[
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"path": ".gitignore",
"content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# IPython Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n\n# Emacs\n*~\n\n\n# Temporary data files\nnotebooks/recipeitems-latest.json\nnotebooks/FremontBridge.csv\nnotebooks/gistemp250.nc\nnotebooks/marathon-data.csv\nnotebooks/my_figure.png\nnotebooks/hello.png"
},
{
"path": ".gitmodules",
"content": "[submodule \"website/plugins/ipynb\"]\n\tpath = website/plugins/ipynb\n\turl = git://github.com/danielfrg/pelican-ipynb.git\n[submodule \"website/plugins/pelican-plugins\"]\n\tpath = website/plugins/pelican-plugins\n\turl = git://github.com/getpelican/pelican-plugins.git\n"
},
{
"path": "LICENSE-CODE",
"content": "The MIT License (MIT)\n\nCopyright (c) 2016 Jacob VanderPlas\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
},
{
"path": "LICENSE-TEXT",
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"path": "README.md",
"content": "# Python Data Science Handbook\n\n[](https://mybinder.org/v2/gh/jakevdp/PythonDataScienceHandbook/master?filepath=notebooks%2FIndex.ipynb)\n[](https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb)\n\nThis repository contains the entire [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do), in the form of (free!) Jupyter notebooks.\n\n\n\n## How to Use this Book\n\n- Read the book in its entirety online at https://jakevdp.github.io/PythonDataScienceHandbook/\n\n- Run the code using the Jupyter notebooks available in this repository's [notebooks](notebooks) directory.\n\n- Launch executable versions of these notebooks using [Google Colab](http://colab.research.google.com): [](https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb)\n\n- Launch a live notebook server with these notebooks using [binder](https://beta.mybinder.org/): [](https://mybinder.org/v2/gh/jakevdp/PythonDataScienceHandbook/master?filepath=notebooks%2FIndex.ipynb)\n\n- Buy the printed book through [O'Reilly Media](http://shop.oreilly.com/product/0636920034919.do)\n\n## About\n\nThe book was written and tested with Python 3.5, though other Python versions (including Python 2.7) should work in nearly all cases.\n\nThe book introduces the core libraries essential for working with data in Python: particularly [IPython](http://ipython.org), [NumPy](http://numpy.org), [Pandas](http://pandas.pydata.org), [Matplotlib](http://matplotlib.org), [Scikit-Learn](http://scikit-learn.org), and related packages.\nFamiliarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project,\n[A Whirlwind Tour of Python](https://github.com/jakevdp/WhirlwindTourOfPython): it's a fast-paced introduction to the Python language aimed at researchers and scientists.\n\nSee [Index.ipynb](http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb) for an index of the notebooks available to accompany the text.\n\n## Software\n\nThe code in the book was tested with Python 3.5, though most (but not all) will also work correctly with Python 2.7 and other older Python versions.\n\nThe packages I used to run the code in the book are listed in [requirements.txt](requirements.txt) (Note that some of these exact version numbers may not be available on your platform: you may have to tweak them for your own use).\nTo install the requirements using [conda](http://conda.pydata.org), run the following at the command-line:\n\n```\n$ conda install --file requirements.txt\n```\n\nTo create a stand-alone environment named ``PDSH`` with Python 3.5 and all the required package versions, run the following:\n\n```\n$ conda create -n PDSH python=3.5 --file requirements.txt\n```\n\nYou can read more about using conda environments in the [Managing Environments](http://conda.pydata.org/docs/using/envs.html) section of the conda documentation.\n\n\n## License\n\n### Code\nThe code in this repository, including all code samples in the notebooks listed above, is released under the [MIT license](LICENSE-CODE). Read more at the [Open Source Initiative](https://opensource.org/licenses/MIT).\n\n### Text\nThe text content of the book is released under the [CC-BY-NC-ND license](LICENSE-TEXT). Read more at [Creative Commons](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode).\n"
},
{
"path": "environment.yml",
"content": "name: data-science-handbook\nchannels:\n - conda-forge\ndependencies:\n - python=3.5\n - pip:\n - -r requirements.txt"
},
{
"path": "notebooks/00.00-Preface.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Preface\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What Is Data Science?\\n\",\n \"\\n\",\n \"This is a book about doing data science with Python, which immediately begs the question: what is *data science*?\\n\",\n \"It's a surprisingly hard definition to nail down, especially given how ubiquitous the term has become.\\n\",\n \"Vocal critics have variously dismissed it as a superfluous label (after all, what science doesn't involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters.\\n\",\n \"\\n\",\n \"In my mind, these critiques miss something important.\\n\",\n \"Data science, despite its hype-laden veneer, is perhaps the best label we have for the cross-disciplinary set of skills that are becoming increasingly important in many applications across industry and academia.\\n\",\n \"This *cross-disciplinary* piece is key: in my mind, the best existing definition of data science is illustrated by Drew Conway's Data Science Venn Diagram, first published on his blog in September 2010 (see the following figure).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"(source: [Drew Conway](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram), used by permission)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"While some of the intersection labels are a bit tongue-in-cheek, this diagram captures the essence of what I think people mean when they say \\\"data science\\\": it is fundamentally an interdisciplinary subject.\\n\",\n \"Data science comprises three distinct and overlapping areas: the skills of a *statistician* who knows how to model and summarize datasets (which are growing ever larger); the skills of a *computer scientist* who can design and use algorithms to efficiently store, process, and visualize this data; and the *domain expertise*—what we might think of as \\\"classical\\\" training in a subject—necessary both to formulate the right questions and to put their answers in context.\\n\",\n \"\\n\",\n \"With this in mind, I would encourage you to think of data science not as a new domain of knowledge to learn, but a new set of skills that you can apply within your current area of expertise.\\n\",\n \"Whether you are reporting election results, forecasting stock returns, optimizing online ad clicks, identifying microorganisms in microscope photos, seeking new classes of astronomical objects, or working with data in any other field, the goal of this book is to give you the ability to ask and answer new questions about your chosen subject area.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Who Is This Book For?\\n\",\n \"\\n\",\n \"In my teaching both at the University of Washington and at various tech-focused conferences and meetups, one of the most common questions I have heard is this: \\\"How should I learn Python?\\\"\\n\",\n \"The people asking are generally technically minded students, developers, or researchers, often with an already strong background in writing code and using computational and numerical tools.\\n\",\n \"Most of these folks don't want to learn Python per se, but want to learn the language with the aim of using it as a tool for data-intensive and computational science.\\n\",\n \"While a large patchwork of videos, blog posts, and tutorials for this audience is available online, I've long been frustrated by the lack of a single good answer to this question; that is what inspired this book.\\n\",\n \"\\n\",\n \"The book is not meant to be an introduction to Python or to programming in general; I assume the reader has familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks.\\n\",\n \"Instead, it is meant to help Python users learn to use Python's data science stack—libraries such as those mentioned in the following section, and related tools—to effectively store, manipulate, and gain insight from data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Why Python?\\n\",\n \"\\n\",\n \"Python has emerged over the last couple of decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets.\\n\",\n \"This may have come as a surprise to early proponents of the Python language: the language itself was not specifically designed with data analysis or scientific computing in mind.\\n\",\n \"The usefulness of Python for data science stems primarily from the large and active ecosystem of third-party packages: *NumPy* for manipulation of homogeneous array-based data, *Pandas* for manipulation of heterogeneous and labeled data, *SciPy* for common scientific computing tasks, *Matplotlib* for publication-quality visualizations, *IPython* for interactive execution and sharing of code, *Scikit-Learn* for machine learning, and many more tools that will be mentioned in the following pages.\\n\",\n \"\\n\",\n \"If you are looking for a guide to the Python language itself, I would suggest the sister project to this book, [https://www.oreilly.com/library/view/a-whirlwind-tour/9781492037859](_A Whirlwind Tour of the Python Language_).\\n\",\n \"This short report provides a tour of the essential features of the Python language, aimed at data scientists who already are familiar with one or more other programming languages.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Outline of the Book\\n\",\n \"\\n\",\n \"Each numbered part of this book focuses on a particular package or tool that contributes a fundamental piece of the Python data science story, and is broken into short self-contained chapters that each discuss a single concept:\\n\",\n \"\\n\",\n \"- *Part I, Jupyter: Beyond Normal Python*, introduces IPython and Jupyter. These packages provide the computational environment in which many Python-using data scientists work.\\n\",\n \"- *Part II, Introduction to NumPy*, focuses on the NumPy library, which provides the `ndarray` for efficient storage and manipulation of dense data arrays in Python.\\n\",\n \"- *Part III, Data Manipulation with Pandas*, introduces the Pandas library, which provides the `DataFrame` for efficient storage and manipulation of labeled/columnar data in Python.\\n\",\n \"- *Part IV, Visualization with Matplotlib*, concentrates on Matplotlib, a library that provides capabilities for a flexible range of data visualizations in Python.\\n\",\n \"- *Part V, Machine Learning*, focuses on the Scikit-Learn library, which provides efficient and clean Python implementations of the most important and established machine learning algorithms.\\n\",\n \"\\n\",\n \"The PyData world is certainly much larger than these six packages, and is growing every day.\\n\",\n \"With this in mind, I make every attempt throughout this book to provide references to other interesting efforts, projects, and packages that are pushing the boundaries of what can be done in Python.\\n\",\n \"Nevertheless, the packages I concentrate on are currently fundamental to much of the work being done in the Python data science space, and I expect they will remain important even as the ecosystem continues growing around them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Using Code Examples\\n\",\n \"\\n\",\n \"Supplemental material (code examples, figures, etc.) is available for download at http://github.com/jakevdp/PythonDataScienceHandbook/. This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.\\n\",\n \"\\n\",\n \"We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: \\\"*Python Data Science Handbook*, 2nd edition, by Jake VanderPlas (O’Reilly). Copyright 2023 Jake VanderPlas, 978-1-098-12122-8.\\\"\\n\",\n \"\\n\",\n \"If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at permissions@oreilly.com.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Installation Considerations\\n\",\n \"\\n\",\n \"Installing Python and the suite of libraries that enable scientific computing is straightforward. This section will outline some of the things to keep in mind when setting up your computer.\\n\",\n \"\\n\",\n \"Though there are various ways to install Python, the one I would suggest for use in data science is the Anaconda distribution, which works similarly whether you use Windows, Linux, or macOS.\\n\",\n \"The Anaconda distribution comes in two flavors:\\n\",\n \"\\n\",\n \"- [Miniconda](http://conda.pydata.org/miniconda.html) gives you the Python interpreter itself, along with a command-line tool called *conda* which operates as a cross-platform package manager geared toward Python packages, similar in spirit to the apt or yum tools that Linux users might be familiar with.\\n\",\n \"\\n\",\n \"- [Anaconda](https://www.continuum.io/downloads) includes both Python and conda, and additionally bundles a suite of other preinstalled packages geared toward scientific computing. Because of the size of this bundle, expect the installation to consume several gigabytes of disk space.\\n\",\n \"\\n\",\n \"Any of the packages included with Anaconda can also be installed manually on top of Miniconda; for this reason I suggest starting with Miniconda.\\n\",\n \"\\n\",\n \"To get started, download and install the Miniconda package—make sure to choose a version with Python 3—and then install the core packages used in this book:\\n\",\n \"\\n\",\n \"```\\n\",\n \"[~]$ conda install numpy pandas scikit-learn matplotlib seaborn jupyter\\n\",\n \"```\\n\",\n \"\\n\",\n \"Throughout the text, we will also make use of other more specialized tools in Python's scientific ecosystem; installation is usually as easy as typing **`conda install packagename`**.\\n\",\n \"If you ever come across packages that are not available in the default conda channel, be sure to check out [*conda-forge*](https://conda-forge.org/), a broad, community-driven repository of conda packages.\\n\",\n \"\\n\",\n \"For more information on conda, including information about creating and using conda environments (which I would *highly* recommend), refer to [conda's online documentation](http://conda.pydata.org/docs/).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/01.00-IPython-Beyond-Normal-Python.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Jupyter: Beyond Normal Python\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are many options for development environments for Python, and I'm often asked which one I use in my own work.\\n\",\n \"My answer sometimes surprises people: my preferred environment is [IPython](http://ipython.org/) plus a text editor (in my case, Emacs or VSCode depending on my mood).\\n\",\n \"Jupyter got its start as the IPython shell, which was created in 2001 by Fernando Perez as an enhanced Python interpreter and has since grown into a project aiming to provide, in Perez's words, \\\"Tools for the entire life cycle of research computing.\\\"\\n\",\n \"If Python is the engine of our data science task, you might think of Jupyter as the interactive control panel.\\n\",\n \"\\n\",\n \"As well as being a useful interactive interface to Python, Jupyter also provides a number of useful syntactic additions to the language; we'll cover the most useful of these additions here.\\n\",\n \"Perhaps the most familiar interface provided by the Jupyter project is the Jupyter Notebook, a browser-based environment that is useful for development, collaboration, sharing, and even publication of data science results.\\n\",\n \"As an example of the usefulness of the notebook format, look no further than the page you are reading: the entire manuscript for this book was composed as a set of Jupyter notebooks.\\n\",\n \"\\n\",\n \"This part of the book will start by stepping through some of the Jupyter and IPython features that are useful to the practice of data science, focusing especially on the syntax they offer beyond the standard features of Python.\\n\",\n \"Next, we will go into a bit more depth on some of the more useful *magic commands* that can speed up common tasks in creating and using data science code.\\n\",\n \"Finally, we will touch on some of the features of the notebook that make it useful for understanding data and sharing results.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/01.01-Help-And-Documentation.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Getting Started in IPython and Jupyter\\n\",\n \"\\n\",\n \"In writing Python code for data science, I generally go between three modes of working: I use the IPython shell for trying out short sequences of commands, the Jupyter Notebook for longer interactive analysis and for sharing content with others, and interactive development environments (IDEs) like Emacs or VSCode for creating reusable Python packages.\\n\",\n \"This chapter focuses on the first two modes: the IPython shell and the Jupyter Notebook.\\n\",\n \"Use of an IDE for software development is an important third tool in the data scientist's repertoire, but we will not directly address that here.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7b582097\",\n \"metadata\": {},\n \"source\": [\n \"## Launching the IPython Shell\\n\",\n \"\\n\",\n \"The text in this part, like most of this book, is not designed to be absorbed passively.\\n\",\n \"I recommend that as you read through it, you follow along and experiment with the tools and syntax we cover: the muscle memory you build through doing this will be far more useful than the simple act of reading about it.\\n\",\n \"Start by launching the IPython interpreter by typing **`ipython`** on the command line; alternatively, if you've installed a distribution like Anaconda or EPD, there may be a launcher specific to your system (we'll discuss this more fully in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\\n\",\n \"\\n\",\n \"Once you do this, you should see a prompt like the following:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"Python 3.9.2 (v3.9.2:1a79785e3e, Feb 19 2021, 09:06:10) \\n\",\n \"Type 'copyright', 'credits' or 'license' for more information\\n\",\n \"IPython 7.21.0 -- An enhanced Interactive Python. Type '?' for help.\\n\",\n \"\\n\",\n \"In [1]:\\n\",\n \"```\\n\",\n \"With that, you're ready to follow along.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1d2d0fb\",\n \"metadata\": {},\n \"source\": [\n \"## Launching the Jupyter Notebook\\n\",\n \"\\n\",\n \"The Jupyter Notebook is a browser-based graphical interface to the IPython shell, and builds on it a rich set of dynamic display capabilities.\\n\",\n \"As well as executing Python/IPython statements, notebooks allow the user to include formatted text, static and dynamic visualizations, mathematical equations, JavaScript widgets, and much more.\\n\",\n \"Furthermore, these documents can be saved in a way that lets other people open them and execute the code on their own systems.\\n\",\n \"\\n\",\n \"Though you'll view and edit Jupyter notebooks through your web browser window, they must connect to a running Python process in order to execute code.\\n\",\n \"You can start this process (known as a \\\"kernel\\\") by running the following command in your system shell:\\n\",\n \"\\n\",\n \"```\\n\",\n \"$ jupyter lab\\n\",\n \"```\\n\",\n \"\\n\",\n \"This command will launch a local web server that will be visible to your browser.\\n\",\n \"It immediately spits out a log showing what it is doing; that log will look something like this:\\n\",\n \"\\n\",\n \"```\\n\",\n \"$ jupyter lab\\n\",\n \"[ServerApp] Serving notebooks from local directory: /Users/jakevdp/PythonDataScienceHandbook\\n\",\n \"[ServerApp] Jupyter Server 1.4.1 is running at:\\n\",\n \"[ServerApp] http://localhost:8888/lab?token=dd852649\\n\",\n \"[ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\\n\",\n \"```\\n\",\n \"\\n\",\n \"Upon issuing the command, your default browser should automatically open and navigate to the listed local URL;\\n\",\n \"the exact address will depend on your system.\\n\",\n \"If the browser does not open automatically, you can open a window and manually open this address (*http://localhost:8888/lab/* in this example).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"92286db8\",\n \"metadata\": {},\n \"source\": [\n \"## Help and Documentation in IPython\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you read no other section in this chapter, read this one: I find the tools discussed here to be the most transformative contributions of IPython to my daily workflow.\\n\",\n \"\\n\",\n \"When a technologically minded person is asked to help a friend, family member, or colleague with a computer problem, most of the time it's less a matter of knowing the answer than of knowing how to quickly find an unknown answer.\\n\",\n \"In data science it's the same: searchable web resources such as online documentation, mailing list threads, and Stack Overflow answers contain a wealth of information, even (especially?) about topics you've found yourself searching on before.\\n\",\n \"Being an effective practitioner of data science is less about memorizing the tool or command you should use for every possible situation, and more about learning to effectively find the information you don't know, whether through a web search engine or another means.\\n\",\n \"\\n\",\n \"One of the most useful functions of IPython/Jupyter is to shorten the gap between the user and the type of documentation and search that will help them do their work effectively.\\n\",\n \"While web searches still play a role in answering complicated questions, an amazing amount of information can be found through IPython alone.\\n\",\n \"Some examples of the questions IPython can help answer in a few keystrokes include:\\n\",\n \"\\n\",\n \"- How do I call this function? What arguments and options does it have?\\n\",\n \"- What does the source code of this Python object look like?\\n\",\n \"- What is in this package I imported? \\n\",\n \"- What attributes or methods does this object have?\\n\",\n \"\\n\",\n \"Here we'll discuss the tools provided in the IPython shell and Jupyter Notebook to quickly access this information, namely the `?` character to explore documentation, the `??` characters to explore source code, and the Tab key for autocompletion.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Accessing Documentation with ?\\n\",\n \"\\n\",\n \"The Python language and its data science ecosystem are built with the user in mind, and one big part of that is access to documentation.\\n\",\n \"Every Python object contains a reference to a string, known as a *docstring*, which in most cases will contain a concise summary of the object and how to use it.\\n\",\n \"Python has a built-in `help` function that can access this information and prints the results.\\n\",\n \"For example, to see the documentation of the built-in `len` function, you can do the following:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [1]: help(len)\\n\",\n \"Help on built-in function len in module builtins:\\n\",\n \"\\n\",\n \"len(obj, /)\\n\",\n \" Return the number of items in a container.\\n\",\n \"```\\n\",\n \"\\n\",\n \"Depending on your interpreter, this information may be displayed as inline text or in a separate pop-up window.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because finding help on an object is so common and useful, IPython and Jupyter introduce the `?` character as a shorthand for accessing this documentation and other relevant information:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [2]: len?\\n\",\n \"Signature: len(obj, /)\\n\",\n \"Docstring: Return the number of items in a container.\\n\",\n \"Type: builtin_function_or_method\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This notation works for just about anything, including object methods:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [3]: L = [1, 2, 3]\\n\",\n \"In [4]: L.insert?\\n\",\n \"Signature: L.insert(index, object, /)\\n\",\n \"Docstring: Insert object before index.\\n\",\n \"Type: builtin_function_or_method\\n\",\n \"```\\n\",\n \"\\n\",\n \"or even objects themselves, with the documentation from their type:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [5]: L?\\n\",\n \"Type: list\\n\",\n \"String form: [1, 2, 3]\\n\",\n \"Length: 3\\n\",\n \"Docstring: \\n\",\n \"Built-in mutable sequence.\\n\",\n \"\\n\",\n \"If no argument is given, the constructor creates a new empty list.\\n\",\n \"The argument must be an iterable if specified.\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Importantly, this will even work for functions or other objects you create yourself!\\n\",\n \"Here we'll define a small function with a docstring:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [6]: def square(a):\\n\",\n \" ....: \\\"\\\"\\\"Return the square of a.\\\"\\\"\\\"\\n\",\n \" ....: return a ** 2\\n\",\n \" ....:\\n\",\n \"```\\n\",\n \"\\n\",\n \"Note that to create a docstring for our function, we simply placed a string literal in the first line.\\n\",\n \"Because docstrings are usually multiple lines, by convention we used Python's triple-quote notation for multiline strings.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we'll use the `?` to find this docstring:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [7]: square?\\n\",\n \"Signature: square(a)\\n\",\n \"Docstring: Return the square of a.\\n\",\n \"File: \\n\",\n \"Type: function\\n\",\n \"```\\n\",\n \"\\n\",\n \"This quick access to documentation via docstrings is one reason you should get in the habit of always adding such inline documentation to the code you write!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Accessing Source Code with ??\\n\",\n \"\\n\",\n \"Because the Python language is so easily readable, another level of insight can usually be gained by reading the source code of the object you're curious about.\\n\",\n \"IPython and Jupyter provide a shortcut to the source code with the double question mark (`??`):\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [8]: square??\\n\",\n \"Signature: square(a)\\n\",\n \"Source: \\n\",\n \"def square(a):\\n\",\n \" \\\"\\\"\\\"Return the square of a.\\\"\\\"\\\"\\n\",\n \" return a ** 2\\n\",\n \"File: \\n\",\n \"Type: function\\n\",\n \"```\\n\",\n \"\\n\",\n \"For simple functions like this, the double question mark can give quick insight into the under-the-hood details.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you play with this much, you'll notice that sometimes the `??` suffix doesn't display any source code: this is generally because the object in question is not implemented in Python, but in C or some other compiled extension language.\\n\",\n \"If this is the case, the `??` suffix gives the same output as the `?` suffix.\\n\",\n \"You'll find this particularly with many of Python's built-in objects and types, including the `len` function from earlier:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [9]: len??\\n\",\n \"Signature: len(obj, /)\\n\",\n \"Docstring: Return the number of items in a container.\\n\",\n \"Type: builtin_function_or_method\\n\",\n \"```\\n\",\n \"\\n\",\n \"Using `?` and/or `??` is a powerful and quick way of finding information about what any Python function or module does.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Exploring Modules with Tab Completion\\n\",\n \"\\n\",\n \"Another useful interface is the use of the Tab key for autocompletion and exploration of the contents of objects, modules, and namespaces.\\n\",\n \"In the examples that follow, I'll use `` to indicate when the Tab key should be pressed.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Tab completion of object contents\\n\",\n \"\\n\",\n \"Every Python object has various attributes and methods associated with it.\\n\",\n \"Like the `help` function mentioned earlier, Python has a built-in `dir` function that returns a list of these, but the tab-completion interface is much easier to use in practice.\\n\",\n \"To see a list of all available attributes of an object, you can type the name of the object followed by a period (\\\"`.`\\\") character and the Tab key:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [10]: L.\\n\",\n \" append() count insert reverse \\n\",\n \" clear extend pop sort \\n\",\n \" copy index remove \\n\",\n \"```\\n\",\n \"\\n\",\n \"To narrow down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [10]: L.c\\n\",\n \" clear() count()\\n\",\n \" copy() \\n\",\n \"\\n\",\n \"In [10]: L.co\\n\",\n \" copy() count()\\n\",\n \"```\\n\",\n \"\\n\",\n \"If there is only a single option, pressing the Tab key will complete the line for you.\\n\",\n \"For example, the following will instantly be replaced with `L.count`:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [10]: L.cou\\n\",\n \"\\n\",\n \"```\\n\",\n \"\\n\",\n \"Though Python has no strictly enforced distinction between public/external attributes and private/internal attributes, by convention a preceding underscore is used to denote the latter.\\n\",\n \"For clarity, these private methods and special methods are omitted from the list by default, but it's possible to list them by explicitly typing the underscore:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [10]: L._\\n\",\n \" __add__ __delattr__ __eq__ \\n\",\n \" __class__ __delitem__ __format__()\\n\",\n \" __class_getitem__() __dir__() __ge__ >\\n\",\n \" __contains__ __doc__ __getattribute__ \\n\",\n \"```\\n\",\n \"\\n\",\n \"For brevity, I've only shown the first few columns of the output.\\n\",\n \"Most of these are Python's special double-underscore methods (often nicknamed \\\"dunder\\\" methods).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Tab completion when importing\\n\",\n \"\\n\",\n \"Tab completion is also useful when importing objects from packages.\\n\",\n \"Here we'll use it to find all possible imports in the `itertools` package that start with `co`:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [10]: from itertools import co\\n\",\n \" combinations() compress()\\n\",\n \" combinations_with_replacement() count()\\n\",\n \"```\\n\",\n \"\\n\",\n \"Similarly, you can use tab-completion to see which imports are available on your system (this will change depending on which third-party scripts and modules are visible to your Python session):\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [10]: import \\n\",\n \" abc anyio \\n\",\n \" activate_this appdirs \\n\",\n \" aifc appnope >\\n\",\n \" antigravity argon2 \\n\",\n \"\\n\",\n \"In [10]: import h\\n\",\n \" hashlib html \\n\",\n \" heapq http \\n\",\n \" hmac \\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Beyond tab completion: Wildcard matching\\n\",\n \"\\n\",\n \"Tab completion is useful if you know the first few characters of the name of the object or attribute you're looking for, but is little help if you'd like to match characters in the middle or at the end of the name.\\n\",\n \"For this use case, IPython and Jupyter provide a means of wildcard matching for names using the `*` character.\\n\",\n \"\\n\",\n \"For example, we can use this to list every object in the namespace whose name ends with `Warning`:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [10]: *Warning?\\n\",\n \"BytesWarning RuntimeWarning\\n\",\n \"DeprecationWarning SyntaxWarning\\n\",\n \"FutureWarning UnicodeWarning\\n\",\n \"ImportWarning UserWarning\\n\",\n \"PendingDeprecationWarning Warning\\n\",\n \"ResourceWarning\\n\",\n \"```\\n\",\n \"\\n\",\n \"Notice that the `*` character matches any string, including the empty string.\\n\",\n \"\\n\",\n \"Similarly, suppose we are looking for a string method that contains the word `find` somewhere in its name.\\n\",\n \"We can search for it this way:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [11]: str.*find*?\\n\",\n \"str.find\\n\",\n \"str.rfind\\n\",\n \"```\\n\",\n \"\\n\",\n \"I find this type of flexible wildcard search can be useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 64-bit ('3.9.6')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Keyboard Shortcuts in the IPython Shell\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you spend any amount of time on a computer, you've probably found a use for keyboard shortcuts in your workflow.\\n\",\n \"Most familiar perhaps are Cmd-c and Cmd-v (or Ctrl-c and Ctrl-v), used for copying and pasting in a wide variety of programs and systems.\\n\",\n \"Power users tend to go even further: popular text editors like Emacs, Vim, and others provide users an incredible range of operations through intricate combinations of keystrokes.\\n\",\n \"\\n\",\n \"The IPython shell doesn't go this far, but does provide a number of keyboard shortcuts for fast navigation while typing commands.\\n\",\n \"While some of these shortcuts do work in the browser-based notebooks, this section is primarily about shortcuts in the IPython shell.\\n\",\n \"\\n\",\n \"Once you get accustomed to these, they can be very useful for quickly performing certain commands without moving your hands from the \\\"home\\\" keyboard position.\\n\",\n \"If you're an Emacs user or if you have experience with Linux-style shells, the following will be very familiar.\\n\",\n \"I'll group these shortcuts into a few categories: *navigation shortcuts*, *text entry shortcuts*, *command history shortcuts*, and *miscellaneous shortcuts*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Navigation Shortcuts\\n\",\n \"\\n\",\n \"While the use of the left and right arrow keys to move backward and forward in the line is quite obvious, there are other options that don't require moving your hands from the \\\"home\\\" keyboard position:\\n\",\n \"\\n\",\n \"| Keystroke | Action |\\n\",\n \"|---------------------------------|--------------------------------------------|\\n\",\n \"| Ctrl-a | Move cursor to beginning of line |\\n\",\n \"| Ctrl-e | Move cursor to end of the line |\\n\",\n \"| Ctrl-b or the left arrow key | Move cursor back one character |\\n\",\n \"| Ctrl-f or the right arrow key | Move cursor forward one character |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Text Entry Shortcuts\\n\",\n \"\\n\",\n \"While everyone is familiar with using the Backspace key to delete the previous character, reaching for the key often requires some minor finger gymnastics, and it only deletes a single character at a time.\\n\",\n \"In IPython there are several shortcuts for removing some portion of the text you're typing; the most immediately useful of these are the commands to delete entire lines of text.\\n\",\n \"You'll know these have become second-nature if you find yourself using a combination of Ctrl-b and Ctrl-d instead of reaching for Backspace to delete the previous character!\\n\",\n \"\\n\",\n \"| Keystroke | Action |\\n\",\n \"|-----------------------------|--------------------------------------------------|\\n\",\n \"| Backspace key | Delete previous character in line |\\n\",\n \"| Ctrl-d | Delete next character in line |\\n\",\n \"| Ctrl-k | Cut text from cursor to end of line |\\n\",\n \"| Ctrl-u | Cut text from beginning of line to cursor |\\n\",\n \"| Ctrl-y | Yank (i.e., paste) text that was previously cut |\\n\",\n \"| Ctrl-t | Transpose (i.e., switch) previous two characters |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Command History Shortcuts\\n\",\n \"\\n\",\n \"Perhaps the most impactful shortcuts discussed here are the ones IPython provides for navigating the command history.\\n\",\n \"This command history goes beyond your current IPython session: your entire command history is stored in a SQLite database in your IPython profile directory.\\n\",\n \"The most straightforward way to access previous commands is by using the up and down arrow keys to step through the history, but other options exist as well:\\n\",\n \"\\n\",\n \"| Keystroke | Action |\\n\",\n \"|-----------------------------------|--------------------------------------------|\\n\",\n \"| Ctrl-p (or the up arrow key) | Access previous command in history |\\n\",\n \"| Ctrl-n (or the down arrow key) | Access next command in history |\\n\",\n \"| Ctrl-r | Reverse-search through command history |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The reverse-search option can be particularly useful.\\n\",\n \"Recall that earlier we defined a function called `square`.\\n\",\n \"Let's reverse-search our Python history from a new IPython shell and find this definition again.\\n\",\n \"When you press Ctrl-r in the IPython terminal, you'll see the following prompt:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [1]:\\n\",\n \"(reverse-i-search)`': \\n\",\n \"```\\n\",\n \"\\n\",\n \"If you start typing characters at this prompt, IPython will autofill the most recent command, if any, that matches those characters:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [1]: \\n\",\n \"(reverse-i-search)`sqa': square??\\n\",\n \"```\\n\",\n \"\\n\",\n \"At any point, you can add more characters to refine the search, or press Ctrl-r again to search further for another command that matches the query. If you followed along earlier, pressing Ctrl-r twice more gives:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [1]: \\n\",\n \"(reverse-i-search)`sqa': def square(a):\\n\",\n \" \\\"\\\"\\\"Return the square of a\\\"\\\"\\\"\\n\",\n \" return a ** 2\\n\",\n \"```\\n\",\n \"\\n\",\n \"Once you have found the command you're looking for, press Return and the search will end.\\n\",\n \"You can then use the retrieved command and carry on with your session:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [1]: def square(a):\\n\",\n \" \\\"\\\"\\\"Return the square of a\\\"\\\"\\\"\\n\",\n \" return a ** 2\\n\",\n \"\\n\",\n \"In [2]: square(2)\\n\",\n \"Out[2]: 4\\n\",\n \"```\\n\",\n \"\\n\",\n \"Note that you can use Ctrl-p/Ctrl-n or the up/down arrow keys to search through your history in a similar way, but only by matching characters at the beginning of the line.\\n\",\n \"That is, if you type **`def`** and then press Ctrl-p, it will find the most recent command (if any) in your history that begins with the characters `def`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Miscellaneous Shortcuts\\n\",\n \"\\n\",\n \"Finally, there are a few miscellaneous shortcuts that don't fit into any of the preceding categories, but are nevertheless useful to know:\\n\",\n \"\\n\",\n \"| Keystroke | Action |\\n\",\n \"|-----------------------------|--------------------------------------------|\\n\",\n \"| Ctrl-l | Clear terminal screen |\\n\",\n \"| Ctrl-c | Interrupt current Python command |\\n\",\n \"| Ctrl-d | Exit IPython session |\\n\",\n \"\\n\",\n \"The Ctrl-c shortcut in particular can be useful when you inadvertently start a very long-running job.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"While some of the shortcuts discussed here may seem a bit obscure at first, they quickly become automatic with practice.\\n\",\n \"Once you develop that muscle memory, I suspect you will even find yourself wishing they were available in other contexts.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/01.03-Magic-Commands.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# IPython Magic Commands\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The previous chapter showed how IPython lets you use and explore Python efficiently and interactively.\\n\",\n \"Here we'll begin discussing some of the enhancements that IPython adds on top of the normal Python syntax.\\n\",\n \"These are known in IPython as *magic commands*, and are prefixed by the `%` character.\\n\",\n \"These magic commands are designed to succinctly solve various common problems in standard data analysis.\\n\",\n \"Magic commands come in two flavors: *line magics*, which are denoted by a single `%` prefix and operate on a single line of input, and *cell magics*, which are denoted by a double `%%` prefix and operate on multiple lines of input.\\n\",\n \"I'll demonstrate and discuss a few brief examples here, and come back to a more focused discussion of several useful magic commands later.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Running External Code: %run\\n\",\n \"As you begin developing more extensive code, you will likely find yourself working in IPython for interactive exploration, as well as a text editor to store code that you want to reuse.\\n\",\n \"Rather than running this code in a new window, it can be convenient to run it within your IPython session.\\n\",\n \"This can be done with the `%run` magic command.\\n\",\n \"\\n\",\n \"For example, imagine you've created a *myscript.py* file with the following contents:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"# file: myscript.py\\n\",\n \"\\n\",\n \"def square(x):\\n\",\n \" \\\"\\\"\\\"square a number\\\"\\\"\\\"\\n\",\n \" return x ** 2\\n\",\n \"\\n\",\n \"for N in range(1, 4):\\n\",\n \" print(f\\\"{N} squared is {square(N)}\\\")\\n\",\n \"```\\n\",\n \"\\n\",\n \"You can execute this from your IPython session as follows:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [6]: %run myscript.py\\n\",\n \"1 squared is 1\\n\",\n \"2 squared is 4\\n\",\n \"3 squared is 9\\n\",\n \"```\\n\",\n \"\\n\",\n \"Note also that after you've run this script, any functions defined within it are available for use in your IPython session:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [7]: square(5)\\n\",\n \"Out[7]: 25\\n\",\n \"```\\n\",\n \"\\n\",\n \"There are several options to fine-tune how your code is run; you can see the documentation in the normal way, by typing **`%run?`** in the IPython interpreter.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Timing Code Execution: %timeit\\n\",\n \"Another example of a useful magic function is `%timeit`, which will automatically determine the execution time of the single-line Python statement that follows it.\\n\",\n \"For example, we may want to check the performance of a list comprehension:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [8]: %timeit L = [n ** 2 for n in range(1000)]\\n\",\n \"430 µs ± 3.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\\n\",\n \"```\\n\",\n \"\\n\",\n \"The benefit of `%timeit` is that for short commands it will automatically perform multiple runs in order to attain more robust results.\\n\",\n \"For multiline statements, adding a second `%` sign will turn this into a cell magic that can handle multiple lines of input.\\n\",\n \"For example, here's the equivalent construction with a `for` loop:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [9]: %%timeit\\n\",\n \" ...: L = []\\n\",\n \" ...: for n in range(1000):\\n\",\n \" ...: L.append(n ** 2)\\n\",\n \" ...: \\n\",\n \"484 µs ± 5.67 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\\n\",\n \"```\\n\",\n \"\\n\",\n \"We can immediately see that list comprehensions are about 10% faster than the equivalent `for` loop construction in this case.\\n\",\n \"We'll explore `%timeit` and other approaches to timing and profiling code in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Help on Magic Functions: ?, %magic, and %lsmagic\\n\",\n \"\\n\",\n \"Like normal Python functions, IPython magic functions have docstrings, and this useful\\n\",\n \"documentation can be accessed in the standard manner.\\n\",\n \"So, for example, to read the documentation of the `%timeit` magic function, simply type this:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [10]: %timeit?\\n\",\n \"```\\n\",\n \"\\n\",\n \"Documentation for other functions can be accessed similarly.\\n\",\n \"To access a general description of available magic functions, including some examples, you can type this:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [11]: %magic\\n\",\n \"```\\n\",\n \"\\n\",\n \"For a quick and simple list of all available magic functions, type this:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [12]: %lsmagic\\n\",\n \"```\\n\",\n \"\\n\",\n \"Finally, I'll mention that it is quite straightforward to define your own magic functions if you wish.\\n\",\n \"I won't discuss it here, but if you are interested, see the references listed in [More IPython Resources](01.08-More-IPython-Resources.ipynb).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 64-bit ('3.9.6')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/01.04-Input-Output-History.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Input and Output History\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Previously you saw that the IPython shell allows you to access previous commands with the up and down arrow keys, or equivalently the Ctrl-p/Ctrl-n shortcuts.\\n\",\n \"Additionally, in both the shell and notebooks, IPython exposes several ways to obtain the output of previous commands, as well as string versions of the commands themselves.\\n\",\n \"We'll explore those here.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## IPython's In and Out Objects\\n\",\n \"\\n\",\n \"By now I imagine you're becoming familiar with the `In [1]:`/`Out[1]:` style of prompts used by IPython.\\n\",\n \"But it turns out that these are not just pretty decoration: they give a clue as to how you can access previous inputs and outputs in your current session.\\n\",\n \"Suppose we start a session that looks like this:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [1]: import math\\n\",\n \"\\n\",\n \"In [2]: math.sin(2)\\n\",\n \"Out[2]: 0.9092974268256817\\n\",\n \"\\n\",\n \"In [3]: math.cos(2)\\n\",\n \"Out[3]: -0.4161468365471424\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We've imported the built-in `math` package, then computed the sine and the cosine of the number 2.\\n\",\n \"These inputs and outputs are displayed in the shell with `In`/`Out` labels, but there's more—IPython actually creates some Python variables called `In` and `Out` that are automatically updated to reflect this history:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [4]: In\\n\",\n \"Out[4]: ['', 'import math', 'math.sin(2)', 'math.cos(2)', 'In']\\n\",\n \"\\n\",\n \"In [5]: Out\\n\",\n \"Out[5]:\\n\",\n \"{2: 0.9092974268256817,\\n\",\n \" 3: -0.4161468365471424,\\n\",\n \" 4: ['', 'import math', 'math.sin(2)', 'math.cos(2)', 'In', 'Out']}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `In` object is a list, which keeps track of the commands in order (the first item in the list is a placeholder so that `In [1]` can refer to the first command):\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [6]: print(In[1])\\n\",\n \"import math\\n\",\n \"```\\n\",\n \"\\n\",\n \"The `Out` object is not a list but a dictionary mapping input numbers to their outputs (if any):\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [7]: print(Out[2])\\n\",\n \"0.9092974268256817\\n\",\n \"```\\n\",\n \"\\n\",\n \"Note that not all operations have outputs: for example, `import` statements and `print` statements don't affect the output.\\n\",\n \"The latter may be surprising, but makes sense if you consider that `print` is a function that returns `None`; for brevity, any command that returns `None` is not added to `Out`.\\n\",\n \"\\n\",\n \"Where this can be useful is if you want to interact with past results.\\n\",\n \"For example, let's check the sum of `sin(2) ** 2` and `cos(2) ** 2` using the previously computed results:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [8]: Out[2] ** 2 + Out[3] ** 2\\n\",\n \"Out[8]: 1.0\\n\",\n \"```\\n\",\n \"\\n\",\n \"The result is `1.0`, as we'd expect from the well-known trigonometric identity.\\n\",\n \"In this case, using these previous results probably is not necessary, but it can become quite handy if you execute a very expensive computation and forget to assign the result to a variable.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Underscore Shortcuts and Previous Outputs\\n\",\n \"\\n\",\n \"The standard Python shell contains just one simple shortcut for accessing previous output: the variable `_` (i.e., a single underscore) is kept updated with the previous output. This works in IPython as well:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [9]: print(_)\\n\",\n \"1.0\\n\",\n \"```\\n\",\n \"\\n\",\n \"But IPython takes this a bit further—you can use a double underscore to access the second-to-last output, and a triple underscore to access the third-to-last output (skipping any commands with no output):\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [10]: print(__)\\n\",\n \"-0.4161468365471424\\n\",\n \"\\n\",\n \"In [11]: print(___)\\n\",\n \"0.9092974268256817\\n\",\n \"```\\n\",\n \"\\n\",\n \"IPython stops there: more than three underscores starts to get a bit hard to count, and at that point it's easier to refer to the output by line number.\\n\",\n \"\\n\",\n \"There is one more shortcut I should mention, however—a shorthand for `Out[X]` is `_X` (i.e., a single underscore followed by the line number):\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [12]: Out[2]\\n\",\n \"Out[12]: 0.9092974268256817\\n\",\n \"\\n\",\n \"In [13]: _2\\n\",\n \"Out[13]: 0.9092974268256817\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Suppressing Output\\n\",\n \"Sometimes you might wish to suppress the output of a statement (this is perhaps most common with the plotting commands that we'll explore in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb)).\\n\",\n \"Or maybe the command you're executing produces a result that you'd prefer not to store in your output history, perhaps so that it can be deallocated when other references are removed.\\n\",\n \"The easiest way to suppress the output of a command is to add a semicolon to the end of the line:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [14]: math.sin(2) + math.cos(2);\\n\",\n \"```\\n\",\n \"\\n\",\n \"The result is computed silently, and the output is neither displayed on the screen nor stored in the `Out` dictionary:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [15]: 14 in Out\\n\",\n \"Out[15]: False\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Related Magic Commands\\n\",\n \"For accessing a batch of previous inputs at once, the `%history` magic command is very helpful.