+TqTrvn9h\nl0vUvfY1P8DOe+5yHj/+gZt9KzTTmppN07ZEDR+9W1hYcAoseFsU3iQ3b2RB0ImoonfsmAWN3mVx\n36OCBF0A2Jwx/kskE3RZs6gYlzxF6IIeM8MwcrPPWUCWSlX9GLrovv/X+f+bfhm+0bn/6/jbcPuW\nhwJviyjqeHhxBwwEnlihSanZ5PFqUD9uJCit5OE6JUbv2DELEr2zLCvzx3BcSNAFYP369VhcXMTc\n3JzrebG6kY/oUBQuvYxjK0KFIPHCR0eDzFHkRRzjTb/8PeXy7zxxGQDAsgfHdhJR99rX/AD/cuRC\n6TgxesenZvk5XnyFZl6jRGlFZYsSNhKUdvJ07/E7Zvw0hyKnXDUfNZ99qR8B11xzDT784Q/jvPPO\ncyI6hmE4vwTYjSdtKZW4sCwL7XYbjUaEnhAxIgpv27Yd4R30F3mv14NpmpiZmZnCFifL8vIyGo3G\nVD0WxXml4rkkE3A8TMyJ0bkjZ17ueswEHYMXdf+4vNNzHf+1fK7z/z3rf4z/++/e4Tme5/EP3Oze\nDi5K1Ov1pp6aNQwD3W4Xa9eujXU9aeDMmTNYu3ZtoH6xbB4X+5emDghBsSwLCwsLaDabSW/KVBDP\nJdu2Ua1WUavVXNf3arWaieMXAOVOUITOB9u2US6Xce+99+Jb3/oW3vKWt+CDH/yg8wugXq/n5UsS\nmCykXFV9bce1FcnCPkdFnKbZ46ZSVezc9Qw2zy6OPP/wT3dgTWW0Ol0kaKSOF3MA8OjCyxUj5QRJ\nzfJeXUzcUWp2csJ8l/081LJwXIo2h0yM3p0+fRqapknnS+YditBJOHPmDB5++GEcOnQIhw4dQrvd\nxlvf+lZceeWVeP3rX481a9bANE10u13Mzs4mvblTx7ZttFotrFmzJulNcWBROCYU+JRdqVSaOHXC\nbrj1ej2iLU4vrVYL9Xo9knRT2OPiJ+L4ANuFFz2Dvq1ja2PgE/ncyjrXWFHQidE5nt1zT3uuVxR0\nQPgonQoxescyAL1eD/1+3xEQUaZmixShO336NNavXz/RZ8d3QGCVs3yaL0j0b1qwqt7169cnvSlT\nhwm6ZrPpTJNh2ZUcRSyVF7LMCLpDhw7htttug2VZuO6667B3796RMbfccgseeughNBoN7Nu3D7t2\n7XJesywLl1xyCbZs2YKvfOUrnuu66KKLcO655+Jtb3sb3va2t+HgwYNoNpv4jd/4DWeMaZrodDqZ\nSTtGzbTScl7wtiKmaQKAIxSiTo+QoAuOLJWqOi5hBBzPBa8+BgCOmAPGF3Qr/cHE69ef9YRyO1SC\nDkAkoo6XZSIvAAAgAElEQVQRJDUbhXVOt9tFr9dL1Y+yuDh16pRzg48KPnpnGIYziT8Ntiis2GPd\nunX+g3MGE3QbNmxwPc+qnnNCtlOulmXh5ptvxuHDh3Huuedi9+7duOKKK7Bjxw5nzEMPPYSnnnoK\nTzzxBB599FHcdNNNOHLkiPP6pz/9aVx44YVYXBxNz4j8x3/8h+tmtnHjRpw6dco1pkgh7bSgivYw\nt/E4JzAXKeU6DnxBg1+hyav3DUScJvk4PYJoAICzzv8ZNsyuABiKuR8tvARrK13XuCDpVpFHXtwu\nFXUyMQcM0q5M1EUFpWajJa5zlrdFmZ2ddebetdttmKaZaMFL0VKuPEVu+wVkRNAdPXoU27dvx7Zt\n2wAAV199NQ4cOOASdAcOHMA111wDANizZw8WFhZw8uRJbNq0CcePH8fBgwfxB3/wB/jTP/1T3/WJ\nX4hms4kf/9h94S76DT7OeVY8sigcE3BZmaScR7xSqWyeIg8Tca5laJD/1uRPK+71s14xFHPAQMhN\nCovOMVSiTsV3lrbhTb/8PfzDv75q4m2Roaqa5UUE64zAV2emKQWYBuK8TmialipblCLfl4rc9gvI\niKA7ceIEtm7d6jzesmULjh496jlmfn4eJ06cwKZNm/ChD30In/zkJ7GwsDDW+pvN5sh7s+CoHydx\nClpVWzRmI5BU0+miXChV+6pKpcrEtUzAAYAt+fHMInW2PrrO+uYW1swMom+8mGtzQkyMzsnwmj/n\nhSo6x+hZJVR0E2/65e/h8L+9Shp1jApR3PET+FUign74TJ802KIU9XgX9X7MyISgm4QHH3wQmzZt\nwq5du/BP//RPY92UN2zYgDNnzow8P60oVd7JU1u0vBHGs08ahSsJ5xsTVtzzrhGrz9fPGoi3NTNd\ntLpV1Gs9AMCLKw00qt7p1HHSrTxho3Q/WdqAl609hTf/0kDU8cQl8IKmZvkemSw9W6TrVtL7KTPI\nNQzDMchlJubTaE5fBChClwHm5+fx7LPPOo+PHz+O+fn5kTHHjh0bGfOFL3wBX/nKV3Dw4EG0220s\nLS3hmmuuwWc/+9nA6282m1JBV2QmjVjJhAIZMicPM/jt9XrodrtO2b9KXAcSca7s3+prVc6kuWyh\nVB2k06vVgaDXVpWQKOaiREy38nzlv1+FV6z7me8yjre8Kwn5wOA0o3eyHpl8ahYoxk0ubeJG13XM\nzMy42luxTiJRzYlM2z5PkyLvO5CRKlfTNPHKV74Shw8fxubNm/GLv/iLuP/++7Fz59AI9ODBg7j7\n7rvx4IMP4siRI7jttttcRREA8Mgjj+BTn/qUb5WriGEYeNvb3oYvf/nLrudXVlZQq9UKOV+l3W47\nc0KCIJr7MqHA0kZpn8iaRquWqBDnKTLjZVnUIJCAKwuPK+LrQzHHhJzGqR0m6gA4Yo4hRueCFEP4\nVbeKLBhD82gvUSeKuZetHRROiVE6GXGKOx6xapaJdTZ5n48QJV2dGQemaWJpaWmky08a4ZvTi7Yo\nYX7krqysQNO0QlTki7TbbViWNeI+we41OSHbVa6lUgl33XUXLr30Use2ZOfOnbjnnnugaRpuvPFG\nXHbZZTh48CDOP/98NBoN3HfffZGtv1qtwjBGbxRFmlclEmTfxUb3rKsGzetJHlUqtVqtot1uO8cI\niEfEWf2BgLf6OkoV03mNF3MifqnWMHhF53ieWtwYKFIHAMdac9jaOIMLXn0MP/rPrZ5jRY05zdRs\ntVp1flRVq9WR+V18ajbrZCliozI1brfbrtSt37FRtb4qAkXedyAjEbo08NrXvhYHDx50PdfpdJxf\nuEVDtu9xm/smCYvQJe29Ny6yY8OOixiZabfb2PP//fXoMjxTqatjyu5+t5og9DRe2HFCDhgVc2Gj\nc0DwCF2Q6ByPKOpUqdayPty/H31PIep8rqqiuJMFGKMQgN9+77UuY3RZ26s0GueGIQ8muyyCzo4L\nb4siOzYsrV6r1RLa4uRotVqOKObJel9egWxH6NKAaqJl0SN0KnPfvEXhsrgfYY6NsipVFHHAiJAT\nRRzgLeQAtZjrtAfmn825luv1VruGVnt4gyqVLPwM7rRKSR/dDgB45cafSp8fF695c31Ld0TdBa86\nhh/+1xZolhiOE94UQMCJRDE37+K/vtf1mFXO8hEiwzASsd6IiixF6FSEtUXJwz6PS5H3HSBBFxjW\nRiRHKn9sLMty/hmG4TL31TSt0CdU0nilUoN4wwGCtQhTDrylCNNNq2JPM4fHmwlAuz94jgk7u88t\nVLfR7w4vPXrZcoQcAJTKFhaXh/N/SiW5UONRiTkA+OHPBn51TNiFjc4Bw9Tr917cjGZ9RTlO5JU/\ndxw//K8truc8Bd4Y4iyqwguxsIIZ57IfBqwyc1rWG4QcP1sUNqZcLhfu2JCgIwKxfv16LCwsuPrB\nFSVCJ7MV0TTNqdgqygmURrsHVbFJmKpUQO4PB8At5BiSqJ0skidG6WTL0sXInfhYIuaCCDwZTNht\nXR++Yn3FqOJ7L24GAJxuzypF3ZnOQIjqq8psw+wKXvlzx3FqZRY//cmgHRHvtxeVuIt6Dp4o7th3\nCnBP3m+1WqlOzabtfI0amS3K4uIiTNPEwsICdF13HZs8fxYA2ZaQoAvI3Nwczpw5MyLo2C+ivOHn\nP8aeL8qJkiZUqVTWoUHslapM4SkiOy6DX5sbO66QA8YSczKkAs8jOidiWjqePj0QVuc1T/mMViMT\ndUzMAYAFDTpsnFoZzlF7yctO4afHVq8fqxHMwOIOSGxGs6ywwmvyPhMQaUjN5l3QibDzf3Z2FqVS\nacQWJW9FLyKWZeVyv4JCgi4gMnPhPEXovCI9slZOxHQRBbZXD1ux4b1LrHlc62SdGgAAbPG84NBH\n062DZQAjykNYrijkALmYGzcSF5SnT29wRJ1XunXFkDf15kUdL+YYTNTxNDa10DrZcFcFS8QdEC56\nN62qWUCdmmXtyCg1myxMxIrRO9GPUOw3mwchpKpyzcO+BYEEXUBU3SKyLOh4W5F+v++Y+8oiPSJ5\nErNBmeY+h02liiLOtSzVYbThjtJZgrCQ3X8Voo9P2do93T2WE31ayYZlCGk5zYbVGy6gUu8HTrWG\njc6JPH16A0xTx1nrlqXvUYk5hkzI8YiibrZqAJsGf396cj1g6FJxB0yWmk3K0JhPzfJdEVhqlm9H\nNg2KFqED1PusskVZWloCgMC2KGlGtu9Z3ZdxIEEXEFm3iKx9UbxsRWSRniDLI6JDTKUy3z5VKtVz\nWX5fTcXrtsa92RRerIyKpzBz7zRZxaxEbfS7JfTFUloNmF3jtimZVMzxvLi4RinqVLS7w+KK2Rm1\nR55M1K0YVTSabfT6JRiLq9W7ORJ3wEDg8V0RmIBYXFxMXWo2TwQRsezz54texG4iKluUtEL3IxJ0\ngdmwYQN+9jO3F1UWolRx2YoU9QIc9fHmo3BhU6nKbQxyaPi5cX7vkUTa2Hs1C7B5EaKI4AUVc5rs\n/aurXVke2paI4m5cTHP4Gb+4OOgCwoSdX3SOZ6VTVYq6lc7octbMdLFiVFEpmzBnB357VkWH3Vu9\neRq62x6GE9dScRfwazktcQf4p2an0bC+aBG6ca5PYW1R0vp58qlmnrRubxyQoAtIs9nEU0895Xou\nrYLOSyREZSuS1n2Pk6gaZ8tSqZVKJXQqVbmdAefMeb5HNZ8OGInuaSySpNvueXYAUFNE0UKKOZF2\nyy2S1qztYHlpBmvWdkbGqqJzvJjjeXFxjfPa2sbo8gB3dI4hE3UyMQcAy52BOO33S9B1G9VqHx1U\nYFo6NN0CX2ul9QVxx0fu2H9XxXUYkpx3JwoIVWp20rldRbtGqURNGERblKzMiyyaeJdBgi4gspQr\nI+kvksxWhHVxKJKtSFqJMpUaaH1jpltd7xWFmW6r36cSfxoGkSaRmpjLVYg5BZpEeSwvzbj+yoRd\nUHiht9QaLI8XdjIxx2CiTiXkRMplE/1+CYZRhq7bsEoW9LIFe1WE2obuMm7W+l5p2eHTYcUdkL7U\nLD+3q1qtjh0dKtL1L2oBy0fvADjROybw0mSLQj6xJOgCk7Y5dH62InFvW1EjdEH3OY5UamTwuxAk\n5QqMCryS7S3kZKtlz0uKImwAuij0JMuRiTkZTNjZtiZNz6qicyqWWjNY2+h4ijnGwlIdlcqoaFXB\nRB0AlKuD91msAITThbahOzYxTlSUT2WbcnEHTBa9Syo1y8/tGjc6VMTennFe+1WmxqItShKmxkX3\noANI0AVGFaGbltls2KrHaZF0dDItiMfHtm3PKGmcIi5UytV2v0dZ5CBiYVTkldVRPLUX3nBjrS4n\n9FgWlxN5QcWcs87VlbK5d0zYeYk5r9eCiLne6hy4Xq8UWtQBcAm7vlGCtirs7J4+bJ/W04GS7Xym\nrpQ3Qzg2k0TvkhR3srldLDrEzq882W5MyjSvx7wtCj8vstvtJmKLQvciEnSB8RN0cTCJrUjcFP3E\nAdypVL/jM80oXNB5czI0Cy5RNrIs1R1dw2jRBGv7FUDMuZbDwUSepmE1itdXLMyNLVmpU1TBrba2\nWpAA+Ai9vg6Ta19Wm+mNjGFijn8sE3WG4X3ZtVc/RyZgrX4JWmUo7Fi1scasXliUjku/eom7SVqN\nJTXvDhgIPD46FCQ1W7SbfJL7m7QtCkXoSNAFplqtotcbvYhHKeiithWJmzS2wooT1hnEMAxXKlV1\nfKaeSp0ExSEUb9ijAs9jmUyY8O8v296iUPY097zF94BViDuZmBu+6H7YXRlE3cqSeX0MXsg57+tU\nHFEnCjkeXtT5CTmGVrIdUQcAetmEtRq5Y96Adk8bmjuz7eM3k98d7vOOso9s3NE6kTCpWXZOFm1a\nSFqux0nYoqRl35OEBF0IVOp/kotGXLYiRDTwqVQm6KOuSo0aXxEmQ7AxUS6bEwS2JlmZzzIG6UFu\nGczbLoCYE+HFHTAQeGHEHE+vPVhWpR4sAggMRJ0eoJtFZ6Uq7Y7hBbN5YcJOX03JMmGnVVZf72mj\nRRNAOMsT5wnu/z6XtGmLOZGgqdler+dMUUnDxP24SaOomZYtiqooIm2fR5yQoAtBVF+MadiKTIO8\nFkaoUt2s0mtmZtgmKg0Czo9x5tQN3ih5WXxOViwRAq2nu1O8nHlx2NPA6pbBvo4jBRYem2Vz+9Br\nl12iThadc95najDNgXIqVeURPnM1emetLieIsOO7Zojb5+zIanpYY0PFeXR8+ttp3SZsv5+48/rM\npjivzg+v1OzCwgIqlQosy8Ly8sBfMA8dEVSkUdCJxGWLUsQCGBESdCFgKTf+SxNE1JCtSPqRiWwx\nlWoYBmzbzoSIExlrXh0vBINeJ8V5dF4CT1bFyouZavColngKsrl3es0MLOYYLFqnSzpjOO8T9tM0\nSiOizpSkYq2+PiLqRAEnoun26HaWLEfUARhamYjVrzJhB4yKO1ZgkWK7k6DwAu/R37zGEQdi6o95\n3jHbjTyIgaz9wA5ii8K3i/O6V2ZBzMYNCboQrF+/HgsLC2g2m85zKkGXtK3INMhyhC6syM6iiGNM\nUiTBEG/0Ywk8XtwF2SbRw04h8Ly+glZnKKrEqJ1MzA1f1Jyes7og1EQxxzCZHYvPvjFR5yfkeJhP\nn2ubWbo3iLDjnxO3kU+jZ8TLLih77v+s839VapbNvUuTp9q4ZF3UTGKLQkURJOhCMTc3h9OnT48I\nOsuyUmsrQgyRpVJVIjvLAk5knM4RvsscR+ApqmADY4xG7zx/T3hE7fzEnOt9nLBTiTnnrSwV6pFa\ntU3dqajVQvSjZcseSXM7r48KNi1A6tVJ064u1/m+TFAwAaRP3PlVzfb7fRiGkfnUbJa21YugtihM\ngGddzEYBCboQiNYlfJSn1WqlylZkGqQ9QmfbtnOhDlI1nCcRpyIOcQe4BV7g6B2LGGm2exJ/EFbF\nnQb3vLvhRijeZ2uwOsPLnsZH33w+EPY+TeEvZ3ORMrsvF3a2Kc6P05Wiji1jJOInCDCGWEgBYGhE\nLBN2NtziThvazERVDZsGIadCLKzgU7Osmj1rqdk8zyNT2aIwAc4CKmJ0Ne/3YR7N54ac4tNx+nz0\nox/F2WefjWPHjmHr1q248soroes6bNtGvV7P7YmkotvtOuXpaUGVSi2Xy9I0ShFEXBCiFHeu5Xqd\nEqq7/QQuBrZX0YHHTmoe8+UASKNivLAThdroYO+XgWG0zpYVYni9XzQRZnYxsl1SRSY9dl/ziUp6\nkWZBp+LxD9zsesynZnu9XqpTs8wSpFarJb0pU4Nd8xcXF1Eul9Hv9x1xXq1WUa/Xk97EqFF+4ShC\n58PS0hIefvhhPPjgg/jCF76AZrOJSy+9FG9+85vRaDRgWRa63W7hxFyaKGoqNUrGsjoJslxZatbv\nLi8GwEIIPI0TQy5x5yXmbAzTueI8PY/UrN0rqduf8eOYwPJKMZsabLOk/mwkaVCbnxMn2T9bl4g6\nRXQPOoaiTlgWE+V5mFMXhDCpWdu2HXGXhtRsEdOOrDc2AKxdu9Z1jAzDyKOgU5K5CN2hQ4dw2223\nwbIsXHfdddi7d+/ImFtuuQUPPfQQGo0G9u3bh127duH48eO45pprcPLkSei6jhtuuAG33HKL57pu\nuukmfO5zn8Mv/dIv4e1vf7tT/Xjttdc6YyzLQrvdRqPRiHxf0w6r+pz2r0GvVGqpVCpkKjVOYove\nhbQ4ATBW9M5rPZ4iw2eOny0IHFGwqebbucapol+KDfObw6c6WI4YkwlUlVCTLMvZrAnvDFkRdyJi\n9I5ZbvR6vZHIUBI/8hcXF1Gv11Gp+LepyxOWZY0ULAJwqmRzhvIikClBZ1kWLrjgAhw+fBjnnnsu\ndu/ejf3792PHjh3OmIceegh33XUXHnzwQTz66KO49dZbceTIETz//PN4/vnnsWvXLiwvL+Piiy/G\ngQMHXO8V+cEPfoCXvvSlWLNmjbPso0eP4nd/93edMbZto9VqOWOKxDQFnSyVygQcpVKnR6bEnWSR\n9qqY8hUU/OuV0cGimGMwseYrvBDQZ0+zAy1rBFtTR9SCpl25g+35eZG4AyBPzQa13IiKhYUFNBoN\nxwakKJimiaWlJczNzbmeL5qgy9RRP3r0KLZv345t27YBAK6++uoRUXbgwAFcc801AIA9e/ZgYWEB\nJ0+exDnnnINzzjkHALBmzRrs3LkTJ06c8BR0F154oetxs9nEwsKCdGxRQ92WNUYeJiB8FM7P+oUE\n3HSIrahCMpHfE1sD+KYOfNTLy/qurw1fl0XgZO/trW5bxVYKOQeWuvXah1VBZWNYyCBjIOS0wIon\n8Fw3WdqV3y9Z+parXxk+GWx1fkyzP2yUBEnN8pYb00jNprlILU6KeP+VkSlBd+LECWzdutV5vGXL\nFhw9etRzzPz8PE6cOIFNmzY5zz399NN47LHHsGfPnlDrF6tcgWJV0IhEXeXq1cuWVQ7zkIhLltjm\n3QnCxCXwVCuR+a6JiC+x9zBh5/dVNvRBVa2i8IKvDGX74Np2SWTMmV/HjRuJyEnVlHs9oRC3Q9wd\nhWqPS2hlRcD5oaqa5S034uxlyijiPYk86AZkStBFwfLyMt75znfi05/+dOg0qUzQAUNhU7QvTxSo\nUqmqNAWJuPQyleid37QkVacKL9HAFxfIBKGwM6zwggm7EYsPfiwTdn5T38xBitRz/2yP6lUvvDz3\nALU/nZ+Ym9Cnjl9NXkQdQxR3vOWGqpdpFKnZot6HVH1ci0amBN38/DyeffZZ5/Hx48cxPz8/MubY\nsWPSMf1+H+985zvx3ve+F1dccUXo9fsJuqIx7n6H6aJBAi6bpMbvjgm8ANWorvEl23fDXW3KVJFB\nZta7+lCWTub3if1f3DdpJE6lgvwEnPQ9wmOx0lVWrRrhJS+L1bBBCZuaZeIubGqWFYsVUdBRhG5A\npgTd7t278eSTT+KZZ57B5s2bsX//ftx///2uMZdffjnuvvtuXHXVVThy5Ajm5uacdOu1116LCy+8\nELfeeutY669UKuj1eiPPF1XQBcUrlZq3NlvEKKkQd7zICSLuXFE7yeviInghKK6PwyXMPJSLprAQ\ncW+DkIoNI+T8onxsmV5ROg2RiLq8CTg/4k7NFk3EAMWNTIpkStCVSiXcdddduPTSSx3bkp07d+Ke\ne+6Bpmm48cYbcdlll+HgwYM4//zzHdsSAPjmN7+Jz33uc3jVq16FX/iFX4CmafjEJz6BX/u1X0t2\npzKMl5ClVCohI2lxp9lwpWVHImayrzPzxSspXudhBRRe915nWz3m/Xl1ahhZluY9ZmS9AQlygJzQ\nY8hlS1ZTNGEHeKdmWTcEwzACpWaLLGooQjcgU7YlaeB1r3sdHnzwQddznU7HaexeJEQPPlUqtVwu\nQ9M0SqUSSuLuVOEnFmy/qJ24gaII8xJLpQBj2DL9omxagOXwiwgj4gIehDhSrtLlFxzeFoVPzfZ6\nPViWNZKaVVl3FIFWq+UIYZ60t2obk3zYlqQBXdcdg2FGkVOutm3DMAwnlcoEHKVSiTBMpVOFxzLF\nwgZH4Kk2xAwRVmLpWy/RaPPjFGNGqlEDjvMixAdNKdfpokrNAhhJzbLrblFh956iQ59ASObm5rCw\nsIANGzY4zxVJ0LFUKovCAYOTiVKpRJTEkprlT1G/QBirTlWIq2GUiq/A9VGlsnl8sssGL8h0qAWa\n2A4s6CVoEhE3MiDkuiUUOeUalCCp2U6n43RMYOKPZUfyjm3b0khcEfadhwRdSObm5nDmzBmXoMs7\nqlRqtVrFysoKarWac+KQgCOiZqriThAV4tw8L9HBonyBpp45gtEraqcN5+8pe7xqnLDzWVZAQgmr\nGCpdSdh5I6uarVar0DQNKysraDQaMAwDKysr0tRsHiny/EEeEnQhUZkLx9kxYdqwqlQm4LxSqZqm\nYde+v0hwa4kiEbu481p30PStYhtlQoVP9dq6h1WKqHZk48SVxSHiYhBbJOAmgxd4R9/9Plf6laVm\nu92uk5pl4i5qQ+MkoaKIASToQrJhwwapoMt6ylWWSuU7NFAqlUgbcVXMBkJMdyrQ7BBjfSJ8g2Vx\nAjDAOK/PJalI3Miic+xBN21+8W8/4/xflZplhsaapjniLuupWZmgy/L+jAsJupCoInRZFHRiKpWd\n/NVqldpsEZkiMXEnKxRQvRZwDp+4LyqRwwfr/Mb4jRshoctZHrtGJIUqNVutVh3POzE1y/5lqTK0\nyIbKIiToQrJhwwa88MILrueyIujCpFIBEnFENklF5C7MONk2rr6u+UX3bEEATbispCARFz9iYQWf\nmrUsC4ZhwDAMtFotlMtll+ddmmH3XorQkaALTbPZxI9+9KOkNyMwlEolioyfuJMJiUTTtyphE2Vk\nLQXiiQRcsojiTtd1zMzMjKRmFxcXU5+aVVW4FhESdCFpNptYWFhwPccidGkJ+1IqlSBGCSoiUpO+\nJYgpkPXUbFruu2mABF1IVEURSaJKpVYqFUqlpg3PmewhxhBTIdH0bY6hQoj0krXULFW4DiFBFxJZ\nUQQwjNJN60skplI1TUOpVKJU6jQJelcKogTCjgm6Pi//siDLIhwykb7NEPS1Sz9hU7NM3E0zNUsR\nuiEk6ELCOkWITKMwgo/CUSo1Ifx8wpLYDi/PsSiFIuEQdfo265FAL3s82TgiewRJzfZ6vamnZilC\nN4QEXUgqlQr6/f5U1iWmUm3bRqlUolRqVARNbab9Dhvl9onLolRwZMg+SqnZcEbEXdBtp69HPlGl\nZuv1OizLQq/Xc+besfsWCz5EKbYsy0rFXL40QIIuIqKK0PGp1H6/D13XKZUaJVGlP4sCRfhiIYoI\nX5BsumpclMtSQV+F4iBLzdZqNdRqNVdqdmlpCQAiTc1ShG4ICboxUH15xhV0qlTq7OwspVInhcTZ\ndCFxFxvjiMBpLIsgeLxSsyxgwadm+XZk40TayLZkCAm6MdB13RFejDCCjlKpMUMiLh14iTtK3xJE\nIRgnNct6zQaJslFRxBASdGOwfv16nDlzBhs3bnQ97yXobNt2BBylUmOARFy6CXp8KMJHELklaGp2\neXkZQLDULKVch5CgG4O5ubkRQSeL0MlSqSy8TKnUCCARl29I3BFEbgmamm232zBNU5matSyrkOJN\nBgm6MVCZCzPhxgoaAFAqNU6CeiUQ2YfStwSRa8KkZnVdd8Sdqsq1iCKPBN0Y8IKOtdnq9XqwLAvd\nbhelUgkzMzOUSp0WXj5sRP6g9C1B5Bq/1Gy/34dhGFheXoZt22i3247A0zStkGIOADSfifx0FZTw\nR3/0Rzh58iS++93v4ud//ufxsY99DLquo9/vo9FouMaSgIsJEnDEuJC4I4jM8vgHbnb+b1kWzpw5\ng3q9jl6v56Rma7Ua1q5dm+BWxory5kcRugD0ej184xvfwNe+9jV89atfxYsvvohf+ZVfwe/8zu/g\njW98I+r1upNu5SExFzEk4ogooMgdQeQCFomr1+uu1KxlWQlvWTJk1rzl0KFD2LFjBy644ALceeed\n0jG33HILtm/fjl27duGxxx4L9V6e66+/HrfffjvWr1+P/fv343Of+xwuvvhivOMd73AicrKiiO++\n/0bnHzEhJOaIOLC14b+gYwmCmDqPf+BmV3QOGK1wZanZ2dnZaW9eKshkhM6yLNx88804fPgwzj33\nXOzevRtXXHEFduzY4Yx56KGH8NRTT+GJJ57Ao48+iptuuglHjhwJ9F6Rv/qrv0K5PPyoHn300ZF+\nrn4+dLyoo8jdGGSlHxKRXWRtEWTfNYrwEcRUEAWcCLX9cpNJQXf06FFs374d27ZtAwBcffXVOHDg\ngEuUHThwANdccw0AYM+ePVhYWMDJkyfxk5/8xPe9IryYA4BmszlS5coIYnJI4m5MSMgR04TanhHE\n1PETcTzkQecmk4LuxIkT2Lp1q/N4y5YtOHr0qO+YEydOBHqvH81mE6dPn3Y9x75AYV2rSdwFhMQc\nka2YgakAACAASURBVHYmaXxKEAUmjIjjoS4RbjIp6MZh3D6rMubm5kZSrsDkvwpI3HlAnnNE1qDo\nHUEoGVfE8VCEzk0mBd38/DyeffZZ5/Hx48cxPz8/MubYsWMjYwzD8H2vH5VKxTEO5gnTz9UPJu5I\n2HGQmHPDf9W8Pho7wJig46JcZ9Bl5QE/cUfRPaIARCHieChC5yaTgm737t148skn8cwzz2Dz5s3Y\nv38/7r//fteYyy+/HHfffTeuuuoqHDlyBHNzc9i0aRPOOuss3/cGQfWrIMpIIEBROwAk5HhUXy9R\nHMnGyQRUkHFRrjPosvJM2Ll5AAk8IrNELeJ4KELnJpOCrlQq4a677sKll14Ky7Jw3XXXYefOnbjn\nnnugaRpuvPFGXHbZZTh48CDOP/98NBoN3HfffZ7vDcu0BB1PYcVd0Stcw36lgowPuswox42zrAIe\nbimUviUyRJwijseyLJRKpamsKwtQp4gxeeMb34gvfelLri9Tp9NxerdOk8KIuyKJOTrzRokiZZw3\nSNwRKWFaIo5neXkZlUoFtVrN9XylUsmznQl1ioiaubk5nDlzBhs3bnSeiztCpyLXkbuiiLgk78uq\ndWshx4QZNw7jpozzDDs/SNgRCZCEiOOhlKsbEnRjwrzo0iDoeHIn7oqSbvUSKHEQVYo06jTqOOOi\nWG/WoTl3xBRJWsgxqCjCDQm6MWEROh5N01LVQy534q4IBImKjUta7vFJFUqEWV7W07c0546ImLSI\nOB6K0LkhQTcmGzZskAq6pCN0KjIp7vIckZMRx1cn4DKDBkLDjvMd4zPOWR8bJ5sWM06aVyXuxGXl\nIcJH4o4YkzSKOB7LskbEW1HFHECCbmxk3SKyQmbEXRHSrQlG4VT3dvFj9xvnNY0r7LJ8x1nucZ5Q\n+nYUSs0SPqRdxPHYtp3n4ofQkKAbk40bN+K5555zPZfmCB2PbdswTRP9fh/f/J/vhq7rKJfL2P25\nfUlvmhpVo/QskkIRp1qmM1710dvCMoOM8xgzso0RRAv5ZfstMzB5EHdA8OgdRflyzb//1vvR6/UA\nAK1WC9VqFeVyOdXRLtW9Ns3bHDck6Mak2Wzi8ccfdz2XZkFnWZYj4kzTRKlUQrlcRrVadX7hpC5y\nlxcBJxLFPLkYRZzva0HmvUVhXCxbngJX+tbjB3ugcbLtKULHC1G0qc4/Ene5QIzEsR/6vV4PKysr\nsCwLlUrF+Ze2SBibP1dkASdCgm5Mms3mSD/XNAk627ZdIs6yLJTLZVQqFczMzPieBKkQd0VIuYZh\n2iJONdZHZLnSol7iKsg4cZ0AII4V9kG2XGk6OO70bZa/skHPNxJ3mcIrnappGsrlMsrlMur1OizL\ngmEYMAwDrVbLCQBUKpVUmPlShesoJOjGhNmWyEjqi8Z+YTERB8A5CUul0tjbROIuQsYRUAEIdS+N\naqwg7DRJgbcormRjZOM818vGBhBhmrn6f5/gQizp27yIu6CQuEsl486J03UdMzMzmJmZgW3b6PV6\nMAwD7XYbuq6jUqlMfG+ZBKpwHYUE3ZjIBF1SX2qWRu33+858uJmZGei6Hvk2kbibkDDpVr90JBsW\npV8c5KJLWYVqBZ+D5Ygr1bjVlbBxtq6YIyOJ2rkifLK38fsUVNzZ3hHGkXWRuBtC4i5Roi5s0DQN\n1WoV1WrVuef0ej0sLy/Dtm0nclepVKZ2H5RVuBYdEnRjMjc3N5JyBYZp1zi/aJZlOSJONR9uGjBx\nZ9s2du37i6mtd7DSHJzIQQRbAAGorDKdUMS5XherUC3J5y+7icuOk7jBimPJr8PWbU9doFmD5ahE\noAsvcRcgfatk3KraHHyVlZCYmxrTqk7VNM0Rb7OzszBNE4ZhoNPpjKRm47wfqSpciyzySNCNSblc\nhmmaI8/HMY+OzYdjIi7sfLhp8M3/+W40Gg1omhZv5C4PQi6u9GjI9/mJuBFWBZZzBDzn0A1e9JxD\nZ/qPYctj4s4uSXaM+06IItCXoOlbxCTuwo7NCiTkpsLjH7gZ/X4frVYrsW0olUqo1+vOvDuWmo17\n3h3NoRuFBN0EyIRbVIKOtxZhwrFcLqNWq8WSSp0EcVtiTctmOd3KCJhKnSjdKqsyFcZ6+b05yCJx\n4vICzKHjBZA4TiWUpMszOWHnc/w1S4NmAVZZvZO+6VvlskOM5yxbIhWCaYZSrrEhq05Ny/1A13XU\najXUajVn3l2v14tl3h3NoRuFBN0ERP3FYXMTmIhj8+GmnUodB1WqmcSdgiBz6SZJt44MDPj+EeNZ\n7/e7RJeXZ1zAaKBmDebQ2T4/5vXecGWW5CrGr0/v82OHO+JnXhxUfGlcoF7cbnEdY0X5MvbVHoHE\n3cR4pVPTJOh4+Hl3s7Ozkc+7S+t+JwkJugkolUro9/sol4cfY9gInWo+HIvE5QkSd6vEkHJ1CTM/\nH7URkREk3+h+r1SgKQSINjozQSrY+HFOYYQokCTr1QcF3Y6w8xKPel8bFDt4RO3E4ozhNrMwm/qt\nrvcESCU7y/Yam6c5dyTuAhN0TlwWhE0c8+6oS8QoJOgmgHnRbdy40XnOT9CJ8+Fs20apVErVfLhx\nCCtkIxV3aRdwInGnXH2ias6wIEKOjZWIMiVeok9Ynl3yXvawMtZ/tbqxukzVVY37PDQuaueIO78U\nbt9jHp8Mtv8hUrhB0r2Uls0v4xQ2ZEHQiajm3a2srDj3Q5aaVcGMj0Wy9llECQm6CWD9XP0EXZbm\nwyUBE3ep6E4xLaaRcuWFo6yWYLVoQCXsHEEWplpWFGc+adggQsYVyZJd38U5easRO5ew84qq9TVo\ntjx1K3s/m8c32J4Jq2rFbRH2pZBz7oBCCbxJq1PTYmY/Lqp5d4uLi07atlKpjLQiy6KQjRsSdBMw\nNzcn9aKzLCvT8+HGIYpikLGjdllKt8oIElHzGeMIO1VUzOP9TNjxc83CrNszeidJw6qKJ0TxIi+K\nWF0sE3ZeUcA+fIUa/9VhqVuAe49flDNGcQfEWFmbdnIevYvSYiRPwkacd8dSs3wrMibwqChiFBJ0\nE7BhwwaXoONTqf1+35kPl+VUalIUUtyN4Us3cq/zi+qtrkMWlXPaZHmJQkkhQCDs1eVGUDgBDNOr\nQcSaOMdOfN1z+aMZHfX6SNzFQ07EXZw+cXm8v/CtyAA4fWY7nQ6Wl5cBAL1eD7quO0GSPH4OYdB8\noirZPXumwF133YWlpSU899xzOOecc3DttddC13XYto16vV6oL1en03HmPsTJ2GnZNIu7CYsk/K1H\nvF/2S7lKhVaIqlX5C5Kn+u7HqrlwMjHJizW/z8Ovgla1b0HEnWzdXrYpSkIG8QPNvWOk+FQIRQbE\n3TTMflutFkqlEmZmZmJfV1qwLAtnzpxBpVJxgicsejc7O5v05sWN8gymCF1IVlZW8PDDD+PAgQP4\n4he/iJe85CX49V//dbzuda/D7OwsLMtCt9stlJgDokm5BuG7778RlmXBNE1c8jf3BX9jmiN3AYsY\nVNg+6Uy/qJ1vylUGExABerNKsd3bJYo5/jkm7Lyignp/IOr87vGa6V6OVRUGeGx3yRj+3xTfB/W6\n2ecaSthR9M6flEbuvvWe/+WIi2mQp5RrUNj+rlmzBgDQ7/edqtkCCDoluYrQnT59GldddRWeeeYZ\nnHfeeXjggQewfv36kXGHDh3CbbfdBsuycN1112Hv3r0AgNtvvx1f/epXUavV8IpXvAL33Xcf1q1b\n57zvjjvuwCc+8QlccskluPzyyzE3N4ef/OQn+P3f/31njGVZaLfbaDQa8e9wimAiNq6LGEtn9/t9\np1NGuVx2DCpzF7mLoArWu8pUvd+q93kuTyLIghIofRukynV1G8xa+PV4FkRArRfM6vhagiJ3EZES\nMff4B26GZVkwDMOZ2M+sOuKcO720tIRarTY1AZkGLMvCwsICms2m63lmXpxzlGdqrgTd3r17sXHj\nRtx+++248847cfr0adxxxx2uMZZl4YILLsDhw4dx7rnnYvfu3di/fz927NiBhx9+GG9605ug6zo+\n8pGPQNM0/Mmf/Inz3h//+MeYm5vDhg0bAABHjx7F3/7t3+KP//iPXctfWVlxfjkUBcMwYNs2ajXF\n3TQkvL1Lvz+4U7M5iX4u44UTd14VnDa8Cwc8rEtUHR2Uwijg1ULvSd6quNfpglCUpT3FMQxe2Hla\no3D7GSbyJhJmvt3IeyMWd9Kvcpivd0pPBYcUiTgVtm27xF1QO46wLC4uol6vF0HIOJimiaWlJczN\nzbmeL7qgy1XK9cCBA3jkkUcAAO973/vwhje8YUTQHT16FNu3b8e2bdsAAFdffTUOHDiAHTt24C1v\neYsz7jWveQ2++MUvut778pe/3PWY2ZbwMKFRxDD4pDB7F1ZUAsApKglj75Krgoog9y2/lCt/4x+x\nxeA6JwhRO/YR6CMGu2y8YjuUqUf1azL7EplQ03tu4aQScwBQ6gaIvAmfB0urMmEXRjfwQjWsuFN1\ns/BEkpb1/NqGSbWmPS2boM1J0HlxmqYp7TiibINVxHsNVbjKyZWge+GFF7Bp0yYAwDnnnIMXXnhh\nZMyJEyewdetW5/GWLVtw9OjRkXH33nsvrr76as/1iVWuQHG/UMyuJSy8R1+/33fsXer1eiQpikyK\nuwmKJHynFbFKVlnEiok7IWpnrQq4sMJOFonzq+TlvelUOMv1OSxaHyhxgs8U5ox7rafc4d43RtB5\n6uKOVR8zj+Qw8+gA788y7eJuCkxa3ODVBguAI+5Er7UgZN2HbhyKKGKDkDlB99a3vhUnT550HrMD\n+7GPfWxk7LgH/OMf/zgqlQre/e53e46bm5vDwsKCdL1F+8KFKYoQPfpYKjVuj76Jxd20hF0Q02F+\n7Bimw3yl54ggkwg7zeIidoqo2EirLN2jSEOyTTpXdKDs9iCMtVTz5STbWOoMRZ3nfEAxatcd/B1H\n2AGTizv2uUutUFTWgWE6TwDBRVua2pDFHJWLq0KVb4PFOiWovNaC3kOKdK8BKEKnInOC7utf/7ry\ntU2bNuHkyZPYtGkTnn/+eZx99tkjY+bn5/Hss886j48fP475+Xnn8b59+3Dw4EH8wz/8g++2lEol\naVRqWhWfWUIsakjaoy+0uEsqBRtBRwm/qJ3jPycWSih2maUxRWEna+Ol9Lbj0rO8kHNelnV7wOhY\nfVVs8cLOq0Cj1Blsd19RCOcl9Ert4f/NunqcF2HE3UhWkfe5Cxi5C915ApCaQacSzzYp4zENmxEe\nTdNcbbB4r7WgPU6LFjwABveTPBr0T0rmBJ0Xl19+Ofbt24e9e/fiM5/5DK644oqRMbt378aTTz6J\nZ555Bps3b8b+/ftx//33AxhUv37yk5/EP//zP0c2ub8oiCKWFTWwdCrrWRvFnJGoUYq7tMyjY0Qt\n7sSbPRcBGplPJ0mvyoSdKg0rRutkIk6G1h+KOq/36N2BqPOrtmXbWl4Z/OWFnWeqV4zarYq7cYUd\n4C3uPL96muz4BBR4Hil6tmz/hQRa1fSYUNhNW8R5USqVHE85WY9TJu5YUYVt24UUdBShk5OrKtdT\np07hXe96F44dO4Zt27bhgQcewNzcHJ577jnccMMN+NrXvgZgINxuvfVWx7bkIx/5CABg+/btMAzD\n6c36mte8Bn/+53/uuc7Xv/71znIZ7Xbb6T1XFEzTRKfTwczMjKsyldmLZLFn7UX3/kXSmxCMCapg\nAYmQUfQt5ZEWLAjPyebQKQXXBBYsjNLq+voKf1WvAgpxft0IAdY/ibhjMG0ijd6FOH1GBF6A94ay\nOAm4zNjJgYgLAiuqYFWzuq474m5xcdFxXigKKysr0DQN9br7pPOKZOaIYtiWJMGb3vQmfPGLX3SJ\nt2l1TUgDrKih1+u5etaWSqVMijgVeRR30nthSO+6SIWdZHt4M1+vitWSsA5R1HmJOcBH0IWv9RlL\n3HlF5UYMkIOyepB9u2PItietHnYFEXEq2BxkwzAcu6iZmRlpA/u8ouqOUXRBV5wQUkywwggW1QPy\nP4dOLGrQdR2lUgmmaebWpfu7197g/D/V4s6v64Rf2lZhcaLZcIol3D1LV9/GCSoxvWpVRkWdXZaI\nOm7bS5L0qqonqyjmgEGVKhN1fmJOM4FyC+jLvMDHEHNA+Pl2SjEnqRgOVFghiB4+BR5E3Nli4Uoa\nbE7GEHJ5EXE8fFFFrVbD0tISADhFFSxyF6aoImtQylUOCboJYV50vKAD8ldKzs+H4ytTWVEDSwkU\ngcfefz1arRYajQZ23feXSW+OHB97EMAdgfHyrxuZD1caFXZMZDDhwZY98ni1iMGuuEWdTMCpYG2+\nALmYY5Q7AXzouH0rtwZ/HWE3ppgTKbXhiBxZJNBPzInoPfdrI/voIXxsDaP7pQuvS9/ov12xE3Cu\n3H9e+wGsrKxIuwTlEV3XnR/Spmk6LbBarZYj7PIWubIsq/DiTQYJugmZm5uTmgvnQdDJ2m1VKhXP\nytQiTNDl9y9VkbuwXzmJ1V4YixNXEYXTr3Tw2ImmCULPqg1EncYJMako8xGken/y4kZV94hya7B8\nY+1kyx+uaPjfUsf9Ul8VvQsh8thnPRTo2sgcuqCGw5o9pofdNE95Sdk2H4nr9Xq5vwYxxPsMXzHL\n7FAMw3CJu7jtoaaBbdvSfSjKcVdBgm5CNm7cKDUXHsdkN2lk7bZY2bxfZWrRTyQgBeIujH+dAq8q\nWEBtQ8JsNERhB6x2d+CEHrMYYdE6c1X0jQg7hagr8+lMj7lvpQ5QAtAbswtfdZDJik7YCdj60OOO\nR7lPilNMJsBY9NQ5nmN42EELUOgtpvineBl4/Kbflj5fhB+VPKp91XUdMzMzmJmZcRVVtNvt2NqQ\nTQvZMS7SMVdBgm5Cms2mVNBlJUIXVbstYLjfRTix/PY1E+LOz+LEIyXrek0ypw4YCDlbd0fpnFRp\nZ9S3zqz4izpezLHlyAQQHwmrDMz4XcLOq7erOOeOCTvNArphs3ghRBh7XmbPojJQ9oqmucR5GA87\nD99C2y8aF7OwU4m4ohL0est3quCLKqJuQzYtinKfCQsJugnZsGEDTpw44Xou7YIu7nZbhJvH3n+9\n81m/5v6/mf4GxC3uFP51I/PquAIKc2Yo6oBhqy2VqGO+cTJEUSemNRmV5YGoCyPmRGqrjWECCbsx\nxNzoBq3+kaSlVZ0rPKNquq2OvPrcH20xGgfhPTHdX//9vf/LSRcGoUg3+3H2lS+qmJ2ddebdRdGG\nbBqovPfSuK3ThgTdhDSbTfzgBz9IejN8mUa7rbQL2Sjx21fV5/3Y+693Ljy5jNx5zKsDVqN0q/dl\nZsWhG4OK1HJnKOZEUaZsI4bheCYSvaj/1EZnQ/gLv7huJuxsDTDWyd4gX844Yk41dqRyuOT+616W\nx3eVrUcyN8435WrDnXaN6J7KInFi54Rx2mLlmUnFq6ZpjlcoL+4maUM2LdK2PWmABN2EpDnlKitq\niLvdVhr2OynCfN6WZeHb17wflmVh9998JpkNjlvcCfPqAHn6VbNWU7P9YYRMJs6CiLogzJwabJco\n7Pyicyqqi8P/G+tWI4GSeXdRirmR5/lUN1+4sjpedng9ix8kRTJ2kGjcBNWw33nftU6nBNbaSeyc\nwFdwqtpiUYRufGRFFZ1OB8vLyy5xl2Qmhypc1ZCgm5A0CTq+qME0zam32yriSSYTcarPm1m/MFiq\n+z+vu9G5QP78X/w/U91+hzDiTvG6X8EEwEXtJNkzY93AvoT1Ww0ScWNUl4frMNbIv4fltnvDZ04N\no3VeYk4lImXRq+ri4HOorM67Y8JOnPvnbKus4GICMaca75pDx9nRhOkoEagCNuQl4HvXf8A5TyzL\nctpdsSpGZlDO/rFJ/nxbLLGCs0g/KuMUr6rPW9WGbFpQhasaEnQTIhN0jGn8UlRVptZqtal3akhL\nZDJO2Odt2zY6nYHa8BLNoohjzbjZDYqNYS19vvmu33TsYf6Pz9w7vR3j8RN3EUTtALW4M6sDYTfz\ns1FRJ4vS8WKOPRZFnSjmGLyokxGk5RiPKHiYsLMlV1qrNDo3UGUaLDMnDiLk/J53qmGZsJOkTaMW\ncf953Y3o9Xro9XpYXFx0xBiLaANDccf6QMvEXa1WQ61WG6ng1DQNuq47Ux3yjErcRI3X5823IZtG\n4KBIEdiwkKCbENYpgkfTtFgrPvmiBtM0nXkQ41SmEv7IRDMAqRs7E3vsL7u5sAsdL+K63a7TMo2J\nuNnZWWd537/hJme5qY3chRF3CoPiwWtucWdVBv9sDWg85142L+pEMceQiToVa04MFrayKfiN0Xdu\nGT9WIeZGnpOIOfZecb6cJem04dUBQh3dkx004T2quXEhPgP+uwzAEQcs8tPtdrGysuKcB3wvbJm4\n4wWeWMG5vLwMy7KcCk72Wh7FXRI/oFUVs6yogom7uIoqqEuEGhJ0E8JaXsWNbJI9iwylpTI1TxE6\nPzuXTqfj3EyYeGPiGoCviGPp2VqtFujClypxN+58O4VB8eC1UXFnrhZNrJwz+Lv+ycGYXkODrQO1\nRe/vGhN1quicyOxJyyXqwqRaAbloikLMjYyVPK82f159jtsXW1cIOe710SdX/4a81IhCToSP/DDR\n1uv10G63HXEni9yx85KdX+Vy2Tkf+S42WbfnCEKS+8FXzLJrIF9UEUcbMorQqSFBFxNRiBt+Ppys\n3RYRLarIp8rOhc1TZBcYJrJ5EWeaJjqdjjMvKIyIU5G4uIuimCKEuGPC7tSFg4WWjIG4s0ruAgAZ\nfmJOfH32ZPhoHRAgLelBUDGnamMmi8wp07GsbdtIdbLHBkYs4pSr4aJpLK3HxB0zwhUjdywa3u12\nnfOO/VgS7TmYWOQjSVkXd2kSN3zFLCCvUI6iDRlF6NSQoIsA1QTNcQTdOO220kIWI3QqTz5Z5JO/\ngQBAv993zZ/Tdd15nRdxlUoF9Xo9lhtHbsXd6ntkc+7MqlvcNX8Y/Xdu7bFBVHZ53n2JjDrVOomY\nG0fIBRmvyVKuzovy5Y8r4lTI0npMHPDijv2IMk3TOe9YRN0wDNcPLPaeer3u8l6zbdtZV1q911Sk\nSdCJiBXKfBELu8aOk2GiKlc1JOgioFwuo9fruYwvg4qbSdptpY2stDwL48knS6eyGwqLIqysrLii\nAwBiFXEqcifuAhRUmFXgxVetprlX55lt+N/DcbopT3EC/tE7AFhzQi7sXNs1Zqq10hrtcmFWAU3o\nFGFVJHPoKpLIZnn0M3PWabm/g3ZFIfCC9nHVohdxKsS0Hi/u+GkPbBy7drJiI9k8Vi/vtTjShHGR\nZkHHE6SoIug8R+beIJKFzyFuSNBFACuMOOuss5znvASdLLXHfslQUUM8qEScyiOOCTlgmEpgUTi2\nPDZ3kr2fpV/5SrykjmUuxF3QallWULGamn3x1ZzAs4HN/9pD+yWTXerWnOgDq8dyaT785HrWV5ZH\nJuZ4ZNG7wBE9VaSOfW5iS6+Au/T9G6cj4lQwQcb+sXOUv9ay865cLjs/sFiEiL3Oizveey1rRsZZ\nEXQ8Xm3I+NdUP4azuM/TggRdBDSbTZw+fdol6ETCpPayStpSrmGNfkURx1IGvIhj83rYhOxKpYJG\no+H8YuQjCN1u15XqSeo4503cAcIkfyGdKFbMnnhDZegzZwNzP7I9o3MlwzvKvPbEMDTWnxm9sfTr\nkueEcWHFXCAhZ6nGBU+3ykhaxAHD+ViqaQzs+trr9dBqtRxBx9KyYlEFW44473UcI+Mkybq48WpD\npkqFZ32f44QEXQTMzc1JzYXZhSPOdluEm7BGv+wfs5rxE3FMoLFoqohXeoi3ZKDIHcYWd4BP9E5m\nh1KGMzns1M8DWl93dUE467H4pgqMI+ZYOpVFHfl9dObTWcPxAEZCgC5vOfZcwCt+GkQcu36yqnCv\naQz8hHx+zla73XYEYFhxF9TIOMlreZ7ETdA2ZKo5dHn5HCaBBF0EbNiwwRF0rNqKXSQAKKNCeSOJ\nCB2bg8iin37dMVTdGkQRZxiGI8bYnDmViFMhiju+ao/EHeIRd2IqsSxG74bP86/89Bf44zr4/zmP\nckZvkmMUJDoXRMyx+XFmbbiNlijyZGlVWTROUvyQZRHHrp1h5qLyP8x4rzte3DHhoBJ3fkbGzAic\nVeAm0TUhTdmQOFC1ITNNE+122xF5NE1piObzpcj3NyYCbNvGH/7hH+L48eP41re+hTe84Q346Ec/\nCk3TYJomZmdnk97EqWGaJrrdbuz7rCok4b2oeIJ0axAjcbwHVtS/wGXrSlOFXWI+d4wgV50AY0Rx\nN4JHUI5vlcXDxNemb5uhBR0Tc6Uue231+RpbqVukiSJO1UUiTyLONE2XPUnU5wP74ccLRnFd/PQL\nmbjj4Sf493q9qRoZ27aN06dPY8OGDbGuJ22cOnUKs7OzzvWTCfd169YlvWnTQnlSkKAbg36/j298\n4xv40pe+hC9/+cvodrt47Wtfi+uuuw579uxBuVx2JnoWSdBZloV2u41GoxH5ssOIuDBGv+yiwIu4\naUbNeDPVfr8f681sHDIh7gKM8xV3PstQCTxg2AdWVojg1SN2sGD1S2rfuezPiRN/1CTxvQ8iJEVx\nJ5uWwe8Tu+4bhhG7kbFlWVhYWECz2Yx0uWnn1KlTaDabTkaIRVVJ0OVU0J0+fRpXXXUVnnnmGZx3\n3nl44IEHsH79+pFxhw4dwm233QbLsnDddddh7969rtc/9alP4cMf/jBefPFF16+g3/u938MjjzyC\nK6+8EldeeSX++7//G9/4xjfw4Q9/2BkzrWhVmrBtG61WC2vWrIlseTKjX1nULKyIm0Y0ICyydFNa\ntg3Ij7gLwkiXCMnHL9qGjLUeU/Ps2gAohJrt8ZqENIq4NEw7YIg/rGTbpmrrJ7se8fNnDWPgQxO1\nkbFpmlhaWsLc3NzEy8oKLCrJBB2DieeCUCxBt3fvXmzcuBG333477rzzTpw+fRp33HGHa4xlhkM4\npwAAIABJREFUWbjgggtw+PBhnHvuudi9ezf279+PHTt2AACOHz+O66+/Hj/84Q/x7W9/2yXoxKbP\n//7v/47Pfvaz+PjHP+5aflzRqrQShaBTVQN7iTi/vqlB0ixpQzYhfFrNr4OQKXHn93FFkb4NuBzf\nRXjMBxRf9yMtIo6de71eL1UiTkWQ6Rde4g5wm83zLbEMw4jMyLjf76PVakmDFXlFFZUkQTcgl0UR\nBw4cwCOPPAIAeN/73oc3vOENI4Lu6NGj2L59O7Zt2wYAuPrqq3HgwAFH0H3oQx/CJz/5SVx++eUj\nyxfnRjSbTWmVa94nrYqMW1Y+jtEvb2DMN+n26tYwacutaSL2uGQmnGLFHhVUrKLqZhBV4YU2HKfq\n8RqmWb1ruX6vefVWFUijiGNFA2GLipJC9Eljwk5sQcZSr7zXHd+CLG4j4zxVuAaF2n55k0tB98IL\nL2DTpk0AgHPOOQcvvPDCyJgTJ05g69atzuMtW7bg6NGjAICvfOUr2Lp1K171qlcFWp9M0DGKeNIF\nIazRr6xbg8zol01Ojrvl1jTh7ROY1xYv7pjwLay4A4KJqbDiToHMEy9ISzDNDhhhC7MvdnpEHDv/\ngtj7ZAVVCzKZxyTfKWYaRsZFvLdQ2y9vMivo3vrWt+LkyZPOY/bl/tjHPjYyNswXoN1u4xOf+AS+\n/vWvu5btxfr167G4uDj2OvMEi0zK9p/NUzFNE6Zphjb6ldmL8JEAZhOSBxGnQmbHwIxU+chBUqRC\n3AUhiLgLyIhIE5e3ui5P0Rfyqzqttlte8Ga+rMIzDyJOhcxjst/vY3l5eeT8CyPuxjUyLloGCIBT\ndSySx2v9OGRW0PGCS2TTpk04efIkNm3ahOeffx5nn332yJj5+Xk8++yzzuPjx49jfn4eTz31FJ5+\n+mlcdNFFsG0bx48fx8UXX4yjR49KlwPAFXbn8RI3eUVMNcuMftlFf9xuDTIR12g0CuVHJHptiS75\nJO5CEFTcBR3H32+CLi8AaRRxsm4pRYA/x/jIuXj+8fPq4jAyLsr1jlG0+2lYMivovLj88suxb98+\n7N27F5/5zGdwxRVXjIzZvXs3nnzySTzzzDPYvHkz9u/fj/vvvx87d+7E888/74x72ctehu985ztj\nlYYXcR7dOEa/fLcGdmHz69ZQLpcLdxNRIbrk8zeXNLQes20bj73/evR6Pez+m88ksg2BCXqviHqc\ngn+7+j3O8UsKdk4zgVFUEadCdf6trKyE6lLBmxgHMTIuYvqR5tB5k0tBt3fvXrzrXe/Cvffei23b\ntuGBBx4AADz33HO44YYb8LWvfQ2lUgl33XUXLr30Use2ZOfOnSPLCirKVF+yIgg63iPOtm10u12n\nCCGI0S8THSqj30m6NRQN2c3FMIyp95VVRXK+e+0NjghIddQuQVgkTtaflD9+cd/EZP1TixYJDwt/\n/vFCOEgLMnb9FI2MNU1ziTsmrJm4ZmbGRRDXFKHzJpe2JUnw5je/GZ///Oddv6T5Fk95Q2X0y3u7\n8YzbrYF9fiTiJkNVeRhlJwyViAuS+i26uPNLp3p9tlEKLDaPixdxSVdU5wVRIAftUiFmLBitVssR\nONMwMk4DKysr0DQN9Xrd9Xy1Ws3l/ioolm1JEjSbTSwsLOCss85ynsvbF0xl9Fuv152LTafTARDO\n6JfdQHjPJ36ZxOTIJnRH0Vc2qjlVmZlvFyFh5sR5pdUnFXcyz8M8FxYlhVgAwaplV1ZWXAbnYuSO\nXXNlc4rZ92F2dtY5p5eXlwFEb2ScBtj3k5BDgi4ims0mTp8+PSLosp5yVRn9ennEsYgdgJGJv2xc\nt9t1ujUwQTE7O5ubC0+aEcUdu5kHFXdxT4zPu7ibtLghCnEn60qSJZ/GrCP6TPLijj8HeXHHX4sB\nONdjwH1OMzsUwzAccccid1k/vpRy9YYEXUQwQceTVUE3idGvpmnOXBDeXZ0XcXQDSQ8yE1Um7sSb\nShLVjXkRd3FVqIYRd3xklvVPpXMweURxJ4ue81MjWEYDgGOgLnapiMPIOA1QUYQ3JOgiQtUtQmZn\nkkbGMfqVdWuYmZlxfkl2u10sLS05Y+gGkm54ccd7YvFivVqtJlbdmDVxN22bEVVBDIvSAP9/e/ce\n1dSZ7g/8GwgECIo3xIqXar2gra21g5fR0XoB74CAJEGSntZTV3uWo/3NdGq7TrtOezqdtmvGrjPr\nOO3qWZ2pBCSJooiigKLVqRe8jmKtlypTblW8UQYQEnL5/eHacSfZCQFCsvfO81mraxXY6ht29t7f\nvO/zvi/stZPUG85PzAQH9gcsZpcYBvsa9GaXCl8tZMwHXIFOCO32Fwp0PuJptwi+6u5Cv97u1uA8\nMy4kJMT+fabYVyg3kGDDNZwaHh4OAPaAwHzKF+TWY+wNUj2t9OvtcR7aFShck4uYh7/ZbIbRaLTX\nItGMVX7iqmtk7qMmk8lh8ll3d6no6ULGfEBDrp5RoPORQYMGoa6uzuF7fBxy7e1Cv552a2APtXIV\nVXsa0qOLNHDcrfjP1RPHPCyYnjs+zILsMtxx7XLv/H2bxPvjPPz7geI8i9ldXWqgl0Ih7jkHcXcj\nGlxbkDHnm7k3Mx+cPYW7nixkHGhca+/Re/YxCnQ+MnDgQHz33XcO3+NDoOvpQr892a0hKirK40PB\neUiPuSGxw52YZmTxmbsQFx0d7fEGzq734dpXNtDn0CXcuQtpzrpznE3CuxDn7f6pfTlblnSft0Gc\nzXlSk692qehqIeNAhzv2M4lwo0DnI+5q6AIR6NytEedpoV92iPO0WwNz8+ltUbynYMB0+wfDQpn+\n1NMQ5w57+IZ9DgHwIhg4hLuvvuj13ze2sAN7yv5fr/+e3vDl/qnO4c7d3sAU7nyrL88hV7hjRlW8\n3aXC3ULG7e3tDjV+/r4/M8Ot9F50jwKdjwQ60LkLccxNwpvdGrpa6NfTUFxvOG84z3T7Mz16fK7p\n4Dvm4WEymRzOYU9DnDueziEv9pX999eRmvQZpLV3ce13o7z+c2MLO/qwVd5xV9foy3PI7olnL6Xx\n8OFDAPwI6ELG3J+Zni/muvD1OXQOd2azude7VLAnaZjNZphMJvzrX/+yhzt/9crTDNeuUaDzkUGD\nBrmdFNFXhZzeLPTLxoQ49icdrhDHPPz9veUW057IyEjObat8vbOBWPVkKM5XuM6hc49BIIduzKNi\nMfGPtUDoo3//2m9GuBzDlxDHFQD8McOYwp3vOO+84a9Z4uxwxz6H7HDH/NxTuGMHPPaHs0AsZEwT\nIrpGgc5HYmJi0Nzc7PC9vg5x3i7029VuDeyaCfZuDYHcN5V9Q/LlzgZiFcgQ54674aDW1la/7ivr\nycTP6h2+7hw1xM2R8MtwK7snDkDA90/tKtyx7z10LT7Ct5032OeQ3XPH1C+z17rztEsF86xxDnf+\nWsiYeui6RoHOR5iZRc6YYdfevOl6s9Av4H63Bq4Qx8f1qZyLgGmm7CN8DHHuOIc751l6/gh3xQd/\ng9SkzzweE1Z7z2Oo6wtcS/3wcestT+GOL5NiAsXbGap8wHUOu9qCjHmmsNe6Y4c7fyxkzDXDlTii\nQNfHelpH19uFft3tm8recov5FMXHEOdOsM+UFVKIc8c5oDOvp6Ojg7e9r77unWMvE8HnEOeOu3DH\npxnPfa0nM1T5hmsLMiaMsa9F5nniLtx5WsiYvdZdbxYyZoaBnQnld+0PFOh8yN0by9tA15OFfrl6\n4thvembHBjFuuRUsM2XFEOLc4Qp3zIy6vgp35lGxkDbc99nf5y2u/VOFFOLcCaZw58sZqnzj7RZk\nzLlmL2RsMpm6DHe9XciYaui6JukibPBrVVyeW7hwIXbs2IGwsDD795ieI6b72hnXQr/MReMuxHW1\nWwP7weFcACv2C4KrmFyIM2XdhTihvY6e4trtwFc1OalJn3UZ6NjDrr3pnWNfi+zV/YPpWnS+Fwkt\n3LmboBLoddn8xXnJKmaiHPtexA537OWvnEeJGOzeQHbJj6ff6cOHDyGRSBAZGenw/fDwcMG8l3zE\n7YulHjofYpYuiY2NtX/PecjVFwv9cu3WIJQ6nL4m5JmyYu6J6y7noXWm5865zqcn721p7d1H/xPa\nN79TIdVT9SWh99wFaoYq33AtXeKu/pUd8LrapaK7Cxm7G3Ilj1Gg8yF3gY4d4Hqy0C/7xuhptwax\nDOH4ihBmylKI6xp7MVPnukn2UFCgz6Nzj6LQ6qn6klDCHd9mqPKNu/pXpp6OfR65wh3XLhXeLmRM\nQ65do0DnQwMHDkRTUxMAx544poehuwv9cu3WwLXlViCXNRAKPs2UpRDXc1xF3H05KcbTcKsYiuID\ngW/hjnpUe8b5ntqTLci6s5Ax08HBDoRMO8gjVEPnQx999BFCQkJw5coVPP/888jOzrYv4BsREeFy\nvLsQ57zQr/PDn70RM+kdrk/kffEwCfaauL7GNWvU03nMmPhOl0OunaOGuAQ65xDHDA/xdRhfSPxZ\nc+cujAe6x14MuHY2YT+zmN8veySKK9w5/53Nzc2QSqX2US4m+Mnlcr+/xgCjGrq+8vDhQ5SXl2PX\nrl3YtWsXJkyYgFWrVmHRokWIioqyF0MznHdrcLfQL3Mx+Hu3hmDTlzNlqSfOfzydR3axdXce1mG1\n9wC43z6NzqNvcS2A68ueOzHPUOUT5zUnPfXAdmeXCgD2XZDYy6EEYaBzS9Q9dE1NTVAoFKipqcGT\nTz6J7du3IyYmxuW4srIyvPHGG7BarVi7di02bdpk/9n//u//4vPPP4dUKsXy5cvxySef2H/2pz/9\nCR9++CESExORnp6O0NBQtLa2Yt26dfZj2J8Au9qtgd3tzzWTiPhHb2bKUk8cfzj3+ACPt63Kmvxu\nlz10hqoPXB7+dB4Dg2vSlzfhLthnqPKN83nkqoF17rljnpNtbW2IiYlxOG/MdRlk3L7hRR3oNm3a\nhMGDB+Ott97Cp59+iqamJodABjx680yYMAGHDh3C8OHDkZiYCL1ej4SEBBw5cgR/+MMfsH//fkil\nUty7dw9DhjxezuDmzZsYMGAABg8eDACoqKjA0aNH8eabb9pvJMwDhT3DkunxYa/1wy6kpuEb/uDq\nnXEeYqMQx3/OvTMvJ37cZaDbeupth+BA+MGbcMc1Q5XOI79wrcvoHO6cz3VERIS9Z4/pxaNA95io\nh1yLi4tx9OhRAMBLL72EF1980SXQnT59GuPHj8fo0aMBAEqlEsXFxUhISMAXX3yBt99+294tzA5z\nAPDUU085fB0REYGqqip7gGPebMwwENNFHBoaag98VEjNb55myjJDAcxOHjR8w1/Ow0CGyx9C8fR7\nHkNdv379/NhC4i13w7JWq9V+bw3mpZuEwt0Ep4cPHzrcW9mTCYHHOx51dHTg9OnTSElJCfAr4Q9R\nP3nu3LmDuLg4AMCwYcNw584dl2MaGhowcuRI+9cjRoxAQ0MDAOD69ev4+9//jpkzZ2L+/Pk4e/as\nx39v+PDhkMlkWLZsGT7++GPcvHkToaGhqK2txZYtW3Dv3uOaHKYrmfm0QTccYWIvokkzjYWBmahE\nhI9rEhnzHxEOdv0kc20y5/DChQvYtm2bfQWJkydP4o033kBaWhouXbrksFtSsBN8D11SUhIaGxvt\nXzNB6fe//73Lsd29iZvNZjQ1NaGyshJnzpxBVlYWqqur3R4/duxYbN++HRaLBVu3bsXatWtx69Yt\nmM1mJCcnIy0tDf3797cvNsx8umxtbbUP5dEsK37hmtXo3BMXjHvKCpHzMJwnfzv5FlpbW2nonIe8\nWS7G1xMqSN9gX5MA7MtwMUPjTDgvLi7G22+/jfDwcEyZMgWbNm1CcnIyXZdOBB/oDh486PZncXFx\naGxsRFxcHG7fvo2hQ4e6HBMfH4/a2lr71/X19YiPjwfwqLcuPT0dAJCYmIiQkBDcv3/fXjPH5aOP\nPoJOp8ODBw+Qnp6OpUuX4s6dOzAYDHjnnXeQlZWFZcuWITIy0mEIyHkPS19tc0S6z5sQxxYse8oK\nkaeFYj3p37+//T3Q0dFBy1oEWHdnqPblbFnSO+zFgz0t3nzv3j3s3LkTRUVFiIuLw9/+9jdYLBYU\nFxdDpVJhypQpeP3116FSqQL4avhF8IHOk5SUFGzduhWbNm1Cbm4uUlNTXY5JTEzEjRs3UFNTgyee\neAJ6vR46nQ4AkJaWhsOHD2PevHm4fv06Ojs7PYY5ABg8eDD+7//+DzNnznS40bz88suor6/Htm3b\nkJKSgokTJ0KpVOKXv/ylQxE982an3h7/6m6Ic8d5wVSTyWRfaFOIe8oKEVexdXcXivW0EDWFO/9w\nN0M1Ojq6R9ckhbvA4epV5bomOzo6UFpaCoPBgLa2NigUChQXF2PgwIH2YxQKBTo6OnDo0CGYTKZA\nvBzeEvUs1wcPHiArKwt1dXUYPXo0tm/fjgEDBuDWrVt49dVXUVJSAuDRsiUbN260L1vy9ttvA3i0\n5Mgrr7yCCxcuQCaTYfPmzZg3b16v22Wz2XDhwgXk5ubi5MmTmD9/PlQqFcaPH+9wHFd3NE239x13\nIc7XocubmbKkd9j7vVosFq93/8iY/J+c39/5/Uec3+faVYAPW4+Jib9mqPZ0KRTiHXe9qs73V6vV\nisrKSuj1ely6dAnLly9HTk4OxowZQ+eBW3AuWyIEZrMZ5eXlyMvLw+3bt5GamorMzEyHnkCuC4Pq\n7XrGXyHO23+fent6zhfhiivQuQtzznoaIokrf+3Y4g6FO9/h6ohwDuQ2mw3V1dXQ6XSoqKjACy+8\nAI1GgxkzZtCH3K5RoBOC5uZmFBYWwmAwICoqCgqFAkuWLIFMJrMfw97fjlm7jurtPAt0iPPULurt\n6R7n31lvAzHXFmDeBjo2rmHe8PBwCgQe8PX9T+Gu+7jq4rh+Zw8ePMCuXbtQVFSEQYMGYc2aNVi+\nfLnDM450iQKd0NTU1CA/Px8lJSWYMmUKlEolZsyY4XBx2Gw2+yehQHyq5TO+hjh3At1DwWd92avp\nq0DHRufSPaH1UFO4c4/rXHJ1LhiNRhw4cAB6vR4///wzVq9eDYVC0WU9OnGLAp1Q2Ww2nD17Frm5\nuTh79iwWLVoElUqFMWPGOBxH9XbCC3HuOD9EgnGmrL923+iLQMdGgcD7Wiq+o3PpOlHFU13cuXPn\noNPpcP78eSxevBhqtRrjx48Pmt9VH6JAJwYmkwmlpaXIz8/H/fv3sWrVKqSnpzvMAAq2ejt3n/jF\nMOGgN3vKClGgHvzsOjpfhjln7oK6GBekFvseqsEW7ryZqGKz2VBTUwO9Xo/y8nI8++yzUKvVmDNn\njijOOY9QoBObpqYmbN++3T5zV6lUIikpCeHh4fZjnOvt+FKj0ltiDnHuiHWmrHOIC0Ro9VegYzBh\nh6k5Yi+RIvRe2GDcQ1Ws4c65Ls5dXWhzczOKiopQWFiIfv36Yc2aNUhJSUFEREQAWy9qFOjErLq6\nGnl5eSgtLcXzzz8PlUqFF154weGiE3pdD9dkELGHOHeEVofkjCvEBTLQ+DvQsbn7XQipJ0vo9xZf\nslgs9mtTiL8Lb+viOjs7UVFRAZ1Oh7t37yIjIwMqlQqxsbEBbH3QoEAXDGw2GyorK5Gbm4uLFy9i\n8eLFUCqVGDVqlMNx7AcIwN96O3czGoMxxLnD15mCXLjed3zovQlkoGMTUq2ZkN53gSKUoNuduriL\nFy+ioKAAZ86cwcKFC6HRaJCQkMCr1xMEKNAFG6PRiJKSEuTn56OlpQUZGRlYtWoV+vfvbz+Gj/V2\nFOJ6jo8PEK7hKObDA18eAszEiECGOWf+mhTS0zYJsWc4kPi4rA172N9TXVxDQwMMBgP279+PhIQE\naDQazJ07N+AfxIIYBbpgdv/+fej1ehQWFiI2NhZKpRKLFi2CVPp457dA1ttRiPO9QM6UdX5Q8CFY\ndiVj8n/yKtCxBbJmVEi9hkLBFe78fa/tqi6upaUFu3fvRmFhIWQyGbKzs5Gamgq5XN6n7SNeoUDX\nXfX19dBoNGhsbERISAheffVVbNiwweW4DRs2oLS0FHK5HFu3bsXUqVMD0FrvXb9+HVqtFhUVFUhM\nTIRSqcTUqVO7rLfzda8KhTj/8NdMWT72DnYHnwMdm7taUl/2kol9hiqf+CPceVsXZzab8c0330Cn\n06G+vh6rVq3CmjVrEBcXx9trWKzP6S5QoOuu27dv4/bt25g6dSpaW1vxwgsvoLi4GAkJCfZjSktL\nsWXLFuzbtw+nTp3Cxo0bUVlZGcBWe89qteLYsWPQarW4cuUKli5dCoVCgfj4eIfjmE/n7Nl4Pb2x\nU4gLLF/PlA1kTwPx/a4ZwThDlU98fT0x17qnnlWbzYbvvvsOBQUFOHHiBF588UVoNBo888wzgriG\nxf6cdsPtiZG6+0GwGzZsGIYNGwYAiI6OxqRJk9DQ0ODwRikuLoZGowEAzJgxA83NzWhsbERcXFxA\n2twdISEhmDt3LubOnYv29nbs2bMHv/3tb2E0GpGZmYnU1FRER0cjNDQUoaGhkMlk9nDX2trqdb2d\nu4dOZGQkhTg/k0gkkEqlkEqlDp/a29vbvQ4DXMXwMpmMQlwAML2t4eHhDuelvb3d65IJrp7VyMhI\nwfSsiklISAhkMhlkMpn9vBiNRofr09vzya6Lk8vlLnVxjY2NMBgM2Lt3L5566iloNBp89tlnggvv\nYn9OdxcFOi/8+OOPuHDhAmbMmOHw/YaGBowcOdL+dXx8PBoaGgT3RomMjIRCoYBCocCdO3eg0+mw\nevVqxMfHQ6lUYv78+QgNDbWHgYiICPuwD9fDw12Ii4qKoocET7CXCukqDND55D92uOsqDFAo57/u\nhDv2+bRYLJBKpZyhvK2tDXv27MGOHTsgkUigUqlQXl6Ofv36BfCV+o7Yn9PeoEDXhdbWVmRmZuLP\nf/4zoqOjA92cPjd06FBs3LgRGzduxPfffw+tVouPPvoIv/zlL6FSqexd8cxDn7nZdHR0wGq12m8w\n9NAXDq4wQOdTuLjCAHM+Q0JCHIbz6Hzyn6fz2dX1abFY8O2336KgoADV1dVITU3FV199hfj4eFGd\n92B7TrtDgc4Ds9mMzMxMqNVqpKamuvw8Pj4edXV19q/r6+tdatCEbPLkyfjkk09gtVpx5MgRfP75\n57hx4wZWrlyJ1atXIzIyEnv37sWtW7ewbt06+ydCs9kMq9UKm80Gm80mqhuHmDE1dhaLBVar1X4+\nme+Fhobav0/4j5ncwFyLzPlkznNISAhCQkJoeFVAmHNqs9ns589qtcJsNuO//uu/sGDBAixYsADV\n1dXQ6/U4evQo5syZg9/+9rcuk9/EItif02wU6Dx45ZVXMHnyZGzcuJHz5ykpKfjLX/4ChUKByspK\nDBgwQJTduCEhIfYbxa1bt/Df//3fmDVrlr0INScnB/3797ffLNjF90ajkRfr2xFu7tY7i4iIsNc4\nsmc9trW1iX5PWSFzN0M1Ojra4Vwx9bDt7e28XR+QPOJNXVx7eztiY2Px+9//Hv/2b/+G2NhYvPzy\nyzh69CiioqIC2Pq+R8/px2iWqxvHjx/H3LlzMWXKFEgkEkgkEvzhD39ATU0NJBIJ1q1bBwBYv349\nysrKIJfL8fXXX2PatGkBbnnfqKqqwnvvvYdvvvkG8+bNw+rVqzF9+nSUlJSguLgYY8aMgVKpxNy5\nc11mUYlxP1kh683aYmLdU1boejpDlb2vLN928AhmXHWOXOvFtbe3o6SkBNu3b0dnZycUCgVmzJiB\niooK7NixA1evXkVKSgr+9Kc/YfDgwQF8RX0jSJ/TtGwJ6Z1//vOfOHbsGFJSUhATE+Py86qqKmi1\nWnz77beYO3culEolJk2a5HCMP9a3I9z6Ys9Q2jkgsHy99h/f9tgNNuwPS56uJ4vFghMnTkCn0+Ha\ntWtYsWIFcnJyMGrUKJfzXl9fj6KiIrz22msICwvz90sifYMCHfEPi8WCiooK5OXloba2FqmpqcjI\nyMDQoUNdjmOvb0dDeL7H7n1hryPYFw9o2tvTP/z1e+YKd3SN9g12iHP3e7bZbPjhhx+g0+lw+PBh\nzJw5E2q1Gr/4xS/ofAQfCnTE/1paWrBr1y7odDqEhYUhKysLy5cvR0REhP0Y5yG80NBQ6uXpJXfb\nfvmrJ1Tou0bwTaB7Qmn7L9/zZh9VALh37x527tyJ3bt3Y+jQoVCr1ViyZAnCw8MD1HLCAxToSGDV\n19cjPz8fe/fuxcSJE6FSqTBr1iyXT6HUy9MzziGOLyEqkHvKChlfQ5RzuGR/AKNw55m3dXEdHR0o\nKyuDwWBAa2srsrKykJWVhYEDBwaw9YRHKNARfrDZbPjHP/4BrVaLkydPYsGCBVCpVBg3bpzDce7q\n7SgIPCaknjB/7SkrZELbQzXQPYdC4G1dnNVqxalTp6DT6XDp0iUsW7YMOTk5GDt2LP0uiTMKdIR/\nOjs7ceDAAeTl5eH27dtIS0tDRkaGy2wsqrd7jGu/R65P+XxGM2UdiWEPVV/vKyt03tyzbDabfb24\ngwcPYtq0adBoNJg5c2ZQXgfEaxToCL81NzejsLAQer0ecrkcCoUCS5YsgUwmsx/j7addsWEWDjWZ\nTLBYLKIaig7WXh4h9a52V7CWTjjXxbkbVXjw4AF27dqFoqIiDBw4EGvWrMGKFSsc7nWEeECBjgjH\njz/+iPz8fOzbtw9TpkyBSqXC9OnTHR4GYn9oiP31cRH7axb76+Mi5g8jgPd1cSaTCQcOHIBer0dT\nUxMyMzOhVCpFuTYc6XMU6Ijw2Gw2nDlzBlqtFmfPnkVSUhKUSiXGjBnjcJy3n4z5joatHhNLD1aw\n9kBy4SoXEGK4605d3Llz56DX63Hu3DksXrwYarUa48ePF9TrJbxDgY4Im8lkQmlpKfLy8vDgwQOk\np6dj1apVLjO/hFZvRw/8rgltpixfZ6jyiRADu7d1cbW1tdDr9SgvL8czzzwDjUaDOXPo+/7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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "***" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "plot2D(x, y, p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ah! The wonders of code reuse! Now, you probably think: \"Well, if I've written this neat little function that does something so useful, I want to use it over and over again. How can I do this without copying and pasting it each time? —If you are very curious about this, you'll have to learn about *packaging*. But this goes beyond the scope of our CFD lessons. You'll just have to Google it if you really want to know." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To learn more about the role of the Poisson equation in CFD, watch **Video Lesson 11** on You Tube:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "heading", - "level": 2, + "data": { + "image/jpeg": 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bEqvqyDqzzi4QnqAwB/uZbvzK6LDWwPPQKgd2J30H4imq1ebZQ+85lRLAzL01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\nmZXlqxrDjj3DrxP1kl9qUUv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+ "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, "metadata": {}, - "source": [ - "Learn More" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('ZjfxA3qq2Lg')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, "metadata": {}, - "source": [ - "To learn more about the role of the Poisson equation in CFD, watch **Video Lesson 11** on You Tube:" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] + } + ], + "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": "3.6.1" + }, + "nbdime-conflicts": { + "local_diff": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('ZjfxA3qq2Lg')" - ], - "language": "python", - "metadata": {}, - "outputs": [ + "diff": [ { - "html": [ - "\n", - " \n", - " " + "diff": [ + { + "key": 0, + "op": "addrange", + "valuelist": [ + "3.6.5" + ] + }, + { + "key": 0, + "length": 1, + "op": "removerange" + } ], - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "" - ] + "key": "version", + "op": "patch" } ], - "prompt_number": 2 - }, + "key": "language_info", + "op": "patch" + } + ], + "remote_diff": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", - "metadata": {}, - "outputs": [ + "diff": [ { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" + "diff": [ + { + "key": 0, + "op": "addrange", + "valuelist": [ + "3.6.4" + ] + }, + { + "key": 0, + "length": 1, + "op": "removerange" + } ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] + "key": "version", + "op": "patch" } ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] + "key": "language_info", + "op": "patch" } - ], - "metadata": {} + ] } - ] + }, + "nbformat": 4, + "nbformat_minor": 1 } diff --git a/lessons/14_Optimizing_Loops_with_Numba.ipynb b/lessons/14_Optimizing_Loops_with_Numba.ipynb deleted file mode 100644 index ac56b58b..00000000 --- a/lessons/14_Optimizing_Loops_with_Numba.ipynb +++ /dev/null @@ -1,552 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook complements the [interactive CFD online](https://bitbucket.org/cfdpython/cfd-python-class/overview) module **12 steps to Navier-Stokes**, addressing the issue of high performance with Python." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Optimizing Loops with Numba\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You will recall from our exploration of [array operations with NumPy](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/06_Array_Operations_with_NumPy.ipynb) that there are large speed gains to be had from implementing our discretizations using NumPy-optimized array operations instead of many nested loops. \n", - "\n", - "[Numba](http://numba.pydata.org/) is a tool that offers another approach to optimizing our Python code. Numba is a library for Python which turns Python functions into C-style compiled functions using LLVM. Depending on the original code and the size of the problem, Numba can provide a significant speedup over NumPy optimized code.\n", - "\n", - "Let's revisit the 2D Laplace Equation:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "##variable declarations\n", - "nx = 81\n", - "ny = 81\n", - "c = 1\n", - "dx = 2.0/(nx-1)\n", - "dy = 2.0/(ny-1)\n", - "\n", - "##initial conditions\n", - "p = np.zeros((ny,nx)) ##create a XxY vector of 0's\n", - "\n", - "##plotting aids\n", - "x = np.linspace(0,2,nx)\n", - "y = np.linspace(0,1,ny)\n", - "\n", - "##boundary conditions\n", - "p[:,0] = 0\t\t##p = 0 @ x = 0\n", - "p[:,-1] = y\t\t##p = y @ x = 2\n", - "p[0,:] = p[1,:]\t\t##dp/dy = 0 @ y = 0\n", - "p[-1,:] = p[-2,:]\t##dp/dy = 0 @ y = 1\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is the function for iterating over the Laplace Equation that we wrote in Step 9:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def laplace2d(p, y, dx, dy, l1norm_target):\n", - " l1norm = 1\n", - " pn = np.empty_like(p)\n", - "\n", - " while l1norm > l1norm_target:\n", - " pn = p.copy()\n", - " p[1:-1,1:-1] = (dy**2*(pn[2:,1:-1]+pn[0:-2,1:-1])+dx**2*(pn[1:-1,2:]+pn[1:-1,0:-2]))/(2*(dx**2+dy**2)) \n", - " p[0,0] = (dy**2*(pn[1,0]+pn[-1,0])+dx**2*(pn[0,1]+pn[0,-1]))/(2*(dx**2+dy**2))\n", - " p[-1,-1] = (dy**2*(pn[0,-1]+pn[-2,-1])+dx**2*(pn[-1,0]+pn[-1,-2]))/(2*(dx**2+dy**2)) \n", - " \n", - " p[:,0] = 0\t\t##p = 0 @ x = 0\n", - " p[:,-1] = y\t\t##p = y @ x = 2\n", - " p[0,:] = p[1,:]\t\t##dp/dy = 0 @ y = 0\n", - " p[-1,:] = p[-2,:]\t##dp/dy = 0 @ y = 1\n", - " l1norm = (np.sum(np.abs(p[:])-np.abs(pn[:])))/np.sum(np.abs(pn[:]))\n", - " \n", - " return p" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use the `%%timeit` cell-magic to see how fast it runs:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%timeit\n", - "laplace2d(p, y, dx, dy, .00001)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1 loops, best of 3: 206 us per loop\n" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ok! Our function `laplace2d` takes around 206 *micro*-seconds to complete. That's pretty fast and we have our array operations to thank for that. Let's take a look at how long it takes using a more 'vanilla' Python version. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def laplace2d_vanilla(p, y, dx, dy, l1norm_target):\n", - " l1norm = 1\n", - " pn = np.empty_like(p)\n", - " nx, ny = len(y), len(y)\n", - "\n", - " while l1norm > l1norm_target:\n", - " pn = p.copy()\n", - " \n", - " for i in range(1, nx-1):\n", - " for j in range(1, ny-1):\n", - " p[i,j] = (dy**2*(pn[i+1,j]+pn[i-1,j])+dx**2*(pn[i,j+1]-pn[i,j-1]))/(2*(dx**2+dy**2))\n", - " \n", - " p[0,0] = (dy**2*(pn[1,0]+pn[-1,0])+dx**2*(pn[0,1]+pn[0,-1]))/(2*(dx**2+dy**2))\n", - " p[-1,-1] = (dy**2*(pn[0,-1]+pn[-2,-1])+dx**2*(pn[-1,0]+pn[-1,-2]))/(2*(dx**2+dy**2)) \n", - " \n", - " p[:,0] = 0\t\t##p = 0 @ x = 0\n", - " p[:,-1] = y\t\t##p = y @ x = 2\n", - " p[0,:] = p[1,:]\t\t##dp/dy = 0 @ y = 0\n", - " p[-1,:] = p[-2,:]\t##dp/dy = 0 @ y = 1\n", - " l1norm = (np.sum(np.abs(p[:])-np.abs(pn[:])))/np.sum(np.abs(pn[:]))\n", - " \n", - " return p" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 29 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%timeit\n", - "laplace2d_vanilla(p, y, dx, dy, .00001)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "10 loops, best of 3: 32 ms per loop\n" - ] - } - ], - "prompt_number": 30 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The simple Python version takes 32 *milli*-seconds to complete. Let's calculate the speedup we gained in using array operations:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "32*1e-3/(206*1e-6)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 35, - "text": [ - "155.33980582524273" - ] - } - ], - "prompt_number": 35 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So NumPy gives us a 155x speed increase over regular Python code. That said, sometimes implementing our discretizations in array operations can be a little bit tricky. \n", - "\n", - "Let's see what Numba can do. We'll start by importing the special function decorator `autojit` from the `numba` library:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from numba import autojit" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 36 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To integrate Numba with our existing function, all we have to do it is prepend the `@autojit` function decorator before our `def` statement: " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "@autojit\n", - "def laplace2d_numba(p, y, dx, dy, l1norm_target):\n", - " l1norm = 1\n", - " pn = np.empty_like(p)\n", - "\n", - " while l1norm > l1norm_target:\n", - " pn = p.copy()\n", - " p[1:-1,1:-1] = (dy**2*(pn[2:,1:-1]+pn[0:-2,1:-1])+dx**2*(pn[1:-1,2:]+pn[1:-1,0:-2]))/(2*(dx**2+dy**2)) \n", - " p[0,0] = (dy**2*(pn[1,0]+pn[-1,0])+dx**2*(pn[0,1]+pn[0,-1]))/(2*(dx**2+dy**2))\n", - " p[-1,-1] = (dy**2*(pn[0,-1]+pn[-2,-1])+dx**2*(pn[-1,0]+pn[-1,-2]))/(2*(dx**2+dy**2)) \n", - " \n", - " p[:,0] = 0\t\t##p = 0 @ x = 0\n", - " p[:,-1] = y\t\t##p = y @ x = 2\n", - " p[0,:] = p[1,:]\t\t##dp/dy = 0 @ y = 0\n", - " p[-1,:] = p[-2,:]\t##dp/dy = 0 @ y = 1\n", - " l1norm = (np.sum(np.abs(p[:])-np.abs(pn[:])))/np.sum(np.abs(pn[:]))\n", - " \n", - " return p" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 38 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The only lines that have changed are the `@autojit` line and also the function name, which has been changed so we can compare performance. Now let's see what happens:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%timeit\n", - "laplace2d_numba(p, y, dx, dy, .00001)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1 loops, best of 3: 137 us per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 39 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ok! So it's not a 155x speed increase like we saw between vanilla Python and NumPy, but it is a non-trivial gain in performance time, especially given how easy it was to implement. Another cool feature of Numba is that you can use the `@autojit` decorator on non-array operation functions, too. Let's try adding it onto our vanilla version:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "@autojit\n", - "def laplace2d_vanilla_numba(p, y, dx, dy, l1norm_target):\n", - " l1norm = 1\n", - " pn = np.empty_like(p)\n", - " nx, ny = len(y), len(y)\n", - "\n", - " while l1norm > l1norm_target:\n", - " pn = p.copy()\n", - " \n", - " for i in range(1, nx-1):\n", - " for j in range(1, ny-1):\n", - " p[i,j] = (dy**2*(pn[i+1,j]+pn[i-1,j])+dx**2*(pn[i,j+1]-pn[i,j-1]))/(2*(dx**2+dy**2))\n", - " \n", - " p[0,0] = (dy**2*(pn[1,0]+pn[-1,0])+dx**2*(pn[0,1]+pn[0,-1]))/(2*(dx**2+dy**2))\n", - " p[-1,-1] = (dy**2*(pn[0,-1]+pn[-2,-1])+dx**2*(pn[-1,0]+pn[-1,-2]))/(2*(dx**2+dy**2)) \n", - " \n", - " p[:,0] = 0\t\t##p = 0 @ x = 0\n", - " p[:,-1] = y\t\t##p = y @ x = 2\n", - " p[0,:] = p[1,:]\t\t##dp/dy = 0 @ y = 0\n", - " p[-1,:] = p[-2,:]\t##dp/dy = 0 @ y = 1\n", - " l1norm = (np.sum(np.abs(p[:])-np.abs(pn[:])))/np.sum(np.abs(pn[:]))\n", - " \n", - " return p" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 41 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%timeit\n", - "laplace2d_vanilla_numba(p, y, dx, dy, .00001)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1 loops, best of 3: 561 us per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 42 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "561 micro-seconds. That's not quite the 155x increase we saw with NumPy, but it's close. And all we did was add one line of code. \n", - "\n", - "So we have:\n", - "\n", - "Vanilla Python: 32 milliseconds \n", - "\n", - "NumPy Python: 206 microseconds \n", - "\n", - "Vanilla + Numba: 561 microseconds\n", - "\n", - "NumPy + Numba: 137 microseconds\n", - "\n", - "Clearly the NumPy + Numba combination is the fastest, but the ability to quickly optimize code with nested loops can also come in very handy in certain applications. \n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 42 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook. We modified a style we found on the GitHub of [CamDavidsonPilon](https://github.com/CamDavidsonPilon), [@Cmrn_DP](https://twitter.com/cmrn_dp).)" - ] - } - ], - "metadata": {} - } - ] -} diff --git a/lessons/14_Step_11.ipynb b/lessons/14_Step_11.ipynb new file mode 100644 index 00000000..81ce5f30 --- /dev/null +++ b/lessons/14_Step_11.ipynb @@ -0,0 +1,668 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final two steps in this interactive module teaching beginning [CFD with Python](https://bitbucket.org/cfdpython/cfd-python-class) will both solve the Navier–Stokes equations in two dimensions, but with different boundary conditions.\n", + "\n", + "The momentum equation in vector form for a velocity field $\\vec{v}$ is:\n", + "\n", + "$$\\frac{\\partial \\vec{v}}{\\partial t}+(\\vec{v}\\cdot\\nabla)\\vec{v}=-\\frac{1}{\\rho}\\nabla p + \\nu \\nabla^2\\vec{v}$$\n", + "\n", + "This represents three scalar equations, one for each velocity component $(u,v,w)$. But we will solve it in two dimensions, so there will be two scalar equations.\n", + "\n", + "Remember the continuity equation? This is where the [Poisson equation](./13_Step_10.ipynb) for pressure comes in!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 11: Cavity Flow with Navier–Stokes\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the system of differential equations: two equations for the velocity components $u,v$ and one equation for pressure:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\\frac{\\partial u}{\\partial t}+u\\frac{\\partial u}{\\partial x}+v\\frac{\\partial u}{\\partial y} = -\\frac{1}{\\rho}\\frac{\\partial p}{\\partial x}+\\nu \\left(\\frac{\\partial^2 u}{\\partial x^2}+\\frac{\\partial^2 u}{\\partial y^2} \\right) $$\n", + "\n", + "\n", + "$$\\frac{\\partial v}{\\partial t}+u\\frac{\\partial v}{\\partial x}+v\\frac{\\partial v}{\\partial y} = -\\frac{1}{\\rho}\\frac{\\partial p}{\\partial y}+\\nu\\left(\\frac{\\partial^2 v}{\\partial x^2}+\\frac{\\partial^2 v}{\\partial y^2}\\right) $$\n", + "\n", + "$$\\frac{\\partial^2 p}{\\partial x^2}+\\frac{\\partial^2 p}{\\partial y^2} = -\\rho\\left(\\frac{\\partial u}{\\partial x}\\frac{\\partial u}{\\partial x}+2\\frac{\\partial u}{\\partial y}\\frac{\\partial v}{\\partial x}+\\frac{\\partial v}{\\partial y}\\frac{\\partial v}{\\partial y} \\right)$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the previous steps, we already know how to discretize all these terms. Only the last equation is a little unfamiliar. But with a little patience, it will not be hard!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discretized equations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's discretize the $u$-momentum equation, as follows:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{split}\n", + "& \\frac{u_{i,j}^{n+1}-u_{i,j}^{n}}{\\Delta t}+u_{i,j}^{n}\\frac{u_{i,j}^{n}-u_{i-1,j}^{n}}{\\Delta x}+v_{i,j}^{n}\\frac{u_{i,j}^{n}-u_{i,j-1}^{n}}{\\Delta y} = \\\\ \n", + "& \\qquad -\\frac{1}{\\rho}\\frac{p_{i+1,j}^{n}-p_{i-1,j}^{n}}{2\\Delta x}+\\nu\\left(\\frac{u_{i+1,j}^{n}-2u_{i,j}^{n}+u_{i-1,j}^{n}}{\\Delta x^2}+\\frac{u_{i,j+1}^{n}-2u_{i,j}^{n}+u_{i,j-1}^{n}}{\\Delta y^2}\\right)\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly for the $v$-momentum equation:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{split}\n", + "&\\frac{v_{i,j}^{n+1}-v_{i,j}^{n}}{\\Delta t}+u_{i,j}^{n}\\frac{v_{i,j}^{n}-v_{i-1,j}^{n}}{\\Delta x}+v_{i,j}^{n}\\frac{v_{i,j}^{n}-v_{i,j-1}^{n}}{\\Delta y} = \\\\\n", + "& \\qquad -\\frac{1}{\\rho}\\frac{p_{i,j+1}^{n}-p_{i,j-1}^{n}}{2\\Delta y}\n", + "+\\nu\\left(\\frac{v_{i+1,j}^{n}-2v_{i,j}^{n}+v_{i-1,j}^{n}}{\\Delta x^2}+\\frac{v_{i,j+1}^{n}-2v_{i,j}^{n}+v_{i,j-1}^{n}}{\\Delta y^2}\\right)\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the discretized pressure-Poisson equation can be written thus:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{split}\n", + "& \\frac{p_{i+1,j}^{n}-2p_{i,j}^{n}+p_{i-1,j}^{n}}{\\Delta x^2}+\\frac{p_{i,j+1}^{n}-2p_{i,j}^{n}+p_{i,j-1}^{n}}{\\Delta y^2} = \\\\\n", + "& \\qquad \\rho \\left[ \\frac{1}{\\Delta t}\\left(\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}+\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right) -\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x} - 2\\frac{u_{i,j+1}-u_{i,j-1}}{2\\Delta y}\\frac{v_{i+1,j}-v_{i-1,j}}{2\\Delta x} - \\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right]\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should write these equations down on your own notes, by hand, following each term mentally as you write it.\n", + "\n", + "As before, let's rearrange the equations in the way that the iterations need to proceed in the code. First, the momentum equations for the velocity at the next time step.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The momentum equation in the $u$ direction:\n", + "\n", + "$$\n", + "\\begin{split}\n", + "u_{i,j}^{n+1} = u_{i,j}^{n} & - u_{i,j}^{n} \\frac{\\Delta t}{\\Delta x} \\left(u_{i,j}^{n}-u_{i-1,j}^{n}\\right) - v_{i,j}^{n} \\frac{\\Delta t}{\\Delta y} \\left(u_{i,j}^{n}-u_{i,j-1}^{n}\\right) \\\\\n", + "& - \\frac{\\Delta t}{\\rho 2\\Delta x} \\left(p_{i+1,j}^{n}-p_{i-1,j}^{n}\\right) \\\\\n", + "& + \\nu \\left(\\frac{\\Delta t}{\\Delta x^2} \\left(u_{i+1,j}^{n}-2u_{i,j}^{n}+u_{i-1,j}^{n}\\right) + \\frac{\\Delta t}{\\Delta y^2} \\left(u_{i,j+1}^{n}-2u_{i,j}^{n}+u_{i,j-1}^{n}\\right)\\right)\n", + "\\end{split}\n", + "$$\n", + "\n", + "The momentum equation in the $v$ direction:\n", + "\n", + "$$\n", + "\\begin{split}\n", + "v_{i,j}^{n+1} = v_{i,j}^{n} & - u_{i,j}^{n} \\frac{\\Delta t}{\\Delta x} \\left(v_{i,j}^{n}-v_{i-1,j}^{n}\\right) - v_{i,j}^{n} \\frac{\\Delta t}{\\Delta y} \\left(v_{i,j}^{n}-v_{i,j-1}^{n})\\right) \\\\\n", + "& - \\frac{\\Delta t}{\\rho 2\\Delta y} \\left(p_{i,j+1}^{n}-p_{i,j-1}^{n}\\right) \\\\\n", + "& + \\nu \\left(\\frac{\\Delta t}{\\Delta x^2} \\left(v_{i+1,j}^{n}-2v_{i,j}^{n}+v_{i-1,j}^{n}\\right) + \\frac{\\Delta t}{\\Delta y^2} \\left(v_{i,j+1}^{n}-2v_{i,j}^{n}+v_{i,j-1}^{n}\\right)\\right)\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Almost there! Now, we rearrange the pressure-Poisson equation:\n", + "\n", + "$$\n", + "\\begin{split}\n", + "p_{i,j}^{n} = & \\frac{\\left(p_{i+1,j}^{n}+p_{i-1,j}^{n}\\right) \\Delta y^2 + \\left(p_{i,j+1}^{n}+p_{i,j-1}^{n}\\right) \\Delta x^2}{2\\left(\\Delta x^2+\\Delta y^2\\right)} \\\\\n", + "& -\\frac{\\rho\\Delta x^2\\Delta y^2}{2\\left(\\Delta x^2+\\Delta y^2\\right)} \\\\\n", + "& \\times \\left[\\frac{1}{\\Delta t}\\left(\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}+\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right)-\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x} -2\\frac{u_{i,j+1}-u_{i,j-1}}{2\\Delta y}\\frac{v_{i+1,j}-v_{i-1,j}}{2\\Delta x}-\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right]\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The initial condition is $u, v, p = 0$ everywhere, and the boundary conditions are:\n", + "\n", + "$u=1$ at $y=2$ (the \"lid\");\n", + "\n", + "$u, v=0$ on the other boundaries;\n", + "\n", + "$\\frac{\\partial p}{\\partial y}=0$ at $y=0$;\n", + "\n", + "$p=0$ at $y=2$\n", + "\n", + "$\\frac{\\partial p}{\\partial x}=0$ at $x=0,2$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Implementing Cavity Flow\n", + "----\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy\n", + "from matplotlib import pyplot, cm\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "nx = 41\n", + "ny = 41\n", + "nt = 500\n", + "nit = 50\n", + "c = 1\n", + "dx = 2 / (nx - 1)\n", + "dy = 2 / (ny - 1)\n", + "x = numpy.linspace(0, 2, nx)\n", + "y = numpy.linspace(0, 2, ny)\n", + "X, Y = numpy.meshgrid(x, y)\n", + "\n", + "rho = 1\n", + "nu = .1\n", + "dt = .001\n", + "\n", + "u = numpy.zeros((ny, nx))\n", + "v = numpy.zeros((ny, nx))\n", + "p = numpy.zeros((ny, nx)) \n", + "b = numpy.zeros((ny, nx))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The pressure Poisson equation that's written above can be hard to write out without typos. The function `build_up_b` below represents the contents of the square brackets, so that the entirety of the PPE is slightly more manageable. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def build_up_b(b, rho, dt, u, v, dx, dy):\n", + " \n", + " b[1:-1, 1:-1] = (rho * (1 / dt * \n", + " ((u[1:-1, 2:] - u[1:-1, 0:-2]) / \n", + " (2 * dx) + (v[2:, 1:-1] - v[0:-2, 1:-1]) / (2 * dy)) -\n", + " ((u[1:-1, 2:] - u[1:-1, 0:-2]) / (2 * dx))**2 -\n", + " 2 * ((u[2:, 1:-1] - u[0:-2, 1:-1]) / (2 * dy) *\n", + " (v[1:-1, 2:] - v[1:-1, 0:-2]) / (2 * dx))-\n", + " ((v[2:, 1:-1] - v[0:-2, 1:-1]) / (2 * dy))**2))\n", + "\n", + " return b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `pressure_poisson` is also defined to help segregate the different rounds of calculations. Note the presence of the pseudo-time variable `nit`. This sub-iteration in the Poisson calculation helps ensure a divergence-free field. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def pressure_poisson(p, dx, dy, b):\n", + " pn = numpy.empty_like(p)\n", + " pn = p.copy()\n", + " \n", + " for q in range(nit):\n", + " pn = p.copy()\n", + " p[1:-1, 1:-1] = (((pn[1:-1, 2:] + pn[1:-1, 0:-2]) * dy**2 + \n", + " (pn[2:, 1:-1] + pn[0:-2, 1:-1]) * dx**2) /\n", + " (2 * (dx**2 + dy**2)) -\n", + " dx**2 * dy**2 / (2 * (dx**2 + dy**2)) * \n", + " b[1:-1,1:-1])\n", + "\n", + " p[:, -1] = p[:, -2] # dp/dx = 0 at x = 2\n", + " p[0, :] = p[1, :] # dp/dy = 0 at y = 0\n", + " p[:, 0] = p[:, 1] # dp/dx = 0 at x = 0\n", + " p[-1, :] = 0 # p = 0 at y = 2\n", + " \n", + " return p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the rest of the cavity flow equations are wrapped inside the function `cavity_flow`, allowing us to easily plot the results of the cavity flow solver for different lengths of time. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def cavity_flow(nt, u, v, dt, dx, dy, p, rho, nu):\n", + " un = numpy.empty_like(u)\n", + " vn = numpy.empty_like(v)\n", + " b = numpy.zeros((ny, nx))\n", + " \n", + " for n in range(nt):\n", + " un = u.copy()\n", + " vn = v.copy()\n", + " \n", + " b = build_up_b(b, rho, dt, u, v, dx, dy)\n", + " p = pressure_poisson(p, dx, dy, b)\n", + " \n", + " u[1:-1, 1:-1] = (un[1:-1, 1:-1]-\n", + " un[1:-1, 1:-1] * dt / dx *\n", + " (un[1:-1, 1:-1] - un[1:-1, 0:-2]) -\n", + " vn[1:-1, 1:-1] * dt / dy *\n", + " (un[1:-1, 1:-1] - un[0:-2, 1:-1]) -\n", + " dt / (2 * rho * dx) * (p[1:-1, 2:] - p[1:-1, 0:-2]) +\n", + " nu * (dt / dx**2 *\n", + " (un[1:-1, 2:] - 2 * un[1:-1, 1:-1] + un[1:-1, 0:-2]) +\n", + " dt / dy**2 *\n", + " (un[2:, 1:-1] - 2 * un[1:-1, 1:-1] + un[0:-2, 1:-1])))\n", + "\n", + " v[1:-1,1:-1] = (vn[1:-1, 1:-1] -\n", + " un[1:-1, 1:-1] * dt / dx *\n", + " (vn[1:-1, 1:-1] - vn[1:-1, 0:-2]) -\n", + " vn[1:-1, 1:-1] * dt / dy *\n", + " (vn[1:-1, 1:-1] - vn[0:-2, 1:-1]) -\n", + " dt / (2 * rho * dy) * (p[2:, 1:-1] - p[0:-2, 1:-1]) +\n", + " nu * (dt / dx**2 *\n", + " (vn[1:-1, 2:] - 2 * vn[1:-1, 1:-1] + vn[1:-1, 0:-2]) +\n", + " dt / dy**2 *\n", + " (vn[2:, 1:-1] - 2 * vn[1:-1, 1:-1] + vn[0:-2, 1:-1])))\n", + "\n", + " u[0, :] = 0\n", + " u[:, 0] = 0\n", + " u[:, -1] = 0\n", + " u[-1, :] = 1 # set velocity on cavity lid equal to 1\n", + " v[0, :] = 0\n", + " v[-1, :] = 0\n", + " v[:, 0] = 0\n", + " v[:, -1] = 0\n", + " \n", + " \n", + " return u, v, p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with `nt = 100` and see what the solver gives us:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "u = numpy.zeros((ny, nx))\n", + "v = numpy.zeros((ny, nx))\n", + "p = numpy.zeros((ny, nx))\n", + "b = numpy.zeros((ny, nx))\n", + "nt = 100\n", + "u, v, p = cavity_flow(nt, u, v, dt, dx, dy, p, rho, nu)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8IYOuQR+dAz66BmJhjONT5EMshDE320tOtofcbA/ZmW6M4X66TjUnwxU7m5vp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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure(figsize=(11,7), dpi=100)\n", + "# plotting the pressure field as a contour\n", + "pyplot.contourf(X, Y, p, alpha=0.5, cmap=cm.viridis) \n", + "pyplot.colorbar()\n", + "# plotting the pressure field outlines\n", + "pyplot.contour(X, Y, p, cmap=cm.viridis) \n", + "# plotting velocity field\n", + "pyplot.quiver(X[::2, ::2], Y[::2, ::2], u[::2, ::2], v[::2, ::2]) \n", + "pyplot.xlabel('X')\n", + "pyplot.ylabel('Y');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that two distinct pressure zones are forming and that the spiral pattern expected from lid-driven cavity flow is beginning to form. Experiment with different values of `nt` to see how long the system takes to stabilize. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "u = numpy.zeros((ny, nx))\n", + "v = numpy.zeros((ny, nx))\n", + "p = numpy.zeros((ny, nx))\n", + "b = numpy.zeros((ny, nx))\n", + "nt = 700\n", + "u, v, p = cavity_flow(nt, u, v, dt, dx, dy, p, rho, nu)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OKtdcxhXv/gQD3mBMnA1HajwTnSKTmGAhMcFCSpKVwrxkDPrpNx59HjdHd+7g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\np1UdL52ZLYgp09L309vM7GszKzCzDWb2npntX4f1dJ3aArSIRM3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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "pyplot.contourf(X, Y, p, alpha=0.5, cmap=cm.viridis)\n", + "pyplot.colorbar()\n", + "pyplot.contour(X, Y, p, cmap=cm.viridis)\n", + "pyplot.quiver(X[::2, ::2], Y[::2, ::2], u[::2, ::2], v[::2, ::2])\n", + "pyplot.xlabel('X')\n", + "pyplot.ylabel('Y');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The quiver plot shows the magnitude of the velocity at the discrete points in the mesh grid we created.\n", + "(We're actually only showing half of the points because otherwise it's a bit of a mess. The `X[::2, ::2]` syntax above is a convenient way to ask for every other point.)