\\n\",\n \"Here is how you can print the first four inputs:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [16]: %history -n 1-3\\n\",\n \" 1: import math\\n\",\n \" 2: math.sin(2)\\n\",\n \" 3: math.cos(2)\\n\",\n \"```\\n\",\n \"\\n\",\n \"As usual, you can type `%history?` for more information and a description of options available (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) for details on the `?` functionality).\\n\",\n \"Other useful magic commands are `%rerun`, which will re-execute some portion of the command history, and `%save`, which saves some set of the command history to a file).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/01.05-IPython-And-Shell-Commands.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# IPython and Shell Commands\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When working interactively with the standard Python interpreter, one of the frustrations is the need to switch between multiple windows to access Python tools and system command-line tools.\\n\",\n \"IPython bridges this gap, and gives you a syntax for executing shell commands directly from within the IPython terminal.\\n\",\n \"The magic happens with the exclamation point: anything appearing after `!` on a line will be executed not by the Python kernel, but by the system command line.\\n\",\n \"\\n\",\n \"The following discussion assumes you're on a Unix-like system, such as Linux or macOS.\\n\",\n \"Some of the examples that follow will fail on Windows, which uses a different type of shell by default, though if you use the *Windows Subsystem for Linux* the examples here should run correctly.\\n\",\n \"If you're unfamiliar with shell commands, I'd suggest reviewing the [Unix shell tutorial](http://swcarpentry.github.io/shell-novice/) put together by the always excellent Software Carpentry Foundation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Quick Introduction to the Shell\\n\",\n \"\\n\",\n \"A full introduction to using the shell/terminal/command line is well beyond the scope of this chapter, but for the uninitiated I will offer a quick introduction here.\\n\",\n \"The shell is a way to interact textually with your computer.\\n\",\n \"Ever since the mid-1980s, when Microsoft and Apple introduced the first versions of their now ubiquitous graphical operating systems, most computer users have interacted with their operating systems through the familiar menu selections and drag-and-drop movements.\\n\",\n \"But operating systems existed long before these graphical user interfaces, and were primarily controlled through sequences of text input: at the prompt, the user would type a command, and the computer would do what the user told it to.\\n\",\n \"Those early prompt systems were the precursors of the shells and terminals that most data scientists still use today.\\n\",\n \"\\n\",\n \"Someone unfamiliar with the shell might ask why you would bother with this, when many of the same results can be accomplished by simply clicking on icons and menus.\\n\",\n \"A shell user might reply with another question: why hunt for icons and menu items when you can accomplish things much more easily by typing?\\n\",\n \"While it might sound like a typical tech preference impasse, when moving beyond basic tasks it quickly becomes clear that the shell offers much more control of advanced tasks—though admittedly the learning curve can be intimidating.\\n\",\n \"\\n\",\n \"As an example, here is a sample of a Linux/macOS shell session where a user explores, creates, and modifies directories and files on their system (`osx:~ $` is the prompt, and everything after the `$` is the typed command; text that is preceded by a `#` is meant just as description, rather than something you would actually type in):\\n\",\n \"\\n\",\n \"```bash\\n\",\n \"osx:~ $ echo \\\"hello world\\\" # echo is like Python's print function\\n\",\n \"hello world\\n\",\n \"\\n\",\n \"osx:~ $ pwd # pwd = print working directory\\n\",\n \"/home/jake # This is the \\\"path\\\" that we're sitting in\\n\",\n \"\\n\",\n \"osx:~ $ ls # ls = list working directory contents\\n\",\n \"notebooks projects \\n\",\n \"\\n\",\n \"osx:~ $ cd projects/ # cd = change directory\\n\",\n \"\\n\",\n \"osx:projects $ pwd\\n\",\n \"/home/jake/projects\\n\",\n \"\\n\",\n \"osx:projects $ ls\\n\",\n \"datasci_book mpld3 myproject.txt\\n\",\n \"\\n\",\n \"osx:projects $ mkdir myproject # mkdir = make new directory\\n\",\n \"\\n\",\n \"osx:projects $ cd myproject/\\n\",\n \"\\n\",\n \"osx:myproject $ mv ../myproject.txt ./ # mv = move file. Here we're moving the\\n\",\n \" # file myproject.txt from one directory\\n\",\n \" # up (../) to the current directory (./).\\n\",\n \"osx:myproject $ ls\\n\",\n \"myproject.txt\\n\",\n \"```\\n\",\n \"\\n\",\n \"Notice that all of this is just a compact way to do familiar operations (navigating a directory structure, creating a directory, moving a file, etc.) by typing commands rather than clicking icons and menus.\\n\",\n \"With just a few commands (`pwd`, `ls`, `cd`, `mkdir`, and `cp`) you can do many of the most common file operations, but it's when you go beyond these basics that the shell approach becomes really powerful.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Shell Commands in IPython\\n\",\n \"\\n\",\n \"Any standard shell command can be used directly in IPython by prefixing it with the `!` character.\\n\",\n \"For example, the `ls`, `pwd`, and `echo` commands can be run as follows:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [1]: !ls\\n\",\n \"myproject.txt\\n\",\n \"\\n\",\n \"In [2]: !pwd\\n\",\n \"/home/jake/projects/myproject\\n\",\n \"\\n\",\n \"In [3]: !echo \\\"printing from the shell\\\"\\n\",\n \"printing from the shell\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Passing Values to and from the Shell\\n\",\n \"\\n\",\n \"Shell commands not only can be called from IPython, but can also be made to interact with the IPython namespace.\\n\",\n \"For example, you can save the output of any shell command to a Python list using the assignment operator, `=`:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [4]: contents = !ls\\n\",\n \"\\n\",\n \"In [5]: print(contents)\\n\",\n \"['myproject.txt']\\n\",\n \"\\n\",\n \"In [6]: directory = !pwd\\n\",\n \"\\n\",\n \"In [7]: print(directory)\\n\",\n \"['/Users/jakevdp/notebooks/tmp/myproject']\\n\",\n \"```\\n\",\n \"\\n\",\n \"These results are not returned as lists, but as a special shell return type defined in IPython:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [8]: type(directory)\\n\",\n \"IPython.utils.text.SList\\n\",\n \"```\\n\",\n \"\\n\",\n \"This looks and acts a lot like a Python list but has additional functionality, such as\\n\",\n \"the `grep` and `fields` methods and the `s`, `n`, and `p` properties that allow you to search, filter, and display the results in convenient ways.\\n\",\n \"For more information on these, you can use IPython's built-in help features.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Communication in the other direction—passing Python variables into the shell—is possible using the `{varname}` syntax:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [9]: message = \\\"hello from Python\\\"\\n\",\n \"\\n\",\n \"In [10]: !echo {message}\\n\",\n \"hello from Python\\n\",\n \"```\\n\",\n \"\\n\",\n \"The curly braces contain the variable name, which is replaced by the variable's contents in the shell command.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Shell-Related Magic Commands\\n\",\n \"\\n\",\n \"If you play with IPython's shell commands for a while, you might notice that you cannot use `!cd` to navigate the filesystem:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [11]: !pwd\\n\",\n \"/home/jake/projects/myproject\\n\",\n \"\\n\",\n \"In [12]: !cd ..\\n\",\n \"\\n\",\n \"In [13]: !pwd\\n\",\n \"/home/jake/projects/myproject\\n\",\n \"```\\n\",\n \"\\n\",\n \"The reason is that shell commands in the notebook are executed in a temporary subshell that does not maintain state from command to command.\\n\",\n \"If you'd like to change the working directory in a more enduring way, you can use the `%cd` magic command:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [14]: %cd ..\\n\",\n \"/home/jake/projects\\n\",\n \"```\\n\",\n \"\\n\",\n \"In fact, by default you can even use this without the `%` sign:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [15]: cd myproject\\n\",\n \"/home/jake/projects/myproject\\n\",\n \"```\\n\",\n \"\\n\",\n \"This is known as an *automagic* function, and the ability to execute such commands without an explicit `%` can be toggled with the `%automagic` magic function.\\n\",\n \"\\n\",\n \"Besides `%cd`, other available shell-like magic functions are `%cat`, `%cp`, `%env`, `%ls`, `%man`, `%mkdir`, `%more`, `%mv`, `%pwd`, `%rm`, and `%rmdir`, any of which can be used without the `%` sign if `automagic` is on.\\n\",\n \"This makes it so that you can almost treat the IPython prompt as if it's a normal shell:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [16]: mkdir tmp\\n\",\n \"\\n\",\n \"In [17]: ls\\n\",\n \"myproject.txt tmp/\\n\",\n \"\\n\",\n \"In [18]: cp myproject.txt tmp/\\n\",\n \"\\n\",\n \"In [19]: ls tmp\\n\",\n \"myproject.txt\\n\",\n \"\\n\",\n \"In [20]: rm -r tmp\\n\",\n \"```\\n\",\n \"\\n\",\n \"This access to the shell from within the same terminal window as your Python session lets you more naturally combine Python and the shell in your workflows with fewer context switches.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/01.06-Errors-and-Debugging.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Errors and Debugging\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Code development and data analysis always require a bit of trial and error, and IPython contains tools to streamline this process.\\n\",\n \"This section will briefly cover some options for controlling Python's exception reporting, followed by exploring tools for debugging errors in code.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Controlling Exceptions: %xmode\\n\",\n \"\\n\",\n \"Most of the time when a Python script fails, it will raise an exception.\\n\",\n \"When the interpreter hits one of these exceptions, information about the cause of the error can be found in the *traceback*, which can be accessed from within Python.\\n\",\n \"With the `%xmode` magic function, IPython allows you to control the amount of information printed when the exception is raised.\\n\",\n \"Consider the following code:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def func1(a, b):\\n\",\n \" return a / b\\n\",\n \"\\n\",\n \"def func2(x):\\n\",\n \" a = x\\n\",\n \" b = x - 1\\n\",\n \" return func1(a, b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"ename\": \"ZeroDivisionError\",\n \"evalue\": \"division by zero\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\\n\\u001b[0;31mZeroDivisionError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mfunc2\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36mfunc2\\u001b[0;34m(x)\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0ma\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mx\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 6\\u001b[0m \\u001b[0mb\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mx\\u001b[0m \\u001b[0;34m-\\u001b[0m \\u001b[0;36m1\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 7\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mfunc1\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0ma\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mb\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36mfunc1\\u001b[0;34m(a, b)\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mfunc1\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0ma\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mb\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0ma\\u001b[0m \\u001b[0;34m/\\u001b[0m \\u001b[0mb\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mfunc2\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0ma\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mx\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mZeroDivisionError\\u001b[0m: division by zero\"\n ]\n }\n ],\n \"source\": [\n \"func2(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Calling `func2` results in an error, and reading the printed trace lets us see exactly what happened.\\n\",\n \"In the default mode, this trace includes several lines showing the context of each step that led to the error.\\n\",\n \"Using the `%xmode` magic function (short for *exception mode*), we can change what information is printed.\\n\",\n \"\\n\",\n \"`%xmode` takes a single argument, the mode, and there are three possibilities: `Plain`, `Context`, and `Verbose`.\\n\",\n \"The default is `Context`, which gives output like that just shown.\\n\",\n \"`Plain` is more compact and gives less information:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception reporting mode: Plain\\n\"\n ]\n }\n ],\n \"source\": [\n \"%xmode Plain\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"ename\": \"ZeroDivisionError\",\n \"evalue\": \"division by zero\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"Traceback \\u001b[0;36m(most recent call last)\\u001b[0m:\\n\",\n \" File \\u001b[1;32m\\\"\\\"\\u001b[0m, line \\u001b[1;32m1\\u001b[0m, in \\u001b[1;35m\\u001b[0m\\n func2(1)\\n\",\n \" File \\u001b[1;32m\\\"\\\"\\u001b[0m, line \\u001b[1;32m7\\u001b[0m, in \\u001b[1;35mfunc2\\u001b[0m\\n return func1(a, b)\\n\",\n \"\\u001b[0;36m File \\u001b[0;32m\\\"\\\"\\u001b[0;36m, line \\u001b[0;32m2\\u001b[0;36m, in \\u001b[0;35mfunc1\\u001b[0;36m\\u001b[0m\\n\\u001b[0;31m return a / b\\u001b[0m\\n\",\n \"\\u001b[0;31mZeroDivisionError\\u001b[0m\\u001b[0;31m:\\u001b[0m division by zero\\n\"\n ]\n }\n ],\n \"source\": [\n \"func2(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `Verbose` mode adds some extra information, including the arguments to any functions that are called:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception reporting mode: Verbose\\n\"\n ]\n }\n ],\n \"source\": [\n \"%xmode Verbose\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"ename\": \"ZeroDivisionError\",\n \"evalue\": \"division by zero\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\\n\\u001b[0;31mZeroDivisionError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mfunc2\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m \\u001b[0;36mglobal\\u001b[0m \\u001b[0;36mfunc2\\u001b[0m \\u001b[0;34m= \\u001b[0m\\n\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36mfunc2\\u001b[0;34m(x=1)\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0ma\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mx\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 6\\u001b[0m \\u001b[0mb\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mx\\u001b[0m \\u001b[0;34m-\\u001b[0m \\u001b[0;36m1\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 7\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mfunc1\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0ma\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mb\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m \\u001b[0;36mglobal\\u001b[0m \\u001b[0;36mfunc1\\u001b[0m \\u001b[0;34m= \\u001b[0m\\u001b[0;34m\\n \\u001b[0m\\u001b[0;36ma\\u001b[0m \\u001b[0;34m= 1\\u001b[0m\\u001b[0;34m\\n \\u001b[0m\\u001b[0;36mb\\u001b[0m \\u001b[0;34m= 0\\u001b[0m\\n\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36mfunc1\\u001b[0;34m(a=1, b=0)\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mfunc1\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0ma\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mb\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0ma\\u001b[0m \\u001b[0;34m/\\u001b[0m \\u001b[0mb\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m \\u001b[0;36ma\\u001b[0m \\u001b[0;34m= 1\\u001b[0m\\u001b[0;34m\\n \\u001b[0m\\u001b[0;36mb\\u001b[0m \\u001b[0;34m= 0\\u001b[0m\\n\\u001b[1;32m 3\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mfunc2\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0ma\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mx\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mZeroDivisionError\\u001b[0m: division by zero\"\n ]\n }\n ],\n \"source\": [\n \"func2(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This extra information can help you narrow in on why the exception is being raised.\\n\",\n \"So why not use the `Verbose` mode all the time?\\n\",\n \"As code gets complicated, this kind of traceback can get extremely long.\\n\",\n \"Depending on the context, sometimes the brevity of `Plain` or `Context` mode is easier to work with.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Debugging: When Reading Tracebacks Is Not Enough\\n\",\n \"\\n\",\n \"The standard Python tool for interactive debugging is `pdb`, the Python debugger.\\n\",\n \"This debugger lets the user step through the code line by line in order to see what might be causing a more difficult error.\\n\",\n \"The IPython-enhanced version of this is `ipdb`, the IPython debugger.\\n\",\n \"\\n\",\n \"There are many ways to launch and use both these debuggers; we won't cover them fully here.\\n\",\n \"Refer to the online documentation of these two utilities to learn more.\\n\",\n \"\\n\",\n \"In IPython, perhaps the most convenient interface to debugging is the `%debug` magic command.\\n\",\n \"If you call it after hitting an exception, it will automatically open an interactive debugging prompt at the point of the exception.\\n\",\n \"The `ipdb` prompt lets you explore the current state of the stack, explore the available variables, and even run Python commands!\\n\",\n \"\\n\",\n \"Let's look at the most recent exception, then do some basic tasks. We'll print the values of `a` and `b`, then type `quit` to quit the debugging session:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"> (2)func1()\\n\",\n \" 1 def func1(a, b):\\n\",\n \"----> 2 return a / b\\n\",\n \" 3 \\n\",\n \"\\n\",\n \"ipdb> print(a)\\n\",\n \"1\\n\",\n \"ipdb> print(b)\\n\",\n \"0\\n\",\n \"ipdb> quit\\n\"\n ]\n }\n ],\n \"source\": [\n \"%debug\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The interactive debugger allows much more than this, though—we can even step up and down through the stack and explore the values of variables there:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"> (2)func1()\\n\",\n \" 1 def func1(a, b):\\n\",\n \"----> 2 return a / b\\n\",\n \" 3 \\n\",\n \"\\n\",\n \"ipdb> up\\n\",\n \"> (7)func2()\\n\",\n \" 5 a = x\\n\",\n \" 6 b = x - 1\\n\",\n \"----> 7 return func1(a, b)\\n\",\n \"\\n\",\n \"ipdb> print(x)\\n\",\n \"1\\n\",\n \"ipdb> up\\n\",\n \"> (1)()\\n\",\n \"----> 1 func2(1)\\n\",\n \"\\n\",\n \"ipdb> down\\n\",\n \"> (7)func2()\\n\",\n \" 5 a = x\\n\",\n \" 6 b = x - 1\\n\",\n \"----> 7 return func1(a, b)\\n\",\n \"\\n\",\n \"ipdb> quit\\n\"\n ]\n }\n ],\n \"source\": [\n \"%debug\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This allows us to quickly find out not only what caused the error, but what function calls led up to the error.\\n\",\n \"\\n\",\n \"If you'd like the debugger to launch automatically whenever an exception is raised, you can use the `%pdb` magic function to turn on this automatic behavior:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Exception reporting mode: Plain\\n\",\n \"Automatic pdb calling has been turned ON\\n\"\n ]\n },\n {\n \"ename\": \"ZeroDivisionError\",\n \"evalue\": \"division by zero\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"Traceback \\u001b[0;36m(most recent call last)\\u001b[0m:\\n\",\n \" File \\u001b[1;32m\\\"\\\"\\u001b[0m, line \\u001b[1;32m3\\u001b[0m, in \\u001b[1;35m\\u001b[0m\\n func2(1)\\n\",\n \" File \\u001b[1;32m\\\"\\\"\\u001b[0m, line \\u001b[1;32m7\\u001b[0m, in \\u001b[1;35mfunc2\\u001b[0m\\n return func1(a, b)\\n\",\n \"\\u001b[0;36m File \\u001b[0;32m\\\"\\\"\\u001b[0;36m, line \\u001b[0;32m2\\u001b[0;36m, in \\u001b[0;35mfunc1\\u001b[0;36m\\u001b[0m\\n\\u001b[0;31m return a / b\\u001b[0m\\n\",\n \"\\u001b[0;31mZeroDivisionError\\u001b[0m\\u001b[0;31m:\\u001b[0m division by zero\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"> (2)func1()\\n\",\n \" 1 def func1(a, b):\\n\",\n \"----> 2 return a / b\\n\",\n \" 3 \\n\",\n \"\\n\",\n \"ipdb> print(b)\\n\",\n \"0\\n\",\n \"ipdb> quit\\n\"\n ]\n }\n ],\n \"source\": [\n \"%xmode Plain\\n\",\n \"%pdb on\\n\",\n \"func2(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, if you have a script that you'd like to run from the beginning in interactive mode, you can run it with the command `%run -d`, and use the `next` command to step through the lines of code interactively.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Partial list of debugging commands\\n\",\n \"\\n\",\n \"There are many more available commands for interactive debugging than I've shown here. The following table contains a description of some of the more common and useful ones:\\n\",\n \"\\n\",\n \"| Command | Description |\\n\",\n \"|---------------|-------------------------------------------------------------|\\n\",\n \"| `l(ist)` | Show the current location in the file |\\n\",\n \"| `h(elp)` | Show a list of commands, or find help on a specific command |\\n\",\n \"| `q(uit)` | Quit the debugger and the program |\\n\",\n \"| `c(ontinue)` | Quit the debugger, continue in the program |\\n\",\n \"| `n(ext)` | Go to the next step of the program |\\n\",\n \"| `` | Repeat the previous command |\\n\",\n \"| `p(rint)` | Print variables |\\n\",\n \"| `s(tep)` | Step into a subroutine |\\n\",\n \"| `r(eturn)` | Return out of a subroutine |\\n\",\n \"\\n\",\n \"For more information, use the `help` command in the debugger, or take a look at `ipdb`'s [online documentation](https://github.com/gotcha/ipdb).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/01.07-Timing-and-Profiling.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Profiling and Timing Code\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the process of developing code and creating data processing pipelines, there are often trade-offs you can make between various implementations.\\n\",\n \"Early in developing your algorithm, it can be counterproductive to worry about such things. As Donald Knuth famously quipped, \\\"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.\\\"\\n\",\n \"\\n\",\n \"But once you have your code working, it can be useful to dig into its efficiency a bit.\\n\",\n \"Sometimes it's useful to check the execution time of a given command or set of commands; other times it's useful to examine a multiline process and determine where the bottleneck lies in some complicated series of operations.\\n\",\n \"IPython provides access to a wide array of functionality for this kind of timing and profiling of code.\\n\",\n \"Here we'll discuss the following IPython magic commands:\\n\",\n \"\\n\",\n \"- `%time`: Time the execution of a single statement\\n\",\n \"- `%timeit`: Time repeated execution of a single statement for more accuracy\\n\",\n \"- `%prun`: Run code with the profiler\\n\",\n \"- `%lprun`: Run code with the line-by-line profiler\\n\",\n \"- `%memit`: Measure the memory use of a single statement\\n\",\n \"- `%mprun`: Run code with the line-by-line memory profiler\\n\",\n \"\\n\",\n \"The last four commands are not bundled with IPython; to use them you'll need to get the `line_profiler` and `memory_profiler` extensions, which we will discuss in the following sections.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Timing Code Snippets: %timeit and %time\\n\",\n \"\\n\",\n \"We saw the `%timeit` line magic and `%%timeit` cell magic in the introduction to magic functions in [IPython Magic Commands](01.03-Magic-Commands.ipynb); these can be used to time the repeated execution of snippets of code:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1.53 µs ± 47.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit sum(range(100))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that because this operation is so fast, `%timeit` automatically does a large number of repetitions.\\n\",\n \"For slower commands, `%timeit` will automatically adjust and perform fewer repetitions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"536 ms ± 15.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%timeit\\n\",\n \"total = 0\\n\",\n \"for i in range(1000):\\n\",\n \" for j in range(1000):\\n\",\n \" total += i * (-1) ** j\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sometimes repeating an operation is not the best option.\\n\",\n \"For example, if we have a list that we'd like to sort, we might be misled by a repeated operation; sorting a pre-sorted list is much faster than sorting an unsorted list, so the repetition will skew the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1.71 ms ± 334 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"import random\\n\",\n \"L = [random.random() for i in range(100000)]\\n\",\n \"%timeit L.sort()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this, the `%time` magic function may be a better choice. It also is a good choice for longer-running commands, when short, system-related delays are unlikely to affect the result.\\n\",\n \"Let's time the sorting of an unsorted and a presorted list:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sorting an unsorted list:\\n\",\n \"CPU times: user 31.3 ms, sys: 686 µs, total: 32 ms\\n\",\n \"Wall time: 33.3 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"import random\\n\",\n \"L = [random.random() for i in range(100000)]\\n\",\n \"print(\\\"sorting an unsorted list:\\\")\\n\",\n \"%time L.sort()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sorting an already sorted list:\\n\",\n \"CPU times: user 5.19 ms, sys: 268 µs, total: 5.46 ms\\n\",\n \"Wall time: 14.1 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"sorting an already sorted list:\\\")\\n\",\n \"%time L.sort()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice how much faster the presorted list is to sort, but notice also how much longer the timing takes with `%time` versus `%timeit`, even for the presorted list!\\n\",\n \"This is a result of the fact that `%timeit` does some clever things under the hood to prevent system calls from interfering with the timing.\\n\",\n \"For example, it prevents cleanup of unused Python objects (known as *garbage collection*) that might otherwise affect the timing.\\n\",\n \"For this reason, `%timeit` results are usually noticeably faster than `%time` results.\\n\",\n \"\\n\",\n \"For `%time`, as with `%timeit`, using the `%%` cell magic syntax allows timing of multiline scripts:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 655 ms, sys: 5.68 ms, total: 661 ms\\n\",\n \"Wall time: 710 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"total = 0\\n\",\n \"for i in range(1000):\\n\",\n \" for j in range(1000):\\n\",\n \" total += i * (-1) ** j\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For more information on `%time` and `%timeit`, as well as their available options, use the IPython help functionality (e.g., type `%time?` at the IPython prompt).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Profiling Full Scripts: %prun\\n\",\n \"\\n\",\n \"A program is made up of many single statements, and sometimes timing these statements in context is more important than timing them on their own.\\n\",\n \"Python contains a built-in code profiler (which you can read about in the Python documentation), but IPython offers a much more convenient way to use this profiler, in the form of the magic function `%prun`.\\n\",\n \"\\n\",\n \"By way of example, we'll define a simple function that does some calculations:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def sum_of_lists(N):\\n\",\n \" total = 0\\n\",\n \" for i in range(5):\\n\",\n \" L = [j ^ (j >> i) for j in range(N)]\\n\",\n \" total += sum(L)\\n\",\n \" return total\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can call `%prun` with a function call to see the profiled results:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" \"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \" 14 function calls in 0.932 seconds\\n\",\n \"\\n\",\n \" Ordered by: internal time\\n\",\n \"\\n\",\n \" ncalls tottime percall cumtime percall filename:lineno(function)\\n\",\n \" 5 0.808 0.162 0.808 0.162 :4()\\n\",\n \" 5 0.066 0.013 0.066 0.013 {built-in method builtins.sum}\\n\",\n \" 1 0.044 0.044 0.918 0.918 :1(sum_of_lists)\\n\",\n \" 1 0.014 0.014 0.932 0.932 :1()\\n\",\n \" 1 0.000 0.000 0.932 0.932 {built-in method builtins.exec}\\n\",\n \" 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%prun sum_of_lists(1000000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result is a table that indicates, in order of total time on each function call, where the execution is spending the most time. In this case, the bulk of the execution time is in the list comprehension inside `sum_of_lists`.\\n\",\n \"From here, we could start thinking about what changes we might make to improve the performance of the algorithm.\\n\",\n \"\\n\",\n \"For more information on `%prun`, as well as its available options, use the IPython help functionality (i.e., type `%prun?` at the IPython prompt).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Line-by-Line Profiling with %lprun\\n\",\n \"\\n\",\n \"The function-by-function profiling of `%prun` is useful, but sometimes it's more convenient to have a line-by-line profile report.\\n\",\n \"This is not built into Python or IPython, but there is a `line_profiler` package available for installation that can do this.\\n\",\n \"Start by using Python's packaging tool, `pip`, to install the `line_profiler` package:\\n\",\n \"\\n\",\n \"```\\n\",\n \"$ pip install line_profiler\\n\",\n \"```\\n\",\n \"\\n\",\n \"Next, you can use IPython to load the `line_profiler` IPython extension, offered as part of this package:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%load_ext line_profiler\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now the `%lprun` command will do a line-by-line profiling of any function. In this case, we need to tell it explicitly which functions we're interested in profiling:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Timer unit: 1e-06 s\\n\",\n \"\\n\",\n \"Total time: 0.014803 s\\n\",\n \"File: \\n\",\n \"Function: sum_of_lists at line 1\\n\",\n \"\\n\",\n \"Line # Hits Time Per Hit % Time Line Contents\\n\",\n \"==============================================================\\n\",\n \" 1 def sum_of_lists(N):\\n\",\n \" 2 1 6.0 6.0 0.0 total = 0\\n\",\n \" 3 6 13.0 2.2 0.1 for i in range(5):\\n\",\n \" 4 5 14242.0 2848.4 96.2 L = [j ^ (j >> i) for j in range(N)]\\n\",\n \" 5 5 541.0 108.2 3.7 total += sum(L)\\n\",\n \" 6 1 1.0 1.0 0.0 return total\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%lprun -f sum_of_lists sum_of_lists(5000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The information at the top gives us the key to reading the results: the time is reported in microseconds, and we can see where the program is spending the most time.\\n\",\n \"At this point, we may be able to use this information to modify aspects of the script and make it perform better for our desired use case.\\n\",\n \"\\n\",\n \"For more information on `%lprun`, as well as its available options, use the IPython help functionality (i.e., type `%lprun?` at the IPython prompt).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Profiling Memory Use: %memit and %mprun\\n\",\n \"\\n\",\n \"Another aspect of profiling is the amount of memory an operation uses.\\n\",\n \"This can be evaluated with another IPython extension, the `memory_profiler`.\\n\",\n \"As with the `line_profiler`, we start by `pip`-installing the extension:\\n\",\n \"\\n\",\n \"```\\n\",\n \"$ pip install memory_profiler\\n\",\n \"```\\n\",\n \"\\n\",\n \"Then we can use IPython to load it:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%load_ext memory_profiler\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The memory profiler extension contains two useful magic functions: `%memit` (which offers a memory-measuring equivalent of `%timeit`) and `%mprun` (which offers a memory-measuring equivalent of `%lprun`).\\n\",\n \"The `%memit` magic function can be used rather simply:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"peak memory: 141.70 MiB, increment: 75.65 MiB\\n\"\n ]\n }\n ],\n \"source\": [\n \"%memit sum_of_lists(1000000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see that this function uses about 140 MB of memory.\\n\",\n \"\\n\",\n \"For a line-by-line description of memory use, we can use the `%mprun` magic function.\\n\",\n \"Unfortunately, this works only for functions defined in separate modules rather than the notebook itself, so we'll start by using the `%%file` cell magic to create a simple module called `mprun_demo.py`, which contains our `sum_of_lists` function, with one addition that will make our memory profiling results more clear:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Overwriting mprun_demo.py\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%file mprun_demo.py\\n\",\n \"def sum_of_lists(N):\\n\",\n \" total = 0\\n\",\n \" for i in range(5):\\n\",\n \" L = [j ^ (j >> i) for j in range(N)]\\n\",\n \" total += sum(L)\\n\",\n \" del L # remove reference to L\\n\",\n \" return total\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now import the new version of this function and run the memory line profiler:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"Filename: /Users/jakevdp/github/jakevdp/PythonDataScienceHandbook/notebooks_v2/mprun_demo.py\\n\",\n \"\\n\",\n \"Line # Mem usage Increment Occurences Line Contents\\n\",\n \"============================================================\\n\",\n \" 1 66.7 MiB 66.7 MiB 1 def sum_of_lists(N):\\n\",\n \" 2 66.7 MiB 0.0 MiB 1 total = 0\\n\",\n \" 3 75.1 MiB 8.4 MiB 6 for i in range(5):\\n\",\n \" 4 105.9 MiB 30.8 MiB 5000015 L = [j ^ (j >> i) for j in range(N)]\\n\",\n \" 5 109.8 MiB 3.8 MiB 5 total += sum(L)\\n\",\n \" 6 75.1 MiB -34.6 MiB 5 del L # remove reference to L\\n\",\n \" 7 66.9 MiB -8.2 MiB 1 return total\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from mprun_demo import sum_of_lists\\n\",\n \"%mprun -f sum_of_lists sum_of_lists(1000000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, the `Increment` column tells us how much each line affects the total memory budget: observe that when we create and delete the list `L`, we are adding about 30 MB of memory usage.\\n\",\n \"This is on top of the background memory usage from the Python interpreter itself.\\n\",\n \"\\n\",\n \"For more information on `%memit` and `%mprun`, as well as their available options, use the IPython help functionality (e.g., type `%memit?` at the IPython prompt).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/01.08-More-IPython-Resources.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# More IPython Resources\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this set of chapters, we've just scratched the surface of using IPython to enable data science tasks.\\n\",\n \"Much more information is available both in print and on the web, and here I'll list some other resources that you may find helpful.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Web Resources\\n\",\n \"\\n\",\n \"- [The IPython website](http://ipython.org): The IPython website provides links to documentation, examples, tutorials, and a variety of other resources.\\n\",\n \"- [The nbviewer website](http://nbviewer.jupyter.org/): This site shows static renderings of any Jupyter notebook available on the internet. The front page features some example notebooks that you can browse to see what other folks are using IPython for!\\n\",\n \"- [A curated collection of Jupyter notebooks](https://github.com/jupyter/jupyter/wiki): This ever-growing list of notebooks, powered by nbviewer, shows the depth and breadth of numerical analysis you can do with IPython. It includes everything from short examples and tutorials to full-blown courses and books composed in the notebook format!\\n\",\n \"- Video tutorials: Searching the internet, you will find many video tutorials on IPython. I'd especially recommend seeking tutorials from the PyCon, SciPy, and PyData conferences by Fernando Perez and Brian Granger, two of the primary creators and maintainers of IPython and Jupyter.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Books\\n\",\n \"\\n\",\n \"- [*Python for Data Analysis* (O'Reilly)](http://shop.oreilly.com/product/0636920023784.do): Wes McKinney's book includes a chapter that covers using IPython as a data scientist. Although much of the material overlaps what we've discussed here, another perspective is always helpful.\\n\",\n \"- [*Learning IPython for Interactive Computing and Data Visualization* (Packt)](https://www.packtpub.com/big-data-and-business-intelligence/learning-ipython-interactive-computing-and-data-visualization): This short book by Cyrille Rossant offers a good introduction to using IPython for data analysis.\\n\",\n \"- [*IPython Interactive Computing and Visualization Cookbook* (Packt)](https://www.packtpub.com/big-data-and-business-intelligence/ipython-interactive-computing-and-visualization-cookbook): Also by Cyrille Rossant, this book is a longer and more advanced treatment of using IPython for data science. Despite its name, it's not just about IPython; it also goes into some depth on a broad range of data science topics.\\n\",\n \"\\n\",\n \"Finally, a reminder that you can find help on your own: IPython's `?`-based help functionality (discussed in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be useful if you use it well and use it often.\\n\",\n \"As you go through the examples here and elsewhere, this can be used to familiarize yourself with all the tools that IPython has to offer.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/02.00-Introduction-to-NumPy.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Introduction to NumPy\\n\",\n \"\\n\",\n \"This part of the book, along with [Part 3](03.00-Introduction-to-Pandas.ipynb), outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\\n\",\n \"The topic is very broad: datasets can come from a wide range of sources and in a wide range of formats, including collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else.\\n\",\n \"Despite this apparent heterogeneity, many datasets can be represented fundamentally as arrays of numbers.\\n\",\n \"\\n\",\n \"For example, images—particularly digital images—can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area.\\n\",\n \"Sound clips can be thought of as one-dimensional arrays of intensity versus time.\\n\",\n \"Text can be converted in various ways into numerical representations, such as binary digits representing the frequency of certain words or pairs of words.\\n\",\n \"No matter what the data is, the first step in making it analyzable will be to transform it into arrays of numbers.\\n\",\n \"(We will discuss some specific examples of this process in [Feature Engineering](05.04-Feature-Engineering.ipynb).)\\n\",\n \"\\n\",\n \"For this reason, efficient storage and manipulation of numerical arrays is absolutely fundamental to the process of doing data science.\\n\",\n \"We'll now take a look at the specialized tools that Python has for handling such numerical arrays: the NumPy package and the Pandas package (discussed in [Part 3](03.00-Introduction-to-Pandas.ipynb)).\\n\",\n \"\\n\",\n \"This part of the book will cover NumPy in detail. NumPy (short for *Numerical Python*) provides an efficient interface to store and operate on dense data buffers.\\n\",\n \"In some ways, NumPy arrays are like Python's built-in `list` type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size.\\n\",\n \"NumPy arrays form the core of nearly the entire ecosystem of data science tools in Python, so time spent learning to use NumPy effectively will be valuable no matter what aspect of data science interests you.\\n\",\n \"\\n\",\n \"If you followed the advice outlined in the Preface and installed the Anaconda stack, you already have NumPy installed and ready to go.\\n\",\n \"If you're more the do-it-yourself type, you can go to http://www.numpy.org/ and follow the installation instructions found there.\\n\",\n \"Once you do, you can import NumPy and double-check the version:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'1.21.2'\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy\\n\",\n \"numpy.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or later.\\n\",\n \"By convention, you'll find that most people in the SciPy/PyData world will import NumPy using `np` as an alias:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Throughout this chapter, and indeed the rest of the book, you'll find that this is the way we will import and use NumPy.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reminder About Built-in Documentation\\n\",\n \"\\n\",\n \"As you read through this part of the book, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab completion feature), as well as the documentation of various functions (using the `?` character). For a refresher on these, refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb).\\n\",\n \"\\n\",\n \"For example, to display all the contents of the NumPy namespace, you can type this:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [3]: np.\\n\",\n \"```\\n\",\n \"\\n\",\n \"And to display NumPy's built-in documentation, you can use this:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [4]: np?\\n\",\n \"```\\n\",\n \"\\n\",\n \"More detailed documentation, along with tutorials and other resources, can be found at http://www.numpy.org.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/02.01-Understanding-Data-Types.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Understanding Data Types in Python\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Effective data-driven science and computation requires understanding how data is stored and manipulated.\\n\",\n \"This chapter outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this.\\n\",\n \"Understanding this difference is fundamental to understanding much of the material throughout the rest of the book.\\n\",\n \"\\n\",\n \"Users of Python are often drawn in by its ease of use, one piece of which is dynamic typing.\\n\",\n \"While a statically typed language like C or Java requires each variable to be explicitly declared, a dynamically typed language like Python skips this specification. For example, in C you might specify a particular operation as follows:\\n\",\n \"\\n\",\n \"```C\\n\",\n \"/* C code */\\n\",\n \"int result = 0;\\n\",\n \"for(int i=0; i<100; i++){\\n\",\n \" result += i;\\n\",\n \"}\\n\",\n \"```\\n\",\n \"\\n\",\n \"While in Python the equivalent operation could be written this way:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"# Python code\\n\",\n \"result = 0\\n\",\n \"for i in range(100):\\n\",\n \" result += i\\n\",\n \"```\\n\",\n \"\\n\",\n \"Notice one main difference: in C, the data types of each variable are explicitly declared, while in Python the types are dynamically inferred. This means, for example, that we can assign any kind of data to any variable:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"# Python code\\n\",\n \"x = 4\\n\",\n \"x = \\\"four\\\"\\n\",\n \"```\\n\",\n \"\\n\",\n \"Here we've switched the contents of `x` from an integer to a string. The same thing in C would lead (depending on compiler settings) to a compilation error or other unintended consequences:\\n\",\n \"\\n\",\n \"```C\\n\",\n \"/* C code */\\n\",\n \"int x = 4;\\n\",\n \"x = \\\"four\\\"; // FAILS\\n\",\n \"```\\n\",\n \"\\n\",\n \"This sort of flexibility is one element that makes Python and other dynamically typed languages convenient and easy to use.\\n\",\n \"Understanding *how* this works is an important piece of learning to analyze data efficiently and effectively with Python.\\n\",\n \"But what this type flexibility also points to is the fact that Python variables are more than just their values; they also contain extra information about the *type* of the value. We'll explore this more in the sections that follow.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## A Python Integer Is More Than Just an Integer\\n\",\n \"\\n\",\n \"The standard Python implementation is written in C.\\n\",\n \"This means that every Python object is simply a cleverly disguised C structure, which contains not only its value, but other information as well. For example, when we define an integer in Python, such as `x = 10000`, `x` is not just a \\\"raw\\\" integer. It's actually a pointer to a compound C structure, which contains several values.\\n\",\n \"Looking through the Python 3.10 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded):\\n\",\n \"\\n\",\n \"```C\\n\",\n \"struct _longobject {\\n\",\n \" long ob_refcnt;\\n\",\n \" PyTypeObject *ob_type;\\n\",\n \" size_t ob_size;\\n\",\n \" long ob_digit[1];\\n\",\n \"};\\n\",\n \"```\\n\",\n \"\\n\",\n \"A single integer in Python 3.10 actually contains four pieces:\\n\",\n \"\\n\",\n \"- `ob_refcnt`, a reference count that helps Python silently handle memory allocation and deallocation\\n\",\n \"- `ob_type`, which encodes the type of the variable\\n\",\n \"- `ob_size`, which specifies the size of the following data members\\n\",\n \"- `ob_digit`, which contains the actual integer value that we expect the Python variable to represent\\n\",\n \"\\n\",\n \"This means that there is some overhead involved in storing an integer in Python as compared to a compiled language like C, as illustrated in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, `PyObject_HEAD` is the part of the structure containing the reference count, type code, and other pieces mentioned before.\\n\",\n \"\\n\",\n \"Notice the difference here: a C integer is essentially a label for a position in memory whose bytes encode an integer value.\\n\",\n \"A Python integer is a pointer to a position in memory containing all the Python object information, including the bytes that contain the integer value.\\n\",\n \"This extra information in the Python integer structure is what allows Python to be coded so freely and dynamically.\\n\",\n \"All this additional information in Python types comes at a cost, however, which becomes especially apparent in structures that combine many of these objects.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## A Python List Is More Than Just a List\\n\",\n \"\\n\",\n \"Let's consider now what happens when we use a Python data structure that holds many Python objects.\\n\",\n \"The standard mutable multielement container in Python is the list.\\n\",\n \"We can create a list of integers as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"L = list(range(10))\\n\",\n \"L\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"int\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"type(L[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or, similarly, a list of strings:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"L2 = [str(c) for c in L]\\n\",\n \"L2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"str\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"type(L2[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because of Python's dynamic typing, we can even create heterogeneous lists:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[bool, str, float, int]\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"L3 = [True, \\\"2\\\", 3.0, 4]\\n\",\n \"[type(item) for item in L3]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But this flexibility comes at a cost: to allow these flexible types, each item in the list must contain its own type, reference count, and other information. That is, each item is a complete Python object.\\n\",\n \"In the special case that all variables are of the same type, much of this information is redundant, so it can be much more efficient to store the data in a fixed-type array.\\n\",\n \"The difference between a dynamic-type list and a fixed-type (NumPy-style) array is illustrated in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"At the implementation level, the array essentially contains a single pointer to one contiguous block of data.\\n\",\n \"The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object like the Python integer we saw earlier.\\n\",\n \"Again, the advantage of the list is flexibility: because each list element is a full structure containing both data and type information, the list can be filled with data of any desired type.\\n\",\n \"Fixed-type NumPy-style arrays lack this flexibility, but are much more efficient for storing and manipulating data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Fixed-Type Arrays in Python\\n\",\n \"\\n\",\n \"Python offers several different options for storing data in efficient, fixed-type data buffers.\\n\",\n \"The built-in `array` module (available since Python 3.3) can be used to create dense arrays of a uniform type:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import array\\n\",\n \"L = list(range(10))\\n\",\n \"A = array.array('i', L)\\n\",\n \"A\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, `'i'` is a type code indicating the contents are integers.\\n\",\n \"\\n\",\n \"Much more useful, however, is the `ndarray` object of the NumPy package.\\n\",\n \"While Python's `array` object provides efficient storage of array-based data, NumPy adds to this efficient *operations* on that data.\\n\",\n \"We will explore these operations in later chapters; next, I'll show you a few different ways of creating a NumPy array.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Creating Arrays from Python Lists\\n\",\n \"\\n\",\n \"We'll start with the standard NumPy import, under the alias `np`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can use `np.array` to create arrays from Python lists:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 4, 2, 5, 3])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Integer array\\n\",\n \"np.array([1, 4, 2, 5, 3])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Remember that unlike Python lists, NumPy arrays can only contain data of the same type.\\n\",\n \"If the types do not match, NumPy will upcast them according to its type promotion rules; here, integers are upcast to floating point:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3.14, 4. , 2. , 3. ])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.array([3.14, 4, 2, 3])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we want to explicitly set the data type of the resulting array, we can use the `dtype` keyword:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1., 2., 3., 4.], dtype=float32)\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.array([1, 2, 3, 4], dtype=np.float32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, unlike Python lists, which are always one-dimensional sequences, NumPy arrays can be multidimensional. Here's one way of initializing a multidimensional array using a list of lists:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[2, 3, 4],\\n\",\n \" [4, 5, 6],\\n\",\n \" [6, 7, 8]])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Nested lists result in multidimensional arrays\\n\",\n \"np.array([range(i, i + 3) for i in [2, 4, 6]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The inner lists are treated as rows of the resulting two-dimensional array.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Creating Arrays from Scratch\\n\",\n \"\\n\",\n \"Especially for larger arrays, it is more efficient to create arrays from scratch using routines built into NumPy.\\n\",\n \"Here are several examples:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create a length-10 integer array filled with 0s\\n\",\n \"np.zeros(10, dtype=int)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 1., 1., 1., 1.],\\n\",\n \" [1., 1., 1., 1., 1.],\\n\",\n \" [1., 1., 1., 1., 1.]])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create a 3x5 floating-point array filled with 1s\\n\",\n \"np.ones((3, 5), dtype=float)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[3.14, 3.14, 3.14, 3.14, 3.14],\\n\",\n \" [3.14, 3.14, 3.14, 3.14, 3.14],\\n\",\n \" [3.14, 3.14, 3.14, 3.14, 3.14]])\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create a 3x5 array filled with 3.14\\n\",\n \"np.full((3, 5), 3.14)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create an array filled with a linear sequence\\n\",\n \"# starting at 0, ending at 20, stepping by 2\\n\",\n \"# (this is similar to the built-in range function)\\n\",\n \"np.arange(0, 20, 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0. , 0.25, 0.5 , 0.75, 1. ])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create an array of five values evenly spaced between 0 and 1\\n\",\n \"np.linspace(0, 1, 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0.09610171, 0.88193001, 0.70548015],\\n\",\n \" [0.35885395, 0.91670468, 0.8721031 ],\\n\",\n \" [0.73237865, 0.09708562, 0.52506779]])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create a 3x3 array of uniformly distributed\\n\",\n \"# pseudorandom values between 0 and 1\\n\",\n \"np.random.random((3, 3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[-0.46652655, -0.59158776, -1.05392451],\\n\",\n \" [-1.72634268, 0.03194069, -0.51048869],\\n\",\n \" [ 1.41240208, 1.77734462, -0.43820037]])\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create a 3x3 array of normally distributed pseudorandom\\n\",\n \"# values with mean 0 and standard deviation 1\\n\",\n \"np.random.normal(0, 1, (3, 3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[4, 3, 8],\\n\",\n \" [6, 5, 0],\\n\",\n \" [1, 1, 4]])\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create a 3x3 array of pseudorandom integers in the interval [0, 10)\\n\",\n \"np.random.randint(0, 10, (3, 3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 0., 0.],\\n\",\n \" [0., 1., 0.],\\n\",\n \" [0., 0., 1.]])\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create a 3x3 identity matrix\\n\",\n \"np.eye(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1., 1., 1.])\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create an uninitialized array of three integers; the values will be\\n\",\n \"# whatever happens to already exist at that memory location\\n\",\n \"np.empty(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## NumPy Standard Data Types\\n\",\n \"\\n\",\n \"NumPy arrays contain values of a single type, so it is important to have detailed knowledge of those types and their limitations.\\n\",\n \"Because NumPy is built in C, the types will be familiar to users of C, Fortran, and other related languages.\\n\",\n \"\\n\",\n \"The standard NumPy data types are listed in the following table.\\n\",\n \"Note that when constructing an array, they can be specified using a string:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"np.zeros(10, dtype='int16')\\n\",\n \"```\\n\",\n \"\\n\",\n \"Or using the associated NumPy object:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"np.zeros(10, dtype=np.int16)\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Data type\\t | Description |\\n\",\n \"|-------------|-------------|\\n\",\n \"| `bool_` | Boolean (True or False) stored as a byte |\\n\",\n \"| `int_` | Default integer type (same as C `long`; normally either `int64` or `int32`)| \\n\",\n \"| `intc` | Identical to C `int` (normally `int32` or `int64`)| \\n\",\n \"| `intp` | Integer used for indexing (same as C `ssize_t`; normally either `int32` or `int64`)| \\n\",\n \"| `int8` | Byte (–128 to 127)| \\n\",\n \"| `int16` | Integer (–32768 to 32767)|\\n\",\n \"| `int32` | Integer (–2147483648 to 2147483647)|\\n\",\n \"| `int64` | Integer (–9223372036854775808 to 9223372036854775807)| \\n\",\n \"| `uint8` | Unsigned integer (0 to 255)| \\n\",\n \"| `uint16` | Unsigned integer (0 to 65535)| \\n\",\n \"| `uint32` | Unsigned integer (0 to 4294967295)| \\n\",\n \"| `uint64` | Unsigned integer (0 to 18446744073709551615)| \\n\",\n \"| `float_` | Shorthand for `float64`| \\n\",\n \"| `float16` | Half-precision float: sign bit, 5 bits exponent, 10 bits mantissa| \\n\",\n \"| `float32` | Single-precision float: sign bit, 8 bits exponent, 23 bits mantissa| \\n\",\n \"| `float64` | Double-precision float: sign bit, 11 bits exponent, 52 bits mantissa| \\n\",\n \"| `complex_` | Shorthand for `complex128`| \\n\",\n \"| `complex64` | Complex number, represented by two 32-bit floats| \\n\",\n \"| `complex128`| Complex number, represented by two 64-bit floats| \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"More advanced type specification is possible, such as specifying big- or little-endian numbers; for more information, refer to the [NumPy documentation](http://numpy.org/).\\n\",\n \"NumPy also supports compound data types, which will be covered in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# The Basics of NumPy Arrays\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas ([Part 3](03.00-Introduction-to-Pandas.ipynb)) are built around the NumPy array.\\n\",\n \"This chapter will present several examples of using NumPy array manipulation to access data and subarrays, and to split, reshape, and join the arrays.\\n\",\n \"While the types of operations shown here may seem a bit dry and pedantic, they comprise the building blocks of many other examples used throughout the book.\\n\",\n \"Get to know them well!\\n\",\n \"\\n\",\n \"We'll cover a few categories of basic array manipulations here:\\n\",\n \"\\n\",\n \"- *Attributes of arrays*: Determining the size, shape, memory consumption, and data types of arrays\\n\",\n \"- *Indexing of arrays*: Getting and setting the values of individual array elements\\n\",\n \"- *Slicing of arrays*: Getting and setting smaller subarrays within a larger array\\n\",\n \"- *Reshaping of arrays*: Changing the shape of a given array\\n\",\n \"- *Joining and splitting of arrays*: Combining multiple arrays into one, and splitting one array into many\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## NumPy Array Attributes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First let's discuss some useful array attributes.\\n\",\n \"We'll start by defining random arrays of one, two, and three dimensions.\\n\",\n \"We'll use NumPy's random number generator, which we will *seed* with a set value in order to ensure that the same random arrays are generated each time this code is run:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"rng = np.random.default_rng(seed=1701) # seed for reproducibility\\n\",\n \"\\n\",\n \"x1 = rng.integers(10, size=6) # one-dimensional array\\n\",\n \"x2 = rng.integers(10, size=(3, 4)) # two-dimensional array\\n\",\n \"x3 = rng.integers(10, size=(3, 4, 5)) # three-dimensional array\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each array has attributes including `ndim` (the number of dimensions), `shape` (the size of each dimension), `size` (the total size of the array), and `dtype` (the type of each element):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"x3 ndim: 3\\n\",\n \"x3 shape: (3, 4, 5)\\n\",\n \"x3 size: 60\\n\",\n \"dtype: int64\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"x3 ndim: \\\", x3.ndim)\\n\",\n \"print(\\\"x3 shape:\\\", x3.shape)\\n\",\n \"print(\\\"x3 size: \\\", x3.size)\\n\",\n \"print(\\\"dtype: \\\", x3.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For more discussion of data types, see [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Array Indexing: Accessing Single Elements\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you are familiar with Python's standard list indexing, indexing in NumPy will feel quite familiar.\\n\",\n \"In a one-dimensional array, the $i^{th}$ value (counting from zero) can be accessed by specifying the desired index in square brackets, just as with Python lists:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([9, 4, 0, 3, 8, 6])\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"9\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"8\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[4]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To index from the end of the array, you can use negative indices:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"6\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[-1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"8\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[-2]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In a multidimensional array, items can be accessed using a comma-separated `(row, column)` tuple:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[3, 1, 3, 7],\\n\",\n \" [4, 0, 2, 3],\\n\",\n \" [0, 0, 6, 9]])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[0, 0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[2, 0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"9\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[2, -1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Values can also be modified using any of the preceding index notation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[12, 1, 3, 7],\\n\",\n \" [ 4, 0, 2, 3],\\n\",\n \" [ 0, 0, 6, 9]])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[0, 0] = 12\\n\",\n \"x2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Keep in mind that, unlike Python lists, NumPy arrays have a fixed type.\\n\",\n \"This means, for example, that if you attempt to insert a floating-point value into an integer array, the value will be silently truncated. Don't be caught unaware by this behavior!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 4, 0, 3, 8, 6])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[0] = 3.14159 # this will be truncated!\\n\",\n \"x1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Array Slicing: Accessing Subarrays\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just as we can use square brackets to access individual array elements, we can also use them to access subarrays with the *slice* notation, marked by the colon (`:`) character.\\n\",\n \"The NumPy slicing syntax follows that of the standard Python list; to access a slice of an array `x`, use this:\\n\",\n \"``` python\\n\",\n \"x[start:stop:step]\\n\",\n \"```\\n\",\n \"If any of these are unspecified, they default to the values `start=0`, `stop=`, `step=1`.\\n\",\n \"Let's look at some examples of accessing subarrays in one dimension and in multiple dimensions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### One-Dimensional Subarrays\\n\",\n \"\\n\",\n \"Here are some examples of accessing elements in one-dimensional subarrays:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 4, 0, 3, 8, 6])\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 4, 0])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[:3] # first three elements\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 8, 6])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[3:] # elements after index 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([4, 0, 3])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[1:4] # middle subarray\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 0, 8])\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[::2] # every second element\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([4, 3, 6])\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[1::2] # every second element, starting at index 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A potentially confusing case is when the `step` value is negative.\\n\",\n \"In this case, the defaults for `start` and `stop` are swapped.\\n\",\n \"This becomes a convenient way to reverse an array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([6, 8, 3, 0, 4, 3])\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[::-1] # all elements, reversed\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([8, 0, 3])\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x1[4::-2] # every second element from index 4, reversed\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Multidimensional Subarrays\\n\",\n \"\\n\",\n \"Multidimensional slices work in the same way, with multiple slices separated by commas.\\n\",\n \"For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[12, 1, 3, 7],\\n\",\n \" [ 4, 0, 2, 3],\\n\",\n \" [ 0, 0, 6, 9]])\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[12, 1, 3],\\n\",\n \" [ 4, 0, 2]])\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[:2, :3] # first two rows & three columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[12, 3],\\n\",\n \" [ 4, 2],\\n\",\n \" [ 0, 6]])\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[:3, ::2] # three rows, every second column\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 9, 6, 0, 0],\\n\",\n \" [ 3, 2, 0, 4],\\n\",\n \" [ 7, 3, 1, 12]])\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[::-1, ::-1] # all rows & columns, reversed\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Accessing array rows and columns\\n\",\n \"\\n\",\n \"One commonly needed routine is accessing single rows or columns of an array.\\n\",\n \"This can be done by combining indexing and slicing, using an empty slice marked by a single colon (`:`):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([12, 4, 0])\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[:, 0] # first column of x2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([12, 1, 3, 7])\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[0, :] # first row of x2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the case of row access, the empty slice can be omitted for a more compact syntax:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([12, 1, 3, 7])\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x2[0] # equivalent to x2[0, :]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Subarrays as No-Copy Views\\n\",\n \"\\n\",\n \"Unlike Python list slices, NumPy array slices are returned as *views* rather than *copies* of the array data.\\n\",\n \"Consider our two-dimensional array from before:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[12 1 3 7]\\n\",\n \" [ 4 0 2 3]\\n\",\n \" [ 0 0 6 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(x2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's extract a $2 \\\\times 2$ subarray from this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[12 1]\\n\",\n \" [ 4 0]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x2_sub = x2[:2, :2]\\n\",\n \"print(x2_sub)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now if we modify this subarray, we'll see that the original array is changed! Observe:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[99 1]\\n\",\n \" [ 4 0]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x2_sub[0, 0] = 99\\n\",\n \"print(x2_sub)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[99 1 3 7]\\n\",\n \" [ 4 0 2 3]\\n\",\n \" [ 0 0 6 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(x2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Some users may find this surprising, but it can be advantageous: for example, when working with large datasets, we can access and process pieces of these datasets without the need to copy the underlying data buffer.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Creating Copies of Arrays\\n\",\n \"\\n\",\n \"Despite the nice features of array views, it is sometimes useful to instead explicitly copy the data within an array or a subarray. This can be most easily done with the `copy` method:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[99 1]\\n\",\n \" [ 4 0]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x2_sub_copy = x2[:2, :2].copy()\\n\",\n \"print(x2_sub_copy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we now modify this subarray, the original array is not touched:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[42 1]\\n\",\n \" [ 4 0]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x2_sub_copy[0, 0] = 42\\n\",\n \"print(x2_sub_copy)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[99 1 3 7]\\n\",\n \" [ 4 0 2 3]\\n\",\n \" [ 0 0 6 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(x2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reshaping of Arrays\\n\",\n \"\\n\",\n \"Another useful type of operation is reshaping of arrays, which can be done with the `reshape` method.\\n\",\n \"For example, if you want to put the numbers 1 through 9 in a $3 \\\\times 3$ grid, you can do the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"grid = np.arange(1, 10).reshape(3, 3)\\n\",\n \"print(grid)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that for this to work, the size of the initial array must match the size of the reshaped array, and in most cases the `reshape` method will return a no-copy view of the initial array.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A common reshaping operation is converting a one-dimensional array into a two-dimensional row or column matrix:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3]])\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.array([1, 2, 3])\\n\",\n \"x.reshape((1, 3)) # row vector via reshape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1],\\n\",\n \" [2],\\n\",\n \" [3]])\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x.reshape((3, 1)) # column vector via reshape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A convenient shorthand for this is to use `np.newaxis` in the slicing syntax:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3]])\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x[np.newaxis, :] # row vector via newaxis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1],\\n\",\n \" [2],\\n\",\n \" [3]])\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x[:, np.newaxis] # column vector via newaxis\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is a pattern that we will utilize often throughout the remainder of the book.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Array Concatenation and Splitting\\n\",\n \"\\n\",\n \"All of the preceding routines worked on single arrays. NumPy also provides tools to combine multiple arrays into one, and to conversely split a single array into multiple arrays.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Concatenation of Arrays\\n\",\n \"\\n\",\n \"Concatenation, or joining of two arrays in NumPy, is primarily accomplished using the routines `np.concatenate`, `np.vstack`, and `np.hstack`.\\n\",\n \"`np.concatenate` takes a tuple or list of arrays as its first argument, as you can see here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 3, 2, 1])\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.array([1, 2, 3])\\n\",\n \"y = np.array([3, 2, 1])\\n\",\n \"np.concatenate([x, y])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can also concatenate more than two arrays at once:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1 2 3 3 2 1 99 99 99]\\n\"\n ]\n }\n ],\n \"source\": [\n \"z = np.array([99, 99, 99])\\n\",\n \"print(np.concatenate([x, y, z]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And it can be used for two-dimensional arrays:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"grid = np.array([[1, 2, 3],\\n\",\n \" [4, 5, 6]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3],\\n\",\n \" [4, 5, 6],\\n\",\n \" [1, 2, 3],\\n\",\n \" [4, 5, 6]])\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# concatenate along the first axis\\n\",\n \"np.concatenate([grid, grid])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3, 1, 2, 3],\\n\",\n \" [4, 5, 6, 4, 5, 6]])\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# concatenate along the second axis (zero-indexed)\\n\",\n \"np.concatenate([grid, grid], axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For working with arrays of mixed dimensions, it can be clearer to use the `np.vstack` (vertical stack) and `np.hstack` (horizontal stack) functions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3],\\n\",\n \" [1, 2, 3],\\n\",\n \" [4, 5, 6]])\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# vertically stack the arrays\\n\",\n \"np.vstack([x, grid])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1, 2, 3, 99],\\n\",\n \" [ 4, 5, 6, 99]])\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# horizontally stack the arrays\\n\",\n \"y = np.array([[99],\\n\",\n \" [99]])\\n\",\n \"np.hstack([grid, y])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Similarly, for higher-dimensional arrays, `np.dstack` will stack arrays along the third axis.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Splitting of Arrays\\n\",\n \"\\n\",\n \"The opposite of concatenation is splitting, which is implemented by the functions `np.split`, `np.hsplit`, and `np.vsplit`. For each of these, we can pass a list of indices giving the split points:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 2 3] [99 99] [3 2 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = [1, 2, 3, 99, 99, 3, 2, 1]\\n\",\n \"x1, x2, x3 = np.split(x, [3, 5])\\n\",\n \"print(x1, x2, x3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that *N* split points leads to *N* + 1 subarrays.\\n\",\n \"The related functions `np.hsplit` and `np.vsplit` are similar:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0, 1, 2, 3],\\n\",\n \" [ 4, 5, 6, 7],\\n\",\n \" [ 8, 9, 10, 11],\\n\",\n \" [12, 13, 14, 15]])\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"grid = np.arange(16).reshape((4, 4))\\n\",\n \"grid\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0 1 2 3]\\n\",\n \" [4 5 6 7]]\\n\",\n \"[[ 8 9 10 11]\\n\",\n \" [12 13 14 15]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"upper, lower = np.vsplit(grid, [2])\\n\",\n \"print(upper)\\n\",\n \"print(lower)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0 1]\\n\",\n \" [ 4 5]\\n\",\n \" [ 8 9]\\n\",\n \" [12 13]]\\n\",\n \"[[ 2 3]\\n\",\n \" [ 6 7]\\n\",\n \" [10 11]\\n\",\n \" [14 15]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"left, right = np.hsplit(grid, [2])\\n\",\n \"print(left)\\n\",\n \"print(right)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Similarly, for higher-dimensional arrays, `np.dsplit` will split arrays along the third axis.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/02.03-Computation-on-arrays-ufuncs.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Computation on NumPy Arrays: Universal Functions\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Up until now, we have been discussing some of the basic nuts and bolts of NumPy. In the next few chapters, we will dive into the reasons that NumPy is so important in the Python data science world: namely, because it provides an easy and flexible interface to optimize computation with arrays of data.\\n\",\n \"\\n\",\n \"Computation on NumPy arrays can be very fast, or it can be very slow.\\n\",\n \"The key to making it fast is to use vectorized operations, generally implemented through NumPy's *universal functions* (ufuncs).\\n\",\n \"This chapter motivates the need for NumPy's ufuncs, which can be used to make repeated calculations on array elements much more efficient.\\n\",\n \"It then introduces many of the most common and useful arithmetic ufuncs available in the NumPy package.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The Slowness of Loops\\n\",\n \"\\n\",\n \"Python's default implementation (known as CPython) does some operations very slowly.\\n\",\n \"This is partly due to the dynamic, interpreted nature of the language; types are flexible, so sequences of operations cannot be compiled down to efficient machine code as in languages like C and Fortran.\\n\",\n \"Recently there have been various attempts to address this weakness: well-known examples are the [PyPy project](http://pypy.org/), a just-in-time compiled implementation of Python; the [Cython project](http://cython.org), which converts Python code to compilable C code; and the [Numba project](http://numba.pydata.org/), which converts snippets of Python code to fast LLVM bytecode.\\n\",\n \"Each of these has its strengths and weaknesses, but it is safe to say that none of the three approaches has yet surpassed the reach and popularity of the standard CPython engine.\\n\",\n \"\\n\",\n \"The relative sluggishness of Python generally manifests itself in situations where many small operations are being repeated; for instance, looping over arrays to operate on each element.\\n\",\n \"For example, imagine we have an array of values and we'd like to compute the reciprocal of each.\\n\",\n \"A straightforward approach might look like this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.11111111, 0.25 , 1. , 0.33333333, 0.125 ])\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"rng = np.random.default_rng(seed=1701)\\n\",\n \"\\n\",\n \"def compute_reciprocals(values):\\n\",\n \" output = np.empty(len(values))\\n\",\n \" for i in range(len(values)):\\n\",\n \" output[i] = 1.0 / values[i]\\n\",\n \" return output\\n\",\n \" \\n\",\n \"values = rng.integers(1, 10, size=5)\\n\",\n \"compute_reciprocals(values)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This implementation probably feels fairly natural to someone from, say, a C or Java background.\\n\",\n \"But if we measure the execution time of this code for a large input, we see that this operation is very slow—perhaps surprisingly so!\\n\",\n \"We'll benchmark this with IPython's `%timeit` magic (discussed in [Profiling and Timing Code](01.07-Timing-and-Profiling.ipynb)):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.61 s ± 192 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"big_array = rng.integers(1, 100, size=1000000)\\n\",\n \"%timeit compute_reciprocals(big_array)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It takes several seconds to compute these million operations and to store the result!\\n\",\n \"When even cell phones have processing speeds measured in gigaflops (i.e., billions of numerical operations per second), this seems almost absurdly slow.\\n\",\n \"It turns out that the bottleneck here is not the operations themselves, but the type checking and function dispatches that CPython must do at each cycle of the loop.\\n\",\n \"Each time the reciprocal is computed, Python first examines the object's type and does a dynamic lookup of the correct function to use for that type.\\n\",\n \"If we were working in compiled code instead, this type specification would be known before the code executed and the result could be computed much more efficiently.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Introducing Ufuncs\\n\",\n \"\\n\",\n \"For many types of operations, NumPy provides a convenient interface into just this kind of statically typed, compiled routine. This is known as a *vectorized* operation.\\n\",\n \"For simple operations like the element-wise division here, vectorization is as simple as using Python arithmetic operators directly on the array object.\\n\",\n \"This vectorized approach is designed to push the loop into the compiled layer that underlies NumPy, leading to much faster execution.\\n\",\n \"\\n\",\n \"Compare the results of the following two operations:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.11111111 0.25 1. 0.33333333 0.125 ]\\n\",\n \"[0.11111111 0.25 1. 0.33333333 0.125 ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(compute_reciprocals(values))\\n\",\n \"print(1.0 / values)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Looking at the execution time for our big array, we see that it completes orders of magnitude faster than the Python loop:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.54 ms ± 383 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit (1.0 / big_array)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Vectorized operations in NumPy are implemented via ufuncs, whose main purpose is to quickly execute repeated operations on values in NumPy arrays.\\n\",\n \"Ufuncs are extremely flexible—before we saw an operation between a scalar and an array, but we can also operate between two arrays:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0. , 0.5 , 0.66666667, 0.75 , 0.8 ])\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.arange(5) / np.arange(1, 6)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And ufunc operations are not limited to one-dimensional arrays. They can act on multidimensional arrays as well:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1, 2, 4],\\n\",\n \" [ 8, 16, 32],\\n\",\n \" [ 64, 128, 256]])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.arange(9).reshape((3, 3))\\n\",\n \"2 ** x\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Computations using vectorization through ufuncs are nearly always more efficient than their counterparts implemented using Python loops, especially as the arrays grow in size.\\n\",\n \"Any time you see such a loop in a NumPy script, you should consider whether it can be replaced with a vectorized expression.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exploring NumPy's Ufuncs\\n\",\n \"\\n\",\n \"Ufuncs exist in two flavors: *unary ufuncs*, which operate on a single input, and *binary ufuncs*, which operate on two inputs.\\n\",\n \"We'll see examples of both these types of functions here.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Array Arithmetic\\n\",\n \"\\n\",\n \"NumPy's ufuncs feel very natural to use because they make use of Python's native arithmetic operators.\\n\",\n \"The standard addition, subtraction, multiplication, and division can all be used:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"x = [0 1 2 3]\\n\",\n \"x + 5 = [5 6 7 8]\\n\",\n \"x - 5 = [-5 -4 -3 -2]\\n\",\n \"x * 2 = [0 2 4 6]\\n\",\n \"x / 2 = [0. 0.5 1. 1.5]\\n\",\n \"x // 2 = [0 0 1 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = np.arange(4)\\n\",\n \"print(\\\"x =\\\", x)\\n\",\n \"print(\\\"x + 5 =\\\", x + 5)\\n\",\n \"print(\\\"x - 5 =\\\", x - 5)\\n\",\n \"print(\\\"x * 2 =\\\", x * 2)\\n\",\n \"print(\\\"x / 2 =\\\", x / 2)\\n\",\n \"print(\\\"x // 2 =\\\", x // 2) # floor division\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There is also a unary ufunc for negation, a `**` operator for exponentiation, and a `%` operator for modulus:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-x = [ 0 -1 -2 -3]\\n\",\n \"x ** 2 = [0 1 4 9]\\n\",\n \"x % 2 = [0 1 0 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"-x = \\\", -x)\\n\",\n \"print(\\\"x ** 2 = \\\", x ** 2)\\n\",\n \"print(\\\"x % 2 = \\\", x % 2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In addition, these can be strung together however you wish, and the standard order of operations is respected:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-1. , -2.25, -4. , -6.25])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"-(0.5*x + 1) ** 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All of these arithmetic operations are simply convenient wrappers around specific ufuncs built into NumPy. For example, the `+` operator is a wrapper for the `add` ufunc:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([2, 3, 4, 5])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.add(x, 2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following table lists the arithmetic operators implemented in NumPy:\\n\",\n \"\\n\",\n \"| Operator | Equivalent ufunc | Description |\\n\",\n \"|-------------|-------------------|-------------------------------------|\\n\",\n \"|`+` |`np.add` |Addition (e.g., `1 + 1 = 2`) |\\n\",\n \"|`-` |`np.subtract` |Subtraction (e.g., `3 - 2 = 1`) |\\n\",\n \"|`-` |`np.negative` |Unary negation (e.g., `-2`) |\\n\",\n \"|`*` |`np.multiply` |Multiplication (e.g., `2 * 3 = 6`) |\\n\",\n \"|`/` |`np.divide` |Division (e.g., `3 / 2 = 1.5`) |\\n\",\n \"|`//` |`np.floor_divide` |Floor division (e.g., `3 // 2 = 1`) |\\n\",\n \"|`**` |`np.power` |Exponentiation (e.g., `2 ** 3 = 8`) |\\n\",\n \"|`%` |`np.mod` |Modulus/remainder (e.g., `9 % 4 = 1`)|\\n\",\n \"\\n\",\n \"Additionally, there are Boolean/bitwise operators; we will explore these in [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Absolute Value\\n\",\n \"\\n\",\n \"Just as NumPy understands Python's built-in arithmetic operators, it also understands Python's built-in absolute value function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([2, 1, 0, 1, 2])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.array([-2, -1, 0, 1, 2])\\n\",\n \"abs(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The corresponding NumPy ufunc is `np.absolute`, which is also available under the alias `np.abs`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([2, 1, 0, 1, 2])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.absolute(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([2, 1, 0, 1, 2])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.abs(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This ufunc can also handle complex data, in which case it returns the magnitude:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([5., 5., 2., 1.])\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.array([3 - 4j, 4 - 3j, 2 + 0j, 0 + 1j])\\n\",\n \"np.abs(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Trigonometric Functions\\n\",\n \"\\n\",\n \"NumPy provides a large number of useful ufuncs, and some of the most useful for the data scientist are the trigonometric functions.\\n\",\n \"We'll start by defining an array of angles:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"theta = np.linspace(0, np.pi, 3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can compute some trigonometric functions on these values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"theta = [0. 1.57079633 3.14159265]\\n\",\n \"sin(theta) = [0.0000000e+00 1.0000000e+00 1.2246468e-16]\\n\",\n \"cos(theta) = [ 1.000000e+00 6.123234e-17 -1.000000e+00]\\n\",\n \"tan(theta) = [ 0.00000000e+00 1.63312394e+16 -1.22464680e-16]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"theta = \\\", theta)\\n\",\n \"print(\\\"sin(theta) = \\\", np.sin(theta))\\n\",\n \"print(\\\"cos(theta) = \\\", np.cos(theta))\\n\",\n \"print(\\\"tan(theta) = \\\", np.tan(theta))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The values are computed to within machine precision, which is why values that should be zero do not always hit exactly zero.\\n\",\n \"Inverse trigonometric functions are also available:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"x = [-1, 0, 1]\\n\",\n \"arcsin(x) = [-1.57079633 0. 1.57079633]\\n\",\n \"arccos(x) = [3.14159265 1.57079633 0. ]\\n\",\n \"arctan(x) = [-0.78539816 0. 0.78539816]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = [-1, 0, 1]\\n\",\n \"print(\\\"x = \\\", x)\\n\",\n \"print(\\\"arcsin(x) = \\\", np.arcsin(x))\\n\",\n \"print(\\\"arccos(x) = \\\", np.arccos(x))\\n\",\n \"print(\\\"arctan(x) = \\\", np.arctan(x))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Exponents and Logarithms\\n\",\n \"\\n\",\n \"Other common operations available in NumPy ufuncs are the exponentials:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"x = [1, 2, 3]\\n\",\n \"e^x = [ 2.71828183 7.3890561 20.08553692]\\n\",\n \"2^x = [2. 4. 8.]\\n\",\n \"3^x = [ 3. 9. 27.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = [1, 2, 3]\\n\",\n \"print(\\\"x =\\\", x)\\n\",\n \"print(\\\"e^x =\\\", np.exp(x))\\n\",\n \"print(\\\"2^x =\\\", np.exp2(x))\\n\",\n \"print(\\\"3^x =\\\", np.power(3., x))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The inverse of the exponentials, the logarithms, are also available.\\n\",\n \"The basic `np.log` gives the natural logarithm; if you prefer to compute the base-2 logarithm or the base-10 logarithm, these are available as well:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"x = [1, 2, 4, 10]\\n\",\n \"ln(x) = [0. 0.69314718 1.38629436 2.30258509]\\n\",\n \"log2(x) = [0. 1. 2. 3.32192809]\\n\",\n \"log10(x) = [0. 0.30103 0.60205999 1. ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = [1, 2, 4, 10]\\n\",\n \"print(\\\"x =\\\", x)\\n\",\n \"print(\\\"ln(x) =\\\", np.log(x))\\n\",\n \"print(\\\"log2(x) =\\\", np.log2(x))\\n\",\n \"print(\\\"log10(x) =\\\", np.log10(x))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are also some specialized versions that are useful for maintaining precision with very small input:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"exp(x) - 1 = [0. 0.0010005 0.01005017 0.10517092]\\n\",\n \"log(1 + x) = [0. 0.0009995 0.00995033 0.09531018]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = [0, 0.001, 0.01, 0.1]\\n\",\n \"print(\\\"exp(x) - 1 =\\\", np.expm1(x))\\n\",\n \"print(\\\"log(1 + x) =\\\", np.log1p(x))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When `x` is very small, these functions give more precise values than if the raw `np.log` or `np.exp` were to be used.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Specialized Ufuncs\\n\",\n \"\\n\",\n \"NumPy has many more ufuncs available, including for hyperbolic trigonometry, bitwise arithmetic, comparison operations, conversions from radians to degrees, rounding and remainders, and much more.\\n\",\n \"A look through the NumPy documentation reveals a lot of interesting functionality.\\n\",\n \"\\n\",\n \"Another excellent source for more specialized ufuncs is the submodule `scipy.special`.\\n\",\n \"If you want to compute some obscure mathematical function on your data, chances are it is implemented in `scipy.special`.\\n\",\n \"There are far too many functions to list them all, but the following snippet shows a couple that might come up in a statistics context:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from scipy import special\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"gamma(x) = [1.0000e+00 2.4000e+01 3.6288e+05]\\n\",\n \"ln|gamma(x)| = [ 0. 3.17805383 12.80182748]\\n\",\n \"beta(x, 2) = [0.5 0.03333333 0.00909091]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Gamma functions (generalized factorials) and related functions\\n\",\n \"x = [1, 5, 10]\\n\",\n \"print(\\\"gamma(x) =\\\", special.gamma(x))\\n\",\n \"print(\\\"ln|gamma(x)| =\\\", special.gammaln(x))\\n\",\n \"print(\\\"beta(x, 2) =\\\", special.beta(x, 2))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"erf(x) = [0. 0.32862676 0.67780119 0.84270079]\\n\",\n \"erfc(x) = [1. 0.67137324 0.32219881 0.15729921]\\n\",\n \"erfinv(x) = [0. 0.27246271 0.73286908 inf]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Error function (integral of Gaussian),\\n\",\n \"# its complement, and its inverse\\n\",\n \"x = np.array([0, 0.3, 0.7, 1.0])\\n\",\n \"print(\\\"erf(x) =\\\", special.erf(x))\\n\",\n \"print(\\\"erfc(x) =\\\", special.erfc(x))\\n\",\n \"print(\\\"erfinv(x) =\\\", special.erfinv(x))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are many, many more ufuncs available in both NumPy and `scipy.special`.\\n\",\n \"Because the documentation of these packages is available online, a web search along the lines of \\\"gamma function python\\\" will generally find the relevant information.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Advanced Ufunc Features\\n\",\n \"\\n\",\n \"Many NumPy users make use of ufuncs without ever learning their full set of features.\\n\",\n \"I'll outline a few specialized features of ufuncs here.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Specifying Output\\n\",\n \"\\n\",\n \"For large calculations, it is sometimes useful to be able to specify the array where the result of the calculation will be stored.\\n\",\n \"For all ufuncs, this can be done using the `out` argument of the function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0. 10. 20. 30. 40.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = np.arange(5)\\n\",\n \"y = np.empty(5)\\n\",\n \"np.multiply(x, 10, out=y)\\n\",\n \"print(y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This can even be used with array views. For example, we can write the results of a computation to every other element of a specified array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1. 0. 2. 0. 4. 0. 8. 0. 16. 0.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"y = np.zeros(10)\\n\",\n \"np.power(2, x, out=y[::2])\\n\",\n \"print(y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we had instead written `y[::2] = 2 ** x`, this would have resulted in the creation of a temporary array to hold the results of `2 ** x`, followed by a second operation copying those values into the `y` array.\\n\",\n \"This doesn't make much of a difference for such a small computation, but for very large arrays the memory savings from careful use of the `out` argument can be significant.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Aggregations\\n\",\n \"\\n\",\n \"For binary ufuncs, aggregations can be computed directly from the object.\\n\",\n \"For example, if we'd like to *reduce* an array with a particular operation, we can use the `reduce` method of any ufunc.\\n\",\n \"A reduce repeatedly applies a given operation to the elements of an array until only a single result remains.\\n\",\n \"\\n\",\n \"For example, calling `reduce` on the `add` ufunc returns the sum of all elements in the array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"15\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.arange(1, 6)\\n\",\n \"np.add.reduce(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Similarly, calling `reduce` on the `multiply` ufunc results in the product of all array elements:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"120\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.multiply.reduce(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we'd like to store all the intermediate results of the computation, we can instead use `accumulate`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1, 3, 6, 10, 15])\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.add.accumulate(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1, 2, 6, 24, 120])\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.multiply.accumulate(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that for these particular cases, there are dedicated NumPy functions to compute the results (`np.sum`, `np.prod`, `np.cumsum`, `np.cumprod`), which we'll explore in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Outer Products\\n\",\n \"\\n\",\n \"Finally, any ufunc can compute the output of all pairs of two different inputs using the `outer` method.\\n\",\n \"This allows you, in one line, to do things like create a multiplication table:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1, 2, 3, 4, 5],\\n\",\n \" [ 2, 4, 6, 8, 10],\\n\",\n \" [ 3, 6, 9, 12, 15],\\n\",\n \" [ 4, 8, 12, 16, 20],\\n\",\n \" [ 5, 10, 15, 20, 25]])\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.arange(1, 6)\\n\",\n \"np.multiply.outer(x, x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `ufunc.at` and `ufunc.reduceat` methods are useful as well, and we will explore them in [Fancy Indexing](02.07-Fancy-Indexing.ipynb).\\n\",\n \"\\n\",\n \"We will also encounter the ability of ufuncs to operate between arrays of different shapes and sizes, a set of operations known as *broadcasting*.\\n\",\n \"This subject is important enough that we will devote a whole chapter to it (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Ufuncs: Learning More\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"More information on universal functions (including the full list of available functions) can be found on the [NumPy](http://www.numpy.org) and [SciPy](http://www.scipy.org) documentation websites.\\n\",\n \"\\n\",\n \"Recall that you can also access information directly from within IPython by importing the packages and using IPython's tab completion and help (`?`) functionality, as described in [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/02.04-Computation-on-arrays-aggregates.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Aggregations: min, max, and Everything in Between\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A first step in exploring any dataset is often to compute various summary statistics.\\n\",\n \"Perhaps the most common summary statistics are the mean and standard deviation, which allow you to summarize the \\\"typical\\\" values in a dataset, but other aggregations are useful as well (the sum, product, median, minimum and maximum, quantiles, etc.).\\n\",\n \"\\n\",\n \"NumPy has fast built-in aggregation functions for working on arrays; we'll discuss and try out some of them here.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Summing the Values in an Array\\n\",\n \"\\n\",\n \"As a quick example, consider computing the sum of all values in an array.\\n\",\n \"Python itself can do this using the built-in `sum` function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"rng = np.random.default_rng()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"52.76825337322368\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"L = rng.random(100)\\n\",\n \"sum(L)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The syntax is quite similar to that of NumPy's `sum` function, and the result is the same in the simplest case:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"52.76825337322366\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.sum(L)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"However, because it executes the operation in compiled code, NumPy's version of the operation is computed much more quickly:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"89.9 ms ± 233 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\",\n \"521 µs ± 8.37 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"big_array = rng.random(1000000)\\n\",\n \"%timeit sum(big_array)\\n\",\n \"%timeit np.sum(big_array)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Be careful, though: the `sum` function and the `np.sum` function are not identical, which can sometimes lead to confusion!\\n\",\n \"In particular, their optional arguments have different meanings (`sum(x, 1)` initializes the sum at `1`, while `np.sum(x, 1)` sums along axis `1`), and `np.sum` is aware of multiple array dimensions, as we will see in the following section.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Minimum and Maximum\\n\",\n \"\\n\",\n \"Similarly, Python has built-in `min` and `max` functions, used to find the minimum value and maximum value of any given array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(2.0114398036064074e-07, 0.9999997912802653)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"min(big_array), max(big_array)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"NumPy's corresponding functions have similar syntax, and again operate much more quickly:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(2.0114398036064074e-07, 0.9999997912802653)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.min(big_array), np.max(big_array)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"72 ms ± 177 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\",\n \"564 µs ± 3.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit min(big_array)\\n\",\n \"%timeit np.min(big_array)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For `min`, `max`, `sum`, and several other NumPy aggregates, a shorter syntax is to use methods of the array object itself:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0114398036064074e-07 0.9999997912802653 499854.0273321711\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(big_array.min(), big_array.max(), big_array.sum())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Whenever possible, make sure that you are using the NumPy version of these aggregates when operating on NumPy arrays!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Multidimensional Aggregates\\n\",\n \"\\n\",\n \"One common type of aggregation operation is an aggregate along a row or column.