\n", + "\n", + "Another way to visualize the flow in the cavity is to use a `streamplot`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Hcb/ot3rot06RtrNczl3n49xtFktZXpu/xuvz1xiL9nGsa4i94cSGZ4sUwhtW\nords5oim7Z2RUpLNx1lOlZlP3mA+ucBKKk4y3UUy3UOuECKV07g8YXB7weB7bwYJWZLj+z7Hyf02\nJw/Y9Hfdm9tpVssBf3FPxyp2FkqotZFspsh7Z8a5cc2bilYzNB757EEOf2IUrfWToi1IKbm9kOTc\n9WmmlmoT4QwNxHnk2DCjw93qBaxQKBQ+XWGNx/YF0AWc3Gfy+9cuIYCR7linq6ZQ7CpiRoQn449w\nMnqEm/kpxvMTLNspxjMLjGcWiBgWR+NDHIsP0WXd36EaQgiiYUE0HGDv0FGkPEDJfZei8yPApliy\nyGVHyK48zp7pYc7fMskWNN752OKdjy1O7Cuja/BLnypwYFC5Mipao4TaOrzyg/NY5r3940skszNJ\npD9b29hjezjxmYMEI+2f6ceVkmS2wFI6x2I6x425ZZYz+Wr6wf29nDi6h75EZ9ZnUygUiu2MEIIv\nnPCej9dT3vqRXcEA0cDdTS6lUCi2hoBmcSRygCORAyyXU4znJ7iRnyRrl3h36RbvLt1iONTFsfgQ\nh2L9HZniX4gAAf00lvYJSu67YL1JwLpOT891Dh3q4lcCj5FaOcz5WxYf3jBZSmtML+t8cMPkqYdK\n/MKzBUZ6d+akNIr2oYTaOsxOrWAYa0ynv0F6R7t4/IUj9Ay1pye2WLZZTOeqomwxlWMpk8NpnAYP\nTRM8/NAQx48MEY2oxoZCoVBshNsZb6zaSHe8wzVRKBQAPWacU+ZxHo8dZbI4x3hugunSPNP5JNP5\nJD+ZvcKR+CAPdw0xGIzfd4+hRsH2HiXnLYpuktv5VwmE3ufTjz/ON54eY27F4M/eCPLGJZO3rlq8\nfdXk608V+fpTRSLBzc9oqXgwUEJtHU595RiB4L1P9BGIWAzs79mSh4QrJalswRNj/mcpnavO1NiM\noWv0dIfp6Q7Tl4gwtq8Xy1S3W6FQKO6GiYznLj7apYSaQrGd0IXOvuAw+4LD5Jw81/OTjOcnyDg5\nLqVmuJSaodsMVScgidzlckubxRNsz1QtbCXnbYpuipvZV7mdfR0hNJ49DQ8dS/D6u09z9dYh3rmx\nzNlrBr/05T8nHFzbUJDPtHExVsW2QrXc12H04UFC4c65B04uJvl4epHFdI7ldB7bbW0Sj4Ytenoi\nJLrDJHxxFo8G1bgzhUKh2ARFx2Y2700CpcanKRTbl7Ae4kT0MMcjh5gvLzOeu83NwhQr5TxvLlzn\nzYXr7A338GjPKPsiiftaNyGsOsHmWdhc8tWpYBPdM/zc5/+c8dsHePm1L5ErRPjDv/wWv/jlPyUS\nyrUs05XtWUNSsf1QQm2bUrJ0dNV/AAAgAElEQVQdvv/WpYY4Xdfo6QqR6I7Q44uyRHcYy1K3UaFQ\nKLaauXwWgLgan6ZQ7AiEEAxYCQasBKfcE9wqTDOen2ChvMztnPf5tbFPEjeDHaibRUD/JJZ2Cpf0\nqvSTB2E4Af/u/3NZXOnjT//qv+DXvp4nHlntBqkZOeAn96HWik6jWvjbFFPXSERDLPmTgBwe6+f0\nk2MY93G2SIVCodjNVHwSdOWdoFDsOEzN4FB4L91mjB8uvg7AaLib2H12gWxGCAOdnpZpgz3w69+Q\n/NvvOywmdf7ilSi/+hUd02h8Bumi/RPTKbYHqtW/TRFC8JVTRxnt9cZFfHx9nu+/fJ75xUyHa6ZQ\nKBS7A8ufOa7kqKmzFYqdyHI5xV8vvgnAcKiLr+x5ZNsPC0l0CX79mzr9PbCUglffVTNB7maUUNvG\nxEIBvvbkMT538iC6rrG8kuP7L5/nzLs3KJdVw0GhUCjaiaV7TiclWz1vFYqdxko5zY+WXsfBZTAY\n52sjj3Rk2v57oTsm+PxTGskMnP1Iki+qWSB3K0qobXOEEBwZ6efXnnucw8O9AFy4MsOf/eU5bk8t\nd7h2CoVC8eAS0L1GXdl1caVqKCkUO4WUneHlpdexpUt/IMbXR05iaTtrtM/R/YKBHiiVPbGm2J0o\nobZDCFkmLzx2mK+dOkosFCCbK/GjVy/zyutXyRfUNK0KhUKx1Vh1ve/KqqZQ7AzSdpYfL53Blg59\ngSg/N3qSgL6zRBp4HfWffsJrpp/50KVUVmJtN6KE2g5jb383v/ypkzx6YAiA67cW+eO/eJer43NI\n1eOrUCgUW4auaejCe02qcWqKnYzrSlz3wW8jZOwcP146Q94t0mOF+bnRRwnqZqerdc+cOChIxCFf\nhLcvPvj3T7GandfFoMA0dE4f28/h4T5e/WichVSOn50d59qNBZ596iDx2P2fdlahUCgeRAK6Ts52\nlUVNsaO5MW3z0ms54lGNrqhGt//pimp0xTTiEQ1du/tJNmYWbd74oMjTjwQY6e9skzLr5Pnx8hly\nboFuM8Q3Rx8j1EGRlspIYhE2NXmJpgk+/bjGn7/q8sY5l6ePCwxje0+GothalFDbwfR3Rfj2M4/w\nwc1pzl6ZYHouxXdfep9PnNzLI8eG0TRlMFUoFIrNYGk6OcrKoqbY0axkXBwXllMuy6nVswgKAbGw\noDuq0xWribjumEZXRFtTHPzs/QKT8w4zSza//EKUvu7OTNaRdwr8zdIZsk6euBnkG6OPETY6M4W9\nlJLf+77LjWnJ3/kFnaG+zZX36EOCV96BVBbeuyx56oQSarsJJdQ6RNGWvDNTYG/cYDRm3HOPi6YJ\nHh/bw9hggp9+dJ3JxRTvfHCb67cWefapg/T3Rre45gqFQrF7qEwoUrTtDtdEobh3Hn/I4vCIyUrG\nJZlx/K3LStrb2g6kspJU1obZ1ccPJDR64zp7+g2G+3QScY3llMvkvNeBUSrDi69m+ZUvRomG7m8n\nccEp8uOls6SdHDEjyDdHHyNqdm6tNCEEIf/0F667DPVtTrzquuBTj2n85esuE7MuTx5XQm03oYRa\nh5jM2PzkdoHugMZvfKJr0+V1hYN8/cljXJ1a4NUL11nyp/I/fmSI3j1DDPdYRALKwqZQKBR3Q2WK\n/rKyqCl2MJomiEcF8ahGc9NPSkmuIFlJu1UBVxFxKxkHQ4e5JZe5JZeLN7zJywKWwGryKszkJC/+\nJMsvvRAlYN4fMVF0S/x4+SwpJ0PECPDNvY8SMzs//OPhg4KLNyQXxiWfe1Jueu22R4/Ay2fgg4/h\nC59UjffdhLrXHeJ2yuudHY1v3S2oTOW/t7+b1y/e5OPpRT68ukhqJoplavzt010kojtjDRGFohkp\nJa7E+7gSR4LrgluJ98OO6+eR0o9rincby5GA44KUIJFIP03K1nGuV5mmPJX0urxAJCDIFNxqnsp8\nP9L/0xAnvbrQEEdDnPQDEoiHdJJ5p67AWt7G67bONW0K1Pal5wtVx6pmhlh3d126wxrJvFs7TtSO\nF3V/mtNa5heNaV1hnVTeRbTIU4kTlfKb8oimvCBYyXTjloJ8cFEwM5HHrbs0mvDDQnhhvxLVeLwG\ncrXcSlpDPlEtS9e9S69pXnylnMpWCOGnUZcmEBpNef1j6/JqgKZ78ZXyFQrwfguRkCAS0hgZaEyr\niLj5ZYepBYfpBZuZRYdiSVIsrS5rYcXluz/O8EsvhDGN9rY3Sm6Zv1k6S9JOE9Ytvjn6KHEz1NZz\nbpQj+wS6DotJmF+GgcTmygtaGl1Rl8UkzC1L9my+f1+xQ1BCrUNMpD2htje29begMpX/oeFevnu1\ngBQaTiHH0rxLIrpJZ2mFAnBcSdGWlG1vazuSsgO2K3H8re164sh2/K0rsV1wHC/NdrxybNffVo6v\nHuOlxUMaC2mHdk1YFjIF+TZNe7ynR2dquT2WGAnMJnemlUcTMJdqT91HXJhc3ko3xRAQYjIHS0sl\n8sUtLLqJnpjGcnr1+KGtYKRfr7qpgS/gNNCFLyYF6JoXp2k1Qadpws+zOk33w0FL4EjveF0XGJon\nOnVdoGtg+NvV+/X5V6cpMdl56kXcgT2eCc1xJWfOF3jrQgulBswtu/zff5JhbI/B558OEgluvWAr\nu2VeWT7Lsp3CEBrfHH2Ubiu85ee5VwKW4PCo4PJNyUfjLgOJzV+DgR7BYlIyt4QSarsIJdQ6gONK\npjK+Ra0NQq3CTDlEUQMNl0huhp+esenpDtHbE2nbORXbF8eVlGz/49SFbUnJphourkpr+vjCq57R\nhMHEUnvG8ARNua5IqzYifWtDpeGpVSwJfniteMsQOG691cKzeGhC1PZ9i4Soi6tYRuotHxXLSeVY\nU4eHh716Vq03omorWm3FqabXWX3q0+u2Go0WtOY2bUt7mGiVtvp4Kdd21dmQhW79HQSeZbKVhbHh\niJbpssES2BzWNTi5N9BklZQNFswGK2X1HHK15VPCeGqZmVya4XiMfYkuXKdmQaXOglrJX7G0Vq2u\nft1c2ViuK2t1qqQFTYiGBa5bq1vFUuvKxvJdVzbl8bfu6rwt75UExwGnxf1pbZtdm0RcY6nFBBWb\nRdPA0CAa1nAcME0wDeF/vP/dStg0BKYp/Lg18pne/r3MbqiooWuC8h0e9xIYn7K5/mKGo/tNTh62\nGO7Vt0x8n8tcYbGcBOAbo4/RE9h+7ZqHxzyhdvG65HNPbr68/gRcvAHzyxLGNl+eYmeghFoHmMk6\n2C6EDEFvmwbdzmVtfnI7D0AoN4cubXRDIxTcueuJKFbjSkm+JMkWXf/jhXNFl2xJels/Ph7SmE9v\nrRXD0LxGUMAQ9MV0r2dcq/WQG5rA0MHQ/N5yzXMHMTRRi9NraYZeOb4W1oXf214vyPzef9Xjrmg3\npalZZucXGR4x+eTBwU5X566puuO6EoknAh23zl3Y9VyEnab9qotxvcuwSzXNqcunISnavvCrWMv9\nDh3Pgu6nOY1W9lZ56nFdKLmQzrl3FAZ3g65RFXFDvTq5giQUEAQDGuGAIBgQhCqfoEbIEoSCAkNX\nz5sKT58I0NetYxoQCmjeNbO8a1csSX72foEb02UKJbh0o8ylG2V6uzROHrY4dsDa9Bi2biNWDf94\n5hKfHzrKUGh7mZmO7vc6D+eXPXHV37O57zzQIwDJ3HKb3EsU2xIl1DpAxe1xM7M9rkXZdrgyvcgP\nJsDFxCxnsMopDuxN8MypMSXUdgBSSgplSa4oyZZcX3j5YqxpP1fa+AO70ols+I2UgCEwdW9rtfro\ntXDAqB1Tn66pnmnFA47mP6N36lrBFStw4//q9vy/lf640mYxV7Y98VeyJWUbymUvrmxX4vx4P1wq\nN+5XwhVPAMcFpyQplCAUcJlb3pg10DQ8N89QUKsJuepH88VeLS5oiQe2MykU0DhxsPX090ZI8OXT\nYaSUzC45fPhxiSu3yiwmXV55p8Br5woc3W9x8rDFQM+9uQQeDu8jqoc5k/yAZDnPn91+nyd69vJU\n7wH0bbI0UTAgODgCxRIsrGxeqFWOn1/2/lcUuwMl1DrAoj8BwFa6PS6lc1y4PcfVqQUyBCiG9yCk\nzcOJEiefOc7QQHzLztVOyo7kzLU8+3tN9vY+2KKy7EgWMw7zKYeFtM182qHsSOZSdzceSwDhgCBs\naUQCGpGAIBzQiFheOBLQCAc0QqYgYArl9qNQ3AWaL2pUw6j9CCH8cWtgtWHWQMeV2A3izpsQo1By\nyRck+WL9xyVflBSKnteC61IVfOncxjwTnj4e4PSjnZ+BsFMIIRjqNRjqNfjME5JLN0p8+HGJpZTL\n+WslLt0ocfKwxdMnggStu7/fQ4E+vtr3Gd5NXeB6YZL3lm9zM7vEC0PH6Atuj6WJEl2Cs+cl++cl\nD2/SXbG3y/MqKZW9NdUUuwMl1DpAsuiiC4hu8kVkOy7jM4tcuD3H7EqmGl+K92NogqcPhjl9ZGCd\nErYPUkquzZX5m4s5UnmXj2fL/Cefij8QFhspJcm8y0LaYT5dE2XL2dW9uImIVhVpQVMQtoQvvnwB\n1izGAp5bjvaA9toqFJ1GVC1qSqjtdHRNoFveRA93g5SSUpmqeKt8CnWCrjHepViGUFA9lysELcHj\nRwI89pDF5LzjCzaH9y6XGJ+w+fpnwvTfw2LZlmbyTPdjjBYGeSP5PkulLH986x2e7j3AE4l9HX83\nWn4reytcd3Vd0NvlWdQWVjZfnmJnoIRaB3D88QD6Pfq7L2fyXLw9x5WpeYrlWs/e/tEEhw72890P\nvPEHJ/Zuj2lq78RKzuFvLuQYn/fWZ4kFNT51JLRqcoSdQLHs+mLME2UVYVZeowM2ZAn6Yzr9MZ2+\nmEFPRCMW1AhbmhoPoVBsAyp9RcqitnsRQhCwIGDpdMfunB886536yaxGCMHogMHogMHsks1Lr+VI\nZl3+6OUMLzwV4tiB1u6Ud2I0OMQ3rM/xVvI8E8VZzi7e4EZ2kc8PHaOng7NBmoY3rqy0RWMsB3oE\n88uS+eWtKU+x/VFCrQPovvu0exeTZDmuy/jMEhdvzzG9nK7GR8MWRw4N8tDBfsIhi8nlMq5ME7YE\nseD28NNeC9uRvDVe4Ox4Htufde/JsSDPHAr5D7fti+tKlnMu82nbE2UpT5SlC61vqi4gEdXpj9dE\nWX9MV4uQKxTbnMoMnTt1jJqiMygX8zszmDD4zpei/OCNPDdnbH7wZp7ZJYdPPx68p+sX1AJ8uvsT\n3CxMcTb5IXOFNH94422e7T/Iye6RjowXNCsWtfLWlBcNQ08MhFAPpN2CEmodoPL8cTbQ3ZbMFrhw\ne44rk/MU6mzn+0Z6OHpogD1D3Q3ugTMrnulmuHvrJyrZSq7Pl/jxhRwrOU/Y7Os1+PzxCL3beEHu\nlZzD5ekSqbzLR5PFVVPUV4gFNV+M6b61zKA7oqkXt0KxA6m4TimLmkKx9YQCGt/8bJgz54ucvVDk\n/Ssl5pYcvvapMJF7mBVbCMGB0AgDVoIzyQ+ZKS3w2vw1rmcW+NzQMeLm/R0zaPlD7bdq1lLHheU0\nFLdI+Cm2P0qodQD9DrOI5YolxmeWuDazxEyd9SwcsjhyaIAjB/uJhAMtj51Oek+D4e7te2vP3cpz\nYbLMSs4lEhA8fyzM0WFrWwrLVN7hykyJy9MlZvzFhXvCGo7rrZHV5wuxvqr7ok7QVFYyheJBoSrU\n7nJtMYVCsTE0TXD60SADCZ0fnskxteDwBz/I8J0vR4iG7q3zNqyHeL7nKT7O3+Ld1AWm8kn+4PpZ\nnuk/yEOxAcLGvblY3i3mFo5RA7DtSrnbr72kaA/btzX/AKO3sKjlS2Wuzy5zbXqRqaVUQ/7R4W6O\nHh5kdLj7jpNrTK94/8VDXdvz1o7PlfjRR3kMDR4ZtXj+WGTT66lsNdmiWxVnk8u1p6vAs/wdHrTY\n32fQHd66xTsVCsX2pOb6qISaQtFODo2afCce5fs/y2IaGt97Jcd3vhjFuEdRIoTgofB+hqw+ziQ/\nYL68zOvz13h9/hpDwThj0T4ORvuIW+0bz18RaiV7a54f5apQ25LiFDsAdas7QEVrlWyXyxPzXJtZ\n5PZCsiFPf2+UsX29HNibWNN61kyu6JLKe/54Q13bz4VwKevw0jlvTtmHRwJ88UR42widfMnl6qwn\nzm4v2g1956M9BkeHLY4MWYTVmDKFYldRm0yks/VQKHYDPXGdbz8f5f99OUM2L3nrYpHTJzfnrhgz\nInw+8QzX8re4np9gsZxkppBippDijYVxeq0IY9E+xmJ99FqRLW2XVF0ft8hVUQm13Ye61feZku2Q\nLRQBjbNXJwgWa1P3JHrCjO3rZWxvL7Ho3T+YKm6PiYhGYJu53xXLLi++k6ZoS/b0GLxwfHuItOmV\nMmeuFbg+X25wRR3u0jk6HODIsLXtJ2VRKBTtQ03Pr1DcX6Jhjec+EeKl13K8c7HIsQMmPbHNdT5r\nvnXtofB+ck6eicIsE8VZZkuLLJayLC5leXvpJnEz6Im2aB9Dwfim2ykVF8Wtcn2szCBtbL++eEWb\nUELtPlB2HG7Pr/Dx9CI35pbJBAfB6kIi6O4KMba3l7F9vXTFN2d+n1nZnuPTpJS8dC7LUtYlGhB8\n84loxyfWyBRcXrmU4/J0icG4jiuhP6ZzbNji6LBFV1g9BRUKhVrwut2c/aiAYQiiIUEkqBEJCSIh\nTY3B2eUcHjXYP2Rwc8bmJ+8U+NZzW9e5G9ZDHIkc4EjkAEW3xGRxjonCLFPFOVLlAueWJzi3PEFI\nN6uibSTcjS7uvtPWqro+bknVKfsulMqitntQt7qNuFJy/uYMZ67cxq0z11imRhE4eniQF052bdn5\nKha17TY+7bWrecbnyxgafOtUrKNT0rtScu5WkZ9dyVOyJQLY02PwtceiJLbxjJMKhaIzKIta+5BS\n8ub5Yku3UsuESEgjEvSEW0XARYKCaEgj7As7a5uNcVZsDUIInjsV5N//ZYabMzYfT9g8tNfc8vME\nNIuDoVEOhkaxXZvp0gK3CzNMFefIO2UuJKe5kJwmoBk8N3iEQ7H+u/wikpF+iEe35neqXB93H+pW\nr8PliXkCoew9HSuByxNzzCW946ORgOfWuK+Xc9Pw3s0ilrW1D52IJRiM6ySi28dV7/J0iTPXCgB8\n8ZFIR0XkTNLmR+ezzKY834GhLp0vnIgwuM2E7XbGlZJUzmUx67CUcSjZkk8d6dxiogpFuzH8XnT7\nbha+VGwIx4HHj1hk85Js3iVb8LZlG0plKJVdllMAzpplWAarhFxlvyuq0R3VCKqxxTuSnpjOqYcD\nnP2oyKvv5tk/ZLRVmBuawd7gEHuDQzjSZa60WHWRLLhFfjh9gUfzIzzTf3DD1rVCESbnoVDa2slE\nDNVs2TWoW70Or1+6iW5tbhCrpgmeOXWAIwcHqj2z+mwOAGeLO2gnlx2SeRdjm6zXlSu5fDxbAuDU\nWJDjIxubFGWrKZZdfnY1z/s3iwAEDMFnjoY4uTdQnXpb0UjZkSz7YswTZS5LWYflrNOwfpyhwemH\nQuo6Ku6I7UhefDdD0BR88lCQvtjOeP0EdM/SXrDXFguKe8MwBJ99YrXLf6nsCbZMk4BrKehsKKVd\nvJVsGu9ROCjIFSThoCAR1+iJ6/T620SXJ+q2w1hpxdo89XCAyzfKJLMuH3xc4smH7087Qhcaw4F+\nhgP9nJLH+SBzhYvZcT5YmWSmkOIzAw8xEIzdsZxM3ttGt6g/s2pRUw5Au4ad8absEKPD3ZjBex83\nFg5ZPHp8hEi4cb0O3e+IcddaSO0eqZS71kLM95tLUyUu+WPAPnukfdPfrluH6SKvXMyRLXrX+uE9\nFs8dC3fU/XI7kS+5LGYclurE2FLGE/xrYWjQE9FJRHUSER3HBU29NBR3YDHjcGPBm/rs0nSJo8MW\npw+F6N3kJAHtJuh3XZfsLRpkorgjlimwTJ2e+Pr5KoIum5dk8i65grf1BJ1DMus993MFSa7gMDHX\nKOQsExJxnURc87Zd3jYeUQIuV3B5/YMCn3ki1NEldAxD8OyjAd66WOTi9RKnjt3/NVc1ofF47Bh9\nZg+vr7zHXCHNn956l6PxQT7ZN0bEWFs8Zn2hFgkp10fFvaFu9To89+xDhCKRLS+3Yn3Yak+aygQd\n20aoTXvWtIf3BO64/ls7ODue59ZCmWxR0hPReOF4hP19W+/jvlNwXclcymFiucztJRvHkdxcXLvx\nGTQFiahOb8Rzp01EdHqjOrGQpixoilVI6S0JLWXt40pvoWgpIVdsbCRfnvaWwxjrM/jEgSADdS7I\n6/261vvp6UIghJdHE2xJgy6ge/UqKKG27diIoCuVJUsph+WUy2LKZTnlsJRySWZcSmWYWXSYWXSA\n2vzpug6JmMbYiEk8orFvyCAW3j2de1JKXnotx+S8w3La5eefi3R0cpdDe01ePpvHdmAx6dLX3ZnO\nndHgID/X/xzn0pe5UZjicmqWq6k5nuzdz2M9ezG01b+RTM7rLIhsUV915TGkZn3cPSih1gGqlq8t\nHpxes6h1ftB7MudUF98+OmzdIffWc3GyyE8v5xHAqQMBPn0kjKHvLnHhuJLZpM3tJZuJJZup5TKl\nurbyHn920HjIE2GJqOaLMs9SptaM6xxSShzXc0H1Pp7rYNmR2K7EdryOHsfP57je1m3ad6QXduvj\nXIkjaYyTXp6eiMZ8yvEFlie0kNLb9+M8AdYoyjbyyIms0el8fcHm+kJm09csHtKq60jWozUJt4Z9\nPPd00ZCvUexJJG5pkKJj8eJPsui61ylW3Wo0hdeJa0j3tpoGliEwDYGh05FOrQcZyxQM9RoM9TbG\n245kJe2y5Au35ZRbFXSOA/MrLq4ss5j0flM9cY19gwb7hgxGB9o7VqrTCCH4zBMhvvs3GabmHb7/\n0xzf+Gzn3qG6JtjTZ3Br1mZizu6YUANvxsjT3Y/zUGk/76Yvslhe4eziDS4mpzndf4iD0b6GDqKq\n6+MWWNSk9J79oMao7SbUre4Alffwg2xRq1jT9vUaRO/zOmSTy2V+8KE3icuTY0E+e2x3THZhO5KZ\npCfKbi+VmVqxaR5WEzAEIz0GowmDkR6D/pihpsHeAsqOpFCWFMouhbJLsVwTWbZTL7g80VW2ZUsR\nVnakl+auvcByf0xnPt2e8VIBA5Zz7XmAtHvSxLXK98RmZfRSq0wbqJhlQSnCjemtt6r1dWssrNSu\nuaF7ay+ZRmXrfSzDcwOrhGtpTWHTD+teOGCAYahOl2YMXdDXra9q9LuuJJV1WUq5TC84mIbN7JIn\n4JZTJc5dLaEJGOzV2TfkCbehhP7ACezBhM63novwvVey3Jq1eem1HF//dLhjS+uMDOjcmrWZnLN5\n/EhnxrvX02f18MXEaW4Wpng/fZm0XeCH0xcYDnXxqf5D9Pvj17JbOEatfi025fq4e1C3ugNUBdUW\nN1y2k0WtItSODd/fB+pKzuHFdzI4Eg4PmnzmaGfGxt0vCmWXS1MlZlMOl6aK2E1t7KApGE0Y7E2Y\njCYM+mK6cltcg0pvZb7s+qJLUih54itf2a+KsUq6F26+7luJJsDU/Ya3IegKaRiaZ3nxrDS+ZUaI\nxnhRl6aJpn0wNIGmee6C9eVAk2WJRotUJa5igRJCNO2DQDTuC5hN2vyHN9IN303X4NG9AZ48ECAW\nat1LLtfYaX7KedPnC1zXt4LVu1/Kze2/dHMKx01y+sA+ArrhWybBceqslM5acf5+Q7gWZ+je9akI\nTdsX7vliq2959wjhnSNoCUIBQdDSCAYqYdEU1qph09ga19GdhqYJumM63TGdgyOeq3yhJJmYtbk1\na3NrxiaZ8UTc9ILDmfNFLBNGBzzRtm/QoDumPRDXbk+fwTc+E+HFV7Ncn7L54Zt5vvxMqCOidHTQ\ngA+LTM47SCm3xfUVQnAgNMJoYJCL2XE+yl5jOp/kT269y7H4EJ/sGyOb9x6qW+H62CDUlOvjrkEJ\ntQ5QecbZW6zUKgKwnY3GjbCQtllIO2gCHhq6f2PCimWX772TIV+WDMR1vvZodFs8zLcaV0puLpQ5\nP1Hi2mwJR8Kebh3bhbAlGPVF2d6EQW9UfyCvwd1gO5J0wSVdcEnlXdJ5F9uVLGacRvFVkpvqPBHC\nE8ZBUxAyNXTNF1gV64YuMHRRE13VsP8xmuO8Xv9OLw6/VdR3EFQE2tMHQ3e0uIs1dxrRK4n6HTLe\nA5GlAulyib17JEPxre98ktITdSVbYtvetmxLyrZvfW2xX2pIax2W0hN+lbh0TgIbe0HoGgR90RYK\nCD+sVcMhSzC2x9gVU98HLcHhvSaH/XW8khmX275ouz1rUyhJxidtxie9lnQ0LNg3aHBo1GDfoImx\ng70W9g4a/NynwvzFz3JcuVVG1+GLT4fu+3tlsEfH0CFflB0dp9YKQzM4GTvCwfBezqUvc7MwxaXU\nDFdSs6xkngaMLXF9LNeNT9vt7/XdhBJqHSAe8nqbc1u0rkaF6mySHV6YdXrFQdfgQJ9J0Lw/L3HX\nlfzF+xkWMw7RgODbp2IPnEvfYsbho8kiFyeLZIq1e9wf03lo0OJLJy0SkQejJ3ejSCnJlyXpvEuq\n4NZtHU+UFdzqjJ/1dIW0NWe21ASELEHQ1KrCy/vU7VueGKtPs3apBWKj9MV0jo9YhEyNJ8eC990l\nejMEdIN0udS2mR+FEBi+a+NW4rouZVuQL3odEfmipFCU5Eve1otz68JefMVi6M2euPb75D/6apRg\n573Q7jtdUY2uqMUjhyxcVzK/4nBrxhNu0wsOmZzkwvUy6ZzLX79V4PEjFo8eDhCwdubz4cAek6+e\nDvPS6zkuXi9jGYLnT91fbxVdFwz3GdyetZmc7+w4tbWI6CGe7X6cI6X9vJu+wEIpSaHgPecW3CX2\nyMSm3hFqxsfdSUdvtxDis8DfA04Bw8C3pZTfWyf/vwH+0xZJF6SUJ/w8vwP846b0WSnl0FbUeSuI\nBHRcCSvZrR1nkojo7CYc3KcAACAASURBVOl2O+76OD5fwnFhuPv+/LyklPz4Yo6bCzaGDj9/Kraj\nGoHrUSi7XJ4u8dFkkemV2u8laAoe3mNxYiTAQPzBtZo5rm8NqxdieZdUwanGbWR5K0ODWEgjHtKI\nB3ViQUEkoHniyxINljDVW9kedE3w1Uejna7GPRGszvy4s9ZS0zSNgAUBa+ON2qoLcFXU+UKuTuDl\nfWEXDqr/E00TDCYMBhMGTx33LJdT8zVrW64gef2DIm9fLHLycIAnjlhEQjvv/XR4r8mXPhnih2fy\npHMuN2fK7L+PHjMA+4cMbEcS3OaC1xu/9ixXUlP8lfTu9WvLH3G9HG8Yv3a3VB4/SqhtDUKI3wZ+\nATgG5IHXgd+SUl7e4PHfAf4AeFFK+fPtqmenb3cEOAf8a+BPN5D/N4F/ULdv+Mf/cVO+j4Av1O1v\nq7drT8R7aeZ9l6utsjpJCVMrDoNdnfV9zPkWjETk/vR4Ta3YXPMX1v76Y1EGuzr9s948JVvy5sd5\nbi+VmUl6P18hYKzf5JGRAGP95gM/i+XUcpk/eDN954xAJCCIBT0hFgvq3jakEQ9625Cp1kVS3DuV\nKfqLu2CKfiFqE5TEI+D7kio2iGkI9g+b7B82cVzJlZtl3r5YZCnl8s7FIu9fLnL8oMUnjlp0b/M1\nBJs5dsBict7h/LUSpbK870Lt1MMBTt2nBa83ixCCfjEEZDAMF02nYfzaE4m9GOLu7v98GiIhjYFe\nScb+/9l7zyA30vTO8/emgQfKO7KKtuibti2b7aZb46SRxulWI+3Kx5mN3bi4uLi4CF1s3O1+2NAX\nnb7cmru9W2lXWmlkZkbSzI5G09Nm2k/7pvdkeW9Q8Ej33ocEUCCbZNMkkAkSv4giQFQhkUj7/t/n\nef6P0ZgVf7B4Fvi3wPu4euJfAy8KIfZKKfO3eqMQYjPwB8AbjV5JX0e0UsofAT+C25vBllKuAWvV\n/wshvgZ04Qq9eiwp5Zx3a+otIU0QDwvyZUk67zDY6Y1Q66j0eVlrkGvb7ZIvu58fCzdnYPzTswWK\nhuSRrWFGB5rfCsBLpJScmzF47bzbpLsv6fYue2g4xJ4N4QeqUXf1u1ajYa4QU2viK1V5LRlR7nvR\n2sZfIg+QUGvjHaoi2LM1xO4tOldnLN4/U2Zu2ebkJYNTlw12jOg8sidMX1frCLbH9oY5fcVgasFm\ncdVuqXVvNoWSO2mdiGr8Yt9z19Svncvc+RC1ODNAvjjKRG6Vvxz7xOvVfeCQUn6p/v9CiN8GFnCz\n/F6/2fuEECrwZ7jZe08DnQ1cTd8javfK7wIvSSnHr3t9hxBiBigD7wL/m5Tyys0WIoQIA/XTNHcX\nl74DOmMq+bLFasFm0KMUwY6Ye8G8We1Ns8gb7ufHQ40XFcs5m7k1GyHgka2t7fC4kLF45UyB6VV3\nMNgZUzi2I8K2/tADGQ1KRRX+6fOdREPtaFgbfwlXusuWWyz1sU0wEEKwbaPO1g0aM4s2758tMz5r\ncWHC5MKEyeYhjWMHw/Q1qVzgXkjGFXaM6FyYMPn4fJkvPPFgtL+5GwqlyqR1RFxTv/Zx9hwrZvqO\nl+eUIgBokTLCY8Ok+4zkdWOGspSyfBvv66g8rnzG3/3vwKKU8j8KIZ6+mxW8E4J/VbgJQogh4MvA\nr133q3eB3wAuAAPAvwDeFkLsk1Iu32Rxv8en69oaSldcYXoVVvPeiarOaDWi5p99rWGtN2RsRsPk\nM9Puube1V2/ZaFPJdHjrQpHjE2UkbgTpidEoD2+JPNCRIiFE06Kybdrcigcp9bFN4xBCsLFfY2O/\nxuKqzQdny1ycNBmftSiVHXaMhDiyO/gTc4d3hWoi89hBpyVr7ppBNaJWX8vZG+ri8z1H72p5/3C1\nwHlMjvRvZd/ACB/yf3qyns1ionCahNq4rKdcoZYOOnXdr/4V8C9v9V7hnnR/CLwppTx1i787hhsk\nOnTXK3qHtKxQA34LSAPXmI9U0imrnBRCvANcxjUh+cObLOv3r/tdkk/vaE/prES/0gXvZmiTUQUh\nXHv+fFmS8KHQu5r2qKtuimcjcaSsCbV9G1sjb70eKSUnp8q8eb5I0XQv6DsHQzy7O0rqJj2l2rS5\nnyibDiAI68EemIaVilCz2xG1Nt7Q16Xy5SdjHM3avHemzNmrJvMrJZbXbJ5/NBroSbrBHo2hHpXZ\nZZsTlwyO7o/4vUqBpCrU4h6Zm2UqE/upeGsK44L2PEJrXAS2oBWAvwAYBuoL3G8nmvZvgAPAUzf7\nAyFEEvgvwH8rpVy6+zW9M1pSqFWU7+8AfyqlvGVFpZQyL4Q4Cey4xd+UqduRzZjNqhqKeOn8qCqC\nVMS1HU8XbF+cDwuVtMdYE9IeJ5YtcmVJRBds629uUfO94jiSV87mmV6xKZqSnoTK83tjbOppre/R\nps3dYjuSP35jjbIpObg5wmNbI02Jwt8NIdW9Xhvt1Mc2HtOZVPn8Y1EGulRe+7jE2TGTdNbhF56O\neTbAbwSHd4WZfbvAyUsGj+4Jt3SvuEZRn/roBetCrb2tP4OslDJzu38shPi/gF8CnpFS3ipIsx3Y\nAvygTicolWVYwC4p5eW7WuNbENyrwK15FhgF/uNn/WGl/mwPMNvolboTuirGH6seG3/4bShS7VnV\njDTEajRt11Ao0LOP1+NIyT+czHN8wqBgODy9K8qvH0u1RVqbBwrDkuTLEsuBD6+W+H9fS/PauQKF\nsr81tjeiaibSFmptGoEQgoM7w3z12RhhHWaXbf7ixRyLq8E93rYPayRjgmJZcm7c9Ht1Asl66uO9\nj4cse72nYatG1IKGcPk3uBb9z0spr37GW84B+3HTHqs/3wderTyfbMR6+t1HLYEruKpsFUIcAlak\nlBNCiN8HNkopf+O6t/4u8O6N8kiFEH8A/ACYAPpxa9RSwH9uxHe4W6qpjyXT7VMT9SgC1RlTmVi2\nSBf9ucBLKemOK8QbXFtkWJKLc24wtZXSHqWUvHgyz9kZA0XA5x+Kt7xTZZsHCyklUoIt3aiYU2mO\n7MhKo2RZfU3iSCrNkyW2dCPJjuO+t2RcK8gsGz64WuLDsRI9CYV9G0OENOWaovn6ZAdR96T+aiMq\n/2iK+6qquNkGmuo+qor7u9rryvrrN8umqEbUyna7Rs0vHEcyPmcxMqC11MTcnbB5UOdXPp/g+28U\nSGcd/uqlHF98IsboSPAm8RRFsHebztisxVoueJMrQUBRYLhPJRm79+M1W5l81zWIhATltjb2gn+L\n63PxVSArhKj2W16TUhYBhBB/AkxLKX9PSlkCrtEdQog0wK3q2u4Vv1MfH8FVolWqdWL/GbcGbQjY\nVP8GIUQH8E3cnmo3Yhi3AV0vsAj8DHjiBs6QvqJrgkRYkCtL0gXvhFpH1N+ImmHBSt6pOVA2ioWM\nxXC3RsmUDHa0Rj2XlJKXThc4PW0ggJ8/mHigRJqUkpW8Q0+iNfZXqyOlG60yLUnZkhjVH7vu+Q1f\nh7IlMS1JKiqYz9g1sVUVWY1db1jKOrx2rnRPyxlIqcxn7mzCShXrwk5VQFMFqiKQODjlTaRVwd/m\n8nQl3YFSOCQI6YJwyK2zC9c/DwnCOoR0gaLcn8Kimcws2Xz/9QIhHbZu0NkxorN5ULvvUu66Uiq/\n8vkEP3q7wMScxQ/fKnB0f5jH9gWvDmxDr867pwxM0+TYweCtn59IKZmYszAt+Fzs3sd39fVpQTeb\naSH+aeXxp9e9/tvAf6o83wT4OhPhdx+1n8LNPUallL91g9fWgJtWI0opv+XFujWDzrhKrmyxmrcZ\n8tqi3yehVnXpyzc4fWkpazO2ZDE6oLfERUtKyatnC5yYdNM1v3wwzq6hB0OkreRszsyUOTNtUDQc\n/ofnuwJvHhE0pJQUDEm+7FR+1p+riitsbiTEnHsUVaqi1tKZb4Ui3NljVbgCR1EEavW1+udC1F6T\n0mFs6cZCShHQn1LdmrW6j69fE3nN67L2B9WXYyEFVRHYjitYbceN+FmVx+r/67El2DZ1arT+E2PY\nJozPWZi2yszi7YtAXeMaERcK3UjUuT+RMETDComYQlhvTs10K1AsS+JRQb4oOT9ucn7cRNdc0TY6\norNlSEO/T0RbJCT46jMx3vikxCcXDGaXbM5eNdizNVj3jEQlUpTzuSVQECmUJKblZgGkEvcu1LJ5\n91qUbKc9eoaU8jMvGFLK5z7j97/l1frcDL8jag80XTGVqRW3l5pXdFZmbrx0k7wTqr3TGl1nUjAq\nud9NMC25V6SUvHa+yMfjrkj74v44eza0Trrm3VA0HM7PGpyZLjO7tn4shjXBUs5iY1fwUnn8wLIl\nBcMhV5LkDYd86dNCLF92yBvyGmFSz3CXxtTqrVPyqi6s1Z+wJtDVymPl/7Xfq9c+19QbCC8FlKoo\nE3cnJgqGw79/+dpeQn1JlaOj0aZNwMhqumZFuF0v4qr/L5oWPxy7CAhe2LEdcAWCYUrKhqRc91j/\nmlnZLablRjZzxc8Wvd0phZWMe/3UVEjEFBJRUXlUSMRE5dF9Hgs/GH0Gd4zojA5rzC7bXJo0uThp\nkivImk28psKWDRo7RnS2DOmEWnwySFEEzx6Joirw4TmD2WWbzUOaJ/VOXpGoZPAYJhimbPlt7iXp\nbCUCFlM8SdVtdcfHNndPW6j5SGflhEt72EutaiZSMNzUpWbPMFYjagVDNrSXW7FS3xINBf/GcGHW\n4MOrbhrX5/fFeGj4/hRptiMZWzQ5PV3myoJZC0oI4fa527sxxPb+1jJ+uReklGRLDktZm3zZYSXv\nfCoiVjLvLOQVCwniYaXy4z5PRRQeGg5fI8TqBZeuuaIqaOiKQBHgyIpA2xFltL+5EXIhXCHq9rO+\n+efaUkXM5wDYNqIQ1W9vosF26oUb14g6w7xW4FUfw7pr0FAsuz0p01mHdBbgJtFHBRJRQTyqkKwX\ndbXnrrC7H8ScEIINvRobejWePhRhfsXm4qTJpUmTTF5yadLi0qTF9o0mPZ0qR3aHWz56/+SBCBNz\nFotph7eOl/j848FpMB3SBSHNLXnIFx1CejutvcpqRah1JtvW/G3ujbZQ85GuSpqilxG1iK4Q0QUl\nU7JWtOlNNncXVyNcjnTrXCINukm2SkQtV3L48ak8vQmV/SMhDmy6//L4S6bDuRmDty8VKRrrwqMv\nqbJvY5jdG0It24z8dimZriCr/izmbJazNmXL3R4buzSmbxL1UoXbHD4RFpXHihCLCOIhhXjE/X8s\n5NZL3S/omuCbjyZxHMnm3mCnMKtCQVMULMfBsOzbFmqqIoiGBdG7mJuxLEmu6JArSnKF+keHXMF9\nni+5dYOZvCSTt5m9gZjr7VTI5Bz6ulT6u1X6K4+dCaWla+eEEAz2aAz2aDx1MMLCqsOlSZNLkwZj\nsxaXpy3XOn5vmP2jrTtBpCiC5x6J8tcv5Tlz1WTfdosNvcEZusVjCkbGPT67Un6vTXBIZ91zsS3U\n2twrwTnbH0C66iJqXkafOqIKJdMmXXDoTXqyyNtGq8ziG5akUJZEGpThVqz1awv2zffDsRKm7dan\nHNkS9Xt1PMWRkpOTZd66UMSwJKoqiIUEezaE2bsxRH/q/ru8WLZkJb8uyJZyNotZm1zpxlFxIaA7\nrtKXUBnoUInXC7FKRCyiPxipazeilVpShBXVFWpNanqtaYLOpErnLa7htiMpFGVNvGWLDvmKqMsW\nHHIFB00RGBZML9pM19XV6Rr0dV4r3rqSrSnehBAMdKsMdKs8eSDMpSmLt0+USGcdXv+4xMfnyxzd\nH2HXZr0lv9+GXo29W3XOXDX56QdFvvWFRGC+RyIqWM1Azqe6+KBSjah1eS7UgrHf2zSP+28k1UJ0\nRBVGurWKqHGIR7xJG+iIqWRKDhmfLPpjoYpQMxy6aUwqRC2iFuBITcl0ODHhpjw+uu3+iqRNLJu8\nerbAUmXWsDuu8OSOKDsGQoEZQNwLUkoyRYfFOkG2lLVZzds3NehIRhR6kyq9SVeY9SZVuuJqy87k\nt7mWsKqRt0zKAeqlpiqCZFzc0mDAdiQrGYeFFZvFVdt9TNuYluukOFNn6KKpuJG3uuhbd6q1xJsQ\ngh0jOts2apy5YvLu6RLZguTFd4t8eK7MkwcibN2gtdzkyLGDES5PmSymHU5cMji0Mxgp9G6dmt02\nFLmOtIepj+0eag82baHmI7qmkC05pAsOSznvhNpASuHCnGR+zSehFlZIF5zbcou7W6pmJUGuUTs+\nUcawoSehsq2vdSIHt2I1b/P6+QKX5t0mLhFdcHQ0ysFN4ZZOy8sUbWbTFuPLFktZi6WsjXmT0yes\niZog660Ist6kSkRv30DvZ6q91AyrtXqpqYqgr1Olr3P9/uI4ktWsK94WVm3mV2yWKuJtdslm9jrx\n1lsfeetS6e5QAn++q4pg/2iI3Vt0jl80+OBMieU1hx+8UWCoV+XYwQgb+1pnCBSLKDx5IMKrH5Z4\n52SJHZt04gEwFolXDEXyt2GU86DgOLLWW64ree/juut7qLV5sGidq9R9Sm9SdYVa1mZzrzeD+Wpd\n2uyaPwOKajpiwWjMDJsjJUUz2DVqli35aMyNpj22LdJys7fXUzYlP7tc5OOxErZ0U/oObgrz5GjU\nsx6AzURKyVLO5tK8yaV5g4WMzUCHes3khiqgu06I9SVVehMaiciDm6r4IBNW3Otqs1IfG4miCHo6\nVHo6VPZsdV9zHEk669SE2+KqK+JMC+aWbeaW684NxRVvzz8Spb872AYSuiZ4ZE+Yh7aH+OBsmU8u\nlJldsvnOy3kOjOo8/lAkUE6Kt+Kh7SFOXzFZWLU5fsHgyQP+Z2o0yqLfdmTgJwNuRrbgusWqyvr2\nuRfaPdQebNpCzWd6EyqX5k2Wct7d/Ac73N26mncomw7hJs/0N9qi33EkG7tUciVJUK/jp6fLFAxJ\nMqK0fL+0xazFh1dLnJ42ANjcq/Hc7ljTjWruFUdKZlatmjhbqxtYCEBX4PFtEXpTKn0Jjc548KMG\nbZqHrrrXNev65mv3CYoi6O5Q6e5Q2b3FfU1KV7zVC7eFVRvDhPkVm3ALze5HQoKnDkY4tCPEu6fL\nnLpsMLds89cv5/nGc/GW6E+lKIJjB8O89lGJc2MGjz/kfyZDNavFNL1d7runypy+YvDInjCHdwUj\nzfN2qTcS8SJtOFOJzrXCMdrGe1prpHUf0lsJiy9nvYt+xcIKqahCpugwn7HZ1NPckzsZdY0S7tR6\n/HbRVIWlrEPZci3OgxbRcaTkg4od/yNbI77fSO+F+TWL77yfpWRKBjtUnhiNsq0v2A599Vi2ZHzZ\n5NK8yeUF4xpXSlWBzb06o/0htvfrga53bOM/mnCPD9O5P4XajRBC0JVS6UpdK97Wcg6Lq05LGhsk\nYgovPBplzxadf3inQLYg+euXc3zz+QQdHjQmbjQjAxr5okPZhOW043tEs3pFVTzedAurNoWSRA3+\nLvkUtfo0j46nTN4hHhX0dbbgxmhzz7SFms/0JtxdsJSzPXV+HOxQyRQdZtNW053VOmMq+bJkvoGp\nl8moQrnittdsZ8vPYilrkQgrlEzJ/hbumTabtvju+1nKlivSvvlosiXqsEqmw9UFk0sLBlcXzWtq\nzcKaYHu/zuhAiM29OqEm9xls07polZGo9QAJtRshRNWNMtgpj5/Fhj6NX34hwfdezbOWc/jrl3N8\n43NxulPB/l7VtgTjcxazy5bvQi1bcKWal7VTUkoWVtwLt9/f727wuofaWs41E4m0JxMfSNpCzWc6\n4wqqANOGTNGhI+bNRWmwQ+PCnNlQsXQz+is3uoWMjWXLhrjeJSMKS1mbzE1s0f1ketVmatVie5/W\n9IbjXjG9avK997MYNmzo0vjGw8mWaBz70ViJ184VrnFmTEQURgfcyNlwt9bSEc42/qEp7nXtfk19\nfBBJxRX+mxfifO/VPCsZh++8nOfrn4tfY7wSRAZ7VMbnLOaWbQ7u8HddqqYZKQ+jkfmS2/BdCOjp\nCPa+uBHrjo/erHu6UhrjVYSuTWvRFmo+oyqC7oRaswH3UqgBzPng/NgZW2+6vZi1Ger0/jBLVoq/\nb9a/yk/yldq8ZLT1bjDgWu//7YdZTBtGujW+9nCyZSJP3XEVR7pOm6MDOjsGQvSn1JZJ1WwTXPR2\nRO2+JB5V+OUX4vzNq3kW0w7ffSXP15+LMdAd3OHRYK97b6k3ePGLqlDzMm20Gk3rTiktOdm5WqlR\n86KHWjXVGLzdxm1ah/ZeDwA9Cfei66WhyEBFqGVLTk04NAshRE2czaYbE9GrCrVsAIVaVTzGWzBN\nYWzR4G8+cEXa5l6Nrz/SOiINYKRH43ee6eC3nu7gqZ0xBjpar19Sm2DSTn28f4mGFb7xfILBHpWy\nIfneq3kWVv0XQTdjsJIOmM46FJt8f7+eqtFFh4dGF4uVbR/0yOaNsGxZSwftTN37NikZEqNi1NIW\nag8m7b0eAKqGItXmwV4Q0kRNAM41SCzdiqGOxgq1RFWoBbDJZrUZd6sJtbWCzZkZA8uBbX06XzuS\nRG+xZs2qIuiKt97NvU3wqZmJtFMf70siIcHXn4uzoVchFlZ450QJ+2bd7X0mElZq0Ro/o2qOI8kU\nvI/21IRaC9anZXIOUkJIh1j43u+f1TTKRFQ0pIykTfBprZHkfUpNqHkYUQPXUARgzoc6taHOxn52\na0TUWuui+v7VEmdnDEa6NX7pSKJ9U2jTpo711MfgRlra3BshXfCVp+OUTcnYrMX5cY895z2kv0tB\n1yBX8O8emC1IpHQddONR7+4X1Whmf5Miat/+cY7vv54nm7/3bblaV5/mRTZHO+2xTXvPB4DeSuRr\nJWd7OoPnZ53aYCX1MV1wGtL4OhldF2pSBmvWs5pq2koRtXzZ4dRUGYCjo9G24UabNtfRNhN5MIiG\nFY7sdt16PzhTxgloVC0WVTAtWMn4dzzWiwivUsyLZaeWOtjX1Xihli+6Td7HZi1P+gJm8g5DPSoj\n/d6se3Ube+Ug2ab1aO/5AJCKKugqOBJW8943vp5bs5ouZiK6QlclZ70RqZfViJppQ9kKzo3UcWQt\n9bGantkKfDxewnZgqENlOMBF9G3a+EW7Ru3BYf9oiLDuRkcuTzc/I+V2qN7S/cx8aITj4+Lquvhr\nRkP1aupod0oh5IGz8cKqzeyy7cmyANLtiNoDT3vPBwAhREPSH3uTKoqAkilZ86GWq1an1oD0R10V\njHRrDHaonorbe6Uq0gQQbcJNxgvKpuSTcTea9uj2aNt8o02bG9B2fXxwCOuCgzurUbVS4LI2AKzK\nbU/zsYxrLd9AI5Gu5gxPq0JtsMebDblSyWDyqq1AO/WxTXvqPCD0JjRm0zbLWRuGvFmmpgr6Uypz\nazZzaxadHln/3y5DnRpnZgzm0o0RUqoimFyxmE3bDHU2t6n3zaimPcbCAqVFBM+JyRJlS9IdVxjt\n92c7mrZsOeOSNneOlBLbAcupPNoSq/Z449csG+zKo+VIbAmqAEWBkCrQVUFIE+iaqPs/66+rAlXh\nnicgqmYi7dTHB4NDO0N8dK7MwqrDxJzF5qFg3GOq2LYrHlUfr5uZRljzV+vTmpD2CDC37E4kD/bc\n+3DYcSTLlVTU7g6vml1XUh8TrWes0sYb2kItIFQjaoseOj+Ca9PvCjWb3R4JwNulatFfTb30OlIz\n0qMxtmQysWxyZEvE02XfLaqATT1aoNIxb4VlSz4cKwHw6DZ/ommreZs/fyfD49sjPLwlEqiInmFJ\nPrha4rFtkba5yg2Q0k31TRds0gWHdMFmreCgCLc21rKlK7LqBJgfKMIVbq6YoybgquKuJ6GiKNAR\nVeiIqqRiClFdXHMsVmvUzAcsoja3bDHQ/eD1IoyGFfaPhvj4vMH7Z8qBE2qBiKg1QKitR9Qa/8Uc\nR9Z6tg14EFHL5B1sG1TVmyijYUoKJXcs0Y6oPbi0hVpAqAq1Zc+dHzWOU/bFor83qaIpburlasGh\n22Pb9E09OlBkasXCkTIQEaxYWGGiMkNnWDLwPcjOzhjky5JEWLBnQ6jpny+l5OUzeUqmZHzJ4uEt\nTV+FmzK+ZPLiqTyZooMjJU/tjPm9Sr7gOJJsyakJsXpBli7YmDe4ZHXFFFZvw41OU9zIf/VRVQSa\nCprivqaqwn1e95qiuPU5lgOmJTFsWXl0I7OmJTEsiVknDB3p1rLebAKlP6WykLn2i4RUSMXUinhT\nEJqNLCWwhMQwpWc1KEFmbMbk714vsGNE5+ceiz4Q37meI7vCHL9osLxmk83bJAPU+sOqRNR8rVGr\npD6mPEp9NExZc01shlBbzToYFuga9HjQ82x5rRJNSykoHhhyVYVwJCSaUq/XJpi0hVpAqDo/pgsO\npiXRPRrgV+vEFjLNFzOqIuhPacykLebSludCrT+lEtYEZUuykLFr5il+EgsrxMOCfFmylLXZ0OX/\nOt2KiWXXfvrhrRFfnB4vzJmML1moCjy/NxaIWXvDkrx+vsDxCbduLxVVKpMC9y+m7fZDqhdi6YLD\nWsFmrehwK+M7gevC2hlT6IipdEYVUlG3F5WmUBFf4lOiTBH3no74WTiOK9gM+3pRV/8IZcuhJ6Gy\nVnBYK9rky+57lrL2df0th8kB//67GaJhQSquMDKg0plUGenXPDVVCAK5okQRcHHSZHnN5itPxehK\nBUesNJpETGFkQGNizuLKtMXBncH57utCzZ/PLxmSsuFttGepUiYRjwriTTDjqtan9Xepngir5Wp9\nmkfnSLrt+NiGtlALDLGwwtY+jUzRYSlrMdTlzcCwK+G6L4Y1wVLWoj/V3AHnUKfKTNpiNm2xd2PY\n02UrQjDcrXF5wU1/DIJQA+hLauTLJktZK9BCzXEk52cNVAV2DjY/mmZYkp+ezQPw2LZIIBpVTy6b\n/Phkvma+c3BTmGd2xQIfGb0TLFsytWIxn7EYWzJJ521y5Vun6qqVtMDOmLouyGLu/1NRJbBpoYoi\nCCuC8B1e9kxbm9pyogAAIABJREFUkik6ZIp2Rbw5LGQNJtYKYOvgqBTLkmLZZiltUy1bS8UFG/s1\nRvo1NvZrnkUa/OKh7SG6Uwp//1aBlYzDX7yY4/NPxBgdvr8nLupJxRWkhMJnnCPNZj310Z9zb2XN\nJhxyJ2K8mlhufn2at0YitYiax/Vp7bTHB5vgjiIfQBwHlnMOiznbM6GmCEEqojC+bDG9ajddqG3s\n0phYtmomG16zqUfn8oLJ5LLFY9sa8hF3TG9SZWzJ9Lze0GtsB2TlMRpq/o3g7UtFcmVJZ0zh0W3R\npn9+PY6UfDRW4rVzRcBt//DF/XE2994fA9J82eHKgsmVRYPxJRPTdiNhQlCLloU0URNfrhhT6Iqp\ndMQUkhHv+iS1Anqlbq2nroB/qSj50wtjRHWNX3/4CJm8w2rWZintMDVvMb9ik8lLMldNzl51I9Ud\ncYWN/SojA65wS8Zab8C1oU/jV7+Y4O/fLjCzaPPDNws8sifM0f1hT6IQQScWdr9jtVYoKJiVvF6/\nImozSzZlA/Zu9W4YudDE+jSA+RW3TGHAAyMRgJVM2/Gxjfe0hVqA6E1pjC9b16Xa3DvD3TrjyxZT\nKyaHNzfXdGNjl85SNs9i1iZTtElFva5Tcw/hqVUT25GBaNTc1yBjGK+x6vLZ1CbfBxazFh9VTEye\n3xvz3fHxrQtFLs4baArs3ehG0cItXI8jpZsOfLkizuava3ofDwu29YUY6tToTbpi7HrzjDbXUdk2\nEgiHBH0hlb4ulZ2b3F8bpmR2yWJqwWZqwRVua3mHtasOZ6rCLaEw3K8y3K8xPKCRiLbGACweVfjG\n5+K8dbzEx+cNPjhbZn7F5ktHo8RaqF/k3RCLVIVacExkHEeSLbi1UH6l216Zdo/p/m7v7um5Ss1b\nMyJqpiVZSrufN+jBd3AcyWrF8dEroZaujCE620LtgaYt1AJEowb4Iz0aXITJlca4L96KWFhhQ5fG\n9KrF5QWTw5u9vQD3JFSiIUHRkMymLYa7/Y+AVPfjUtZu+va+E6pGC4qg6UYsZ6fLSAk7BnS29jU/\n7bKec7Nl3rviisYX9kY5tNnf6N7dYlqS8WWzFjnLX5eqNdihsq0vxLZ+nf7Ug+fid69Uh0o366kV\n0gWbh/SaO2DZlMwuWkwtuOJtYdVmLeewlnM4fcUd5O7cpDM6rLN9WAt8dEpVBM8cjjLQrfLSe0Um\n5y3eOVHi2MEIkfD9O5CMVoRoMUARteU1h7IhkY70ZRBfKDnMLrnjlG0bvLnn5osOkws24ZBgqK/x\nQm1x1UZKiEcEidi9n3vpnIPtuBHOVNybc7kR7Q/atB5toRYgakIt4+0Af7BDQ1OgaEhWcg49yebm\nSmzv15letbg0b3ge0RNCMNKtc2HOYHI5GEKtO+E2Gi9brlue11FEr6j14WnyPcBxJB+NlYmGBE+M\n+iuKFjIWPz7h1sk9sjXSkiJtbs09tz64WqK+xZeuwuZenW19Ibb26STu88hHo6lej2+393FYF2zZ\noLOlMpAtG5KZJYupeVe4lQyHCxMmFyZMOpMKD+8Os3uLHth6vyq7Nofo7VT58TsFzlw10XVXwN2v\nrEfUgiPUphfdlL2hXn8E/tiM+/l9XQpJj+owr1aW2ZlwjYgaTbU+baDHm0mran1aT4c3y7NtSb4k\nScaEpw3F27QebaEWILrjjRngq4pgQ6VWbHLFbLpQGx0I8fp510a/ZDpEdG8vOpt6NC7MGUysmBzF\n/wGDqgi6EypLWZvFrPfpnl5RTX3Umnyjz5fdpsVlU9baUvhBoezwdx/lsBzY0qvz9C7/j507IVt0\nePNCgTMzBqmIwHZcw49t/a44G+7WAj/obyUE1dTHuxuwh0OCrRt0tlaEW65gc/KyyfGLBumsw8vv\nF3n3VInDu8I8tD0UaCv8ng6VYwcj/O1rBU5cNDi0M9zyxik3oybUGlRnfTdUhdqGJkSebsSVGTci\n7FU0DeBqdZkbmzMs9dpIZKWSXu6VkUgm70boSoYkFg3utaBN42kLtQChqYLuuMpSzrWE9nKAP9yt\nV4SaxaHNni32tuiKu0X5yzmbq4smezZ46/5YtU6fXbUwbel7vRO40dGqtff2fr/X5sZUoy9qk7dX\ntuTe0BIRxbfed7Yj+cEnOTJFh86Ywi8cigeiD9/tYFqS968Wef9KqZa+uqFL4xe2RBjq0NopjQ2i\nulVvN6L2WSRiKkf3qzy8O8ypywYfnS+TK0re+KTE+2fKHNwR4uDOENGAphVuGtQYGVCZnLf52ckS\nX3ji/uwzqKuCng6FjoSCbTuozU5BuA4pJdML7jV0Y3/zh3CWLRmfdYXito3eCDXLlkzMucvc6qH4\nuxU1I5Fub7ZhfUTNC+qNRNrX9AebYN4BHmB6G1WnVrkYTa2YN62xaCTb+92L76V50/Nld8YUEhEF\nW8LMavMbe9+I6n68volukFiPqDX3c7OVFCI/U/GmVkyyRQddha8dSXoe5W0EUkpOT5f5o9fTvHPJ\nFWkbujR+7WiKXziUZEOn3r6hN5D11Edvr58hXXBkd5jf+kqSFx6N0pFQKBmSd0+Xee2jkqef5SVC\nCI4ddFPZz46ZLKaDe627F9byDstrDgurtu8iDSCddSiWJaoCAx4aedwuk/MWlg2JqKCvy5vtUVtm\nTNDb2fhtXCg5ZPLueezVNqz1UGtb87fxmPYREDD6Uo0RatU6tYIhWck3P4VjdMA1jBhbNGqNOr1C\nCFFzf6w2cPabdUORYAjHG6EIQSqqND0CWY2oJX0Uau9dKbFWdDi8OdL0VOC7YTlr82fvZPiHE3ly\nZUkqqvCVQ3G+9XiSoc52YkQzqEXUGrR8TRU8tD3Eb/x8gi8/GaWvS+HILm+zD7xmoFtjx4g7Cff2\n8eCKynthqWoZ3xmM68T04nrKnh+pzdVasq0bvZsYqi1zQ3Mmm6ppj90phXDIm3qydLYSUfO42XVb\nqLVp3+EDxrqhiLcDfE0VDHVqTK64Nv31/YGawWCHSjwsyJclUysmWzx2+tvWp5MtOsytNd/Z8kb0\np1S29Wks55yGtCXwgmREIVtyyAJl0yHcpKhSrmJz7ZdQc6RkLu2eX7uG/HWcvB3m1yxePZdnOWcT\nUuGx7VEe3hJp1581mUZF1K5HUQQ7N4XYMdIaEdKjB8JcnjIZm3WNUoYH7q9hRTVS2KzeXp9FtT5t\nY1/zt7OUsmbL71UtmZSSq9VlbmjOd6r2axvwqD4tnXNwJIR0PHGQhHZErc067SMgYPQl3QvVat7x\nPPJUdUScXGl+lEcIwfZ+d1DciPTHzb06M2mLiWXrUz2j/CAeVrEcWCs6nJgs+706NyQRUeiMKUgJ\n48vNOyayPgu1lZyNYbs2yr1NnrC4U6qGJ9MrNhs7NX7n2U4e3x5tizQfqI+oNSN9vBVEGkBXUmV0\nk05fp8JqwHtH3g2Lq+71KjARtYWKkUh/89dnYdUhX5ToGgx7VB+3lHbIFSWaStNE/tS8RTgk2OTR\nd6ilPXrY9qQt1NpUaR8BASMeFkR1gQSWc/dXndrogCsULy8Ynn9+RFfYUUmvPDkVDGF0cJNbv3Fy\nsoztBMfauZ6tfe4+ubpoNO0z/RZqs5UZ8sFUsHtX2Y7kv36SI1ty6IopfOVwgnhAjSUeBKpnsMA/\nEbWasXn1w2ItqhEUNvWrLKYdrswEN9X7brAdWRuEByGilsk7ZAsSIWCop/kRtepxt2nQO0fZRizz\nVliWZG7ZpmxIBno9cnzMOPR2Kp4JTSllTah1BnwysU3jad/1A4YQomF1akOdGqri2qOvFppfpzbS\nraOrkCvLhkS99o+49RznZg1Mj6ORd8P2fp14WFAwJBfnmieE7oRqs+mri80T7xs7dTqiAsP2x+56\ndq3SgyjgtV2vnyswuWKhq/DVFjE8aWU+K4PBdtzjVVX82w9nx0xOXDT46FwwJqOqVCc8ZHAc7D1h\nNeNapId075oY3wsT8ybREPR3qb60b6ilKHrk9gjX1qc1g5klG9txzVC8aha+sGKzlHZqrRzulVxR\nYjugCEh6lErZpnVp3/kDyLrzY2Pq1MCNqjUbTRU1YXBpwXvhMtKt0RFVMKxgCCNVERyoiMfjE8Ea\nWFUZ7tLQVFe8ez0xcDNMW7JWlCz4lKJarU8LslA7P2vw0bh7zHz5YKIlDE9aGdOW/Mmba7x+voBh\n3Viw2bIq1PwbOO0fDSGEaygRJJfFqnZ1fMjUaCRLlW3c2+ldStu9cPaqSdGAvduaI2rqyeQdFtMO\nQsCWIW+unfmiw/yKu423Nqk+baqSOjrc710rk+px4lV6bDWalowrgc76aNMc2kItgFTr1JYaMHAe\nrqQ/TjaxJqme0Qba9Ash2DfsCqNTAUl/3D8SQQiYWrUC6QCpqaLWh+7qYnPE+0jVodOHWknLlqzk\nK6mPARZq06smnTGFh7eEaym9bRrHxTmD1YLD+1dK/PEba1yY/XR6tl35vyr8u20mYwqjw+75evxC\nMK5xANXxrnOfRdRmKsYdQahPW0rbzCzaCOFto+nbpdqQeqhHJeZR2no1mjbQrRKPNue8qgk1j9IU\nS4YkW3CvDb0eC7V2fVobaAu1QNJX14PL63S0kYqhiF91alv7dRTh1t+t5r0Xovs2uoPayRWLdAOW\nf6ckI0pNnAY1qratr8lCrXIMLmVtCuXmjuyEWD+/gjpPKaXk+ESZdMHh8OZg2rPbjuSl03nWCv6f\nY16wd2OYrz2coCOqkCs5/OCTHK+dK1yTQh2EiBrAwR3uNe78uEmpyefPzVBqjpg+r4iHOI7k6qxF\nV1Jh+3DzhdH1HL/oZolsH9ZIxJo/dLsy7W2Ta1gXf82KphmmZL5ize+VGcpyJZqWjAlPrP6hLdTa\nXEv7KAggPQkVIaBkSvJlb+98Q50aqnDrxNI+1KlFdKUW1bvcgPTHVFRlS697Izk1HQxhVDUVOTNd\nvmlalZ9UDUVm0hYls/HHRCys1NwWm+1AqiqiZgoxHpCee9eTK0sc6YrKZMT/mfwb8erZAscnyvzV\ne9nAGuXcKdv7Q/zm0x0cHY2gKjC2ZPLTs4Xa76sN4v2sUQPY0KfSkRB0JBQyPvTEvBHVY+A+ORQA\nuDxtkStISoZkyCPTibulbEjOj7v3y4OjzZ+8KZuyFonyypbfsiUTc+s92ZrB7JKFI11R5ZUIWqxL\nj/WKdSOR9hC9TVuoBRJNFXTHq1E1bweyuipqKV9TPqSeAQ216Qd4qJL+eHqqHIiaiU09Gl1xBcOG\nszPBEI/1pKIqPQnVtelfam7646QPtZJVIT/WpAjinZIprjcED2J9wvGJUi06/Lk9Md8jTF6iq4In\nd8T4xcMJlnNua41qPW8touZzrZIQgnhUYXnNYSkdDKG2uGqjqc2LjDSDamrp/u0h39thnB0zMC3o\n6VDY6IMt/8ScheNAZ1Khy6OGzpPzFpbtmnr0dTZnKDq5UImmedgGwOv6NGg3u25zLe2jIKBs7tFI\nRoTnhiLguhEOd2vMZ/wZqFZt+mdWLXIl71OntvfrRHRBriwDMRgXQnBwxI2qHZ8o+5Jy+llsaXb6\nY6UubsKHqFZVqI0v+5P++1lkiu5NOuVT+4LPIl92iOiCo6NRRu/T+rnt/SEeqqRRn51xIxnrqY/+\n7hcpJctr7rr0BsAyXkrJhQkTy75/BpaLqzbTizaKcA1c/ERKyYlK2qNrJtN80Xhp0mBjn8rerQ1w\ne9zYvMbuU/PuZ454lPYI1CZLGhFRu1/Opzb3RvsoCCjxsEK2JFnMej9jOtSpMbVicWHWn4FqKqqy\nd0OIeFhwsQFRNU0V7N3g3lyDYiqybziEprgtF2bSwTMV2Tmgs7VXZ3zJrPU5ayQj3Ro9CQVNESw0\necJgqFNDV6FoSBYywauxCqmCvqSK5v8Y/IZ8NFamZEp2Dfpft9NIdgy615BsZTLJrqU++htdyRUk\nZUOiCOhO+X8Ln1qwyRYkIR22NymFrdF8UommjY7ovtSD1TM5b7OadQhpsGdL80XjWs7hwoTF9KLt\n2f6VUja9Pq1sShZWva1Pc+r67PV6FBUsGe75DW2h1salfRQElP5qLzWPUx/BHaiGVCiaknmfBqoD\nHRq5suTkZGOEVDX98fKC2XTDihsR0RV2V8RjEE1Fhjo1SpZDrix5/Vzhs99wj0R0hf6UxmLW5u2L\npYZ/Xj2qsu50OdakVM87IRZWWMzaTK1aFA3/j93rCWmuUDGDp3E9ZaLijBut9K9bT33097ZZrYnp\nSim+p+QBnLnqRnt2bgqhaf6vz71SLDucH3evC4d2+h8xPnHRvV/s3hrypXfa8crnbxrU6O7wZvZo\ndtkmERVs6FU9jW7diplFCyld8ZOMe3MOr+UcLBs01TtRtZZzz+9YRKDfB+dTm3unLdQCSl/KvXit\n5B1Mjw0oVEWwyec6nT0bQqjCjTDNr3kvRvtSGgMdKo5cT13ym6qpyIVZIxDisR4hBC/sjQNuw/Bm\n9Nl7fHsUgSump1ebexxuDnCd2mCHSl9SxbKDKeo3dKl0RJVA1lt6hZSSi3OVqEolvbNqz+933eBY\nJRLhZarV3WKYkkuT7vp4mRbnJ6cuG9gO9HerDPb4u40zeYcrlRTBAz6kYBqm5PQV9/7ppWg9e9Vk\nbtkhFVeaJu6raY/DHtb4VSdNejpUz64LuUI7mtbmWtpHQkCJhxXiYffEb0Qj4pqhgk8RhWhIYftA\nY9MT91eiaienglEXNtihMdihYsvgpGTWM9Ch1Rp0v3KmgNNgC7eehFqLfL5xvtjUfbStT2d0QGc5\nZzMZMPdHIQSPbnVF/cfjJSzb/2O3nv3DEdaKDh+Nl/l4vLnR0GZgO7Li9iiI6OvXyiDUqK1kbM6N\nm/R0KL7XTgFcnHRr0zqTiu+ixgtsZ70e7NAOf+rB6jl12UBKV1z0eBTNuhPOjhkYprt/vWpybVmS\nCxPuNt67rXnHcH2ja6+o1qd5aYayfVjnn/1yil84FvNsmW1am7ZQCzDVxteNMBSpDj5m0xZl05+B\nYFVInZ0xGjIY3TXk1oUt52zm1oKRp1WNqp2YDIYj5fUc2xklogsWszbHG5SWWs/RHVE0BaZXLa40\nMbrVEVOJ6ApFU/KjE3nKTWhLcCfsHAqRjCgUDBmYiHCVTT0aj293j+NXzhQ4MXH/iLVM0eYv383y\n0XiZTMnhq0eStfQjv2vUTEvy928VMC2IhgVDPgsjy5KcvmzQ16mwd2vzDCEayZUpi1xREg0Ldmzy\nN0Jo2ZJTl91z/8CO5lvySyk5fqHSEsBD0XppysQwIRUXnka3bkXJkCysutf4kQY4Pnpt6qNpomkN\nwNsEn/aREGCqdWqNMDzoiKl0xRUcCRM+WKQDbOrVSEYUypbk4rz3g9GIrtQMAYISwdo1FCKiC9aK\nTiDT7mIhhWM7ogC8daHY8BTNZETh8GZ30P/m+WJTxevn9sToiCpkSw6vnG18Xd6doCqCI1vCKAJO\nTpUClSorhODYjigPV6J+Pz1X4Mcncw1pYN9MriwY/OlbGWbTFmFN8EuHEwx3rw/WTcf9frpPEbVX\nPyyyvOYQiwi+dDTmewrmWydKzC7bFA3J4QDUct0rjiM5O2aga667ot/1f5cmTYplSTwqPOtddieM\nz1muiYkOe7d6t3+rNY17tjYvYjldiaZ1JRVPBVCth5oP0c42Dw5toRZg+hpoKAL+95NShGDfcGOF\nVDW17txM2fNav7tBVwX7NgbXVATgwKYwfUmVsiV582Kx4Z/32PYIYU2wlLObGj0KaYIvH4wjgDPT\nBhdmgxW5OjASYedgiNm0zXfezzalGfntIoTg2V1RHt0WYbhL49SUwR+9vsbffJBlfCmYbQ9uRq7k\n8MqZPH/zYY6SKRlIqfyTY6naJE+Vsu0OysJa8wfNp68YnL1qIgR8+WjM99n2sRmTTyrRlhcejaJp\nrT+U+OSCwdUZi3hU8d1ERErJxUmTRNTt4+ZHFLe6f/d6aGKSyTtMzrvn0d4mOljW0h49jKaVDFmr\nJwtCvWib+5fWv7rex/TXUh/thkQa6uvU/BpYPbTRFVITyxZrBe9n5Ee6NXoTKj1JlTMBMT84sClC\nf0plrWgzG0CrfkUInt/r5sefnCwz1wCzl3oiusJj29zozNsXi02tydrYpfNYJY3vJ6fz5JrQmuB2\nCWmCJ3dEiYXcVNS/ejcTOLH2zK4Yj2yLsq3Sh+/Kosl33s/yJ29mODFZwvSpvk5KecuUbtOWnJsp\n8933s/yHV9OMLbki6PDmMN96IkVn7NMDr7LjngfhJvdNWEzbvPqhO2HyxENhTwebd0Oh5PDiu+76\nHNoZYstQ65uIpLM2b590U3gf3h0mGvZ3aHTmqsmVaQtHCg7taL5oXMnYjM+6x/tBDz//bCWaNjKg\nkmqiWcZ6fZp352417TEVF4RDrZ/22ya4tIVagOmMK+gqWA6s5r0foI1066jCbbC7WvBnANgRU9nU\n4w48Tk97L6SEEBzc5EYl3rtSqtWZ+El33HX1W845vHQ6H8hateFunT2VdgKvnMk3XMgf3hIhERaU\nDIf3rzY+ilfP0dEo/SmVkin5h5O5QEWDuuIqv/xYEl2FxazD//fTtcBZ9m/q0fn6I0l++5kODm0O\no6uwlLP5yakC/+HVNG9eKLCcs5qyXV23RoP/9MYa/+7l1WscZaWUTK+a/ORUnv/nlTQ/PJ53J6lw\nU37/yZMpnt8bv2nKW9muCrXmCaVcwebv38xj27B5SOPRvc2vVapHSslP3itSLEt6OhSOHYz4uj5e\nIKXkpfeK2LYrIPZt81d4lg3JW8fXRWPYB9F4vGKosnWDRmfSG3EjpaylPXqZSvlZ5Is2miqIhr02\nEqn2T2tH09o0Fn+n5trcEkUIepMas2mLxYxFT8LbC4KuCTZ2a0wsW4wtmnTH/bngPDQcZmLZ4tSU\nwROjURSP89b3DUd451KJTNHhzLTB/hF/BzsAz+yKcWneZCFj88l4mSNbgjfgcdfRYDZtc2baYN9w\n47abrgo+/1CcF0/leftiiYGUxrb+5tzMVUXw8wcT/Je31hhfsvhkolyrmwsCfUmNLx9I8P2Pc5Qt\nyf/9SppjOyLsG44Q93nmv57uuMoLe+Mc2xHl1FSZj8fLZIoO714ucXq6jGFBT0KhJ+E2O+9NqHQn\nVJIR5Z5rVaSUjC9ZvHmhcE1vyOMTJUK6wmLGYilrUzDWxWIyorBvY4i9G8N03ca1r5r6GGpSRG1s\n1uTFnxWJRQR9nfDFJ6K+G3acuGQwNmOhKvClozHf67i84ORlg+lFG02FFx6N+b6N3ztdoliWdCUV\nT6NZt0vZkLXI16Gd3l3zpxZsMvlKY/Th5onhyXmbuWWbvk6FWMTD+rRVd+KmLdTaNJq2UAs4/SmV\n2bTFQtZmdwOWv7lXd4XakumbWBgdCBHWCmRLDhPLVi0l0yt0VfDotgivnSvy7uUiezf6k/NfTyys\n8PSuKC+dLvDWhQI7B0MkPLyJeEEiovDEaJQ3zhd5/XyB0QGdsN64ddzap7O9P8SJyTI/PJ7n146q\nnk9O3IyehMozu2O8cqbA6+cKbOrRm/bZt8OOwRBHt4d553IZR8IbF0q8eaHE5l6dvRtCjA6EAtMc\nNaIrPLI1ypHNES4vmFxaMDg3Y+BImE3bzKavTXEOaaIi4NTaT3dcJawLHAccCY6UOA7Ydc/zZYdM\nyWY15zC2ZLJyg6yDk1PX1h2OdGskowr7NoYZ6dbuaFDerIiaZbsRlWqNUCwi+MVn4r6n480tWbzx\niRvpeepQ5L4YoGbzDm9VvtOTByK+965aydi1/f7MkQiqD0L49BUD04KeDoWRAe/2cX1j9GZeq65W\n+tBt2eDtuGJ5TdKZVBjw2PGxTZvraQu1gNOfrBqKNMZRbWuvzhvni0yumFi29GWGVFcFezaE+GSi\nzKmpsudCDeDgSIT3rpRYKzqcnTFqJiN+cmAkzKmpMnNrNj89V+ArhxJ+r9KneHhLhFNTZVbzDu9c\nKvLcnnjDPktUauOWczbTqxZ/91GWXzuaItJAcVjPoU1hLi8YTCxbvHQqz5cOxOm4Qa2SXzy5M86p\naYNsyY0KSdz60rElE13N88X9cXYN+X9cV1EUwY7BEDsGQ3zhIclq3mY5Z7OUs1nOus/TBQfDkp8S\ncPGwIF++91TJzpjC1j6dvqRGX0qlJ6Ggq3d3PNXMRNTGHRPLazb/8E6h1p/p4I4QTx2MNK0p8M1Y\nStv84M0Cgz0qmip8ifR4jZSSlz8oYlgw1KP6/p2klLz+cQlHuimHftT+OY7k+EW3BOHQzrBn0cWy\nsd4YvZmppY4jGZ+rCDWP+sAB2LZkKW1jO9BzH0xYtAk2wZrCb/Mp+lLuxWWhQc6PvUmVeFhg2W4v\nK7+oCqdLc0ZDanB0bb2J8LuXiw1v5nw7CCH4uX2u6+D5WcO35uO3QlUEz+9xjUU+Gi+z1ICeftd/\n3i8dTpCMKKzmHX74SfNq+IQQfGl/gl2DOlOrFn/+TiZwZi+7byLETJtPRaqChKq4ady7hsIc2xHj\nl44k+e1nOvkfv9DFbz6V4iuH4hwdjbBz0I1k1qciCgGaAroKEV0QCwniIfe1zxpGHt4c4fm9cfaP\nhBns0O5apEFjI2pSSk5cLPPtF3MspR2iYcEvPh3juYejvou0uWWL776Sp1CSGIbk54/5n4LpBefG\nTMZn3TTOn3ss6nu7g6szFuOzFooCTx/2J7vl6oxFJi+JhAS7NnsnqKqN0btTCgPdzRM2c8s2ZcP9\nPl42ZF/JONgOhHTXTKRNm0bSjqgFnN6kigAKhiRfdjyvSRFCsKVX5/S0KxQ2NyCadTsMdGj0JVUW\ns65FeyPSMA9tivD+lRLpgsPZWYN9G/2PPgx0aBzaHObj8TIvn87zm091BK7uY0tfiO39OpcXTF49\nW+CXH002dKAWCyt87eEE334nw9iSyevnizy3O9awz6snEVF4Zlec5Vy25rT4pQMJdg0FI4KwfUDn\n/aufbjDQPAyxAAAgAElEQVQtgI6YQErZUoPoqoDrTV57K3IcB4lAEXzm96nWp713pcjkinXd77xb\n13Wh5u1As1h2eOm9Ilem3eVvGtT4wuNR3y34we0/9f3X8xgWDPaofPXZOKGApNjeC/miw2sfu+fR\n4w+F6fa5D5Zlu9E0gMM7Q3R5ZOBxp3xywY2m7dvubXrimSsVE5FtzeudBjA2u35OeSnEF1bdSbH+\nLrWlrrdtWhNf7wRCiGeEED8QQswIIaQQ4muf8ffPVf7u+p/d1/3dN4UQZ4QQ5crj1xv7TRqHrgq6\n4u5ualRUbXOdTb+fVKNqp6bKDXGI0zXBI9Wo2qVgRNUAju2IEQ8L0gWH9658ehAeBJ7bE0NVqJi+\nNL7NQX9K40sH3DTLD6+WGuIIejOSUYVvPZFiW5+O5cB//STHu5eLgXCDHOrUiF7X0yiiCyTwypki\n3/sgR6YBbS6ajaIoqIq4rUGQEIItfTr/6PEU33oiWWsVAHAPAbRrsBwHu7L/vRRq47Mmf/ajHFem\n3cjO04cjfO1Z//ukgWtm8jevuSJtuF/l68/FidwnNuQ//bBI2ZD0dSkc2e3/hN0nFwzWcm4z88f2\n+RNNW0zbTC3YCAEHR72bmFpZs5lddpe728Mo3e0wNuOOabZu8DYmUS/U2rQud6pBKu8JCyH+tRBi\nvKIxLgshfqeR6+n33SAOHAf++R2+bxcwVPdzsfoLIcRR4C+BPwUOVh7/SgjxuBcr7Af9tfTHxgzA\nqkJtKWv72kdqz4YQqnD7xjXqux7aHCGiC1YLDucC0uA4rAs+V0kvfO9KkdV88AbanTGVp3ZEGe7S\nePlMoSkpgbuGwjxe7XF2Kt/UNMSQJvjqwwmObHEHcW9eKPLjk3nf2zsoQrCtf32ws2dDiP/+cx08\nuztaEdImf/FuhpdO5QN5HDWajV1uq4BfP5bi+b0x9noUNa9G0wSg32ONmuO49TrffSXHW8dL5EuS\n7pTCr3w+wZFd3tUF3QsXJ01+8EYB23YHuV99Nu5Z02O/uTBhcGnKQhHw+cdivhtL5YsO7512J+iO\nHYz4tp2PV6Jpo8M6ybh3Q8OqiciWIa2pExC5gsNipdZz06C3Qm2xItT62kKt1bkbDfJXwAvA7+Jq\nkV8Fznm/auv4KtSklD+SUv4LKeX37vCtC1LKubqf+hHJ/wT8REr5+1LKc1LK3wderrzekvSl3IvB\nSq4xA69YSGGgkvoxvuSfeImGFEYH3Zm8kw2K2oTqomo/u1wMTA+znYMhNvdq2A683IS+ZXfD4S0R\nNFVgO/C9D7KsNEEIHNsRZXu/ju3A332UbepEgiIEn9sT54W9MQRwetrgO+9nfe9jdmAkTEh1mzN/\n+UAcTXVdFn/9WAcHN4XJliTHJ8v80etrfP+jbODq7JpBf0rj8OaIZ2l6VaEW0u4+1alQcgfkf/yD\nLD98q8DUgk2uKHlsX4hvfSERmEHfmSsGP3q7gOPAzk06v/DU/WHDD66r4nunyyRjgof3hAOxzd86\nXsK0YKBbZc8Wf0oPsnmHpbRNd0rh4E7vommOIzk7VjURaW76eDXtcbBH9dSW33FkTai1I2qtzZ1q\nECHEl4BngZ+XUr4kpRyTUr4npXy7kevpd0TtbvlYCDErhHhZCPG56353FHjxutd+DDx5s4VVQpmp\n6g+Q9Hh974nBlEpEg5kGDrj2DIWIheDygr/pj/uHQwx1qsyvWZTNxoiVw9WoWt7hfECiakIIXtgb\nR1VgfMniwlww1qseVRH84uEEAx1uc+jvvt944SSE4MsHEvQkVPJlyUun8xTKzRVKhzZH+PojCUIq\nTK1YfPudjK/Rqg1dOv/88108vzd+jWjoSah8bk+Mf/RYkq2V9L+L8yZ//k6Gv/hZhssLRiAnAFqB\nmuPjXRiJzC1b/PhnBf7o+1neOVkmV5REw4JH94b51S8kOLo/GpjWCp9cKPOT94pI6brzffGJqO8R\nJ6/IFx3+7rU8y2sOvZ0qj+3zP+VxbtmqCZnnHo74Fk1951SJ+RU39XJDr3fiY2zWolByj/ctHqcf\nfvZnu9vVS7dHgHTWwbJBU6Ez2apD6PueZP2YXgjh1cn+S8AHwP8qhJgWQlwQQvyBECLq0fJvSKuZ\nicwC/x3wIRAGfh14WQjxnJTy9crfDALz171vvvL6zfg94P/weF09oy+lUbKgZDmUTKchduUbu3UK\n54qMLfln0w+wqUfn5TMFVvMOp6bKPLzV+3z9kCZ4eEuEty4W+dmlIruGQp432b4buuIqj22L8s6l\nIq+eLbClN0Q4YOlGIU3wjYeTfPtnGdIFh+99kOVXHk82tL9aWBd87UiC188XuDhv8u2fZfjmo0k6\nm2idv7UvxLeOpvibD3KsFhz+/J0MXz2SYLjbnxnwmw3ohBCM9OiM9OgsZS0+uFri7IzB9KrF9Ic5\nuuNu9G3PhtB9EyVpBkXbHfTdbn2aZUsuTpgcv2gwv7Iu6ge6XRv4HZv0QG1/y5a8fbzEiUvuBNHh\nXSGePuSfcPAaw5R8//U8mbykI6Hwc49Ffd/+Ukp++qGb8rhnq85gjz/DsaW0zdmr7vF97KC3+/zq\njEk04tamNVPw27ZksmrL36D6tL4u1Xen0FbjRPoqEatxNZilXK3Gf+q6X/0r4F968BHbgKeAEvB1\noBf4d0A30LA6tZYSalLK88D5upfeEUKMAP8L8Hr9n173VnGD1+r5feAP6/6f5NM72jeiIYWOqMJa\n0WEhY7Opx/tB8UBKJREW5MqSiWWTbf3+uNwJ4aYm/uRUgQ/HShzeHG7IxfDw5ggfjpVYyTtcmDXY\nvcH/2VWAx7ZFODtTJl1wePtigc/tbVzfsrslFlb45qNJvv1OhsWszd99lOMbjyQbOvDpjKs8tTPG\nfCZLuuDw7XcyfOORJAMdzbuE9SU1/vGTKf72wyxzazZ//V6WL+yPB8I99Eb0JjW+dCDBsZ0OH42V\nODFZZiXv8OKpPG9dLHBkc4TRgRDdAWrsHVQKpjuQjem3FubZvMPJywanLhsUK33gVMVNITywI+Tb\nYPxWzK9YvPizIisZh419KiMDGo/tC0atnBc4juTv3y6wsOq2PfjaszFPU+HulrNXTeZXbEIaHDvg\nj4EIwFsn3MHt6Ijm6fG5mrU5ddlEUWC/h+Ykt8PMko1huc3ivU5PbNen3T1J7RGieuPGNLqWrz4d\nBrJ1v/KqlkbB1RL/WEq5BiCE+J+B7wgh/pmUsujR53zqQ1udnwE76v4/x6ejZ/18OspWQ/7/7L1X\ncFznmff5e0/o3I1E5EgABAMYwUxKVLSsYEm2xvZ8sseemW++2trLvdub3dqL3aqt+nZvtvbi2636\ndmZnduwZZ0mWZVmyAilKzBkkQBA5Z6Bz94l7cQCQohVIEN19KOJXQkEIBA66zzn9/t/nef5/287a\nth1bfuPzT7ArWJ4hm4zmpv1RCEFLpXMzLXT747YaLwGPIJ6xctYC6FXFSgTA6b6Ma1rCFFnwTLtz\nI7s0lGUqR8/3g1IckHltXxiPDCPzBn+8mvu5utKQzOuHIpSHZVKazS/PxBjKs1Np0Cvxw4MR2qpU\nLBvevZrk056Ua86fLyLsk3hiS4D/5skijm32E1oKk74xrvFPn0T5x+OLfNyVYmROL7hZiltJGs59\nKOj5ywWnbduMTBm8fTLJP70d59yNLOmsTSggOLLTy398JcxzhwKuE2mmaXPqWoZfvJ9kPua0vXVs\n8XJw+zenkmbbNh+eTzM0YaDI8MqxAMUFsr6/k6xurwikA+2+grl8jk4bDI47xipH1lgsXrrpXDMN\nVUre4waW3R4bq5U1P5fXHR8fCuJ3rult214roTYBjC2LtCW6cIpBdWv0O/6Cb4JQ24Pz4C1zCvjW\nXd/zHJDTYb9cU7nk/DgVzd1sTOuSm1yhZ1kUWbC7wXnRODeQOxHV0ejFqwjmEqarZsKaNqhsrvZg\nAx9cd6exCDgZcK90hJEE9ExqfNSVe8ES8kn89cEw9aUKmumYmnSP58+6H5zIjO/sDnGg+bbQf/dq\nsqCOqfeCV5XY3+znPz1ZzMt7goS8Tk7ZQsriwmCGX56N818+WOTtywm6xrNkdHf/PfkkuVxR8zj3\nyFTGondU58SlNL/7KMlvP0rSN2pg246V/UtHA/z9d8Ls3+ZzRfXmbmYXTX7xfoKz17PYtlPx+5sX\nQjTXFqaVN1ecu5Hler+OEPDCEfeI5U+vZAj4BMVhid1raN5xP9i2zcnLjljc3rK22W3prLXi9rh3\nc/47DpaNRDZWr+35bNv2ulB7tPkUqBFChO74XBtgkcMuvILetZb+2NY7PrVRCLEbmLdte1gI8b8C\ntbZt/3Tp+/87YBC4DniAvwH+aultmf8DOCGE+O+BN4FXgWdx+kofWpZbvHJZYakrVfHIkMzaTEZN\nqosLd3rsavRytj/NdMxkdN6gvmztFxBeVaKjycep3jSnejO0VeU3jPOreHJLgIFpjYmoydWRLLsa\nCtca81U0blB5YWeQP1xJcmkoS9ArcbAlp3O1eFWJ1/aF+ePVJD2TGn+4kiSl2TkJSf8yhBA8vjlA\nSVDmTF+a3imNW1Ma+zf62bfR5xpziC9ClgRtVV7aqrxkdZuhWZ2+GY2BaZ20bnNzQuPmhIYQUFui\n0Fyu0lLx6LZI2rbNQsrEThUxPBig50acxfhtEVtTLqMqsKXJw65NHsoKHJ78VViWzYXuLKc7s1gW\n+DyCp/b5aGtwR6D7WnJjQOPUNWcT54kOn2tEaPegxrVe5/r64bNB5ALNyvWOGEzNm6gKHNi+tmLq\n6i0N03TETG1Ffq+HaMJiPmYhxNrb8seSNpoOkgSlEfdtwKxzf9yvBgF+DvyPwD8JIf4nnBm1/w34\nx1y1PULhZ9T2AR/d8fHynNg/A3+Hk5HWcMfXPcD/DtQCaRzB9pJt2+8sf4Nt258JIf4D8L8A/zPQ\nB/y1bdtncvQ33BNDszqf3ExREpR5aXfo6//BXSy3PkbTFmnNwu9Z+5uEIguayj30TGr0TmkFFWoB\nj0R7nZcrw1nOD2RyItQAOpq8XBzMMJcwuTWp01btjgVLyCdxtC3A8e4UXeMajWUqxUF3LgC31HhJ\nZm0+7k5xsidN0CuthJfnCkUWfGd3kI+6BJeGsnzUlSKRsXh8sz+vYnt7nZfKInkp583ks940V0cy\nHG0LsK3WHSY1X4VXFbRVe2ir9mDZNhOLBv3TOn3TOnMJZ5NkdN7gxM00JQGJjeUqNSUqZSGJkqD8\njXEEvBPTspmJmY4By4LO2IJBSisDnL56Z/MUyookasoVGqtk6ipUvC4Pg16Imbx3Js3knFMRaK5V\neHqf3xXh2mvN0KTOB2edddPeLR52bXLHHOlCzOTD885x7d/qLViFz7Rut152bPYSXMOqr2HaXLnl\nVNM6tuR/83PZ7bFmg7zm1+RyNW1DkVQwgb3OmnJfGsS27YQQ4lvA/4nj/jiHk6v2P+TyIAsq1Gzb\n/hint/PLvv53d338n4H/fA8/99fArx/w8NacqZiJZq6uNcynShQHJBZTjqFI44bcvLi2Vqr0TGr0\nTes8vjknv+Ke2dvk48pwlv4ZZ9FYloMdfZ8qsbfJy9CcwbmBNM0V7nFj293gZTJq0DWu8ealBD86\nFHFtpWbvRh/JrMW5gQzvdSYJegUby3MreoVwgsKDXomTPWnODWRIahbPbQ/mVUCUhxVePxTh5qTG\nJzfTxNIWf7qW5OJghie2BFYC5d2OJAS1JSq1JSqPb4ZoyqRvWqd/WmNk3mAhZeFZMLg4lF36figO\nSJSF5NtvYZmSgOyaa+heyOqOQF0WZRNRA+MvOswt8GTYUhegrS5A9QYFn8uF2TK2bXO5R+PTqxlM\nEzwqPNnhZ0uT6poOgrVkZsHknZMprKWWzqO73NGNoBs2f/g0hW447bEH17iKdT909mlEE465SseW\ntT2O7kGddNYmHBC01uf/3rfc9thUs/a/201GIhe7M1//Tet8JferQZY+181fjlfllEJX1B4ZSoKO\nsIqmLEzLXtVCsiKisJjSmIwaOVv8bSxXkQTMJUwWkiYlBazilARlWitVeqd0LgxkeG5HbtyC9m70\nc3l4kZRmc2Ewk/PWvXtFkpz2uqFZndm4yXudSV7cFXTt4urxzX6SWYsb4xpvXUrwwwORnFdlhRAc\nbPET9Eq815nkxphGKmvz8p7QmoUd3+txbKn20lrh4dJQhjN9GWbiJr8+F6e5XOXY5gBlLjAxuB+K\nAjIdTTIdTT40w2ZwVmchYazcHzQT5pMW80mLW1O3TV0EUBy8S8CFZEqD+RdwummTylqkNJtk1iKl\nWaSyNinNIpm1SWQsJhaNv7AE9qmCmhKF2hKF2mKF34xcxMTk0I5dFPkfDuENMLNgcPxihrEZZ4HZ\nUKXw7AE/4cA3r4oGjuvmmyeSaEti6FsH81th/yqOX0wzF3XE0fOHAwWzdtd0mzOdzmbLwe1ePGsY\nAWPbNhe7nZ+9e7M37xV3w7AZnVoSamucnwbuMRIxTJsz190zV79OblkXanki7JNQJDAsiKWtVQmg\nqiKZnsnczqn5VIm6UoXhOYO+aY19GwsrWvZt9NE7pXNjPMvRNmdBvtZ4FMGxLQHevZrkdF+arTUe\nIn53LKrDPomX94T41dk43RMaVUVKTrLl1gIhBM/tCJLSnEX9b8/Hef1whNI8iP3tdV6CXsFblxIM\nzur86myMl/eE8v48KrJgf7Of9jovp3vTKxXhgdkoO+u8HNnkJ5CDczjXeBRBW5UH8HCw1VmQJTI2\ncwmDuYTFXMJcecsaNgtJi4WkRe9dAq66WMbGMWRRJIEiC1SZpfcCRQZ15fNf8DXZ+Zpl2WR0m1TW\nJqn9pQBLaxbJrIX+Nd5Ly8dT5JccUVaiUFuiUhqSVhb4WdPAHHF+0LKZiNuZWTQ525mhd9SgdmmG\n7vHdPra3uGcOd63JajZvnkiSTNuUFUl857Ggayq73YMa1/uda+H5w4GCtpte7HZcSYtCEttb1rbr\nYWDcYCFu4VFhe3P+xwiGp3RKwhLhgERZ0do+xrZtMz3vjopaLLlu9PQosS7U8oQQgpKgzEx89ZWq\nFUORWO6cHwFaKjwMzxn0TukFF2o1xQrVxTITiyaXh5zZn1ywrcbD1ZEs4wsGx7vTvLzn/ucIc0Vd\nqcoTWwJ81JXi+M0UFRE5ZzN7D4osiSVhGWMyavKbc3FePxQhlAfnu43lHn54IMLvLsSRBPzLyRjP\ntAfYUp3/xWnAI/H0tiC7G318cjNF75TOlZEsXeNZDrT46WjyobpkEbkahBCE/YKw30NT+e3P27ZN\nMmt/Trgtv2V0G4RgYuHBN5qWcyXvBVlyno+gVxDwSAS8EgGPIOiVKA1JlIUUwl9xfi47PnpkGVV2\nxwbOlzGzYHLmeoa+0duPcVFY4rmDASKhh2+D4F7RDYu3T6aYi1oE/YJXjgVdMzM4HzX54Jwzl3aw\n3bvmBhf3QzJtcfGmU/E6utO35hWv5WrajhbPmlbq7pXeEYOZRYvairW35U+mLQzTRgjYUFxgoZZY\nF2qPEutCLY+UBKUloba6i6wy4twcYmlnBzmQA0MRcObUPuqC8QUjp7/nXhBCsK/Jz+8vJ7g87Cxy\nc7HAFULwzLYA//ppjJ5JjaFZ3VWzRXsab8+r/f5ygp8cKSLsUhMAjyL43t4w/3Y6xmLK4rfn4/z1\nwTBeNffHW12s8JMjRbx5KU7WsHnnSpLeKZ1n2gMFOY9LgzKvdoQZmdM53p1iKmZysseptD3W5mdr\nzTerwiGEIOQThHzS564f27ZJaTaxlElSs9FNG8Nk6b2Nbt31sem09xjW5793+f8jfgkhnPD1oGdZ\ngIklQSbhXxJiAY+ER+GBHuPlDDU3V9Om5x2B1j92W6C1NagcaPe62oVyLchqNm+fdGa/PAq8eixI\nJOiOe6NuOGHbhum0Yh5oL6ypydnrWXQDKktlWuvXdvk3NW8wNmMiCdjVlv+/0zRt+secTZXWurW/\nVqfmLXQD6ivkgs+KR9crao8U60ItjzhVNJ2F1OoqYl5VoiQgsZCymI4aNOXIrCHilykPO9W//mk9\n5w5+X0drlbqyg359NMvuxty0/lVEFHY3erk0lOXDG0l++liRa1zthBB8a3uQ2bjJTNzkrUtx/vpg\nxDWtPXcT8Er81f4w/3YqRkqz+O2FON/ZFc6LuAz7JV4/FOFsX4bTfWl6JjXGFnSe2x6kuaIwrp71\nZSo/PhKhe1zjk5408YzFH68muTiU4cktAepK3SsC1gIhBEGvyEnrcq5JLVXUgi4UalPzJmc6MwyM\nP3oCDW7PpM1FLbwqvPZUsOBtaXfy8QVnLi3gK+xcGsBC3KSzz9l0eGz32oeaX+x2fnZbo1qQGciR\naYOsDgGfoHrD2p8Dy26pbqhMR9crao8UhT/jHiFKAs7NYyG5+tbFvLU/Vt4Ovy40khArc1kXBjNY\nOQxVPrLJT8AjmE9aXBx0l6uSKgte6QjhUwWTUZMPu1KFPqSvpDgg8/39YSJ+ifEFk5+dijK+Bm1v\n94IsCQ5v8vOjwxFKgxLJrM3vLiR471oSzShMgLgQgq21Xv7+WBGPtfnxyE6A/S/OxHn7UoKROd21\n4eaPMm6sqE3OGbx5Ism/v5dgYNxACNjcqPKTF0O8cCTwSIi0mQWTX/w54bQ7+gSvPR2i0iWB1uDk\nuN0YcMK2Cz2XBvDZ1QyW7Zhs1FWscb5YwuLWiLOh0VGAgGuA3pHb1bRcCOKpeee1q7K08OfYeuvj\no8W6UMsjy86Pq219hNt5apM5NBQBaF2qPAzO6uirjBRYS7bXevGpgsWURd8dBgVrjU+VeHyzMwd3\nqtepfLiJ4oDMS7sc98trI1mujrhLTN5NeUThpV0hNoRkklmbX56NcWMsm7ffX1mk8DdHi9i7FIZ9\nbTTLP5+MMjqfu3Po61Blx6nyH54oZleDFyEgkTH55dk4/8/xKKdupYmusuq+ztqT0B2hFvQUPmNx\ncs7gjeNJfvF+ksElgbalSeUnL4R4/nCA0sg3X6CBk5X1qw8SK8YhP/xWqOBOfHcyFzX56PztubT6\nysIu7ifnDHpHnDVDLuIKLvVksW2or1QKUtG0LHtlLjMXkQC2bTO1ZCRSVVb482y99fHRYl2o5ZFl\nA5F4xlq1+KmMLFXUorldyFVEZMI+CcOE4dnCLWqXURXBrgZnp+58jitd7bUeqosVdBOOd7uvatVU\n7uGxNsfk5cPrKSYW81OlWi1FAZnXD0doqVAxLfjj1STHu1M5rYzeiSoLntwa4IcHnOpeLG3xizNx\nPu5KYRRwEyLglXi2PcjfPV5ERZGMR3YC7T/rTfNfj0f51ZKo1QtUAVzHIa45Gwshb2GEmmXZDI7r\nKwJtaMIRaFubnAratw8FKCmAQLMsuyDXT2efxlsnbueR/eCZkGtm0mBpLu1TZy6tvlJh/7bCjg7Y\nts3pTuc1c+tGdc2NMLKazfV+ZzNj75bCXCOj0yYZzcbvFdSWr/21sBCz0HRQZNbcTfJ+sW17vaL2\niOGeu9sjgN8j4VtyQlpcZftjxZJQi2csUtncXaxCCFoqnJ2p3unCCzWA3Q0+ZOGYnIwv5O6Ylo1F\nBHBzQmN4zh1//50caPbRWqli2vDWxTjJHJ4La4FHEbzaEeJgi7Obe34gwxsXEmT1/B13fZnKT48W\nrcxcXhjM8K+fxXIad3EvlAZlnt4W4r99poQXdgZpWGrfGp4z+OPVJP/Xhwv86VqS0fn11shCENMd\noRbx5W/Bbds2k3MGxy+m+ce34rx5IsXMvCPQtm1U+emLIZ47FKCkQNl8o9MG//5egrPX81cdt22b\nz65m+OBcGtt2hOp3n3CPu+MyH51PMx9z2jGfP+wv6FwaQNeAzuiUSWOVzOHta19Nu9anoRuOgCmU\no2XvqPMa3VKr5OTxXp5PqyiVC/58ZjQbzd17s+usMYVvtn3EKAlITEQd58fyyP3/e68qKAs5la6p\nmMHGHBmKALRUqIzM6yQyJpZtIxXYnS7kk2iv8zKXMLg0lKWmJHczI5VFCjsbvFwZzvLhjRQ/ORpx\njbEIOGLy+R0hfpaIspC0ePtygu/vD7vqGO9GCMFjbQE2hGT+dC3JwIzOz0/F+O7ecN6C1b2q4Ns7\ngrRWqLzXmWQuYfLzUzEOtfg50LL2dtX3gyoLttV62VbrJZY2uT6mcX00SzRt0TmapXM0S3FAor3W\ny7Za92T9fdOJLVXUwt7cC7W5qMnNIZ2eIf1z7U1ej2DPZg+bGjwUFdDMIJqwOHklvdJGl8xoHNjm\nRcmxC55h2vz5bJqbQ86C/EC7l0Pbva5zTL3er9E1uDSXdiRAIA+xJF9FMm1x4lIa04L6SpXwGlce\nTdPmco9zfXRsLszz4bQ9Ls2n5aDtEWByue2xtPD33GUjkaDPXef+OrljXajlmZKg7Ai1B5hBqSpS\nuD6mMbGYW6FWX6YuZSIZjM4bNLggu2vfRh//9EkU2zbZ02hQU5K7U/ixNj89ExpzCZNLQ5mCZ8rd\njVcVvNoR5mefRRmdNzhxM8VTW4OFPqyvZUuNl+KgzJsX4swnLX5+KsZ3dofyGofQUunhb0sUPrie\npGdS57PeNP0zGs/vDFEWKvyLccQvc7jVz6EWH2MLBp2jWXomNRZTFp/eSvPprTSNZQrtdV5aKz0P\ndSabmzEsk5ThLAJzVVGLJix6hjVuDunMRW+LM0WGljqVtgaVxioFuYDPsabbnLuR5dLNLKYFQsD2\nFg+Hd+RepGU0mz+cTDI67Vi/P73fT3sBwpS/jtlFk48vOHNph7Z719ywYzV8fCFNVoeKEpk9m9f+\nMesZ1lFlRzS0NRZmfTAxa5LK2Hg9groczQJOzTkbE1UuMKtZFmrh0Po9/1Gh8GfdI8Zy5eBBnB+X\nhdp4jmeTZEmwqdLDtdEs3ROaK4RaSVCmvdZD56jGp7dS/ODAKsqS98iysch7nUlO3Uqzpdqbl+Dm\n+zmtCJoAACAASURBVKEsJPPCzhBvXUpwcTBLVZHC1prCzkTcC1VFCj8+UsRbF+NMRE1+cz7OU1sD\n7G7I365swCPxnd0huic0PrieYjJq8rvzcTZXq+xv9uPLQ+7b1yGEoK5Upa5U5eltNrcmNa6PZRmZ\nNxiac948SorN1R62L81Wuq3K8DAT05zZG1WW8CprJ+CTaccl7+aQvtJWBSBJjitfW4NKc61a8Lwm\n27bpGtT57EqGZMZpu62vlDm2x5+X0N9Y0uLN40nmYxYeBV48GqCxuvCvQ3eTyVq8d8aZS2uoKvxc\nGsCtEZ3eUQNJwLMH1r4F0zBtTl3LEE/ZPL3PV7ComGW3yeZaJScdEYZhM7voiKNKNxiJLAm1ogJE\nIKxTGNaFWp5Zdn6cfwDXnupi52mbXDSxbTunC7PN1Y5QuzWp8cy2gCta6w61+LkxpjE8ZzA8p+dU\nQG6v83BtJMNE1OTEzRQv7grl7Hetlk1VHg40+zjbn+G9a0nKQvLKLKObCfkkfngwwvudSW6Ma3x4\nI8Vs3OTpPJ5nQgi21nipK3FaIbO6xdn+LFeGNfY3+9jT6MNT4MXyMh5F0F7npb3OSzRlcn0sy/Ux\njVja4tpIlmsjWUqCEluqPdSVqlQXK+uVtgckrt9ue3zQ+2xGs+kd0ekZ1hidNrlz3LC+UqatwUNr\nvYrPJTNX47MGJy5mVtzuikISj+/20Vybn82AqXmTt04kSWVsQn7BK8fclZG2jG7Y/P5kinTGprZc\n5tuH/AXfLMlo9kp1b99Wb04et+v9GvGUTdAn2NJUmAqnbdsr82m5CLkGmF40sWwnny0cKPy1GUsu\nV9TWhdqjgvtXc98w1qKiVh6WUSTIGjbzSSunrVr1pQoBjyCl2QzP6TlttbxXigIyO+u9XB7OcrIn\nzeuHcrdwEELwdHuQn30Wo2tcY2e97spw4qNtfqZiBkOzBn++nuTVjvBDES6syILndwbZEJY5cTPN\n1ZEs8wmTl/eECOTx+MN+J6D71qTGp7fSzCVMTvakuTCY4WCzn50NXleJnqKAzJFNAQ63+hmdN+gc\nc1oj42mLs30ZTvVmkARURmRqS1VqSxRqSxT8HvefE24iqjlueatpe7QsZyd+Ys5gdMpgYNzAvGN/\nrqpMpq3BaW0sdMbWncSTFievZOgZdhbAHgX2t/vY3ebJW9VkYFznj585zo4biiVeORYsSIjy12Ga\nNn/4NMX4jIlHhWMd/oLPpQF8cilNKmNTEpHY37721T3dsFeMZA5s9xas8js5Z5JM23gUcmZkslzx\nriyVCy7A4XZFLeLC62Gd3LAu1PJM8VLodUa3SWvWqhZOkiSoKlIYXTCYWDRyKtQkSbCpysOV4Sw3\nJzRXCDWAgy1+OkezTCwaDMzoNFfk7riqihR21nu5OpLlgxspfnIkUnDnp7uRhOClXSHeu5ZkaFbn\nV2fj/PBAOK9iZ7UIIdjf7KcsJPOHK0lGFwx+dirGdztClOe5MripykNLpcrNCY3PbqVZTFl83J3i\n/ECaQ61+ttd5XVFVXkYIQX2ZSn2ZyjPbbIZndW5OaowuGCQyFhNRk4moyfkB5/vLQjK1JQp1JQq1\npcq6IcnXEF8xEvn6+0sqYzExazI5ZzIxazA1b2Is7cdVl8mYluOM19agsrmxsKYgX4Ru2FzoynKh\nO7ty3O3NKod3+PIqJK/cynD8opPL1VCp8OJjAbyqe665ZSzL5r0zaYYmDBQZXjkWdEWW29Ckzo0B\nR2Q/e8CfE3F95ZZGKmNTFJRo31i4NcFy2+PGWjVnmwhTc4XPT4smLEamDEIBwULcOR43RVKsk1vW\nhVqe8SiCkFeQyNosJM1V73BXFztCbXzRWLEbzxVbqh2h1julY5h2wXrR7yTkk9jd6OP8QIZPb6XZ\nWK7mdLfrsTY/PZMaWd3m0mCGvc3uMhYBJ/7h2JYAvzwTYy5h8utzcX5wILzqc+zGWJamDWrexF5z\nhYcfHZZ540KcxZTFv52O8cKuEJsq87sQkJbaIduqPNwY01aCz/98PcW5/gyHW/1srfUU3AX1bjyK\noLXKQ2uVx8naSVuMLRiMLhiMzevMJy3mEiZzCZOrI0sCxCdRV6osiTeV0pDkil1jt7Ds+Hh3Rc1c\nqpZNzhmOOJs1vzCE1qM6BgQttQpP78/PXNf9Yts2PcM6Jy9nSKSdfsyacpkn9vipyKPLXVaz+fhi\nmrmo0xa6baPK0/v9rtoYWca2bT46n6ZnWEeS4KXHAtSWF345pek2H55zWh53bfJQs2Htjymr2Zzv\ncq6LQzu8BTO5sW2nlRhy5/YITlg4FHY+7cNzaYanPu9J8N6ZdIGOZp18U/g7yyNIXZnC0IzOfMJc\ntcX88pxaPsKOa0uUFXE5OKvTmueF85dxoNnH1eEM0zGTW5M6bdW5Oy6/R+Jb24O8cznBJz1p6jeo\nrpwDKwnK/OBAhF+ciTETN/nNuTjfPxC+b2OMmxNZ/ng1SXFA4nv7wpTmyT6/LCTzo8MR3r6cYHjO\n4K2LCY5u8nOwxZd3ASFLgh31XrbWeLg6kuVMX5po2uLda0nO9qc5sslPW5XHlcJGCEFRQKYoILOt\n1hEZqawj3MYWdMYWDKZiJvGMRde4Rte4Y5rhU8VKm2RdqUpFRHblQjlfLAs1j/DSN6ozMWcyeVe1\n7E5KIxLVG2SqyhSqN8iURtwtfCfnDE5cyjAx6/wx4YDg8d1+Wuvza0ozOKHzwdk0iaU2tic7fOzc\n5M5ry7ZtTl7J0Nm/ZMN/KECTSwxOTl3LEEvahAOCIzvXPjMN4EJ3lqxmr1SHC8XUvEk8ZaMq0JSj\ntsdUxiKWdDYvqkoL93pfWiQxPPX5z6Uy65majwruW2k+AngVibQOC6kHNxSZjZtohp1TwwMhBG1V\nHi4OOe2PbhFqfo/E3o0+TvVm+PRWitYqNadVjk2VKk3lKn3TOu9cSfLjIxFXzS0tUxqS+eGBCL84\nG2MqtiTW9kfuq32oPKxQ5JecytapGN/dG6I2h7l1d+L3SLy2L8zx7hSXhrJcGc4wuqDzrfYgRYH8\n72oqsqCjyceOei+XhjKc688wn7R4+3KS8nCGo21+mnNc0V0LAl6JTVUeNlU5169m2EwsGowu6IzN\nO23UGd2mb1qnb1oH0igyNJc7bUURn0TYJxH2S0R8MmG/5AqjlamowaneNBOLBq/tC1NZtLqXNdu2\nyeg2iaxFImOxkDSZHI9gZcv5YFwBUp/7fu9Staxqg0x1mSPO3Ba+/GVEkyZnrmXpGnQqEooM+7d5\n6dice7v9O9F0m08upensd46jOCzx3EE/1TmoBK0V525kudjtbGw8s9/PpgKKlTuZmDW43HP7uDw5\naBdNZqyV3LTDO3wFHQHoHXU2qZuq1Zyds8tGOiVhqaDX9uZGdeW5XefRw713w28wy9WJ+QcwFAn5\nJCJ+iVjaYjKa+4yzzdWOUOub1tBN2zUCZW+Tn0tDWeaTTmWgvTZ3baBCCJ7bHuSfT0Yds4mbKZ7a\n5s7csrKwzA/2h/nl2TiTUZPfno/zV/vD97ywLg3JvH44whsXnH//q7NxXtwZymnV8k5kSfD0Nsdk\n5NpIlqFZg//3ZJQjrX72NhVmgaDKggPNfnbV+7gwmOHCQJqZuMkbFxJUF8sc3RTIaxbcg+JRBI0b\n1JVjNi2bqai5UnEbW3CE20LSYib+xfcqnyruEG/O+/DSvSnskwh5pZw9VxOLBqd70/TP6CufG180\nvlComZZNckmAJTKOGItnlj5e+byFccfeWWVExkiFVz4uK5KoKpOp3qBQXSZT4vJq2d3Yts34jMnV\nXo2RKYOs5uzIb2lSObrTRyjP5gQjUwZ/PptaqVjsbvNwZKev4JEEX8WVW1lOXXOEyuN7fK7Jc1sO\nBAfY2qTmLMLg/I0suuEYazTXFm75aNs2c4smkgSbctr2WPj5NHAe70hQrFwr6zxarAu1ArAi1BKr\nF2rgVNViaY3xhdwLtepiZUUYDszotFW54wXKqwr2N/v45GZ6KevMk9NWrYBX4vmdQX57PsHFoSwb\nyz00lbtzcV4eUfj+/jC/OhtnfNHgd+fjvLYvfM8LoaBX4ocHIvzhSoK+aZ3fX07wRMYRSvlaoO6s\n91FXovL+9eRSqHea7gmNb20PUrXKysmD4lUFRzb52dPo5dxAhkuDGSYWnZnA+lKFo5v81LrQGfTr\nkCVBTYlCTYnCfpYWQwmT+aTJQtIRNvG0RWzpfdZwKlAZ3fxSIScEhLy3hduyoPN7hFP9dv5zvnfp\n+7nz/dIH4o6fN7ag0z2ufWHEyeSiwRkjfVuELQmxZPbeFzjL4rO8WDBtzyB5M/ynx7fhewiMeb4I\nTbfpHtK4ekv7XKD2zlYPWzeqeQ/x1Q2bT69kuHLLqRBEgoJvHQjkLKx4rege1Pj4guMCeqDdqT66\nhXM3sszHLPxewbE9uWl5jCUtrvU6z9mRnflvRb+TiTmTwQmDgE/QWJ07EbVsJFLo/DQhBG0NnpXZ\nQHDarEcKeEzr5A933xm/oZQuuX0tpixMy161sKguUrg5oeVlTm25/fH8QIabE5prhBrAngYfFwYy\nRNMWnaNZdjXk5oVqmY3lHnY3OPEA715L8NPHigi41Pa8smhJrJ2LM7pg8MbFON/dG77niqiqCF7p\nCPFRV4rLQ1mOdztzWk9tDeTNTMNp5QzTOapx4maK6ZjJzz+L0dHk5cimQMHa7/weiWObA+xt8nGm\nL83VYSeI+qMup0Wuvc7LlmrPQ2uJL4RgQ1hhQ/iLXyayuk0848y5xdLW7fdLYi6RsbBsHIGX+byo\nKg1KD5Ql+WXcGP/y9iBJOJsPYZ9EyCcR8glCXuf/Vz7nlVbMkgZjC9ww5igJ+B9KkTYXNbnWq9E1\noKEtvUQostNGtXOTtyAOheOzBu+fSbMYd5777S0eHt/ty0mb3lrSP6avmDfsbvNwaLt7RNrMosn5\nG84C/ql9uTtXz17PYFpQVyFTX1lY4dK91LLbWKXguc/563vFtm1M08bnKXxFDZzr9k6h9tQ+P1cK\neDzr5I91oVYAwj4JRQbDhGjaWrVRQ03JbUORXAdfg+P+eH4gQ/+0lvO5uPtBVQQHW/x81JXidG+a\nbbW5z7w6tiXA8JzjpPfnziQv7wm5tg2qqljhtX0hfnMuvmLQ8WpH6J7dOyUheHprgCK/xPHuNJeH\nssTTFi/tDuWtBVYIx9ijpULlo64U3RMaFwaz9EzqPNseyGk8w9cR9Eo8vS3Ivo0+TvdlmIoaTMdM\npm6kON6VoqXSw/Y6D40bcjtDmW+8qsCrKmwIf/HXLdsmlbWXRJz5OTGnygKfamFjs/QfsPR+6eM7\nA6GXP29aFospmy+rj5UGJaqLlRXRdVuUSQQ84r6u0cWlDLUif243ftYS07LpHzO4eivL6PTtKmdx\nWGJnq4dtGz0FmbUxTJvT1zJcvKlh2xDyC5494M9Zi95aMjJl8M6nKWzbaSs8tqew1aQ7sSyn5dGy\noaVWobUuN0u6hZi5Yvlf6GqaYdr0DDkbMrkM2p6PWYzOmCgybCgq/EZNWZGEwLkX1pTLVJS44xxc\nJ/esC7UCIISgNCgzHTOZT5irFmrlYRlZQFq3iaYsinPszFcRkSkOOAYTfdMaW2vcs6u4s97L+YEM\n8YzFleEM+zbm1j5flQUv7grx81Mxbk3pdI5q7Kh3z+NxN7UlKq/tC/Ob83EGZ3V+fynBKx2he67m\nCiHYt9FPxCfzzlWnFfKXZ2J8b29+s9oCXomXdofYVqvx5+spYmmL311IsLnaw1NbAwUN+Y74ZZ7b\nHiSlWXSPa3SOZpmJm/RMavRMaoR8Eu21HtprvSvB999kJCGcqpVPYi1famzbMTz57Fb6L1ou9zb5\n2LlGFfWFrCPUiv3uva6XSaQtOvs0Ovs0kksW+0JAc43Czk1e6isLF9Y7NW/y3ukU8zGnira1SeWJ\nDv9DYbwyOWfw+0+SmBa01Ck8e8DvGpEGcLlHY3reCdt+cl/uju3UNSfXbmONUnCjl4Fxg6zuiP26\nitzdR8dnnDJ0VZmMLBdeqAkhUBTQDXh8lxdYNxd5VCj82feIshaGIoosqChyfs54ntofNy+ZSdyc\ncNdNQpEFh1sdcXa2L4Nm5H7otrLImUcC+KgryeIDPJf5oK5U5Xt7wygS9M/ovH05gWnd3+PUVu3h\nBwfC+FTBZNTk56diDzxruRo2lnv4u8eK2LfRh8A5H//pRJSrIxlsu7AD1wGPREeTj58+VsTfHImw\np9GLTxUkMhZn+jL844ko/346RudoNi/n6TcNIQStlR5+cjTCK3tCbAjdXqytZaZTNOu0uhX73FlR\ns22b0aVqzz+9FedMZ5Zk2ibgExzY5uXvvxPmO48HaajKr9X+MqZpc+pahl+8n2A+ZhHwCV5+PMBz\nhwIPhUibXTR543gK3XCCt58/HCioy+HdzEUNPrvqbCY8vttPKEeh5NML5kqw9OEcWf7fD92Dztpj\nc5Mnp8/H2Izzulbjgnw8cFrN9aVlXkmB5rPXKQzrz3aBWBZqCw+4uK8uVphYNJlYNFbyknLJ5moP\nZ/oyDM7oZHULb476w1dDe62Hc/1pFlIWFwczHGrNfSj1vmYfA7M6o/MG71xN8B8ORlz1Yn43DWUq\nr3aEeeNCnN4pJ2bgpV3B+zrm2hKVHx2O8Nvzt4OpX+0IUZdnAw1VETyxJcCWag/vdSaZjpm835mi\na0zj2e1BykKFr1pVFilUFikc2xygb1rn+liWwZnbroof3kjSVuVhe52X2pLCLKgfVoQQbKry0Fqp\n0jOpMxk1aKlYu3NwuaLmttbHrG7TPeiYgyxXqQBqNsjs3OShtU4tWAjxMrOLThVtZtE5vrYGlSf3\n+vA/JLN+i3GT332cJKvZVJXJvPRY4J5bxfOBYdr86XSa0iKJ4pBEe3Pu7r2nlsRgW4NKeYED29NZ\ni8FxR61sbcrt681yRc0NQeYAsYRzLfk8Aq8qyGS/5h+s843BHWfgI0jJkqHIfOLBBuprihUuks2L\noQjAhpC8YgTQO63n1A7/fpEkweFNft65kuT8QIadDd6cm3xIQvDCziD/cjLGxKLJ6b4MRzblXiA+\nCE3lKq90hHjzYoKeSQ1Zgud3Bu9rfqokKPP6oQhvXIw7jodn4zy/K8iW6vyfD5VFCj8+HOHSUJaT\nt1KMLhj8fyejHGzxs7/Z54oFliI71ejN1R7iGYsbY1muj2ZZSFlcH9O4PqZRHJBor/WyrdZDxF94\nkfmwsFzp37yG0RGWba+EXRe7QKjZts30gsn1fp3uQW1lZ11VYHOjh52bPAVfRIMzM3WhO8vpziyW\n5Swqn9rno63BPeZTX0ciZfG7j5OkMk6o86tPBF1ndnL8YoaZBcfl8eXHc9fyODZjMDhhIAQc2lH4\n1/qeIR3LhooSibKi3J3v8aRFPGUjhDuMRMBx3QQoCj0cmx3rrB3rQq1A3Nn6+CBGIMvB1zNxMy/5\nZs6iyMup3jR9U7nNLVsNW6o93JrUmImbnO5N83Qecs4ifpln2gO8cyXJ6b40TRvUFaMXt9Jc4eHl\nPSF+fylB17iGJODbO4L3dR4GvBI/OBDhnSsJeqd0/nA5SSxtsX9j/ofNJUmwd6OP1kqVD26kGJjR\n+aw3TfdElm9tD+a92vdVhH0SB1v8HGj2Mb5o0DmqcXMiy2LK4tNbaT69laZxg8L2Oi+tFR5XCM1H\njZiWwcJx5A15CyMydMNmZMpZKA9N6Pi90u0A3ohjDrK1qTDmIF/ExKzB6WtZhqccFdlcq/D0Pj/B\nHLXk5YLFuMmH59PEUzZFIYnvPRnE55LHd5nuQWcWEeDbh/yEc5R/Z9v2Smtl+0aVknDhBctyQHsu\nTUTAEagAFSWya0R6dEmoRYIPz/W0ztrg7tXkN5hlM4GMbpPWbALe1d0Mwj6JqiIZScBU1MjLgnRL\njYfeSY3eKZ1k1iqogcPdCCHY1eDj1+fiXB7KsqPOS3kk96f51hov/dM63RMaf7ya4CdHi1zjivll\ntFZ6eGlXiLevJLg+piFJgmfb7892X5UFL+8Jcbw7xcXBLJ/cTBNLWzy9tTDzHEUBme/tDXFzUuOj\nGynmkxa/OBNnZ72Xxzf78bmoVVcIQW2JSm2JytNbA/RMaVwfdSz+h2adt7AvRU2xQuMGDw1lCkWB\nwi+WHgVW2h59+dt0sG2bhbjF4ITB4LjB+IyBeUfDhWFatDUobG/xUldROHOQu5lZMPnsWobBcYOK\nEgmvCsc6/GxtUl1zjPfCxKzB7z9Jkc7atNQqHOtwn8ici5p8cM6ZnTzQ7s2pa2bfqI5l2YQDggPb\nC19Vno+ZTM2bCOFY1eeS8dnl+TT33G+XWx/XK2qPHutCrUCoslgJkJ5Pmqt2zhPC+Tk9k87cSz6E\nWmlQRlEcm9gbY1n2N7ur1a9xg0pblTO38uGNFD88GM7LguGZ9gBjCwaLKYuPulJ8e0fuq3kPSlu1\nhxfsIO91JplY0HnrYoIXd4XuS2RKQvDU1iBFfpmPulJcGc4SS1u8uDOIrwAZYkIItlR7aSxT+eRm\nmmujWa6OOLNhB5p9bK/35jQUfTWoiqC91kt7rZfFlMn10exKO+TNSZ2bk85OcpFfoqFMpaFMob5M\nddUmyTeJxRXHx9wuUO+umsWSnzeXiQQFjdUqTdUK9ZXKPYfV54P5qMnpzuyK0YQQsKFY5tUng3l1\ngl0Lekd13j2VwjSdtrqnXFgJ1HSbdz5NYZhQX6lwsD133Sy6YXPiUoZ4yuZAuydnVbv7YSU7rVoh\n4Mvt8SzPp7nFSATWK2qPMu45Cx9BSoPyilB7EIFVW+KIktF5g4Mta3iAX8H2Wi8Tiymuj2mO857L\ndk6f2BKgfzrK6IJB90R+ogR8qsQLO4P88mycztEszeUqm1wUDP5lbK3x4lXgrUtJZhMW/346xnf3\nhu57TqqjyUfYJ/HOlQSaYfGvn8V4YVeQ2pLCtB36PRLP7QiytcbD+9eThLwSf76R4kx/hv3NPrbX\n5T5vbzUUB2SOtgU4ssnPxKLBwIzO8JzBRNQgmra4Nprl2qgzP1UWkmkoU2goU6krVVxVMXyYWdSW\nHB/XWKh9XdVMlhzzgsYahaZqhZKw5Lp7azRhcaYzQ/eQvpJ119agcmi7l5KIeyoQ98qlm1lOXHKE\neVONwguHA65pd1vGtm0+PJ9mPmYR9AueP+zPacfCuRtZ4imnmrZva+GrabZtr7g95tpEJJO1mIs6\nF2XNBvecz+sVtUeXdaFWQEpDEoOzMJ98MEOR2qV5qPFFA8u28xKqu7naw0ddKeYSJpNRc2VWzi1E\n/DIHW/x8eivNie4ULRWevLQi1pep7G/2ca4/w3udyZXwXbfTXOHlhwck3ryYYCbu2O6/2hG+7+d1\nU5WH1wMR3roYJ5q2+cXpOAdbfBxq9ResilVfpvLTo0V0jmWYT5rEMxYf3khxpi/Nvo0+dtb7XNmm\nKoSgpkSlpkTlKKAZNqPzOsPzBsOzOjNxk7mE83ZpKIsAKotk6stUGspUaksUVwrRh4HFNXR8fJir\nZncST1mcvZ7lRr/GcqpHc63C4R0+NrjAyOR+sSybTy5nuNzjCIAdrR6e7PC50rW3s0/j5pCOEPDC\nkUBOK0oLMZOL3c5G0LEOvyvOx7Fpk3jKxqNCc01+2h5LIlLOK3f3im3bd1TUCv98rJNf3LW6fsRY\nseh/wByq8oiMRxFohs1MzKQyDxkbXlViU5WHrnFnrsZtQg1g30YfnaNZommL031pjm0O5OX3Ht3k\nZ2hWRzdt3u9M3lewdCGpKVH58ZEIvzufYDZh8sszMb69I8iW+6xGVkQUfnK0iA9upOga1zjdl2Fw\nVufFXaGCBT0rsmB3g5/ttc45cbbfCUc/3p3mTF+GvU0+djd6XV2R8iiC5goPzRVOlTalWYzOGwzP\n6QzP6SwkLSajzsbJuf4MsoDqEoWGUpX6MoXqYuWhOA/dwMIDtD6aps1s1GRsxnxoq2Z3kspYnO/K\ncvWWtvJ3NFYpHNrhparMfff9e0E3bP50KkXfmNPidnSXj71bPK58HqbnTY5fdM7Hozt9ObWLt22b\njy9mMC2nxbCl1h3Pb9dSNW1TvYqSY+G40vboompaKmNjmk57cXi99fGRwx1X4SPKWoRegzMjVFOs\nMDjrzKnlQ6gBtNd66RrX6J7QeGJrwHW794oseGpbgDcuJLgwkGF7rZfSPGRryZLgO7tD/NupKP1J\np3rzbHvAlYuAu4n4ZV4/HOEPlxP0z+j84UqS+aTF4db7a2/1qhIv7grRXJ7lz9dTTEZN/uXTKE9t\nDbCjzluwx0KRBbsbfeyo93JjTONsf3rFbfHcQIaORi97mnw5j3VYCwIeibYqD21L7bXxtMXIvL4k\n3AziGUfIjc4b0Auq7LRJ15UqVBfJlIYUgl7xUJyX+cS0LWLa1ws1y7KJJixmoxbzUZO5qMVc1GQx\nblEUkliI31ZnD0vV7E4yms3F7iyXe7IrcQA15TJHdviorXh4lw6pjMVbJ1JMzZvIEjx3yO/a+ICs\nZvOHT5OYllO97NiS2+PsGzUYnjSQJXiywx0jDbph07s0B7k1x26PcDvo2i35aXB7Pi3kF+ubbY8g\n7jkTH0GWRUMiY6GbFqq8+sVhXemyUNPpaMpPT3lDmULYJxHPWPRO5WcO7H5pqfDQXK7SP6PzUVeK\n1/aF8vLiUxKU+fbOEG9cSHB1JEt5WGZ3Y+F7/e8FjyJ4dW+IE91pLgxmONWbZj5p8u0dwfsW41tq\nvNSUKLx7NcnIvMH7nSn6p3We215YwwFZEuyo99Je6+HmpMaZvgxzCScH78Jghl0NPvZt9D1UZh1h\nv8S2Wi/bar3Yts1iyloRbSNzOmndZnBWZz5pcrLHeeFXZedcLQnKlASk2/8flO6pumjbNlNRk54p\njaFZna01HvZtdJe50P0SzWawAUWSCHpUp+0oYTEfc4TYsiBbiFmfq5TdSVa3aaqWqa9SH4qqXU9d\nfgAAIABJREFU2Z1ous3lniwXurNozvqYilKZIzu8NFQ93KHsCzGTN44niSVtfB7Bdx4PuGpBfie2\nbfP+mRSxpE0kKPjWwdxu9umGzfFLzmzm3q1eil1gxw/QP6ajGc5mR65dGHXDySsEdxmJrM+nPdq4\n50x8BAl4BGUhibmExWLKojy8+otweU5tdN54oFy2+0EIQXuth9N9Ga6PZl0p1ACe3BpgaDbK4KxO\n37ROa2V+dk9bKjw83ubnk540H3alKA3JNJStTX+9Zti8ezXB0U0BynLwgioJwZNbA5SGJD64nuLm\nhEYsZfLq3vB9i5eIX+YHB8KcH8hwsidN37TOP5+M8vzOIBvLC7uTLUmCrTVetlR76J3SOd2XZjpm\ncn4gw6WhDDvqvezf6HvoAqiFECuia1eDs+ibjZtLlTaTvmmdaNpCN2E6ZjId+8uqvl8VK6LtTgFX\n5JeYjZv0TOrcmtKIpW+rFVXWH1qhZts28YxF11QaO1GKRIB/fy/JfMzE+JKmB0WG0iKZsiIngLes\nSKIsIhMKPHyVSsOwudqrcb4rSzrrDKGVFUkc3uGjufbhFmgAY9MGvz+ZIqvZFAUlXn0ikDPzE9Oy\n+eBsmh2tHqo3rG6ZdemmRt+YU9168Wju89zOXs+SSDmicN9W97yW35mdlutzcGrOxLIg6BeumgWL\nrgu1R5p1oVZAhBB4FAmwmEuYlIdX/3RUFSnIAlKas5Oer1mg9jovp/syDM0ZxNKmKxe0JUGZfc0+\nzvRl+KgrReMGNW9tmvubfcwmTLrGNX5/KcGPD0coXoPn5kR3iltTOqMLMX5wIPxA585XsbPeR3FA\n5veXEkxETX72meMIWXGf2XRCCPY3+2ncoPLOlSRzCZPfnk+wu8HLsS2Fb5sVQrCpykNrpcrAjM7p\nvgwTiwaXh7JcHc7SXuvlQLNvTZ67QiCEoDyirGQKPrnVWUxGUxYLSZOFlMlCcun/kyaJrE1at0kv\nGowv3vvvifilvBka3Qu2bWNYToUra9hkdYusYZPR7ZXPRVMWswmDuYSFZtiAACrIABkchSZLjrlA\n2edEmUwk+PAJsrsxTZvr/Rpnb2RJph2BVhyWOLTdS1vDw5WF9mX0DGu8dzqNaUFVmczLj+fOkMO0\nbN79LEXvqGMg8/cvh++71XV8xuDkFaf99tgeH5Wlub3vzMdMLt50DESecImBCEA8ZTI04fTdbsmx\n2yPcDrquLXfXxoRh2NSUO/ecdR491oVagSkLyUwsGsw9oKGIIguqihXGFpyZlHwJteKATF2pwui8\nwY0xjUOt7txNP9js5/qYs/t/rj/DkU35OU4hBM9tD7KQdEwe3riY4PVDEbwPaP98tM3PRNRgOmby\nyzNxfnAgfN/i6V5pKFP50eEIv7sQZyHp2Pe/uCu0qspkRUThx0cinLyZ4uJQlsvDWYbnHKORfM1W\nfhVCOIYdG8tVRuYNTvemGZk3uDaapXM0y5YaDwdb/JTlYdYx18iSoDQkf+HcpmbYXyjg5uIm+leY\n1HaNa3SNayiSc09SZYEqOzlxqnznG5//WOEvvr7y7xWBJMAwlwSWYd8hvO58b618XdNtMkuft+wv\nP15VBv2OW68kQPUYZEWK+g1edjaWUFYkURSSXOkG+CCksxZdAzpXe7NEE86DFA4IDm73sbVJ/Ub8\nvbZtc6Fb49Ml0dNSp/DtQ4GcCRHTsnn3lCPSluff7vd3pTIWf/wshW07sQc7WnPbdWDbNh9fyGBZ\nTjzBxprC34eX6R7UCQcEdZUKJXloxRyfcV/QNcDEnMn4jMmOFnfOUq6TW9xzRT6ilC2VsucfUKiB\n0/44tmAwtqCzoz5/rQvba72MzhtcH8tysMUdA8h3oyqCJ7cEePtygnP9adprPRQF8nMzVmTBqx1h\nfvZZlLmEyTtXEry6N/RAVQe/R+IHB8L85lycyajJr87G+f7+cM7ETklQ5keHI/z+UoLhOYM3LyY4\nttm/qgw9VRY8tc1pe3z3WoL5pJO5Vl0sc7jVx4awSqjAJhdCiKVgaZWxBZ0zfRkGZvQVIdJWpbJ/\no4+q4sJkxOUajyKoLFL+4nyybZvhWZ33rieJpb9cARkWGJYjrNaC6iKZiejq75EC8KoCryLwqgKf\nKvAozvtiv0RRUGZDyGnv/GXfNSZTCXZubqW1/Jv1/FqWExVwvV+nf0zHtKB2g4xuWBxo99Le7EFx\nmSnUarEsm48uZOjscxwDd7d5eHx37uz3LcvmT6fS9I44Iu2lxwI0Vd/f+bP8MxJpm5KIxDP7/Tm/\nD/aOGIxMOcf8xB73vH5bls21Xo14yqY+D+Y1lmUzMffgQddj0wanOzM0Vqtr1kIaS663Pj7KrAu1\nArO8Mz+XeLAsNXAMRc72w9iC8cA/635oq/LwwY0kiymLsQXjgcK7v46UZq3aka+tSqW+VGFk3uDj\n7hSvdoTX+Oi+nJBP4tWOML84E6N/Rudkz4PHBfhUie/vD/Ob8wkmFg1+dTbOX+2//+yz+/l9r+0L\n8+GNFFdHspy46ZiMPNseXJUTVVO5yt8+VsT7nUluTelMLJr89nwS+LzJRWVEZk+jr2ALyNoSldf2\nqUxFDU73pemd0umZ1IlnHDOJrTUetlR7H4q8vAdFCEFjuYf/eEzlTF+a032ZldBjgB8djlDkl9BN\ne+mNO/7fRjduf2x80fcYX/BvTPCpAr/qiKxlweVTBV5FwqMKfIr4nBBb/rojxiRUmXtagNq2zXzG\nMVQoCbizO2A1RBMWN/o1bgxoJO4Q2OUlEu0tKq31Hte0u60Fmm7zx89SDC61zR3b42PP5txtXlqW\nzZ9Op7k1oiNJ8OLRABtXkfd19kaW4SkDRYaXjuY+eFvTbU4sGYjsc5GBCMDQpEE8ZeP1CDbV537D\nZGbBpLxEBhvKIqu/l0/Nm4xOm/i8Anjwc8407ZVrNrIu1B5J1oVagVkWagtJE9OyH8h6tWZpgb6Y\nskhkrLwtHFVF0Fbl4fqYRudoNidCLZW1eK8zycSiwT88UbyqgGIhBE9vC/Avn8bondIZnNFpyuOO\neVWxwnM7grxzJcm5/gzlIZmttQ92I/cuibXfno8ztmDw67MxXtsfprYkN3+XLAmebQ9QFpL5uCtF\n56jGYsrilT0h/KsQ0H6PxMt7QvzbqdjnKiZ3mlzcnHBEW75MYL6MyiKFVzvCzMYNzg9kGJjRSGkw\nHUtzojtNQ5nC1lovmyrzE65eSGRJcGRTgKYNHv54NcFiykIAG8JywecNH4SkoaNZJoLVZai5iWVb\n8xsDGqPTt68tr0ewpVFlW7OHihL3LMzXimjC5E+nU0zMWsgyPH84QGtd7u7zyyKtZ9gRaS8dDdBc\ne/+/r3dUY3DcMc54ep8/L/NIZ69nSKTdZyACcK3XqYRu25j77DSAkWmnvbC5VnmgquvsonOtbVij\n5y+esrBtx7go4H14763rrJ51eV5gwj5nt9eyHYH1IHhVifKlHbGxBX0tDu+e2V7n3OR7JrWlgfy1\nxasK5hImKc3m/EBm1T9nQ1iho9E51g+7kphfNcCSA7bWOKYUAH9aEp4PikcR/NW+MPWlCpoJvzkX\nZ3Q+d8+/EIKOJh/f2xfCIztOoz8/FWMuvrq/RQjB9w9E8H7JtlHYJ6grdc+e0oawwvM7Q/zt48U8\nsy1ATbGCDQzNGbx7Ncl/+WCBty8n6J/W8n5+5ZuaEifc/GCLj6e3Fd4U5kFZrqYV+X3I0sP38mjb\nNpNzBh+cS/Nf34zx3pn0ikhrrFJ44Yif//RqmCf3+r+RIq17UOPnf0qgGxD0wfefDuZcpL13Zkmk\nCXjxyOpE2sSswbun0kzNWxze4WXrxtxvSs1HTS7ddMTQk3v9eRFD90osaTEw7rye5Gsua2TS+X31\nlQ/2WjO7tOFYVrw219dyhlok+PBEfKyztjx8r0TfMIQQK8P8azGntrygHc1z+2NtiUJxQEI34dak\ntuY/X5YEj7U5rUjnB9Iks6sXtYdb/QQ8goWkxcXB1Yu+1fJYm5/mChXTgjcvxolnHrztVVUE39sX\nprFMQV8Sa8NzuRXrG8s9vH44QsQvEUtbvH0lwdWRDLZ9/+LEowgea/viVtCWCo8rBUDAI7G70cfr\nhyP8w7EijmzyUxKUMCy4OaHxuwsJ/u8PF/ngRpLxBWNVj8vDwPJz97DkBP7/7L1ncBxpmuf3ezMr\nyxe8IzxBEATovW2y2d7N9Njdsbt7d9LFxd2nlRT6sCeFdKdTxN0H6XQXIZ30Yf3ezkzvzOzMdPdM\n+56ma3rQkyBAEN6bAlC+KjNffciCaTa7m0RZkvWLQACFKiTS5/t/n+f5P1+GPxYGoPQRi6aFoyad\nXTH+/t0gb3wQ4npvnHjC6j11YIuDf/p1H9886qGt8fGpQVtJLC5573SY985EiCesOtjvveilpjxz\nEzymKfngXITbA5ZIe+WQm3WrEIWzCwZvHg9jGJaZRzYiW1JKfn8xgimtRtqrSdPMJIt1hQ3VasZa\nKKxENySj09aYqT6FejjTlMzOW8/ziuL0DK8XgoW0xyedwpHPA5br1NJhKGLdcEdmsyvUrJ5q1gPm\n+kgsI/+jrcZOTbFKwoDTdyKrXo5DUziywY3DJuidiDMXTn2/PwxCCF7b5qXcqxKKSX7TGSBhpD6I\n11TBN3f5WFupoZvwqwsB+qcyK9YqfDZ+dKCILQ12pgMmH1wP8+uLwVUJ6S0NDkrcn78lXR6M8Tcn\n5+mdiOet2CnxqBxodfFPDxfzo4NF7Gxy4LYLIgnJ5YEYPz2zwF8en+fTnjD+UHbPtwIPzmzs0alP\nM03J3ZEEb58M8Re/CXDicpSZeSvdr71J49vPePgnX/Oxd5OTIs/j+6gfndb5yXsBugYSCAH7Njn4\n7nMefBk0izJNyYfnInT1W//z5YOrS68MRUx+/UmIaFxSXaby6kF3Vtw2e4YSDE8aqCoc2ZFf5/pi\nuwiALa3ZScccn7F6JbqdgvIUBNZc0Kpb1mzpM/5YMhJ5jK/hAl9O4cjnAeWe9Am1xYjaVMAg+mU+\n2hlgY52VojA8q2dE/Aghlgw4rg7FmE1hwLuxzk59mY2ROYP3roWyLgDsNsE3d3lxaoKJeYP307QO\nNlXw+g4vLVWWWPv1xQB3J9Mf4VyJ26Hw7EYPT7e7UAXcnUrw1yfm6X7IyOrKqClApU/luY7l6Oev\nO4P84nyAyYXsTkI8DEIIaoptPLPRw794poRv7/bSUWvHplqpzafvRPnL4/P8/afzdPZHCacQGS6Q\nfh4FIxH/gsHJK1H+4s0Ab50I0zusY0qoLlN5dreTf/6NIl464KahOr96QaUb05ScuRblFx+FWAhZ\ndVbffdbD/i2Zc3YEKxr10fkIt5Ii7ZUD7lWZXcQSkt8cCxEIS4q9Cq8fyVzbgJVE48tCaE+HI++c\nBHtHEoSjEo9T0FKXnZT3oYnlaFoq18zMnHU/LytS03btLTa7LkTUnlzyp/DjCaYsjRE1j0OhxK0w\nFzYZ9eu0VGXPgKHIpbKpzo4/ZHJ9OPaFqWyp0FCu0VKpWc6Jt8O8vkrnRiEERzvcDM7MMzyrc2kg\nxs7m7KY7lbhVvr7Dyy/PB+gai1PhU9m3LvUB4qJYe/tykDsTCX7TGeTrO1bX9+xBUYRg91oXzcmG\n1lMBg7cuBemotfPsRjdO7cEeMm01dhrKYkwGDF7d5qHCZ6Ojzsm5uxEu9kcZnNH5u1MLbK63c2i9\nO6+dFhVFsLbSztpKO3Fd0jsR59ZYnP7pBOPzBuPzYT7pCtNcodFRa6e16vFy3nsUWY6o5U/qo5SS\nKb/J4ITO6FSCvtHl54TLIWhv1ti41k5FmmpiHgXmgybvnQ4zNmPti/YmjaO7XDjsmb1+FkXazb5k\nJO2Ai/WNDy/SDEPy25NhpuZMXA7Bt456MtaA+14+uRhhcNxgXZ2NXXlmIALLJiKb1tlTMld7GBaF\nWrrq0ypK0ncsCxG1AgWhlgesdH40pUypvxZYg90Rv874XHaFGlj1RG9eCuIPGexf58pIPcThDS76\nphL0TCQY9evUlq7uNC5xqxzZ4Oajm2FO3A7TXKlRlqVG4Ys0lms802Gtw8nuCBVelXVpEFSqIvja\ndi+/uxKiezzOW5eCvLbdS1tNZs+HCp/V0Pr0nQjneqPcGo0zNKvz8hYPTRVfPaCxjEV8mCZL545D\nExze4GZbo4MTtyN0jcW5Phzn9licPS1WL7d8rGFbid0m6Khz0FHnIBQzuT0W59ZojPF5g76pBH1T\nCTQ1xMY6B2tKbDSU2ShyPTkD73wgbhgEE9YgsdSV24jafNBkcNzqbzU0oRONW9H2+ioVIaBpjY1N\na+2srbWh5vm5n06klHT1J/jkYoS4DnYNntnlor0588+5RZF2464l0l7a76Kt8eH/r5RWbdvQhI5m\ng2887claVKt7MM7tZIro7o2OvKtXnF2wrO2FgM0t2Rm7xBOSiaTgb0xRqM0kHR/T6di5sMJMpMCT\nSUGo5QHFbgVVsZrELoRNSlIUC+VelXN3oxim5GBbmlbyAVlXpeFzKgSiJt3jcTamaD9/Pyp8NjbW\nWe0Ajt8O8719vlWnGWxrdNAzEWcw6dj3/f2+lIXyw7K9ycl00ODKYIzzfRE8DoWaNPRCUxXBa9s8\nKAK6xuK8fTnIq9s8tK/J7CyqlcLopqXSzrtXg/jDJr84H2BHk4PDG77aGVARAuU+l0CRS+W17V52\nNFl98MbmdD7tiXB1MMrhDW46au2PRKqXx6Gws9nJzmYnsyGDW6Mxbo3EmY+YTC3oXBm0ajyLXAr1\nZTbqS63+f8XugutXJlmMprk1DaeW3UdjJGYyPGEwOKEzNK4vOb0totmstKx19RovHbDhdT15g7ZY\nXPLxBcthEaC2QuWl/e6spIRJKfn4QnRJpL24z8WGptUJiVNXoksGJK8eclNdlp0JmVDE5PcXLPOs\nPRsdGTVaWS2L0bS1tTZ8WRImI1NW6nCxR0n5XJpeNBJJU3Q7npBEYgUzkSed/LtSn0AUISjzqEwF\nDGZCRspCraHcilxMzFt1ag+adpYOFEWwrdHBye4IlweiGRFqAIfWu7k9FmfEr9M7mVh1Wp8Qgpe2\nePibkwuMzVn9sfa2ZH82/ZkONwLLOOOXFwL84V4flUWpX56KInhlmwdVgRsjcX53OYRpkrHjspJF\n6/bjt8NcHoxxaSBG/3SCV7Z6U2rKXVtq4wf7fdwej3PidoSFiMk7V0N09kc52uHOaMP1dFPmUTm0\n3s3BVhdjczqDMwmkhPEFg4WIyc2RODdHrMGL1yGoL7NEW32ZRmnBrjmtzEQtx8eyLNSn6bpkZNoS\nZUMTOpP+zwozRUBNuUpDjY3GahvV5WrW0sDykZEpnfdOhwmE5ZJhyJ6NjqwYb1gOidElJ8IX960+\ngne5O8bFLms5z+110bwmO/eqxSheNC6pKlXYuyn/Uh4TuuRW36KJSPYygZbTHlMbdyV0uVRPlooh\nyUoWo2lOu8CR4ebnBfKXglDLE8q9SaEWNFhXldqyfE6FUo+CP2QyPKtnvVHwlnoHp3sijM0bjM/r\n1BSn/zTzuayoxLm7UU7cDtNSqa36oV3kUjna7ub96yE+7Y7QUqlR4cvupaEqVnrfxILO2JzBz88H\n+N6+oqW02FRQkmJUEYJrwzEu9EUJxQx2r3VlfKCv2QTPbfKwrsrOe9eC+EMmPz2zwL4WJ/tbXase\nfAohaF/joLXKTmd/lLO9ESYWDN44G2B9tcbhDW5Ks5zGmgpCCGpLNWpLNfa3QlyXjPp1hv0Jhmat\nNOZgTNI1FqdrzBrMeByCutJF4Waj3Ju+AvYnkdlFoeZJv1AzTcmk32BoQmdwXGds2sC4x0emvFih\nodpGY42Nukob9sLADMOUnLse4/ytGDIZ9XjpgIs1Fdm5Pxum5OTl6FKkJxWR1jOU4FinFdE6sMXB\nxiz0Slvkem+cgTEdVYEX97vzUvR3DyaIJVtKNNVk7/m7JNRS/J8zyfo0t1Okrd6wYCRSAApCLW9I\nZy81sGqf/KEYgzOrjzatFrdDoW2NnVujcS4PRHl5qzcj/2dPizPp/mhyfSTG1obVGwBsrrfTMxGn\nbyrBO1dD/PBAUdYfZnab4Nu7ffz8XIDJBYNfnFvge/uKUo6wgiUEXtjsxusUnL8b5fhtg5mgyfOb\nPFmpU2iu1PiTw8V8dCNM11icM71R7k4leHWrl3Lf6rfPpgr2rnOxud5hpUEOxeiZSNA7Oc+OJif7\nW51ZjSinC7tN0Fyp0VxpzbgnDMnYnM7QbILhWZ2xOZ1QTNI9Hl9y13RpVmPwxahbha8g3B6GmWTq\nYzoialJas+uDSWE2PKETu6dThtclliJmDdU2PE9gOuOXMRcweO9MhPFk/VBHs2UYki0BG4tLfnsq\nzNCETn2VSkezfdWNqEcmrYggwNZWO3s2Zi+iNRcwOH7JEoiHtjnTWj+VThbF8JZWR9buW+GoyXTS\nqTGV/mnA0nIyUZ9WMBJ5sikItTwhnb3UABrLNK4MxhiayY2V+fZGJ7dGrdn/I+0mbnv6bzROTWH/\nOhefdIU53ROho9axalMJIQQvbvbwNyfnmVwwONcb5cD67KdAOjWF7+zx8Q9nA8wEFyNrvrQYSwgh\nONDqwmFTONYV5sZIHH/I5PWdXjyOzD8InJrCa9u9tFbH+PBGmMkFg7/7dJ7DbW52Nqf2cHY7FJ7f\n7GF7k4NjXRH6pxNc7I9yYyTGgVYX2xodeTmL/KBoqqCxXKMxmdasJ4Xb8KwVdRv160QSkp4Jy2QH\nwKkJ6kptNJRp1JXaKPepeW+6kktmV5n6GItLZhcMZhdM/MnvCV0yPPnZe7ldswaDjcmoWYmvkLp6\nP6SU3OpL8ElnhETSMOS5Pasz7lgtcwGDN0+E8S+YaDbYscFBS93q0hRn5g3eOhHCMGFdnY2ndzqz\ndtxNU/L+mQi6YRnRbG/L7qTtgzIxazAxa6AosHFt9lLXhyet8VF5sZJyFGwmg46PRZ7CfeJJpiDU\n8oRFoRbTJVLKlG/k9clC4emgQShmZmUgvpI1JSpVRSqTCwbXh2MZq/va1uigcyDKQsSksz+akr29\n16nw7EY3v7sS4kxvhJYqjeoMpG1+FW67wh/s9fHGmQX8YZOfn7PSINNhRS+EYNdaJ+VelbcvBxmd\n0/mvny7wzZ3erG3rhjUO6ko13r8eom8qwSddYXon47y81ZOyIK3w2fjOHh99U3GOdUWYCRr8/laY\n22NWxHXDGnveOZ2tBpsqaCjXkvWoLgxTMj5vCbehWUu4RROS3skEvZMJaopVxucNvE6FUrdCiUel\nxK1Q6lYp8SiUuJ9sEZcwDebjlonL/YSalJJgxBJk/gXzM6IsHP18/8P6KhVFgTXlKo01VsSsukzN\nSk3Vo0woYnCsM0rPkDWArqtUeXG/O6uOdyNTOm+fCBONS7wuwetHPFSWru6+FAhbDa1jCVhTofLy\ngew0tF7kYleMsRkDuwYv7HPn7cTAtTvWtddar2WtTQHA0IQlrlK15QeYTjo+VqQxoraY+phvve4K\nZJeCUMsTit0Chw38IZNA1Ex5wOq2K1T6rLq3odlExp3+7kUIwY4mJ+9dC3FlMMbutc6MuCnaVKtJ\n8u+uhDjXG2FLvQN3CqK0fY2dnvE4PRMJ3r0W4kcHinIysPc4FL6718cbZwPMJV0T/3CvL6VtW0lz\npcYPDxbx64sB/CGTn51Z4OWtHjZk6TzxOhW+tcvLtaEYn3SFGZrV+ZsT8xztcLO5PvXUl7WVdprK\nNa4NxzjVE0EIePdaiOO3w2xvdLKtMbXzJN9QFatera5UY986S7hNLhhLqZLW3jQIRk2CUZOh2c9H\n2r0OQYlHtcSbW6E0KeZKPNkVcVJKBqYT3ByNs77azvoMt5SA5UbXTpuNSERhZCKxLMoClihLfEly\ngtclKC1SKStSKC1SqCxRqSxVC33xHhDDkFy5E+fs9SgVJSqKgP1bHOxqz45hyCJd/XE+PBfBMKGq\nVOXrR9yrdtiMxa2G1sGIpLRI4fXDbmxZPB+m/AZnrlsC6OhO10OL3XRMGD8Isbjk9oCVBbA1iyYi\nkL7+aVJKZpKOj+Vp7GdYsOYvAAWhljfYFAWfUyUWNJgOGGlJdWsst1lCbUbPulAD2LDGzrGuMAsR\nk77JRFr6g92P9jV2LvRFmVwwONMb4dmNnlUvSwjB85s8DM/OMx2wlpeJxt0PQpFLXYqszQQNS6zt\n86Wt5qrMo/LDA0X89nKI/ukEb18OMR0wOLg+8yYjYO3rrY1OGss13rkWYtSvc204xvWROEfb3Sk5\nQ8KiA6mT9jUOrg1FmY/ECEZNPr0T4ezdCB21dnY2OdPirplvqIpgTYmNNSU29rZYA4lIQjIXMpkL\nG/jDxvLPIZOYLgnGJMGYFZW7l0URV+JWPxeRS5cY0Q3JrdE4F/ujSyngCxEzbUJNSknCgFhCEtcl\nkYTJfMRkNmgw4I9hBtYS1u383WDwvn+vCCj2KZYY8y2LsrIitWD8kQJ9owmOX4oyF7AGpdKE773g\noaose9ellJIz12Ocu2EJm3X1Nl7a7171uZ3QTd46EWZm3sTjFHzzaQ/OLE4M6YbkvTNhTNPalvbm\nh08nPH4pSiwuObzDiSuD6379boyKYhWXU1Bbmb36uYWQyXzQRAioS7E+LRy1bPSFgPKi9O2rHRsc\n+BeMvK0rLJAdHr8RyiNMuU9lOmgwHTRoSdH5ESyb/ov9lqFILtBUweZ6Bxf6olwajGZMqAkhOLLB\nzS/OB7gyGGNns5MS9+pvbIv1Tm9dCnKuN8q6KnvKomG1lLhV/mBvEW+cXWAqYPDL8wG+u6cobVa9\nTk3hW7u9HO+KcLE/ypneKNNBg1e2erFnafa3xKPyvX0+Lg/EOHMnTCQBPzm9wIY1dg63uShO4ViC\n1TB7d4uLHc1OesYtITA+b3B92Gqc3VhuY2ezk5ZKLW9Tg1JFCIHbLnDblfs2iI/ETeZSQOGsAAAg\nAElEQVTCJv6QsSTe5sIGc2GTaOLLRVxDuY1AxERTBTbVuu4/+2W5f2qqwKYINNtnP5MwTLpG4/RP\n68T0z6YRqoo1iDZMKy08rsvl74l7Xj/A+yup8KlMBxbryARgTWZpNpaiY5YYs34u9iqPdJ1jvjE7\nb3D8cpSBMeuccjkEB7c62bh29Q6+q0HXLev6xf5suzocHNq6+qi+rkt+dypCwpDYkw2tsx0ROX0t\nysy8icsheHb3w0+8jc/oXO62zD02NGs01WRm/Q1Tcrk7TjAseW5PdiYIF1mMptWUqSk/T6eT9WnF\nXiWtUdNNWWr6XSC/yalQE0IcAf5HYBewBviWlPLXX/L5bwP/EtiO9VS9AfwbKeV7Kz7zb4D/9Z4/\nnZBS1qR37dNPhVflNjATSI+hSH2phhAwFzZZiKQnSvewbG+0hNrAtM5syKAsQ5bpTRUazRUa4/NW\nL7TnN60+qgbQVmOnfY2drrE4714N8uNDxTmr4Snzqnx3r2UwMj5v8KuLAb6zx5e29VGE4GiHmwqf\nyofXQ9yZSPCzMwt8Y6c3ZZH0MOuws9nJ+ho7p7oto5PbY3HujMfZ0exk37rU3RtVRdBe66C91sGo\nP8HF/hg941az88GZIKVuhR3NTjbVObImUvMFl13BZVfuOyGxKOLmQgb+cFLAJX+OJiSmad1jMsHk\ngsF/es+P+fkysFWjCMtV020XNJTZKPOqjESnmDHm2Leukr1rqx9bwZ4PROOSs9ejXOmJIyUoCuxo\ns7NnkzPrvaJCUZO3T4QZnzFQBDy7x5XS4DiekLx1IsTwpFUX9o2jq69vWy3DkzqdyV5tz+91PXTN\nl2lKPj5vpQJvaNJoqsmcuUfPYIJgWOJ2ilVF/VJhcNwS5umoT5uZMyj2KlltK1AgfQgh/hWWFlmD\npSv+VEp54ks+/6dYWqQRmAZ+AfyZlDKaifXL9VnlAa4AfwX88gE+fwT4APjXwBzwT4G3hBD7pJSX\nVnzuBvD8itfpUT4ZZtFQZDpNzo8OTVBTrDI2ZzA4o7O5PvtCrdit0lKpcXcqwZXBKM90pCagvoyn\n2928cWaeK4MxWqs0mitTm416dqObodkEsyGTU90RjnbkJgUSoNJn47t7fPzDuQAjfp2fn53nO7t9\nOOzpO6ab6x2UeRR+0xlkKmDw958u8PpOb1YbSPucCi9v9bKzWedYV5jBGUt4Xx9Or3vjYs+yhYjB\npYEY14Zi+MMmH98Mc6o7wpYGBzuaHDmZ3Mg3vkrEhWMmUd1KK0wYEl2XJIzl18tfy691Q5JI/s1s\nyPhCISYEn3nProJdU3DYBHabWP6uLb/+3Hs2gV1bfm1T+JwQ+6tbtxHxKLWl2Z3Vf5IwTcm13jhn\nrsWIxq2D2lJn4/B2JyUptOhYLdNzBm8eDxEISxx2wdcOualPYdAei0t+czzE2LSB3QavH/ZQm6V+\nb0vrkJC8f8ZyL93Uoq3KqfJyd5ypOROHXXBkx+pb3nwVUkoudlmpptvWZ9fkSUrJxKxBTblKY03q\n596U30qjdDsL945HDSHE94D/BPwr4BTwL4B3hBAbpZSD9/n8j4D/APwz4FOgDfjr5Nv/XSbWMadC\nTUr5DvAOfP7B+QWf/9N7fvWvhRDfAL4OrBRqupRyPF3rmS0qfMu91Ewp02K+0ViuMTZnMDSTYHN9\n9uvUALY3Obg7leDGcJyn1q8+7/+rqPCpbKp3cLE/xkc3w/zxU1pKUSeXXeHFzR5+dTHIxf4ordVa\nVkXLvVQX2/j2bi//cDbA2LzJ//3RPFsb7Gyud1JTnJ6eWbWlGj86WMRvOoNMLhj8/FyA5za5U+pR\ntxqqiixh2jeV4PjtZffGywNRDm9w01qdnjTFIpfK0+1uDrS6uDESo7M/ylzY5EJflIv9UdZX29nV\n7KC2NHfHPZ9ZFHGp0j+V4PjtMFP3ZBPUl9o42uFeElmZEFG6aTAXtyZCyz25m4x5nBkc1zl+KbJs\nuFCscGSHi8YcRSD6xxK8cypMXLfS1b5xxE1p0eoH7JGY5e446TdxaPDNox5qyrO/bcc7IwTCkiKP\n4MiOh3dAXgiZnL5mXQtPbXNm1IFxcEJnes5qf5BtE5FJv8l8UBKOGmk5TlOLjo9pNBIpkDX+e+Av\npJR/nnz9p0KIl7AiZn92n88fAE5JKX+SfN0vhPgpsDdTK/hIW8kIIRTAB8ze89Z6IcSoEKJPCPEz\nIUTLVyzHIYQoWvxKLjPrFLsVbAroJsynKZWoISksBmcSSJnG/KGHoLlCo8StENMlt0ZjGf1fB9e7\n8ToEc2GTc72RlJfXUmVnU52dIpfCidthIvHMpHg9KHWlGjubl0XT1aE4Pzm9wF8en+dUT5jZUOrR\n2CKXyvf3F9FWY8eU8MH1MB/dDGGmM//sARBC0FJl548PFfH8Jjduu8AfNnnzUpA3zgYYn0tfj0C7\nzXIp/WdHivnmTi+N5TakhO7xOD89E+Ann87TNRrDyPI+eFJortT48aEiXtriweNYFmNep0KRS8Wh\nZa7nmD9mDUydNhsuLddJJo8XcwGrh9ivPgkxM2/itAuO7nLyw5e8ORNpV3pivHncEml1lSrfe8GT\nkkgLRUx++bEl0lwOwXee9eZEpPUOJ7jZZ6XzvbjP/dAGN1JKfn/R6rlWV6myqSWzk1Odt6z0zE0t\n9qwarQAMjFn7qbHahppiJE83JP4Fa1xQEGp5g2/lmF4Icd8ohRDCjlV69f49b70PHPyCZZ8Edgkh\n9iaX0QK8Cvw2Pav+eR71p9L/gJU++Q8rfncW+GOgG6gG/mfgUyHEJinlzBcs58/4fF1b1lGEoMxr\n9R6bDhqUpqGeq7bUhiogGJP4w2bGasS+DCEE2xudfNIV5tJgjC0NqduvfxF2m+CZjZYRyPm7UTrq\nHClv8zMdbt44u8DonME7V0N8a5c3p+lRT7W5uDQQxVihGefCJmfuRDlzJ8rrO72sT9G4RVMFX9vu\n4WyvyqmeCJcHYswGDb623ZuWCMrDsOje2FHr4NzdCBf7ooz4df7+9ALta+wc3uBKW4qiEIJ11XbW\nVduZXNDp7I/SNRpnbN7gt1dCeG9H2NHoYEuDI+v74XFHEZb50IYaOxf6o3SPx1M+jx+EmWSj61J3\n9hoRP+7EEpLzN6Jc7o5jmFYK67b1dvZtcmRtUC6l5N3TEfwBw3JdtAuOX7Jq4wA61mo8t9uV0kA9\nEDL5x09CzAVMPC7Bt5/xUJaC6Fv1eoRNrvTEEMDOdvuqXAzvDOv0j+ooCqsyIHkYpvwGgxM6QsD2\ntuxn+vQnDWya1qQuRmcXTEwJDrvA5y7cP76M85PD2EOZ6akLEA8tTc4P3/PWvwX+zX3+pAJQgYl7\nfj8B3NfXQkr5MyFEJXBSWBeJDfh/pZT/YZWr/ZU8skJNCPEDrB3/DSnl5OLvk+mUi1wTQpwGeoE/\nAf7jFyzu39/zno/PH+isUJEUajMBg/XVqS9PUwW1pTaGZnWGZhI5EWoAm+rtnOwOMx0wGPHrGU0h\nXF+tsbZSo28qwUc3Qnx3jy+lh45Ds+qmfnp6gb6pBGd7o+xvzdzN5qtQFcH6asvo5H7Y0jQOEkKw\nv9VFuU/lnStBBmd0fnJ6gW/u9FGeg5oSu03wVJubbQ1OTvaEuTkSp2ssTs9EnJ1NluGII02tC8BK\nv3x5q5fDG0yuDMa4MhglGDU50R3h0kCUf360pNDAOANoNsGBVhcHsnSNLQq1Mnch7TFVTFNysy/B\n6WvRpUbgTTU2Du9wZt1i/O6IvuTk+MHZCKaUDI5bGQcHtzrY3ZHahOFcwOAffx9aSjX89jPenDQm\n1g3Jb0+GmZg1aK23sX/Lw6epx+KSYxetQe7uDgdlGT5WncnatPUNWtb3WTRmMj5jnQfNa9KQ9ui3\nllVZkrmo/+NCm2sDTnfmfAqiZmjxx3ogsOKtr0rlujdVRtznd9YbQhwF/iesmrazQCvwn4UQY1LK\nf/eQq/xAPJJTwsniv78A/lBK+eGXfVZKGQKuAeu/5DMxKeXC4hefPcBZJd2GImDZ9AMMzqQvVexh\ncWoKm+oc1Jao3BjJbPqjEIJnN7qxKdY2f5GgeRiqimw8l3SSPNUToX86Ny0PFmmtvr/QfX6Tm7Up\nmqjcy/pqOz/YX0SRS2EubPLutSA3R2I5S6X1uRRe2erlxweLaCizYZhwvi/Knx+bT0Ya07teHofC\nwfUu/vnREl7a4qHSp9Jem91GvAUyx0yy2XW5J3eTL48DI5M6b3wQ5KPzEcJRSYlP4fUjbr7xtDvr\nIs00JZ9eXTZg6x/TGRw3sKnw6iE3ezamFj2dnTf4xceWSCvxKXz32dyINIBjnVEmZg0cdsFT212r\nMuU4dTVKKHnM9mzMbIQrEDKXBPTO9uxH0wYndKS06iR9aWibMF2oT8tHAivH9FLKLxp0TmOZDd4b\nPavi81G2Rf4d8HdSyj+XUl6TUv4Ky+Dwz5LlWGnnkRNqyUjaXwM/lFJ+ZU5oMje1AxjL8KqlhUVD\nkXRZ9IPV+Bpg1J/ANHNXY7W90cHonNW/ajaNQvR+lLhV9q2zBl6f3AoTTaS+3ZvrrZQ3gN9eDrIQ\nyZ2ZaHOFnfs9j2+NxjNSR1dZZONHB4poq9Hwh6wU0DcvBQnHcnc+VRfb+IO9Pr65y0uZRyGakHx8\nM8zfnJznzkQ87ULSluwL+EeHiji0vjCof1xYTn0sHNPVMDlr8PH5CL9I1mnZNTi83cmPX/aytjY3\nvQlv9SeYXfj8venFfS7WN6SWzTHlt0RaKCIpL1b47rOetAz4V8P13jjXe62JyJcPuFYlFsemda7d\nsZbx7O7VCb2H4VJ3DFNCfZVKdVn2xU3/6GLaY3oSypYjagWh9qghpYwDF4EX7nnrBSxHx/vhBu69\nuRhYUbiMXDw5FWpCCK8QYrsQYnvyV2uTrxuT7/97IcTfrvj8D4C/xapNOyOEqEl+Fa/4zP8hhHha\nCLFWCLEPq79BEfA3WduwFFiMqM2GjLRFBmqKbdSWqoRjkqlA7gbWFUU2Wqqsh+SFvoy0m/gMu9c6\nKfUohOOSU92pG4sAPNvhpqpIJZqQvHUpiG7kJqrk0MRSpFQRcLDVid0mGPHr/OzMQkZEpNuh8No2\nL3taXCgC7kwk+OuT8/RMpB6xXC1CCNZV2fmTp4p5bqMbl13gD5n8pjPIby8HuTuZfsEmhMiqlXSB\nzKGb5rLjY0GoPTBSSvpHE/zy4yA/fT9Iz1ACuwab19n5k9d87Gx3pGzSsFp047PRtJVcuBXHSOGe\nPT6j88uPg0RikqpSle8868Hjys0wanxG55NkuuKBLQ6aV1FvZZiSj5I90zrWamnpKfZlxOJySVjm\nIpompWRg3BJqq9lf91veUkQty/3yCqSN/wj8t0KIfyaE6BBC/F9Y/dH+PwAhxN8KIf79is+/BfxL\nIcT3kzrjBawo25tSyozM3uc6orYby1Z/0Vr/PyZ//t+Sr9dg7bBF/gVWXd3/gxUhW/z6zys+Uw/8\nFLgN/CMQB/ZLKQcyswnppciloKlW76C5UHpElaoI3HYFE+ibym3K3t4WK3/+5kiMYDSzotGmiqXG\n15cHY4zPp576aVMFr+/w4tQE4/MGx7rCKS9ztexb56Su1Ma3dnk5sN7N9/f78DoEsyGTn5xeYHIh\n/amuiiLYt87Fjw4WUeFVicQlb3YGefdqkFgaopaprNf2Jif/zZES9q1zoqkwvmDwq4tB/urEPJcH\noiT0gmNjgc/ij1mDVIdNxW0vtGD4KnRDcuNunP/6TpDfHA8zPGkghBWd+P4LXp7b8/ANltPNO6fC\nS/Vx9zLpNxieWt19cWRS51e/DxFLwJoKlW8/48GVZbfCRcJRk9+eDGOYVi+61aYrXrodX3LjPLw9\n8y1YrvXGSehW2mE66sMelim/STgq0WzWMUyVQFgSS1iTpWVFuR5OF1gNUso3gD8F/hfgMla/5ldX\naIZGLC2yyP8O/J/J7zexyrDew9InGSHXfdQ+4UtChVLKf3LP66MPsMzvp7peuUQIQYVXZWzecn5M\nl2lDc4XGnYkEfdOJnBph1JVq1JXaGPHrXOyP8nR7Zgv4G8s1Omrt3BqN8+H1ED88WJRyf7pit8or\nW63+apcHY9SW2Oioy/7sYH2Zxvf3Lw8uK302fnCgiH+8EGQmaPDGmQVe3+mjqSL9A9CqIhs/OljE\np3ciXLgb5cZInMEZnZe2eDLy/x4Uh2YZjmxvcnLhbpRrwzH8IZOPboY51WM1st7eWGhkXcBi2Uik\n0Oj6y4jGJdfuxLjcHV8SQXabFUHb1uagKEepfytZjKTdHf28EHNoUFqkUlWmsmYV1vkDYwnePhlG\nN6yUva8f9jy0/X26ME3JO5+GCUasmrIX97lXde7OB03OXrcij0d2ODMuOg1DcrnbKhXauSFzzs9f\nRn/Slr+h2paWrIjFaFppkVLIsniEkVL+F+C/fMF7R+95rWO5SP7bzK+ZxSPr+vg4U+5bFGo6G0iP\nMcTaSmvwPObXiSZMnGl0x3tY9rY4+dXFIFcGo+xb58z4ujzd7ubuZIKJBYMrgzF2NKU+c9hSZWf/\nOidneqO8fyNEZZFKhS/3l5PVA83Hm51BhmZ1/vFCgJe2eNiYASFpUwVHNrhZV6Xx7tUQc2GTX5wP\nsL3JwZEN7pSajaeK16FwtMPNwfUurg/HuDRgNbI+fzfKhb4obTV2djY5qS3N/TErkDsWjUQKjo/3\nZz5ocrk7xo27ViQEwOsSbN/gYHOLHYc9Pwan03MG754OLzXU9jgFO9vtVJfZKC1ScDlW3yz97kiC\n352yolfNa2y8dsiNzZa77T51NcrwpIFmg6895V7VMZBS8vGFyJLwbG/O/OTa7cEEoYjE4xJsaMrN\nZN6yLX+hPq3Ao0NhlJKHVHhtQDythiJFLpUyj8JsyGRgWmfDmsz3J/oi1lZqVHhVpoOWcFo0/cgU\nHofCU20uProZ5uObYXxOQWt16sLlwHoXY3M6AzM6b14K8qMDxThyNMu6Eqem8O3dPt69FuL2WJx3\nroYIRE32tmSmT1RdqcYfHSrm+O0wVwZjXB6IMTCd4OUt3pwLIbtNsLPZyfYmB3cnE3T2Rxma1bk9\nFuf2WJw1xSo71zpZX21HLbg4PnEsRdQKjo+fYXxGp7Mrzp3hBIslnhUlCrvaHaxv0HJWf3YvUkqu\n9MQ5ednqK+lyCJ7f66KlLj1C4PZAjPfPRDEltNbbePmAO6fb3j0Yp7PLqvF6Yd/q3TS7BxMMjuuo\nCjy7J/PRZCnlkiX/9rbc1C9G43KFLX96zo/FiFploT6tQAbJfb5Cgc+RCYt+YMm2vX86d+YPYKV3\n7knWqnX2R0lkwZBja6MDNXm2/6YzxMnucMr/VxGCV7d78TkV/CGT964Fc2ZZfy82VfDaNg+71lr7\n+WR3hI9uhjEztH52m1UP+J3dVp2cP2TyszMLnOwOp90ufzUoQtBabecP9xXxR4eK2FRnuWaOzRv8\n9nKIPz82z7neSEYcMwvkL7Ox5dTHJx0pJXdHEvzioyBvfBCiZ8gSaU01Nr511MMPX/LS3mzPG5EW\nipj85liYY53RpWjXj17xpkWkSSk5fzNK5+04poT2Jo1XDuZWpE3PGXx4zooA72q3r9q9MhozOdZp\npTzu3eSgNAs9MQfGdGbmTTQbbFmXm0niwXHrfC4rUtKWqjs1Zz0vCtb8BTJJIaKWh1T4VDTVMgHR\nDRObmp6bSnOlxsX+KP1TCaSUOa3J2LDGzqmeCAsRkxvDMbanIR3xy1CEoLbEavwNcLY3yu2xOM9t\n9NBcufoHu9uu8PUdXn52ZoGeiQQX+6PsXpsfgz4hBEfb3RQ5FX5/y4p2hWImr27zZiwtsblS408O\nF/PxzTC3RuOc7Y1yZzzGMxs9NJbnxqr7Xr6skfXpOxE21TnY0excmjAp8Hiimyb+mDVgfZKFmm5I\nuvoTdHbF8CddgRUBG5o0drQ78jKtq3c4wUfnI0RiElW12gFsbbWn5f6S0CUfnoss9fpKR3PsVInF\nJb89FSahQ0O1ysGtq39enrwSxecWuByCXVlyXrxwy7rONq/LXbpsutMe4wnJfHBRqBViHgUyR0Go\n5SFuO6gCpgMGM0GT6uL03ATqS23YVAjGJFMBg6qi3B1+VRHsXuvk45thLvRF2dqQ+QbCdaXLQg1g\nLmzyywsBNqyxJ63dV7ef15TYeKbDzUc3wxy/HaGm2EZ9Wf44yO1sduJ1KPzuapA7Ewl+fi7At3Z5\nV729X4VTU3h1m5fW6jgfXA8xE5L84nwQAZR5FUo9KsVulRKXQmWRSm2JLSeDoMVG1ntbnHSNxens\njzIVMLgyFOPKUIy1lRo7m5w0VeRm/QpklpWOj54n0PExEjO52hPnSk+cSCxpEKJZEY9tbQ587vwb\nfCZ0yfHOCNfvWiKqskThpQPpa6i9EDJ5+0SIqTkTRcDTu5xsbc2+UdRKpJS8dybMXMDE5xa8fMC9\n6mdlV3+cG3cTCAHfe96TlQjh6LROJAY15Qo72nKzL6WUDIylz5YfltMePS6Rc6fTAo83BaGWhyiK\nQmWRJSqmAwbVxek5TDZV0FimcXcqQf9UIqdCDawG0qd7IsxHTLrH47TXZvYm/kXC5PZYHLdd8OxG\nz6qXva3Rweiczq3ROG9fDvJHh4rx5Mi6+X60rbHjdvj49cUgY3M6Pz2zwHd2+yh2Z262vK3GTl2p\njb88NkfcAAnMBE1mgiaw3CbiW7u8tFTlrmZysZH1pjo7w7OWG2nvZIK+Keur3Kuys9lBR60jpwYp\nBdLLk+j4KKVkdNqgbyTBlZ44ejK73ue2DEI2tdjzos72fozP6Lx3JsJcMuq3q93O/i3OtLntDU/q\n/O5UmEhM4nIIXjvkpq4q90Okczdi9I1a9WSvPeVetShYCJr8Ptl3bc9GB9WrcL9cDWeuxZhdMNnU\nouWsMfjU3LItf21lep55U4v90/Iw4lzg8SL3d6EC96XSpzI0qzMV0IH0CZjmSkuo9U0n2JthE4+v\nQlMFO5qdfNoT4dzdKBvWpCd15YtwO+6/bI9D0L4mtX0shOCFTR4mFwxmggZvXwry3b2+vDKoqC/T\n+MH+In55IYA/2Wvt27t9aZsIuB8eh8KPD/r4qxMB7leppipQ6smPB50QVhPxhnKNuZBB50CU68Mx\nZoIGH1wPc3UwRk2JjbYaO/WltoxHgAtkltmk42PpY572KKVk0m/QPZCgezBh2bp7FXQDqkoVdrY7\naG3Q8upetRLTlFy4FePs9RimtFwnX9zvTltzZikl1+7EOdZpmYZUlSq89pQnL1oO9I0mOHPdMuF4\nZreL6rLVbbNpWlG5eAJqylX2bcpOZGtkUmdoQkdRYO/GzPdp+yL6R62Jwfqq9NjyA8zO59bx8YOz\nuevhWiC7FIRanmJZvceYSqPzIyzb9I/6dWIJmfPZ0+2NDs7fjTAVMOifTiwZnmQCz30iag4b/PhQ\nMd40RL80m9UM++8/nWfYr3PmToRDbfll+13uU/nhgSL+8UKAqYDBG2cXeH2Hl+YM7vdSr8autU4u\n9EU/9962BkfeCLWVlHhUnt3o4dB6F9eH43QORNFUkjVtMVx2wfpqO201dhrKCqLtUWQmaSRS/hgK\nNSklM/Mm3YOWOFuspQGr/9maCpUX97uoKVfzOpo4O2/w4fkwY9PW+q9v0Hh2jwtnmuqcDEPySWeU\n672WwVZbo8bze11oObTfX2QuYPDeaesc3dJqZ1PL6u/RF27FGJ22LP1TSZ18GKSUnL5m3fM3tdgp\n8uZO+M7Om9hU0tpke2LWwO0U1JRnf7viCcmd4dU1bi/w6FEQanlKVZE1eJ1aMNJq/FHiVil1K/jD\nJoMzCdbX5C7lDKx0xK0NDi72xzh/N5pRoeZdkTJSVaSyEDGIJuBcbySltMeVlHlVXtrqobM/xpne\nKC67ws7m3M0k3g+vU+F7+4p481KAwRmdY10RpgMGu9Zmxr4fYN86J9eHY0QTn42rdQ7EiCQkT7W5\n8rIJtUNT2LXWyY5mB4MzCUrGEtyZiBOJS64Oxbg6FMOlCVprlkVbvkYmCnyW2aUeao+PUPMvGEvi\nbHZhWZzZVFhbq9HWpNG8Jn1RhUwRT0jO34xx6XaMylIVzQZHd7noaE6fIVEoavLbk2HGpq3J0EPb\nnOxqz2xWx4MST0g+Oh8hloyAHdmx+mfI+Iy+HJXb5aI4S4JpeNJgZMpAVaxUy1wRjpp0D1l1eWtr\n0zPkNUzJ9JyJYZK2+siHYSFkUFakMJT1/1wgFxSEWp5S5lURQCQhCcUkXmf6Hh7NlRr+gRj907kX\nagC7ml1cGogxNKszNqezpiQzp2WpR+WZDjcOm6Cjzs7gdIJfXghyaSBGS6U9JffHlbTVOPCHTEb8\nOr+/FcbrUGjLYd+6++HQBN/e7eNYV5hbozGO3Y4w7Nd5aYsnIyYjTk3hQKuL39+yZog1FRrLNXon\nE9wajdMzHmfXWid7W1zY82A2+14UIWiusNNcYef5TW6GZnW6x+PcGY8TSUiuDcW4NhTDqVltANpq\nNBrL8zed7EnHMM0lM5FHPfVxIWgNRLsH40z5l8WZqlgOd22NGmtrNex5Wnu2EiktB8pTV6KEotak\njs+j8PIBd1oFxsSsztsnwgQjErsGrxxw01ybH4YyhiF5+2SYSb9BQ7XKi/vcqxbW8YTk3dMRpLSi\nhdlobA2fjaZtXmfPqTFN36iOlJbxjC9N2Rv+BUuk2TWyJnxXshCSn5mIKfB4UxBqeYqmCkqTDaqn\nAzpeZ/oG+msrNS4NxOjLA5t+AJ9LoaPWzo2ROOfuRvjGTl/G/tfK6FZzpZ3tjQ4uD8Z491qQP3mq\nOG0iZW+Lk0DUsn//3dUgbocvr5wgwXLefKbDTZlH5ZNbYXonE/zdqQW+tj0zjaq3NTq4PBDFHzY5\n2uFma4OT8XmdT26FGfHrnO2Ncm0oxqH1LjbXZ94FdLWoiqC5QqO5QuP5jcuirSC17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7bn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1L53lE/P3k9SGdrbsKql2J00uDtE/Gv2Ec4cz3p6PYMU/LhuQSfXrJJWmeriz99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1sD/ZEWRs3qZzzDWGiWccbo+6EVmfCm21bqSt2RNtK0IIQSQgco22H/wbyZpyQcTlo3B5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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure(figsize=(11, 7), dpi=100)\n", + "pyplot.contourf(X, Y, p, alpha=0.5, cmap=cm.viridis)\n", + "pyplot.colorbar()\n", + "pyplot.contour(X, Y, p, cmap=cm.viridis)\n", + "pyplot.streamplot(X, Y, u, v)\n", + "pyplot.xlabel('X')\n", + "pyplot.ylabel('Y');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn More" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The interactive module **12 steps to Navier–Stokes** is one of several components of the Computational Fluid Dynamics class taught by Prof. Lorena A. Barba in Boston University between 2009 and 2013. \n", + "\n", + "For a sample of what the other components of this class are, you can explore the **Resources** section of the Spring 2013 version of [the course's Piazza site](https://piazza.com/bu/spring2013/me702/resources).\n", + "\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> (The cell above executes the style for this notebook.)" + ] + } + ], + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lessons/15_Step_11.ipynb b/lessons/15_Step_11.ipynb deleted file mode 100644 index 778ebe97..00000000 --- a/lessons/15_Step_11.ipynb +++ /dev/null @@ -1,584 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The final two steps in this interactive module teaching beginning [CFD with Python](https://bitbucket.org/cfdpython/cfd-python-class) will both solve the Navier-Stokes equations in two dimensions, but with different boundary conditions.\n", - "\n", - "The momentum equation in vector form for a velocity field $\\vec{v}$ is:\n", - "\n", - "$$\\frac{\\partial \\vec{v}}{\\partial t}+(\\vec{v}\\cdot\\nabla)\\vec{v}=-\\frac{1}{\\rho}\\nabla p + \\nu \\nabla^2\\vec{v}$$\n", - "\n", - "This represents three scalar equations, one for each velocity component $(u,v,w)$. But we will solve it in two dimensions, so there will be two scalar equations.\n", - "\n", - "Remember the continuity equation? This is where the [Poisson equation](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/13_Step_10.ipynb) for pressure comes in!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 11: Cavity Flow with Navier-Stokes\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is the system of differential equations: two equations for the velocity components $u,v$ and one equation for pressure:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\\frac{\\partial u}{\\partial t}+u\\frac{\\partial u}{\\partial x}+v\\frac{\\partial u}{\\partial y} = -\\frac{1}{\\rho}\\frac{\\partial p}{\\partial x}+\\nu \\left(\\frac{\\partial^2 u}{\\partial x^2}+\\frac{\\partial^2 u}{\\partial y^2} \\right) $$\n", - "\n", - "\n", - "$$\\frac{\\partial v}{\\partial t}+u\\frac{\\partial v}{\\partial x}+v\\frac{\\partial v}{\\partial y} = -\\frac{1}{\\rho}\\frac{\\partial p}{\\partial y}+\\nu\\left(\\frac{\\partial^2 v}{\\partial x^2}+\\frac{\\partial^2 v}{\\partial y^2}\\right) $$\n", - "\n", - "$$\\frac{\\partial^2 p}{\\partial x^2}+\\frac{\\partial^2 p}{\\partial y^2} = -\\rho\\left(\\frac{\\partial u}{\\partial x}\\frac{\\partial u}{\\partial x}+2\\frac{\\partial u}{\\partial y}\\frac{\\partial v}{\\partial x}+\\frac{\\partial v}{\\partial y}\\frac{\\partial v}{\\partial y} \\right)$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the previous steps, we already know how to discretize all these terms. Only the last equation is a little unfamiliar. But with a little patience, it will not be hard!" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Discretized equations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's discretize the $u$-momentum equation, as follows:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\begin{eqnarray}\n", - "&&\\frac{u_{i,j}^{n+1}-u_{i,j}^{n}}{\\Delta t}+u_{i,j}^{n}\\frac{u_{i,j}^{n}-u_{i-1,j}^{n}}{\\Delta x}+v_{i,j}^{n}\\frac{u_{i,j}^{n}-u_{i,j-1}^{n}}{\\Delta y}\\\\\\ \n", - "&&=-\\frac{1}{\\rho}\\frac{p_{i+1,j}^{n}-p_{i-1,j}^{n}}{2\\Delta x}+\\nu\\left(\\frac{u_{i+1,j}^{n}-2u_{i,j}^{n}+u_{i-1,j}^{n}}{\\Delta x^2}+\\frac{u_{i,j+1}^{n}-2u_{i,j}^{n}+u_{i,j-1}^{n}}{\\Delta y^2}\\right)\\end{eqnarray}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly for the $v$-momentum equation:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\begin{eqnarray}\n", - "&&\\frac{v_{i,j}^{n+1}-v_{i,j}^{n}}{\\Delta t}+u_{i,j}^{n}\\frac{v_{i,j}^{n}-v_{i-1,j}^{n}}{\\Delta x}+v_{i,j}^{n}\\frac{v_{i,j}^{n}-v_{i,j-1}^{n}}{\\Delta y}\\\\\\\n", - "&&=-\\frac{1}{\\rho}\\frac{p_{i,j+1}^{n}-p_{i,j-1}^{n}}{2\\Delta y}\n", - "+\\nu\\left(\\frac{v_{i+1,j}^{n}-2v_{i,j}^{n}+v_{i-1,j}^{n}}{\\Delta x^2}+\\frac{v_{i,j+1}^{n}-2v_{i,j}^{n}+v_{i,j-1}^{n}}{\\Delta y^2}\\right)\\end{eqnarray}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, the discretized pressure-Poisson equation can be written thus:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$ \\frac{p_{i+1,j}^{n}-2p_{i,j}^{n}+p_{i-1,j}^{n}}{\\Delta x^2}+\\frac{p_{i,j+1}^{n}-2*p_{i,j}^{n}+p_{i,j-1}^{n}}{\\Delta y^2} \n", - "=\\rho\\left[\\frac{1}{\\Delta t}\\left(\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}+\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right)\\right.$$\n", - "\n", - "$$-\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\n", - "- \\ 2\\frac{u_{i,j+1}-u_{i,j-1}}{2\\Delta y}\\frac{v_{i+1,j}-v_{i-1,j}}{2\\Delta x}\n", - "-\\left.\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right]\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You should write these equations down on your own notes, by hand, following each term mentally as you write it.\n", - "\n", - "As before, let's rearrange the equations in the way that the iterations need to proceed in the code. First, the momentum equations for the velocity at the next time step.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The momentum equation in the $u$ direction:\n", - "\n", - "$$\n", - "u_{i,j}^{n+1} = u_{i,j}^{n} - u_{i,j}^{n}\\frac{\\Delta t}{\\Delta x}(u_{i,j}^{n}-u_{i-1,j}^{n})\n", - "- v_{i,j}^{n}\\frac{\\Delta t}{\\Delta y}(u_{i,j}^{n}-u_{i,j-1}^{n})$$\n", - "$$-\\frac{\\Delta t}{\\rho 2\\Delta x}(p_{i+1,j}^{n}-p_{i-1,j}^{n})\n", - "+\\nu\\left(\\frac{\\Delta t}{\\Delta x^2}(u_{i+1,j}^{n}-2u_{i,j}^{n}+u_{i-1,j}^{n})\\right.\n", - "+\\left.\\frac{\\Delta t}{\\Delta y^2}(u_{i,j+1}^{n}-2u_{i,j}^{n}+u_{i,j-1}^{n})\\right)\n", - "$$\n", - "\n", - "The momentum equation in the $v$ direction:\n", - "\n", - "$$v_{i,j}^{n+1} = v_{i,j}^{n}-u_{i,j}^{n}\\frac{\\Delta t}{\\Delta x}(v_{i,j}^{n}-v_{i-1,j}^{n})\n", - "- v_{i,j}^{n}\\frac{\\Delta t}{\\Delta y}(v_{i,j}^{n}-v_{i,j-1}^{n})$$\n", - "$$\n", - "-\\frac{\\Delta t}{\\rho 2\\Delta y}(p_{i,j+1}^{n}-p_{i,j-1}^{n})\n", - "+\\nu\\left(\\frac{\\Delta t}{\\Delta x^2}(v_{i+1,j}^{n}-2v_{i,j}^{n}+v_{i-1,j}^{n})\\right.\n", - "+\\left.\\frac{\\Delta t}{\\Delta y^2}(v_{i,j+1}^{n}-2v_{i,j}^{n}+v_{i,j-1}^{n})\\right)$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Almost there! Now, we rearrange the pressure-Poisson equation:\n", - "\n", - "$$\n", - "p_{i,j}^{n}=\\frac{(p_{i+1,j}^{n}+p_{i-1,j}^{n})\\Delta y^2+(p_{i,j+1}^{n}+p_{i,j-1}^{n})\\Delta x^2}{2(\\Delta x^2+\\Delta y^2)}-\\frac{\\rho\\Delta x^2\\Delta y^2}{2(\\Delta x^2+\\Delta y^2)} \\times$$\n", - "\n", - "$$\\left[\\frac{1}{\\Delta t}\\left(\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}+\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right)-\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\right. $$\n", - "\n", - "$$ -2\\frac{u_{i,j+1}-u_{i,j-1}}{2\\Delta y}\\frac{v_{i+1,j}-v_{i-1,j}}{2\\Delta x}-\\left.\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right]$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The initial condition is $u, v, p = 0$ everywhere, and the boundary conditions are:\n", - "\n", - "$u=1$ at $y=2$ (the \"lid\");\n", - "\n", - "$u, v=0$ on the other boundaries;\n", - "\n", - "$\\frac{\\partial p}{\\partial y}=0$ at $y=0$;\n", - "\n", - "$p=0$ at $y=2$\n", - "\n", - "$\\frac{\\partial p}{\\partial x}=0$ at $x=0,2$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Implementing Cavity Flow\n", - "----\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "nx = 41\n", - "ny = 41\n", - "nt = 500\n", - "nit=50\n", - "c = 1\n", - "dx = 2.0/(nx-1)\n", - "dy = 2.0/(ny-1)\n", - "x = np.linspace(0,2,nx)\n", - "y = np.linspace(0,2,ny)\n", - "Y,X = np.meshgrid(y,x)\n", - "\n", - "rho = 1\n", - "nu = .1\n", - "dt = .001\n", - "\n", - "u = np.zeros((ny, nx))\n", - "v = np.zeros((ny, nx))\n", - "p = np.zeros((ny, nx)) \n", - "b = np.zeros((ny, nx))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The pressure Poisson equation that's written above can be hard to write out without typos. The function `buildUpB` below represents the contents of the square brackets, so that the entirety of the PPE is slightly more manageable. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def buildUpB(b, rho, dt, u, v, dx, dy):\n", - " \n", - " b[1:-1,1:-1]=rho*(1/dt*((u[2:,1:-1]-u[0:-2,1:-1])/(2*dx)+(v[1:-1,2:]-v[1:-1,0:-2])/(2*dy))-\\\n", - "\t\t((u[2:,1:-1]-u[0:-2,1:-1])/(2*dx))**2-\\\n", - "\t\t2*((u[1:-1,2:]-u[1:-1,0:-2])/(2*dy)*(v[2:,1:-1]-v[0:-2,1:-1])/(2*dx))-\\\n", - "\t\t((v[1:-1,2:]-v[1:-1,0:-2])/(2*dy))**2)\n", - "\t\n", - " return b" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The function `presPoisson` is also defined to help segregate the different rounds of calculations. Note the presence of the pseudo-time variable `nit`. This sub-iteration in the Poisson calculation helps ensure a divergence-free field. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def presPoisson(p, dx, dy, b):\n", - " pn = np.empty_like(p)\n", - " pn = p.copy()\n", - " \n", - " for q in range(nit):\n", - "\t\tpn = p.copy()\n", - "\t\tp[1:-1,1:-1] = ((pn[2:,1:-1]+pn[0:-2,1:-1])*dy**2+(pn[1:-1,2:]+pn[1:-1,0:-2])*dx**2)/\\\n", - "\t\t\t(2*(dx**2+dy**2)) -\\\n", - "\t\t\tdx**2*dy**2/(2*(dx**2+dy**2))*b[1:-1,1:-1]\n", - "\t\t\n", - "\t\tp[-1,:] =p[-2,:]\t\t##dp/dy = 0 at y = 2\n", - "\t\tp[0,:] = p[1,:]\t \t##dp/dy = 0 at y = 0\n", - "\t\tp[:,0]=p[:,1]\t\t ##dp/dx = 0 at x = 0\n", - "\t\tp[:,-1]=0\t\t ##p = 0 at x = 2\n", - " \n", - " return p" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, the rest of the cavity flow equations are wrapped inside the function `cavityFlow`, allowing us to easily plot the results of the cavity flow solver for different lengths of time. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def cavityFlow(nt, u, v, dt, dx, dy, p, rho, nu):\n", - " un = np.empty_like(u)\n", - " vn = np.empty_like(v)\n", - " b = np.zeros((ny, nx))\n", - " \n", - " for n in range(nt):\n", - " un = u.copy()\n", - " vn = v.copy()\n", - " \n", - " b = buildUpB(b, rho, dt, u, v, dx, dy)\n", - " p = presPoisson(p, dx, dy, b)\n", - " \n", - " u[1:-1,1:-1] = un[1:-1,1:-1]-\\\n", - " un[1:-1,1:-1]*dt/dx*(un[1:-1,1:-1]-un[0:-2,1:-1])-\\\n", - " vn[1:-1,1:-1]*dt/dy*(un[1:-1,1:-1]-un[1:-1,0:-2])-\\\n", - " dt/(2*rho*dx)*(p[2:,1:-1]-p[0:-2,1:-1])+\\\n", - " nu*(dt/dx**2*(un[2:,1:-1]-2*un[1:-1,1:-1]+un[0:-2,1:-1])+\\\n", - " dt/dy**2*(un[1:-1,2:]-2*un[1:-1,1:-1]+un[1:-1,0:-2]))\n", - "\t\n", - " v[1:-1,1:-1] = vn[1:-1,1:-1]-\\\n", - " un[1:-1,1:-1]*dt/dx*(vn[1:-1,1:-1]-vn[0:-2,1:-1])-\\\n", - " vn[1:-1,1:-1]*dt/dy*(vn[1:-1,1:-1]-vn[1:-1,0:-2])-\\\n", - " dt/(2*rho*dy)*(p[1:-1,2:]-p[1:-1,0:-2])+\\\n", - " nu*(dt/dx**2*(vn[2:,1:-1]-2*vn[1:-1,1:-1]+vn[0:-2,1:-1])+\\\n", - " (dt/dy**2*(vn[1:-1,2:]-2*vn[1:-1,1:-1]+vn[1:-1,0:-2])))\n", - "\n", - " u[0,:] = 0\n", - " u[:,0] = 0\n", - " u[:,-1] = 1\n", - " v[0,:] = 0\n", - " v[-1,:]=0\n", - " v[:,0] = 0\n", - " v[:,-1] = 0\n", - " u[-1,:] = 0\n", - " \n", - " return u, v, p" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start with `nt = 200` and see what the solver gives us:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = np.zeros((ny, nx))\n", - "v = np.zeros((ny, nx))\n", - "p = np.zeros((ny, nx))\n", - "b = np.zeros((ny, nx))\n", - "nt = 200\n", - "u, v, p = cavityFlow(nt, u, v, dt, dx, dy, p, rho, nu)\n", - "fig = plt.figure(figsize=(11,7), dpi=100)\n", - "plt.contourf(X,Y,p,alpha=0.5) ###plnttong the pressure field as a contour\n", - "plt.colorbar()\n", - "plt.contour(X,Y,p) ###plotting the pressure field outlines\n", - "plt.quiver(X[::2,::2],Y[::2,::2],u[::2,::2],v[::2,::2]) ##plotting velocity\n", - "plt.xlabel('X')\n", - "plt.ylabel('Y')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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cEof3FhCXEOGWH5UOh0vzQHx3I0nqj43mYt9kWcZW46Cmyk5NVe0J7+pydZUd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\n" - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can see that two distinct pressure zones are forming and that the spiral pattern expected from lid-driven cavity flow is beginning to form. Experiment with different values of `nt` to see how long the system takes to stabilize. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = np.zeros((ny, nx))\n", - "v = np.zeros((ny, nx))\n", - "p = np.zeros((ny, nx))\n", - "b = np.zeros((ny, nx))\n", - "nt = 700\n", - "u, v, p = cavityFlow(nt, u, v, dt, dx, dy, p, rho, nu)\n", - "fig = plt.figure(figsize=(11,7), dpi=100)\n", - "plt.contourf(X,Y,p,alpha=0.5)\n", - "plt.colorbar()\n", - "plt.contour(X,Y,p)\n", - "plt.quiver(X[::2,::2],Y[::2,::2],u[::2,::2],v[::2,::2])\n", - "plt.xlabel('X')\n", - "plt.ylabel('Y')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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RpPYKxc+sY9+BUlavz+aDz7bzwuvrUCoF+iVHkDGkM+mDYugWH8TgSe/y4qOj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3akXPXCisS4dekx21ac4vt1fRQzyCtnHyRXHlva9hbTSPt9khI1zBq0mT7jmuJa3Pnvf+FfcnTH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\nALCUERERERmA/wP6+pv1TyBZQgAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 7 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Learn More" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The interactive module **12 steps to Navier-Stokes** is one of several components of the Computational Fluid Dynamics class taught by Prof. Lorena A. Barba in Boston University between 2009 and 2013. \n", - "\n", - "For a sample of what the othe components of this class are, you can explore the **Resources** section of the Spring 2013 version of [the course's Piazza site](https://piazza.com/bu/spring2013/me702/resources).\n", - "\n", - "***" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> (The cell above executes the style for this notebook.)" - ] - } - ], - "metadata": {} - } - ] -} diff --git a/lessons/15_Step_12.ipynb b/lessons/15_Step_12.ipynb new file mode 100644 index 00000000..30d1a927 --- /dev/null +++ b/lessons/15_Step_12.ipynb @@ -0,0 +1,677 @@ +{ + "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved BSD-3 license. (c) Lorena A. Barba, Gilbert F. Forsyth 2017. Thanks to NSF for support via CAREER award #1149784." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[@LorenaABarba](https://twitter.com/LorenaABarba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "12 steps to Navier–Stokes\n", + "=====\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Did you make it this far? This is the last step! How long did it take you to write your own Navier–Stokes solver in Python following this interactive module? Let us know!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Step 12: Channel Flow with Navier–Stokes\n", + "----\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The only difference between this final step and Step 11 is that we are going to add a source term to the $u$-momentum equation, to mimic the effect of a pressure-driven channel flow. Here are our modified Navier–Stokes equations:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\\frac{\\partial u}{\\partial t}+u\\frac{\\partial u}{\\partial x}+v\\frac{\\partial u}{\\partial y}=-\\frac{1}{\\rho}\\frac{\\partial p}{\\partial x}+\\nu\\left(\\frac{\\partial^2 u}{\\partial x^2}+\\frac{\\partial^2 u}{\\partial y^2}\\right)+F$$\n", + "\n", + "$$\\frac{\\partial v}{\\partial t}+u\\frac{\\partial v}{\\partial x}+v\\frac{\\partial v}{\\partial y}=-\\frac{1}{\\rho}\\frac{\\partial p}{\\partial y}+\\nu\\left(\\frac{\\partial^2 v}{\\partial x^2}+\\frac{\\partial^2 v}{\\partial y^2}\\right)$$\n", + "\n", + "$$\\frac{\\partial^2 p}{\\partial x^2}+\\frac{\\partial^2 p}{\\partial y^2}=-\\rho\\left(\\frac{\\partial u}{\\partial x}\\frac{\\partial u}{\\partial x}+2\\frac{\\partial u}{\\partial y}\\frac{\\partial v}{\\partial x}+\\frac{\\partial v}{\\partial y}\\frac{\\partial v}{\\partial y}\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discretized equations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With patience and care, we write the discretized form of the equations. It is highly recommended that you write these in your own hand, mentally following each term as you write it.\n", + "\n", + "The $u$-momentum equation:\n", + "\n", + "$$\n", + "\\begin{split}\n", + "& \\frac{u_{i,j}^{n+1}-u_{i,j}^{n}}{\\Delta t}+u_{i,j}^{n}\\frac{u_{i,j}^{n}-u_{i-1,j}^{n}}{\\Delta x}+v_{i,j}^{n}\\frac{u_{i,j}^{n}-u_{i,j-1}^{n}}{\\Delta y} = \\\\\n", + "& \\qquad -\\frac{1}{\\rho}\\frac{p_{i+1,j}^{n}-p_{i-1,j}^{n}}{2\\Delta x} \\\\\n", + "& \\qquad +\\nu\\left(\\frac{u_{i+1,j}^{n}-2u_{i,j}^{n}+u_{i-1,j}^{n}}{\\Delta x^2}+\\frac{u_{i,j+1}^{n}-2u_{i,j}^{n}+u_{i,j-1}^{n}}{\\Delta y^2}\\right)+F_{i,j}\n", + "\\end{split}\n", + "$$\n", + "\n", + "The $v$-momentum equation:\n", + "\n", + "$$\n", + "\\begin{split}\n", + "& \\frac{v_{i,j}^{n+1}-v_{i,j}^{n}}{\\Delta t}+u_{i,j}^{n}\\frac{v_{i,j}^{n}-v_{i-1,j}^{n}}{\\Delta x}+v_{i,j}^{n}\\frac{v_{i,j}^{n}-v_{i,j-1}^{n}}{\\Delta y} = \\\\\n", + "& \\qquad -\\frac{1}{\\rho}\\frac{p_{i,j+1}^{n}-p_{i,j-1}^{n}}{2\\Delta y} \\\\\n", + "& \\qquad +\\nu\\left(\\frac{v_{i+1,j}^{n}-2v_{i,j}^{n}+v_{i-1,j}^{n}}{\\Delta x^2}+\\frac{v_{i,j+1}^{n}-2v_{i,j}^{n}+v_{i,j-1}^{n}}{\\Delta y^2}\\right)\n", + "\\end{split}\n", + "$$\n", + "\n", + "And the pressure equation:\n", + "\n", + "$$\n", + "\\begin{split}\n", + "& \\frac{p_{i+1,j}^{n}-2p_{i,j}^{n}+p_{i-1,j}^{n}}{\\Delta x^2} + \\frac{p_{i,j+1}^{n}-2p_{i,j}^{n}+p_{i,j-1}^{n}}{\\Delta y^2} = \\\\\n", + "& \\qquad \\rho\\left[\\frac{1}{\\Delta t}\\left(\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}+\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right) - \\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x} - 2\\frac{u_{i,j+1}-u_{i,j-1}}{2\\Delta y}\\frac{v_{i+1,j}-v_{i-1,j}}{2\\Delta x} - \\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right]\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As always, we need to re-arrange these equations to the form we need in the code to make the iterations proceed. \n", + "\n", + "For the $u$- and $v$ momentum equations, we isolate the velocity at time step `n+1`:\n", + "\n", + "$$\n", + "\\begin{split}\n", + "u_{i,j}^{n+1} = u_{i,j}^{n} & - u_{i,j}^{n} \\frac{\\Delta t}{\\Delta x} \\left(u_{i,j}^{n}-u_{i-1,j}^{n}\\right) - v_{i,j}^{n} \\frac{\\Delta t}{\\Delta y} \\left(u_{i,j}^{n}-u_{i,j-1}^{n}\\right) \\\\\n", + "& - \\frac{\\Delta t}{\\rho 2\\Delta x} \\left(p_{i+1,j}^{n}-p_{i-1,j}^{n}\\right) \\\\\n", + "& + \\nu\\left[\\frac{\\Delta t}{\\Delta x^2} \\left(u_{i+1,j}^{n}-2u_{i,j}^{n}+u_{i-1,j}^{n}\\right) + \\frac{\\Delta t}{\\Delta y^2} \\left(u_{i,j+1}^{n}-2u_{i,j}^{n}+u_{i,j-1}^{n}\\right)\\right] \\\\\n", + "& + \\Delta t F\n", + "\\end{split}\n", + "$$\n", + "\n", + "$$\n", + "\\begin{split}\n", + "v_{i,j}^{n+1} = v_{i,j}^{n} & - u_{i,j}^{n} \\frac{\\Delta t}{\\Delta x} \\left(v_{i,j}^{n}-v_{i-1,j}^{n}\\right) - v_{i,j}^{n} \\frac{\\Delta t}{\\Delta y} \\left(v_{i,j}^{n}-v_{i,j-1}^{n}\\right) \\\\\n", + "& - \\frac{\\Delta t}{\\rho 2\\Delta y} \\left(p_{i,j+1}^{n}-p_{i,j-1}^{n}\\right) \\\\\n", + "& + \\nu\\left[\\frac{\\Delta t}{\\Delta x^2} \\left(v_{i+1,j}^{n}-2v_{i,j}^{n}+v_{i-1,j}^{n}\\right) + \\frac{\\Delta t}{\\Delta y^2} \\left(v_{i,j+1}^{n}-2v_{i,j}^{n}+v_{i,j-1}^{n}\\right)\\right]\n", + "\\end{split}\n", + "$$\n", + "\n", + "And for the pressure equation, we isolate the term $p_{i,j}^n$ to iterate in pseudo-time:\n", + "\n", + "$$\n", + "\\begin{split}\n", + "p_{i,j}^{n} = & \\frac{\\left(p_{i+1,j}^{n}+p_{i-1,j}^{n}\\right) \\Delta y^2 + \\left(p_{i,j+1}^{n}+p_{i,j-1}^{n}\\right) \\Delta x^2}{2(\\Delta x^2+\\Delta y^2)} \\\\\n", + "& -\\frac{\\rho\\Delta x^2\\Delta y^2}{2\\left(\\Delta x^2+\\Delta y^2\\right)} \\\\\n", + "& \\times \\left[\\frac{1}{\\Delta t} \\left(\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x} + \\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right) - \\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x} - 2\\frac{u_{i,j+1}-u_{i,j-1}}{2\\Delta y}\\frac{v_{i+1,j}-v_{i-1,j}}{2\\Delta x} - \\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right]\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The initial condition is $u, v, p=0$ everywhere, and at the boundary conditions are:\n", + "\n", + "$u, v, p$ are periodic on $x=0,2$\n", + "\n", + "$u, v =0$ at $y =0,2$\n", + "\n", + "$\\frac{\\partial p}{\\partial y}=0$ at $y =0,2$\n", + "\n", + "$F=1$ everywhere.\n", + "\n", + "Let's begin by importing our usual run of libraries:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "from matplotlib import pyplot, cm\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In step 11, we isolated a portion of our transposed equation to make it easier to parse and we're going to do the same thing here. One thing to note is that we have periodic boundary conditions throughout this grid, so we need to explicitly calculate the values at the leading and trailing edge of our `u` vector." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def build_up_b(rho, dt, dx, dy, u, v):\n", + " b = numpy.zeros_like(u)\n", + " b[1:-1, 1:-1] = (rho * (1 / dt * ((u[1:-1, 2:] - u[1:-1, 0:-2]) / (2 * dx) +\n", + " (v[2:, 1:-1] - v[0:-2, 1:-1]) / (2 * dy)) -\n", + " ((u[1:-1, 2:] - u[1:-1, 0:-2]) / (2 * dx))**2 -\n", + " 2 * ((u[2:, 1:-1] - u[0:-2, 1:-1]) / (2 * dy) *\n", + " (v[1:-1, 2:] - v[1:-1, 0:-2]) / (2 * dx))-\n", + " ((v[2:, 1:-1] - v[0:-2, 1:-1]) / (2 * dy))**2))\n", + " \n", + " # Periodic BC Pressure @ x = 2\n", + " b[1:-1, -1] = (rho * (1 / dt * ((u[1:-1, 0] - u[1:-1,-2]) / (2 * dx) +\n", + " (v[2:, -1] - v[0:-2, -1]) / (2 * dy)) -\n", + " ((u[1:-1, 0] - u[1:-1, -2]) / (2 * dx))**2 -\n", + " 2 * ((u[2:, -1] - u[0:-2, -1]) / (2 * dy) *\n", + " (v[1:-1, 0] - v[1:-1, -2]) / (2 * dx)) -\n", + " ((v[2:, -1] - v[0:-2, -1]) / (2 * dy))**2))\n", + "\n", + " # Periodic BC Pressure @ x = 0\n", + " b[1:-1, 0] = (rho * (1 / dt * ((u[1:-1, 1] - u[1:-1, -1]) / (2 * dx) +\n", + " (v[2:, 0] - v[0:-2, 0]) / (2 * dy)) -\n", + " ((u[1:-1, 1] - u[1:-1, -1]) / (2 * dx))**2 -\n", + " 2 * ((u[2:, 0] - u[0:-2, 0]) / (2 * dy) *\n", + " (v[1:-1, 1] - v[1:-1, -1]) / (2 * dx))-\n", + " ((v[2:, 0] - v[0:-2, 0]) / (2 * dy))**2))\n", + " \n", + " return b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll also define a Pressure Poisson iterative function, again like we did in Step 11. Once more, note that we have to include the periodic boundary conditions at the leading and trailing edge. We also have to specify the boundary conditions at the top and bottom of our grid. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def pressure_poisson_periodic(p, dx, dy):\n", + " pn = numpy.empty_like(p)\n", + " \n", + " for q in range(nit):\n", + " pn = p.copy()\n", + " p[1:-1, 1:-1] = (((pn[1:-1, 2:] + pn[1:-1, 0:-2]) * dy**2 +\n", + " (pn[2:, 1:-1] + pn[0:-2, 1:-1]) * dx**2) /\n", + " (2 * (dx**2 + dy**2)) -\n", + " dx**2 * dy**2 / (2 * (dx**2 + dy**2)) * b[1:-1, 1:-1])\n", + "\n", + " # Periodic BC Pressure @ x = 2\n", + " p[1:-1, -1] = (((pn[1:-1, 0] + pn[1:-1, -2])* dy**2 +\n", + " (pn[2:, -1] + pn[0:-2, -1]) * dx**2) /\n", + " (2 * (dx**2 + dy**2)) -\n", + " dx**2 * dy**2 / (2 * (dx**2 + dy**2)) * b[1:-1, -1])\n", + "\n", + " # Periodic BC Pressure @ x = 0\n", + " p[1:-1, 0] = (((pn[1:-1, 1] + pn[1:-1, -1])* dy**2 +\n", + " (pn[2:, 0] + pn[0:-2, 0]) * dx**2) /\n", + " (2 * (dx**2 + dy**2)) -\n", + " dx**2 * dy**2 / (2 * (dx**2 + dy**2)) * b[1:-1, 0])\n", + " \n", + " # Wall boundary conditions, pressure\n", + " p[-1, :] =p[-2, :] # dp/dy = 0 at y = 2\n", + " p[0, :] = p[1, :] # dp/dy = 0 at y = 0\n", + " \n", + " return p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have our familiar list of variables and initial conditions to declare before we start." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "##variable declarations\n", + "nx = 41\n", + "ny = 41\n", + "nt = 10\n", + "nit = 50 \n", + "c = 1\n", + "dx = 2 / (nx - 1)\n", + "dy = 2 / (ny - 1)\n", + "x = numpy.linspace(0, 2, nx)\n", + "y = numpy.linspace(0, 2, ny)\n", + "X, Y = numpy.meshgrid(x, y)\n", + "\n", + "\n", + "##physical variables\n", + "rho = 1\n", + "nu = .1\n", + "F = 1\n", + "dt = .01\n", + "\n", + "#initial conditions\n", + "u = numpy.zeros((ny, nx))\n", + "un = numpy.zeros((ny, nx))\n", + "\n", + "v = numpy.zeros((ny, nx))\n", + "vn = numpy.zeros((ny, nx))\n", + "\n", + "p = numpy.ones((ny, nx))\n", + "pn = numpy.ones((ny, nx))\n", + "\n", + "b = numpy.zeros((ny, nx))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the meat of our computation, we're going to reach back to a trick we used in Step 9 for Laplace's Equation. We're interested in what our grid will look like once we've reached a near-steady state. We can either specify a number of timesteps `nt` and increment it until we're satisfied with the results, or we can tell our code to run until the difference between two consecutive iterations is very small. \n", + "\n", + "We also have to manage **8** separate boundary conditions for each iteration. The code below writes each of them out explicitly. If you're interested in a challenge, you can try to write a function which can handle some or all of these boundary conditions. If you're interested in tackling that, you should probably read up on Python [dictionaries](http://docs.python.org/2/tutorial/datastructures.html#dictionaries). " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "udiff = 1\n", + "stepcount = 0\n", + "\n", + "while udiff > .001:\n", + " un = u.copy()\n", + " vn = v.copy()\n", + "\n", + " b = build_up_b(rho, dt, dx, dy, u, v)\n", + " p = pressure_poisson_periodic(p, dx, dy)\n", + "\n", + " u[1:-1, 1:-1] = (un[1:-1, 1:-1] -\n", + " un[1:-1, 1:-1] * dt / dx * \n", + " (un[1:-1, 1:-1] - un[1:-1, 0:-2]) -\n", + " vn[1:-1, 1:-1] * dt / dy * \n", + " (un[1:-1, 1:-1] - un[0:-2, 1:-1]) -\n", + " dt / (2 * rho * dx) * \n", + " (p[1:-1, 2:] - p[1:-1, 0:-2]) +\n", + " nu * (dt / dx**2 * \n", + " (un[1:-1, 2:] - 2 * un[1:-1, 1:-1] + un[1:-1, 0:-2]) +\n", + " dt / dy**2 * \n", + " (un[2:, 1:-1] - 2 * un[1:-1, 1:-1] + un[0:-2, 1:-1])) + \n", + " F * dt)\n", + "\n", + " v[1:-1, 1:-1] = (vn[1:-1, 1:-1] -\n", + " un[1:-1, 1:-1] * dt / dx * \n", + " (vn[1:-1, 1:-1] - vn[1:-1, 0:-2]) -\n", + " vn[1:-1, 1:-1] * dt / dy * \n", + " (vn[1:-1, 1:-1] - vn[0:-2, 1:-1]) -\n", + " dt / (2 * rho * dy) * \n", + " (p[2:, 1:-1] - p[0:-2, 1:-1]) +\n", + " nu * (dt / dx**2 *\n", + " (vn[1:-1, 2:] - 2 * vn[1:-1, 1:-1] + vn[1:-1, 0:-2]) +\n", + " dt / dy**2 * \n", + " (vn[2:, 1:-1] - 2 * vn[1:-1, 1:-1] + vn[0:-2, 1:-1])))\n", + "\n", + " # Periodic BC u @ x = 2 \n", + " u[1:-1, -1] = (un[1:-1, -1] - un[1:-1, -1] * dt / dx * \n", + " (un[1:-1, -1] - un[1:-1, -2]) -\n", + " vn[1:-1, -1] * dt / dy * \n", + " (un[1:-1, -1] - un[0:-2, -1]) -\n", + " dt / (2 * rho * dx) *\n", + " (p[1:-1, 0] - p[1:-1, -2]) + \n", + " nu * (dt / dx**2 * \n", + " (un[1:-1, 0] - 2 * un[1:-1,-1] + un[1:-1, -2]) +\n", + " dt / dy**2 * \n", + " (un[2:, -1] - 2 * un[1:-1, -1] + un[0:-2, -1])) + F * dt)\n", + "\n", + " # Periodic BC u @ x = 0\n", + " u[1:-1, 0] = (un[1:-1, 0] - un[1:-1, 0] * dt / dx *\n", + " (un[1:-1, 0] - un[1:-1, -1]) -\n", + " vn[1:-1, 0] * dt / dy * \n", + " (un[1:-1, 0] - un[0:-2, 0]) - \n", + " dt / (2 * rho * dx) * \n", + " (p[1:-1, 1] - p[1:-1, -1]) + \n", + " nu * (dt / dx**2 * \n", + " (un[1:-1, 1] - 2 * un[1:-1, 0] + un[1:-1, -1]) +\n", + " dt / dy**2 *\n", + " (un[2:, 0] - 2 * un[1:-1, 0] + un[0:-2, 0])) + F * dt)\n", + "\n", + " # Periodic BC v @ x = 2\n", + " v[1:-1, -1] = (vn[1:-1, -1] - un[1:-1, -1] * dt / dx *\n", + " (vn[1:-1, -1] - vn[1:-1, -2]) - \n", + " vn[1:-1, -1] * dt / dy *\n", + " (vn[1:-1, -1] - vn[0:-2, -1]) -\n", + " dt / (2 * rho * dy) * \n", + " (p[2:, -1] - p[0:-2, -1]) +\n", + " nu * (dt / dx**2 *\n", + " (vn[1:-1, 0] - 2 * vn[1:-1, -1] + vn[1:-1, -2]) +\n", + " dt / dy**2 *\n", + " (vn[2:, -1] - 2 * vn[1:-1, -1] + vn[0:-2, -1])))\n", + "\n", + " # Periodic BC v @ x = 0\n", + " v[1:-1, 0] = (vn[1:-1, 0] - un[1:-1, 0] * dt / dx *\n", + " (vn[1:-1, 0] - vn[1:-1, -1]) -\n", + " vn[1:-1, 0] * dt / dy *\n", + " (vn[1:-1, 0] - vn[0:-2, 0]) -\n", + " dt / (2 * rho * dy) * \n", + " (p[2:, 0] - p[0:-2, 0]) +\n", + " nu * (dt / dx**2 * \n", + " (vn[1:-1, 1] - 2 * vn[1:-1, 0] + vn[1:-1, -1]) +\n", + " dt / dy**2 * \n", + " (vn[2:, 0] - 2 * vn[1:-1, 0] + vn[0:-2, 0])))\n", + "\n", + "\n", + " # Wall BC: u,v = 0 @ y = 0,2\n", + " u[0, :] = 0\n", + " u[-1, :] = 0\n", + " v[0, :] = 0\n", + " v[-1, :]=0\n", + " \n", + " udiff = (numpy.sum(u) - numpy.sum(un)) / numpy.sum(u)\n", + " stepcount += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that we've also included a variable `stepcount` to see how many iterations our loop went through before our stop condition was met. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "499\n" + ] + } + ], + "source": [ + "print(stepcount)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to see how the number of iterations increases as our `udiff` condition gets smaller and smaller, try defining a function to perform the `while` loop written above that takes an input `udiff` and outputs the number of iterations that the function runs. \n", + "\n", + "For now, let's look at our results. We've used the quiver function to look at the cavity flow results and it works well for channel flow, too. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Jpk2bhpSUFMyZMwdXr15VOkeyWbNmITExEV999RWys7OVzpFs/vz5iI+Px8yZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure(figsize = (11,7), dpi=100)\n", + "pyplot.quiver(X[::3, ::3], Y[::3, ::3], u[::3, ::3], v[::3, ::3]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The structures in the `quiver` command that look like `[::3, ::3]` are useful when dealing with large amounts of data that you want to visualize. The one used above tells `matplotlib` to only plot every 3rd data point. If we leave it out, you can see that the results can appear a little crowded. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3R+Tn59OMGTO425I2bNjA3ZYUFBTEdGekPEVFRTRz5kzutqTNmzczvRpQnlu3\nbtHixYu52pLUajXNmjWLuy1px44d3J539+7df8TzeNqS9u7dWyXPY3k1oDz/hOfxtiXFxsZWyfMW\nLFjA3ZZ05MgRbs9LSkqi2bNnc7clQTTRCAQCgUAgEAhYEE00AoFAIBAIBIIXhthACgQCgUAgEAiY\nEBtIgUAgEAgEAgETYgMpEAgEAoFAIGDildlAZmVlcX8sXaPRICsri3vt9PR0bm12drbiSJOKaLVa\nZGZmcq+dnp6uKL5AHzk5OdxzExEyMjK4tEDV5s7NzVUcaVIRIqrSv3VV5maJqtBHVR7vjIwM7rkL\nCgoUx+4YWrsqWt65CwsLFcfXGFqbl8zMTEVRQ/qQInB4eVlzq9Vq5Obmcq9dlbmzsrIURfboo6Sk\nBNnZ2dxrV9U7hOex8TK942Wdy3JzcxXHM/3TvPAmmj///BMJCQmoXr06U7L/kydP0KJFC9y+fRsl\nJSVwcXFRnJBvZGSEvn37Yvv27cjIyGBOyN+5cye+/PJLxMfHo1q1anB2dlY8d2ZmJpo1a4abN29C\nrVYzJeSrVCp88cUX2LhxI9LT05kT8g8cOIAvvvgCcXFxMDMzY0rIz83NRbNmzRAUFITi4mKmhHyV\nSoXhw4fDw8MDT58+ZU7IP378OPr374+HDx/C1NSUae7CwkI0b94cly9fRmFhIVO7ikqlwg8//IDl\ny5cjNTWVuRUmMDAQH374IaKjo2FsbMzUrlJSUoKWLVvi/PnzyM/PZ25X+emnn7Bo0SI8efIE1tbW\ncHBwUDz35cuX0aNHDzx48AAqlYppbo1GgzfffBMBAQHIz89nblf59ddfMXfuXKSkpKBmzZpM7So3\nb95E586dERUVJc/N0q7y9ttvw8/PD7m5uahXrx5q1qypWDt37lxMnToVycnJqFGjBlO7SkREBNq3\nb4979+6BiJjaVVQqFTp27AhfX19kZ2czt8IsXrwYEyZMQFJSEiwtLeHo6Kh47piYGLRp0wYRERHQ\naDTMc3fr1g0HDx5EdnY26tSpw9QKs3r1aowZMwaPHj2ChYUFk3c8evQIrVq1QlhYGDQaDZydnRW3\nwhgZGaFXr17Ys2cPMjMzmVthvLy8MHLkSCQkJMDc3JypXSU1NRXNmzfn9rz+/ftj27ZtyMjIYG4U\n27VrF7fnZWVloVmzZrhx4waX5w0ePBgbNmzg8ryDBw/i888/R2xsLLPn5eXlVcnzRowYgTVr1nB7\nXr9+/RDHNlK6AAAgAElEQVQTEwMTExO4uLgonruoqIjb84BXsIlGOmxtbWnYsGF0/vz5SrOKNm7c\nSCYmJrJWSsj/448/Ks0VS05OJmtra521XV1dacqUKYpaGKTWCemQEvIDAgIq1W7fvl1O5cffCfnd\nunUjDw+PSjMpMzIy5HYS6ZAS8uPi4ipdW2qdkA4pId/f379Srbe3N5mZmclaIyMj6tq1K61atarS\nXLGCggKqVauWztpSQn5MTEyla0utE9JhZWVFn3/+OR0/frzSfK7Dhw9TtWrVZK1KpaLOnTvTsmXL\nKs3n0mq1ZGtrq7N2gwYNaPz48YraDMaMGaOjlVphDh8+XOncfn5+cguCNHeHDh1o8eLFlJubW+na\njo6OOmu7uLjQmDFjKDw8vFLtpEmTdLQWFhY0YMAAOnDgQKVznz17liwsLHT07dq1o4ULFyrKdmzU\nqJGO1snJiX744Qe6c+dOpVqpdUI6qlevTn379iVvb+9K57569SpZWlrq6N966y1yd3enjIyMSteW\n2jKko169evTtt9/SzZs3K9XOnz9fbkYBQNWqVaM+ffrQzp07K81xCwkJkZuWpOPNN9+k2bNnK8pI\nbN++vY62Tp069M0331BQUFCl2qVLl5KRkZGsNTMzow8//JC2bt1aaQbovXv35LYM6WjZsiXNmDGD\nnjx5Uuna7777ro7W3t6eRowYQZcuXapUu3btWrnRBX+3wvTs2ZO8vLwqzdKMjY2lmjVr6qwttcIo\nydft1avXS/G8lJSUKnnegAEDdLQ2NjY0ZMgQRZ63Y8cOvZ63du3aSj0vMzNTr+dNnDhRkee5ubnp\n9Tw/P79Ktfv37+f2vMLCQoOepySn9ptvvtHreceOHav0XHb06FG5NU7yjk6dOinyPKKq5UC+tA0k\naxXWtWvXZKOqU6eOXIWlxFxzcnKoU6dOOic9T09PRb+QREQLFy6U52atwrp586Z8ApJOekqrsAoK\nCuQTZ/kqrIcPHyqae8WKFc+c9JRWYd25c4fs7OwIYK/CUqvV9P777+uc9FatWkUPHjxQNPcff/wh\nz920aVOaMmWK4iqs8PBwuaKOtQpLq9XSRx99pHPSW758ueIqrG3btj2z0VdahRUZGUkuLi46Jz2W\nKqxPPvlEPum98847tGTJEoqIiFAUiLtv375nTnpKq7BiYmKoYcOG8knviy++oB07dtDTp08VzT1k\nyBCdjT5L/eORI0fkuevXr0/jx4+nkydPKioLiI+PpzfeeENno79t2zZFmxkiopEjR+ps9BcuXKi4\n/tHPz0/eiLm4uNDYsWPpr7/+UhT4nJycLNebShv9zZs3K65/HDdunPyYtW/fnubNm0e3bt1SNHdg\nYKC8oZE2+krrH1NTU+WqN6n+cePGjYoDn6dMmaKz0Z87dy4FBwcr8o6LFy/KF5V169alb7/9lo4e\nPaqoLCAjI0PedEsb/fXr11NCQoKiuWfNmqWz0Z81a5ZizwsKCpIvciTPO3TokGLP69y5M7fnLVq0\n6BnPu3LliiLvuHXrlrx5Le95Si4oCwoKqFu3bjob/TVr1ii68UBEtHLlSm7PCw0NlWtZpY3+/v37\nFXtez549uT1v/fr1z2z0lXpeREQE1atXT2ejv3fvXkUXwkSv2Abyjz/+UPzkK09oaCjNnDmTq6mj\ntLSUfv31V8VPvors2LGDu6nj/v37NH36dMVPvvJoNBqaPn06HThwgKupY+/evUxPvvI8fPiQfv31\nV7p48SLz3FqtlmbPns3d1HHw4EFauXKl4idfeRITE2nKlClcTR1arZbmzZvH9OQrz9GjR2nZsmV0\n//59Zu2TJ09o8uTJdObMGa6mjkWLFtGuXbu4mjr8/PxoyZIlXE0d6enpNGnSJDp9+jRXU8fSpUu5\nmzoCAgLot99+o7CwMOa5s7OzaeLEidztVKtWraKtW7dyNXVcuHCBu50qPz+fJk2apHjDWREPDw/u\ndqqrV69yN3UUFRXR5MmTudupNmzYwN3UcfPmTZozZ47iDWd51Go1TZkyhbudasuWLdyeFxYWVmXP\n422n2rlzJ7fnRUZG0rRp07jaqTQaDc2YMYPb87y9vWn16tVcnhcbG0u//vorVzvVP+V5PO1USUlJ\n3J5HVLUNpGiiEQgEAoFAIPgXIppoBAKBQCAQCAQvDLGBFAgEAoFAIBAwITaQAoFAIBAIBAImxAZS\nIBAIBAKBQMDEC99A+vj4cDUSZGZmwsPDA7GxsVzrbt++HZcvX+ZqJLhy5QoOHDiAnJwcZm1ubi7W\nrl2LmJgYZi1QFuh64cIFrkaC69evY9++fVyNBAUFBVizZg2ioqKYtQDg7e2Nc+fOcTUS3Lp1C3v2\n7OFK9i8uLsbq1avlkGZWDhw4gMDAQK5GgrCwMOzcuRNpaWnM2tLSUqxduxbh4eFccx86dAinTp3i\naiS4f/8+tm/fjtTUVGatRqPB2rVrERoayjW3r68v/P39uVp4YmJisGXLFjx+/JhZS0Tw9PRESEgI\n19x//fUXTpw4wdXCEx8fDy8vLyQnJzNriQjr16/HzZs3uVphTp06BV9fX64WnuTkZGzYsAGJiYnM\nWqAsVPv69etcc585cwaHDx9GXl4es/bp06dYt24dEhISmLUAsGXLFly9epXLOy5cuMDteVlZWS/V\n8/7880+uFp68vDysXbsW0dHRzFoA2L17N7fnBQcHw9vbm6vNprCwsMqed/bsWS7PCwkJ4fY8tVqN\nNWvWcHteleD9+DbPgb9z6szMzKhXr17k4eGhOJcqNTWV2rVrRwCoRYsWTDEBxcXFNHv2bDmX6quv\nvmKKCbh+/TqZmJiQqakpvf/++0wxAU+fPqWuXbsSAGrWrBlTTEBJSYmcx2Vra0tffvkl7du3T1Eu\nFRHR7du3ydzcnExMTKhHjx5MMQHp6elyluMbb7xBkydPVhwToNFoaPny5XIulZubG+3Zs0dxNE5E\nRARZWlqSsbExvfvuu0zROBkZGdSnTx8CQI0aNaIJEyZQYGCgomgcrVZLHh4eBIBq1qxJgwYNYorG\niYqKIhsbGzIyMqIuXbrQ4sWLFUfjZGVl0aeffkoA6PXXX6cff/yRTp06pTgaZ/PmzXKm4WeffcYU\njfPw4UOyt7eXA2gXLVpEoaGhiubOycmRsxxfe+01GjduHFM0zu7duwkAWVpa0ieffEJbtmxRHI2T\nkJAgB6j/5z//YYrGyc3NlcN7nZ2dafTo0XTixAnF0Tg+Pj5ypmH//v3Jy8tLcTROYmIiNWjQgADQ\n22+/zRSNk5+fT6NHjyYA5OjoSN999x35+voqjsb566+/CACZm5vTxx9/zBSNk5KSQq6urgSA2rZt\nS7Nnz6br168ripgpKCign3/+Wc5iHDVqFFM0TmBgoJzF+NFHH9G6desoPj5ekfbx48fUunVrAkCt\nW7emmTNn0tWrVxXNXVRURFOnTiUAVLt2bfr666/Jx8dHcRzcpUuXdDxv7dq1iqNxUlNT6T//+Q+X\n56nVapozZ46O5/3555+KPS84OJhMTU3JxMRE9jwlgdhEZZ73zjvvyDm+v/zyS5U9T2k0zp07d8jc\n3JyMjY2pe/futGLFCibP++CDD3Q87+zZs4o9T8pd5vW8GjVqyDm+S5cupXv37ik6J2RmZlLfvn25\nPI/oFcuB1He89957dPfu3ef+JZctW6ZX6+TkRHv27HnuA/3o0SO9WlNTU/r5558r3ZB16dJFr/6d\nd96h27dvP1crbUgqHg4ODrRt27bnnsTS0tL0ak1MTGjMmDGVhkxLwaYVj44dO1JwcPBztdKGpOJh\nb29PGzdufO5JLD8/X6/W2NiYvv3220pDpvv166dX365dO7p69epztXv27NGrtbW1JU9Pz+eeDLRa\nrV6tkZERffXVV5VubAYNGqRX/+abb9KFCxeeqz106JBerY2NDa1atarSk0H59h3pUKlUNGTIkErD\nmkeMGKF37ZYtW9KZM2eeq/Xz89OrtbKyoiVLllS6Aa7YOiHN/fnnn1e6QZA2UhWPpk2bVtq2dO7c\nOb1aS0tLWrhwYaUbSSm4t+IxYMCASjcIEydO1Ktt3LgxHTt27LnaoKAgvdrq1avT3LlzKw31lkLf\nKx59+vSpNHe1YvOPdLz++ut06NCh556DQ0ND9WrNzc1p+vTplW4kpfD0ikevXr0qvbhcsGCBXq2L\niwvt37//uXM/ePBAr9bMzIwmT55cacbi22+/rVffo0cPCgsLe65WugiveDg6OtKuXbueO3diYqJe\nrVLPk256VDy6du1KISEhz9WuW7dOr9bBwYG2bNnyXM9LT0/Xq1XqedIGsOLRoUMHun79+nO1W7du\n1atV4nkFBQV6tcbGxjRq1KhKL+Yrtt1JR7t27ejKlSvP1Xp7e+vV1qpVizw8PCrdAAOv0AZSamBg\nvaKKiYmht956iwDdZhSl6fbSSVu6olq1apXiK6rTp0/LDQysV1SxsbHUsWNH+R/0yy+/VNyMUlxc\nLF/9lr+iUtqMcu7cOXljwdqMEh8fL7fgWFtby1dUSppRSktL5Tu+PFdUV65ckRsYpCsqpc0oiYmJ\n8klEakbZuXOnomYUrVYrtw5JdxFZmlGCg4PlBoYGDRrIdxGV3I1LSUmhjz/+mID/3kVkaUaRzEal\nUlHHjh1p0aJFiptRQkJC5AYGqRnFz89P0d24J0+eyHdOLSws5LuISptRyptN+/btaf78+RQSEqJo\n7tDQUPkOpJOTE40ePVpxM0paWppceyY1o3h5eSluRilvNtJdxBs3biiaOzw8XL4DWa9ePaa7iJmZ\nmfTVV18RoNuMoqSejqisXECaW2oDU3oX8f79+3J7j4ODA1MbWHZ2Nn333Xfy5ku6i6j0FajyF1jl\n28CUzB0VFUWtWrWSNwMsbWB5eXlyew9PG9iJEyd0PI+lGSUmJkbegLK2gRUWFup4ntSMwuJ5Uh0h\nj+dJLTisbWDFxcU0bdo0Hc9jaQM7f/68XOvH6nkJCQnUvXt32fNY2sBKS0vlO768nidVlLK2gSUl\nJcmVmTxtYK/UBpK3GSUjI4OmT5/O1YxCVNYcobSWqCJnz55lqiUqT05ODk2fPl1xLVFFPD09uZtR\nLl26xPTkK09+fj5Nnz6duxllw4YNtHv3bq5mlKCgIKYqvvIUFRXR9OnTuZtRtmzZwvTkK09ISAh3\nM0pJSQnNmjVLcRVfRXbs2MHdjBIeHs5UxVce6WKBtxll79693M0oUVFRTFV85dFqteTu7k7Hjh1T\ntOGsyIEDB2jjxo1czShxcXHczSjSRY7SKr6KHDlyhLsZJTk5mWbNmsXVjEJE9Pvvv3O3gZ04cYLp\nLU/lSU1NpZkzZ3K1gRGVvfrF24xy6tQpbs/LzMyskuetXr26Sp7H2waWm5tL06dP525GqYrnXb58\nmbsNrKCggGbMmMHteRs3buRuA7t+/Tq35xUXF9OMGTO4Pa8qG0jRRCMQCAQCgUDwL0Q00QgEAoFA\nIBAIXhhiAykQCAQCgUAgYEJsIAUCgUAgEAgETIgNpEAgEAgEAoGACWN3d/cXtti8efPca9SogcDA\nQFhbW8PBwQEqlfL3bgYFBWHhwoVQqVRwcXGBqampYq1arcb333+P1NRUODo6wsrKimn2NWvWwN/f\nH1ZWVqhXrx7T3CEhIZg7dy6IiHnu0tJS/PDDD0hOTkbdunVRs2ZNprk3bNgAX19fWFpaMs8dERGB\nadOmyXObmZkp1mq1WowZMwbx8fGoW7curK2tmebesmULfHx8YGlpCUdHR6a5Y2JiMGnSJGg0Gri4\nuKBatWqKtUSEn376CTExMXBwcICNjQ3T3Lt27YK3tzeqV68OJycnGBkpv0ZLSEjATz/9hJKSEjg7\nO8Pc3Jxp7kmTJuH+/fuoXbs2bG1tmebev38/duzYAXNzc+a5Hz9+jLFjx6K4uJh5bgD49ddfERoa\nCnt7e9jZ2TFpjxw5gk2bNqFatWpwcnKCsbGxYm16ejpGjx6NgoICODs7o3r16kxrz5o1Czdu3ICt\nrS3s7e2Zfkf/+usvrFu3DqampnB2dmaaOzs7Gz/88APy8vLg5OQECwsLprnnzZuHa9euoVatWqhd\nuzbT3AEBAVi1ahVMTEzg7OwMExMTxdq8vDx89913yM7OhqOjIywtLZnm/v3333HhwgXY2NigTp06\nTHNfvHgRS5cuhbGxMfPchYWF+P7775Geng5HR0fUqFGDae7ly5cjICAANWvWRN26dZnmvn79OhYs\nWFAlz3vy5Anq1avH7HkeHh7w8/ODlZUV89y3b9/G7NmzAYDL80aPHo2kpCRuzzt69Chq1KjB7Hn3\n7t3DtGnToNVquTxv7Nix3J63detWHDhwABYWFnB0dGQ6B8fExGDy5Mlcnjdv3jy4u7vPYxpWgvfj\n2zwHAPrxxx91glzHjBmjqAHi1q1b5OHhIWclWVhYUP/+/RVFgOTm5pKnpyd169ZNJ6BTaQOEj48P\nTZo0SSfI9fvvv1cUAXLnzh3y8PCgWrVqEcDWAFFYWEienp46geBt27alOXPmKMpuO3LkiJwjCbA1\nQISHh5OnpyfVrl1bzpzr3bs3rVu3rtIIELVaTZ6entS7d295bakBQkkEyPHjx+UcSQBUp04d+uab\nbxQ1QERGRpKnpyc5OTnJmXNKW4+0Wi15enrSgAED5LVZ8kr9/f1p/vz5OgG0SluPYmJiyNPTU84H\nLN8AoSQCZP369fTZZ5/Ja7O0HgUEBNBvv/0ma1laj+Li4sjT05OaNGnyTF6pkgaITZs2yXmMAFvr\n0dmzZ2np0qWkUqkIYGuASExMJE9PT2revLk8N0vr0datW+U8RoCtAeLixYu0YsUKMjY2JoCt9ejx\n48fk6elJbdq00ckrVdp6tHPnTho1apQ89+uvv04//fSTotajq1ev0urVq8nMzEwnr1RJ61FaWhp5\nenpS+/bt5bxSltajvXv36gTHs7QeBQcH05o1a6h69eoEsLUeZWZmkqenp5xrCLC1Hu3fv1/H81ha\njyTPs7KykvNKlbYe6fM8ltYjHx8fmjx5so7nKc0rDQ0NJQ8PD7K1teX2vPKB4CytR0ePHpVzJFk9\nLyIigjw9PcnBwUH2PKWtRyUlJeTp6Sm3oJX3PCV5pSdOnNDxPJbWo6ioKL2ep7T1CK9SDqS+w97e\nnsaNG/fck5ChJhpTU1Pq1avXc9PaDTXRAKDmzZtXmtZuqInG1taWvv/+++eGJhtqopE2COfPnzeo\nNdREA4BcXV1p5cqVzzUrQ000tWrVopEjRz73yWyoiUbaIAQEBBjUGmqiAcraNiprKDHURGNtbU0j\nRox47pPZUBONFO7q5+dnUGuoiQYoC3etrKHEUBONlZUVDR069LkbQUNNNCqVijp37ky+vr4GtUT6\nm2gAUP369cnd3f25J31DTTSWlpY0ePDg524EDTXRqFQq6tChA/n4+DzXrPQ10QBlF5czZ8587snT\nUBONhYUFffbZZxQREWFQa6iJBii7uPT29n7u3IaaaJycnGjq1KnPvWAw1ERjbm5On3zyCYWGhhrU\nGmqiAUBvvfUW7dy587lzG2qiqVu3Lk2ePPm5Yc+GmmiqVatGffv2pZs3bxrUGmqiAcqamiprKDHU\nRFOnTh366aefnrvxNtREY2ZmRr17935uQ4mhJhqgrKlp/fr1z72wNNREo8TzDDXRSJ53+fJlg1pD\nTTRAmeetXbv2uZ5nqIlGiecZaqKRAs3PnTtnUGuoiQZQ5nmGmmhsbGwq9TxDTTTGxsbUrVs3On36\ntEGtoSYa4L+e97wLHUNNNNbW1vTVV1891/MMNdEYGRlR165d6a+//jKoJXrFNpDSxqRly5Y0ffp0\nxU00RUVFFB8fTzVq1GDus9ZoNJSdnU0DBw4kU1NT6tmzJ1OfdV5entzZK93ZURruWlxcTMnJyVSr\nVi2ytbWlYcOGKe72lOYeOnQoGRsbM/dZ5+XlyZ29rH3WxcXF9OTJE6pTpw7Z2NjQkCFDFIe7arVa\nys7OppEjR3L1Wefn59OJEyfkJ9/EiRMVd3uq1WpKS0sjJycn5j5rae5x48Zx9Vnn5+fTmTNnCPjv\nnR2l4a5qtZoyMjKoYcOGXH3W2dnZNGnSJPnOzm+//aa4z7qgoIAuX75MAHufdUlJCWVmZlKzZs3I\n0tKSBg4cyBRonp2dTTNnziSAvc+6oKCAbt68SSqVSufOjpJg8JKSEsrKyqI2bdrId3ZYAs1zcnLk\njQnLqxlEZXdZwsLCyNjYWOfVDCVNNKWlpZSdnU2dOnXi6rPOycmRNybSqxlKA80LCwspMjKSzMzM\nyMHBganPWpq7R48eOq9mKO2zlu6oAWyvZhCVecfDhw+pevXqOnd2lDTRSOfgPn36yBs3Dw8PxX3W\nubm5tGXLFtnzWNrXioqKKCEhgaysrMjOzo6pz1qa+9NPP+Xqs87Ly5MvxllezSD6r+fZ2trK7WtK\nPU86B3/55Zey57H0Wefl5ckX4zyel5qaSg4ODszta9Lco0aNIiMjI9nzlDbR5Ofnyz31rH3Wkuc5\nOzvLnqe0fY3oFdtAnjx5UnENVEUiIiIU10BVpLi4mHx8fLhS+YnKqp2UPvkqEhkZqfjJV5GSkhI6\nePCg4jL5ipw5c4ariYao7GVVpTVQFdFoNHTw4EFFTz59nD9/XvGTryJxcXGKa6AqotVqycfHh6uJ\nhqjs5Uml1YcVSUxMVFx9WBGtVkuHDh1SXH1YkStXrnA10RCVvayqtPpQH0eOHFFcfViRoKAgxdWH\nFUlLS1NcfagPX19fxdWHFblx44bi6sOKZGVlKa4+1Mfx48cVVx9W5Pbt2xQUFMTVRJObm6u4+lAf\nfn5+XE00RERhYWF09epVLu8oLCxUvOHUx6vqeQEBAdyeFxUVxd2+9m/2PJ4mGqKy6mFez6vKBlI0\n0QgEAoFAIBD8CxFNNAKBQCAQCASCF4bYQAoEAoFAIBAImBAbSIFAIBAIBAIBEy98A1laWgqNRsOt\nV6vVL0Wr0WhQWlr6Utauilar1aKkpOSlrK1Wq8H7nlciemmP2b9x7n9i7apoX8W5S0pKoNVqX8ra\n/8a5X2XveFXnFp7Hrn0VvYOXF95EM3fuXLz33ns4e/YsSktLmVPTlyxZgnnz5iEjI4O5cSMqKgo9\nevRAXFwcc+OGSqXCRx99hFOnTkGtVjM3bqxZswYzZsxARkYG7OzsmBo34uPj0bVrVzx8+JCrcWPA\ngAE4duwY1Go1nJycmBo3Nm7ciMmTJyMtLU2eW2myf0pKCrp06YIHDx7A1NQULi4uiudWqVQYNGgQ\nfHx8UFRUBGdnZ6bGjR07dmD8+PF4+vQpc+NGeno6OnbsiPv37zM3bqhUKgwfPhx79+5FYWEhnJyc\nmBo3/vzzT7kxydramqlxIzs7Gx07dsTdu3e5Gje+++47bN++HQUFBahXrx5T44avry9GjBiBx48f\nM7dM5efno0OHDrhz5w5X48b48ePh5eWFvLw85pap06dPY/DgwUhJSWFumSouLkanTp1w8+ZNAOyN\nG1OmTIGHhwdyc3OZGzcuXryIgQMHIikpiblxo7S0FF26dMG1a9dAxN4yNXPmTKxYsQLZ2dnMjRs3\nbtxAnz59kJiYyNwypdVq8e677+LixYtcjRsLFizAb7/9hqysLOaWqbCwMHzwwQdISEjgapl6//33\ncebMGa6WqWXLlmHu3LnIzMxk9rwHDx6ge/fuiIuLk72DxfN69+4Nf39/rrk9PDwwffp0pKenM7dM\n/ROe5+vrK7djsXjepk2bMGnSJKSlpTG3TD1+/BidOnWSPY+lZUqlUmHw4ME4ePCg7B0snrdr1y6M\nGzeO2fNeqSYaNzc3nVBYKZdxzZo1z81lPHbsGLm5uem0m0BhLmNaWhq5ubmRm5sbWVtb64SiKmnc\nmDt3Lrm5uVHr1q11QlGlXMYHDx4Y1Pr7+5Obm9sz4dhKMqpycnLkuaVUf/wdiqokl3HRokXk5uZG\nbdu21QlFVZLLeObMGXJzc6NPPvnkmVDUynIZi4qK5Lnr1Kkja5XmMi5dupTc3NyoXbt2z4SiVpbL\neOnSJXJzc9NpZYHCXEatVivPXT4o2srKSlEu4+rVq8nNzY06dOgga8vnMoaFhRmc+/r16+Tm5kZf\nfPGFztz169dXlMs4fPhwcnNzI2dnZ1kr5TJW1rjxxx9/kJubm07bBhTmMt6+fZvc3Nxo0KBBciMM\noLxxY9SoUeTm5kb169eXtUpbpjZv3kxubm707rvv6sytJJcxPDyc3NzcaPDgwWRiYiJrlbZMjR49\nmtzc3HSCuZXmMu7cuZPc3NyoR48eOnMraZl68OCB/DsqNcIAyhs3fvrpJ3Jzc5ObgwDlLVP79u0j\nNze3ZwoKlOQyxsfHy3NLjTCA8papKVOmkJubGzVt2lTWKm2ZOnToELm5udGHH36oM7eSlqmUlBR5\nbqkRBlDeMjV9+vRnPE9py5TkeeXbTcp73vOi4dLT0+W5y4f1V8XzlLZMnTx5ktvzcnNz5bnt7Ox0\nPE9Jy1RVPO/s2bN6PU9JLmNxcXGVPG/ZsmXk5uYmNzVJnqcki/jy5csGPe/HH3+stGUKVYjxUX57\n4h8iPDwcaWlp8v+XlJQgIiICDRs2xOuvv47XXntN712TtLQ0hIeHP3ObNjY2FuHh4WjYsCFcXV1R\nu3btZ7SlpaUIDw8HoHubNyMjQ177jTfeQNu2bfXOLK1Rfu7S0lJZ27BhQ9SvX1/v3YeMjAyEh4c/\n81JAfHy8rHd1dUXdunWf0Wo0Gnnu4uJi+etZWVny37lJkyZo166d3rnj4uIQHh6OjIwMnZ957949\nee4GDRrovYrPzMxEeHj4My+9xMfHy2s3bdoUjo6Oz2i1Wq08d0FBgfz1nJwcnbkNXZEmJCQgPDwc\nmZmZOj+z/NwNGzbUezWcnZ2N8PDwZ15GePTokc7viYuLi961pbnz8/Plr+Xm5sr/Vk2aNDF4RZqY\nmIq4IIcAACAASURBVIjw8HBkZ2fLXyMi3L9/X2dufVeV0mNTcW7pZ0qPd/369fXOHRERAbVajby8\nPPlr+fn5srZx48YG72YmJSUhPDwcOTk5Ol+PjIyUZ27UqJHeO5K5ubnyY1ae5ORk+TFr2rQpGjZs\nqHfue/fuITc3V2ftgoICnbkN9fCmpKQgPDxc5+8MlL3SUP7fS98dSWkNADqP+ePHj3V+T5o0aaJ3\n7qioKDx9+lTn37qoqEjWNmrUyODdNWmN8s8NoOxuUfnnh747ZIWFhXrnfvLkic7cTZs21Tv3gwcP\nkJiYqPPcKi4u1pnb0F0qaY3CwkKdr8fExOjMre8OmbQGAJ1zSmpqqs7cLVq00Dt3TEwMoqOjdc5l\narVa5xxs6G7P06dPER4ernMOBYCHDx/qnP/t7e2f0arVannu8i+LSn4kaVu3bq13bmmN9PR0+WsV\nvcOQ56WnpyvyvDp16jyjLSkp0esdSj1P8o7ynid5R6NGjWTv0Od5kndU9DzpZyr1vKKiIvnrWVlZ\nOnMb8jzJn3g8T/LVim/vSEhI0PFqJyenZ7TlPa/886O85zVu3JjZ8yp6B6vnlT8HG/K8KsG78+Q5\nAJBWq6V3331X7uVkDdP9448/qF69eop7OcuTlJRENjY29PHHH9P69euZw3R79epFbdq0UdzLWZ5t\n27aRg4MDjRw5kjlMNzU1lezs7OReTtYw3QEDBlDr1q1pxowZino5y7Nv3z65veHgwYNMYbqZmZlU\np04d+uCDD2jt2rXMYbrSlfu0adOYw3SPHj1KdnZ2NHz4cPrzzz+ZwnRzc3PJ0dGR3n//fVq1ahVz\nmO4333xDTZs2pV9++YU5QP7UqVNUq1YtGjp0KHl7ezOF6RYWFtJrr70m3yVgDdMdO3YsNWnShCZN\nmsQcpnvhwgW5vWH37t1MYbrFxcXUqFEjeuedd2jp0qXMAfKTJ0+W7xKwhukGBwdTzZo16YsvvmBq\nbyAqa1dp1qwZde7cmX7//XfmAPlZs2ZRgwYN5LsELAHyYWFhZGVlRZ9++ilt27aNKUBeo9FQmzZt\nqGPHjrRo0SLmAPlFixaRi4sLjR07ljlAPjIykmrWrCl3UbMEyGu1WurQoQO1b9+e5s+fzxwgv3Ll\nSnJycqIffviBOUA+Li6OrK2tqV+/fuTl5cUcIN+9e3d66623aO7cuXTjxg2mc/D69etlzzt69CiT\n5yUnJ5ONjQ316dOHy/M++ugjatOmDc2aNYs5QH779u3cnvf06VPZ8zw9PZk9b+DAgdSqVSuaMWMG\nXblyhck79u/fT/b29jRixIgX7nlDhw6l5s2b09SpU5k9z9fXV/a8/fv3K/Y8vEpB4sXFxUhLS9N7\n50oJCQkJcHZ2Znr/icTTp09hYWHB9J40idLSUqSkpHDv4qsyd1paGszNzZnekyah1WqRmJiI1157\njVkLlF3FODo6Mr3/RCI9PR2mpqZM7+2SICIkJCQYvONWGVWZOzMzE0ZGRkzv7ZIgIsTHx6NBgwbM\nWqDsjmPdunWZ3rsokZ2dDa1Wi1q1anGtHRcXxz13UlIS6tSpw/QeQInc3Fyo1Wqm90iVJz4+Hq+9\n9pri9ymVJzk5Gfb29kzvAZTIz89HYWGh3jtXSqjK3CkpKbC1tWV6D6BEYWEhcnJy4ODgwKwFqjb3\n48ePYWNjw/ReOoni4mJkZGSgXr16zFqganM/efIENWvWZHovnURJSQlSU1P13rlSwsvyPI1Gg+Tk\nZOF5DGRkZMDExOSleF5iYiLq1avHPHdVgsRFE41AIBAIBALBvxDRRCMQCAQCgUAgeGGIDaRAIBAI\nBAKBgIlKN5AqlWqLSqV6olKpQg38eTeVSpWlUqlu/X3M+ufHFAgEAoFAIBD8r6DkDuQ2AB9W8j0X\niOitv4+Flf3AhIQEnY/Zs1BcXIx79+5xp73ri0VQSmJiok6sAQslJSW4e/cu99z37t17JoZCKcnJ\nyUhNTeXSajQahIWFcc99//59nTgGFh4/fozHjx9zabVaLUJDQ7nnjoyMfCauRCmpqalITk7m0hIR\n7ty5w90UEhUVpRNBxEJaWhoePXrEpQWA0NBQ7rmjo6OfieNRSmZmJuLj47m0QFlING9TSExMDHJz\nc7m02dnZiI2N5dICVZs7NjZWJ4KIhby8PERHR3NpAeDu3bvcDSfx8fE6UScsFBQUIDIykksLlHkH\nb8NJQkKCTpQPC8XFxYiIiOA+l0kRXzwkJibi6dOnXNrS0lLheYxUxfMk73iRnzOptInG3d09Yd68\nedUADHV3d19f8c/nzZvXAEAXd3d378oWmzdvnru7uzuys7PRsGFDnDx5krnhxMTEBIMGDcLChQsR\nGxsLMzMzprT3w4cPo1evXggJCWFuOCkoKEDDhg3x119/Mae9Gxsb4+uvv8acOXMQExMDExMTpmYW\nPz8/dO/eHTdv3mRuOFGr1WjSpAmOHj2K1NRU2NjYKG44MTIywtixY/Hrr78iOjoaxsbGcHFxUfwp\n4bNnz6JLly4IDg5GQUEBHB0dFX+yTqPRwNXVFT4+PswNJyqVCpMnT8aECRPw4MEDGBkZwdnZWfGn\nhK9du4b27dsjKCiIq+GkefPm2L9/P3PDiUqlwuzZszFmzBjZ7FgaTm7fvo02bdrg6tWrzA0nRkZG\naNOmDXbt2oXk5GTmhpNFixZh5MiR8gUeS8PJvXv30LJlS1y+fFn+hLDST8GbmJigXbt22LJlC1fD\nycqVKzFs2DBEREQwN5zExcXB1dUVFy9eZG44MTU1RZcuXbBhwwauhpP169dj0KBB8oaMZe7k5GQ0\nadIE586dY271MjU1Rc+ePbFmzRokJCQwt3pt27YNn3zyCUJDQ5kbTtLS0tC4cWMEBgYyt3qZmJig\nX79+WLp0qU4zi9Jz8L59+9CnTx/cuXOHueEkJyenSp43ePBgLFiwALGxscytXkeOHMEHH3wgex5L\nw0lBQQEaN26MEydOMHuekZERvvnmG8yaNQsPHz5kbvXy9/dHt27dZM9zdHTk9jyWVi8jIyOMGzdO\nx/NY5j537hw6deqE4OBg5OfnM3meVquFq6srDh48yOV5v/zyC3766SdERUUpbvX6P2+iAVAfQKiB\nP+sGIA3AbQAnADR/zs8hZ2dncnZ21ml/QLmGkxs3bujNKtqwYYOsrVmzpo5WSnv39vbWm5uUnJws\na+vWraujlRpOlixZYrCtY+DAgbLe1NRUb8PJtWvX9Gq3bdsma8u34ODvhpPPP/+cdu3apTcrMCMj\nQ9aWb0ZBhYYTQ20dQ4YMkfXlWytQruHk0qVLerXe3t6ytnyTAco1nGzfvl1v5l5BQYGsdXR01NGi\nXMOJoUyyb775RtZXq1ZNR+vi4kJjxoyhc+fO6dUePnxY1taqVUtHKzWcbNmyRW/mnlarlbVOTk7P\nzN2uXTuaN2+ewUyyMWPGyHpzc3MdrdRwEhgYqFfr5+cna8u3DqFcw8nGjRsNZtc1atTI4NxSw4mh\n1ouJEyfKa5dvCUG5hpOTJ0/qzdw7e/asrC3fHIFyDSd//PGHwXaUFi1ayPqKc0sNJ4byLKdPny5r\nLSwsdLRSw8mJEyf0zn316lVZa29vr6Mt33BiKAPu7bfflvXlG3gAUMuWLWnatGkUERGhVztv3jxZ\na2lpqaOVGk58fX31zh0SEiJra9euraM1NTWVG04MZcB16dJF1hsZGeltOAkLC9OrXbJkiaytUaOG\njlZqOPHx8dGbFXjv3j1ZW76pA/hvq9eKFSsM5oe+//77st7Y2Fhvw0lISIhe7Zo1a2Rt+TYZ4L+t\nXvv379frHbGxsbLWwcFBR1u+4cRQfmifPn2e63kTJkyg4OBgvdqNGzdye15KSspzPU9qODHkeZ9+\n+im35+3YseO5nvfZZ5/Rrl279ObMZmZmVup5ixYtMpjDOXToUG7P27dvX6Wet23bNr2eV1hYqMjz\nDDU9jRw50qDnSa1ehjzvyJEjlXre5s2bDea1Avw5kIq6sOfNm2cDw3cg0wGsJCKPefPmZQLY4e7u\n7mHg57i/8cYbqFGjBp48eSK/3NW4cWP069cP/fr1Q/v27fVeWWVlZcm787y8PDx58gQAYG1tjd69\ne6N///748MMP9V6hlJaWIjExEa6urrC3t0dERASAsiuNzp07y2vXr1/fYMNI7dq14erqqnNbvWHD\nhujfvz/69euHjh07Gpy7tLQUrq6uKCwsREpKCgDAysoKH374Ifr164ePPvpI790tjUaDhIQEuW1A\nSrpXqVTo1KmTPHfDhg0NNozY2dnB1dUVDx48kF+SrV+/Pvr164f+/fujc+fOeq+ssrOzUVxcDFdX\nV6jVaiQlJQEALC0t5bl79+6t9y4RESE2Nhaurq5wdHREWFiYPHeHDh3kx6xJkyZ6505OToaNjQ1c\nXV0RExMjN3a4uLigb9++6NevH7p27ap37tzcXBQUFMDV1RUajUZ+SdbCwgIffPAB+vXrhz59+hi8\nSxQdHS031dy5c0f+ert27eTH29XV1WAzSs2aNeHq6or4+Hj5JVlHR0dZ+8477+i9IszLy0NeXp78\ns6WXZM3NzdGzZ0/0798fH3/8scG7RNHR0WjSpAkaNGiAkJAQ+ett27aV127WrJneuZ88eQJLS0u4\nuroiMTFRboWpW7cu+vbti/79+6Nbt2567ybm5+cjOzsbrq6uMDExkV+SrVbt/7H33lFRXe3790VR\nxK7YUOwFO5bYKyLWwVhikifFGJM8UaOxG6MxJmrsNfZYwIKoKHbFAoKCiBUFBUGl9zrS29zvH/PO\nftgzZ8o55pcs1/dca7GWZ5x79j2HmX3ts2e4PlYYNmwYXFxcoFAo9GYlvn79mlEWHj9+zG53cHBg\nr5MuXboI9p2WloYqVarA3t4eKSkpyMnJAQA0aNAAY8eOhYuLC4YNGya4K1dYWIisrCzY29vDysoK\nb968AQBUrlwZjo6OrG99WYnR0dFo3rw52rRpgydPnrCPjTp37szeW127dhXclUtPT0flypVhb2+P\ntLQ09nWeevXqYcyYMXBxcYGTk5PgrlxRUREyMjJgb2+PqlWrso+SK1WqhCFDhrBzpi8rMSYmBk2b\nNkW7du3w5MkTNgd37NiRvU66d+8u2HdmZiYsLCxgb2+PrKws9nWeunXrsr6dnZ0Fd+WKi4uRmpoK\ne3t71KxZE5GRkQDUu2uDBg1iY9vZ2Qn2HRsbi8aNG6Ndu3Z49uwZ++je3t6ePecPPvhAsG/N+bW3\nt4dSqWQfbdauXRujR4+Gi4sLRo4cKbgrV1JSguTkZNjb26N27dqIiIgAoP5EaeDAgaxvfZmD8fHx\naNSoEezt7bmPwDWeN27cOPTu3VvQO7Kzs5nn5efns482a9asiVGjRrG+pXie5py1aNHCqOdFREQw\nz2vZsiV7zoY8r7S01KDnjR49Wq/nxcbGwt7eHg0bNkRYWBgAtXf07duXnTN9npeUlIS6devq9TwX\nFxe9npebm4uioiLY29ujtLSU87wRI0Yw7xDyPJVKZdDzNGPr87zk5GTmeW/evGFfQ7Kzs2PPecCA\nAXq9Q+N5KpWKeZ61tTXzvLFjx7J8YD8/P7i5ucHPzw9+fn7w9/f/93YgBe4bDaCunv8jIjW70RQ2\npZCKi4vJycnJKI9Zn37//XeT2JRCevjwoUk8ZiGVlpbSyJEjjfKY9WndunUm8ZiFFBoaSgMGDKA1\na9bQs2fPRPVdXl5OCoXCJB6zkLZt22YSj1lIkZGR1K9fP6M8ZiGpVCqaOHEizZgxgy5fviyKOkGk\npj9oeMxiqRMxMTHUr18/ozxmfX1/+umnjMcshjpBpN7xNoXHLKSkpCTq37+/UR6zPk2ZMsUkHrOQ\njh8/znYrY2NjRdWmp6dT//79jfKY9em///0v4zGLoU4QqTnLmt3K6OhoUbXZ2dk0cOBAozxmfZo1\na5ZJPGYhXb58me1WiiUt5ebm0uDBg43ymPVpwYIFjMcshrREROTj40OOjo60ZcsWgzxmIRUWFpKj\noyPjMYv1jqVLlzIesxjSEpGu54mZE4qLi2n48OFGecz6tHLlSuZ5YkhLRESPHj2S7HllZWU0atQo\nk3jMQlq/fr1kzwsLC6P+/fvTH3/8IcnzXFxc6IcffhBNWiJS73hrSEtiPS8qKor69+9Pq1atEk1a\nUqlUNGnSJJo+fbooz8M77ECaFCRuZmbWAsBFIuoi8H8NiSj1//93bwCniKiFnschIoJKpZKUTg+o\nV/pmZmaSaAKa+ncZ+/9a35rXh9z3P1P7vvb9b44t9/3+1P6bY7/Pfb+P3vG+9v2+zsFS+/5/SqIx\nMzM7DmAoABsAqQBWAKgM9ar1LzMzsx8AzABQCqAQwDwiCtbzWGTKglWWLFmyZMmSJUvW/1vJKENZ\nsmTJkiVLlixZoiSjDGXJkiVLlixZsmT9Y5IXkLJkyZIlS5YsWbJE6V9bQJ44cUIyUSYoKAg3b96U\nlK6fk5ODI0eOSCbKnDp1SnK6/oMHD3D9+nVJ6fp5eXlwc3OTnK5/5swZySn1T548wdWrVyWl6xcW\nFsLV1VVyuv65c+e4mBQxCg0NxaVLlyQRZUpKSuDq6iqZKHPx4kU8fPhQEpklPDwc58+fl0SUKSsr\ng6urq2SizJUrVxAcHCyp76ioKJw9e1YSUUalUsHV1VUyUeb69esICgqSRGaJjo7G6dOnWXyRGBER\nDh8+zGKAxMrHxwcBAQGS+o6Pj8fJkyclEWWICEePHpVMlPH394e/v78kokxKSgo8PDwkE2Xc3d0l\nE2UCAgJw69YtSUSZjIwMHDt2TDJR5sSJE5KJMvfu3ZPseUqlEocPH5ZMlPH09JTseQ8fPsS1a9ck\neV5+fj7c3NxYZJ9YeXl5Sfa8kJCQd/Y8TXyRWJ0/fx6PHz+W1HdYWJhkz5Mik3Ig/y79/vvvv339\n9ddQKpW4fv06PvnkExw9epSl6xsiyiiVSqSmpkKpVCIvLw/Dhg3Dtm3bTErXLysrQ3x8PMs2/Omn\nnzB37lxcv37dpHT9lJQUZGZmQqlUwt/fHx999BEOHz7MiDKGUurfvn2LlJQUKJVKFBQUwMnJCVu2\nbDEpXV+TY6hUKlFYWIjffvsNs2fPhre3N1JTU40SZVJTU1nfd+/excSJE+Hq6opXr17B3NzcIFEm\nLy8PycnJ7Jw5Oztj48aNJqXrq1QqxMXFQalUIj8/H+vWrcOMGTNw5coVpKSkoGbNmmjUqJHevtPS\n0pCRkQGlUolHjx7hww8/xIEDB0xK18/Pz2d9l5aWYvTo0Vi3bh2Cg4ORm5sLW1tbvUQZImJ95+Xl\nYdu2bfjvf/+LixcvmkSUSU9PZ32HhobCxcUF+/fvx8uXLxmZRV/fBQUFSEpKglKpRHl5OVxcXLBm\nzRqTiTKxsbFQKpXIzc3F3r178e233+L8+fNITEw0SpTJyMhAeno6lEolIiMjMWbMGOzbt49d4NnZ\n2eklyhQWFrK+VSoVJk6ciJUrVyIwMBBKpdIomSU+Ph45OTl4+/Yt3Nzc8PXXX8PLywsJCQmoWrUq\nGjdurPevEjMzM1nfMTExGDlyJPbs2YPnz58bJcoUFRUhMTGR9f3JJ59gxYoVuHPnDrKzs9GgQQOW\nmyakhIQEZGdnQ6lUwsPDA1OmTIGnpyfi4+ONEmWysrKQlpYGpVKJpKQkODs7Y9euXQgLCzNKZiku\nLmZ9l5eX46uvvsKyZcvg7++P7Oxs1KtXzyBRJjExkfXt5eWFzz//HCdOnEBsbKxRokx2djbrOz09\nHU5OTti5cycjyjRp0kQvmaWkpAQJCQlQKpUoKyvD999/jyVLlsDX1xeZmZmoV6+eQaJMUlISsrKy\noFQqcenSJfznP//B8ePHERMTY5RGlpOTw7wjJycHw4YNw59//omQkBCjRJnS0lLWd2lpKebMmYOF\nCxfi5s2byMjIQN26dVGvXj29763k5GTW940bN0R5XkXv0Hje1q1b8fjxY1GeV1RUhCVLlnCeV7t2\nbZM9z8/PT5Tn5ebmsr4LCwsxfPhwbN68WbTnFRQU4Pfff8fs2bNx9epV0Z4XFBSEiRMn4tChQ4xG\nZqrnlZSUYMSIEdiwYYMkz1u/fj3zPE0+sFTPAwzTyCp6XllZGfO8e/fuIS8vz6DnAf8Aiebv+oFW\nOrv2T82aNWnBggWCWWwbN240WGtubk7Ozs707Nkzndr4+HiDtQCoVatWdPToUcHcpQEDBhisrV69\nOv3444+CmWY7duwwWGtmZkbDhg0TpChkZGQY7bt58+Z08OBBwey74cOHG6ytVq0azZgxQzDT7MCB\nA0bHHjx4MN2/f1+nNj8/32itnZ0d7dmzRzD7zsXFxWCttbU1ffvtt4LZYO7u7kbH7t+/P929e1en\nVqVSGa1t3Lgx7dixQzD77uOPPzZYW6VKFZo6dSolJyfr1Hp5eRkdu3fv3uTv769TS0Q69ALtn4YN\nG9LmzZsFM+S++uorg7VWVlb0+eefC+ZKXr161WjfPXr0oJs3bwr2rU180P6pX78+rV27VjBDbvr0\n6QZrK1WqRB9//LFgrqSfn5/Rvh0cHOjq1auCfWtTMrR/bGxsaOXKlYIZcvPmzTNYa2lpSRMnThQk\nBwUHBxvtu1OnTnThwgXBvlu1amWwtk6dOvTLL78I5o8uXbrUYK2FhQWNGzdOMJ/x2bNnRvtu3749\nnTlzRnAO7tSpk8Ha2rVr008//US5ubk6tatWrTJYa25uTqNHjxYkB0VFRRntu02bNuTh4SHYd8+e\nPQ3W1qxZk+bPny+Y47lp0yajfevzvISEBKN9t2rVio4cOSLY98CBAw3WVq9enWbPni3oebt27TJY\na2ZmRo6OjvTo0SOd2szMTKN9G/I8Z2dng7XVqlWj6dOnC2ZAHzp0yOjYgwYNEvS8goICo7WGPG/c\nuHEGaw15noeHh9Gx+/XrR4GBgTq1RP9ADuTfJTMzM3J3dwegTkPfv38/AKB3796M/ODg4CC4Sg8P\nD2eEjfz8fMyYMQPl5eVo0qQJS3l3dHQUvJIsKCjAuXPn2PHmzZvx+PFjWFtbY/jw4SypvXHjxoJ9\n37hxg23/3717F7t27QIA9OzZk6M3CPUdGRmJhw8fAlDvHnz//fcoLS2Fra0to6o4OTkJXkkWFxfj\nzJkz7HjHjh24d+8eqlSpAicnJ9a3PnqDr68v++j44cOH2Lp1KwCgW7durO+ePXsK7ji8fv0awcHq\nNKbS0lJMnz4dRUVFaNiwIet7+PDheikIp06dYsd79+7FnTt3ULlyZY5Ooo/e4O/vzygAz549w/r1\n6wEAXbt2ZWP37t1bsO+YmBjcvXsXgPpqdubMmcjLy0P9+vUZncTZ2VnwioyI4OHxP6T7oUOH4OPj\ng0qVKmHo0KGs75YtWwr2HRAQgLi4OADq1+vq1asBAJ06dWLnu0+fPoI7DvHx8bhz5w7rY/bs2cjO\nzoaNjQ2jfIwcOVLvLuSJEyfYx87Hjh3D1atXYWlpiSFDhrBz1rp1a8HaoKAgRpB5/fo1fv31VwBA\n+/btWd/9+vUTvHJPSkqCn58f63v+/PlIS0tDnTp1MGbMGCgUCowaNUrvLqSnpyf7OPHUqVM4f/48\nLCwsGJ1EoVCgXbt2grX3799nH8HGx8djyZIlAIC2bduyvvXRG1JTU+Hj48OOFy9ejMTEREa20hCi\n9O3mnT17ln1MdO7cOXh6esLc3BwDBgwwSix6/PgxI5qkpKRgwYIFAIDWrVuz5zxo0CDBXd/MzExc\nu3aNHf/yyy+Ijo5GjRo1GJ1k9OjResk/Fy5cYF8xuHLlCtzd3WFubo5+/fqx10nHjh0F+3769Cmj\nYWVlZWH27NkAgBYtWrDnPHjwYMFd35ycHFy5coUdr1y5Ei9fvkT16tUxcuRIKBQKjBkzBg0aNBDs\n+/Lly+yj+hs3bsDNzY2jfCgUCr3EorCwMDx79gyAeldv5syZ7BMBTd9Dhw4V3PXNzc3FxYsX2fG6\ndesQGhqKqlWrcnSSRo0aCfbt7e3NSDj+/v7466+/AAC9evViY5vieQUFBZg+fTrneQqFAsOGDTPJ\n8zSfeknxvKCgIOzcuRPA/zxPoVCgR48eJnne9OnTUVJSIsnzdu7ciaCgIFhZWTHPUygUJnneo0eP\nsGXLFgDiPa+srAzTp09HYWEhGjZsyLxj+PDhgruQ2p63b98+3L59m/O8sWPHonnz5oJ96/O8Ll26\nsL71eV5sbCwCAwMBqD3vhx9+QG5uLurVq8f6HjFihN5dyHf5K+x/fAdSo9WrV9OBAwcEd2OMydvb\nm37//Xd6/PixqKR2IjX9YdasWXTp0iXRdBIidTr+vn37RNNJiIh8fX1pxYoV9ODBA9G0jNzcXJo1\naxadP39eNOWDiGjLli20e/duvSxOQ7pz5w798ssvFBwcLLrvgoICmj17Nnl5eQnuDhjTjh07aOfO\nnXr504YUHBxMP//8M929e1c05aO4uJjmzJlDp0+fFk35IFKTbLZv366XP21IISEh9NNPP1FAQIDo\nvktLS2nevHl08uRJvTxkQzp48CBt3bqVoqKiRNe+ePGCFi5cSP7+/qLpJOXl5bRgwQI6fvw4ZWVl\niR77yJEjtGnTJr3cbEN69eoVzZs3j3x9fUVTPlQqFf3000907Ngx0WQrIvXuwfr16+nFixei57K4\nuDiaM2eOJLKVSqWiZcuW0eHDh0XTSYiIzpw5Q2vWrKHQ0FDRfScnJ9OPP/5I165dE022IiJasWIF\nHTp0SDTlg4jo4sWLtHr1anr69KnovjMyMmj27Nl05coV0XQSIrXn7d+/n5KSkkTXvovn5eTk0KxZ\ns+jixYvv5HliyVZEas/79ddfJXleXl4ezZ49+1/xvICAAPrll18kka3+Ds/bsWOHJM+7f/++aM/D\n+7QD+U+OJ0uWLFmyZMmSJUtYcg6kLFmyZMmSJUuWrH9M8gJSlixZsmTJkiVLlijJC0hZsmTJ9/Nx\nQQAAIABJREFUkiVLlixZoiQvIGXJkiVLlixZsmSJ0j8eJK4ZLzQ0FBs3boSVlRXs7Oz0htcKqby8\nHAsXLkRWVpbBEFh9cnV1hY+PD2xsbPTGXejTy5cvsWbNGqMhsEJSqVRYvHgx0tLSDIbA6pO7uzu8\nvb1Rp04dg+G1QoqOjsZvv/0mqW8iws8//4zExEQ0adJEbwisPp06dQoXLlwwGtgupMTERCxbtgwW\nFhYGQ2D19f3rr78iNjbWYAisPp07dw6nT59GrVq10LBhQ1F9p6WlYfHixSz4XEzfgDrc9dWrV0ZD\nYIV05coVHD9+3Gh4rZCys7OxaNEiqFQq2NnZ6Q2v1ac1a9bgxYsXsLW1NRh8LqSbN2/i8OHDqFGj\nhui+c3NzMX/+fJSVlaFp06Z6g8/1aePGjXj69CkaNWqEWrVqiar19/fHgQMHUK1aNTRu3FhU3wUF\nBZg3bx6Kiook9b1t2zY8fPjQaGC7kIKCgrB7926jge1CKi4uxvz585Gfnw87Ozu9ge36tHv3bty9\ne9doYLuQHj9+jG3bthkNPhdSaWkp5s+fj7dv36Jp06Z6A9v1af/+/fD39zcafC6ksLAwbNiw4Z08\nLzMzU5Lnubm54ebNm6hbty5sbGxEvUYjIyOxevVqo4HtQnpXzzt+/DiuXLliNLBdSDExMVixYgUs\nLS3RtGlT0Z63dOlSyZ7n6emJ8+fPGw0+F1JSUhKWLl0qyfMAYPny5YiJiTHZ894lSPwf/yvsY8eO\nAVD/gubOnYvMzEzUqVMHo0ePZplxQhPKixcvWCYWoM69u3TpEiwsLDBw4ECWD2Vvb69TW1BQgLNn\nz7LjmJgY/PLLLwDUmXGabKqBAwcKGqYmvV/T96JFi5CSkoJatWqx7LVRo0YJTigvX75kmViAGil4\n9uxZmJubo3///izjqX379jovsuLiYpw+fZodJyUlYfHixQCAVq1asec8ePBgQePx8fHhEIJLly5F\nXFwcatSogZEjR7IMM6FF9KtXr1gmFqBG8508eRJmZmZcZlynTp10+i4rK8PJkyfZcXp6OubNmwdA\nnRmnqR0yZIig8fj5+bFMLAD47bff8OrVK1SrVo1lr40dO1YwMy4mJoZlYgHAtWvXcPToUZiZmXF5\no127dtXpm4hw/PhxdpyTk4NZs2YBUJMANH07OjoKGs+dO3dYDiQA/PHHHwgPD0fVqlXh7OzM+hbK\njIuPj8ft27fZsa+vLw4dOgQA+OCDD9jrpFu3boKTkYeHB8uBzMvLw8yZM6FSqdCkSRMoFAooFAo4\nOTkJGk9QUBCH4tMspqytrbnsNaHMuKSkJNy6dYsdBwQEYO/evQCAHj16sHPWo0cPQcP09PRkeLai\noiJMnz4dZWVlsLW1ZRlmTk5OghP4/fv3ERUVxY63b9+OBw8ewMrKissbbdq0qU5tamoqbt68yT3W\nn3/+CQBwcHBgtb169RLs28vLi+VAanJSi4uL0aBBAy5vVGgCf/ToEcuBBIA9e/YgMDAQlStXhqOj\nIxtbKDMuIyODy4F88uQJNm/eDADo3LkzlxknZJjnz59nOZCanNT8/HyWGadQKDBixAjBxX9ISAjL\ngQSAgwcP4tatW6hUqRKGDBnCxhbKSc3JycHly5fZ8fPnz7F27VoAQMeOHdnrpF+/foJ9X7p0ieVA\nqlQqzJ49G0qlEnXr1uVyUoUW/6GhoSwHEgCOHDmC69evw9LSEoMHD2Zjt2nTRqc2NzcXFy5cYMdR\nUVH4/Xe119rb27Pf1YABAwSN/urVqywHkogwb948ZGRkoHbt2lzeqJDnhYeH4/Hjx+xY2/M0fZvi\nebGxsVi2bBkAoE2bNux3ZYrnAcCiRYuQnJzMPE+hUGD06NGCnhcZGYkHDx6wYy8vL3h5eXGep1Ao\n0KFDB6Oel5ycjEWLFgFQe57mOZvqecuWLUNsbCzzPE3eaP369XVqX79+jXv37rHjS5cu4cSJEzAz\nM0Pfvn3ZOTPF8zIyMjB37lwAQPPmzdlzHjp0qKDn+fv7IyEhgR0Led6YMWPQsGFDnVptz7t+/TqO\nHDnCPE9zzoQ8D3jPciCN/fTp04du3bqlk1VkjEQDgBo1akQbN27UyUQzhURjZWVFn332GcXHx+uM\nbYxEA4B69uxJ169f16k1RqIBQA0aNKA//vhDJxPNFBJN5cqV6eOPPxbMjDJGogHUtI1Lly7p1JpC\noqlXrx6tWLFCJ1vMFBJNpUqVaMKECfTq1SudsY2RaABQ586d6ezZszqZaKaQaOrWrUvLli3ToW2Y\nQqKxtLQkFxcXioiI0OnbGIkGAHXo0IE8PT11+jaFRFO7dm1avHixIKnJGInGwsKCRo8eTWFhYTq1\nxkg0AKht27bk7u6u07cpJJpatWrRvHnzBHMpjZFoNLSNkJAQnVpjJBpATdtwdXXVyXIzhURTo0YN\nmjVrlmAupTESjYa28fDhQ51aYyQaQE3b+Ouvv3T6NoVEU61aNfr+++8F8x2NkWjMzMxo0KBBFBQU\npFNrjEQDgJo2bUq7du3SyaAzhURTtWpV+uabbwTzHY2RaAA1YerOnTs6tcZINICaMLV161ad/FJT\nSDRVqlShKVOmCOY7GiPRAGrClK+vr06tMRINoPa8DRs26HieKSQajecJ5SQaI9EAasKUt7e3Tq0x\nEg2g3/NMIdFUrlyZJk+eTNHR0TpjGyPRAPo9zxQSjT7PM4VEY8jzjJFoADVhSsjzTCHR1KlTh5Yu\nXSqYpwlIz4H8xxeQsbGxFBsbS5GRkWRjY0NVqlShsWPH0t69ew0GlSqVSlYbGxtLn332GQGg7t27\n0/Lly+n+/ft6Az9LS0u52hMnThCgxrt98803dO7cOYNBpSkpKaz29evX1KhRI7KysqLRo0fTrl27\nBFFpGr19+5Ybe9q0aQSAunbtSsuWLTMYVFpWVsbVnjt3jgA13m3q1Kl05swZwcWERqmpqaz2zZs3\n1KxZM6pUqRKNGDGCduzYIfgG1Cg3N5cbe+bMmexFvGTJEgoMDNQbVFpeXs7VahYZNjY2NGXKFDp1\n6pTBcO60tDRWGxMTQ23btiVLS0tycnKibdu2Cb4BNcrLy+PGnj9/PgHqhduiRYvo9u3bekOuVSoV\nV+vr68vefJ9//jmdOHFCEN2lUXp6Otd3586dycLCghwdHWnz5s2CiDeN8vPzubE1Zt2uXTtasGAB\n3bp1y2DIdcXagIAAMjc3p1q1atGnn35K7u7ugrhKjTIyMrj6Dz74gMzNzWnw4MG0ceNGCg8P1xte\nXFBQwNWuXLmSAFDr1q1p7ty55OPjY7DvuLg4Vnv//n2ytLSkmjVr0scff0xHjhwxGHKdmZnJjT1w\n4EAyNzenAQMG0Lp16+j58+d6+y4sLORqN2zYQACoZcuW9OOPP9L169cNhnMnJCSw2idPnpCVlRVV\nr16dJk2aRG5ubpSamqq3NisrixvbycmJzMzMqG/fvvTHH3/Qs2fP9PZdVFTE1W7fvp0AULNmzeiH\nH34gb29vgyHXiYmJrDY0NJSqVatGVatWpfHjx9PBgwcNhnNnZ2dzY48dO5YtgFatWkVPnjzR23dx\ncTFXu2/fPgLUeLfp06fT5cuXDYZcJyUlsdrw8HCqVasWWVtbk4uLC/31118GwQ45OTnc2JMmTSJA\nfdH/22+/0aNHj/T2XVJSwtUePnyYAJCtrS199913dOHCBUHso0bJycmc59WrV4/zPKENC420Pe/z\nzz8nANStWzfRnnfy5Ml38jxbW1uqXLkyjRo1SrTnffPNN8zzli5dSkFBQSZ73vnz5yV7XnR0NDVv\n3pwqVapEzs7O9Oeff9KbN2/01mp73g8//CDZ865du8Y878svvxTtee3atWOet3XrVlGet2DBAgLU\naFBjnkf0ni0gNYqIiKDz588bfPPpU1lZGbm5uRl88xmSt7e3JKoKkZpacfbsWUkJ8+Xl5eTm5iYp\nYZ6I6MaNGxQUFCSaTkKkXmCcPn3a4JtPn1QqFR05csTgm8+QfH19JVFViNSGJ5WqolKp6NixY5Ko\nKkREt2/flkRVIVJPZB4eHgYXnIbk4eEhiapCRHT37l2jC059ysrKomPHjhlccBrSyZMnJVFViNS7\nazdv3hRNVSFSm5axBachnT59msLCwiT1/ejRI8lUlfz8fDp8+LDBBachnT17VhJVhYjo6dOnkqkq\nRUVF5ObmJokkRkR04cIFSVQVIqLnz59LJomVlJSQq6urJJIYEdHly5fp4cOHkrzj5cuXkqkqGs+T\nQlUhIrp27Zpkz3v9+rVkqkp5eTkdPnxYsufdvHlTEkmM6H+eJ4UkpvE8KSQxIqJbt27RnTt3/nHP\nIyLRnvcuC0iZRCNLlixZsmTJkvV/UDKJRpYsWbJkyZIlS9Y/JnkBKUuWLFmyZMmSJUuU5AWkLFmy\nZMmSJUuWLFH61xaQRMRy66To36r9N8eW+35/av/NsVUqFd7lu8Zy3+Jr38e+6X9/3PiPj/2ufb+P\nc8L72ve/Obbc9z8/thj9ayQaMzMzfPHFF7hy5QqICHZ2dqIoDMeOHcNPP/0EpVIpmh6RlpYGZ2dn\nREdHo1q1arC1tRVFBfjmm29w9uxZlJeXo2nTpqIoDKdPn8a8efOQnZ0tmsKQnZ0NJycnREVFwdra\nWjSFYebMmTh58iTKyspgZ2cnisJw6dIlzJw5E9nZ2ahfvz7q1q1rcm1ubi6cnJwQHh4uiR4xb948\nHD16FCUlJaIpDDdv3sQ333yDzMxM0fSIwsJCODk5ISwsTBKFYcmSJTh48CCKi4tF9x0QEIAvv/wS\n6enpoikMJSUlGDFiBJ48eSKJPPTbb79h9+7dKCoqEk2PePjwIT7++GOkp6ejdu3aoshD5eXlGDVq\nFB48eABLS0vY2dmJojCsXbsW27ZtQ0FBgWh6RGhoKMaPH4/U1FTR9AgigkKhwN27d2Fubi6aHrF1\n61asX78e+fn5oolJkZGRUCgUSEpKkkQemjBhAvz8/BgxSQx5aPfu3Vi1ahXy8vJEE5NiYmIwevRo\nJCQkoHr16qL7/vTTT3H9+nUAEN33wYMHsXz5cuTm5qJRo0aiiElJSUkYMWIEYmNjUb16ddja2prc\nt5mZGb788ktcunQJKpVKNHnI3d0dixcvhlKpFE0eSk9Ph5OTE968eSOJPPTtt99K9rwzZ85gzpw5\nyMnJEe15OTk5cHJyQmRkpCTP++GHH3DixAlJnnf58mXMmDEDWVlZoj0vLy/vnTxv/vz5OHLkiCTP\n8/HxwbRp00z2vPeKRDN9+nR2HB4eDn9/fwCAlZUVozC4uLjo0CNu3LgBLy8vdlxaWoqDBw+y465d\nu7K09969e3O/rOzsbCxdupR7vKtXryI2NhYAUL9+fUaPGDFihM4EvmnTJrx+/ZodR0VFwcfHBwBQ\nuXJlDB06lKW9t2jRgqv18/PjEurLy8tx4MABduXfqVMn9pz79OnDGX1eXh5L4dfo+vXrjBpSr149\njBkzBgqFAiNHjtSZCLdv384RL968ecMmXUtLS44e0apVK642MDAQGmoQoL6qOXjwIMrLywEA7du3\nZ7X9+vXjDLOkpARz5szhHs/X1xeRkZEAgLp16zIKw8iRI3Umwj179nDkiLi4OFy5cgUAYGFhgUGD\nBrGx27Zty9Xev38frq6u7JiI4Orqymgn7dq1Y6+TgQMHcn0TEWbOnMk93u3bt/HixQsAQO3atRl5\naPTo0ToT4YEDB/Do0SN2nJSUxCgW5ubmGDBgAOvb3t6eM56QkBDs27ePe7zDhw8z2knr1q1Z7aBB\ng3QMc/bs2SgrK2PHgYGBCA0NBQDUrFmTIw9pTyiHDx/mCAxpaWnsvWZubo5+/fqxsbXpEc+fP8fO\nnTu5x3N3d0dubi4AoGXLlhx5SNswFyxYgIKCAnYcHBzMiFPVq1fn+tamRxw/fhx37txhx5mZmfD0\n9ASgNus+ffqwvjt37sz1HRkZia1bt3KPd+LECeTk5AAAmjVrxl4njo6OOoa5ZMkSRkYB1HQZDX2j\nWrVqHHlImx5x+vRpNn8AgFKphIeHBzvu1asX69vBwYHrOyYmBuvXr9d5vIyMDACAnZ0dO9/Dhg3T\nMczly5ez+wLA06dPERQUBACwtrbG8OHD2fO2tbXlas+fPw9vb292nJ+fj6NHj7Ljnj17sr67d+/O\n9Z2YmIjVq1dzj3fu3DlGDbG1tWV9Ozk56Vy0rFy5EsnJyew4LCwMAQEBAIAqVarAycmJEZfs7Oy4\n2itXruDixYvsuKioCG5ubuy4W7durO+ePXty3pGWloYVK1Zwj3fx4kVGymrYsCFHHtK+aFm7di1H\np4qIiICfnx8A3vMUCgWaNWvG1d68eRNnzpxhx0Kepzln2p6Xk5ODn3/+mXs8fZ7n7Oyss/jX9rxX\nr14xclOlSpUwdOhQds60Pc/f3x8nTpxgx/o8T6FQoG/fvpzn5efnY+HChdzj3bhxg/ViY2PDkYe0\nPe/PP/9EeHg4O46OjmbkJo3nac5Z69atudq7d+9yr2eVSoVDhw6xefVdPE9D29OQh4x5Xnx8PCM3\nVfQ8hUKBdu3acbUPHz7kXhfante2bVvW94ABA3S8413+ClscZPFvUEWcVUXjKC4uhp+fHywtLVGp\nUiV8/vnn3CTy5s0brlZbz549g4WFBSwsLFCvXj0OS1VUVKRTm5mZyf6dnp4Ob29vWFpaMnRQxcnv\n7t27HI5QY+qA+oXj7+8PCwsLWFpa4ssvv+QWoDExMQb7fv78OSwtLWFpaQkbGxsOS1VaWqpTq8Fi\nAWpckre3NywsLFC1alWMGTOG6zs4OJhNsprzoFFZWRnu3LnDxv7yyy+5N2PFF7BGFS82IiIiuL47\nduzI/q+8vFynNjs7m3sO165dg4WFBapUqYJx48ZxfT948IDDzBUXF3OPHRgYyMauX78+92ZMSkrS\nGVuz6AXUC4crV67AwsICdevWRdeuXbn7atdqFhSaf2sQaFWqVMH48eO5Sfvx48dcveYNDKgno6Cg\nIK7vigu5lJQUnbFLS0vZv1+/fo2rV6/CwsICderUQffu3bn7ent7c+ep4uLm7du3uHHjBut74sSJ\n3KT99OlTbuyK46pUKty7dw+WlpawsLBA/fr1uYVcenq6Tt8VX2fR0dG4evUqLC0tUbt2bfTq1Yu7\n7/Xr13V61SgvLw83btyAhYUFKleujMmTJ3OTdlhYGDd2xd8zEeH+/fvc+a6IkMzKytLpu+J8pLlo\nsbCwQO3atdG3b1/uvjdv3kRaWho71iyYAbUJ+vj4sLns008/5SbtFy9ecGNrf+T08OFDru8mTZqw\n/9NGAmqPnZCQwM53zZo1MXDgQO6+t27d4hY0GqwhoJ7XfH19Wd//+c9/uIXzy5cvubG1Nx8eP37M\nXif16tXjFkS5ubkG31vJycms7xo1amDo0KHcfW/fvs3MGFCfY42Kiorg6+sLCwsL5h0VF86vXr0y\n2HdISAg73/Xq1eNQjAUFBQbn4NTUVOYdNWrUwLBhw7i5LCAggF3IaR5Po4qeZ2lpiS+++EKS52n6\nlup5VatWxciRI7m+g4KCOBxhRc8rLS3F7du3Oe+o6HmxsbFGPa+iV4vxvMzMTFy7do31LeR5FS8q\nhTxPc86mTJnCeV5CQoLO2BXfmxU9r27duujUqRN3P0Oel52dzbzD2tpax/MePnyIGzdusOOK3lFe\nXo6AgAB2zurXr89tXhjzvKioKPbeEvK8d5LUAEkpP6gQJE6kRpGZmuivrVu3bplMsdFWcXExNWvW\nzCSKjZDmzZtncqK/tu7du0dWVlYmJfprq7S0lNq2bWsSxUZIS5cuNTnRX1shISFkZWVFI0aMoD//\n/NMgxUZb5eXl1KlTJ5MS/YW0atUqkxP9tRUeHk5WVlYmUWy0pVKpqGfPniZRbIS0adMmkyk22nrz\n5g1ZW1vT0KFDjVJshPoeOHCgyRQbbe3atctkio22EhISqFq1ajR48GDasGGDQYqNkJydnU2m2Gjr\n0KFDVKNGDZo8ebLoUPG0tDSqUaOGSRQbIY0bN45atGhBs2fPNkqx0ZaHhwej2Li6uooKFc/OzqY6\ndeqYRLER0ieffMIoNlevXhUVKn727FmTKTbays3Npfr161OvXr1o5cqVBik2Qpo6dSo1adLEJIqN\ntry9vU2m2GiroKCAGjdubBLFRkgzZsyQ7Hl+fn4mU2y0VVxcTC1atDCJYiOk+fPnU8OGDWnatGmi\nQRrBwcHv5Hnt2rWjLl26GKXYCGnZsmWSPe/p06dkZWVlEsVGW+Xl5dSlSxfmeWJBGqtXr+Y8T0yo\neEREBPM8YxQboveURKNSqSg0NFRSMj6RmlEqhWJDpEagSaXYENE79f3q1StJif5EarOQmuhPpO5b\nSjI+kXpBI4ViQ6SmhEil2BARhYWFSe47OjpacqJ/Xl6eZIoNkZqYIYViQ6SmKAgxmE1RYWGhZIoN\nkbpvKRQbIjV3XirFpri4WDLFhojoxYsXkig2RGr6g1SKTVlZmWSKDZF6wpdCsSFSo/KkUmzKy8sp\nNDT0nfqWQrEhUpOapFJsVCqV6IVyRUVGRkqi2BCpcaViNisqStP3u3ieFIoNkdrzpFJsiP49z8vJ\nyflXPU8KxYZI7XlSKTZE7+Z5MTExojzvXRaQMolGlixZsmTJkiXr/6BkEo0sWbJkyZIlS5asf0zy\nAlKWLFmyZMmSJUuWKMkLSFmyZMmSJUuWLFmi9K8uIBMSErg/Vxejt2/fcnlmYhUdHS2ZwpCYmMhF\npohRXl4eF/8hVu/Sd1JSEhdrIEYFBQUss02K3qXv5ORkLkZCjIqKipCUlCSpFlD3LTXZPyUlhYvt\nEKOSkhIkJCRIqgXU8VFS+05LS+PiXcSorKyMi4kRq5iYGC6CQozS09O5SBsxUqlUiImJkVQLqKNL\npPadmZnJRRmJEREhOjpaUi2gjiuqmB8qRllZWVxUiRi9a9/x8fFc3JQY5eTkcNEwYvXmzRvJc9m/\n6Xnv0rfseeL1vnqeGP1rJBoAePLkCXr06IGQkBAUFxeLol6YmZnBwcEBnp6ekmgdq1evxowZM/D6\n9WtYWlqiadOmJtM6IiIi0KVLFzx69AiFhYWiqBfm5ubo1asXjh8/Lol6sXnzZkybNg2vX7+GhYWF\nKOpFdHQ02rdvjwcPHqCgoEAU9cLCwgIDBw6Em5sbUlNTUatWLTRs2NDkvnfv3o3PP/8cUVFRomkd\nSUlJaNeuHe7du4e8vDw0btzYZOqFhYUFhg8fjn379iE5OVk0rcPV1RWTJ0/Gy5cvAYijXmRkZKBN\nmzYIDAxEbm4ubG1tTaZemJubw8XFBTt27EBiYqJo6oWHhwc+/PBDREREiKZeKJVKtG7dGv7+/nj7\n9i0aNmxoMunJ3NwcH3/8MTZt2oTExERGvTC173PnzmHUqFF48eIFVCoV7OzsTKZeFBQUoE2bNvD1\n9UVOTo4oWoeZmRmmTp2K1atXIz4+XjSt49q1a3B0dERYWBjKyspE0TpKSkrQrl07XLt2TTTpyczM\nDDNnzsTy5csRFxcnmnrh5+eHQYMGITQ0FKWlpaJoHeXl5ejQoQMuXbqErKwsUaQnMzMzLFiwAIsX\nL0ZsbCwjPZnad3BwMPr06YOnT59KonV06tQJZ8+eRUZGBmxsbGBjY2Pya/SXX37BnDlzEB0dLZpQ\nFRISgu7du+PJkycoKiqCnZ2dKM/r3r07Tp06hfT0dNSpU0cU6emPP/6Q7HkvX75E586dJXmehYUF\n+vTpg2PHjiEtLU20523ZsgVff/01Xr16JdrzYmJi3snzBg8ejEOHDiElJUW05+3Zs4fzPDs7O5O9\nIzk5Ge3atUNQUJAkzxsxYgT27t2LpKQk1KhRw6B3vFckmorhm4A6VFfTg7m5Ofr37w8XFxd89NFH\nHB3F1dUVmzdv5moTExO5MNpWrVrBxcUF48aNg6OjIzthKSkpGD58OFebl5fHUvkBoEaNGox6MWHC\nBO6X9eWXXzI6hkbh4eFsh8fMzAx9+/ZlfVekoxw/fhxr1qzhapOSkrgr9xYtWkChUGDcuHEYPnw4\n6zsnJ0cnCLigoIC7ctcEn7u4uGDixImc0X/33XeMMqHRy5cv2Y6DmZkZevfuDRcXF0yaNAnt27dn\n9/Py8sKvv/7K1aakpHBhtE2bNmV9jxgxgk3+hYWFOoHRhYWFjKADAFWrVoWzszMUCgUmTZrEBaPO\nmjWL0Ro0ioyM5HYcPvjgA9Z3xdfU5cuX8dNPP3G1aWlpSE9PZ8eNGzdmfY8aNYpNokSELl26cLXF\nxcV49eoVO65SpQqjdUyaNIkzzIULF3KkDkAdYlzxyr179+6stmKg682bNzF37lyuNj09nbtyb9So\nEaMojBkzhptEe/Towe1slJSUICoqih1bWVlh2LBhbOwGDRqw/1u2bBnOnz/Pjf369Wvuyt3BwYG9\nxiqGmAcEBKAiXQpQ76hVvHJv0KABo14oFApuEu3fvz8XHl5WVsYW68D/SE+a91bFMPCVK1fi1KlT\n3NjR0dHcrm/nzp1Z3x988AG7/cGDB/j666+52qysLI52oiE9aSgOFReEjo6O3GuqvLycoz5VqlSJ\nkZ4mTZrEhYFv2LABR44c4caOjY3ldn07dOjA5qKKIeahoaH4z3/+w9Xm5OQwMgqgJj1pCFUffvgh\ntyAcNWoUt7NNRIy0BKhpHRrqxUcffcQRwbZv3479+/dzY8fFxXG7vvb29lAoFBg/fjwGDBjA5rLI\nyEhMnDiRq1UqlVwvtWvXZrSODz/8kFtYjR8/nnsfavetucBVKBT46KOPODrKvn37sGPHDm7shIQE\nbte3TZs2bNzBgwezvuPi4jBmzBiuNjc3l9tlr1mzJiNUjR8/nlugfPLJJ3j+/DlXr8/zJk2axNFR\n3NzcsGnTJq5W2/NatmzJ+q7oeampqXBycuJq9XmeQqHAhAkTuIvbKVOm4PHjx1x9RESj8Vx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PQ9dOhQnZ3Ev/76S4eWtGTJEuTm5jJspIuLC8aOHatDGPH19eXoLwDg6enJ0Fq9evViYzs4OHB9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\nDoXhw4dD//79RZ/yWFpagqenJ3nOhg0bBh4eHqJ9N2zYENq3b098d+zYUbR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = pyplot.figure(figsize = (11,7), dpi=100)\n", + "pyplot.quiver(X, Y, u, v);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learn more\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### What is the meaning of the $F$ term?\n", + "\n", + "Step 12 is an exercise demonstrating the problem of flow in a channel or pipe. If you recall from your fluid mechanics class, a specified pressure gradient is what drives Poisseulle flow. \n", + "\n", + "Recall the $x$-momentum equation:\n", + "\n", + "$$\\frac{\\partial u}{\\partial t}+u \\cdot \\nabla u = -\\frac{\\partial p}{\\partial x}+\\nu \\nabla^2 u$$\n", + "\n", + "What we actually do in Step 12 is split the pressure into steady and unsteady components $p=P+p'$. The applied steady pressure gradient is the constant $-\\frac{\\partial P}{\\partial x}=F$ (interpreted as a source term), and the unsteady component is $\\frac{\\partial p'}{\\partial x}$. So the pressure that we solve for in Step 12 is actually $p'$, which for a steady flow is in fact equal to zero everywhere.\n", + "\n", + "Why did we do this?\n", + "\n", + "Note that we use periodic boundary conditions for this flow. For a flow with a constant pressure gradient, the value of pressure on the left edge of the domain must be different from the pressure at the right edge. So we cannot apply periodic boundary conditions on the pressure directly. It is easier to fix the gradient and then solve for the perturbations in pressure.\n", + "\n", + "Shouldn't we always expect a uniform/constant $p'$ then?\n", + "\n", + "That's true only in the case of steady laminar flows. At high Reynolds numbers, flows in channels can become turbulent, and we will see unsteady fluctuations in the pressure, which will result in non-zero values for $p'$. \n", + "\n", + "In step 12, note that the pressure field itself is not constant, but it's the pressure perturbation field that is. The pressure field varies linearly along the channel with slope equal to the pressure gradient. Also, for incompressible flows, the absolute value of the pressure is inconsequential.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### And explore more CFD materials online" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The interactive module **12 steps to Navier–Stokes** is one of several components of the Computational Fluid Dynamics class taught by Prof. Lorena A. Barba in Boston University between 2009 and 2013. \n", + "\n", + "For a sample of what the othe components of this class are, you can explore the **Resources** section of the Spring 2013 version of [the course's Piazza site](https://piazza.com/bu/spring2013/me702/resources).\n", + "\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "def css_styling():\n", + " styles = open(\"../styles/custom.css\", \"r\").read()\n", + " return HTML(styles)\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(The cell above executes the style for this notebook.)" + ] + } + ], + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/lessons/16_Step_12.ipynb b/lessons/16_Step_12.ipynb deleted file mode 100644 index 03b29dd7..00000000 --- a/lessons/16_Step_12.ipynb +++ /dev/null @@ -1,633 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Lorena A. Barba, 2013. Thanks: Gilbert Forsyth for help writing the notebooks. NSF for support via CAREER award #1149784." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[@LorenaABarba](https://twitter.com/LorenaABarba)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "12 steps to Navier-Stokes\n", - "=====\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Did you make it this far? This is the last step! How long did it take you to write your own Navier-Stokes solver in Python following this interactive module? Let us know!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Step 12: Channel Flow with Navier-Stokes\n", - "----\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The only difference between this final step and Step 11 is that we are going to add a source term to the $u$-momentum equation, to mimic the effect of a pressure-driven channel flow. Here are our modified Navier-Stokes equations:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\\frac{\\partial u}{\\partial t}+u\\frac{\\partial u}{\\partial x}+v\\frac{\\partial u}{\\partial y}=-\\frac{1}{\\rho}\\frac{\\partial p}{\\partial x}+\\nu\\left(\\frac{\\partial^2 u}{\\partial x^2}+\\frac{\\partial^2 u}{\\partial y^2}\\right)+F$$\n", - "\n", - "$$\\frac{\\partial v}{\\partial t}+u\\frac{\\partial v}{\\partial x}+v\\frac{\\partial v}{\\partial y}=-\\frac{1}{\\rho}\\frac{\\partial p}{\\partial y}+\\nu\\left(\\frac{\\partial^2 v}{\\partial x^2}+\\frac{\\partial^2 v}{\\partial y^2}\\right)$$\n", - "\n", - "$$\\frac{\\partial^2 p}{\\partial x^2}+\\frac{\\partial^2 p}{\\partial y^2}=-\\rho\\left(\\frac{\\partial u}{\\partial x}\\frac{\\partial u}{\\partial x}+2\\frac{\\partial u}{\\partial y}\\frac{\\partial v}{\\partial x}+\\frac{\\partial v}{\\partial y}\\frac{\\partial v}{\\partial y}\\right)\n", - "$$" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Discretized equations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With patience and care, we write the discretized form of the equations. It is highly recommended that you write these in your own hand, mentally following each term as you write it.\n", - "\n", - "The $u$-momentum equation:\n", - "\n", - "\\begin{eqnarray}\n", - "&&\\frac{u_{i,j}^{n+1}-u_{i,j}^{n}}{\\Delta t}+u_{i,j}^{n}\\frac{u_{i,j}^{n}-u_{i-1,j}^{n}}{\\Delta x}+v_{i,j}^{n}\\frac{u_{i,j}^{n}-u_{i,j-1}^{n}}{\\Delta y}\\\\\\\n", - "&&=-\\frac{1}{\\rho}\\frac{p_{i+1,j}^{n}-p_{i-1,j}^{n}}{2\\Delta x}\\\\\\\n", - "&&+\\nu\\left(\\frac{u_{i+1,j}^{n}-2u_{i,j}^{n}+u_{i-1,j}^{n}}{\\Delta x^2}+\\frac{u_{i,j+1}^{n}-2u_{i,j}^{n}+u_{i,j-1}^{n}}{\\Delta y^2}\\right)+F_{i,j}\n", - "\\end{eqnarray}\n", - "\n", - "The $v$-momentum equation:\n", - "\n", - "\\begin{eqnarray}\n", - "&&\\frac{v_{i,j}^{n+1}-v_{i,j}^{n}}{\\Delta t}+u_{i,j}^{n}\\frac{v_{i,j}^{n}-v_{i-1,j}^{n}}{\\Delta x}+v_{i,j}^{n}\\frac{v_{i,j}^{n}-v_{i,j-1}^{n}}{\\Delta y}\\\\\\\n", - "&&=-\\frac{1}{\\rho}\\frac{p_{i,j+1}^{n}-p_{i,j-1}^{n}}{2\\Delta y}\\\\\\\n", - "&&+\\nu\\left(\\frac{v_{i+1,j}^{n}-2v_{i,j}^{n}+v_{i-1,j}^{n}}{\\Delta x^2}+\\frac{v_{i,j+1}^{n}-2v_{i,j}^{n}+v_{i,j-1}^{n}}{\\Delta y^2}\\right)\n", - "\\end{eqnarray}\n", - "\n", - "And the pressure equation:\n", - "\n", - "\\begin{eqnarray}\n", - "&&\\frac{p_{i+1,j}^{n}-2p_{i,j}^{n}+p_{i-1,j}^{n}}{\\Delta x^2}+\\frac{p_{i,j+1}^{n}-2*p_{i,j}^{n}+p_{i,j-1}^{n}}{\\Delta y^2}\\\\\\\n", - "&&=\\rho\\left(\\frac{1}{\\Delta t}\\left(\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}+\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right)\\right.\\\\\\\n", - "&&-\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\\\\\\n", - "&&-2\\frac{u_{i,j+1}-u_{i,j-1}}{2\\Delta y}\\frac{v_{i+1,j}-v_{i-1,j}}{2\\Delta x}\\\\\\\n", - "&&-\\left.\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right)\n", - "\\end{eqnarray}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As always, we need to re-arrange these equations to the form we need in the code to make the iterations proceed. \n", - "\n", - "For the $u$- and $v$ momentum equations, we isolate the velocity at time step `n+1`:\n", - "\n", - "$$u_{i,j}^{n+1} = u_{i,j}^{n} - u_{i,j}^{n}\\frac{\\Delta t}{\\Delta x}(u_{i,j}^{n}-u_{i-1,j}^{n})-v_{i,j}^{n}\\frac{\\Delta t}{\\Delta y}(u_{i,j}^{n}-u_{i,j-1}^{n})$$\n", - "$$-\\frac{\\Delta t}{\\rho 2\\Delta x}(p_{i+1,j}^{n}-p_{i-1,j}^{n})+\\nu\\left[\\frac{\\Delta t}{\\Delta x^2}(u_{i+1,j}^{n}-2u_{i,j}^{n}+u_{i-1,j}^{n})\\right.$$\n", - "$$+\\left.\\frac{\\Delta t}{\\Delta y^2}(u_{i,j+1}^{n}-2u_{i,j}^{n}+u_{i,j-1}^{n})\\right] + F\\Delta t$$\n", - "\n", - "$$v_{i,j}^{n+1}=v_{i,j}^{n} - u_{i,j}^{n}\\frac{\\Delta t}{\\Delta x}(v_{i,j}^{n}-v_{i-1,j}^{n})-v_{i,j}^{n}\\frac{\\Delta t}{\\Delta y}(v_{i,j}^{n}-v_{i,j-1}^{n})$$\n", - "$$-\\frac{\\Delta t}{\\rho 2\\Delta y}(p_{i,j+1}^{n}-p_{i,j-1}^{n})+\\nu\\left[\\frac{\\Delta t}{\\Delta x^2}(v_{i+1,j}^{n}-2v_{i,j}^{n}+v_{i-1,j}^{n})\\right.$$\n", - "$$+\\left.\\frac{\\Delta t}{\\Delta y^2}(v_{i,j+1}^{n}-2v_{i,j}^{n}+v_{i,j-1}^{n})\\right]$$\n", - "\n", - "And for the pressure equation, we isolate the term $p_{i,j}^n$ to iterate in pseudo-time:\n", - "\n", - "$$p_{i,j}^{n} = \\frac{(p_{i+1,j}^{n}+p_{i-1,j}^{n})\\Delta y^2+(p_{i,j+1}^{n}+p_{i,j-1}^{n})\\Delta x^2}{2(\\Delta x^2+\\Delta y^2)}-\\frac{\\rho\\Delta x^2\\Delta y^2}{2(\\Delta x^2+\\Delta y^2)}\\times$$\n", - "$$\\left[\\frac{1}{\\Delta t}\\left(\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}+\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right)- \\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x}\\frac{u_{i+1,j}-u_{i-1,j}}{2\\Delta x} \\right.$$\n", - "$$- 2\\frac{u_{i,j+1}-u_{i,j-1}}{2\\Delta y}\\frac{v_{i+1,j}-v_{i-1,j}}{2\\Delta x}-\\left.\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\frac{v_{i,j+1}-v_{i,j-1}}{2\\Delta y}\\right]$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The initial condition is $u, v, p=0$ everywhere, and at the boundary conditions are:\n", - "\n", - "$u, v, p$ are periodic on $x=0,2$\n", - "\n", - "$u, v =0$ at $y =0,2$\n", - "\n", - "$\\frac{\\partial p}{\\partial y}=0$ at $y =0,2$\n", - "\n", - "$F=1$ everywhere.\n", - "\n", - "Let's begin by importing our usual run of libraries:\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib import cm\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In step 11, we isolated a portion of our transposed equation to make it easier to parse and we're going to do the same thing here. One thing to note is that we have periodic boundary conditions throughout this grid, so we need to explicitly calculate the values at the leading and trailing edge of our `u` vector." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def buildUpB(rho, dt, dx, dy, u, v):\n", - " b = np.zeros_like(u)\n", - " \n", - " b[1:-1,1:-1]=rho*(1/dt*((u[2:,1:-1]-u[0:-2,1:-1])/(2*dx)+(v[1:-1,2:]-v[1:-1,0:-2])/(2*dy))-\\\n", - "\t\t((u[2:,1:-1]-u[0:-2,1:-1])/(2*dx))**2-\\\n", - "\t\t2*((u[1:-1,2:]-u[1:-1,0:-2])/(2*dy)*(v[2:,1:-1]-v[0:-2,1:-1])/(2*dx))-\\\n", - "\t\t((v[1:-1,2:]-v[1:-1,0:-2])/(2*dy))**2)\t\n", - "\t\n", - "\t####Periodic BC Pressure @ x = 2\n", - " b[-1,1:-1]=rho*(1/dt*((u[0,1:-1]-u[-2,1:-1])/(2*dx)+(v[-1,2:]-v[-1,0:-2])/(2*dy))-\\\n", - "\t\t((u[0,1:-1]-u[-2,1:-1])/(2*dx))**2-\\\n", - "\t\t2*((u[-1,2:]-u[-1,0:-2])/(2*dy)*(v[0,1:-1]-v[-2,1:-1])/(2*dx))-\\\n", - "\t\t((v[-1,2:]-v[-1,0:-2])/(2*dy))**2)\t\n", - "\n", - "\t####Periodic BC Pressure @ x = 0\n", - " b[0,1:-1]=rho*(1/dt*((u[1,1:-1]-u[-1,1:-1])/(2*dx)+(v[0,2:]-v[0,0:-2])/(2*dy))-\\\n", - "\t\t((u[1,1:-1]-u[-1,1:-1])/(2*dx))**2-\\\n", - "\t\t2*((u[0,2:]-u[0,0:-2])/(2*dy)*(v[1,1:-1]-v[-1,1:-1])/(2*dx))-\\\n", - "\t\t((v[0,2:]-v[0,0:-2])/(2*dy))**2)\n", - " \n", - " return b" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll also define a Pressure Poisson iterative function, again like we did in Step 11. Once more, note that we have to include the periodic boundary conditions at the leading and trailing edge. We also have to specify the boundary conditions at the top and bottom of our grid. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def presPoissPeriodic(p, dx, dy):\n", - " pn = np.empty_like(p)\n", - " \n", - " for q in range(nit):\t\n", - "\t\tpn[:]=p[:]\n", - "\t\tp[1:-1,1:-1] = ((pn[2:,1:-1]+pn[0:-2,1:-1])*dy**2+(pn[1:-1,2:]+pn[1:-1,0:-2])*dx**2)/\\\n", - "\t\t\t(2*(dx**2+dy**2)) -\\\n", - "\t\t\tdx**2*dy**2/(2*(dx**2+dy**2))*b[1:-1,1:-1]\n", - "\n", - "\t\t####Periodic BC Pressure @ x = 2\n", - "\t\tp[-1,1:-1] = ((pn[0,1:-1]+pn[-2,1:-1])*dy**2+(pn[-1,2:]+pn[-1,0:-2])*dx**2)/\\\n", - "\t\t\t(2*(dx**2+dy**2)) -\\\n", - "\t\t\tdx**2*dy**2/(2*(dx**2+dy**2))*b[-1,1:-1]\n", - "\n", - "\t\t####Periodic BC Pressure @ x = 0\n", - "\t\tp[0,1:-1] = ((pn[1,1:-1]+pn[-1,1:-1])*dy**2+(pn[0,2:]+pn[0,0:-2])*dx**2)/\\\n", - "\t\t\t(2*(dx**2+dy**2)) -\\\n", - "\t\t\tdx**2*dy**2/(2*(dx**2+dy**2))*b[0,1:-1]\n", - "\t\t\n", - "\t\t####Wall boundary conditions, pressure\n", - "\t\tp[-1,:] =p[-2,:]\t##dp/dy = 0 at y = 2\n", - "\t\tp[0,:] = p[1,:]\t\t##dp/dy = 0 at y = 0\n", - " \n", - " return p" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have our familiar list of variables and initial conditions to declare before we start." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "##variable declarations\n", - "nx = 41\n", - "ny = 41\n", - "nt = 10\n", - "nit=50 \n", - "c = 1\n", - "dx = 2.0/(nx-1)\n", - "dy = 2.0/(ny-1)\n", - "x = np.linspace(0,2,nx)\n", - "y = np.linspace(0,2,ny)\n", - "Y,X = np.meshgrid(y,x)\n", - "\n", - "\n", - "##physical variables\n", - "rho = 1\n", - "nu = .1\n", - "F = 1\n", - "dt = .01\n", - "\n", - "#initial conditions\n", - "u = np.zeros((ny,nx)) ##create a XxY vector of 0's\n", - "un = np.zeros((ny,nx)) ##create a XxY vector of 0's\n", - "\n", - "v = np.zeros((ny,nx)) ##create a XxY vector of 0's\n", - "vn = np.zeros((ny,nx)) ##create a XxY vector of 0's\n", - "\n", - "p = np.ones((ny,nx)) ##create a XxY vector of 0's\n", - "pn = np.ones((ny,nx)) ##create a XxY vector of 0's\n", - "\n", - "b = np.zeros((ny,nx))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the meat of our computation, we're going to reach back to a trick we used in Step 9 for Laplace's Equation. We're interested in what our grid will look like once we've reached a near-steady state. We can either specify a number of timesteps `nt` and increment it until we're satisfied with the results, or we can tell our code to run until the difference between two consecutive iterations is very small. \n", - "\n", - "We also have to manage **8** separate boundary conditions for each iteration. The code below writes each of them out explicitly. If you're interested in a challenge, you can try to write a function which can handle some or all of these boundary conditions. If you're interested in tackling that, you should probably read up on Python [dictionaries](http://docs.python.org/2/tutorial/datastructures.html#dictionaries). " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "udiff = 1\n", - "stepcount = 0\n", - "\n", - "while udiff > .001:\n", - " un = u.copy()\n", - " vn = v.copy()\n", - "\t\n", - " b = buildUpB(rho, dt, dx, dy, u, v)\n", - " p = presPoissPeriodic(p, dx, dy)\n", - "\t\t\n", - " u[1:-1,1:-1] = un[1:-1,1:-1]-\\\n", - "\t\tun[1:-1,1:-1]*dt/dx*(un[1:-1,1:-1]-un[0:-2,1:-1])-\\\n", - "\t\tvn[1:-1,1:-1]*dt/dy*(un[1:-1,1:-1]-un[1:-1,0:-2])-\\\n", - "\t\tdt/(2*rho*dx)*(p[2:,1:-1]-p[0:-2,1:-1])+\\\n", - "\t\tnu*(dt/dx**2*(un[2:,1:-1]-2*un[1:-1,1:-1]+un[0:-2,1:-1])+\\\n", - "\t\tdt/dy**2*(un[1:-1,2:]-2*un[1:-1,1:-1]+un[1:-1,0:-2]))+F*dt\n", - "\t\n", - " v[1:-1,1:-1] = vn[1:-1,1:-1]-\\\n", - "\t\tun[1:-1,1:-1]*dt/dx*(vn[1:-1,1:-1]-vn[0:-2,1:-1])-\\\n", - "\t\tvn[1:-1,1:-1]*dt/dy*(vn[1:-1,1:-1]-vn[1:-1,0:-2])-\\\n", - "\t\tdt/(2*rho*dy)*(p[1:-1,2:]-p[1:-1,0:-2])+\\\n", - "\t\tnu*(dt/dx**2*(vn[2:,1:-1]-2*vn[1:-1,1:-1]+vn[0:-2,1:-1])+\\\n", - "\t\t(dt/dy**2*(vn[1:-1,2:]-2*vn[1:-1,1:-1]+vn[1:-1,0:-2])))\n", - "\t\n", - "\t####Periodic BC u @ x = 2\n", - " u[-1,1:-1] = un[-1,1:-1]-\\\n", - "\t\tun[-1,1:-1]*dt/dx*(un[-1,1:-1]-un[-2,1:-1])-\\\n", - "\t\tvn[-1,1:-1]*dt/dy*(un[-1,1:-1]-un[-1,0:-2])-\\\n", - "\t\tdt/(2*rho*dx)*(p[0,1:-1]-p[-2,1:-1])+\\\n", - "\t\tnu*(dt/dx**2*(un[0,1:-1]-2*un[-1,1:-1]+un[-2,1:-1])+\\\n", - "\t\tdt/dy**2*(un[-1,2:]-2*un[-1,1:-1]+un[-1,0:-2]))+F*dt\n", - "\n", - "\t####Periodic BC u @ x = 0\n", - " u[0,1:-1] = un[0,1:-1]-\\\n", - "\t\tun[0,1:-1]*dt/dx*(un[0,1:-1]-un[-1,1:-1])-\\\n", - "\t\tvn[0,1:-1]*dt/dy*(un[0,1:-1]-un[0,0:-2])-\\\n", - "\t\tdt/(2*rho*dx)*(p[1,1:-1]-p[-1,1:-1])+\\\n", - "\t\tnu*(dt/dx**2*(un[1,1:-1]-2*un[0,1:-1]+un[-1,1:-1])+\\\n", - "\t\tdt/dy**2*(un[0,2:]-2*un[0,1:-1]+un[0,0:-2]))+F*dt\n", - "\n", - "\t####Periodic BC v @ x = 2\n", - " v[-1,1:-1] = vn[-1,1:-1]-\\\n", - "\t\tun[-1,1:-1]*dt/dx*(vn[-1,1:-1]-vn[-2,1:-1])-\\\n", - "\t\tvn[-1,1:-1]*dt/dy*(vn[-1,1:-1]-vn[-1,0:-2])-\\\n", - "\t\tdt/(2*rho*dy)*(p[-1,2:]-p[-1,0:-2])+\\\n", - "\t\tnu*(dt/dx**2*(vn[0,1:-1]-2*vn[-1,1:-1]+vn[-2,1:-1])+\\\n", - "\t\t(dt/dy**2*(vn[-1,2:]-2*vn[-1,1:-1]+vn[-1,0:-2])))\n", - "\n", - "\t####Periodic BC v @ x = 0\n", - " v[0,1:-1] = vn[0,1:-1]-\\\n", - "\t\tun[0,1:-1]*dt/dx*(vn[0,1:-1]-vn[-1,1:-1])-\\\n", - "\t\tvn[0,1:-1]*dt/dy*(vn[0,1:-1]-vn[0,0:-2])-\\\n", - "\t\tdt/(2*rho*dy)*(p[0,2:]-p[0,0:-2])+\\\n", - "\t\tnu*(dt/dx**2*(vn[1,1:-1]-2*vn[0,1:-1]+vn[-1,1:-1])+\\\n", - "\t\t(dt/dy**2*(vn[0,2:]-2*vn[0,1:-1]+vn[0,0:-2])))\n", - "\n", - "\t####Wall BC: u,v = 0 @ y = 0,2\n", - " u[:,0] = 0\n", - " u[:,-1] = 0\n", - " v[:,0] = 0\n", - " v[:,-1]=0\n", - "\t\n", - " udiff = (np.sum(u)-np.sum(un))/np.sum(u)\n", - " stepcount += 1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can see that we've also included a variable `stepcount` to see how many iterations our loop went through before our stop condition was met. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print stepcount" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "499\n" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to see how the number of iterations increases as our `udiff` condition gets smaller and smaller, try defining a function to perform the `while` loop written above that takes an input `udiff` and outputs the number of iterations that the function runs. \n", - "\n", - "For now, let's look at our results. We've used the quiver function to look at the cavity flow results and it works well for channel flow, too. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig = plt.figure(figsize = (11,7), dpi=100)\n", - "plt.quiver(X[::3, ::3], Y[::3, ::3], u[::3, ::3], v[::3, ::3])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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GjRsNfl1zR0CTXnFcsGABevfurX1X2qNHD7N4t9GYhQsXonv37tqpPSQkxCK6lyxZgoCA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\n1niutEwmo86dOwvdERERRne7urparJvo/69iKJVKatu2rdHdLi4uJu0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TAAAN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\nAQAAAEAUDI4AAAAAIAoGRwAAAAAQ5f8BzzdmkaqFw50AAAAASUVORK5CYII=\n" - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The structures in the `quiver` command that look like `[::3, ::3]` are useful when dealing with large amounts of data that you want to visualize. The one used above tells `matplotlib` to only plot every 3rd data point. If we leave it out, you can see that the results can appear a little crowded. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig = plt.figure(figsize = (11,7), dpi=100)\n", - "plt.quiver(X, Y, u, v)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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jx7nbjRITE2nmzJncTQ+LFy9muvqvy6lTp7ibHlJTU2n69OncTQ/Lly/nbjc6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\nb9++uGbNGlEGZ2dnp7Z2q1atcM6cOXjnzh29tbNnz1artbCwwA8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- } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Learn more\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#####What is the meaning of the $F$ term?\n", - "\n", - "Step 12 is an exercise demonstrating the problem of flow in a channel or pipe. If you recall from your fluid mechanics class, a specified pressure gradient is what drives Poisseulle flow. \n", - "\n", - "Recall the $x$-momentum equation:\n", - "\n", - "$$\\frac{\\partial u}{\\partial t}+u \\cdot \\nabla u = -\\frac{\\partial p}{\\partial x}+\\nu \\nabla^2 u$$\n", - "\n", - "What we actually do in Step 12 is split the pressure into steady and unsteady components $p=P+p'$. The applied steady pressure gradient is the constant $-\\frac{\\partial P}{\\partial x}=F$ (interpreted as a source term), and the unsteady component is $\\frac{\\partial p'}{\\partial x}$. So the pressure that we solve for in Step 12 is actually $p'$, which for a steady flow is in fact equal to zero everywhere.\n", - "\n", - "Why did we do this?\n", - "\n", - "Note that we use periodic boundary conditions for this flow. For a flow with a constant pressure gradient, the value of pressure on the left edge of the domain must be different from the pressure at the right edge. So we cannot apply periodic boundary conditions on the pressure directly. It is easier to fix the gradient and then solve for the perturbations in pressure.\n", - "\n", - "Shouldn't we always expect a uniform/constant $p'$ then?\n", - "\n", - "That's true only in the case of steady laminar flows. At high Reynolds numbers, flows in channels can become turbulent, and we will see unsteady fluctuations in the pressure, which will result in non-zero values for $p'$. \n", - "\n", - "In step 12, note that the pressure field itself is not constant, but it's the pressure perturbation field that is. The pressure field varies linearly along the channel with slope equal to the pressure gradient. Also, for incompressible flows, the absolute value of the pressure is inconsequential.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#####And explore more CFD materials online" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The interactive module **12 steps to Navier-Stokes** is one of several components of the Computational Fluid Dynamics class taught by Prof. Lorena A. Barba in Boston University between 2009 and 2013. \n", - "\n", - "For a sample of what the othe components of this class are, you can explore the **Resources** section of the Spring 2013 version of [the course's Piazza site](https://piazza.com/bu/spring2013/me702/resources).\n", - "\n", - "***" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(The cell above executes the style for this notebook.)" - ] - } - ], - "metadata": {} - } - ] -} diff --git a/lessons/17_NumbaPro.ipynb.bak b/lessons/17_NumbaPro.ipynb.bak deleted file mode 100644 index 8c6088ac..00000000 --- a/lessons/17_NumbaPro.ipynb.bak +++ /dev/null @@ -1,536 +0,0 @@ -{ - "metadata": { - "name": "17 - NumbaPro" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Further Optimization using NumbaPro\n", - "---\n", - "\n", - "One of the most exciting new products from [Continuum Analytics](www.continuum.io) is called NumbaPro, which allows code written in Python to target CUDA-capable GPUs for parallelized computation. \n", - "\n", - "For a quick primer on how parallel computation on GPUs works, check out ???