\\n\",\n \"Say you have some data stored in a two-dimensional array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0 3 1 2]\\n\",\n \" [1 9 7 0]\\n\",\n \" [4 8 3 7]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"M = rng.integers(0, 10, (3, 4))\\n\",\n \"print(M)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"NumPy aggregations will apply across all elements of a multidimensional array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"45\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"M.sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Aggregation functions take an additional argument specifying the *axis* along which the aggregate is computed. For example, we can find the minimum value within each column by specifying `axis=0`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0, 3, 1, 0])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"M.min(axis=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The function returns four values, corresponding to the four columns of numbers.\\n\",\n \"\\n\",\n \"Similarly, we can find the maximum value within each row:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 9, 8])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"M.max(axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The way the axis is specified here can be confusing to users coming from other languages.\\n\",\n \"The `axis` keyword specifies the dimension of the array that will be *collapsed*, rather than the dimension that will be returned.\\n\",\n \"So, specifying `axis=0` means that axis 0 will be collapsed: for two-dimensional arrays, values within each column will be aggregated.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Other Aggregation Functions\\n\",\n \"\\n\",\n \"NumPy provides several other aggregation functions with a similar API, and additionally most have a `NaN`-safe counterpart that computes the result while ignoring missing values, which are marked by the special IEEE floating-point `NaN` value (see [Handling Missing Data](03.04-Missing-Values.ipynb)).\\n\",\n \"\\n\",\n \"The following table provides a list of useful aggregation functions available in NumPy:\\n\",\n \"\\n\",\n \"|Function name | NaN-safe version| Description |\\n\",\n \"|-----------------|-------------------|-----------------------------------------------|\\n\",\n \"| `np.sum` | `np.nansum` | Compute sum of elements |\\n\",\n \"| `np.prod` | `np.nanprod` | Compute product of elements |\\n\",\n \"| `np.mean` | `np.nanmean` | Compute mean of elements |\\n\",\n \"| `np.std` | `np.nanstd` | Compute standard deviation |\\n\",\n \"| `np.var` | `np.nanvar` | Compute variance |\\n\",\n \"| `np.min` | `np.nanmin` | Find minimum value |\\n\",\n \"| `np.max` | `np.nanmax` | Find maximum value |\\n\",\n \"| `np.argmin` | `np.nanargmin` | Find index of minimum value |\\n\",\n \"| `np.argmax` | `np.nanargmax` | Find index of maximum value |\\n\",\n \"| `np.median` | `np.nanmedian` | Compute median of elements |\\n\",\n \"| `np.percentile` | `np.nanpercentile`| Compute rank-based statistics of elements |\\n\",\n \"| `np.any` | N/A | Evaluate whether any elements are true |\\n\",\n \"| `np.all` | N/A | Evaluate whether all elements are true |\\n\",\n \"\\n\",\n \"You will see these aggregates often throughout the rest of the book.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: What Is the Average Height of US Presidents?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Aggregates available in NumPy can act as summary statistics for a set of values.\\n\",\n \"As a small example, let's consider the heights of all US presidents.\\n\",\n \"This data is available in the file *president_heights.csv*, which is a comma-separated list of labels and values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"order,name,height(cm)\\n\",\n \"1,George Washington,189\\n\",\n \"2,John Adams,170\\n\",\n \"3,Thomas Jefferson,189\\n\"\n ]\n }\n ],\n \"source\": [\n \"!head -4 data/president_heights.csv\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll use the Pandas package, which we'll explore more fully in [Part 3](03.00-Introduction-to-Pandas.ipynb), to read the file and extract this information (note that the heights are measured in centimeters):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[189 170 189 163 183 171 185 168 173 183 173 173 175 178 183 193 178 173\\n\",\n \" 174 183 183 168 170 178 182 180 183 178 182 188 175 179 183 193 182 183\\n\",\n \" 177 185 188 188 182 185 191 182]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"data = pd.read_csv('data/president_heights.csv')\\n\",\n \"heights = np.array(data['height(cm)'])\\n\",\n \"print(heights)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have this data array, we can compute a variety of summary statistics:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Mean height: 180.04545454545453\\n\",\n \"Standard deviation: 6.983599441335736\\n\",\n \"Minimum height: 163\\n\",\n \"Maximum height: 193\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Mean height: \\\", heights.mean())\\n\",\n \"print(\\\"Standard deviation:\\\", heights.std())\\n\",\n \"print(\\\"Minimum height: \\\", heights.min())\\n\",\n \"print(\\\"Maximum height: \\\", heights.max())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that in each case, the aggregation operation reduced the entire array to a single summarizing value, which gives us information about the distribution of values.\\n\",\n \"We may also wish to compute quantiles:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"25th percentile: 174.75\\n\",\n \"Median: 182.0\\n\",\n \"75th percentile: 183.5\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"25th percentile: \\\", np.percentile(heights, 25))\\n\",\n \"print(\\\"Median: \\\", np.median(heights))\\n\",\n \"print(\\\"75th percentile: \\\", np.percentile(heights, 75))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see that the median height of US presidents is 182 cm, or just shy of six feet.\\n\",\n \"\\n\",\n \"Of course, sometimes it's more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib (we'll discuss Matplotlib more fully in [Part 4](04.00-Introduction-To-Matplotlib.ipynb)). For example, this code generates the following chart:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(heights)\\n\",\n \"plt.title('Height Distribution of US Presidents')\\n\",\n \"plt.xlabel('height (cm)')\\n\",\n \"plt.ylabel('number');\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Computation on Arrays: Broadcasting\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We saw in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops.\\n\",\n \"This chapter discusses *broadcasting*: a set of rules by which NumPy lets you apply binary operations (e.g., addition, subtraction, multiplication, etc.) between arrays of different sizes and shapes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Introducing Broadcasting\\n\",\n \"\\n\",\n \"Recall that for arrays of the same size, binary operations are performed on an element-by-element basis:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([5, 6, 7])\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = np.array([0, 1, 2])\\n\",\n \"b = np.array([5, 5, 5])\\n\",\n \"a + b\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Broadcasting allows these types of binary operations to be performed on arrays of different sizes—for example, we can just as easily add a scalar (think of it as a zero-dimensional array) to an array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([5, 6, 7])\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a + 5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can think of this as an operation that stretches or duplicates the value `5` into the array `[5, 5, 5]`, and adds the results.\\n\",\n \"\\n\",\n \"We can similarly extend this idea to arrays of higher dimension. Observe the result when we add a one-dimensional array to a two-dimensional array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 1., 1.],\\n\",\n \" [1., 1., 1.],\\n\",\n \" [1., 1., 1.]])\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"M = np.ones((3, 3))\\n\",\n \"M\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 2., 3.],\\n\",\n \" [1., 2., 3.],\\n\",\n \" [1., 2., 3.]])\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"M + a\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here the one-dimensional array `a` is stretched, or broadcasted, across the second dimension in order to match the shape of `M`.\\n\",\n \"\\n\",\n \"While these examples are relatively easy to understand, more complicated cases can involve broadcasting of both arrays. Consider the following example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0 1 2]\\n\",\n \"[[0]\\n\",\n \" [1]\\n\",\n \" [2]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = np.arange(3)\\n\",\n \"b = np.arange(3)[:, np.newaxis]\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0, 1, 2],\\n\",\n \" [1, 2, 3],\\n\",\n \" [2, 3, 4]])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a + b\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just as before we stretched or broadcasted one value to match the shape of the other, here we've stretched *both* `a` and `b` to match a common shape, and the result is a two-dimensional array!\\n\",\n \"The geometry of these examples is visualized in the following figure. (Code to produce this plot can be found in the online [appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Broadcasting), and is adapted from a source published in the [astroML](http://astroml.org) documentation. Used by permission.)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The light boxes represent the broadcasted values. This way of thinking about broadcasting may raise questions about its efficiency in terms of memory use, but worry not: NumPy broadcasting does not actually copy the broadcasted values in memory. Still, this can be a useful mental model as we think about broadcasting.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Rules of Broadcasting\\n\",\n \"\\n\",\n \"Broadcasting in NumPy follows a strict set of rules to determine the interaction between the two arrays:\\n\",\n \"\\n\",\n \"- Rule 1: If the two arrays differ in their number of dimensions, the shape of the one with fewer dimensions is *padded* with ones on its leading (left) side.\\n\",\n \"- Rule 2: If the shape of the two arrays does not match in any dimension, the array with shape equal to 1 in that dimension is stretched to match the other shape.\\n\",\n \"- Rule 3: If in any dimension the sizes disagree and neither is equal to 1, an error is raised.\\n\",\n \"\\n\",\n \"To make these rules clear, let's consider a few examples in detail.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Broadcasting Example 1\\n\",\n \"\\n\",\n \"Suppose we want to add a two-dimensional array to a one-dimensional array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"M = np.ones((2, 3))\\n\",\n \"a = np.arange(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's consider an operation on these two arrays, which have the following shapes:\\n\",\n \"\\n\",\n \"- `M.shape` is `(2, 3)`\\n\",\n \"- `a.shape` is `(3,)`\\n\",\n \"\\n\",\n \"We see by rule 1 that the array `a` has fewer dimensions, so we pad it on the left with ones:\\n\",\n \"\\n\",\n \"- `M.shape` remains `(2, 3)`\\n\",\n \"- `a.shape` becomes `(1, 3)`\\n\",\n \"\\n\",\n \"By rule 2, we now see that the first dimension disagrees, so we stretch this dimension to match:\\n\",\n \"\\n\",\n \"- `M.shape` remains `(2, 3)`\\n\",\n \"- `a.shape` becomes `(2, 3)`\\n\",\n \"\\n\",\n \"The shapes now match, and we see that the final shape will be `(2, 3)`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 2., 3.],\\n\",\n \" [1., 2., 3.]])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"M + a\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Broadcasting Example 2\\n\",\n \"\\n\",\n \"Now let's take a look at an example where both arrays need to be broadcast:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"a = np.arange(3).reshape((3, 1))\\n\",\n \"b = np.arange(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Again, we'll start by determining the shapes of the arrays:\\n\",\n \"\\n\",\n \"- `a.shape` is `(3, 1)`\\n\",\n \"- `b.shape` is `(3,)`\\n\",\n \"\\n\",\n \"Rule 1 says we must pad the shape of `b` with ones:\\n\",\n \"\\n\",\n \"- `a.shape` remains `(3, 1)`\\n\",\n \"- `b.shape` becomes `(1, 3)`\\n\",\n \"\\n\",\n \"And rule 2 tells us that we must upgrade each of these ``1``s to match the corresponding size of the other array:\\n\",\n \"\\n\",\n \"- `a.shape` becomes `(3, 3)`\\n\",\n \"- `b.shape` becomes `(3, 3)`\\n\",\n \"\\n\",\n \"Because the results match, these shapes are compatible. We can see this here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0, 1, 2],\\n\",\n \" [1, 2, 3],\\n\",\n \" [2, 3, 4]])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a + b\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Broadcasting Example 3\\n\",\n \"\\n\",\n \"Next, let's take a look at an example in which the two arrays are not compatible:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"M = np.ones((3, 2))\\n\",\n \"a = np.arange(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is just a slightly different situation than in the first example: the matrix `M` is transposed.\\n\",\n \"How does this affect the calculation? The shapes of the arrays are as follows:\\n\",\n \"\\n\",\n \"- `M.shape` is `(3, 2)`\\n\",\n \"- `a.shape` is `(3,)`\\n\",\n \"\\n\",\n \"Again, rule 1 tells us that we must pad the shape of `a` with ones:\\n\",\n \"\\n\",\n \"- `M.shape` remains `(3, 2)`\\n\",\n \"- `a.shape` becomes `(1, 3)`\\n\",\n \"\\n\",\n \"By rule 2, the first dimension of `a` is then stretched to match that of `M`:\\n\",\n \"\\n\",\n \"- `M.shape` remains `(3, 2)`\\n\",\n \"- `a.shape` becomes `(3, 3)`\\n\",\n \"\\n\",\n \"Now we hit rule 3—the final shapes do not match, so these two arrays are incompatible, as we can observe by attempting this operation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"operands could not be broadcast together with shapes (3,2) (3,) \",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mM\\u001b[0m \\u001b[0;34m+\\u001b[0m \\u001b[0ma\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m: operands could not be broadcast together with shapes (3,2) (3,) \"\n ]\n }\n ],\n \"source\": [\n \"M + a\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note the potential confusion here: you could imagine making `a` and `M` compatible by, say, padding `a`'s shape with ones on the right rather than the left.\\n\",\n \"But this is not how the broadcasting rules work!\\n\",\n \"That sort of flexibility might be useful in some cases, but it would lead to potential areas of ambiguity.\\n\",\n \"If right-side padding is what you'd like, you can do this explicitly by reshaping the array (we'll use the `np.newaxis` keyword introduced in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) for this):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(3, 1)\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a[:, np.newaxis].shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 1.],\\n\",\n \" [2., 2.],\\n\",\n \" [3., 3.]])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"M + a[:, np.newaxis]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Also notice that while we've been focusing on the `+` operator here, these broadcasting rules apply to *any* binary ufunc.\\n\",\n \"For example, here is the `logaddexp(a, b)` function, which computes `log(exp(a) + exp(b))` with more precision than the naive approach:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1.31326169, 1.31326169],\\n\",\n \" [1.69314718, 1.69314718],\\n\",\n \" [2.31326169, 2.31326169]])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.logaddexp(M, a[:, np.newaxis])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For more information on the many available universal functions, refer to [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Broadcasting in Practice\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Broadcasting operations form the core of many examples you'll see throughout this book.\\n\",\n \"We'll now take a look at some instances of where they can be useful.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Centering an Array\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb), we saw that ufuncs allow a NumPy user to remove the need to explicitly write slow Python loops. Broadcasting extends this ability.\\n\",\n \"One commonly seen example in data science is subtracting the row-wise mean from an array of data.\\n\",\n \"Imagine we have an array of 10 observations, each of which consists of 3 values.\\n\",\n \"Using the standard convention (see [Data Representation in Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb#Data-Representation-in-Scikit-Learn)), we'll store this in a $10 \\\\times 3$ array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"rng = np.random.default_rng(seed=1701)\\n\",\n \"X = rng.random((10, 3))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can compute the mean of each column using the `mean` aggregate across the first dimension:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.38503638, 0.36991443, 0.63896043])\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Xmean = X.mean(0)\\n\",\n \"Xmean\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And now we can center the `X` array by subtracting the mean (this is a broadcasting operation):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"X_centered = X - Xmean\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To double-check that we've done this correctly, we can check that the centered array has a mean near zero:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 4.99600361e-17, -4.44089210e-17, 0.00000000e+00])\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_centered.mean(0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To within machine precision, the mean is now zero.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Plotting a Two-Dimensional Function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"One place that broadcasting often comes in handy is in displaying images based on two-dimensional functions.\\n\",\n \"If we want to define a function $z = f(x, y)$, broadcasting can be used to compute the function across the grid:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# x and y have 50 steps from 0 to 5\\n\",\n \"x = np.linspace(0, 5, 50)\\n\",\n \"y = np.linspace(0, 5, 50)[:, np.newaxis]\\n\",\n \"\\n\",\n \"z = np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll use Matplotlib to plot this two-dimensional array, shown in the following figure (these tools will be discussed in full in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(z, origin='lower', extent=[0, 5, 0, 5])\\n\",\n \"plt.colorbar();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result is a compelling visualization of the two-dimensional function.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/02.06-Boolean-Arrays-and-Masks.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Comparisons, Masks, and Boolean Logic\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This chapter covers the use of Boolean masks to examine and manipulate values within NumPy arrays.\\n\",\n \"Masking comes up when you want to extract, modify, count, or otherwise manipulate values in an array based on some criterion: for example, you might wish to count all values greater than a certain value, or remove all outliers that are above some threshold.\\n\",\n \"In NumPy, Boolean masking is often the most efficient way to accomplish these types of tasks.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Counting Rainy Days\\n\",\n \"\\n\",\n \"Imagine you have a series of data that represents the amount of precipitation each day for a year in a given city.\\n\",\n \"For example, here we'll load the daily rainfall statistics for the city of Seattle in 2015, using Pandas (see [Part 3](03.00-Introduction-to-Pandas.ipynb)):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"365\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"from vega_datasets import data\\n\",\n \"\\n\",\n \"# Use DataFrame operations to extract rainfall as a NumPy array\\n\",\n \"rainfall_mm = np.array(\\n\",\n \" data.seattle_weather().set_index('date')['precipitation']['2015'])\\n\",\n \"len(rainfall_mm)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The array contains 365 values, giving daily rainfall in millimeters from January 1 to December 31, 2015.\\n\",\n \"\\n\",\n \"As a first quick visualization, let's look at the histogram of rainy days in the following figure, which was generated using Matplotlib (we will explore this tool more fully in [Part 4](04.00-Introduction-To-Matplotlib.ipynb)):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(rainfall_mm, 40);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This histogram gives us a general idea of what the data looks like: despite the city's rainy reputation, the vast majority of days in Seattle saw near zero measured rainfall in 2015.\\n\",\n \"But this doesn't do a good job of conveying some information we'd like to see: for example, how many rainy days were there in the year? What was the average precipitation on those rainy days? How many days were there with more than 10 mm of rainfall?\\n\",\n \"\\n\",\n \"One approach to this would be to answer these questions by hand: we could loop through the data, incrementing a counter each time we see values in some desired range.\\n\",\n \"But for reasons discussed throughout this chapter, such an approach is very inefficient from the standpoint of both time writing code and time computing the result.\\n\",\n \"We saw in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) that NumPy's ufuncs can be used in place of loops to do fast element-wise arithmetic operations on arrays; in the same way, we can use other ufuncs to do element-wise *comparisons* over arrays, and we can then manipulate the results to answer the questions we have.\\n\",\n \"We'll leave the data aside for now, and discuss some general tools in NumPy to use *masking* to quickly answer these types of questions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Comparison Operators as Ufuncs\\n\",\n \"\\n\",\n \"[Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) introduced ufuncs, and focused in particular on arithmetic operators. We saw that using `+`, `-`, `*`, `/`, and other operators on arrays leads to element-wise operations.\\n\",\n \"NumPy also implements comparison operators such as `<` (less than) and `>` (greater than) as element-wise ufuncs.\\n\",\n \"The result of these comparison operators is always an array with a Boolean data type.\\n\",\n \"All six of the standard comparison operations are available:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"x = np.array([1, 2, 3, 4, 5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ True, True, False, False, False])\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x < 3 # less than\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([False, False, False, True, True])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x > 3 # greater than\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ True, True, True, False, False])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x <= 3 # less than or equal\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([False, False, True, True, True])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x >= 3 # greater than or equal\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ True, True, False, True, True])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x != 3 # not equal\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([False, False, True, False, False])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x == 3 # equal\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is also possible to do an element-wise comparison of two arrays, and to include compound expressions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([False, True, False, False, False])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"(2 * x) == (x ** 2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As in the case of arithmetic operators, the comparison operators are implemented as ufuncs in NumPy; for example, when you write `x < 3`, internally NumPy uses `np.less(x, 3)`.\\n\",\n \" A summary of the comparison operators and their equivalent ufuncs is shown here:\\n\",\n \"\\n\",\n \"| Operator | Equivalent ufunc | Operator | Equivalent ufunc |\\n\",\n \"|-------------|-------------------|------------|------------------|\\n\",\n \"|`==` |`np.equal` |`!=` |`np.not_equal` |\\n\",\n \"|`<` |`np.less` |`<=` |`np.less_equal` |\\n\",\n \"|`>` |`np.greater` |`>=` |`np.greater_equal`|\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just as in the case of arithmetic ufuncs, these will work on arrays of any size and shape.\\n\",\n \"Here is a two-dimensional example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[9, 4, 0, 3],\\n\",\n \" [8, 6, 3, 1],\\n\",\n \" [3, 7, 4, 0]])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"rng = np.random.default_rng(seed=1701)\\n\",\n \"x = rng.integers(10, size=(3, 4))\\n\",\n \"x\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[False, True, True, True],\\n\",\n \" [False, False, True, True],\\n\",\n \" [ True, False, True, True]])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x < 6\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In each case, the result is a Boolean array, and NumPy provides a number of straightforward patterns for working with these Boolean results.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Working with Boolean Arrays\\n\",\n \"\\n\",\n \"Given a Boolean array, there are a host of useful operations you can do.\\n\",\n \"We'll work with `x`, the two-dimensional array we created earlier:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[9 4 0 3]\\n\",\n \" [8 6 3 1]\\n\",\n \" [3 7 4 0]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Counting Entries\\n\",\n \"\\n\",\n \"To count the number of `True` entries in a Boolean array, `np.count_nonzero` is useful:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"8\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# how many values less than 6?\\n\",\n \"np.count_nonzero(x < 6)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see that there are eight array entries that are less than 6.\\n\",\n \"Another way to get at this information is to use `np.sum`; in this case, `False` is interpreted as `0`, and `True` is interpreted as `1`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"8\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.sum(x < 6)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The benefit of `np.sum` is that, like with other NumPy aggregation functions, this summation can be done along rows or columns as well:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 2, 3])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# how many values less than 6 in each row?\\n\",\n \"np.sum(x < 6, axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This counts the number of values less than 6 in each row of the matrix.\\n\",\n \"\\n\",\n \"If we're interested in quickly checking whether any or all the values are `True`, we can use (you guessed it) `np.any` or `np.all`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# are there any values greater than 8?\\n\",\n \"np.any(x > 8)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# are there any values less than zero?\\n\",\n \"np.any(x < 0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# are all values less than 10?\\n\",\n \"np.all(x < 10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# are all values equal to 6?\\n\",\n \"np.all(x == 6)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`np.all` and `np.any` can be used along particular axes as well. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([False, False, True])\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# are all values in each row less than 8?\\n\",\n \"np.all(x < 8, axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here all the elements in the third row are less than 8, while this is not the case for others.\\n\",\n \"\\n\",\n \"Finally, a quick warning: as mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb), Python has built-in `sum`, `any`, and `all` functions. These have a different syntax than the NumPy versions, and in particular will fail or produce unintended results when used on multidimensional arrays. Be sure that you are using `np.sum`, `np.any`, and `np.all` for these examples!\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Boolean Operators\\n\",\n \"\\n\",\n \"We've already seen how we might count, say, all days with less than 20 mm of rain, or all days with more than 10 mm of rain.\\n\",\n \"But what if we want to know how many days there were with more than 10 mm and less than 20 mm of rain? We can accomplish this with Python's *bitwise logic operators*, `&`, `|`, `^`, and `~`.\\n\",\n \"Like with the standard arithmetic operators, NumPy overloads these as ufuncs that work element-wise on (usually Boolean) arrays.\\n\",\n \"\\n\",\n \"For example, we can address this sort of compound question as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"16\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.sum((rainfall_mm > 10) & (rainfall_mm < 20))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This tells us that there were 16 days with rainfall of between 10 and 20 millimeters.\\n\",\n \"\\n\",\n \"The parentheses here are important. Because of operator precedence rules, with the parentheses removed this expression would be evaluated as follows, which results in an error:\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"rainfall_mm > (10 & rainfall_mm) < 20\\n\",\n \"```\\n\",\n \"\\n\",\n \"Let's demonstrate a more complicated expression. Using De Morgan's laws, we can compute the same result in a different manner:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"16\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.sum(~( (rainfall_mm <= 10) | (rainfall_mm >= 20) ))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Combining comparison operators and Boolean operators on arrays can lead to a wide range of efficient logical operations.\\n\",\n \"\\n\",\n \"The following table summarizes the bitwise Boolean operators and their equivalent ufuncs:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Operator | Equivalent ufunc | Operator | Equivalent ufunc |\\n\",\n \"|-------------|-------------------|-------------|-------------------|\\n\",\n \"|`&` |`np.bitwise_and` || |`np.bitwise_or` |\\n\",\n \"|`^` |`np.bitwise_xor` |`~` |`np.bitwise_not` |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using these tools, we can start to answer many of the questions we might have about our weather data.\\n\",\n \"Here are some examples of results we can compute when combining masking with aggregations:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Number days without rain: 221\\n\",\n \"Number days with rain: 144\\n\",\n \"Days with more than 10 mm: 34\\n\",\n \"Rainy days with < 5 mm: 83\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Number days without rain: \\\", np.sum(rainfall_mm == 0))\\n\",\n \"print(\\\"Number days with rain: \\\", np.sum(rainfall_mm != 0))\\n\",\n \"print(\\\"Days with more than 10 mm: \\\", np.sum(rainfall_mm > 10))\\n\",\n \"print(\\\"Rainy days with < 5 mm: \\\", np.sum((rainfall_mm > 0) &\\n\",\n \" (rainfall_mm < 5)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Boolean Arrays as Masks\\n\",\n \"\\n\",\n \"In the preceding section we looked at aggregates computed directly on Boolean arrays.\\n\",\n \"A more powerful pattern is to use Boolean arrays as masks, to select particular subsets of the data themselves. Let's return to our `x` array from before:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[9, 4, 0, 3],\\n\",\n \" [8, 6, 3, 1],\\n\",\n \" [3, 7, 4, 0]])\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Suppose we want an array of all values in the array that are less than, say, 5. We can obtain a Boolean array for this condition easily, as we've already seen:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[False, True, True, True],\\n\",\n \" [False, False, True, True],\\n\",\n \" [ True, False, True, True]])\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x < 5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, to *select* these values from the array, we can simply index on this Boolean array; this is known as a *masking* operation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([4, 0, 3, 3, 1, 3, 4, 0])\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x[x < 5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"What is returned is a one-dimensional array filled with all the values that meet this condition; in other words, all the values in positions at which the mask array is `True`.\\n\",\n \"\\n\",\n \"We are then free to operate on these values as we wish.\\n\",\n \"For example, we can compute some relevant statistics on our Seattle rain data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Median precip on rainy days in 2015 (mm): 3.8\\n\",\n \"Median precip on summer days in 2015 (mm): 0.0\\n\",\n \"Maximum precip on summer days in 2015 (mm): 32.5\\n\",\n \"Median precip on non-summer rainy days (mm): 4.1\\n\"\n ]\n }\n ],\n \"source\": [\n \"# construct a mask of all rainy days\\n\",\n \"rainy = (rainfall_mm > 0)\\n\",\n \"\\n\",\n \"# construct a mask of all summer days (June 21st is the 172nd day)\\n\",\n \"days = np.arange(365)\\n\",\n \"summer = (days > 172) & (days < 262)\\n\",\n \"\\n\",\n \"print(\\\"Median precip on rainy days in 2015 (mm): \\\",\\n\",\n \" np.median(rainfall_mm[rainy]))\\n\",\n \"print(\\\"Median precip on summer days in 2015 (mm): \\\",\\n\",\n \" np.median(rainfall_mm[summer]))\\n\",\n \"print(\\\"Maximum precip on summer days in 2015 (mm): \\\",\\n\",\n \" np.max(rainfall_mm[summer]))\\n\",\n \"print(\\\"Median precip on non-summer rainy days (mm):\\\",\\n\",\n \" np.median(rainfall_mm[rainy & ~summer]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By combining Boolean operations, masking operations, and aggregates, we can very quickly answer these sorts of questions about our dataset.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Using the Keywords and/or Versus the Operators &/|\\n\",\n \"\\n\",\n \"One common point of confusion is the difference between the keywords `and` and `or` on the one hand, and the operators `&` and `|` on the other.\\n\",\n \"When would you use one versus the other?\\n\",\n \"\\n\",\n \"The difference is this: `and` and `or` operate on the object as a whole, while `&` and `|` operate on the elements within the object.\\n\",\n \"\\n\",\n \"When you use `and` or `or`, it is equivalent to asking Python to treat the object as a single Boolean entity.\\n\",\n \"In Python, all nonzero integers will evaluate as `True`. Thus:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(True, False)\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bool(42), bool(0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bool(42 and 0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bool(42 or 0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When you use `&` and `|` on integers, the expression operates on the bitwise representation of the element, applying the *and* or the *or* to the individual bits making up the number:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0b101010'\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bin(42)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0b111011'\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bin(59)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0b101010'\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bin(42 & 59)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0b111011'\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bin(42 | 59)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that the corresponding bits of the binary representation are compared in order to yield the result.\\n\",\n \"\\n\",\n \"When you have an array of Boolean values in NumPy, this can be thought of as a string of bits where `1 = True` and `0 = False`, and `&` and `|` will operate similarly to in the preceding examples:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ True, True, True, False, True, True])\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"A = np.array([1, 0, 1, 0, 1, 0], dtype=bool)\\n\",\n \"B = np.array([1, 1, 1, 0, 1, 1], dtype=bool)\\n\",\n \"A | B\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But if you use `or` on these arrays it will try to evaluate the truth or falsehood of the entire array object, which is not a well-defined value:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_93010/3447948156.py\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mA\\u001b[0m \\u001b[0;32mor\\u001b[0m \\u001b[0mB\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\"\n ]\n }\n ],\n \"source\": [\n \"A or B\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Similarly, when evaluating a Boolean expression on a given array, you should use `|` or `&` rather than `or` or `and`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([False, False, False, False, False, True, True, True, False,\\n\",\n \" False])\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.arange(10)\\n\",\n \"(x > 4) & (x < 8)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Trying to evaluate the truth or falsehood of the entire array will give the same `ValueError` we saw previously:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_93010/2869511139.py\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m \\u001b[0;34m>\\u001b[0m \\u001b[0;36m4\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mand\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m \\u001b[0;34m<\\u001b[0m \\u001b[0;36m8\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\"\n ]\n }\n ],\n \"source\": [\n \"(x > 4) and (x < 8)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"So, remember this: `and` and `or` perform a single Boolean evaluation on an entire object, while `&` and `|` perform multiple Boolean evaluations on the content (the individual bits or bytes) of an object.\\n\",\n \"For Boolean NumPy arrays, the latter is nearly always the desired operation.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 64-bit ('3.9.6')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/02.07-Fancy-Indexing.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Fancy Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The previous chapters discussed how to access and modify portions of arrays using simple indices (e.g., `arr[0]`), slices (e.g., `arr[:5]`), and Boolean masks (e.g., `arr[arr > 0]`).\\n\",\n \"In this chapter, we'll look at another style of array indexing, known as *fancy* or *vectorized* indexing, in which we pass arrays of indices in place of single scalars.\\n\",\n \"This allows us to very quickly access and modify complicated subsets of an array's values.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exploring Fancy Indexing\\n\",\n \"\\n\",\n \"Fancy indexing is conceptually simple: it means passing an array of indices to access multiple array elements at once.\\n\",\n \"For example, consider the following array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[90 40 9 30 80 67 39 15 33 79]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"rng = np.random.default_rng(seed=1701)\\n\",\n \"\\n\",\n \"x = rng.integers(100, size=10)\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Suppose we want to access three different elements. We could do it like this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[30, 15, 9]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[x[3], x[7], x[2]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Alternatively, we can pass a single list or array of indices to obtain the same result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([30, 15, 80])\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ind = [3, 7, 4]\\n\",\n \"x[ind]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When using arrays of indices, the shape of the result reflects the shape of the *index arrays* rather than the shape of the *array being indexed*:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[30, 15],\\n\",\n \" [80, 67]])\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ind = np.array([[3, 7],\\n\",\n \" [4, 5]])\\n\",\n \"x[ind]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Fancy indexing also works in multiple dimensions. Consider the following array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0, 1, 2, 3],\\n\",\n \" [ 4, 5, 6, 7],\\n\",\n \" [ 8, 9, 10, 11]])\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X = np.arange(12).reshape((3, 4))\\n\",\n \"X\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Like with standard indexing, the first index refers to the row, and the second to the column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 2, 5, 11])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"row = np.array([0, 1, 2])\\n\",\n \"col = np.array([2, 1, 3])\\n\",\n \"X[row, col]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that the first value in the result is `X[0, 2]`, the second is `X[1, 1]`, and the third is `X[2, 3]`.\\n\",\n \"The pairing of indices in fancy indexing follows all the broadcasting rules that were mentioned in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb).\\n\",\n \"So, for example, if we combine a column vector and a row vector within the indices, we get a two-dimensional result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 2, 1, 3],\\n\",\n \" [ 6, 5, 7],\\n\",\n \" [10, 9, 11]])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X[row[:, np.newaxis], col]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, each row value is matched with each column vector, exactly as we saw in broadcasting of arithmetic operations.\\n\",\n \"For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0, 0, 0],\\n\",\n \" [2, 1, 3],\\n\",\n \" [4, 2, 6]])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"row[:, np.newaxis] * col\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is always important to remember with fancy indexing that the return value reflects the *broadcasted shape of the indices*, rather than the shape of the array being indexed.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Combined Indexing\\n\",\n \"\\n\",\n \"For even more powerful operations, fancy indexing can be combined with the other indexing schemes we've seen. For example, given the array `X`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0 1 2 3]\\n\",\n \" [ 4 5 6 7]\\n\",\n \" [ 8 9 10 11]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(X)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can combine fancy and simple indices:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([10, 8, 9])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X[2, [2, 0, 1]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also combine fancy indexing with slicing:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 6, 4, 5],\\n\",\n \" [10, 8, 9]])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X[1:, [2, 0, 1]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And we can combine fancy indexing with masking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0, 2],\\n\",\n \" [ 4, 6],\\n\",\n \" [ 8, 10]])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mask = np.array([True, False, True, False])\\n\",\n \"X[row[:, np.newaxis], mask]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All of these indexing options combined lead to a very flexible set of operations for efficiently accessing and modifying array values.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Selecting Random Points\\n\",\n \"\\n\",\n \"One common use of fancy indexing is the selection of subsets of rows from a matrix.\\n\",\n \"For example, we might have an $N$ by $D$ matrix representing $N$ points in $D$ dimensions, such as the following points drawn from a two-dimensional normal distribution:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(100, 2)\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mean = [0, 0]\\n\",\n \"cov = [[1, 2],\\n\",\n \" [2, 5]]\\n\",\n \"X = rng.multivariate_normal(mean, cov, 100)\\n\",\n \"X.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using the plotting tools we will discuss in [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb), we can visualize these points as a scatter plot (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"\\n\",\n \"plt.scatter(X[:, 0], X[:, 1]);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's use fancy indexing to select 20 random points. We'll do this by first choosing 20 random indices with no repeats, and using these indices to select a portion of the original array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([82, 84, 10, 55, 14, 33, 4, 16, 34, 92, 99, 64, 8, 76, 68, 18, 59,\\n\",\n \" 80, 87, 90])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"indices = np.random.choice(X.shape[0], 20, replace=False)\\n\",\n \"indices\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(20, 2)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"selection = X[indices] # fancy indexing here\\n\",\n \"selection.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now to see which points were selected, let's overplot large circles at the locations of the selected points (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(X[:, 0], X[:, 1], alpha=0.3)\\n\",\n \"plt.scatter(selection[:, 0], selection[:, 1],\\n\",\n \" facecolor='none', edgecolor='black', s=200);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This sort of strategy is often used to quickly partition datasets, as is often needed in train/test splitting for validation of statistical models (see [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)), and in sampling approaches to answering statistical questions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Modifying Values with Fancy Indexing\\n\",\n \"\\n\",\n \"Just as fancy indexing can be used to access parts of an array, it can also be used to modify parts of an array.\\n\",\n \"For example, imagine we have an array of indices and we'd like to set the corresponding items in an array to some value:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0 99 99 3 99 5 6 7 99 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = np.arange(10)\\n\",\n \"i = np.array([2, 1, 8, 4])\\n\",\n \"x[i] = 99\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can use any assignment-type operator for this. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 0 89 89 3 89 5 6 7 89 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x[i] -= 10\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice, though, that repeated indices with these operations can cause some potentially unexpected results. Consider the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[6. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = np.zeros(10)\\n\",\n \"x[[0, 0]] = [4, 6]\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Where did the 4 go? This operation first assigns `x[0] = 4`, followed by `x[0] = 6`.\\n\",\n \"The result, of course, is that `x[0]` contains the value 6.\\n\",\n \"\\n\",\n \"Fair enough, but consider this operation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([6., 0., 1., 1., 1., 0., 0., 0., 0., 0.])\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"i = [2, 3, 3, 4, 4, 4]\\n\",\n \"x[i] += 1\\n\",\n \"x\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You might expect that `x[3]` would contain the value 2 and `x[4]` would contain the value 3, as this is how many times each index is repeated. Why is this not the case?\\n\",\n \"Conceptually, this is because `x[i] += 1` is meant as a shorthand of `x[i] = x[i] + 1`. `x[i] + 1` is evaluated, and then the result is assigned to the indices in `x`.\\n\",\n \"With this in mind, it is not the augmentation that happens multiple times, but the assignment, which leads to the rather nonintuitive results.\\n\",\n \"\\n\",\n \"So what if you want the other behavior where the operation is repeated? For this, you can use the `at` method of ufuncs and do the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0. 0. 1. 2. 3. 0. 0. 0. 0. 0.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = np.zeros(10)\\n\",\n \"np.add.at(x, i, 1)\\n\",\n \"print(x)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `at` method does an in-place application of the given operator at the specified indices (here, `i`) with the specified value (here, 1).\\n\",\n \"Another method that is similar in spirit is the `reduceat` method of ufuncs, which you can read about in the [NumPy documentation](https://numpy.org/doc/stable/reference/ufuncs.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Binning Data\\n\",\n \"\\n\",\n \"You could use these ideas to efficiently do custom binned computations on data.\\n\",\n \"For example, imagine we have 100 values and would like to quickly find where they fall within an array of bins.\\n\",\n \"We could compute this using `ufunc.at` like this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"rng = np.random.default_rng(seed=1701)\\n\",\n \"x = rng.normal(size=100)\\n\",\n \"\\n\",\n \"# compute a histogram by hand\\n\",\n \"bins = np.linspace(-5, 5, 20)\\n\",\n \"counts = np.zeros_like(bins)\\n\",\n \"\\n\",\n \"# find the appropriate bin for each x\\n\",\n \"i = np.searchsorted(bins, x)\\n\",\n \"\\n\",\n \"# add 1 to each of these bins\\n\",\n \"np.add.at(counts, i, 1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The counts now reflect the number of points within each bin—in other words, a histogram (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the results\\n\",\n \"plt.plot(bins, counts, drawstyle='steps');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Of course, it would be inconvenient to have to do this each time you want to plot a histogram.\\n\",\n \"This is why Matplotlib provides the `plt.hist` routine, which does the same in a single line:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"plt.hist(x, bins, histtype='step');\\n\",\n \"```\\n\",\n \"\\n\",\n \"This function will create a nearly identical plot to the one just shown.