\n", - "\n", - "Now, for a brief proof-of-concept look at the capabilities of NumbaPro, let's return to our old friend, 1D Nonlinear Convection. \n", - "\n", - "Yes, this is a trivial problem, but it is a good demonstration of the potential for speed gains using NumbaPro and GPU computation. \n", - "\n", - "We'll start by importing the usual libraries, plus the `time` library, so we can measure our performance gains, and also the appropriate libraries from `numbapro`. \n", - "\n", - "`autojit` is the same library we used with regular `numba`, and in fact we'll be using it the same way, to provide a comparison between regular Numba and NumbaPro. \n", - "\n", - "`cuda` is the NumbaPro library that provides the CUDA intrinsics which allow us to target the GPU for computation. \n", - "\n", - "`float32` is a data type. Python generally takes care of whether we want an `int` or a `str` for us, but when we start delving into the dark depths of memory management, it can be helpful (and sometimes required) to be a bit more specific concerning our data formats. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import time\n", - "from numbapro import autojit, cuda, jit, float" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first function we're trying out is a simple implementation using array operations in Numpy. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "###1-D Nonlinear convection implemented using Numpy\n", - "def NonLinNumpy(u, un, nx, nt, dx, dt):\n", - "\n", - " ###Run through nt timesteps and plot/animate each step\n", - " for n in range(nt): ##loop across number of time steps\n", - " un = u.copy()\n", - " u[1:] = -un[1:]*dt/dx*(un[1:]-un[:-1])+un[1:]\n", - " \n", - " return u" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The 'vanilla' version is what we used for Step 2, two nested loops and not that efficient. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "###1-D Nonlinear convection implemented using 'vanilla' Python\n", - "def NonLinVanilla(u, nx, nt, dx, dt):\n", - "\n", - " for n in range(nt):\n", - " for i in range(1,nx-1):\n", - " u[i+1] = -u[i]*dt/dx*(u[i]-u[i-1])+u[i]\n", - "\n", - " return u" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we've implemented the same 'vanilla' version, but we've added the `@autojit` decorator, which will tell Numba to JIT compile this function for a nice speed boost. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "###1-D Nonlinear convection implemented using Numba JIT compiler (similar to LLVM)\n", - "@autojit\n", - "def NonLinNumba(u,un, nx, nt, dx, dt):\n", - "\n", - " for n in range(nt):\n", - " for i in range(1,nx):\n", - " un[i] = -u[i]*dt/dx*(u[i]-u[i-1])+u[i]\n", - "\n", - " return un" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CUDA JIT\n", - "---\n", - "\n", - "There's a lot going on here that will be new to you, so we'll go through it piece by piece. \n", - "\n", - "`@jit(argtypes=[float32[:], float32, float32, float32, float32[:]], target='gpu')`\n", - "\n", - "Instead of `@autojit` which automatically figures out data-types for us, we have to specify what kind of variables will be sent to this function (which is actually a CUDA 'kernel'). The `argtypes` above tell the kernel that there will be five variables, three scalar floats and two float arrays. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "###1-D Nonlinear convection implemented using NumbaPro CUDA-JIT\n", - "d@jit(argtypes=[float32[:], float32, float32, float32, float32[:]], target='gpu')\n", - "def NonLinCudaJit(u, dx, dt, nt, un):\n", - " tid = cuda.threadIdx.x\n", - " blkid = cuda.blockIdx.x\n", - " blkdim = cuda.blockDim.x\n", - " i = tid + blkid * blkdim\n", - "\n", - " if i >= u.shape[0]:\n", - " return\n", - "\n", - " for n in range(nt):\n", - " un[i] = -u[i]*dt/dx*(u[i]-u[i-1])+u[i]\n", - " \n", - " cuda.syncthreads()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def main(nx):\n", - " ##System Conditions \n", - " #nx = 500 \n", - " nt = 500\n", - " c = 1\n", - " xmax = 15.0\n", - " dx = xmax/(nx-1)\n", - " sigma = 0.25\n", - " dt = sigma*dx\n", - "\n", - " ##Initial Conditions for wave\n", - " ui = np.ones(nx) ##create a 1xn vector of 1's\n", - " ui[.5/dx:1/dx+1]=2 ##set hat function I.C. : .5<=x<=1 is 2\n", - " un = np.ones(nx) \n", - "\n", - " if nx < 20001:\n", - " t1 = time.time()\n", - " u = NonLinVanilla(ui, nx, nt, dx, dt)\n", - " t2 = time.time()\n", - " print \"Vanilla version took: %.6f seconds\" % (t2-t1)\n", - " \n", - " \n", - " ui = np.ones(nx) ##create a 1xn vector of 1's\n", - " ui[.5/dx:1/dx+1]=2 ##set hat function I.C. : .5<=x<=1 is 2\n", - " \n", - " t1 = time.time()\n", - " u = NonLinNumpy(ui, un, nx, nt, dx, dt)\n", - " t2 = time.time()\n", - " print \"Numpy version took: %.6f seconds\" % (t2-t1)\n", - " numpytime = t2-t1\n", - " #plt.plot(numpy.linspace(0,xmax,nx),u[:],marker='o',lw=2)\n", - "\n", - " \n", - " ui = np.ones(nx) ##create a 1xn vector of 1's\n", - " ui[.5/dx:1/dx+1]=2 ##set hat function I.C. : .5<=x<=1 is 2\n", - " \n", - " t1 = time.time()\n", - " u = NonLinNumba(ui, un, nx, nt, dx, dt)\n", - " t2 = time.time()\n", - " print \"Numbapro Vectorize version took: %.6f seconds\" % (t2-t1)\n", - " vectime = t2-t1\n", - " #plt.plot(numpy.linspace(0,xmax,nx),u[:],marker='o',lw=2)\n", - "\n", - " u = np.ones(nx)\n", - " u = ui.copy()\n", - " griddim = 320, 1\n", - " blockdim = 768, 1, 1\n", - " NonLinCudaJit_conf = NonLinCudaJit[griddim, blockdim]\n", - " t1 = time.time()\n", - " NonLinCudaJit(u, dx, dt, nt, un)\n", - " t2 = time.time()\n", - "\n", - " print \"Numbapro Cuda version took: %.6f seconds\" % (t2-t1)\n", - " cudatime = t2-t1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 14 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "main(500)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Vanilla version took: 0.581475 seconds\n", - "Numpy version took: 0.007635 seconds\n", - "Numbapro Vectorize version took: 0.000966 seconds\n", - "Numbapro Cuda version took: 0.002658 seconds\n" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "main(1000)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Vanilla version took: 1.140803 seconds\n", - "Numpy version took: 0.008878 seconds\n", - "Numbapro Vectorize version took: 0.001837 seconds\n", - "Numbapro Cuda version took: 0.002678 seconds\n" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "main(5000)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Vanilla version took: 5.336566 seconds\n", - "Numpy version took: 0.023648 seconds\n", - "Numbapro Vectorize version took: 0.009166 seconds\n", - "Numbapro Cuda version took: 0.002717 seconds\n" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "main(10000)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Vanilla version took: 10.719647 seconds\n", - "Numpy version took: 0.043988 seconds\n", - "Numbapro Vectorize version took: 0.018464 seconds" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Numbapro Cuda version took: 0.002899 seconds\n" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "main(20000)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Vanilla version took: 21.414605 seconds\n", - "Numpy version took: 0.083821 seconds" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Numbapro Vectorize version took: 0.036616 seconds\n", - "Numbapro Cuda version took: 0.002943 seconds\n" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "main(50000)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Numpy version took: 0.207808 seconds\n", - "Numbapro Vectorize version took: 0.093922 seconds" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Numbapro Cuda version took: 0.003228 seconds\n" - ] - } - ], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "main(100000)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Numpy version took: 0.456931 seconds\n", - "Numbapro Vectorize version took: 0.189677 seconds" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Numbapro Cuda version took: 0.004876 seconds\n" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "main(200000)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Numpy version took: 1.255342 seconds\n", - "Numbapro Vectorize version took: 0.393786 seconds" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Numbapro Cuda version took: 0.005403 seconds\n" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "" - ], - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} diff --git a/lessons/18_Burgers_equation.ipynb b/lessons/18_Burgers_equation.ipynb deleted file mode 100644 index 45931fe7..00000000 --- a/lessons/18_Burgers_equation.ipynb +++ /dev/null @@ -1,77842 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Evaluating Burgers Equation with different CFD Schemes\n", - "===\n", - "\n", - "We've already examined Burgers equation in both 1D and 2D ([Step 4](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/05_Step_4.ipynb) and [Step 8](http://nbviewer.ipython.org/urls/github.com/barbagroup/CFDPython/blob/master/lessons/10_Step_8.ipynb), respectively). Here we are going to restrict ourselves to the 1D Burgers equation and examine the role that different schemes play in discretizing non-linear 1st order hyperbolic equations. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Consider the 1D Burgers equation:\n", - "\n", - "$$\\frac{\\partial u}{\\partial t} = -u \\frac{\\partial u}{\\partial x}$$\n", - "\n", - "We want to represent this in conservative form so that we can better deal with potential shocks, which gives us:\n", - "\n", - "$$\\frac{\\partial u}{\\partial t} = - \\frac{\\partial }{\\partial x}(\\frac{u^2}{2})$$\n", - "\n", - "We can also write this as:\n", - "\n", - "$$\\frac{\\partial u}{\\partial t} = - \\frac{\\partial E}{\\partial x}$$\n", - "\n", - "if we take $E = \\frac{u^2}{2}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initial Conditions\n", - "-----\n", - "For each scheme, the initial conditions are \n", - "\n", - "$$u(x,0) = \\left\\{ \\begin{array}{cc}\n", - "1 & 0 \\leq x < 2 \\\\\n", - "0 & 2 \\leq x \\leq 4 \\\\ \\end{array} \\right.$$\n", - "\n", - "Assignment\n", - "----\n", - "First investigate the behavior of the Burgers function with $\\Delta t = \\Delta x = 1$, that is, with a Courant number of $\\frac{\\Delta t}{\\Delta x}=1$, then change the Courant number to $\\frac{\\Delta t}{\\Delta x}=0.5$. \n", - "\n", - "Also experiment with different mesh sizes to see what kind of behavior each scheme exhibits under different circumstances. \n", - "\n", - "Helper Code\n", - "---\n", - "Start by importing `numpy` and `matplotlib.pyplot`. The initial conditions are also defined below, along with a few helper functions to return values that are frequently required. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "#Basic initial condition parameters\n", - "#defining grid size, time steps, CFL condition, etc...\n", - "nx = 81\n", - "nt = 70\n", - "sigma = 1\n", - "dx = 4.0/nx\n", - "dt = sigma*dx\n", - "\n", - "#Define a quick function to set up the initial square wave condition\n", - "def u_ic():\n", - " u = np.ones(nx)\n", - " u[(nx-1)/2:]=0\n", - " return u\n", - "\n", - "#Define two lambda functions to help with common operations encountered\n", - "#with the Euler equations\n", - "utoE = lambda u: (u/2)**2\n", - "utoA = lambda u: u**2" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Lax-Friedrichs\n", - "\n", - "Lax-Friedrichs is an explicit, 1st order scheme, using forward-difference in time and central-difference in space. However, notice that the $u^n_i$ value is calculated as the average of its adjacent cells. \n", - "\n", - "$$\\frac{u_i^{n+1}-\\frac{1}{2}(u^n_{i+1}+u^n_{i-1})}{\\Delta t} = -\\frac{E^n_{i+1}-E^n_{i-1}}{2 \\Delta x}$$\n", - "\n", - "Transposing this to solve for $u^{n+1}_i$ yields:\n", - "\n", - "$$u_i^{n+1} = \\frac{1}{2}(u^n_{i+1}+u^n_{i-1}) - \\frac{\\Delta t}{2 \\Delta x}(E^n_{i+1}-E^n_{i-1})$$\n", - "\n", - "This scheme is implemented in the function below. All the schemes in this notebook are wrapped in their own functions to help with displaying animations of the results. This is also good coding practice!\n", - "\n", - "In order to display our animations, we're going to hold the results of each timestep in `u` in a 2D array. So our array `un` will have `nt` rows and `nx` columns." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def laxfriedrichs(u, nt, dt, dx):\n", - " #initialize our results array with dimensions nt by nx\n", - " un = np.zeros((nt,len(u))) \n", - " #copy the initial u array into each row of our new array\n", - " un[:,:] = u.copy() \n", - " \n", - " '''\n", - " Now, for each timestep, we're going to calculate u^n+1, \n", - " then set the value of u equal to u^n+1 so we can calculate \n", - " the next iteration. For every timestep, the entire vector\n", - " u^n is saved in a single row of our results array un.\n", - " '''\n", - " for i in range(1,nt):\n", - " E = utoE(u)\n", - " un[i,1:-1] = .5*(u[2:]+u[:-2]) - dt/(2*dx)*(E[2:]-E[:-2])\n", - " un[i,0] = 1\n", - " u = un[i].copy()\n", - " \n", - " return un" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above cell just defines the function which can execute the Lax-Friedrichs scheme, now it needs to be called. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Lax Friedrichs Test 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's try to display the results of our Lax Friedrichs scheme. First, we need to generate the results by calling our function `laxfriedrics`. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = u_ic() #make sure that u is set to our expected initial conditions\n", - "un = laxfriedrichs(u,nt,dt,dx)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can ask NumPy to tell us the shape of our new array `un` and see if we have the dimensions we're expecting. We should have `nt` rows and `nx` columns. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "un.shape" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "(70, 81)" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks good! Now in order to view the results in an animation, we'll import a few extra libraries. The `JSAnimation` library is not part of the standard IPython install (yet!) but you can click [here](http://dummy) for instructions on how to install it." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from matplotlib import animation\n", - "from JSAnimation.IPython_display import display_animation" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To view a nice interactive animation of our results, we'll need to define a few parameters.\n", - "\n", - "* First, we need a `figure`, which we'll also make a little larger than the default size for easier viewing. \n", - "* We also want to define an axis in the figure with some plotting limits. \n", - "* Then, we want to initialize a `line` object that will be a part of our plot, but we don't need to give it any data yet, so we'll just assign it empty lists for its `x` and `y` coordinates.\n", - "\n", - "Now we need a functions to \"draw\" each frame of our animation:\n", - "\n", - "* `animate` takes a single row of our results array `un` and then plots a line.\n", - "\n", - "Now to set up the `animation` object, we pass is a few values:\n", - "\n", - "* `fig` is the figure we've already defined and we're telling `FuncAnimation` to use that to draw in\n", - "* `animate` is the function we're using to actually plot our results\n", - "* `frames=un` tells `FuncAnimation` that it should use each row of our array `un` to generate sequential frames of the animation.\n", - "* `interval=50` sets the default amount of time in milliseconds between frames\n", - "\n", - "You'll notice a set of controls below the animation -- you can use the **+** and **-** buttons to speed up or slow down the animation." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig = plt.figure();\n", - "ax = plt.axes(xlim=(0,4),ylim=(-.5,2));\n", - "line, = ax.plot([],[],lw=2);\n", - "\n", - "def animate(data):\n", - " x = np.linspace(0,4,nx)\n", - " y = data\n", - " line.set_data(x,y)\n", - " return line,\n", - "\n", - "anim = animation.FuncAnimation(fig, animate, frames=un, interval=50)\n", - "display_animation(anim, default_mode='loop')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "
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\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice the strange \"step\" behavior on the leading edge of the wave? Check out a separate notebook [here](http://nbviewer.ipython.org/github/barbagroup/CFDPython/blob/master/lessons/19_Odd_Even_Decoupling.ipynb?create=1) to learn about \"Odd-Even Decoupling.\"\n", - "\n", - "Also take a look at how the change in CFL number affected the wave." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Lax-Wendroff Scheme\n", - "\n", - "The Lax-Wendroff scheme starts with taking the Taylor series expansion about $u^{n+1}$:\n", - "\n", - "$$u^{n+1} = u^n + u_t \\Delta t + \\frac{(\\Delta t)^2}{2}u_{tt} + ...$$\n", - "\n", - "Now substitute spatial derivatives for the time derivatives\n", - "\n", - "$$E_t = -AE_x$$ \n", - "\n", - "$$u_t = -E_x$$\n", - "\n", - "where $A = \\frac{\\partial E}{\\partial u} = u$ is the Jacobian for Burgers equation.\n", - "\n", - "This, when plugged into the full Burgers equation, should yield\n", - "\n", - "$$\\frac{u_i^{n+1} - u_i^n}{\\Delta t} = \\frac{-E^n_{i+1}-E^n_{i-1}}{2 \\Delta x} + \\frac{\\Delta t}{2} \\left(\\frac{(A \\frac{\\partial E}{\\partial x})^n_{i+\\frac{1}{2}}-(A \\frac{\\partial E}{\\partial x})^n_{i-\\frac{1}{2}}}{\\Delta x}\\right)$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Approximate the last term in the above equation as:\n", - "$$\\left(\\frac{(A \\frac{\\partial E}{\\partial x})^n_{i+\\frac{1}{2}}-(A \\frac{\\partial E}{\\partial x})^n_{i-\\frac{1}{2}}}{\\Delta x}\\right) \\approx \\frac{A^n_{i+\\frac{1}{2}}\\left(\\frac{E^n_{i+1}-E^n_{i}}{\\Delta x}\\right)-A^n_{i-\\frac{1}{2}}\\left(\\frac{E^n_i-E^n_{i-1}}{\\Delta x}\\right)}{\\Delta x}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And evaluate the Jacobian at the midpoints:\n", - "\n", - "$$\\frac{\\frac{1}{2 \\Delta x}(A^n_{i+1}+A^n_i)(E^n_{i+1}-E^n_i)-\\frac{1}{2 \\Delta x}(A^n_i+A^n_{i-1})(E^n_i-E^n_{i-1})}{\\Delta x}$$\n", - "\n", - "So our equation now reads:\n", - "\n", - "$$\\frac{u_i^{n+1} - u_i^n}{\\Delta t} = \\frac{\\frac{1}{2 \\Delta x}(A^n_{i+1}+A^n_i)(E^n_{i+1}-E^n_i)-\\frac{1}{2 \\Delta x}(A^n_i+A^n_{i-1})(E^n_i-E^n_{i-1})}{\\Delta x}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We substitute $A = u$ and then solve for $u^{n+1}_i$, the Lax-Wendroff Scheme results:\n", - "\n", - "\n", - "$$u^{n+1}_i = u^n_i - \\frac{\\Delta t}{2 \\Delta x}(E^n_{i+1}-E^n_{i-1})+$$\n", - "$$\\frac{\\Delta t^2}{4 \\Delta x^2}[(u^n_{i+1}+u^n_i)(E^n_{i+1}-E^n_i)-(u^n_i+u^n_{i-1})(E^n_i-E^n_{i-1})]$$\n", - "\n", - "\n", - "Lax-Wendroff is a little bit long. Remember that you can use \\ slashes to split up a statement across several lines. This can help make code easier to parse (and also easier to debug!). " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def laxwendroff(u, nt, dt, dx):\n", - " un = np.zeros((nt,len(u)))\n", - " un[:] = u.copy()\n", - " \n", - " for i in range(1,nt):\n", - " E = utoE(u) \n", - " un[i,1:-1] = u[1:-1] - dt/(2*dx) * (E[2:]-E[:-2]) + dt**2/(4*dx**2) *\\\n", - " ((u[2:]+u[1:-1])*(E[2:]-E[1:-1]) -\\\n", - " (u[1:-1]+u[:-2])*(E[1:-1]-E[:-2]))\n", - " un[i,0]=1\n", - " \n", - " u = un[i].copy() \n", - " \n", - " return un" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that's we've defined a function for the Lax Wendroff scheme, we can use the seem procedure as above to animate and view our results. \n", - "\n", - "###With CFL = 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = u_ic()\n", - "sigma = 1\n", - "dt = sigma*dx\n", - "un = laxwendroff(u,nt,dt,dx)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig = plt.figure();\n", - "ax = plt.axes(xlim=(0,4),ylim=(-.5,2));\n", - "line, = ax.plot([],[],lw=2);\n", - "\n", - "anim = animation.FuncAnimation(fig, animate, frames=un, interval=50)\n", - "display_animation(anim, default_mode='once')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "
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When each term used is from the previous time step, that is an explicit method. \n", - "\n", - "The Beam & Warming scheme does not cleanly separate out all of the $u^{n+1}$ terms and it is computed in a different way from the other examples. \n", - "\n", - "Taking a Taylor series expansion of $u^{n+1}_i$:\n", - "\n", - "\n", - "$$u^{n+1}_i = u^n_i + \\frac{1}{2} \\left[\\left. \\frac{\\partial u}{\\partial t} \\right|^{n}_i + \\left. \\frac{\\partial u}{\\partial t} \\right|^{n+1}_i \\right] \\Delta t + O(\\Delta t^3)$$\n", - "\n", - "For Burgers, $\\frac{\\partial u}{\\partial t} = -\\frac{ \\partial E}{\\partial x}$ , so:\n", - "\n", - "$$\\frac{u^{n+1}_i - u^n_i}{\\Delta t} = - \\frac{1}{2} \\left[ \\left.\\frac{\\partial E}{\\partial x} \\right|^n_i + \\left. \\frac{\\partial E}{\\partial x}\\right|^{n+1}_i \\right] + O(\\Delta t^2)$$\n", - "\n", - "$$\\therefore \\frac{u^{n+1}_i - u^n_i}{\\Delta t} = -\\frac{1}{2} \\left( \\left.\\frac{\\partial E}{\\partial x}\\right|^n_i + \\left.\\frac{\\partial E}{\\partial x}\\right|^n_i + \\frac{\\partial}{\\partial x} \\left[ A(u^{n+1}_i - u^n_i)\\right] \\right)$$\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using 2nd order central difference for the Jacobian A yields:\n", - "\n", - "$$\\frac{\\partial}{\\partial x} \\left[A(u^{n+1}_i-u^n_i)\\right] = $$\n", - "\n", - "$$\\frac{1}{2 \\Delta x} \\left(A^n_{i+1} u^{n+1}_{i+1} - A^n_{i-1} u^{n+1}_{i-1} \\right) - \\frac{1}{2 \\Delta x} \\left(A^n_{i+1}u^n_{i+1} - A^n_{i-1}u^n_{i-1} \\right)$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "which results in a tri-diagonal system:\n", - "\n", - "$$- \\frac{\\Delta t}{4 \\Delta x} \\left( A^n_{i-1} u^{n+1}_{i-1}\\right) + u^{n+1}_i + \\frac{\\Delta t}{4 \\Delta x} \\left( A^n_{i+1} u^{n+1}_{i+1} \\right) = $$\n", - "\n", - "$$ = u^n_i - \\frac{1}{2} \\frac{\\Delta t}{\\Delta x} \\left( E^n_{i+1} - E^n_{i-1} \\right) + \\frac{\\Delta t}{4 \\Delta x} \\left( A^n_{i+1} u^n_{i+1} - A^n_{i-1} u^n_{i-1} \\right) $$\n", - "\n", - "If you are unfamiliar with tri-diagonal systems, check out the [Wikipedia page](http://en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm) for a brief explanation. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy import linalg\n", - "\n", - "def beamwarming(u, nt, dt, dx):\n", - " ##Tridiagonal setup##\n", - " a = np.zeros_like(u)\n", - " b = np.ones_like(u)\n", - " c = np.zeros_like(u)\n", - " d = np.zeros_like(u)\n", - " \n", - " un = np.zeros((nt,len(u)))\n", - " un[:]=u.copy()\n", - " \n", - " for n in range(1,nt): \n", - " u[0] = 1\n", - " E = utoE(u)\n", - " au = utoA(u)\n", - " \n", - " a[0] = -dt/(4*dx)*u[0]\n", - " a[1:] = -dt/(4*dx)*u[0:-1]\n", - " a[-1] = -dt/(4*dx)*u[-1]\n", - " \n", - " #b is all ones\n", - " \n", - " c[:-1] = dt/(4*dx)*u[1:]\n", - " \n", - " d[1:-1] = u[1:-1]-.5*dt/dx*(E[2:]-E[0:-2])+dt/(4*dx)*(au[2:]-au[:-2])\n", - " \n", - " ###subtract a[0]*LHS B.C to 'fix' thomas algorithm\n", - " d[0] = u[0] - .5*dt/dx*(E[1]-E[0])+dt/(4*dx)*(au[1]-au[0]) - a[0] \n", - " \n", - " ab = np.matrix([c,b,a])\n", - " u = linalg.solve_banded((1,1), ab, d)\n", - " u[0]=1\n", - " un[n] = u.copy() \n", - " return un" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Why does the Thomas algorithm need to be 'fixed'?\n", - "\n", - "If you examine the [Wikipedia page](http://en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm), it explains that tridiagonal systems of equations have the form\n", - "\n", - "$$a_i x_{i-1} + b_i x_i + c_i x_{i+1} = d_i$$\n", - "\n", - "where $a_1 = 0$ and $c_n = 0$ so that the system of equations can be written out as follows\n", - "\n", - "$$\\begin{bmatrix}\n", - "b_1 & c_1 & & & 0\\\\\n", - "a_2 & b_2 & c_2 & & \\\\\n", - " & a_3 & b_3 & \\ddots & \\\\\n", - " & & \\ddots & \\ddots & c_{n-1} \\\\\n", - "0 & & & a_n & b_n \n", - "\\end{bmatrix}\n", - "\\begin{bmatrix}\n", - "x_1 \\\\\n", - "x_2 \\\\\n", - "x_3 \\\\\n", - "\\vdots \\\\\n", - "x_n\n", - "\\end{bmatrix}\n", - "=\n", - "\\begin{bmatrix}\n", - "d_1 \\\\\n", - "d_2 \\\\\n", - "d_3 \\\\\n", - "\\vdots \\\\\n", - "d_n\n", - "\\end{bmatrix}\n", - "$$\n", - "\n", - "** But ** what if $a_1 \\neq 0$? And what about boundary conditions?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Modified Thomas Algorithm Matrix\n", - "\n", - "To make use of the Thomas Algorithm, we want to apply the algorithm only to the internal elements of our problem and not to the boundary elements. Take the answer to be a vector spanning from $u_0$ to $u_{n+1}$\n", - "\n", - "$$u_0\\ \\ \\ u_1\\ \\ \\ u_2\\ \\ \\ \\dots \\ \\ \\ u_n \\ \\ \\ u_{n+1}$$\n", - "\n", - "where $u_0 = B_0$ and $u_{n+1} = B_{n+1}$ and where $B_0$ and $B_{n+1}$ are general boundary conditions. Then $[u_1 \\ \\ \\dots\\ \\ u_n]$ correspond to $[x_1 \\ \\ \\dots \\ \\ x_n]$\n", - "\n", - "Now we can write down a modified tri-diagonal system which accounts for $a_1 \\neq 0$ and boundary conditions\n", - "\n", - "$$\\begin{bmatrix}\n", - "1 & 0 & 0 & \\dots & &0 \\\\\n", - "a_1 & b_1 & c_1 & & & \\vdots\\\\\n", - "0 & a_2 & b_2 & c_2 & & \\\\\n", - "\\vdots& & a_3 & b_3 & \\ddots & \\\\\n", - "& & & \\ddots & \\ddots & c_n\\\\\n", - "0 & &\\dots & &0 & 1\n", - "\\end{bmatrix}\n", - "\\begin{bmatrix}\n", - "u_0 \\\\\n", - "u_1 \\\\\n", - "u_2 \\\\\n", - "u_3 \\\\\n", - "\\vdots \\\\\n", - "u_n \\\\\n", - "u_{n+1}\n", - "\\end{bmatrix}\n", - "=\n", - "\\begin{bmatrix}\n", - "B_0 \\\\\n", - "d_1 \\\\\n", - "d_2 \\\\\n", - "d_3 \\\\\n", - "\\vdots \\\\\n", - "d_n \\\\\n", - "B_{n+1}\n", - "\\end{bmatrix}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Examining this system, you should note that the boundary conditions are properly observed, as $u_0 = B_0$ and $u_{n+1} = B_{n+1}$. \n", - "All of the internal equations in the system are the same as the Wikipedia example, except the first and last equations. If we take $a_1 \\neq 0$ and $c_n \\neq 0$ then the corresponding equations are\n", - "\n", - "$$a_1 u_0 + b_1 u_1 + c_1 u_2 = d_1$$\n", - "$$a_n u_{n-1} + b_n u_n + c_n u_{n+1} = d_n $$\n", - "\n", - "Taking the $a_1$ and $c_n$ terms to the right hand sides of their respective equations yields a slightly modified tridiagonal matrix that can be solved using existing tridiagonal solvers.\n", - "\n", - "$$\\begin{bmatrix}\n", - "b_1 & c_1 & & & 0\\\\\n", - "a_2 & b_2 & c_2 & & \\\\\n", - " & a_3 & b_3 & \\ddots & \\\\\n", - " & & \\ddots & \\ddots & c_{n-1} \\\\\n", - "0 & & & a_n & b_n \n", - "\\end{bmatrix}\n", - "\\begin{bmatrix}\n", - "x_1 \\\\\n", - "x_2 \\\\\n", - "x_3 \\\\\n", - "\\vdots \\\\\n", - "x_n\n", - "\\end{bmatrix}\n", - "=\n", - "\\begin{bmatrix}\n", - "d_1 - a_1 u_0\\\\\n", - "d_2 \\\\\n", - "d_3 \\\\\n", - "\\vdots \\\\\n", - "d_n - c_n u_{n+1}\n", - "\\end{bmatrix}\n", - "$$\n", - "\n", - "$u_0$ and $u_{n+1}$ are just the boundary conditions specified. For the Burger's problem, $u_0 = B_0 = 1$ and $u_{n+1} = B_{n+1} = 0$ so the final matrix is \n", - "\n", - "$$\\begin{bmatrix}\n", - "b_1 & c_1 & & & 0\\\\\n", - "a_2 & b_2 & c_2 & & \\\\\n", - " & a_3 & b_3 & \\ddots & \\\\\n", - " & & \\ddots & \\ddots & c_{n-1} \\\\\n", - "0 & & & a_n & b_n \n", - "\\end{bmatrix}\n", - "\\begin{bmatrix}\n", - "x_1 \\\\\n", - "x_2 \\\\\n", - "x_3 \\\\\n", - "\\vdots \\\\\n", - "x_n\n", - "\\end{bmatrix}\n", - "=\n", - "\\begin{bmatrix}\n", - "d_1 -a_1\\\\\n", - "d_2 \\\\\n", - "d_3 \\\\\n", - "\\vdots \\\\\n", - "d_n\n", - "\\end{bmatrix}\n", - "$$\n", - "\n", - "So for the problem at hand, we have to make a change to the first element of the `d` vector to correct for the usual assumptions made when using the Thomas algorithm. \n", - "\n", - "In the function above, this is accomplished with the line\n", - "\n", - " d[0] = u[0] - .5*dt/dx*(E[1]-E[0])+dt/(4*dx)*(au[1]-au[0]) - a[0]" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = u_ic()\n", - "sigma = .5\n", - "dt = sigma*dx\n", - "nt=60\n", - "\n", - "un = beamwarming(u,nt,dt,dx)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig = plt.figure();\n", - "ax = plt.axes(xlim=(0,4),ylim=(-.5,2));\n", - "line, = ax.plot([],[],lw=2);\n", - "\n", - "anim = animation.FuncAnimation(fig, animate, frames=un, interval=50)\n", - "display_animation(anim, default_mode='once')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "
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\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Yikes.\n", - "\n", - "You may have noticed that we've made `nt` smaller than it has been for the other schemes. \n", - "With our CFL number equal to 0.5, this implicit method can handle about 60 iterations before it blows up to numbers that Python can't handle (which then get replaced with `Inf`, which then breaks the banded matrix solver).\n", - "\n", - "So we need to calm things down a little bit and prevent it from going nuclear so quickly.\n", - "\n", - "##Beam-Warming with damping" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Implicit methods can work well in many situations, but this isn't one of them. As you can see above, the undamped Beam-Warming quickly turns into nonsense. We can add damping to help keep things under control. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add a damping term 'D' where:\n", - "\n", - "$$D = -\\epsilon_e (u^n_{i+2} - 4u^n_{i+1} + 6u^n_i - 4^n_{i-1} + u^n_{i-2})$$\n", - "\n", - "Note below that while we can easily include the damping term for most values of the `d` vector, `d[0]` and `d[1]` require a little extra attention. There are no periodic boundary conditions being used, so any value of $u_i$ where $i < 0$ is set equal to 1." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def dampit(u,eps,dt,dx):\n", - "\td = u[2]-.5*dt/dx*(u[3]**2/2-u[1]**2/2)+dt/(4*dx)*(u[3]**2-u[1]**2)\\\n", - "\t\t-eps*(u[4]-4*u[3]+6*u[2]-4*u[1]+u[0])\n", - "\treturn d\n", - "\n", - "def beamwarming_damp(u, nt, dt, dx):\n", - " ##Tridiagonal setup##\n", - " a = np.zeros_like(u)\n", - " b = np.ones_like(u)\n", - " c = np.zeros_like(u)\n", - " d = np.zeros_like(u)\n", - " \n", - " un = np.zeros((nt,len(u)))\n", - " un[:] = u.copy()\n", - " \n", - " eps = .125\n", - "\n", - " for n in range(1,nt): \n", - " u[0] = 1\n", - " E = utoE(u)\n", - " au = utoA(u)\n", - " \n", - " a[0] = -dt/(4*dx)*u[0]\n", - " a[1:] = -dt/(4*dx)*u[0:-1]\n", - " a[-1] = -dt/(4*dx)*u[-1]\n", - " \n", - " #b is all ones\n", - " \n", - " c[:-1] = dt/(4*dx)*u[1:]\n", - " \n", - " ###Calculate the damping factor for MOST of our u_vector\n", - " d[2:-2] = u[2:-2]-.5*dt/dx*(E[3:-1]-E[1:-3])+dt/(4*dx)\\\n", - " *(au[3:-1]-au[1:-3])\\\n", - " -eps*(u[4:]-4*u[3:-1]+6*u[2:-2]-4*u[1:-3]+u[:-4])\n", - " \n", - "\n", - " ###Calculate the damping factor for d[0] and d[1]\n", - " damp = np.concatenate((np.ones(2), u[:3]))\t\n", - " d[0] = dampit(damp,eps,dt,dx)\n", - " damp = np.concatenate((np.ones(1), u[:4]))\n", - " d[1] = dampit(damp,eps,dt,dx)\n", - " \n", - " ###subtract a[0]*LHS B.C to 'fix' thomas algorithm\n", - " d[0] = d[0] - u[0] * a[0] \n", - " \n", - " ab = np.matrix([c,b,a])\n", - " u = linalg.solve_banded((1,1), ab, d)\n", - " u[0]=1\n", - " un[n] = u.copy()\n", - " return un" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "u = u_ic()\n", - "sigma = 0.5\n", - "dt = sigma*dx\n", - "nt = 120\n", - "un = beamwarming_damp(u,nt,dt,dx)\n", - "\n", - "fig = plt.figure();\n", - "ax = plt.axes(xlim=(0,4),ylim=(-.5,2));\n", - "line, = ax.plot([],[],lw=2);\n", - "\n", - "anim = animation.FuncAnimation(fig, animate, frames=un, interval=50)\n", - "display_animation(anim, default_mode='once')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "
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\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 20, - "text": [ - "" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's certainly more stable than what we had before. It certainly doesn't behave as nicely as some of our other schemes, but it is remaining finite, at least. This animation has a damping factor $\\epsilon = .125$. Try playing around with different values for the damping factor and see what happens." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"../styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "" - ] - } - ], - "prompt_number": 21 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/lessons/19_Odd_Even_Decoupling.ipynb b/lessons/19_Odd_Even_Decoupling.ipynb deleted file mode 100644 index 9a149202..00000000 --- a/lessons/19_Odd_Even_Decoupling.ipynb +++ /dev/null @@ -1,272 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Odd-Even Decoupling\n", - "\n", - "What is odd-even decoupling? Let's pull up a video from the Burgers' Equation exercise and take a look. \n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import HTML\n", - "from base64 import b64encode\n", - "video = open(\"../videos/laxfricfl1.mp4\", \"rb\").read()\n", - "video_encoded = b64encode(video)\n", - "video_tag = '