\\n\",\n \"To compute the binning, Matplotlib uses the `np.histogram` function, which does a very similar computation to what we did before. Let's compare the two here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"NumPy histogram (100 points):\\n\",\n \"33.8 µs ± 311 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\\n\",\n \"Custom histogram (100 points):\\n\",\n \"17.6 µs ± 113 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f\\\"NumPy histogram ({len(x)} points):\\\")\\n\",\n \"%timeit counts, edges = np.histogram(x, bins)\\n\",\n \"\\n\",\n \"print(f\\\"Custom histogram ({len(x)} points):\\\")\\n\",\n \"%timeit np.add.at(counts, np.searchsorted(bins, x), 1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Our own one-line algorithm is twice as fast as the optimized algorithm in NumPy! How can this be? If you dig into the `np.histogram` source code (you can do this in IPython by typing `np.histogram??`), you'll see that it's quite a bit more involved than the simple search-and-count that we've done; this is because NumPy's algorithm is more flexible, and particularly is designed for better performance when the number of data points becomes large:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"NumPy histogram (1000000 points):\\n\",\n \"84.4 ms ± 2.82 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\",\n \"Custom histogram (1000000 points):\\n\",\n \"128 ms ± 2.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = rng.normal(size=1000000)\\n\",\n \"print(f\\\"NumPy histogram ({len(x)} points):\\\")\\n\",\n \"%timeit counts, edges = np.histogram(x, bins)\\n\",\n \"\\n\",\n \"print(f\\\"Custom histogram ({len(x)} points):\\\")\\n\",\n \"%timeit np.add.at(counts, np.searchsorted(bins, x), 1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"What this comparison shows is that algorithmic efficiency is almost never a simple question. An algorithm efficient for large datasets will not always be the best choice for small datasets, and vice versa (see [Big-O Notation](02.08-Sorting.ipynb#Big-O-Notation)).\\n\",\n \"But the advantage of coding this algorithm yourself is that with an understanding of these basic methods, the sky is the limit: you're no longer constrained to built-in routines, but can create your own approaches to exploring the data.\\n\",\n \"Key to efficiently using Python in data-intensive applications is not only knowing about general convenience routines like `np.histogram` and when they're appropriate, but also knowing how to make use of lower-level functionality when you need more pointed behavior.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/02.08-Sorting.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Sorting Arrays\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Up to this point we have been concerned mainly with tools to access and operate on array data with NumPy.\\n\",\n \"This chapter covers algorithms related to sorting values in NumPy arrays.\\n\",\n \"These algorithms are a favorite topic in introductory computer science courses: if you've ever taken one, you probably have had dreams (or, depending on your temperament, nightmares) about *insertion sorts*, *selection sorts*, *merge sorts*, *quick sorts*, *bubble sorts*, and many, many more.\\n\",\n \"All are means of accomplishing a similar task: sorting the values in a list or array.\\n\",\n \"\\n\",\n \"Python has a couple of built-in functions and methods for sorting lists and other iterable objects. The `sorted` function accepts a list and returns a sorted version of it:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[1, 1, 2, 3, 4, 5, 6, 9]\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"L = [3, 1, 4, 1, 5, 9, 2, 6]\\n\",\n \"sorted(L) # returns a sorted copy\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By contrast, the `sort` method of lists will sort the list in-place:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1, 1, 2, 3, 4, 5, 6, 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"L.sort() # acts in-place and returns None\\n\",\n \"print(L)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Python's sorting methods are quite flexible, and can handle any iterable object. For example, here we sort a string:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['h', 'n', 'o', 'p', 't', 'y']\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sorted('python')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These built-in sorting methods are convenient, but as previously discussed, the dynamism of Python values means they are less performant than routines designed specifically for uniform arrays of numbers.\\n\",\n \"This is where NumPy's sorting routines come in.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Fast Sorting in NumPy: np.sort and np.argsort\\n\",\n \"\\n\",\n \"The `np.sort` function is analogous to Python's built-in `sorted` function, and will efficiently return a sorted copy of an array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4, 5])\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"x = np.array([2, 1, 4, 3, 5])\\n\",\n \"np.sort(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Similarly to the `sort` method of Python lists, you can also sort an array in-place using the array `sort` method:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 2 3 4 5]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x.sort()\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A related function is `argsort`, which instead returns the *indices* of the sorted elements:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 0 3 2 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = np.array([2, 1, 4, 3, 5])\\n\",\n \"i = np.argsort(x)\\n\",\n \"print(i)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first element of this result gives the index of the smallest element, the second value gives the index of the second smallest, and so on.\\n\",\n \"These indices can then be used (via fancy indexing) to construct the sorted array if desired:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4, 5])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x[i]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You'll see an application of `argsort` later in this chapter.\\n\",\n \"\\n\",\n \"### Sorting Along Rows or Columns\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A useful feature of NumPy's sorting algorithms is the ability to sort along specific rows or columns of a multidimensional array using the `axis` argument. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0 7 6 4 4 8]\\n\",\n \" [0 6 2 0 5 9]\\n\",\n \" [7 7 7 7 5 1]\\n\",\n \" [8 4 5 3 1 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"rng = np.random.default_rng(seed=42)\\n\",\n \"X = rng.integers(0, 10, (4, 6))\\n\",\n \"print(X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0, 4, 2, 0, 1, 1],\\n\",\n \" [0, 6, 5, 3, 4, 8],\\n\",\n \" [7, 7, 6, 4, 5, 9],\\n\",\n \" [8, 7, 7, 7, 5, 9]])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# sort each column of X\\n\",\n \"np.sort(X, axis=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0, 4, 4, 6, 7, 8],\\n\",\n \" [0, 0, 2, 5, 6, 9],\\n\",\n \" [1, 5, 7, 7, 7, 7],\\n\",\n \" [1, 3, 4, 5, 8, 9]])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# sort each row of X\\n\",\n \"np.sort(X, axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Keep in mind that this treats each row or column as an independent array, and any relationships between the row or column values will be lost!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Partial Sorts: Partitioning\\n\",\n \"\\n\",\n \"Sometimes we're not interested in sorting the entire array, but simply want to find the *k* smallest values in the array. NumPy enables this with the `np.partition` function. `np.partition` takes an array and a number *K*; the result is a new array with the smallest *K* values to the left of the partition and the remaining values to the right:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([2, 1, 3, 4, 6, 5, 7])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.array([7, 2, 3, 1, 6, 5, 4])\\n\",\n \"np.partition(x, 3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that the first three values in the resulting array are the three smallest in the array, and the remaining array positions contain the remaining values.\\n\",\n \"Within the two partitions, the elements have arbitrary order.\\n\",\n \"\\n\",\n \"Similarly to sorting, we can partition along an arbitrary axis of a multidimensional array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0, 4, 4, 7, 6, 8],\\n\",\n \" [0, 0, 2, 6, 5, 9],\\n\",\n \" [1, 5, 7, 7, 7, 7],\\n\",\n \" [1, 3, 4, 5, 8, 9]])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.partition(X, 2, axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result is an array where the first two slots in each row contain the smallest values from that row, with the remaining values filling the remaining slots.\\n\",\n \"\\n\",\n \"Finally, just as there is an `np.argsort` function that computes indices of the sort, there is an `np.argpartition` function that computes indices of the partition.\\n\",\n \"We'll see both of these in action in the following section.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: k-Nearest Neighbors\\n\",\n \"\\n\",\n \"Let's quickly see how we might use the `argsort` function along multiple axes to find the nearest neighbors of each point in a set.\\n\",\n \"We'll start by creating a random set of 10 points on a two-dimensional plane.\\n\",\n \"Using the standard convention, we'll arrange these in a $10\\\\times 2$ array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"X = rng.random((10, 2))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To get an idea of how these points look, let's generate a quick scatter plot (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], s=100);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we'll compute the distance between each pair of points.\\n\",\n \"Recall that the squared distance between two points is the sum of the squared differences in each dimension;\\n\",\n \"using the efficient broadcasting ([Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)) and aggregation ([Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb)) routines provided by NumPy we can compute the matrix of square distances in a single line of code:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"dist_sq = np.sum((X[:, np.newaxis] - X[np.newaxis, :]) ** 2, axis=-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This operation has a lot packed into it, and it might be a bit confusing if you're unfamiliar with NumPy's broadcasting rules. When you come across code like this, it can be useful to break it down into its component steps:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10, 10, 2)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# for each pair of points, compute differences in their coordinates\\n\",\n \"differences = X[:, np.newaxis] - X[np.newaxis, :]\\n\",\n \"differences.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10, 10, 2)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# square the coordinate differences\\n\",\n \"sq_differences = differences ** 2\\n\",\n \"sq_differences.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10, 10)\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# sum the coordinate differences to get the squared distance\\n\",\n \"dist_sq = sq_differences.sum(-1)\\n\",\n \"dist_sq.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As a quick check of our logic, we should see that the diagonal of this matrix (i.e., the set of distances between each point and itself) is all zeros:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dist_sq.diagonal()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With the pairwise square distances converted, we can now use `np.argsort` to sort along each row. The leftmost columns will then give the indices of the nearest neighbors:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0 9 3 5 4 8 1 6 2 7]\\n\",\n \" [1 7 2 6 4 8 3 0 9 5]\\n\",\n \" [2 7 1 6 4 3 8 0 9 5]\\n\",\n \" [3 0 4 5 9 6 1 2 8 7]\\n\",\n \" [4 6 3 1 2 7 0 5 9 8]\\n\",\n \" [5 9 3 0 4 6 8 1 2 7]\\n\",\n \" [6 4 2 1 7 3 0 5 9 8]\\n\",\n \" [7 2 1 6 4 3 8 0 9 5]\\n\",\n \" [8 0 1 9 3 4 7 2 6 5]\\n\",\n \" [9 0 5 3 4 8 6 1 2 7]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"nearest = np.argsort(dist_sq, axis=1)\\n\",\n \"print(nearest)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that the first column gives the numbers 0 through 9 in order: this is due to the fact that each point's closest neighbor is itself, as we would expect.\\n\",\n \"\\n\",\n \"By using a full sort here, we've actually done more work than we need to in this case. If we're simply interested in the nearest $k$ neighbors, all we need to do is partition each row so that the smallest $k + 1$ squared distances come first, with larger distances filling the remaining positions of the array. We can do this with the `np.argpartition` function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"K = 2\\n\",\n \"nearest_partition = np.argpartition(dist_sq, K + 1, axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In order to visualize this network of neighbors, let's quickly plot the points along with lines representing the connections from each point to its two nearest neighbors (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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C0adOGiIgIVCoV2dnZjBkzhiNHjpCTk8OQIUMYMGBAnmNER0djYWGhlzdQGJmZmbJPjf03kanwCpsrIyODK1euEBcXl+vPtWvX0GqfDPFTKpXUqFEDJycnnJyceP3113FycqJWrVov9TP6Kt+rm4+yCT3/kL3xj8nIljA3VtCutjU93MpRzcZY1+7Ro0d89tlnHD58GIAWLVowe/bsArtYSvq/X3H6dya1Ws2HH37IoUOH+PHHH+nUqZPsmdLT0wvXlSMVYOrUqVJ4eLjua09PT93fIyIipODgYCkzM1PKzMyU+vTpI506dSrPMY4fP17QafTq/Pnzsp4/PyJT4f0716NHj6QjR45Iy5cvlz7//HOpa9euUu3atSWFQiEBEiCpVCrJ1dVV6tWrl/TNN99I69atk06dOiVlZGToJZM+HDhwQKpWrZoESMbGxtJ3330ne6ZXYYi5ns2kVqslX19fCZCWLl1qEJkkqfB1s8DuFHd3dyIjI+ncuTMxMTE4OzvrnitXrhxmZmaYmJigUCiwtrbOdZdeEP6LBw8eEBsby549e3jw4IGuGyQxMVHXxsTEBBcXF5o2bUr//v113SCvv/56id9/smXLlty4cYNp06YxYcIEJkyYwIIFC9i4cWOJ6KstCXJychgwYABbt27ll19+YeDAgXJHemkFFnFvb28OHjxIUFAQkiQxdepUli9fjoODA15eXhw6dIiAgACUSiXu7u60bNmyOHILpcidO3d0Q/We/XPr1i1dG3Nzc1xdXWndujWurq66Yl27dm1UqtI9Unbs2LF8/PHH+Pv7s2vXLjw9PWnbti1btmwxmPU7SiJJkhg6dChr165l2rRpfPzxx3JHeiUF/vQrlUomTpyY6zEnJyfd30eMGMGIESOKPplQqkiSxO3bt/OdEHP37l1dOysrK9zc3PDx8dEVahMTE7y8vMr0xghWVlbs3LmTY8eO0atXLyIjI6lcuTJjxoxh0qRJcscrcSRJ4rPPPmPJkiV8/fXXJXokUOm+hBGKnSRJXL9+Pd9inZKSomtXvnx53Nzc8PPzyzXGukaNGnlGOcXGxpbpAv6spk2bcu3aNWbMmMG4ceOYPHkyixYtYt26dQa3pochmzNnDgsXLuSTTz4p8f8JiiIuvBKtVsvVq1dzTYR5+vfU1FRdu8qVK+Pm5kbv3r1zFesqVaqIIan/weeff86wYcMICgoiPDwcLy8vGjduzK5duwxqhT1DNH36dBYuXMigQYP4+eefS/zPoSjiwgtpNBri4+PznRCTkZGha1e1alXc3Nx47733dIXa1dWVypUry5i+dLOysmL79u2cOHGCnj17Eh0djb29PaNGjWLatGni00s+5s6dy9ixY+nSpQsLFy4s8QUcRBEX/ketVvP3339z7ty5XMX64sWLqNVqXTsHBwfc3Nxo06ZNrmJdoUIFGdOXbe7u7ly9epWvvvqKmTNn8sMPP7B06VLWrFmDj4+P3PEMxvLly/n444/x9fVl4sSJsm1sXNREES9jMjMzuXjxYp4r67///huN5skqegqFglq1auHm5kanTp1yzV60traW+R0Iz9O3b1/GjRtHcHAwoaGhdOzYkRYtWhAWFoadnZ3c8WS1YcMGBg0aRIcOHdiwYQPx8fFyRyoyooiXUmlpaVy4cCFPsY6Pj9fNXjQyMsLJyQk3Nzd69uxJuXLl8Pb2xsXFRdYZtsKrs7CwYMuWLZw+fZoePXpw+PBhqlWrxieffMKMGTPKZBfLtm3bCA4OpmXLloSGhmJqaip3pCIlingJ9/DhQ2JjY/OMs7569aqujbGxMc7OzjRq1Ig+ffrorqzr1KmT6wc6NjYWV1dXGd6FUNQaNGhAXFwcCxYsYOTIkcycOZMVK1awcuVKunbtKne8YhMREYG/vz8NGzZk+/btpfLiRBTxEuL+/fv5Dtu7ceOGro2pqSlvvPEGLVq04P3339cVaycnJ4yNjV9wdKG0GjZsGO+99x79+vVj06ZNdOvWjaZNmxIWFka1atXkjqdXBw8exNfXF2dnZ3bv3o2NjY3ckfRCFHEDIkkSd+7cybdYJyUl6dpZWFjg5uZGu3btcg3bq1WrVqm5WSMUHTMzM0JCQjh37hw9evTg2LFj1KxZk+HDhzN79uxS2cUSHR1N586dqVGjBn/88Qe2trZyR9IbUcRlIEkSSUlJ+U6KeXY7LxsbG9zc3OjSpUuuYl2zZs1S+Ysn6FfdunW5dOkSS5YsYcSIEcydO5fVq1ezbNkyevToIXe8InP27Fk6dOiAra0te/bsoUqVKnJH0itRxPVIq9WSmJiY75X1v7fzcnNz45133slVrKtVq1YqxrEKhmXQoEH069ePAQMGsH79enr27Im7uzuhoaE4ODjIHe8/uXz5Mt7e3piZmREREUGNGjXkjqR3oogXgWe383r2BmNsbCxpaWm6ds9u52Vra6vrDvn3dl6CoG8mJiasXbuWCRMm4Ofnx4kTJ6hVqxaDBw9m3rx5JfKTXkJCAl5eXmg0Gvbv359rjafSTBTxl1DY7byqV6+Om5sbgwYNyjUhpmLFiro2YiSIYAhcXFyIjY1l5cqVfPjhhyxcuJC1a9eyePFiAgIC5I5XaLdu3aJ9+/Y8fvyYyMjIMvW7JYp4PrKysrh8+XKeYn3p0qU823m5urrSvn37XMU6v+28BMGQ9e/fnz59+vD++++zatUqAgMDmTJlCr/99luujdEN0d27d/H29ubWrVtERETQsGFDuSMVqzJdxNPT0/OdvRgXF0dOzpNNbRUKhW5CTLdu3XTF2sXFRexILpQqKpWKlStXMn78ePz8/Dh9+jS1atVi4MCBLFy40CDXbX/48CE+Pj7ExcWxc+dOmjdvLnekYmd4/ypAwr00FkfFE3byJmlZGixNVfg1qsYHnrVxrGj50sdLS0vj2LFjeYr1lStXkP63xaiRkRF16tShXr16BAQE6Iq1s7Mz5ubmRf0WBcFgOTk5cebMGdatW8fgwYNZunQpGzZsYOHChfTp00fueDppaWl07tyZM2fOEBYWRps2beSOJAuDK+KRF5Pz7AqemqVh/dFENkffYH6wO21d8l8HIiUlJc/NxfPnz3Pt2jVdm9K8nZcgFKXevXvj7+/PsGHDWLp0KcHBwUybNo2tW7fKftMwMzMTX19f/vrrL0JCQujcubOseeRkUEU84V4aw1efICM7J89zGq2ERpvD8NUnWNvXlce3E164nZeZmRmurq54enpSqVIl3ap7ZWE7L0EoKiqVisWLFzNu3Dh8fX05deoUderUITg4mGXLlsnyu5SdnY2/vz979uzh119/pVevXsWewZAYVDVbHBVPdo5W93VOWgrqO1fJvpf45M/da2TfS8R98kNdm6fbeXXo0CHXGGtHR0fd7EUxEkQQ/htHR0diYmLYtGmT7ubnli1bmDdvHv379y+2HDk5OQQHB7N9+3YWLFhA3759i+3chsqginjYyZu6LhSAGwsHImmerGWtMLXEpJIDFq+/haX9a6wc2fO523kJgqAf77zzDj179uTDDz9k0aJFDBgwgOnTpxMWFoaLi4tez63Vahk0aBAhISH8+OOPDB06VK/nKykMakR/WpYm19c2LQIABcaVHKk2aAH2wTOo2GkE5u7d8fHxoWbNmqKAC0IxUyqVLFiwgISEBBo3bsyFCxdwdXXl3XffzbWBSFGSJIlPPvmEFStW8O233zJq1Ci9nKckMqgibmma+4NBeY8gKr8zHk3KbZLWfEl2yu0n7UwM6gOEIJRJNWrU4Pjx44SFhVGuXDnWrVtH+fLlWbJkSZGeR5Ikxo4dy9y5cxk9ejTjx48v0uOXdAZVxP0aVUOlzH1lbeHUlCpBU9BmpnJ79WhykuPp0ai6TAkFQfg3X19f7t27x8cff0xWVhYffPABzs7OnDt3rkiOP3XqVL7//nuGDh3KDz/8ID59/4tBFfEPPGtjbJQ3kmn1N7Dv8wMKpTE313yJm5QgQzpBEJ5HqVTyyy+/cOPGDZo1a8bly5epX78+/v7+ZGZmvvJxZ82axbhx4+jbty/z5s0TBTwfBlXEHStaMj/YHXNjozxX5OZ2Drz23k84ODowIKgnISEhMqUUBOF57O3tOXLkCOHh4VSoUIFNmzbRokUL5s+f/9LHWrRoEZ999hm9evVi2bJlJXJRruJgcN+Vti527PrUk97NHLAyVaFQgJWpit7NHNgzvicnjhymadOmBAUFMXfuXLnjCoKQj86dO3Pnzh1Gjx5NdnY2H374IU5OTpw+fbpQr1+zZg1Dhw6lU6dOrF27VszteAGD/M44VrRkkl89JvnVy+dZS/744w+CgoL4+OOPuX37NpMmTRIfswTBwCiVSmbMmIGvry9ffvklhw4d4s0338TPz49169ZhZmaW7xIbb2TFEvbjaFq3bs3mzZvFTOoCGGQRL4i5uTmbN29m2LBhTJkyhdu3bxvsAj2CUNZVrFiRgwcPsnv3boKDgwkLC6N8+fIMGjWOPcomuZbYuBN7hHObJ2FWtQ6jf1wq1i0qBIPrTikslUrFokWLGDduHEuXLqVXr16kp6fLHUsQhOfw8fEhKSmJMWPGoNFomDf1Gy7/0p+0m5cByLx2hjuhUzCu5ECld75ldNglEu6lFXBUocQWcXiyTOykSZOYO3cu27Zto0OHDrn2qBQEwbAolUqmTZvGiGV7MatZj5xHydxe+Sk3VowkedN3qMpVoUrgJIzMrMjO0bIk6orckQ1eiS7iT3344Yds2LCBY8eO4enpyfXr1+WOJAjCC/wel0GVd6djFzQFzKzRJF1C0qixC5yEkcWTTVU0WonQkzdkTmr4SkURB/D392fnzp0kJibi4eFBbGys3JEEQXiOp0tsmDu+ieXrzQCwdO+KyrpS7nZqTZ7XCrmVmiIO0K5dO/7880/UajVvv/02hw8fljuSIAj5eLrEhlarJS12P6hMsG03KG87scRGgUpVEQdo1KgRhw4dokKFCnh5eREeHi53JEEQ/uXpEhuPj4VCTjaWbq3zTOZRKRViiY1CKHVFHKB27docPHgQV1dXfH19CQ0NlTuSIAjPeLrExqOjWwAFtm3fz9PG2EjJIM9axR+uhCmwiGu1WsaPH09gYCB9+/YlISH3uiV//vknAQEB+Pv78+233+r2rJRblSpV2LdvH23btuXrr7/m+++/N5hsglDWOVa0ZGCtx2jTH2JWsy5Ks382HVcpFZgbGzE/2P2V9tQtawos4hEREajVajZs2MCoUaOYPn267rnU1FRmzJjBwoUL2bhxI9WrV+fBgwd6DfwyrK2tCQ8Pp3PnzowZM4bPPvsMrVZb8AsFQdC7VbMmAdB35Hd5ltjY9annc/fSFXJTSAVcnk6bNo0GDRrQpUsXADw9PYmKigIgKiqK0NBQjI2NSUxMxN/fnx49euQ5RnR0NBYWFnqIXzjp6enMnj2bVatW0aVLF6ZMmSL7VN7MzEzMzMxkzfBvhpgJDDOXyFR4+eW6ceMG3t7e1KxZk927dxtEJrn9O1N6ejqNGzcu8HUF3vpNTU3FyuqfjzpGRkZoNBpUKhUPHjzgyJEjhIWFYWFhQZ8+fWjYsCG1auXtx5Jzj8vY2FhWrlxJ3bp1GTNmDFlZWWzZsgVra2tZMxnavp+GmAkMM5fIVHj55friiy+AJxeJcmQ2xO/VvzNFR0cX6nUFdqdYWVmRlvbP1FetVqtbo6R8+fLUr1+fypUrY2lpSZMmTQx2fLZCoeDLL79k+fLlREZG0rZtW5KTk+WOJQhlTmZmJjt37qRcuXL06dNH7jglXoFF3N3dnf379wMQExODs7Oz7rm6dety6dIl7t+/j0aj4dSpU7z++uv6S1sEBgwYQFhYGOfPn6dly5bEx8fLHUkQypRvvvmGnJwchgwZIneUUqHA7hRvb28OHjxIUFAQkiQxdepUli9fjoODA15eXowaNYpBg54M0u/YsWOuIm+ounbtyp49e+jSpQseHh7s2rWLhg0byh1LEMqERYsWoVKpmDRpktxRSoUCi7hSqWTixIm5HnNyctL9vUuXLrqbniVJixYtOHDgAD4+PrRq1YqtW7fStm1buWMJQqm2evVqHj16hK+vr+yDC0qLUjnZp7Dc3Nw4fPgwNWvWpGPHjmzcuFHuSIJQqk2YMAGAX375ReYkpUeZLuIANWrUICoqiqZNmxIYGMi8efPkjiQIpVJMTAzx8fG8+eabODg4yB2n1CjzRRzA1taW33//na5du/LRRx/xzTffiNmdglDERowYAcBPP/0kc5LSRRTx/7GwsGDLli28//77TJ48mcGDB6PRiGUwBaEopKSkcODAAezt7fHy8pI7Tqki1nl8hkqlYvHixdjb2zNlyhSSk5NZv3692OdPEP6jzz77DEmS+Pzzz+WOUuqIK/F/USgUTJ48mTlz5ui2fDOk9WAEoaTRarWsX78eMzMzPv30U7njlDqiiD/HRx99xPr16zly5Aienp7cuCG2iRKEV7Fy5UoyMzMJCgrKs2a48N+J7+gLBAQEsHPnTq5du4aHhwcXLlyQO5IglDjLli1DoVDw888/yx2lVBJFvABeXl7s27ePzMxMWrZsyV9//SV3JEEoMfbs2cO9e/d4++23KV++vNxxSiVRxAvB3d1dt+Vbu3bt2LFjh9yRBKFEGDVqFCAm9+iTKOKF5OTkpNvyrXv37qxcuVLuSIJg0K5du8apU6eoUaOGWJtIj0QRfwlPt3xr06YNAwYM4IcffhCTggThOZ5O7vnoo49kTlK6iSL+kp5u+RYYGMiXX37JyJEjxZZvgvAvarWa8PBwbGxs6N69u9xxSjUx2ecVmJqasnbtWqpUqcKsWbNISkpixYoVYlU2Qfifb775Bo1GI9YMLwaiiL8ipVLJrFmzqFq1KmPHjuXu3bts3rxZ1i3fBMFQ/N///R8qlYrJkycTFxcnd5xSTXSn/AcKhYIxY8awbNky9u7dS7t27cSWb0KZt2bNGh4+fEjHjh3Fp9NiIIp4EXjvvfcICwvj3LlztGzZkitXrsgdSRBkM378eADmzp0rc5KyQRTxItK1a1ciIiK4d+8eHh4exMTEyB1JEIrd6dOniY+Pp379+jg6Osodp0wQRbwIeXh4cODAAVQqFa1bt2bfvn1yRxKEYvXxxx8DYs3w4iSKeBFzc3Pj0KFD1KhRAx8fHzZt2iR3JEEoFikpKURFRWFnZ4e3t7fcccoMUcT1oGbNmkRFRdGkSRMCAgKYP3++3JEEQe9Gjx6NJEmMHj1a7ihliijiemJra8sff/xBly5d+PDDDxk/fryY3SmUWlqtljVr1mBqaqpbL0UoHqKI65GFhQWhoaEMHDiQSZMmMWTIELHlm1Aq/fLLL2LNcJmIyT56plKpWLJkCfb29kydOpXk5GTWrVsndyxBKFLff/89CoWCmTNnyh2lzBH/ZRYDhULBlClT+OWXX/jtt9/o0KEDDx8+lDuWIBSJvXv3cvv2bVq2bImtra3cccocUcSL0ccff8y6des4cuQI/fr1E1u+CaXC0z7w2bNny5ykbBJFvJgFBgayY8cObty4IbZ8E0q869evExMTw2uvvYa7u7vcccokUcRl0L59e3799VcyMzN5++23OXLkiNyRBOGVPJ3c891338mcpOwSRVwmTycFlStXjnbt2rFz5065IwnCS1Gr1Wzfvh1ra2v69esnd5wySxRxGTk5OXHo0CFcXFzo1q0bv/76q9yRBKHQJkyYgEaj4YMPPpA7SpkmirjMnt3yrX///syYMUNMChJKhIULF2JkZMSUKVPkjlKmiSJuAGxsbAgPDycgIIAvvviCUaNGiS3fBIO2YcMGUlJS6NixI2ZmZnLHKdPEZB8DYWpqyrp166hSpQo///wzSUlJLF++XCyqLxikcePGATBnzhyZkwiiiBsQpVLJ7NmzqVq1Kl999ZVuyzcrKyu5owmCztmzZ/n777+pX78+tWrVkjtOmSe6UwyMQqFg7NixLF26lIiICNq1a8edO3fkjiUIOk+HFc6YMUPmJAIUoohrtVrGjx9PYGAgffv2JSEhId82gwYNEmuCFKGBAwcSGhrKmTNnxJZvgsF49OgRf/75J3Z2dvj4+MgdR6AQRTwiIgK1Ws2GDRsYNWoU06dPz9Nm1qxZPHr0SC8By7Lu3bsTERHB3bt38fDw4NSpU3JHEsq4UaNGIUmSWG7WgCikAsazTZs2jQYNGtClSxcAPD09iYqK0j2/a9cuYmNjUalUVKpUid69e+c5RnR0NBYWFkUcvfAyMzMN7g76y2S6fPkyQ4YMITU1lblz59KsWTPZMxUnQ8xVFjNptVoaN26MJEmcOHGi0EvOlsXv1av4d6b09HQaN25c4OsKvLGZmpqa68aakZERGo0GlUrFpUuX2L59O7/88gvz5s174XFcXV0LDKMvsbGxsp4/Py+TydXVlUaNGuHj48PgwYNZu3YtvXr1kjVTcTLEXGUx0+zZs8nKyqJv377UrVvXYHK9ipKQKTo6ulCvK/C/UisrK9LS0nRfa7VaVKontT8sLIykpCT69+9PaGgoK1asYP/+/S+bXSiEmjVrcuDAARo3boy/vz8LFiyQO5JQxjxdM3zWrFlyRxGeUeCVuLu7O5GRkXTu3JmYmBicnZ11z33xxRe6v8+ZM4dKlSrRqlUr/SQVsLW1JSIigsDAQIYPH87t27f59ttvUSgUckcTSrl9+/Zx69YtPDw8xJrhBqbAIu7t7c3BgwcJCgpCkiSmTp3K8uXLcXBwwMvLqzgyCs94uuXb4MGDmThxIrdv32bevHm6T0eCoA9izXDDVeBvvlKpZOLEibkec3JyytPu6dhRQf9UKhVLly7F3t6eadOm6bZ8M7QbNULJlXAvjcVR8YSdvMnDO7e5fuIE5SpXo3Itw+pHFsRknxJLoVAwdepUZs+eTVhYGB06dCAlJUXuWEIpEHkxmY6zolh/NJHULA339iwCwKSpPx1nRRF5MVnmhMKzRBEv4UaMGMG6dev466+/aNWqFTdv3pQ7klCCJdxLY/jqE2Rk56DRSmg1ajL+PoLCxByL+t5kZOcwfPUJEu6lFXwwoViIIl4KBAUFsWPHDq5cuYKHhwcXL16UO5JQQi2Oiic7558VNB8eXA/aHKwa/DM7MztHy5IoMYPYUIgiXkq0b9+effv2kZGRQcuWLcWWb8IrCTt5E432n/l/j45uBqB863927tFoJUJPik2+DYUo4qVI48aNOXjwoNjyTXhlaVmavA8qjVCqci+JnKbOp50gC1HES5nXX3+dgwcP4uzsTPfu3cWWb8JLsTTNPWDNpMrroM1Bk557bSRLEzGk1VCIIl4K2dvb8+eff9KqVSv69+/Pjz/+KHckoYTwa1QNlfKfyWNmjg0ASL9wQPeYSqmgR6PqxZ5NyJ8o4qWUjY0NO3bsICAggM8//1xs+SYUygeetTE2+qcsWLq1BiAz/pjuMWMjJYM8xWYQhkIU8VLs6ZZvH330ETNnzqRfv36o1Wq5YwkGzLGiJfOD3TE3NkKlVGBS+TVQGKFOikOlVGBubMT8YHccK1rKHVX4H9GxVcoplUp++eUXqlatytdff83du3fZtGmT2PJNeK62Lnbs+tSTJVFXCD15AyPriuQ8vkNgkxoMbu0kCriBEVfiZYBCoeCrr75i8eLF/PHHH2LLN6FAjhUtmeRXj7Pf+eDn7QmSRPfqGaKAGyBRxMuQQYMG5dry7erVq3JHEkqA7t27A7B+/XqZkwj5EUW8jHm65dudO3fw8PDg9OnTckcSDNw777wDwJ9//ilzEiE/ooiXQS1btuTAgQMolUo8PT3FL6fwQhYWFtjY2HD58mW5owj5EEW8jKpbty6HDh2ievXq+Pj4sGXLFrkjCQbMxcWF1NRUsSG6ARJFvAxzcHAgKiqKRo0a8c4774g+T+G52rZtC0BISIjMSYR/E0W8jKtYsSJ79uyhc+fOTJw4kW+//RZJkgp+oVCm9O7dG4Dt27fLnET4N1HEBd2Wb35+fnz33XcMHTqUnJwcuWMJBqRhw4YYGRkVegd2ofiIyT4CAMbGxkyZMoU33niD6dOnc+fOHdauXSu2fBN0qlWrJjYdMUDiSlzQUSgUTJs2jZ9//pnQ0FB8fHzElm+CTpMmTcjJyeH48eNyRxGeIYq4kMenn37K2rVrOXz4sNjyTdDp1q0bABs2bJA5ifAsUcSFfPXu3Zvw8HCx5Zug4+/vD0BkZKTMSYRniSIuPJe3tzf79u0jPT2dli1bcvToUbkjCTKysrLC2tqaS5cuyR1FeIYo4sILPd3yzcbGhrZt27Jr1y65IwkycnZ25vHjx6SmpsodRfgfUcSFAtWpU4dDhw7h7OxMt27dWLVqldyRBJm0bv1kk4iNGzfKnER4ShRxoVCebvnm6elJv379xJZvZZSY9GN4xDhxodBsbGzYuXMnffv25fPPP+f27dv88MMPJD7IYHFUPGEnb5KWpcHSVIVfo2p84FlbrD9dyjRp0gQjIyMxzNCAiCIuvJSnW77Z2dnx008/cepyAol1+6NBiUb7ZLp+apaG9UcT2Rx9g/nB7rR1sZM5tVCUqlatyo0bN+SOIfyP6E4RXpqRkRFz5sxh9FfjifhtEwnrv0WdmZ6rjUYrkZGdw/DVJ0i4lyZTUkEf3N3dycnJISYmRu4oAqKIC69IoVBg2vQdKncaQebVkySt/5qc9Id52mXnaFkSdUWGhIK+dOnSBYB169bJnEQAUcSF/yDs5E0sGnSgco+vUCdf4frcvjw6E5GrjUYrEXpSfPQuTYKCggAx6cdQiCIuvLK0LA0AFnWaU77t+yBpebBjFo9O7MjdTq2RI56gJzY2NlhaWopZvAZCFHHhlVma/nNf3LpRZ0ABwIM/5nNv97x/2pmI++eljbOzM48ePSI9Pb3gxoJeiSIuvDK/RtVQKZ8UbqVSiZFVBVAoUZhakhqzk1urv0BJDj0aVZc5qVCUEu6loarmCoBj4HjqTdjNuLAz4ga2TAos4lqtlvHjxxMYGEjfvn1JSEjI9fyKFSvw9/fH39+fuXPn6i2oYHg+8KyNsdE/P0LGdk4gabHzn4iqQnXUN86TOH8gPd2sZUwpFKXIi8l0nBXFDVt3ANIu/6UbUtpxVhSRF5NlTlj2FFjEIyIiUKvVbNiwgVGjRjF9+nTdc4mJifz222+sX7+ekJAQDhw4wIULF/QaWDAcjhUtmR/sjrmxESqlAnOnpgBk/P0XNQcvxLJOMzSP7/F2IzcxHK0USLiXxvDVJ8jIzsHI3hkUCtS3LgNiSKmcCizi0dHReHp6Ak+2aDp79qzuOXt7e5YsWYKRkREKhQKNRoOpqan+0goGp62LHbs+9aR3Mwfs3mwDQHbiGfo0f41zh/fy1VdfkZ6eTpMmTcQ61CXc4qh4snO0wP+6zywrkJN6L1cbMaS0+BV4xyk1NRUrKyvd10ZGRmg0GlQqFcbGxtja2iJJEj/88ANubm7UqlUr3+PExsYWXeqXlJmZKev581PaMr3rYsS7LvVoPM8c1aMbvOtiRHryNYKDg7G1teWLL74gKCiIvXv38umnnxZbLn0pi5m2RCfqZuVq1ZkoLSqQk3qfrKQ4TKs4AU+uyDdHX+NdF6Niy/UqSlOmAou4lZUVaWn/fDzSarWoVP+8LCsri6+++gpLS0smTJjw3OO4urq+dLiiEhsbK+v581NaMzk7O3Pq1Clq1aql25/T1dUVLy8v3n77bRYtWsSNGzf47bffUCoLd1+9tH6vipq+M6Wlnyc97jjpFw6QEXcMSZMFKFAoc5eRDI2UK0dZ/F69in9nKuym1AX+Frm7u7N//34AYmJicHZ21j0nSRLDhw/HxcWFiRMnYmRk9LzDCGVEq1atANi8eXOuxxs2bMjVq1epXr064eHhuLq6ijWpS4CMjAy2bNlCUFAQiXP6cHfrdDITz2JZvz1Vek/D4fMwTCo75nqNGFJavAr8bnt7e3Pw4EGCgoKQJImpU6eyfPlyHBwc0Gq1HD16FLVaTVRUFAAjR46kUaNGeg8uGKaAgADmzJnDtm3b6NOnT67nKlWqxNWrV2nbti0HDhygZs2aHD9+HCcnJ5nSCvnJzMxk165dhISE8Ntvv5GWlkblypVp1K47tys2QlW9Lgpl/hdsKqVCDCktZgUWcaVSycSJE3M99uwv3ZkzZ4o+lVBieXh4oFQqn7uVm0qlIioqimHDhrFw4UJcXV3Ztm0bPj4+xZxUeFZmZia7d+/WFe7U1FQqVapEnz59CAgIoHXr1tx4mEXHWVFkZOc89zjGRkoGeeZ/X0zQDzHZRyhSSqWSKlWqkJiY+MJ2CxYsYP78+Wg0Gjp16sTMmTOLKaHwVFZWFtu2baNv377Y2dnh5+fHrl276N27N3/88Qe3bt3i//7v//Dy8kKlUuUZUvoslVKBubER84PdxRryxUwUcaHINWzYEI1GU+Cd9mHDhrF3715MTEwYNWoU/fv3L6aEZZdarSY8PJz+/ftjZ2dH9+7dCQ8PJyAggN27d3P79m0WLVpE+/btcw1geOrZIaVWpioUCrAyVdG7mQO7PvUUa8fLQNyBEIpc586d2blzJ2vXrmXSpEkvbNumTRsuXbqEu7s7v/76K+fOnePQoUOYmJgUU9rST61WExERQUhICGFhYTx8+JDy5cvTq1cvAgIC8PLywtjYuNDHc6xoySS/ekzyq6fH1EJhiStxocg9Xap0z549hWrv4ODA9evXqV+/PtHR0Tg6OnL79m19Riz1srOz2bVrFwMHDqRKlSp06dKFsLAw/Pz8CA8PJykpiWXLltGxY8eXKuCC4RFX4kKRq1SpEhYWFi81ccHMzIzTp0/j7+/Ppk2bqFWrFvv27eOtt97SY9LSJTs7m99//52QkBBCQ0O5f/8+NjY2+Pn5ERAQQPv27cWM6lJIFHFBL5ycnDhz5gyZmZm6ST+FsXHjRiZPnsw333yDh4cHS5YsoXnz5npMWrJpNBoiIyMJCQlh06ZNpKSkYG1tja+vLwEBAXTo0EEU7lJOdKcIevF0vZ2tW7e+9GvHjRtHWFgYSqWSgQMH5lp0TXhSuPfs2cOQIUOoWrUqHTp0YP369bRs2ZKwsDCSk5NZtWoV3bp1EwW8DBBFXNALf39/4NWKOICvry+nT5/G2tqaX3/9lfbt26PVaosyYomSk5NDZGQkw4YNo1q1arRv3541a9bg7e1NaGgoycnJzJgxA19f35f65COUfKI7RdCLVq1aoVAoOHLkyCsfw9XVlWvXrlGvXj327NnD66+/TkxMDDY2NkWY1HDl5OQQFRVFSEgImzdvJjk5GQsLC7p160ZAQACdOnXC3Nxc7piCzEQRF/SisJN+ClK+fHl2797NJ598wp49e6hRowaHDx+mbt26RZTUsOTk5HDw4EFdH3dSUhLm5uZ07dqVgIAAOnfujIWFhdwxBQMiulMEvXnzzTfJzs7+zxvqKpVKIiIi+Oyzz3j8+DENGzZ85W4aQ6TVajlw4AAjRoygZs2atG7dmmXLluHp6UlISAh37twhJCSEd955RxRwIQ9RxAW96dSpEwDr1q0rkuPNnDmTZcuWodVq8fPzY8qUKUVyXDlotVoOHTrEp59+Ss2aNfH09GTx4sW0aNGC9evXk5yczMaNG/H398fSUkxjF55PFHFBb3r37g082eKvqLz33nscOHAAMzMzxo0bp7uBWhJotVoOHz7MyJEjcXR0pGXLlixcuJBmzZqxdu1akpOT2bx5M4GBgbk2YhGEFxF94oLe2NnZYW5uzvnz54v0uC1atODKlSs0bNiQTZs20aBBA44ePWqQozIkSeLo0aOEhISwceNGEhMTMTExoVOnTnz//fd07dq1zNyoFfRDFHFBr5ycnDh79ixqtbpI10Oxt7fn2rVreHh4EB0dTY0aNThx4gQODg5Fdo5XJUkSx48f1xXuhIQEjI2N6dixI1OnTqVbt26UK1dO7phCKSG6UwS9evvttwH47bffivzYJiYmHD9+nH79+nHv3j2cnZ3Zt29fkZ+nMJ4W7i+//JLatWvTrFkzZs+eTb169Vi5ciXJycn89ttvBAcHiwIuFClRxAW9etpnHRoaqrdzrFy5kh9//BG1Wk27du2YP3++3s71LEmSOHHiBGPGjMHJyYmmTZsyc+ZMXF1dWb58OUlJSWzfvp1+/fpRvnz5YskklD2iO0XQqzZt2qBQKJ67009RGTVqFPXq1aNbt258+OGHnD59moULFxb5eSRJ4tSpU8yfP5+9e/cSFxeHSqWiffv2jBs3Dj8/P2xtbYv8vILwPKKIC3qlVCqxs7Pj2rVrej+Xj48PsbGxNGnShP/7v//j7Nmz7Nu3L9/NDV6GJEmcPn2ajRs3EhISwuXLlzEyMsLLy4uxY8fi5+dHxYoVi+hdCMLLEUVc0Ls333yT33//nbi4OL1viuzk5ERiYiLu7u4cPHiQ1157jZiYGNIU5iyOiifs5E3SsjRYmqrwa1SNDzxr57udmCRJnD17lpCQEEJCQrh06RJKpZJ27drx+eefU7duXTw8PPT6XgShMESfuKB3TzdBXrNmTbGcz8rKigsXLtClSxdu3LhBTQcH2oxZwfqjiaRmaZCA1CwN648m0nFWFJEXk3WvPXfuHBMmTMDNzY0GDRowdepUatasycKFC7l9+zZ//PEHH3zwARUqVCiW9yIIBRFFXNC7d999FyjaST8FUSqVbN++neGfjCYzI4OrS0fw8OyfudpotBIZ2Tm8/3Mon335NXXr1qVevXpMnjyZqlWrsmDBAm7dukVERARDhgyhcuXKxZZfEApLdKcIemdvb4+5uTnnzp0r9nNXaNMPuysKkrf9xN1tP6C+c5UKrfuRfS+RtAsHSL8QRfbda8xSKGjdqhXz5s2jZ8+e2NvbF3tWQXgVoogLepdwLw3LSlW5mxiP4+hQrC0tXtgfXZQ2HYxFqzLDsoE3aaf/4NFfITw6Fgo52YAC05p1qdB+CJXrt2LfzHf1mkUQ9EEUcUGvIi8mM3z1CbIqOkNiPOnxx1E4e7D+aCKbo28wP9idti52r3x8tVpNdHQ0R44c4fTp01y+fJnr169z9+5d0tPT899IQqulQvshWDh7oLJ+MqpErXjlCIIgK1HEBb1JuJfG8NUnyMjOwdzZg8cxu0i/eBhLZw80WgmNNofhq0+w61PP516Ra7VaEhISOHbsGCdPnuTChQtcvXqV5ORkHj16hEajyfMaIyMjrK2tcXJy4qbGCkX5apjYvYZpdVdUFR1QKvPeCrI0Eb8KQskkfnIFvVkcFU92zpMrYVPHhoAC9a3ca4tn52j5ZcdJ2lZIITo6mnPnzhEfH8/Nmzd58OABmZmZeY6rUCiwsLCgatWqVK9eHScnJxo0aECTJk1o3rx5rjW3x4WdYf3RRDRa6bk5VUoFPRpVL5L3LAjFTRRxQW/CTt7UFU+lUonC3BrNg1vcXjMGzeM7aDMeIWVnMlOSmPmv15qYmFC+fHnq1KlD5cqVadasGY0bN8bDw4Nq1aoVOsMHnrXZHH0DjTbnuW2MjZQM8qz1Km9REGQnirigN2lZubs6lGaW5GQ8Iuv6WVAoUZiYoSpvj8qmMgM6edCwYUPeeustGjRokKvLIzY2FldX11fK4FjRkvnB7gxffYLsHG2uK3KVUoGxkZL5we56v8EqCPoiirigN5amKlKfKeT2AxeQdS0G8xpuKE3+6fKwMlWx4DsfveVo62LHrk89WRJ1hdCTN0hTa7A0UdGjUXUGedYSBVwo0UQRF/TGr1G1XP3RKpUKVe0mudoUV3+0Y0VLJvnVY5JfPb2fSxCKk5ixKejNB561MTZ68Y+Y6I8WhP9GFHFBb572R5sbG6FS5h6IrVIqMDc2Ev3RgvAfiSIu6NXT/ujezRywMlWhUDzpA+/dzIFdn3r+p4k+giCIPnGhGIj+aEHQnwKvxLVaLePHjycwMJC+ffuSkJCQ6/mQkBB69uxJQEAAkZGRegsqCIIg5FXglXhERARqtZoNGzYQExPD9OnTWbBgAQB37txh1apVbN68maysLN59911atmxZpLuaC4IgCM9X4JV4dHQ0np6eADRs2JCzZ8/qnjt9+jSNGjXCxMQEa2trHBwcuHDhgv7SCoIgCLkUeCWempqKlZWV7msjIyM0Gg0qlYrU1FSsra11z1laWpKamprvcaKjo4sg7quT+/z5EZkKzxBziUyFZ4i5SkumAou4lZUVaWlpuq+1Wq1u49l/P5eWlparqD/VuHHjlw4mCIIgFKzA7hR3d3f2798PQExMDM7OzrrnGjRoQHR0NFlZWTx+/Ji4uLhczwuCIAj6pZAk6flrdPLkyvvbb7/l0qVLSJLE1KlT2b9/Pw4ODnh5eRESEsKGDRuQJIkhQ4boNsUVBEEQ9K/AIi4IgiAYrlIz2efpJ4aLFy9iYmLC5MmTcXR01D2/YsUKwsPDAWjdujUfffSRQeRas2YNW7ZsQaFQMHDgQDp37ix7pqdtBg8ejJeXF71795Y90+TJkzlx4gSWlk+m6M+fPz/f+y/FmenPP/9k3rx5SJJE3bp1mTBhAgqF/vd5e1Gu2NhYpk6dqmsbExPDvHnzaNWqlWyZAJYtW8b27dtRKBQMHToUb29vveYpTKZFixYRHh6OlZUVgwYNom3btnrP9NSpU6f48ccfWbVqVa7H9+7dy7x581CpVPTq1YuAgICCDyaVErt375a+/PJLSZIk6eTJk9LQoUN1z127dk3q0aOHpNFoJK1WKwUGBkqxsbGy57p3757UpUsXSa1WS48fP5ZatWolabVaWTM99dNPP0n+/v7S2rVr9Z6nMJmCgoKke/fuFUuWwmR6/Pix1KVLF12mRYsWFVu+wvz7SZIk7dixQxo5cqTsmR4+fCi1bt1aysrKklJSUqQ2bdrInunChQtSt27dpMzMTCkzM1Py8/OT0tPTiyXXokWLpK5du0r+/v65Hler1VL79u2llJQUKSsrS+rZs6d0586dAo9XatZOedF4dnt7e5YsWYKRkREKhQKNRoOpqansuWxtbQkLC8PY2Ji7d+9iampaLFdyL8oEsGvXLhQKha5NcXhRpqf7bI4fP56goCA2bdoke6aTJ0/i7OzM999/z7vvvkulSpWwtbWVPddT6enpzJkzh6+//lr2TObm5lSrVo2MjAwyMjKK5We8oExxcXE0a9YMU1NTTE1NcXR05OLFi887VJFycHBgzpw5eR6Pi4vDwcGBcuXKYWJiQuPGjTl27FiBxys1Rfx549kBjI2NsbW1RZIkvv/+e9zc3KhVq3iWP31RLniyxvbq1asJDAyke/fusme6dOkS27dv55NPPimWLIXJlJ6eTnBwMDNmzGDJkiWsXbu2WCaVvSjTgwcPOHLkCKNHj2bx4sWsXLmSK1eu6D1TQbme2rRpEx07diy2/1gKylS1alW6dOlCjx496Nevn+yZXFxcOH78OKmpqTx48ICTJ0+SkZFRLLl8fHx0w7T/nbew826eVWqK+IvGswNkZWUxevRo0tLSmDBhgsHkAggODiYqKopjx47x119/yZopLCyMpKQk+vfvT2hoKCtWrNANMZUrk7m5Of369cPc3BwrKyuaN29eLEX8RZnKly9P/fr1qVy5MpaWljRp0oTY2Fi9Zyoo11Pbtm3D39+/WPIUlGn//v0kJyezZ88e9u3bR0REBKdPn5Y1k5OTE3369GHQoEFMmjSJN998kwoVKug904sUdt7Nv5WaIv6i8eySJDF8+HBcXFyYOHEiRkZGBpErPj6ejz76CEmSMDY2xsTEJNfeknJk+uKLL9i4cSOrVq2iR48eDBgwQO83xQrKdPXqVXr37k1OTg7Z2dmcOHGCunXrypqpbt26XLp0ifv376PRaDh16hSvv/663jMVlAvg8ePHqNVqqlatWix5CspUrlw5zMzMMDExwdTUFGtrax49eiRrpvv375OWlsb69ev57rvvuHXrFnXq1NF7phdxcnIiISGBlJQU1Go1x48fp1GjRgW+rtSMTvH29ubgwYMEBQXpxrMvX74cBwcHtFotR48eRa1WExUVBcDIkSML9Q3SZy4vLy/eeOMNAgMDdX3QzZo1kz2THArK5OvrS0BAAMbGxvj6+hbLL1xBmUaNGsWgQYMA6NixY7FNdCso15UrV6heXf9b3r1MpkOHDhEQEIBSqcTd3Z2WLVvKmqldu3bEx8fTq1cvjI2N+eKLL4r14u5Z27ZtIz09ncDAQMaMGcP777+PJEn06tWLKlWqFPh6MU5cEAShBCs13SmCIAhlkSjigiAIJZgo4oIgCCWYKOKCIAglmCjigiAIJZgo4oIgCCWYKOKCIAgl2P8DrlVSTvfYSgUAAAAASUVORK5CYII=\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(X[:, 0], X[:, 1], s=100)\\n\",\n \"\\n\",\n \"# draw lines from each point to its two nearest neighbors\\n\",\n \"K = 2\\n\",\n \"\\n\",\n \"for i in range(X.shape[0]):\\n\",\n \" for j in nearest_partition[i, :K+1]:\\n\",\n \" # plot a line from X[i] to X[j]\\n\",\n \" # use some zip magic to make it happen:\\n\",\n \" plt.plot(*zip(X[j], X[i]), color='black')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each point in the plot has lines drawn to its two nearest neighbors.\\n\",\n \"At first glance, it might seem strange that some of the points have more than two lines coming out of them: this is due to the fact that if point A is one of the two nearest neighbors of point B, this does not necessarily imply that point B is one of the two nearest neighbors of point A.\\n\",\n \"\\n\",\n \"Although the broadcasting and row-wise sorting of this approach might seem less straightforward than writing a loop, it turns out to be a very efficient way of operating on this data in Python.\\n\",\n \"You might be tempted to do the same type of operation by manually looping through the data and sorting each set of neighbors individually, but this would almost certainly lead to a slower algorithm than the vectorized version we used. The beauty of this approach is that it's written in a way that's agnostic to the size of the input data: we could just as easily compute the neighbors among 100 or 1,000,000 points in any number of dimensions, and the code would look the same.\\n\",\n \"\\n\",\n \"Finally, I'll note that when doing very large nearest neighbor searches, there are tree-based and/or approximate algorithms that can scale as $\\\\mathcal{O}[N\\\\log N]$ or better rather than the $\\\\mathcal{O}[N^2]$ of the brute-force algorithm. One example of this is the KD-Tree, [implemented in Scikit-Learn](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/02.09-Structured-Data-NumPy.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Structured Data: NumPy's Structured Arrays\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This chapter demonstrates the use of NumPy's *structured arrays* and *record arrays*, which provide efficient storage for compound, heterogeneous data. While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas ``DataFrame``s, which we'll explore in [Part 3](03.00-Introduction-to-Pandas.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Imagine that we have several categories of data on a number of people (say, name, age, and weight), and we'd like to store these values for use in a Python program.\\n\",\n \"It would be possible to store these in three separate arrays:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"name = ['Alice', 'Bob', 'Cathy', 'Doug']\\n\",\n \"age = [25, 45, 37, 19]\\n\",\n \"weight = [55.0, 85.5, 68.0, 61.5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But this is a bit clumsy. There's nothing here that tells us that the three arrays are related; NumPy's structured arrays allow us to do this more naturally by using a single structure to store all of this data.\\n\",\n \"\\n\",\n \"Recall that previously we created a simple array using an expression like this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"x = np.zeros(4, dtype=int)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can similarly create a structured array using a compound data type specification:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[('name', '`, means \\\"little endian\\\" or \\\"big endian,\\\" respectively, and specifies the ordering convention for significant bits.\\n\",\n \"The next character specifies the type of data: characters, bytes, ints, floating points, and so on (see the table below).\\n\",\n \"The last character or characters represent the size of the object in bytes.\\n\",\n \"\\n\",\n \"| Character | Description | Example |\\n\",\n \"| --------- | ----------- | ------- | \\n\",\n \"| `'b'` | Byte | `np.dtype('b')` |\\n\",\n \"| `'i'` | Signed integer | `np.dtype('i4') == np.int32` |\\n\",\n \"| `'u'` | Unsigned integer | `np.dtype('u1') == np.uint8` |\\n\",\n \"| `'f'` | Floating point | `np.dtype('f8') == np.int64` |\\n\",\n \"| `'c'` | Complex floating point| `np.dtype('c16') == np.complex128`|\\n\",\n \"| `'S'`, `'a'` | String | `np.dtype('S5')` |\\n\",\n \"| `'U'` | Unicode string | `np.dtype('U') == np.str_` |\\n\",\n \"| `'V'` | Raw data (void) | `np.dtype('V') == np.void` |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## More Advanced Compound Types\\n\",\n \"\\n\",\n \"It is possible to define even more advanced compound types.\\n\",\n \"For example, you can create a type where each element contains an array or matrix of values.\\n\",\n \"Here, we'll create a data type with a `mat` component consisting of a $3\\\\times 3$ floating-point matrix:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(0, [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]])\\n\",\n \"[[0. 0. 0.]\\n\",\n \" [0. 0. 0.]\\n\",\n \" [0. 0. 0.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tp = np.dtype([('id', 'i8'), ('mat', 'f8', (3, 3))])\\n\",\n \"X = np.zeros(1, dtype=tp)\\n\",\n \"print(X[0])\\n\",\n \"print(X['mat'][0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now each element in the `X` array consists of an `id` and a $3\\\\times 3$ matrix.\\n\",\n \"Why would you use this rather than a simple multidimensional array, or perhaps a Python dictionary?\\n\",\n \"One reason is that this NumPy `dtype` directly maps onto a C structure definition, so the buffer containing the array content can be accessed directly within an appropriately written C program.\\n\",\n \"If you find yourself writing a Python interface to a legacy C or Fortran library that manipulates structured data, structured arrays can provide a powerful interface.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Record Arrays: Structured Arrays with a Twist\\n\",\n \"\\n\",\n \"NumPy also provides record arrays (instances of the `np.recarray` class), which are almost identical to the structured arrays just described, but with one additional feature: fields can be accessed as attributes rather than as dictionary keys.\\n\",\n \"Recall that we previously accessed the ages in our sample dataset by writing:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([25, 45, 37, 19], dtype=int32)\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['age']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we view our data as a record array instead, we can access this with slightly fewer keystrokes:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([25, 45, 37, 19], dtype=int32)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_rec = data.view(np.recarray)\\n\",\n \"data_rec.age\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The downside is that for record arrays, there is some extra overhead involved in accessing the fields, even when using the same syntax:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"121 ns ± 1.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\\n\",\n \"2.41 µs ± 15.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\\n\",\n \"3.98 µs ± 20.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit data['age']\\n\",\n \"%timeit data_rec['age']\\n\",\n \"%timeit data_rec.age\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Whether the more convenient notation is worth the (slight) overhead will depend on your own application.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## On to Pandas\\n\",\n \"\\n\",\n \"This chapter on structured and record arrays is purposely located at the end of this part of the book, because it leads so well into the next package we will cover: Pandas.\\n\",\n \"Structured arrays can come in handy in certain situations, like when you're using NumPy arrays to map onto binary data formats in C, Fortran, or another language.\\n\",\n \"But for day-to-day use of structured data, the Pandas package is a much better choice; we'll explore it in depth in the chapters that follow.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/03.00-Introduction-to-Pandas.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Data Manipulation with Pandas\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In [Part 2](02.00-Introduction-to-NumPy.ipynb), we dove into detail on NumPy and its `ndarray` object, which enables efficient storage and manipulation of dense typed arrays in Python.\\n\",\n \"Here we'll build on this knowledge by looking in depth at the data structures provided by the Pandas library.\\n\",\n \"Pandas is a newer package built on top of NumPy that provides an efficient implementation of a `DataFrame`.\\n\",\n \"``DataFrame``s are essentially multidimensional arrays with attached row and column labels, often with heterogeneous types and/or missing data.\\n\",\n \"As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs.\\n\",\n \"\\n\",\n \"As we've seen, NumPy's `ndarray` data structure provides essential features for the type of clean, well-organized data typically seen in numerical computing tasks.\\n\",\n \"While it serves this purpose very well, its limitations become clear when we need more flexibility (e.g., attaching labels to data, working with missing data, etc.) and when attempting operations that do not map well to element-wise broadcasting (e.g., groupings, pivots, etc.), each of which is an important piece of analyzing the less structured data available in many forms in the world around us.\\n\",\n \"Pandas, and in particular its `Series` and `DataFrame` objects, builds on the NumPy array structure and provides efficient access to these sorts of \\\"data munging\\\" tasks that occupy much of a data scientist's time.\\n\",\n \"\\n\",\n \"In this part of the book, we will focus on the mechanics of using `Series`, `DataFrame`, and related structures effectively.\\n\",\n \"We will use examples drawn from real datasets where appropriate, but these examples are not necessarily the focus.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Installing and Using Pandas\\n\",\n \"\\n\",\n \"Installation of Pandas on your system requires NumPy to be installed, and if you're building the library from source, you will need the appropriate tools to compile the C and Cython sources on which Pandas is built.\\n\",\n \"Details on the installation process can be found in the [Pandas documentation](http://pandas.pydata.org/).\\n\",\n \"If you followed the advice outlined in the [Preface](00.00-Preface.ipynb) and used the Anaconda stack, you already have Pandas installed.\\n\",\n \"\\n\",\n \"Once Pandas is installed, you can import it and check the version; here is the version used by this book:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'1.3.5'\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas\\n\",\n \"pandas.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just as we generally import NumPy under the alias `np`, we will import Pandas under the alias `pd`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This import convention will be used throughout the remainder of this book.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reminder About Built-in Documentation\\n\",\n \"\\n\",\n \"As you read through this part of the book, don't forget that IPython gives you the ability to quickly explore the contents of a package (by using the tab completion feature) as well as the documentation of various functions (using the `?` character). Refer back to [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb) if you need a refresher on this.\\n\",\n \"\\n\",\n \"For example, to display all the contents of the Pandas namespace, you can type:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [3]: pd.\\n\",\n \"```\\n\",\n \"\\n\",\n \"And to display the built-in Pandas documentation, you can use this:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [4]: pd?\\n\",\n \"```\\n\",\n \"\\n\",\n \"More detailed documentation, along with tutorials and other resources, can be found at http://pandas.pydata.org/.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/03.01-Introducing-Pandas-Objects.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Introducing Pandas Objects\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"At a very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.\\n\",\n \"As we will see during the course of this chapter, Pandas provides a host of useful tools, methods, and functionality on top of the basic data structures, but nearly everything that follows will require an understanding of what these structures are.\\n\",\n \"Thus, before we go any further, let's take a look at these three fundamental Pandas data structures: the `Series`, `DataFrame`, and `Index`.\\n\",\n \"\\n\",\n \"We will start our code sessions with the standard NumPy and Pandas imports:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The Pandas Series Object\\n\",\n \"\\n\",\n \"A Pandas `Series` is a one-dimensional array of indexed data.\\n\",\n \"It can be created from a list or array as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 0.25\\n\",\n \"1 0.50\\n\",\n \"2 0.75\\n\",\n \"3 1.00\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.Series([0.25, 0.5, 0.75, 1.0])\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `Series` combines a sequence of values with an explicit sequence of indices, which we can access with the `values` and `index` attributes.\\n\",\n \"The `values` are simply a familiar NumPy array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.25, 0.5 , 0.75, 1. ])\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `index` is an array-like object of type `pd.Index`, which we'll discuss in more detail momentarily:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RangeIndex(start=0, stop=4, step=1)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.5\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1 0.50\\n\",\n \"2 0.75\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data[1:3]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we will see, though, the Pandas `Series` is much more general and flexible than the one-dimensional NumPy array that it emulates.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Series as Generalized NumPy Array\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From what we've seen so far, the `Series` object may appear to be basically interchangeable with a one-dimensional NumPy array.\\n\",\n \"The essential difference is that while the NumPy array has an *implicitly defined* integer index used to access the values, the Pandas `Series` has an *explicitly defined* index associated with the values.\\n\",\n \"\\n\",\n \"This explicit index definition gives the `Series` object additional capabilities. For example, the index need not be an integer, but can consist of values of any desired type.\\n\",\n \"So, if we wish, we can use strings as an index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 0.25\\n\",\n \"b 0.50\\n\",\n \"c 0.75\\n\",\n \"d 1.00\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.Series([0.25, 0.5, 0.75, 1.0],\\n\",\n \" index=['a', 'b', 'c', 'd'])\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And the item access works as expected:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.5\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['b']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can even use noncontiguous or nonsequential indices:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2 0.25\\n\",\n \"5 0.50\\n\",\n \"3 0.75\\n\",\n \"7 1.00\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.Series([0.25, 0.5, 0.75, 1.0],\\n\",\n \" index=[2, 5, 3, 7])\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.5\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data[5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Series as Specialized Dictionary\\n\",\n \"\\n\",\n \"In this way, you can think of a Pandas `Series` a bit like a specialization of a Python dictionary.\\n\",\n \"A dictionary is a structure that maps arbitrary keys to a set of arbitrary values, and a `Series` is a structure that maps typed keys to a set of typed values.\\n\",\n \"This typing is important: just as the type-specific compiled code behind a NumPy array makes it more efficient than a Python list for certain operations, the type information of a Pandas `Series` makes it more efficient than Python dictionaries for certain operations.\\n\",\n \"\\n\",\n \"The `Series`-as-dictionary analogy can be made even more clear by constructing a `Series` object directly from a Python dictionary, here the five most populous US states according to the 2020 census:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 39538223\\n\",\n \"Texas 29145505\\n\",\n \"Florida 21538187\\n\",\n \"New York 20201249\\n\",\n \"Pennsylvania 13002700\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"population_dict = {'California': 39538223, 'Texas': 29145505,\\n\",\n \" 'Florida': 21538187, 'New York': 20201249,\\n\",\n \" 'Pennsylvania': 13002700}\\n\",\n \"population = pd.Series(population_dict)\\n\",\n \"population\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From here, typical dictionary-style item access can be performed:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"39538223\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"population['California']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Unlike a dictionary, though, the `Series` also supports array-style operations such as slicing:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 39538223\\n\",\n \"Texas 29145505\\n\",\n \"Florida 21538187\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"population['California':'Florida']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll discuss some of the quirks of Pandas indexing and slicing in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Constructing Series Objects\\n\",\n \"\\n\",\n \"We've already seen a few ways of constructing a Pandas `Series` from scratch. All of them are some version of the following:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"pd.Series(data, index=index)\\n\",\n \"```\\n\",\n \"\\n\",\n \"where `index` is an optional argument, and `data` can be one of many entities.\\n\",\n \"\\n\",\n \"For example, `data` can be a list or NumPy array, in which case `index` defaults to an integer sequence:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 2\\n\",\n \"1 4\\n\",\n \"2 6\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.Series([2, 4, 6])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or `data` can be a scalar, which is repeated to fill the specified index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"100 5\\n\",\n \"200 5\\n\",\n \"300 5\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.Series(5, index=[100, 200, 300])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or it can be a dictionary, in which case `index` defaults to the dictionary keys:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2 a\\n\",\n \"1 b\\n\",\n \"3 c\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.Series({2:'a', 1:'b', 3:'c'})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In each case, the index can be explicitly set to control the order or the subset of keys used:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1 b\\n\",\n \"2 a\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.Series({2:'a', 1:'b', 3:'c'}, index=[1, 2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The Pandas DataFrame Object\\n\",\n \"\\n\",\n \"The next fundamental structure in Pandas is the `DataFrame`.\\n\",\n \"Like the `Series` object discussed in the previous section, the `DataFrame` can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary.\\n\",\n \"We'll now take a look at each of these perspectives.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### DataFrame as Generalized NumPy Array\\n\",\n \"If a `Series` is an analog of a one-dimensional array with explicit indices, a `DataFrame` is an analog of a two-dimensional array with explicit row and column indices.\\n\",\n \"Just as you might think of a two-dimensional array as an ordered sequence of aligned one-dimensional columns, you can think of a `DataFrame` as a sequence of aligned `Series` objects.\\n\",\n \"Here, by \\\"aligned\\\" we mean that they share the same index.\\n\",\n \"\\n\",\n \"To demonstrate this, let's first construct a new `Series` listing the area of each of the five states discussed in the previous section (in square kilometers):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 423967\\n\",\n \"Texas 695662\\n\",\n \"Florida 170312\\n\",\n \"New York 141297\\n\",\n \"Pennsylvania 119280\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"area_dict = {'California': 423967, 'Texas': 695662, 'Florida': 170312, \\n\",\n \" 'New York': 141297, 'Pennsylvania': 119280}\\n\",\n \"area = pd.Series(area_dict)\\n\",\n \"area\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have this along with the `population` Series from before, we can use a dictionary to construct a single two-dimensional object containing this information:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" population area\\n\",\n \"California 39538223 423967\\n\",\n \"Texas 29145505 695662\\n\",\n \"Florida 21538187 170312\\n\",\n \"New York 20201249 141297\\n\",\n \"Pennsylvania 13002700 119280\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"states = pd.DataFrame({'population': population,\\n\",\n \" 'area': area})\\n\",\n \"states\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Like the `Series` object, the `DataFrame` has an `index` attribute that gives access to the index labels:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['California', 'Texas', 'Florida', 'New York', 'Pennsylvania'], dtype='object')\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"states.index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Additionally, the `DataFrame` has a `columns` attribute, which is an `Index` object holding the column labels:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['population', 'area'], dtype='object')\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"states.columns\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Thus the `DataFrame` can be thought of as a generalization of a two-dimensional NumPy array, where both the rows and columns have a generalized index for accessing the data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### DataFrame as Specialized Dictionary\\n\",\n \"\\n\",\n \"Similarly, we can also think of a `DataFrame` as a specialization of a dictionary.\\n\",\n \"Where a dictionary maps a key to a value, a `DataFrame` maps a column name to a `Series` of column data.\\n\",\n \"For example, asking for the `'area'` attribute returns the `Series` object containing the areas we saw earlier:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 423967\\n\",\n \"Texas 695662\\n\",\n \"Florida 170312\\n\",\n \"New York 141297\\n\",\n \"Pennsylvania 119280\\n\",\n \"Name: area, dtype: int64\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"states['area']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice the potential point of confusion here: in a two-dimensional NumPy array, `data[0]` will return the first *row*. For a `DataFrame`, `data['col0']` will return the first *column*.\\n\",\n \"Because of this, it is probably better to think about ``DataFrame``s as generalized dictionaries rather than generalized arrays, though both ways of looking at the situation can be useful.\\n\",\n \"We'll explore more flexible means of indexing ``DataFrame``s in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Constructing DataFrame Objects\\n\",\n \"\\n\",\n \"A Pandas `DataFrame` can be constructed in a variety of ways.\\n\",\n \"Here we'll explore several examples.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### From a single Series object\\n\",\n \"\\n\",\n \"A `DataFrame` is a collection of `Series` objects, and a single-column `DataFrame` can be constructed from a single `Series`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" population\\n\",\n \"California 39538223\\n\",\n \"Texas 29145505\\n\",\n \"Florida 21538187\\n\",\n \"New York 20201249\\n\",\n \"Pennsylvania 13002700\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.DataFrame(population, columns=['population'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### From a list of dicts\\n\",\n \"\\n\",\n \"Any list of dictionaries can be made into a `DataFrame`.\\n\",\n \"We'll use a simple list comprehension to create some data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" a b\\n\",\n \"0 0 0\\n\",\n \"1 1 2\\n\",\n \"2 2 4\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = [{'a': i, 'b': 2 * i}\\n\",\n \" for i in range(3)]\\n\",\n \"pd.DataFrame(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Even if some keys in the dictionary are missing, Pandas will fill them in with `NaN` values (i.e., \\\"Not a Number\\\"; see [Handling Missing Data](03.04-Missing-Values.ipynb)):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" a b c\\n\",\n \"0 1.0 2 NaN\\n\",\n \"1 NaN 3 4.0\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.DataFrame([{'a': 1, 'b': 2}, {'b': 3, 'c': 4}])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### From a dictionary of Series objects\\n\",\n \"\\n\",\n \"As we saw before, a `DataFrame` can be constructed from a dictionary of `Series` objects as well:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" population area\\n\",\n \"California 39538223 423967\\n\",\n \"Texas 29145505 695662\\n\",\n \"Florida 21538187 170312\\n\",\n \"New York 20201249 141297\\n\",\n \"Pennsylvania 13002700 119280\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.DataFrame({'population': population,\\n\",\n \" 'area': area})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### From a two-dimensional NumPy array\\n\",\n \"\\n\",\n \"Given a two-dimensional array of data, we can create a `DataFrame` with any specified column and index names.\\n\",\n \"If omitted, an integer index will be used for each:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\"\n ],\n \"text/plain\": [\n \" A B\\n\",\n \"0 0 0.0\\n\",\n \"1 0 0.0\\n\",\n \"2 0 0.0\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.DataFrame(A)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The Pandas Index Object\\n\",\n \"\\n\",\n \"As you've seen, the `Series` and `DataFrame` objects both contain an explicit *index* that lets you reference and modify data.\\n\",\n \"This `Index` object is an interesting structure in itself, and it can be thought of either as an *immutable array* or as an *ordered set* (technically a multiset, as `Index` objects may contain repeated values).\\n\",\n \"Those views have some interesting consequences in terms of the operations available on `Index` objects.\\n\",\n \"As a simple example, let's construct an `Index` from a list of integers:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Int64Index([2, 3, 5, 7, 11], dtype='int64')\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ind = pd.Index([2, 3, 5, 7, 11])\\n\",\n \"ind\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Index as Immutable Array\\n\",\n \"\\n\",\n \"The `Index` in many ways operates like an array.\\n\",\n \"For example, we can use standard Python indexing notation to retrieve values or slices:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ind[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Int64Index([2, 5, 11], dtype='int64')\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ind[::2]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`Index` objects also have many of the attributes familiar from NumPy arrays:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5 (5,) 1 int64\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(ind.size, ind.shape, ind.ndim, ind.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"One difference between `Index` objects and NumPy arrays is that the indices are immutable—that is, they cannot be modified via the normal means:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"Index does not support mutable operations\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_83282/393126374.py\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mind\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m~/.local/share/virtualenvs/python-data-science-handbook-2e-u_kwqDTB/lib/python3.9/site-packages/pandas/core/indexes/base.py\\u001b[0m in \\u001b[0;36m__setitem__\\u001b[0;34m(self, key, value)\\u001b[0m\\n\\u001b[1;32m 4583\\u001b[0m \\u001b[0;34m@\\u001b[0m\\u001b[0mfinal\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4584\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m__setitem__\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkey\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mvalue\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 4585\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0mTypeError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Index does not support mutable operations\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 4586\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4587\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m__getitem__\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkey\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mTypeError\\u001b[0m: Index does not support mutable operations\"\n ]\n }\n ],\n \"source\": [\n \"ind[1] = 0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This immutability makes it safer to share indices between multiple ``DataFrame``s and arrays, without the potential for side effects from inadvertent index modification.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Index as Ordered Set\\n\",\n \"\\n\",\n \"Pandas objects are designed to facilitate operations such as joins across datasets, which depend on many aspects of set arithmetic.\\n\",\n \"The `Index` object follows many of the conventions used by Python's built-in `set` data structure, so that unions, intersections, differences, and other combinations can be computed in a familiar way:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"indA = pd.Index([1, 3, 5, 7, 9])\\n\",\n \"indB = pd.Index([2, 3, 5, 7, 11])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Int64Index([3, 5, 7], dtype='int64')\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"indA.intersection(indB)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Int64Index([1, 2, 3, 5, 7, 9, 11], dtype='int64')\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"indA.union(indB)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Int64Index([1, 2, 9, 11], dtype='int64')\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"indA.symmetric_difference(indB)\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n 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"path": "notebooks/03.02-Data-Indexing-and-Selection.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Data Indexing and Selection\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In [Part 2](02.00-Introduction-to-NumPy.ipynb), we looked in detail at methods and tools to access, set, and modify values in NumPy arrays.\\n\",\n \"These included indexing (e.g., `arr[2, 1]`), slicing (e.g., `arr[:, 1:5]`), masking (e.g., `arr[arr > 0]`), fancy indexing (e.g., `arr[0, [1, 5]]`), and combinations thereof (e.g., `arr[:, [1, 5]]`).\\n\",\n \"Here we'll look at similar means of accessing and modifying values in Pandas `Series` and `DataFrame` objects.\\n\",\n \"If you have used the NumPy patterns, the corresponding patterns in Pandas will feel very familiar, though there are a few quirks to be aware of.\\n\",\n \"\\n\",\n \"We'll start with the simple case of the one-dimensional `Series` object, and then move on to the more complicated two-dimensional `DataFrame` object.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Data Selection in Series\\n\",\n \"\\n\",\n \"As you saw in the previous chapter, a `Series` object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary.\\n\",\n \"If you keep these two overlapping analogies in mind, it will help you understand the patterns of data indexing and selection in these arrays.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Series as Dictionary\\n\",\n \"\\n\",\n \"Like a dictionary, the `Series` object provides a mapping from a collection of keys to a collection of values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 0.25\\n\",\n \"b 0.50\\n\",\n \"c 0.75\\n\",\n \"d 1.00\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"data = pd.Series([0.25, 0.5, 0.75, 1.0],\\n\",\n \" index=['a', 'b', 'c', 'd'])\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.5\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['b']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also use dictionary-like Python expressions and methods to examine the keys/indices and values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'a' in data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['a', 'b', 'c', 'd'], dtype='object')\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.keys()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[('a', 0.25), ('b', 0.5), ('c', 0.75), ('d', 1.0)]\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(data.items())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`Series` objects can also be modified with a dictionary-like syntax.\\n\",\n \"Just as you can extend a dictionary by assigning to a new key, you can extend a `Series` by assigning to a new index value:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 0.25\\n\",\n \"b 0.50\\n\",\n \"c 0.75\\n\",\n \"d 1.00\\n\",\n \"e 1.25\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['e'] = 1.25\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This easy mutability of the objects is a convenient feature: under the hood, Pandas is making decisions about memory layout and data copying that might need to take place, and the user generally does not need to worry about these issues.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Series as One-Dimensional Array\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A `Series` builds on this dictionary-like interface and provides array-style item selection via the same basic mechanisms as NumPy arrays—that is, slices, masking, and fancy indexing.\\n\",\n \"Examples of these are as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 0.25\\n\",\n \"b 0.50\\n\",\n \"c 0.75\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# slicing by explicit index\\n\",\n \"data['a':'c']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 0.25\\n\",\n \"b 0.50\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# slicing by implicit integer index\\n\",\n \"data[0:2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"b 0.50\\n\",\n \"c 0.75\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# masking\\n\",\n \"data[(data > 0.3) & (data < 0.8)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 0.25\\n\",\n \"e 1.25\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# fancy indexing\\n\",\n \"data[['a', 'e']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Of these, slicing may be the source of the most confusion.\\n\",\n \"Notice that when slicing with an explicit index (e.g., `data['a':'c']`), the final index is *included* in the slice, while when slicing with an implicit index (e.g., `data[0:2]`), the final index is *excluded* from the slice.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Indexers: loc and iloc\\n\",\n \"\\n\",\n \"If your `Series` has an explicit integer index, an indexing operation such as `data[1]` will use the explicit indices, while a slicing operation like `data[1:3]` will use the implicit Python-style indices:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1 a\\n\",\n \"3 b\\n\",\n \"5 c\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5])\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'a'\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# explicit index when indexing\\n\",\n \"data[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3 b\\n\",\n \"5 c\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# implicit index when slicing\\n\",\n \"data[1:3]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because of this potential confusion in the case of integer indexes, Pandas provides some special *indexer* attributes that explicitly expose certain indexing schemes.\\n\",\n \"These are not functional methods, but attributes that expose a particular slicing interface to the data in the `Series`.\\n\",\n \"\\n\",\n \"First, the `loc` attribute allows indexing and slicing that always references the explicit index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'a'\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.loc[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1 a\\n\",\n \"3 b\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.loc[1:3]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `iloc` attribute allows indexing and slicing that always references the implicit Python-style index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'b'\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.iloc[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3 b\\n\",\n \"5 c\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.iloc[1:3]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"One guiding principle of Python code is that \\\"explicit is better than implicit.\\\"\\n\",\n \"The explicit nature of `loc` and `iloc` makes them helpful in maintaining clean and readable code; especially in the case of integer indexes, using them consistently can prevent subtle bugs due to the mixed indexing/slicing convention.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Data Selection in DataFrames\\n\",\n \"\\n\",\n \"Recall that a `DataFrame` acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of `Series` structures sharing the same index.\\n\",\n \"These analogies can be helpful to keep in mind as we explore data selection within this structure.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### DataFrame as Dictionary\\n\",\n \"\\n\",\n \"The first analogy we will consider is the `DataFrame` as a dictionary of related `Series` objects.\\n\",\n \"Let's return to our example of areas and populations of states:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n 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\"\n ],\n \"text/plain\": [\n \" area pop\\n\",\n \"California 423967 39538223\\n\",\n \"Texas 695662 29145505\\n\",\n \"Florida 170312 21538187\\n\",\n \"New York 141297 20201249\\n\",\n \"Pennsylvania 119280 13002700\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"area = pd.Series({'California': 423967, 'Texas': 695662,\\n\",\n \" 'Florida': 170312, 'New York': 141297,\\n\",\n \" 'Pennsylvania': 119280})\\n\",\n \"pop = pd.Series({'California': 39538223, 'Texas': 29145505,\\n\",\n \" 'Florida': 21538187, 'New York': 20201249,\\n\",\n \" 'Pennsylvania': 13002700})\\n\",\n \"data = pd.DataFrame({'area':area, 'pop':pop})\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The individual `Series` that make up the columns of the `DataFrame` can be accessed via dictionary-style indexing of the column name:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 423967\\n\",\n \"Texas 695662\\n\",\n \"Florida 170312\\n\",\n \"New York 141297\\n\",\n \"Pennsylvania 119280\\n\",\n \"Name: area, dtype: int64\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['area']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Equivalently, we can use attribute-style access with column names that are strings:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 423967\\n\",\n \"Texas 695662\\n\",\n \"Florida 170312\\n\",\n \"New York 141297\\n\",\n \"Pennsylvania 119280\\n\",\n \"Name: area, dtype: int64\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.area\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Though this is a useful shorthand, keep in mind that it does not work for all cases!\\n\",\n \"For example, if the column names are not strings, or if the column names conflict with methods of the `DataFrame`, this attribute-style access is not possible.\\n\",\n \"For example, the `DataFrame` has a `pop` method, so `data.pop` will point to this rather than the `pop` column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.pop is data[\\\"pop\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In particular, you should avoid the temptation to try column assignment via attributes (i.e., use `data['pop'] = z` rather than `data.pop = z`).\\n\",\n \"\\n\",\n \"Like with the `Series` objects discussed earlier, this dictionary-style syntax can also be used to modify the object, in this case adding a new column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" area pop density\\n\",\n \"California 423967 39538223 93.257784\\n\",\n \"Texas 695662 29145505 41.896072\\n\",\n \"Florida 170312 21538187 126.463121\\n\",\n \"New York 141297 20201249 142.970120\\n\",\n \"Pennsylvania 119280 13002700 109.009893\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['density'] = data['pop'] / data['area']\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This shows a preview of the straightforward syntax of element-by-element arithmetic between `Series` objects; we'll dig into this further in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### DataFrame as Two-Dimensional Array\\n\",\n \"\\n\",\n \"As mentioned previously, we can also view the `DataFrame` as an enhanced two-dimensional array.\\n\",\n \"We can examine the raw underlying data array using the `values` attribute:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[4.23967000e+05, 3.95382230e+07, 9.32577842e+01],\\n\",\n \" [6.95662000e+05, 2.91455050e+07, 4.18960717e+01],\\n\",\n \" [1.70312000e+05, 2.15381870e+07, 1.26463121e+02],\\n\",\n \" [1.41297000e+05, 2.02012490e+07, 1.42970120e+02],\\n\",\n \" [1.19280000e+05, 1.30027000e+07, 1.09009893e+02]])\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With this picture in mind, many familiar array-like operations can be done on the `DataFrame` itself.\\n\",\n \"For example, we can transpose the full `DataFrame` to swap rows and columns:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" California Texas Florida New York Pennsylvania\\n\",\n \"area 4.239670e+05 6.956620e+05 1.703120e+05 1.412970e+05 1.192800e+05\\n\",\n \"pop 3.953822e+07 2.914550e+07 2.153819e+07 2.020125e+07 1.300270e+07\\n\",\n \"density 9.325778e+01 4.189607e+01 1.264631e+02 1.429701e+02 1.090099e+02\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.T\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When it comes to indexing of a `DataFrame` object, however, it is clear that the dictionary-style indexing of columns precludes our ability to simply treat it as a NumPy array.\\n\",\n \"In particular, passing a single index to an array accesses a row:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([4.23967000e+05, 3.95382230e+07, 9.32577842e+01])\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.values[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"and passing a single \\\"index\\\" to a `DataFrame` accesses a column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 423967\\n\",\n \"Texas 695662\\n\",\n \"Florida 170312\\n\",\n \"New York 141297\\n\",\n \"Pennsylvania 119280\\n\",\n \"Name: area, dtype: int64\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['area']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Thus, for array-style indexing, we need another convention.\\n\",\n \"Here Pandas again uses the `loc` and `iloc` indexers mentioned earlier.\\n\",\n \"Using the `iloc` indexer, we can index the underlying array as if it were a simple NumPy array (using the implicit Python-style index), but the `DataFrame` index and column labels are maintained in the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" area pop\\n\",\n \"California 423967 39538223\\n\",\n \"Texas 695662 29145505\\n\",\n \"Florida 170312 21538187\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.iloc[:3, :2]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Similarly, using the `loc` indexer we can index the underlying data in an array-like style but using the explicit index and column names:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" area pop\\n\",\n \"California 423967 39538223\\n\",\n \"Texas 695662 29145505\\n\",\n \"Florida 170312 21538187\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.loc[:'Florida', :'pop']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Any of the familiar NumPy-style data access patterns can be used within these indexers.\\n\",\n \"For example, in the `loc` indexer we can combine masking and fancy indexing as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" pop density\\n\",\n \"Florida 21538187 126.463121\\n\",\n \"New York 20201249 142.970120\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.loc[data.density > 120, ['pop', 'density']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Any of these indexing conventions may also be used to set or modify values; this is done in the standard way that you might be accustomed to from working with NumPy:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" area pop density\\n\",\n \"California 423967 39538223 90.000000\\n\",\n \"Texas 695662 29145505 41.896072\\n\",\n \"Florida 170312 21538187 126.463121\\n\",\n \"New York 141297 20201249 142.970120\\n\",\n \"Pennsylvania 119280 13002700 109.009893\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.iloc[0, 2] = 90\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To build up your fluency in Pandas data manipulation, I suggest spending some time with a simple `DataFrame` and exploring the types of indexing, slicing, masking, and fancy indexing that are allowed by these various indexing approaches.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Additional Indexing Conventions\\n\",\n \"\\n\",\n \"There are a couple of extra indexing conventions that might seem at odds with the preceding discussion, but nevertheless can be useful in practice.\\n\",\n \"First, while *indexing* refers to columns, *slicing* refers to rows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" area pop density\\n\",\n \"Florida 170312 21538187 126.463121\\n\",\n \"New York 141297 20201249 142.970120\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['Florida':'New York']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Such slices can also refer to rows by number rather than by index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \" area pop density\\n\",\n \"Florida 170312 21538187 126.463121\\n\",\n \"New York 141297 20201249 142.970120\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data[data.density > 120]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These two conventions are syntactically similar to those on a NumPy array, and while they may not precisely fit the mold of the Pandas conventions, they are included due to their practical utility.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Operating on Data in Pandas\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"One of the strengths of NumPy is that it allows us to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more complicated operations (trigonometric functions, exponential and logarithmic functions, etc.).\\n\",\n \"Pandas inherits much of this functionality from NumPy, and the ufuncs introduced in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) are key to this.\\n\",\n \"\\n\",\n \"Pandas includes a couple of useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will *preserve index and column labels* in the output, and for binary operations such as addition and multiplication, Pandas will automatically *align indices* when passing the objects to the ufunc.\\n\",\n \"This means that keeping the context of data and combining data from different sources—both potentially error-prone tasks with raw NumPy arrays—become essentially foolproof with Pandas.\\n\",\n \"We will additionally see that there are well-defined operations between one-dimensional `Series` structures and two-dimensional `DataFrame` structures.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Ufuncs: Index Preservation\\n\",\n \"\\n\",\n \"Because Pandas is designed to work with NumPy, any NumPy ufunc will work on Pandas `Series` and `DataFrame` objects.\\n\",\n \"Let's start by defining a simple `Series` and `DataFrame` on which to demonstrate this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 0\\n\",\n \"1 7\\n\",\n \"2 6\\n\",\n \"3 4\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"rng = np.random.default_rng(42)\\n\",\n \"ser = pd.Series(rng.integers(0, 10, 4))\\n\",\n \"ser\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" A B C D\\n\",\n \"0 4 8 0 6\\n\",\n \"1 2 0 5 9\\n\",\n \"2 7 7 7 7\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.DataFrame(rng.integers(0, 10, (3, 4)),\\n\",\n \" columns=['A', 'B', 'C', 'D'])\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we apply a NumPy ufunc on either of these objects, the result will be another Pandas object *with the indices preserved:*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 1.000000\\n\",\n \"1 1096.633158\\n\",\n \"2 403.428793\\n\",\n \"3 54.598150\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.exp(ser)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is true also for more involved sequences of operations:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" A B C D\\n\",\n \"0 1.224647e-16 -2.449294e-16 0.000000 -1.000000\\n\",\n \"1 1.000000e+00 0.000000e+00 -0.707107 0.707107\\n\",\n \"2 -7.071068e-01 -7.071068e-01 -0.707107 -0.707107\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.sin(df * np.pi / 4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Any of the ufuncs discussed in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) can be used in a similar manner.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Ufuncs: Index Alignment\\n\",\n \"\\n\",\n \"For binary operations on two `Series` or `DataFrame` objects, Pandas will align indices in the process of performing the operation.\\n\",\n \"This is very convenient when working with incomplete data, as we'll see in some of the examples that follow.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Index Alignment in Series\\n\",\n \"\\n\",\n \"As an example, suppose we are combining two different data sources and wish to find only the top three US states by *area* and the top three US states by *population*:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"area = pd.Series({'Alaska': 1723337, 'Texas': 695662,\\n\",\n \" 'California': 423967}, name='area')\\n\",\n \"population = pd.Series({'California': 39538223, 'Texas': 29145505,\\n\",\n \" 'Florida': 21538187}, name='population')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's see what happens when we divide these to compute the population density:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Alaska NaN\\n\",\n \"California 93.257784\\n\",\n \"Florida NaN\\n\",\n \"Texas 41.896072\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"population / area\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The resulting array contains the *union* of indices of the two input arrays, which could be determined directly from these indices:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['Alaska', 'California', 'Florida', 'Texas'], dtype='object')\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"area.index.union(population.index)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Any item for which one or the other does not have an entry is marked with `NaN`, or \\\"Not a Number,\\\" which is how Pandas marks missing data (see further discussion of missing data in [Handling Missing Data](03.04-Missing-Values.ipynb)).\\n\",\n \"This index matching is implemented this way for any of Python's built-in arithmetic expressions; any missing values are marked by `NaN`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 NaN\\n\",\n \"1 5.0\\n\",\n \"2 9.0\\n\",\n \"3 NaN\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"A = pd.Series([2, 4, 6], index=[0, 1, 2])\\n\",\n \"B = pd.Series([1, 3, 5], index=[1, 2, 3])\\n\",\n \"A + B\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If using `NaN` values is not the desired behavior, the fill value can be modified using appropriate object methods in place of the operators.\\n\",\n \"For example, calling ``A.add(B)`` is equivalent to calling ``A + B``, but allows optional explicit specification of the fill value for any elements in ``A`` or ``B`` that might be missing:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 2.0\\n\",\n \"1 5.0\\n\",\n \"2 9.0\\n\",\n \"3 5.0\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"A.add(B, fill_value=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Index Alignment in DataFrames\\n\",\n \"\\n\",\n \"A similar type of alignment takes place for *both* columns and indices when performing operations on `DataFrame` objects:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \" a b c\\n\",\n \"0 13.0 7.0 NaN\\n\",\n \"1 23.0 18.0 NaN\\n\",\n \"2 NaN NaN NaN\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"A + B\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that indices are aligned correctly irrespective of their order in the two objects, and indices in the result are sorted.\\n\",\n \"As was the case with `Series`, we can use the associated object's arithmetic methods and pass any desired `fill_value` to be used in place of missing entries.\\n\",\n \"Here we'll fill with the mean of all values in `A`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" a b c\\n\",\n \"0 13.00 7.00 10.25\\n\",\n \"1 23.00 18.00 15.25\\n\",\n \"2 17.25 13.25 14.25\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"A.add(B, fill_value=A.values.mean())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following table lists Python operators and their equivalent Pandas object methods:\\n\",\n \"\\n\",\n \"| Python operator | Pandas method(s) |\\n\",\n \"|-----------------|---------------------------------|\\n\",\n \"| `+` | `add` |\\n\",\n \"| `-` | `sub`, `subtract` |\\n\",\n \"| `*` | `mul`, `multiply` |\\n\",\n \"| `/` | `truediv`, `div`, `divide` |\\n\",\n \"| `//` | `floordiv` |\\n\",\n \"| `%` | `mod` |\\n\",\n \"| `**` | `pow` |\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Ufuncs: Operations Between DataFrames and Series\\n\",\n \"\\n\",\n \"When performing operations between a `DataFrame` and a `Series`, the index and column alignment is similarly maintained, and the result is similar to operations between a two-dimensional and one-dimensional NumPy array.\\n\",\n \"Consider one common operation, where we find the difference of a two-dimensional array and one of its rows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[4, 4, 2, 0],\\n\",\n \" [5, 8, 0, 8],\\n\",\n \" [8, 2, 6, 1]])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"A = rng.integers(10, size=(3, 4))\\n\",\n \"A\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0, 0, 0, 0],\\n\",\n \" [ 1, 4, -2, 8],\\n\",\n \" [ 4, -2, 4, 1]])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"A - A[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"According to NumPy's broadcasting rules (see [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb)), subtraction between a two-dimensional array and one of its rows is applied row-wise.\\n\",\n \"\\n\",\n \"In Pandas, the convention similarly operates row-wise by default:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" Q R S T\\n\",\n \"0 0 0 0 0\\n\",\n \"1 1 4 -2 8\\n\",\n \"2 4 -2 4 1\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.DataFrame(A, columns=['Q', 'R', 'S', 'T'])\\n\",\n \"df - df.iloc[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you would instead like to operate column-wise, you can use the object methods mentioned earlier, while specifying the `axis` keyword:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \" Q R S T\\n\",\n \"0 0.0 NaN 0.0 NaN\\n\",\n \"1 1.0 NaN -2.0 NaN\\n\",\n \"2 4.0 NaN 4.0 NaN\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df - halfrow\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This preservation and alignment of indices and columns means that operations on data in Pandas will always maintain the data context, which prevents the common errors that might arise when working with heterogeneous and/or misaligned data in raw NumPy arrays.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Handling Missing Data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The difference between data found in many tutorials and data in the real world is that real-world data is rarely clean and homogeneous.\\n\",\n \"In particular, many interesting datasets will have some amount of data missing.\\n\",\n \"To make matters even more complicated, different data sources may indicate missing data in different ways.\\n\",\n \"\\n\",\n \"In this chapter, we will discuss some general considerations for missing data, look at how Pandas chooses to represent it, and explore some built-in Pandas tools for handling missing data in Python.\\n\",\n \"Here and throughout the book, I will refer to missing data in general as *null*, *NaN*, or *NA* values.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Trade-offs in Missing Data Conventions\\n\",\n \"\\n\",\n \"A number of approaches have been developed to track the presence of missing data in a table or `DataFrame`.\\n\",\n \"Generally, they revolve around one of two strategies: using a *mask* that globally indicates missing values, or choosing a *sentinel value* that indicates a missing entry.\\n\",\n \"\\n\",\n \"In the masking approach, the mask might be an entirely separate Boolean array, or it might involve appropriation of one bit in the data representation to locally indicate the null status of a value.\\n\",\n \"\\n\",\n \"In the sentinel approach, the sentinel value could be some data-specific convention, such as indicating a missing integer value with –9999 or some rare bit pattern, or it could be a more global convention, such as indicating a missing floating-point value with `NaN` (Not a Number), a special value that is part of the IEEE floating-point specification.\\n\",\n \"\\n\",\n \"Neither of these approaches is without trade-offs. Use of a separate mask array requires allocation of an additional Boolean array, which adds overhead in both storage and computation. A sentinel value reduces the range of valid values that can be represented, and may require extra (often nonoptimized) logic in CPU and GPU arithmetic, because common special values like `NaN` are not available for all data types.\\n\",\n \"\\n\",\n \"As in most cases where no universally optimal choice exists, different languages and systems use different conventions.\\n\",\n \"For example, the R language uses reserved bit patterns within each data type as sentinel values indicating missing data, while the SciDB system uses an extra byte attached to every cell to indicate an NA state.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Missing Data in Pandas\\n\",\n \"\\n\",\n \"The way in which Pandas handles missing values is constrained by its reliance on the NumPy package, which does not have a built-in notion of NA values for non-floating-point data types.\\n\",\n \"\\n\",\n \"Perhaps Pandas could have followed R's lead in specifying bit patterns for each individual data type to indicate nullness, but this approach turns out to be rather unwieldy.\\n\",\n \"While R has just 4 main data types, NumPy supports *far* more than this: for example, while R has a single integer type, NumPy supports 14 basic integer types once you account for available bit widths, signedness, and endianness of the encoding.\\n\",\n \"Reserving a specific bit pattern in all available NumPy types would lead to an unwieldy amount of overhead in special-casing various operations for various types, likely even requiring a new fork of the NumPy package. Further, for the smaller data types (such as 8-bit integers), sacrificing a bit to use as a mask would significantly reduce the range of values it can represent.\\n\",\n \"\\n\",\n \"Because of these constraints and trade-offs, Pandas has two \\\"modes\\\" of storing and manipulating null values:\\n\",\n \"\\n\",\n \"- The default mode is to use a sentinel-based missing data scheme, with sentinel values `NaN` or `None` depending on the type of the data.\\n\",\n \"- Alternatively, you can opt in to using the nullable data types (dtypes) Pandas provides (discussed later in this chapter), which results in the creation an accompanying mask array to track missing entries. These missing entries are then presented to the user as the special `pd.NA` value.\\n\",\n \"\\n\",\n \"In either case, the data operations and manipulations provided by the Pandas API will handle and propagate those missing entries in a predictable manner. But to develop some intuition into *why* these choices are made, let's dive quickly into the trade-offs inherent in `None`, `NaN`, and `NA`. As usual, we'll start by importing NumPy and Pandas:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### None as a Sentinel Value\\n\",\n \"\\n\",\n \"For some data types, Pandas uses `None` as a sentinel value. `None` is a Python object, which means that any array containing `None` must have `dtype=object`—that is, it must be a sequence of Python objects.\\n\",\n \"\\n\",\n \"For example, observe what happens if you pass `None` to a NumPy array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, None, 2, 3], dtype=object)\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vals1 = np.array([1, None, 2, 3])\\n\",\n \"vals1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This `dtype=object` means that the best common type representation NumPy could infer for the contents of the array is that they are Python objects.\\n\",\n \"The downside of using `None` in this way is that operations on the data will be done at the Python level, with much more overhead than the typically fast operations seen for arrays with native types:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.73 ms ± 288 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit np.arange(1E6, dtype=int).sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"92.1 ms ± 3.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit np.arange(1E6, dtype=object).sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Further, because Python does not support arithmetic operations with `None`, aggregations like `sum` or `min` will generally lead to an error:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'NoneType'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_91333/1181914653.py\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mvals1\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0msum\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m~/.local/share/virtualenvs/python-data-science-handbook-2e-u_kwqDTB/lib/python3.9/site-packages/numpy/core/_methods.py\\u001b[0m in \\u001b[0;36m_sum\\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\\u001b[0m\\n\\u001b[1;32m 46\\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\\n\\u001b[1;32m 47\\u001b[0m initial=_NoValue, where=True):\\n\\u001b[0;32m---> 48\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mumr_sum\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0ma\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0maxis\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdtype\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mout\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkeepdims\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minitial\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mwhere\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 49\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 50\\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\\n\",\n \"\\u001b[0;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'\"\n ]\n }\n ],\n \"source\": [\n \"vals1.sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this reason, Pandas does not use `None` as a sentinel in its numerical arrays.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### NaN: Missing Numerical Data\\n\",\n \"\\n\",\n \"The other missing data sentinel, `NaN` is different; it is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1., nan, 3., 4.])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vals2 = np.array([1, np.nan, 3, 4]) \\n\",\n \"vals2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that NumPy chose a native floating-point type for this array: this means that unlike the object array from before, this array supports fast operations pushed into compiled code.\\n\",\n \"Keep in mind that `NaN` is a bit like a data virus—it infects any other object it touches.\\n\",\n \"Regardless of the operation, the result of arithmetic with `NaN` will be another `NaN`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"nan\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"1 + np.nan\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"nan\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"0 * np.nan\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This means that aggregates over the values are well defined (i.e., they don't result in an error) but not always useful:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(nan, nan, nan)\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vals2.sum(), vals2.min(), vals2.max()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"That said, NumPy does provide ``NaN``-aware versions of aggregations that will ignore these missing values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(8.0, 1.0, 4.0)\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.nansum(vals2), np.nanmin(vals2), np.nanmax(vals2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The main downside of `NaN` is that it is specifically a floating-point value; there is no equivalent `NaN` value for integers, strings, or other types.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### NaN and None in Pandas\\n\",\n \"\\n\",\n \"`NaN` and `None` both have their place, and Pandas is built to handle the two of them nearly interchangeably, converting between them where appropriate:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 1.0\\n\",\n \"1 NaN\\n\",\n \"2 2.0\\n\",\n \"3 NaN\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.Series([1, np.nan, 2, None])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For types that don't have an available sentinel value, Pandas automatically typecasts when NA values are present.\\n\",\n \"For example, if we set a value in an integer array to ``np.nan``, it will automatically be upcast to a floating-point type to accommodate the NA:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 0\\n\",\n \"1 1\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = pd.Series(range(2), dtype=int)\\n\",\n \"x\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 NaN\\n\",\n \"1 1.0\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x[0] = None\\n\",\n \"x\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that in addition to casting the integer array to floating point, Pandas automatically converts the ``None`` to a ``NaN`` value.\\n\",\n \"\\n\",\n \"While this type of magic may feel a bit hackish compared to the more unified approach to NA values in domain-specific languages like R, the Pandas sentinel/casting approach works quite well in practice and in my experience only rarely causes issues.\\n\",\n \"\\n\",\n \"The following table lists the upcasting conventions in Pandas when NA values are introduced:\\n\",\n \"\\n\",\n \"|Typeclass | Conversion when storing NAs | NA sentinel value |\\n\",\n \"|--------------|-----------------------------|------------------------|\\n\",\n \"| ``floating`` | No change | ``np.nan`` |\\n\",\n \"| ``object`` | No change | ``None`` or ``np.nan`` |\\n\",\n \"| ``integer`` | Cast to ``float64`` | ``np.nan`` |\\n\",\n \"| ``boolean`` | Cast to ``object`` | ``None`` or ``np.nan`` |\\n\",\n \"\\n\",\n \"Keep in mind that in Pandas, string data is always stored with an ``object`` dtype.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Pandas Nullable Dtypes\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In early versions of Pandas, `NaN` and `None` as sentinel values were the only missing data representations available. The primary difficulty this introduced was with regard to the implicit type casting: for example, there was no way to represent a true integer array with missing data.\\n\",\n \"\\n\",\n \"To address this difficulty, Pandas later added *nullable dtypes*, which are distinguished from regular dtypes by capitalization of their names (e.g., `pd.Int32` versus `np.int32`). For backward compatibility, these nullable dtypes are only used if specifically requested.\\n\",\n \"\\n\",\n \"For example, here is a `Series` of integers with missing data, created from a list containing all three available markers of missing data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 1\\n\",\n \"1 \\n\",\n \"2 2\\n\",\n \"3 \\n\",\n \"4 \\n\",\n \"dtype: Int32\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.Series([1, np.nan, 2, None, pd.NA], dtype='Int32')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This representation can be used interchangeably with the others in all the operations explored through the rest of this chapter.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Operating on Null Values\\n\",\n \"\\n\",\n \"As we have seen, Pandas treats `None`, `NaN`, and `NA` as essentially interchangeable for indicating missing or null values.\\n\",\n \"To facilitate this convention, Pandas provides several methods for detecting, removing, and replacing null values in Pandas data structures.\\n\",\n \"They are:\\n\",\n \"\\n\",\n \"- ``isnull``: Generates a Boolean mask indicating missing values\\n\",\n \"- ``notnull``: Opposite of ``isnull``\\n\",\n \"- ``dropna``: Returns a filtered version of the data\\n\",\n \"- ``fillna``: Returns a copy of the data with missing values filled or imputed\\n\",\n \"\\n\",\n \"We will conclude this chapter with a brief exploration and demonstration of these routines.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Detecting Null Values\\n\",\n \"Pandas data structures have two useful methods for detecting null data: `isnull` and `notnull`.\\n\",\n \"Either one will return a Boolean mask over the data. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"data = pd.Series([1, np.nan, 'hello', None])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 False\\n\",\n \"1 True\\n\",\n \"2 False\\n\",\n \"3 True\\n\",\n \"dtype: bool\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.isnull()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As mentioned in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb), Boolean masks can be used directly as a `Series` or `DataFrame` index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 1\\n\",\n \"2 hello\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data[data.notnull()]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `isnull()` and `notnull()` methods produce similar Boolean results for ``DataFrame`` objects.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dropping Null Values\\n\",\n \"\\n\",\n \"In addition to these masking methods, there are the convenience methods `dropna`\\n\",\n \"(which removes NA values) and `fillna` (which fills in NA values). For a `Series`,\\n\",\n \"the result is straightforward:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 1\\n\",\n \"2 hello\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.dropna()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For a ``DataFrame``, there are more options.\\n\",\n \"Consider the following ``DataFrame``:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Hierarchical Indexing\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in Pandas `Series` and `DataFrame` objects, respectively.\\n\",\n \"Often it is useful to go beyond this and store higher-dimensional data—that is, data indexed by more than one or two keys.\\n\",\n \"Early Pandas versions provided `Panel` and `Panel4D` objects that could be thought of as 3D or 4D analogs to the 2D `DataFrame`, but they were somewhat clunky to use in practice. A far more common pattern for handling higher-dimensional data is to make use of *hierarchical indexing* (also known as *multi-indexing*) to incorporate multiple index *levels* within a single index.\\n\",\n \"In this way, higher-dimensional data can be compactly represented within the familiar one-dimensional `Series` and two-dimensional `DataFrame` objects.\\n\",\n \"(If you're interested in true *N*-dimensional arrays with Pandas-style flexible indices, you can look into the excellent [Xarray package](https://xarray.pydata.org/).)\\n\",\n \"\\n\",\n \"In this chapter, we'll explore the direct creation of `MultiIndex` objects; considerations when indexing, slicing, and computing statistics across multiply indexed data; and useful routines for converting between simple and hierarchically indexed representations of data.\\n\",\n \"\\n\",\n \"We begin with the standard imports:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## A Multiply Indexed Series\\n\",\n \"\\n\",\n \"Let's start by considering how we might represent two-dimensional data within a one-dimensional `Series`.\\n\",\n \"For concreteness, we will consider a series of data where each point has a character and numerical key.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### The Bad Way\\n\",\n \"\\n\",\n \"Suppose you would like to track data about states from two different years.\\n\",\n \"Using the Pandas tools we've already covered, you might be tempted to simply use Python tuples as keys:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(California, 2010) 37253956\\n\",\n \"(California, 2020) 39538223\\n\",\n \"(New York, 2010) 19378102\\n\",\n \"(New York, 2020) 20201249\\n\",\n \"(Texas, 2010) 25145561\\n\",\n \"(Texas, 2020) 29145505\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"index = [('California', 2010), ('California', 2020),\\n\",\n \" ('New York', 2010), ('New York', 2020),\\n\",\n \" ('Texas', 2010), ('Texas', 2020)]\\n\",\n \"populations = [37253956, 39538223,\\n\",\n \" 19378102, 20201249,\\n\",\n \" 25145561, 29145505]\\n\",\n \"pop = pd.Series(populations, index=index)\\n\",\n \"pop\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With this indexing scheme, you can straightforwardly index or slice the series based on this tuple index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(California, 2020) 39538223\\n\",\n \"(New York, 2010) 19378102\\n\",\n \"(New York, 2020) 20201249\\n\",\n \"(Texas, 2010) 25145561\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop[('California', 2020):('Texas', 2010)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"But the convenience ends there. For example, if you need to select all values from 2010, you'll need to do some messy (and potentially slow) munging to make it happen:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(California, 2010) 37253956\\n\",\n \"(New York, 2010) 19378102\\n\",\n \"(Texas, 2010) 25145561\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop[[i for i in pop.index if i[1] == 2010]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This produces the desired result, but is not as clean (or as efficient for large datasets) as the slicing syntax we've grown to love in Pandas.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### The Better Way: The Pandas MultiIndex\\n\",\n \"Fortunately, Pandas provides a better way.\\n\",\n \"Our tuple-based indexing is essentially a rudimentary multi-index, and the Pandas `MultiIndex` type gives us the types of operations we wish to have.\\n\",\n \"We can create a multi-index from the tuples as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"index = pd.MultiIndex.from_tuples(index)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The `MultiIndex` represents multiple *levels* of indexing—in this case, the state names and the years—as well as multiple *labels* for each data point which encode these levels.\\n\",\n \"\\n\",\n \"If we reindex our series with this `MultiIndex`, we see the hierarchical representation of the data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 2010 37253956\\n\",\n \" 2020 39538223\\n\",\n \"New York 2010 19378102\\n\",\n \" 2020 20201249\\n\",\n \"Texas 2010 25145561\\n\",\n \" 2020 29145505\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop = pop.reindex(index)\\n\",\n \"pop\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Here the first two columns of the Series representation show the multiple index values, while the third column shows the data.\\n\",\n \"Notice that some entries are missing in the first column: in this multi-index representation, any blank entry indicates the same value as the line above it.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now to access all data for which the second index is 2020, we can use the Pandas slicing notation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 39538223\\n\",\n \"New York 20201249\\n\",\n \"Texas 29145505\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop[:, 2020]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The result is a singly indexed Series with just the keys we're interested in.\\n\",\n \"This syntax is much more convenient (and the operation is much more efficient!) than the home-spun tuple-based multi-indexing solution that we started with.\\n\",\n \"We'll now further discuss this sort of indexing operation on hierarchically indexed data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### MultiIndex as Extra Dimension\\n\",\n \"\\n\",\n \"You might notice something else here: we could easily have stored the same data using a simple `DataFrame` with index and column labels.\\n\",\n \"In fact, Pandas is built with this equivalence in mind. The `unstack` method will quickly convert a multiply indexed `Series` into a conventionally indexed `DataFrame`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" 2010 2020\\n\",\n \"California 37253956 39538223\\n\",\n \"New York 19378102 20201249\\n\",\n \"Texas 25145561 29145505\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop_df = pop.unstack()\\n\",\n \"pop_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Naturally, the ``stack`` method provides the opposite operation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 2010 37253956\\n\",\n \" 2020 39538223\\n\",\n \"New York 2010 19378102\\n\",\n \" 2020 20201249\\n\",\n \"Texas 2010 25145561\\n\",\n \" 2020 29145505\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop_df.stack()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Seeing this, you might wonder why would we would bother with hierarchical indexing at all.\\n\",\n \"The reason is simple: just as we were able to use multi-indexing to manipulate two-dimensional data within a one-dimensional `Series`, we can also use it to manipulate data of three or more dimensions in a `Series` or `DataFrame`.\\n\",\n \"Each extra level in a multi-index represents an extra dimension of data; taking advantage of this property gives us much more flexibility in the types of data we can represent. Concretely, we might want to add another column of demographic data for each state at each year (say, population under 18); with a `MultiIndex` this is as easy as adding another column to the ``DataFrame``:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" total under18\\n\",\n \"California 2010 37253956 9284094\\n\",\n \" 2020 39538223 8898092\\n\",\n \"New York 2010 19378102 4318033\\n\",\n \" 2020 20201249 4181528\\n\",\n \"Texas 2010 25145561 6879014\\n\",\n \" 2020 29145505 7432474\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop_df = pd.DataFrame({'total': pop,\\n\",\n \" 'under18': [9284094, 8898092,\\n\",\n \" 4318033, 4181528,\\n\",\n \" 6879014, 7432474]})\\n\",\n \"pop_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"In addition, all the ufuncs and other functionality discussed in [Operating on Data in Pandas](03.03-Operations-in-Pandas.ipynb) work with hierarchical indices as well.\\n\",\n \"Here we compute the fraction of people under 18 by year, given the above data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" 2010 2020\\n\",\n \"California 0.249211 0.225050\\n\",\n \"New York 0.222831 0.206994\\n\",\n \"Texas 0.273568 0.255013\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"f_u18 = pop_df['under18'] / pop_df['total']\\n\",\n \"f_u18.unstack()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This allows us to easily and quickly manipulate and explore even high-dimensional data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Methods of MultiIndex Creation\\n\",\n \"\\n\",\n \"The most straightforward way to construct a multiply indexed `Series` or `DataFrame` is to simply pass a list of two or more index arrays to the constructor. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" data1 data2\\n\",\n \"a 1 0.748464 0.561409\\n\",\n \" 2 0.379199 0.622461\\n\",\n \"b 1 0.701679 0.687932\\n\",\n \" 2 0.436200 0.950664\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.DataFrame(np.random.rand(4, 2),\\n\",\n \" index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],\\n\",\n \" columns=['data1', 'data2'])\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The work of creating the ``MultiIndex`` is done in the background.\\n\",\n \"\\n\",\n \"Similarly, if you pass a dictionary with appropriate tuples as keys, Pandas will automatically recognize this and use a ``MultiIndex`` by default:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"California 2010 37253956\\n\",\n \" 2020 39538223\\n\",\n \"New York 2010 19378102\\n\",\n \" 2020 20201249\\n\",\n \"Texas 2010 25145561\\n\",\n \" 2020 29145505\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = {('California', 2010): 37253956,\\n\",\n \" ('California', 2020): 39538223,\\n\",\n \" ('New York', 2010): 19378102,\\n\",\n \" ('New York', 2020): 20201249,\\n\",\n \" ('Texas', 2010): 25145561,\\n\",\n \" ('Texas', 2020): 29145505}\\n\",\n \"pd.Series(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Nevertheless, it is sometimes useful to explicitly create a `MultiIndex`; we'll look at a couple of methods for doing this next.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Explicit MultiIndex Constructors\\n\",\n \"\\n\",\n \"For more flexibility in how the index is constructed, you can instead use the constructor methods available in the `pd.MultiIndex` class.\\n\",\n \"For example, as we did before, you can construct a `MultiIndex` from a simple list of arrays giving the index values within each level:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"MultiIndex([('a', 1),\\n\",\n \" ('a', 2),\\n\",\n \" ('b', 1),\\n\",\n \" ('b', 2)],\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Or you can construct it from a list of tuples giving the multiple index values of each point:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"MultiIndex([('a', 1),\\n\",\n \" ('a', 2),\\n\",\n \" ('b', 1),\\n\",\n \" ('b', 2)],\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"You can even construct it from a Cartesian product of single indices:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"MultiIndex([('a', 1),\\n\",\n \" ('a', 2),\\n\",\n \" ('b', 1),\\n\",\n \" ('b', 2)],\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.MultiIndex.from_product([['a', 'b'], [1, 2]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Similarly, you can construct a `MultiIndex` directly using its internal encoding by passing `levels` (a list of lists containing available index values for each level) and `codes` (a list of lists that reference these labels):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"MultiIndex([('a', 1),\\n\",\n \" ('a', 2),\\n\",\n \" ('b', 1),\\n\",\n \" ('b', 2)],\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.MultiIndex(levels=[['a', 'b'], [1, 2]],\\n\",\n \" codes=[[0, 0, 1, 1], [0, 1, 0, 1]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Any of these objects can be passed as the `index` argument when creating a `Series` or `DataFrame`, or be passed to the `reindex` method of an existing `Series` or `DataFrame`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### MultiIndex Level Names\\n\",\n \"\\n\",\n \"Sometimes it is convenient to name the levels of the `MultiIndex`.\\n\",\n \"This can be accomplished by passing the `names` argument to any of the previously discussed `MultiIndex` constructors, or by setting the `names` attribute of the index after the fact:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state year\\n\",\n \"California 2010 37253956\\n\",\n \" 2020 39538223\\n\",\n \"New York 2010 19378102\\n\",\n \" 2020 20201249\\n\",\n \"Texas 2010 25145561\\n\",\n \" 2020 29145505\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop.index.names = ['state', 'year']\\n\",\n \"pop\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With more involved datasets, this can be a useful way to keep track of the meaning of various index values.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### MultiIndex for Columns\\n\",\n \"\\n\",\n \"In a `DataFrame`, the rows and columns are completely symmetric, and just as the rows can have multiple levels of indices, the columns can have multiple levels as well.\\n\",\n \"Consider the following, which is a mock-up of some (somewhat realistic) medical data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"subject Bob Guido Sue \\n\",\n \"type HR Temp HR Temp HR Temp\\n\",\n \"year visit \\n\",\n \"2013 1 30.0 38.0 56.0 38.3 45.0 35.8\\n\",\n \" 2 47.0 37.1 27.0 36.0 37.0 36.4\\n\",\n \"2014 1 51.0 35.9 24.0 36.7 32.0 36.2\\n\",\n \" 2 49.0 36.3 48.0 39.2 31.0 35.7\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# hierarchical indices and columns\\n\",\n \"index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]],\\n\",\n \" names=['year', 'visit'])\\n\",\n \"columns = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']],\\n\",\n \" names=['subject', 'type'])\\n\",\n \"\\n\",\n \"# mock some data\\n\",\n \"data = np.round(np.random.randn(4, 6), 1)\\n\",\n \"data[:, ::2] *= 10\\n\",\n \"data += 37\\n\",\n \"\\n\",\n \"# create the DataFrame\\n\",\n \"health_data = pd.DataFrame(data, index=index, columns=columns)\\n\",\n \"health_data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This is fundamentally four-dimensional data, where the dimensions are the subject, the measurement type, the year, and the visit number.\\n\",\n \"With this in place we can, for example, index the top-level column by the person's name and get a full `DataFrame` containing just that person's information:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"type HR Temp\\n\",\n \"year visit \\n\",\n \"2013 1 56.0 38.3\\n\",\n \" 2 27.0 36.0\\n\",\n \"2014 1 24.0 36.7\\n\",\n \" 2 48.0 39.2\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"health_data['Guido']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Indexing and Slicing a MultiIndex\\n\",\n \"\\n\",\n \"Indexing and slicing on a `MultiIndex` is designed to be intuitive, and it helps if you think about the indices as added dimensions.\\n\",\n \"We'll first look at indexing multiply indexed `Series`, and then multiply indexed `DataFrame` objects.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Multiply Indexed Series\\n\",\n \"\\n\",\n \"Consider the multiply indexed `Series` of state populations we saw earlier:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state year\\n\",\n \"California 2010 37253956\\n\",\n \" 2020 39538223\\n\",\n \"New York 2010 19378102\\n\",\n \" 2020 20201249\\n\",\n \"Texas 2010 25145561\\n\",\n \" 2020 29145505\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We can access single elements by indexing with multiple terms:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"37253956\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop['California', 2010]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The `MultiIndex` also supports *partial indexing*, or indexing just one of the levels in the index.\\n\",\n \"The result is another `Series`, with the lower-level indices maintained:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"year\\n\",\n \"2010 37253956\\n\",\n \"2020 39538223\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop['California']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Partial slicing is available as well, as long as the `MultiIndex` is sorted (see the discussion in [Sorted and Unsorted Indices](#Sorted-and-unsorted-indices)):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state year\\n\",\n \"California 2010 37253956\\n\",\n \" 2020 39538223\\n\",\n \"New York 2010 19378102\\n\",\n \" 2020 20201249\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop.loc['California':'New York']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With sorted indices, partial indexing can be performed on lower levels by passing an empty slice in the first index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state\\n\",\n \"California 37253956\\n\",\n \"New York 19378102\\n\",\n \"Texas 25145561\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop[:, 2010]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Other types of indexing and selection (discussed in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb)) work as well; for example, selection based on Boolean masks:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state year\\n\",\n \"California 2010 37253956\\n\",\n \" 2020 39538223\\n\",\n \"Texas 2010 25145561\\n\",\n \" 2020 29145505\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop[pop > 22000000]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Selection based on fancy indexing also works:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state year\\n\",\n \"California 2010 37253956\\n\",\n \" 2020 39538223\\n\",\n \"Texas 2010 25145561\\n\",\n \" 2020 29145505\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop[['California', 'Texas']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Multiply Indexed DataFrames\\n\",\n \"\\n\",\n \"A multiply indexed `DataFrame` behaves in a similar manner.\\n\",\n \"Consider our toy medical `DataFrame` from before:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"subject Bob Guido Sue \\n\",\n \"type HR Temp HR Temp HR Temp\\n\",\n \"year visit \\n\",\n \"2013 1 30.0 38.0 56.0 38.3 45.0 35.8\\n\",\n \" 2 47.0 37.1 27.0 36.0 37.0 36.4\\n\",\n \"2014 1 51.0 35.9 24.0 36.7 32.0 36.2\\n\",\n \" 2 49.0 36.3 48.0 39.2 31.0 35.7\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"health_data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Remember that columns are primary in a `DataFrame`, and the syntax used for multiply indexed `Series` applies to the columns.\\n\",\n \"For example, we can recover Guido's heart rate data with a simple operation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"year visit\\n\",\n \"2013 1 56.0\\n\",\n \" 2 27.0\\n\",\n \"2014 1 24.0\\n\",\n \" 2 48.0\\n\",\n \"Name: (Guido, HR), dtype: float64\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"health_data['Guido', 'HR']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Also, as with the single-index case, we can use the `loc`, `iloc`, and `ix` indexers introduced in [Data Indexing and Selection](03.02-Data-Indexing-and-Selection.ipynb). For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"subject Bob \\n\",\n \"type HR Temp\\n\",\n \"year visit \\n\",\n \"2013 1 30.0 38.0\\n\",\n \" 2 47.0 37.1\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"health_data.iloc[:2, :2]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"These indexers provide an array-like view of the underlying two-dimensional data, but each individual index in `loc` or `iloc` can be passed a tuple of multiple indices. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"year visit\\n\",\n \"2013 1 30.0\\n\",\n \" 2 47.0\\n\",\n \"2014 1 51.0\\n\",\n \" 2 49.0\\n\",\n \"Name: (Bob, HR), dtype: float64\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"health_data.loc[:, ('Bob', 'HR')]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Working with slices within these index tuples is not especially convenient; trying to create a slice within a tuple will lead to a syntax error:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"invalid syntax (3311942670.py, line 1)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;36m File \\u001b[0;32m\\\"/var/folders/xc/sptt9bk14s34rgxt7453p03r0000gp/T/ipykernel_86488/3311942670.py\\\"\\u001b[0;36m, line \\u001b[0;32m1\\u001b[0m\\n\\u001b[0;31m health_data.loc[(:, 1), (:, 'HR')]\\u001b[0m\\n\\u001b[0m ^\\u001b[0m\\n\\u001b[0;31mSyntaxError\\u001b[0m\\u001b[0;31m:\\u001b[0m invalid syntax\\n\"\n ]\n }\n ],\n \"source\": [\n \"health_data.loc[(:, 1), (:, 'HR')]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"You could get around this by building the desired slice explicitly using Python's built-in `slice` function, but a better way in this context is to use an `IndexSlice` object, which Pandas provides for precisely this situation.\\n\",\n \"For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"subject Bob Guido Sue\\n\",\n \"type HR HR HR\\n\",\n \"year visit \\n\",\n \"2013 1 30.0 56.0 45.0\\n\",\n \"2014 1 51.0 24.0 32.0\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"idx = pd.IndexSlice\\n\",\n \"health_data.loc[idx[:, 1], idx[:, 'HR']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"As you can see, there are many ways to interact with data in multiply indexed `Series` and ``DataFrame``s, and as with many tools in this book the best way to become familiar with them is to try them out!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Rearranging Multi-Indexes\\n\",\n \"\\n\",\n \"One of the keys to working with multiply indexed data is knowing how to effectively transform the data.\\n\",\n \"There are a number of operations that will preserve all the information in the dataset, but rearrange it for the purposes of various computations.\\n\",\n \"We saw a brief example of this in the `stack` and `unstack` methods, but there are many more ways to finely control the rearrangement of data between hierarchical indices and columns, and we'll explore them here.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Sorted and Unsorted Indices\\n\",\n \"\\n\",\n \"Earlier I briefly mentioned a caveat, but I should emphasize it more here.\\n\",\n \"*Many of the `MultiIndex` slicing operations will fail if the index is not sorted.*\\n\",\n \"Let's take a closer look.\\n\",\n \"\\n\",\n \"We'll start by creating some simple multiply indexed data where the indices are *not lexographically sorted*:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"char int\\n\",\n \"a 1 0.280341\\n\",\n \" 2 0.097290\\n\",\n \"c 1 0.206217\\n\",\n \" 2 0.431771\\n\",\n \"b 1 0.100183\\n\",\n \" 2 0.015851\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"index = pd.MultiIndex.from_product([['a', 'c', 'b'], [1, 2]])\\n\",\n \"data = pd.Series(np.random.rand(6), index=index)\\n\",\n \"data.index.names = ['char', 'int']\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"If we try to take a partial slice of this index, it will result in an error:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"KeyError 'Key length (1) was greater than MultiIndex lexsort depth (0)'\\n\"\n ]\n }\n ],\n \"source\": [\n \"try:\\n\",\n \" data['a':'b']\\n\",\n \"except KeyError as e:\\n\",\n \" print(\\\"KeyError\\\", e)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Although it is not entirely clear from the error message, this is the result of the `MultiIndex` not being sorted.\\n\",\n \"For various reasons, partial slices and other similar operations require the levels in the `MultiIndex` to be in sorted (i.e., lexographical) order.\\n\",\n \"Pandas provides a number of convenience routines to perform this type of sorting, such as the `sort_index` and `sortlevel` methods of the `DataFrame`.\\n\",\n \"We'll use the simplest, `sort_index`, here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"char int\\n\",\n \"a 1 0.280341\\n\",\n \" 2 0.097290\\n\",\n \"b 1 0.100183\\n\",\n \" 2 0.015851\\n\",\n \"c 1 0.206217\\n\",\n \" 2 0.431771\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = data.sort_index()\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With the index sorted in this way, partial slicing will work as expected:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"char int\\n\",\n \"a 1 0.280341\\n\",\n \" 2 0.097290\\n\",\n \"b 1 0.100183\\n\",\n \" 2 0.015851\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['a':'b']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Stacking and Unstacking Indices\\n\",\n \"\\n\",\n \"As we saw briefly before, it is possible to convert a dataset from a stacked multi-index to a simple two-dimensional representation, optionally specifying the level to use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Combining Datasets: concat and append\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Some of the most interesting studies of data come from combining different data sources.\\n\",\n \"These operations can involve anything from very straightforward concatenation of two different datasets to more complicated database-style joins and merges that correctly handle any overlaps between the datasets.\\n\",\n \"`Series` and ``DataFrame``s are built with this type of operation in mind, and Pandas includes functions and methods that make this sort of data wrangling fast and straightforward.\\n\",\n \"\\n\",\n \"Here we'll take a look at simple concatenation of `Series` and ``DataFrame``s with the `pd.concat` function; later we'll dive into more sophisticated in-memory merges and joins implemented in Pandas.\\n\",\n \"\\n\",\n \"We begin with the standard imports:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For convenience, we'll define this function, which creates a `DataFrame` of a particular form that will be useful in the following examples:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\\\"\\\"\\\"\\n\",\n \" def __init__(self, *args):\\n\",\n \" self.args = args\\n\",\n \" \\n\",\n \" def _repr_html_(self):\\n\",\n \" return '\\\\n'.join(self.template.format(a, eval(a)._repr_html_())\\n\",\n \" for a in self.args)\\n\",\n \" \\n\",\n \" def __repr__(self):\\n\",\n \" return '\\\\n\\\\n'.join(a + '\\\\n' + repr(eval(a))\\n\",\n \" for a in self.args)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The use of this will become clearer as we continue our discussion in the following section.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Recall: Concatenation of NumPy Arrays\\n\",\n \"\\n\",\n \"Concatenation of `Series` and `DataFrame` objects behaves similarly to concatenation of NumPy arrays, which can be done via the `np.concatenate` function, as discussed in [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb).\\n\",\n \"Recall that with it, you can combine the contents of two or more arrays into a single array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4, 5, 6, 7, 8, 9])\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = [1, 2, 3]\\n\",\n \"y = [4, 5, 6]\\n\",\n \"z = [7, 8, 9]\\n\",\n \"np.concatenate([x, y, z])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first argument is a list or tuple of arrays to concatenate.\\n\",\n \"Additionally, in the case of multidimensional arrays, it takes an `axis` keyword that allows you to specify the axis along which the result will be concatenated:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 1, 2],\\n\",\n \" [3, 4, 3, 4]])\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = [[1, 2],\\n\",\n \" [3, 4]]\\n\",\n \"np.concatenate([x, x], axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Simple Concatenation with pd.concat\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `pd.concat` function provides a similar syntax to `np.concatenate` but contains a number of options that we'll discuss momentarily:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"# Signature in Pandas v1.3.5\\n\",\n \"pd.concat(objs, axis=0, join='outer', ignore_index=False, keys=None,\\n\",\n \" levels=None, names=None, verify_integrity=False,\\n\",\n \" sort=False, copy=True)\\n\",\n \"```\\n\",\n \"\\n\",\n \"`pd.concat` can be used for a simple concatenation of `Series` or `DataFrame` objects, just as `np.concatenate` can be used for simple concatenations of arrays:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1 A\\n\",\n \"2 B\\n\",\n \"3 C\\n\",\n \"4 D\\n\",\n \"5 E\\n\",\n \"6 F\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser1 = pd.Series(['A', 'B', 'C'], index=[1, 2, 3])\\n\",\n \"ser2 = pd.Series(['D', 'E', 'F'], index=[4, 5, 6])\\n\",\n \"pd.concat([ser1, ser2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It also works to concatenate higher-dimensional objects, such as ``DataFrame``s:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df1\\n\",\n \" A B\\n\",\n \"1 A1 B1\\n\",\n \"2 A2 B2\\n\",\n \"\\n\",\n \"df2\\n\",\n \" A B\\n\",\n \"3 A3 B3\\n\",\n \"4 A4 B4\\n\",\n \"\\n\",\n \"pd.concat([df1, df2])\\n\",\n \" A B\\n\",\n \"1 A1 B1\\n\",\n \"2 A2 B2\\n\",\n \"3 A3 B3\\n\",\n \"4 A4 B4\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df1 = make_df('AB', [1, 2])\\n\",\n \"df2 = make_df('AB', [3, 4])\\n\",\n \"display('df1', 'df2', 'pd.concat([df1, df2])')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It's default behavior is to concatenate row-wise within the `DataFrame` (i.e., `axis=0`).\\n\",\n \"Like `np.concatenate`, `pd.concat` allows specification of an axis along which concatenation will take place.\\n\",\n \"Consider the following example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df3\\n\",\n \" A B\\n\",\n \"0 A0 B0\\n\",\n \"1 A1 B1\\n\",\n \"\\n\",\n \"df4\\n\",\n \" C D\\n\",\n \"0 C0 D0\\n\",\n \"1 C1 D1\\n\",\n \"\\n\",\n \"pd.concat([df3, df4], axis='columns')\\n\",\n \" A B C D\\n\",\n \"0 A0 B0 C0 D0\\n\",\n \"1 A1 B1 C1 D1\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df3 = make_df('AB', [0, 1])\\n\",\n \"df4 = make_df('CD', [0, 1])\\n\",\n \"display('df3', 'df4', \\\"pd.concat([df3, df4], axis='columns')\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We could have equivalently specified ``axis=1``; here we've used the more intuitive ``axis='columns'``. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Duplicate Indices\\n\",\n \"\\n\",\n \"One important difference between `np.concatenate` and `pd.concat` is that Pandas concatenation *preserves indices*, even if the result will have duplicate indices!\\n\",\n \"Consider this short example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"x\\n\",\n \" A B\\n\",\n \"0 A0 B0\\n\",\n \"1 A1 B1\\n\",\n \"\\n\",\n \"y\\n\",\n \" A B\\n\",\n \"0 A2 B2\\n\",\n \"1 A3 B3\\n\",\n \"\\n\",\n \"pd.concat([x, y])\\n\",\n \" A B\\n\",\n \"0 A0 B0\\n\",\n \"1 A1 B1\\n\",\n \"0 A2 B2\\n\",\n \"1 A3 B3\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = make_df('AB', [0, 1])\\n\",\n \"y = make_df('AB', [2, 3])\\n\",\n \"y.index = x.index # make indices match\\n\",\n \"display('x', 'y', 'pd.concat([x, y])')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice the repeated indices in the result.\\n\",\n \"While this is valid within ``DataFrame``s, the outcome is often undesirable.\\n\",\n \"`pd.concat` gives us a few ways to handle it.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Treating repeated indices as an error\\n\",\n \"\\n\",\n \"If you'd like to simply verify that the indices in the result of `pd.concat` do not overlap, you can include the `verify_integrity` flag.\\n\",\n \"With this set to `True`, the concatenation will raise an exception if there are duplicate indices.\\n\",\n \"Here is an example, where for clarity we'll catch and print the error message:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ValueError: Indexes have overlapping values: Int64Index([0, 1], dtype='int64')\\n\"\n ]\n }\n ],\n \"source\": [\n \"try:\\n\",\n \" pd.concat([x, y], verify_integrity=True)\\n\",\n \"except ValueError as e:\\n\",\n \" print(\\\"ValueError:\\\", e)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Ignoring the index\\n\",\n \"\\n\",\n \"Sometimes the index itself does not matter, and you would prefer it to simply be ignored.\\n\",\n \"This option can be specified using the `ignore_index` flag.\\n\",\n \"With this set to `True`, the concatenation will create a new integer index for the resulting `DataFrame`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"x\\n\",\n \" A B\\n\",\n \"0 A0 B0\\n\",\n \"1 A1 B1\\n\",\n \"\\n\",\n \"y\\n\",\n \" A B\\n\",\n \"0 A2 B2\\n\",\n \"1 A3 B3\\n\",\n \"\\n\",\n \"pd.concat([x, y], keys=['x', 'y'])\\n\",\n \" A B\\n\",\n \"x 0 A0 B0\\n\",\n \" 1 A1 B1\\n\",\n \"y 0 A2 B2\\n\",\n \" 1 A3 B3\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display('x', 'y', \\\"pd.concat([x, y], keys=['x', 'y'])\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can use the tools discussed in [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb) to transform this multiply indexed `DataFrame` into the representation we're interested in.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Concatenation with Joins\\n\",\n \"\\n\",\n \"In the short examples we just looked at, we were mainly concatenating ``DataFrame``s with shared column names.\\n\",\n \"In practice, data from different sources might have different sets of column names, and `pd.concat` offers several options in this case.\\n\",\n \"Consider the concatenation of the following two ``DataFrame``s, which have some (but not all!) columns in common:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df5\\n\",\n \" A B C\\n\",\n \"1 A1 B1 C1\\n\",\n \"2 A2 B2 C2\\n\",\n \"\\n\",\n \"df6\\n\",\n \" B C D\\n\",\n \"3 B3 C3 D3\\n\",\n \"4 B4 C4 D4\\n\",\n \"\\n\",\n \"pd.concat([df5, df6])\\n\",\n \" A B C D\\n\",\n \"1 A1 B1 C1 NaN\\n\",\n \"2 A2 B2 C2 NaN\\n\",\n \"3 NaN B3 C3 D3\\n\",\n \"4 NaN B4 C4 D4\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df5 = make_df('ABC', [1, 2])\\n\",\n \"df6 = make_df('BCD', [3, 4])\\n\",\n \"display('df5', 'df6', 'pd.concat([df5, df6])')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The default behavior is to fill entries for which no data is available with NA values.\\n\",\n \"To change this, we can adjust the `join` parameter of the `concat` function.\\n\",\n \"By default, the join is a union of the input columns (`join='outer'`), but we can change this to an intersection of the columns using `join='inner'`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df5\\n\",\n \" A B C\\n\",\n \"1 A1 B1 C1\\n\",\n \"2 A2 B2 C2\\n\",\n \"\\n\",\n \"df6\\n\",\n \" B C D\\n\",\n \"3 B3 C3 D3\\n\",\n \"4 B4 C4 D4\\n\",\n \"\\n\",\n \"pd.concat([df5, df6], join='inner')\\n\",\n \" B C\\n\",\n \"1 B1 C1\\n\",\n \"2 B2 C2\\n\",\n \"3 B3 C3\\n\",\n \"4 B4 C4\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display('df5', 'df6',\\n\",\n \" \\\"pd.concat([df5, df6], join='inner')\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Another useful pattern is to use the `reindex` method before concatenation for finer control over which columns are dropped:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df1\\n\",\n \" A B\\n\",\n \"1 A1 B1\\n\",\n \"2 A2 B2\\n\",\n \"\\n\",\n \"df2\\n\",\n \" A B\\n\",\n \"3 A3 B3\\n\",\n \"4 A4 B4\\n\",\n \"\\n\",\n \"df1.append(df2)\\n\",\n \" A B\\n\",\n \"1 A1 B1\\n\",\n \"2 A2 B2\\n\",\n \"3 A3 B3\\n\",\n \"4 A4 B4\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display('df1', 'df2', 'df1.append(df2)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Keep in mind that unlike the `append` and `extend` methods of Python lists, the `append` method in Pandas does not modify the original object; instead it creates a new object with the combined data.\\n\",\n \"It also is not a very efficient method, because it involves creation of a new index *and* data buffer.\\n\",\n \"Thus, if you plan to do multiple `append` operations, it is generally better to build a list of `DataFrame` objects and pass them all at once to the `concat` function.\\n\",\n \"\\n\",\n \"In the next chapter, we'll look at a more powerful approach to combining data from multiple sources: the database-style merges/joins implemented in `pd.merge`.\\n\",\n \"For more information on `concat`, `append`, and related functionality, see the [\\\"Merge, Join, Concatenate and Compare\\\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/03.07-Merge-and-Join.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Combining Datasets: merge and join\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"One important feature offered by Pandas is its high-performance, in-memory join and merge operations, which you may be familiar with if you have ever worked with databases.\\n\",\n \"The main interface for this is the `pd.merge` function, and we'll see a few examples of how this can work in practice.\\n\",\n \"\\n\",\n \"For convenience, we will again define the `display` function from the previous chapter after the usual imports:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"class display(object):\\n\",\n \" \\\"\\\"\\\"Display HTML representation of multiple objects\\\"\\\"\\\"\\n\",\n \" template = \\\"\\\"\\\"
\\n\",\n \"
{0}
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\\\"\\\"\\\"\\n\",\n \" def __init__(self, *args):\\n\",\n \" self.args = args\\n\",\n \" \\n\",\n \" def _repr_html_(self):\\n\",\n \" return '\\\\n'.join(self.template.format(a, eval(a)._repr_html_())\\n\",\n \" for a in self.args)\\n\",\n \" \\n\",\n \" def __repr__(self):\\n\",\n \" return '\\\\n\\\\n'.join(a + '\\\\n' + repr(eval(a))\\n\",\n \" for a in self.args)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Relational Algebra\\n\",\n \"\\n\",\n \"The behavior implemented in `pd.merge` is a subset of what is known as *relational algebra*, which is a formal set of rules for manipulating relational data that forms the conceptual foundation of operations available in most databases.\\n\",\n \"The strength of the relational algebra approach is that it proposes several fundamental operations, which become the building blocks of more complicated operations on any dataset.\\n\",\n \"With this lexicon of fundamental operations implemented efficiently in a database or other program, a wide range of fairly complicated composite operations can be performed.\\n\",\n \"\\n\",\n \"Pandas implements several of these fundamental building blocks in the `pd.merge` function and the related `join` method of `Series` and `DataFrame` objects.\\n\",\n \"As you will see, these let you efficiently link data from different sources.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Categories of Joins\\n\",\n \"\\n\",\n \"The `pd.merge` function implements a number of types of joins: *one-to-one*, *many-to-one*, and *many-to-many*.\\n\",\n \"All three types of joins are accessed via an identical call to the `pd.merge` interface; the type of join performed depends on the form of the input data.\\n\",\n \"We'll start with some simple examples of the three types of merges, and discuss detailed options a bit later.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### One-to-One Joins\\n\",\n \"\\n\",\n \"Perhaps the simplest type of merge is the one-to-one join, which is in many ways similar to the column-wise concatenation you saw in [Combining Datasets: Concat & Append](03.06-Concat-And-Append.ipynb).\\n\",\n \"As a concrete example, consider the following two `DataFrame` objects, which contain information on several employees in a company:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df1\\n\",\n \" employee group\\n\",\n \"0 Bob Accounting\\n\",\n \"1 Jake Engineering\\n\",\n \"2 Lisa Engineering\\n\",\n \"3 Sue HR\\n\",\n \"\\n\",\n \"df2\\n\",\n \" employee hire_date\\n\",\n \"0 Lisa 2004\\n\",\n \"1 Bob 2008\\n\",\n \"2 Jake 2012\\n\",\n \"3 Sue 2014\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\\n\",\n \" 'group': ['Accounting', 'Engineering',\\n\",\n \" 'Engineering', 'HR']})\\n\",\n \"df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\\n\",\n \" 'hire_date': [2004, 2008, 2012, 2014]})\\n\",\n \"display('df1', 'df2')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To combine this information into a single `DataFrame`, we can use the `pd.merge` function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" employee group hire_date\\n\",\n \"0 Bob Accounting 2008\\n\",\n \"1 Jake Engineering 2012\\n\",\n \"2 Lisa Engineering 2004\\n\",\n \"3 Sue HR 2014\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df3 = pd.merge(df1, df2)\\n\",\n \"df3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `pd.merge` function recognizes that each `DataFrame` has an `employee` column, and automatically joins using this column as a key.\\n\",\n \"The result of the merge is a new `DataFrame` that combines the information from the two inputs.\\n\",\n \"Notice that the order of entries in each column is not necessarily maintained: in this case, the order of the `employee` column differs between `df1` and `df2`, and the `pd.merge` function correctly accounts for this.\\n\",\n \"Additionally, keep in mind that the merge in general discards the index, except in the special case of merges by index (see the `left_index` and `right_index` keywords, discussed momentarily).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Many-to-One Joins\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Many-to-one joins are joins in which one of the two key columns contains duplicate entries.\\n\",\n \"For the many-to-one case, the resulting `DataFrame` will preserve those duplicate entries as appropriate.\\n\",\n \"Consider the following example of a many-to-one join:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df3\\n\",\n \" employee group hire_date\\n\",\n \"0 Bob Accounting 2008\\n\",\n \"1 Jake Engineering 2012\\n\",\n \"2 Lisa Engineering 2004\\n\",\n \"3 Sue HR 2014\\n\",\n \"\\n\",\n \"df4\\n\",\n \" group supervisor\\n\",\n \"0 Accounting Carly\\n\",\n \"1 Engineering Guido\\n\",\n \"2 HR Steve\\n\",\n \"\\n\",\n \"pd.merge(df3, df4)\\n\",\n \" employee group hire_date supervisor\\n\",\n \"0 Bob Accounting 2008 Carly\\n\",\n \"1 Jake Engineering 2012 Guido\\n\",\n \"2 Lisa Engineering 2004 Guido\\n\",\n \"3 Sue HR 2014 Steve\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df4 = pd.DataFrame({'group': ['Accounting', 'Engineering', 'HR'],\\n\",\n \" 'supervisor': ['Carly', 'Guido', 'Steve']})\\n\",\n \"display('df3', 'df4', 'pd.merge(df3, df4)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The resulting `DataFrame` has an additional column with the \\\"supervisor\\\" information, where the information is repeated in one or more locations as required by the inputs.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Many-to-Many Joins\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Many-to-many joins may be a bit confusing conceptually, but are nevertheless well defined.\\n\",\n \"If the key column in both the left and right arrays contains duplicates, then the result is a many-to-many merge.\\n\",\n \"This will be perhaps most clear with a concrete example.\\n\",\n \"Consider the following, where we have a `DataFrame` showing one or more skills associated with a particular group.\\n\",\n \"By performing a many-to-many join, we can recover the skills associated with any individual person:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df1\\n\",\n \" employee group\\n\",\n \"0 Bob Accounting\\n\",\n \"1 Jake Engineering\\n\",\n \"2 Lisa Engineering\\n\",\n \"3 Sue HR\\n\",\n \"\\n\",\n \"df5\\n\",\n \" group skills\\n\",\n \"0 Accounting math\\n\",\n \"1 Accounting spreadsheets\\n\",\n \"2 Engineering software\\n\",\n \"3 Engineering math\\n\",\n \"4 HR spreadsheets\\n\",\n \"5 HR organization\\n\",\n \"\\n\",\n \"pd.merge(df1, df5)\\n\",\n \" employee group skills\\n\",\n \"0 Bob Accounting math\\n\",\n \"1 Bob Accounting spreadsheets\\n\",\n \"2 Jake Engineering software\\n\",\n \"3 Jake Engineering math\\n\",\n \"4 Lisa Engineering software\\n\",\n \"5 Lisa Engineering math\\n\",\n \"6 Sue HR spreadsheets\\n\",\n \"7 Sue HR organization\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df5 = pd.DataFrame({'group': ['Accounting', 'Accounting',\\n\",\n \" 'Engineering', 'Engineering', 'HR', 'HR'],\\n\",\n \" 'skills': ['math', 'spreadsheets', 'software', 'math',\\n\",\n \" 'spreadsheets', 'organization']})\\n\",\n \"display('df1', 'df5', \\\"pd.merge(df1, df5)\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These three types of joins can be used with other Pandas tools to implement a wide array of functionality.\\n\",\n \"But in practice, datasets are rarely as clean as the one we're working with here.\\n\",\n \"In the following section we'll consider some of the options provided by `pd.merge` that enable you to tune how the join operations work.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Specification of the Merge Key\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We've already seen the default behavior of `pd.merge`: it looks for one or more matching column names between the two inputs, and uses this as the key.\\n\",\n \"However, often the column names will not match so nicely, and `pd.merge` provides a variety of options for handling this.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### The on Keyword\\n\",\n \"\\n\",\n \"Most simply, you can explicitly specify the name of the key column using the `on` keyword, which takes a column name or a list of column names:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df1\\n\",\n \" employee group\\n\",\n \"0 Bob Accounting\\n\",\n \"1 Jake Engineering\\n\",\n \"2 Lisa Engineering\\n\",\n \"3 Sue HR\\n\",\n \"\\n\",\n \"df2\\n\",\n \" employee hire_date\\n\",\n \"0 Lisa 2004\\n\",\n \"1 Bob 2008\\n\",\n \"2 Jake 2012\\n\",\n \"3 Sue 2014\\n\",\n \"\\n\",\n \"pd.merge(df1, df2, on='employee')\\n\",\n \" employee group hire_date\\n\",\n \"0 Bob Accounting 2008\\n\",\n \"1 Jake Engineering 2012\\n\",\n \"2 Lisa Engineering 2004\\n\",\n \"3 Sue HR 2014\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display('df1', 'df2', \\\"pd.merge(df1, df2, on='employee')\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This option works only if both the left and right ``DataFrame``s have the specified column name.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### The left_on and right_on Keywords\\n\",\n \"\\n\",\n \"At times you may wish to merge two datasets with different column names; for example, we may have a dataset in which the employee name is labeled as \\\"name\\\" rather than \\\"employee\\\".\\n\",\n \"In this case, we can use the `left_on` and `right_on` keywords to specify the two column names:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \"df1a\\n\",\n \" group\\n\",\n \"employee \\n\",\n \"Bob Accounting\\n\",\n \"Jake Engineering\\n\",\n \"Lisa Engineering\\n\",\n \"Sue HR\\n\",\n \"\\n\",\n \"df3\\n\",\n \" name salary\\n\",\n \"0 Bob 70000\\n\",\n \"1 Jake 80000\\n\",\n \"2 Lisa 120000\\n\",\n \"3 Sue 90000\\n\",\n \"\\n\",\n \"pd.merge(df1a, df3, left_index=True, right_on='name')\\n\",\n \" group name salary\\n\",\n \"0 Accounting Bob 70000\\n\",\n \"1 Engineering Jake 80000\\n\",\n \"2 Engineering Lisa 120000\\n\",\n \"3 HR Sue 90000\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display('df1a', 'df3', \\\"pd.merge(df1a, df3, left_index=True, right_on='name')\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All of these options also work with multiple indices and/or multiple columns; the interface for this behavior is very intuitive.\\n\",\n \"For more information on this, see the [\\\"Merge, Join, and Concatenate\\\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Specifying Set Arithmetic for Joins\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In all the preceding examples we have glossed over one important consideration in performing a join: the type of set arithmetic used in the join.\\n\",\n \"This comes up when a value appears in one key column but not the other. Consider this example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df6\\n\",\n \" name food\\n\",\n \"0 Peter fish\\n\",\n \"1 Paul beans\\n\",\n \"2 Mary bread\\n\",\n \"\\n\",\n \"df7\\n\",\n \" name drink\\n\",\n \"0 Mary wine\\n\",\n \"1 Joseph beer\\n\",\n \"\\n\",\n \"pd.merge(df6, df7)\\n\",\n \" name food drink\\n\",\n \"0 Mary bread wine\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df6 = pd.DataFrame({'name': ['Peter', 'Paul', 'Mary'],\\n\",\n \" 'food': ['fish', 'beans', 'bread']},\\n\",\n \" columns=['name', 'food'])\\n\",\n \"df7 = pd.DataFrame({'name': ['Mary', 'Joseph'],\\n\",\n \" 'drink': ['wine', 'beer']},\\n\",\n \" columns=['name', 'drink'])\\n\",\n \"display('df6', 'df7', 'pd.merge(df6, df7)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we have merged two datasets that have only a single \\\"name\\\" entry in common: Mary.\\n\",\n \"By default, the result contains the *intersection* of the two sets of inputs; this is what is known as an *inner join*.\\n\",\n \"We can specify this explicitly using the `how` keyword, which defaults to `\\\"inner\\\"`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" name food drink\\n\",\n \"0 Mary bread wine\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.merge(df6, df7, how='inner')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Other options for the `how` keyword are `'outer'`, `'left'`, and `'right'`.\\n\",\n \"An *outer join* returns a join over the union of the input columns, and fills in all missing values with NAs:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df6\\n\",\n \" name food\\n\",\n \"0 Peter fish\\n\",\n \"1 Paul beans\\n\",\n \"2 Mary bread\\n\",\n \"\\n\",\n \"df7\\n\",\n \" name drink\\n\",\n \"0 Mary wine\\n\",\n \"1 Joseph beer\\n\",\n \"\\n\",\n \"pd.merge(df6, df7, how='outer')\\n\",\n \" name food drink\\n\",\n \"0 Peter fish NaN\\n\",\n \"1 Paul beans NaN\\n\",\n \"2 Mary bread wine\\n\",\n \"3 Joseph NaN beer\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display('df6', 'df7', \\\"pd.merge(df6, df7, how='outer')\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The *left join* and *right join* return joins over the left entries and right entries, respectively.\\n\",\n \"For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df6\\n\",\n \" name food\\n\",\n \"0 Peter fish\\n\",\n \"1 Paul beans\\n\",\n \"2 Mary bread\\n\",\n \"\\n\",\n \"df7\\n\",\n \" name drink\\n\",\n \"0 Mary wine\\n\",\n \"1 Joseph beer\\n\",\n \"\\n\",\n \"pd.merge(df6, df7, how='left')\\n\",\n \" name food drink\\n\",\n \"0 Peter fish NaN\\n\",\n \"1 Paul beans NaN\\n\",\n \"2 Mary bread wine\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display('df6', 'df7', \\\"pd.merge(df6, df7, how='left')\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The output rows now correspond to the entries in the left input. Using\\n\",\n \"`how='right'` works in a similar manner.\\n\",\n \"\\n\",\n \"All of these options can be applied straightforwardly to any of the preceding join types.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Overlapping Column Names: The suffixes Keyword\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Last, you may end up in a case where your two input ``DataFrame``s have conflicting column names.\\n\",\n \"Consider this example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df8\\n\",\n \" name rank\\n\",\n \"0 Bob 1\\n\",\n \"1 Jake 2\\n\",\n \"2 Lisa 3\\n\",\n \"3 Sue 4\\n\",\n \"\\n\",\n \"df9\\n\",\n \" name rank\\n\",\n \"0 Bob 3\\n\",\n \"1 Jake 1\\n\",\n \"2 Lisa 4\\n\",\n \"3 Sue 2\\n\",\n \"\\n\",\n \"pd.merge(df8, df9, on=\\\"name\\\")\\n\",\n \" name rank_x rank_y\\n\",\n \"0 Bob 1 3\\n\",\n \"1 Jake 2 1\\n\",\n \"2 Lisa 3 4\\n\",\n \"3 Sue 4 2\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df8 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\\n\",\n \" 'rank': [1, 2, 3, 4]})\\n\",\n \"df9 = pd.DataFrame({'name': ['Bob', 'Jake', 'Lisa', 'Sue'],\\n\",\n \" 'rank': [3, 1, 4, 2]})\\n\",\n \"display('df8', 'df9', 'pd.merge(df8, df9, on=\\\"name\\\")')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because the output would have two conflicting column names, the `merge` function automatically appends the suffixes ``_x`` and ``_y`` to make the output columns unique.\\n\",\n \"If these defaults are inappropriate, it is possible to specify a custom suffix using the ``suffixes`` keyword:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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name
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rank_R
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\"\n ],\n \"text/plain\": [\n \" name rank_L rank_R\\n\",\n \"0 Bob 1 3\\n\",\n \"1 Jake 2 1\\n\",\n \"2 Lisa 3 4\\n\",\n \"3 Sue 4 2\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.merge(df8, df9, on=\\\"name\\\", suffixes=[\\\"_L\\\", \\\"_R\\\"])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These suffixes work in any of the possible join patterns, and also work if there are multiple overlapping columns.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For more information on these patterns, see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb), where we dive a bit deeper into relational algebra.\\n\",\n \"Also see the [\\\"Merge, Join, Concatenate and Compare\\\" section](http://pandas.pydata.org/pandas-docs/stable/merging.html) of the Pandas documentation for further discussion of these topics.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: US States Data\\n\",\n \"\\n\",\n \"Merge and join operations come up most often when combining data from different sources.\\n\",\n \"Here we will consider an example of some data about US states and their populations.\\n\",\n \"The data files can be found at [http://github.com/jakevdp/data-USstates](http://github.com/jakevdp/data-USstates):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Following are commands to download the data\\n\",\n \"# repo = \\\"https://raw.githubusercontent.com/jakevdp/data-USstates/master\\\"\\n\",\n \"# !cd data && curl -O {repo}/state-population.csv\\n\",\n \"# !cd data && curl -O {repo}/state-areas.csv\\n\",\n \"# !cd data && curl -O {repo}/state-abbrevs.csv\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's take a look at the three datasets, using the Pandas ``read_csv`` function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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pop.head()
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state/region
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ages
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year
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population
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AL
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1
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AL
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areas.head()
\\n\",\n \"\\n\",\n \"
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\\n\",\n \"
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state
\\n\",\n \"
area (sq. mi)
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0
\\n\",\n \"
Alabama
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52423
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1
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Alaska
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656425
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2
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Arizona
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114006
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3
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Arkansas
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53182
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4
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California
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163707
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abbrevs.head()
\\n\",\n \"\\n\",\n \"
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\\n\",\n \"
\\n\",\n \"
state
\\n\",\n \"
abbreviation
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0
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Alabama
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AK
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AR
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4
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California
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CA
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\"\n ],\n \"text/plain\": [\n \"pop.head()\\n\",\n \" state/region ages year population\\n\",\n \"0 AL under18 2012 1117489.0\\n\",\n \"1 AL total 2012 4817528.0\\n\",\n \"2 AL under18 2010 1130966.0\\n\",\n \"3 AL total 2010 4785570.0\\n\",\n \"4 AL under18 2011 1125763.0\\n\",\n \"\\n\",\n \"areas.head()\\n\",\n \" state area (sq. mi)\\n\",\n \"0 Alabama 52423\\n\",\n \"1 Alaska 656425\\n\",\n \"2 Arizona 114006\\n\",\n \"3 Arkansas 53182\\n\",\n \"4 California 163707\\n\",\n \"\\n\",\n \"abbrevs.head()\\n\",\n \" state abbreviation\\n\",\n \"0 Alabama AL\\n\",\n \"1 Alaska AK\\n\",\n \"2 Arizona AZ\\n\",\n \"3 Arkansas AR\\n\",\n \"4 California CA\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop = pd.read_csv('data/state-population.csv')\\n\",\n \"areas = pd.read_csv('data/state-areas.csv')\\n\",\n \"abbrevs = pd.read_csv('data/state-abbrevs.csv')\\n\",\n \"\\n\",\n \"display('pop.head()', 'areas.head()', 'abbrevs.head()')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Given this information, say we want to compute a relatively straightforward result: rank US states and territories by their 2010 population density.\\n\",\n \"We clearly have the data here to find this result, but we'll have to combine the datasets to do so.\\n\",\n \"\\n\",\n \"We'll start with a many-to-one merge that will give us the full state names within the population `DataFrame`.\\n\",\n \"We want to merge based on the `state/region` column of `pop` and the `abbreviation` column of `abbrevs`.\\n\",\n \"We'll use `how='outer'` to make sure no data is thrown away due to mismatched labels:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" state/region ages year population state\\n\",\n \"0 AL under18 2012 1117489.0 Alabama\\n\",\n \"1 AL total 2012 4817528.0 Alabama\\n\",\n \"2 AL under18 2010 1130966.0 Alabama\\n\",\n \"3 AL total 2010 4785570.0 Alabama\\n\",\n \"4 AL under18 2011 1125763.0 Alabama\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged = pd.merge(pop, abbrevs, how='outer',\\n\",\n \" left_on='state/region', right_on='abbreviation')\\n\",\n \"merged = merged.drop('abbreviation', axis=1) # drop duplicate info\\n\",\n \"merged.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's double-check whether there were any mismatches here, which we can do by looking for rows with nulls:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state/region False\\n\",\n \"ages False\\n\",\n \"year False\\n\",\n \"population True\\n\",\n \"state True\\n\",\n \"dtype: bool\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged.isnull().any()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Some of the ``population`` values are null; let's figure out which these are!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" state/region ages year population state\\n\",\n \"2448 PR under18 1990 NaN NaN\\n\",\n \"2449 PR total 1990 NaN NaN\\n\",\n \"2450 PR total 1991 NaN NaN\\n\",\n \"2451 PR under18 1991 NaN NaN\\n\",\n \"2452 PR total 1993 NaN NaN\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged[merged['population'].isnull()].head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It appears that all the null population values are from Puerto Rico prior to the year 2000; this is likely due to this data not being available in the original source.\\n\",\n \"\\n\",\n \"More importantly, we see that some of the new `state` entries are also null, which means that there was no corresponding entry in the `abbrevs` key!\\n\",\n \"Let's figure out which regions lack this match:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array(['PR', 'USA'], dtype=object)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged.loc[merged['state'].isnull(), 'state/region'].unique()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can quickly infer the issue: our population data includes entries for Puerto Rico (PR) and the United States as a whole (USA), while these entries do not appear in the state abbreviation key.\\n\",\n \"We can fix these quickly by filling in appropriate entries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state/region False\\n\",\n \"ages False\\n\",\n \"year False\\n\",\n \"population True\\n\",\n \"state False\\n\",\n \"dtype: bool\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged.loc[merged['state/region'] == 'PR', 'state'] = 'Puerto Rico'\\n\",\n \"merged.loc[merged['state/region'] == 'USA', 'state'] = 'United States'\\n\",\n \"merged.isnull().any()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"No more nulls in the `state` column: we're all set!\\n\",\n \"\\n\",\n \"Now we can merge the result with the area data using a similar procedure.\\n\",\n \"Examining our results, we will want to join on the `state` column in both:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" state/region ages year population state area (sq. mi)\\n\",\n \"0 AL under18 2012 1117489.0 Alabama 52423.0\\n\",\n \"1 AL total 2012 4817528.0 Alabama 52423.0\\n\",\n \"2 AL under18 2010 1130966.0 Alabama 52423.0\\n\",\n \"3 AL total 2010 4785570.0 Alabama 52423.0\\n\",\n \"4 AL under18 2011 1125763.0 Alabama 52423.0\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"final = pd.merge(merged, areas, on='state', how='left')\\n\",\n \"final.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Again, let's check for nulls to see if there were any mismatches:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state/region False\\n\",\n \"ages False\\n\",\n \"year False\\n\",\n \"population True\\n\",\n \"state False\\n\",\n \"area (sq. mi) True\\n\",\n \"dtype: bool\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"final.isnull().any()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are nulls in the ``area`` column; we can take a look to see which regions were ignored here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array(['United States'], dtype=object)\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"final['state'][final['area (sq. mi)'].isnull()].unique()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see that our ``areas`` ``DataFrame`` does not contain the area of the United States as a whole.\\n\",\n \"We could insert the appropriate value (using the sum of all state areas, for instance), but in this case we'll just drop the null values because the population density of the entire United States is not relevant to our current discussion:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" state/region ages year population state area (sq. mi)\\n\",\n \"0 AL under18 2012 1117489.0 Alabama 52423.0\\n\",\n \"1 AL total 2012 4817528.0 Alabama 52423.0\\n\",\n \"2 AL under18 2010 1130966.0 Alabama 52423.0\\n\",\n \"3 AL total 2010 4785570.0 Alabama 52423.0\\n\",\n \"4 AL under18 2011 1125763.0 Alabama 52423.0\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"final.dropna(inplace=True)\\n\",\n \"final.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we have all the data we need. To answer the question of interest, let's first select the portion of the data corresponding with the year 2010, and the total population.\\n\",\n \"We'll use the `query` function to do this quickly (this requires the NumExpr package to be installed; see [High-Performance Pandas: `eval()` and `query()`](03.12-Performance-Eval-and-Query.ipynb)):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" state/region ages year population state area (sq. mi)\\n\",\n \"3 AL total 2010 4785570.0 Alabama 52423.0\\n\",\n \"91 AK total 2010 713868.0 Alaska 656425.0\\n\",\n \"101 AZ total 2010 6408790.0 Arizona 114006.0\\n\",\n \"189 AR total 2010 2922280.0 Arkansas 53182.0\\n\",\n \"197 CA total 2010 37333601.0 California 163707.0\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data2010 = final.query(\\\"year == 2010 & ages == 'total'\\\")\\n\",\n \"data2010.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's compute the population density and display it in order.\\n\",\n \"We'll start by re-indexing our data on the state, and then compute the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"data2010.set_index('state', inplace=True)\\n\",\n \"density = data2010['population'] / data2010['area (sq. mi)']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state\\n\",\n \"District of Columbia 8898.897059\\n\",\n \"Puerto Rico 1058.665149\\n\",\n \"New Jersey 1009.253268\\n\",\n \"Rhode Island 681.339159\\n\",\n \"Connecticut 645.600649\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"density.sort_values(ascending=False, inplace=True)\\n\",\n \"density.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result is a ranking of US states, plus Washington, DC, and Puerto Rico, in order of their 2010 population density, in residents per square mile.\\n\",\n \"We can see that by far the densest region in this dataset is Washington, DC (i.e., the District of Columbia); among states, the densest is New Jersey.\\n\",\n \"\\n\",\n \"We can also check the end of the list:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"state\\n\",\n \"South Dakota 10.583512\\n\",\n \"North Dakota 9.537565\\n\",\n \"Montana 6.736171\\n\",\n \"Wyoming 5.768079\\n\",\n \"Alaska 1.087509\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"density.tail()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see that the least dense state, by far, is Alaska, averaging slightly over one resident per square mile.\\n\",\n \"\\n\",\n \"This type of data merging is a common task when trying to answer questions using real-world data sources.\\n\",\n \"I hope that this example has given you an idea of some of the ways you can combine the tools we've covered in order to gain insight from your data!\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Aggregation and Grouping\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A fundamental piece of many data analysis tasks is efficient summarization: computing aggregations like `sum`, `mean`, `median`, `min`, and `max`, in which a single number summarizes aspects of a potentially large dataset.\\n\",\n \"In this chapter, we'll explore aggregations in Pandas, from simple operations akin to what we've seen on NumPy arrays to more sophisticated operations based on the concept of a `groupby`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For convenience, we'll use the same `display` magic function that we used in the previous chapters:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"class display(object):\\n\",\n \" \\\"\\\"\\\"Display HTML representation of multiple objects\\\"\\\"\\\"\\n\",\n \" template = \\\"\\\"\\\"
\\n\",\n \"
{0}
{1}\\n\",\n \"
\\\"\\\"\\\"\\n\",\n \" def __init__(self, *args):\\n\",\n \" self.args = args\\n\",\n \" \\n\",\n \" def _repr_html_(self):\\n\",\n \" return '\\\\n'.join(self.template.format(a, eval(a)._repr_html_())\\n\",\n \" for a in self.args)\\n\",\n \" \\n\",\n \" def __repr__(self):\\n\",\n \" return '\\\\n\\\\n'.join(a + '\\\\n' + repr(eval(a))\\n\",\n \" for a in self.args)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Planets Data\\n\",\n \"\\n\",\n \"Here we will use the Planets dataset, available via the [Seaborn package](http://seaborn.pydata.org/) (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\\n\",\n \"It gives information on planets that astronomers have discovered around other stars (known as *extrasolar planets*, or *exoplanets* for short). It can be downloaded with a simple Seaborn command:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1035, 6)\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"planets = sns.load_dataset('planets')\\n\",\n \"planets.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \" A B\\n\",\n \"0 0.155995 0.020584\\n\",\n \"1 0.058084 0.969910\\n\",\n \"2 0.866176 0.832443\\n\",\n \"3 0.601115 0.212339\\n\",\n \"4 0.708073 0.181825\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.DataFrame({'A': rng.rand(5),\\n\",\n \" 'B': rng.rand(5)})\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"A 0.477888\\n\",\n \"B 0.443420\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.mean()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By specifying the `axis` argument, you can instead aggregate within each row:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 0.088290\\n\",\n \"1 0.513997\\n\",\n \"2 0.849309\\n\",\n \"3 0.406727\\n\",\n \"4 0.444949\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.mean(axis='columns')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Pandas `Series` and `DataFrame` objects include all of the common aggregates mentioned in [Aggregations: Min, Max, and Everything In Between](02.04-Computation-on-arrays-aggregates.ipynb); in addition, there is a convenience method, `describe`, that computes several common aggregates for each column and returns the result.\\n\",\n \"Let's use this on the Planets data, for now dropping rows with missing values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" number orbital_period mass distance year\\n\",\n \"count 498.00000 498.000000 498.000000 498.000000 498.000000\\n\",\n \"mean 1.73494 835.778671 2.509320 52.068213 2007.377510\\n\",\n \"std 1.17572 1469.128259 3.636274 46.596041 4.167284\\n\",\n \"min 1.00000 1.328300 0.003600 1.350000 1989.000000\\n\",\n \"25% 1.00000 38.272250 0.212500 24.497500 2005.000000\\n\",\n \"50% 1.00000 357.000000 1.245000 39.940000 2009.000000\\n\",\n \"75% 2.00000 999.600000 2.867500 59.332500 2011.000000\\n\",\n \"max 6.00000 17337.500000 25.000000 354.000000 2014.000000\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"planets.dropna().describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This method helps us understand the overall properties of a dataset.\\n\",\n \"For example, we see in the `year` column that although exoplanets were discovered as far back as 1989, half of all planets in the dataset were not discovered until 2010 or after.\\n\",\n \"This is largely thanks to the *Kepler* mission, which aimed to find eclipsing planets around other stars using a specially designed space telescope.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following table summarizes some other built-in Pandas aggregations:\\n\",\n \"\\n\",\n \"| Aggregation | Returns |\\n\",\n \"|--------------------------|---------------------------------|\\n\",\n \"| ``count`` | Total number of items |\\n\",\n \"| ``first``, ``last`` | First and last item |\\n\",\n \"| ``mean``, ``median`` | Mean and median |\\n\",\n \"| ``min``, ``max`` | Minimum and maximum |\\n\",\n \"| ``std``, ``var`` | Standard deviation and variance |\\n\",\n \"| ``mad`` | Mean absolute deviation |\\n\",\n \"| ``prod`` | Product of all items |\\n\",\n \"| ``sum`` | Sum of all items |\\n\",\n \"\\n\",\n \"These are all methods of `DataFrame` and `Series` objects.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To go deeper into the data, however, simple aggregates are often not enough.\\n\",\n \"The next level of data summarization is the `groupby` operation, which allows you to quickly and efficiently compute aggregates on subsets of data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## groupby: Split, Apply, Combine\\n\",\n \"\\n\",\n \"Simple aggregations can give you a flavor of your dataset, but often we would prefer to aggregate conditionally on some label or index: this is implemented in the so-called `groupby` operation.\\n\",\n \"The name \\\"group by\\\" comes from a command in the SQL database language, but it is perhaps more illuminative to think of it in the terms first coined by Hadley Wickham of Rstats fame: *split, apply, combine*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Split, Apply, Combine\\n\",\n \"\\n\",\n \"A canonical example of this split-apply-combine operation, where the \\\"apply\\\" is a summation aggregation, is illustrated in this figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"([figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Split-Apply-Combine))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This illustrates what the `groupby` operation accomplishes:\\n\",\n \"\\n\",\n \"- The *split* step involves breaking up and grouping a `DataFrame` depending on the value of the specified key.\\n\",\n \"- The *apply* step involves computing some function, usually an aggregate, transformation, or filtering, within the individual groups.\\n\",\n \"- The *combine* step merges the results of these operations into an output array.\\n\",\n \"\\n\",\n \"While this could certainly be done manually using some combination of the masking, aggregation, and merging commands covered earlier, an important realization is that *the intermediate splits do not need to be explicitly instantiated*. Rather, the `groupby` can (often) do this in a single pass over the data, updating the sum, mean, count, min, or other aggregate for each group along the way.\\n\",\n \"The power of the `groupby` is that it abstracts away these steps: the user need not think about *how* the computation is done under the hood, but rather can think about the *operation as a whole*.\\n\",\n \"\\n\",\n \"As a concrete example, let's take a look at using Pandas for the computation shown in the following figure.\\n\",\n \"We'll start by creating the input `DataFrame`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" key data\\n\",\n \"0 A 0\\n\",\n \"1 B 1\\n\",\n \"2 C 2\\n\",\n \"3 A 3\\n\",\n \"4 B 4\\n\",\n \"5 C 5\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\\n\",\n \" 'data': range(6)}, columns=['key', 'data'])\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The most basic split-apply-combine operation can be computed with the `groupby` method of the `DataFrame`, passing the name of the desired key column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.groupby('key')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that what is returned is a `DataFrameGroupBy` object, not a set of `DataFrame` objects.\\n\",\n \"This object is where the magic is: you can think of it as a special view of the `DataFrame`, which is poised to dig into the groups but does no actual computation until the aggregation is applied.\\n\",\n \"This \\\"lazy evaluation\\\" approach means that common aggregates can be implemented efficiently in a way that is almost transparent to the user.\\n\",\n \"\\n\",\n \"To produce a result, we can apply an aggregate to this `DataFrameGroupBy` object, which will perform the appropriate apply/combine steps to produce the desired result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" data\\n\",\n \"key \\n\",\n \"A 3\\n\",\n \"B 5\\n\",\n \"C 7\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.groupby('key').sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `sum` method is just one possibility here; you can apply most Pandas or NumPy aggregation functions, as well as most `DataFrame` operations, as you will see in the following discussion.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### The GroupBy Object\\n\",\n \"\\n\",\n \"The `GroupBy` object is a flexible abstraction: in many ways, it can be treated as simply a collection of ``DataFrame``s, though it is doing more sophisticated things under the hood. Let's see some examples using the Planets data.\\n\",\n \"\\n\",\n \"Perhaps the most important operations made available by a `GroupBy` are *aggregate*, *filter*, *transform*, and *apply*.\\n\",\n \"We'll discuss each of these more fully in the next section, but before that let's take a look at some of the other functionality that can be used with the basic `GroupBy` operation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Column indexing\\n\",\n \"\\n\",\n \"The `GroupBy` object supports column indexing in the same way as the `DataFrame`, and returns a modified `GroupBy` object.\\n\",\n \"For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"planets.groupby('method')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"planets.groupby('method')['orbital_period']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we've selected a particular `Series` group from the original `DataFrame` group by reference to its column name.\\n\",\n \"As with the `GroupBy` object, no computation is done until we call some aggregate on the object:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"method\\n\",\n \"Astrometry 631.180000\\n\",\n \"Eclipse Timing Variations 4343.500000\\n\",\n \"Imaging 27500.000000\\n\",\n \"Microlensing 3300.000000\\n\",\n \"Orbital Brightness Modulation 0.342887\\n\",\n \"Pulsar Timing 66.541900\\n\",\n \"Pulsation Timing Variations 1170.000000\\n\",\n \"Radial Velocity 360.200000\\n\",\n \"Transit 5.714932\\n\",\n \"Transit Timing Variations 57.011000\\n\",\n \"Name: orbital_period, dtype: float64\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"planets.groupby('method')['orbital_period'].median()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This gives an idea of the general scale of orbital periods (in days) that each method is sensitive to.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Iteration over groups\\n\",\n \"\\n\",\n \"The `GroupBy` object supports direct iteration over the groups, returning each group as a `Series` or `DataFrame`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Astrometry shape=(2, 6)\\n\",\n \"Eclipse Timing Variations shape=(9, 6)\\n\",\n \"Imaging shape=(38, 6)\\n\",\n \"Microlensing shape=(23, 6)\\n\",\n \"Orbital Brightness Modulation shape=(3, 6)\\n\",\n \"Pulsar Timing shape=(5, 6)\\n\",\n \"Pulsation Timing Variations shape=(1, 6)\\n\",\n \"Radial Velocity shape=(553, 6)\\n\",\n \"Transit shape=(397, 6)\\n\",\n \"Transit Timing Variations shape=(4, 6)\\n\"\n ]\n }\n ],\n \"source\": [\n \"for (method, group) in planets.groupby('method'):\\n\",\n \" print(\\\"{0:30s} shape={1}\\\".format(method, group.shape))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This can be useful for manual inspection of groups for the sake of debugging, but it is often much faster to use the built-in `apply` functionality, which we will discuss momentarily.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Dispatch methods\\n\",\n \"\\n\",\n \"Through some Python class magic, any method not explicitly implemented by the `GroupBy` object will be passed through and called on the groups, whether they are `DataFrame` or `Series` objects.\\n\",\n \"For example, using the `describe` method is equivalent to calling `describe` on the `DataFrame` representing each group:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \" method \\n\",\n \"count Astrometry 2.0\\n\",\n \" Eclipse Timing Variations 9.0\\n\",\n \" Imaging 38.0\\n\",\n \" Microlensing 23.0\\n\",\n \" Orbital Brightness Modulation 3.0\\n\",\n \" ... \\n\",\n \"max Pulsar Timing 2011.0\\n\",\n \" Pulsation Timing Variations 2007.0\\n\",\n \" Radial Velocity 2014.0\\n\",\n \" Transit 2014.0\\n\",\n \" Transit Timing Variations 2014.0\\n\",\n \"Length: 80, dtype: float64\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"planets.groupby('method')['year'].describe().unstack()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Looking at this table helps us to better understand the data: for example, the vast majority of planets until 2014 were discovered by the Radial Velocity and Transit methods, though the latter method became common more recently.\\n\",\n \"The newest methods seem to be Transit Timing Variation and Orbital Brightness Modulation, which were not used to discover a new planet until 2011.\\n\",\n \"\\n\",\n \"Notice that these dispatch methods are applied *to each individual group*, and the results are then combined within `GroupBy` and returned.\\n\",\n \"Again, any valid `DataFrame`/`Series` method can be called in a similar manner on the corresponding `GroupBy` object.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Aggregate, Filter, Transform, Apply\\n\",\n \"\\n\",\n \"The preceding discussion focused on aggregation for the combine operation, but there are more options available.\\n\",\n \"In particular, `GroupBy` objects have `aggregate`, `filter`, `transform`, and `apply` methods that efficiently implement a variety of useful operations before combining the grouped data.\\n\",\n \"\\n\",\n \"For the purpose of the following subsections, we'll use this ``DataFrame``:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" key data1 data2\\n\",\n \"0 A 0 5\\n\",\n \"1 B 1 0\\n\",\n \"2 C 2 3\\n\",\n \"3 A 3 3\\n\",\n \"4 B 4 7\\n\",\n \"5 C 5 9\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"rng = np.random.RandomState(0)\\n\",\n \"df = pd.DataFrame({'key': ['A', 'B', 'C', 'A', 'B', 'C'],\\n\",\n \" 'data1': range(6),\\n\",\n \" 'data2': rng.randint(0, 10, 6)},\\n\",\n \" columns = ['key', 'data1', 'data2'])\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Aggregation\\n\",\n \"\\n\",\n \"You're now familiar with `GroupBy` aggregations with `sum`, `median`, and the like, but the `aggregate` method allows for even more flexibility.\\n\",\n \"It can take a string, a function, or a list thereof, and compute all the aggregates at once.\\n\",\n \"Here is a quick example combining all of these:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" data1 data2 \\n\",\n \" min median max min median max\\n\",\n \"key \\n\",\n \"A 0 1.5 3 3 4.0 5\\n\",\n \"B 1 2.5 4 0 3.5 7\\n\",\n \"C 2 3.5 5 3 6.0 9\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.groupby('key').aggregate(['min', np.median, max])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Another common pattern is to pass a dictionary mapping column names to operations to be applied on that column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" data1 data2\\n\",\n \"key \\n\",\n \"A 0 5\\n\",\n \"B 1 7\\n\",\n \"C 2 9\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.groupby('key').aggregate({'data1': 'min',\\n\",\n \" 'data2': 'max'})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Filtering\\n\",\n \"\\n\",\n \"A filtering operation allows you to drop data based on the group properties.\\n\",\n \"For example, we might want to keep all groups in which the standard deviation is larger than some critical value:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df\\n\",\n \" key data1 data2\\n\",\n \"0 A 0 5\\n\",\n \"1 B 1 0\\n\",\n \"2 C 2 3\\n\",\n \"3 A 3 3\\n\",\n \"4 B 4 7\\n\",\n \"5 C 5 9\\n\",\n \"\\n\",\n \"df.groupby('key').std()\\n\",\n \" data1 data2\\n\",\n \"key \\n\",\n \"A 2.12132 1.414214\\n\",\n \"B 2.12132 4.949747\\n\",\n \"C 2.12132 4.242641\\n\",\n \"\\n\",\n \"df.groupby('key').filter(filter_func)\\n\",\n \" key data1 data2\\n\",\n \"1 B 1 0\\n\",\n \"2 C 2 3\\n\",\n \"4 B 4 7\\n\",\n \"5 C 5 9\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def filter_func(x):\\n\",\n \" return x['data2'].std() > 4\\n\",\n \"\\n\",\n \"display('df', \\\"df.groupby('key').std()\\\",\\n\",\n \" \\\"df.groupby('key').filter(filter_func)\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The filter function should return a Boolean value specifying whether the group passes the filtering. Here, because group A does not have a standard deviation greater than 4, it is dropped from the result.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Transformation\\n\",\n \"\\n\",\n \"While aggregation must return a reduced version of the data, transformation can return some transformed version of the full data to recombine.\\n\",\n \"For such a transformation, the output is the same shape as the input.\\n\",\n \"A common example is to center the data by subtracting the group-wise mean:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" data1 data2\\n\",\n \"0 -1.5 1.0\\n\",\n \"1 -1.5 -3.5\\n\",\n \"2 -1.5 -3.0\\n\",\n \"3 1.5 -1.0\\n\",\n \"4 1.5 3.5\\n\",\n \"5 1.5 3.0\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def center(x):\\n\",\n \" return x - x.mean()\\n\",\n \"df.groupby('key').transform(center)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### The apply method\\n\",\n \"\\n\",\n \"The `apply` method lets you apply an arbitrary function to the group results.\\n\",\n \"The function should take a `DataFrame` and returns either a Pandas object (e.g., `DataFrame`, `Series`) or a scalar; the behavior of the combine step will be tailored to the type of output returned.\\n\",\n \"\\n\",\n \"For example, here is an `apply` operation that normalizes the first column by the sum of the second:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" key data1 data2\\n\",\n \"0 A 0.000000 5\\n\",\n \"1 B 0.142857 0\\n\",\n \"2 C 0.166667 3\\n\",\n \"3 A 0.375000 3\\n\",\n \"4 B 0.571429 7\\n\",\n \"5 C 0.416667 9\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def norm_by_data2(x):\\n\",\n \" # x is a DataFrame of group values\\n\",\n \" x['data1'] /= x['data2'].sum()\\n\",\n \" return x\\n\",\n \"\\n\",\n \"df.groupby('key').apply(norm_by_data2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`apply` within a `GroupBy` is flexible: the only criterion is that the function takes a `DataFrame` and returns a Pandas object or scalar. What you do in between is up to you!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Specifying the Split Key\\n\",\n \"\\n\",\n \"In the simple examples presented before, we split the `DataFrame` on a single column name.\\n\",\n \"This is just one of many options by which the groups can be defined, and we'll go through some other options for group specification here.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### A list, array, series, or index providing the grouping keys\\n\",\n \"\\n\",\n \"The key can be any series or list with a length matching that of the `DataFrame`. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \" data1 data2\\n\",\n \"key \\n\",\n \"A 3 8\\n\",\n \"B 5 7\\n\",\n \"C 7 12\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.groupby(df['key']).sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### A dictionary or series mapping index to group\\n\",\n \"\\n\",\n \"Another method is to provide a dictionary that maps index values to the group keys:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"df2\\n\",\n \" data1 data2\\n\",\n \"key \\n\",\n \"A 0 5\\n\",\n \"B 1 0\\n\",\n \"C 2 3\\n\",\n \"A 3 3\\n\",\n \"B 4 7\\n\",\n \"C 5 9\\n\",\n \"\\n\",\n \"df2.groupby(mapping).sum()\\n\",\n \" data1 data2\\n\",\n \"key \\n\",\n \"consonant 12 19\\n\",\n \"vowel 3 8\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df2 = df.set_index('key')\\n\",\n \"mapping = {'A': 'vowel', 'B': 'consonant', 'C': 'consonant'}\\n\",\n \"display('df2', 'df2.groupby(mapping).sum()')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Any Python function\\n\",\n \"\\n\",\n \"Similar to mapping, you can pass any Python function that will input the index value and output the group:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" survived pclass sex age sibsp parch fare embarked class \\\\\\n\",\n \"0 0 3 male 22.0 1 0 7.2500 S Third \\n\",\n \"1 1 1 female 38.0 1 0 71.2833 C First \\n\",\n \"2 1 3 female 26.0 0 0 7.9250 S Third \\n\",\n \"3 1 1 female 35.0 1 0 53.1000 S First \\n\",\n \"4 0 3 male 35.0 0 0 8.0500 S Third \\n\",\n \"\\n\",\n \" who adult_male deck embark_town alive alone \\n\",\n \"0 man True NaN Southampton no False \\n\",\n \"1 woman False C Cherbourg yes False \\n\",\n \"2 woman False NaN Southampton yes True \\n\",\n \"3 woman False C Southampton yes False \\n\",\n \"4 man True NaN Southampton no True \"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.head()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As the output shows, this contains a number of data points on each passenger on that ill-fated voyage, including sex, age, class, fare paid, and much more.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Pivot Tables by Hand\\n\",\n \"\\n\",\n \"To start learning more about this data, we might begin by grouping according to sex, survival status, or some combination thereof.\\n\",\n \"If you read the previous chapter, you might be tempted to apply a `groupby` operation—for example, let's look at survival rate by sex:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" survived\\n\",\n \"sex \\n\",\n \"female 0.742038\\n\",\n \"male 0.188908\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.groupby('sex')[['survived']].mean()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This gives us some initial insight: overall, three of every four females on board survived, while only one in five males survived!\\n\",\n \"\\n\",\n \"This is useful, but we might like to go one step deeper and look at survival rates by both sex and, say, class.\\n\",\n \"Using the vocabulary of `groupby`, we might proceed using a process like this:\\n\",\n \"we first *group by* class and sex, then *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and finally *unstack* the hierarchical index to reveal the hidden multidimensionality. In code:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"class First Second Third\\n\",\n \"sex \\n\",\n \"female 0.968085 0.921053 0.500000\\n\",\n \"male 0.368852 0.157407 0.135447\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This gives us a better idea of how both sex and class affected survival, but the code is starting to look a bit garbled.\\n\",\n \"While each step of this pipeline makes sense in light of the tools we've previously discussed, the long string of code is not particularly easy to read or use.\\n\",\n \"This two-dimensional `groupby` is common enough that Pandas includes a convenience routine, `pivot_table`, which succinctly handles this type of multidimensional aggregation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Pivot Table Syntax\\n\",\n \"\\n\",\n \"Here is the equivalent to the preceding operation using the `DataFrame.pivot_table` method:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"class First Second Third\\n\",\n \"sex \\n\",\n \"female 0.968085 0.921053 0.500000\\n\",\n \"male 0.368852 0.157407 0.135447\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.pivot_table('survived', index='sex', columns='class', aggfunc='mean')\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is eminently more readable than the manual `groupby` approach, and produces the same result.\\n\",\n \"As you might expect of an early 20th-century transatlantic cruise, the survival gradient favors both higher classes and people recorded as females in the\\n\",\n \"data. First-class females survived with near certainty (hi, Rose!), while only one in eight or so third-class males survived (sorry, Jack!).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Multilevel Pivot Tables\\n\",\n \"\\n\",\n \"Just as in a `groupby`, the grouping in pivot tables can be specified with multiple levels and via a number of options.\\n\",\n \"For example, we might be interested in looking at age as a third dimension.\\n\",\n \"We'll bin the age using the `pd.cut` function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"fare (-0.001, 14.454] (14.454, 512.329] \\\\\\n\",\n \"class First Second Third First \\n\",\n \"sex age \\n\",\n \"female (0, 18] NaN 1.000000 0.714286 0.909091 \\n\",\n \" (18, 80] NaN 0.880000 0.444444 0.972973 \\n\",\n \"male (0, 18] NaN 0.000000 0.260870 0.800000 \\n\",\n \" (18, 80] 0.0 0.098039 0.125000 0.391304 \\n\",\n \"\\n\",\n \"fare \\n\",\n \"class Second Third \\n\",\n \"sex age \\n\",\n \"female (0, 18] 1.000000 0.318182 \\n\",\n \" (18, 80] 0.914286 0.391304 \\n\",\n \"male (0, 18] 0.818182 0.178571 \\n\",\n \" (18, 80] 0.030303 0.192308 \"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fare = pd.qcut(titanic['fare'], 2)\\n\",\n \"titanic.pivot_table('survived', ['sex', age], [fare, 'class'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result is a four-dimensional aggregation with hierarchical indices (see [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb)), shown in a grid demonstrating the relationship between the values.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Additional Pivot Table Options\\n\",\n \"\\n\",\n \"The full call signature of the `DataFrame.pivot_table` method is as follows:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"# call signature as of Pandas 1.3.5\\n\",\n \"DataFrame.pivot_table(data, values=None, index=None, columns=None,\\n\",\n \" aggfunc='mean', fill_value=None, margins=False,\\n\",\n \" dropna=True, margins_name='All', observed=False,\\n\",\n \" sort=True)\\n\",\n \"```\\n\",\n \"\\n\",\n \"We've already seen examples of the first three arguments; here we'll take a quick look at some of the remaining ones.\\n\",\n \"Two of the options, `fill_value` and `dropna`, have to do with missing data and are fairly straightforward; I will not show examples of them here.\\n\",\n \"\\n\",\n \"The `aggfunc` keyword controls what type of aggregation is applied, which is a mean by default.\\n\",\n \"As with `groupby`, the aggregation specification can be a string representing one of several common choices (`'sum'`, `'mean'`, `'count'`, `'min'`, `'max'`, etc.) or a function that implements an aggregation (e.g., `np.sum()`, `min()`, `sum()`, etc.).\\n\",\n \"Additionally, it can be specified as a dictionary mapping a column to any of the desired options:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" fare survived \\n\",\n \"class First Second Third First Second Third\\n\",\n \"sex \\n\",\n \"female 106.125798 21.970121 16.118810 91 70 72\\n\",\n \"male 67.226127 19.741782 12.661633 45 17 47\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.pivot_table(index='sex', columns='class',\\n\",\n \" aggfunc={'survived':sum, 'fare':'mean'})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice also here that we've omitted the `values` keyword; when specifying a mapping for `aggfunc`, this is determined automatically.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"At times it's useful to compute totals along each grouping.\\n\",\n \"This can be done via the ``margins`` keyword:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"class First Second Third All\\n\",\n \"sex \\n\",\n \"female 0.968085 0.921053 0.500000 0.742038\\n\",\n \"male 0.368852 0.157407 0.135447 0.188908\\n\",\n \"All 0.629630 0.472826 0.242363 0.383838\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.pivot_table('survived', index='sex', columns='class', margins=True)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, this automatically gives us information about the class-agnostic survival rate by sex, the sex-agnostic survival rate by class, and the overall survival rate of 38%.\\n\",\n \"The margin label can be specified with the `margins_name` keyword; it defaults to `\\\"All\\\"`.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Birthrate Data\\n\",\n \"\\n\",\n \"As another example, let's take a look at the freely available data on births in the United States, provided by the Centers for Disease Control (CDC).\\n\",\n \"This data can be found at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv\\n\",\n \"(this dataset has been analyzed rather extensively by Andrew Gelman and his group; see, for example, the [blog post on signal processing using Gaussian processes](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/)):\\n\",\n \"\\n\",\n \"[^1]: The CDC dataset used in this section uses the sex assigned at birth, which it calls \\\"gender,\\\" and limits the data to male and female. While gender is a spectrum independent of biology, I will be using the same terminology while discussing this dataset for consistency and clarity.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# shell command to download the data:\\n\",\n \"# !cd data && curl -O \\\\\\n\",\n \"# https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"births = pd.read_csv('data/births.csv')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Taking a look at the data, we see that it's relatively simple—it contains the number of births grouped by date and gender:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" year month day gender births\\n\",\n \"0 1969 1 1.0 F 4046\\n\",\n \"1 1969 1 1.0 M 4440\\n\",\n \"2 1969 1 2.0 F 4454\\n\",\n \"3 1969 1 2.0 M 4548\\n\",\n \"4 1969 1 3.0 F 4548\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"births.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can start to understand this data a bit more by using a pivot table.\\n\",\n \"Let's add a `decade` column, and take a look at male and female births as a function of decade:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \"gender F M\\n\",\n \"decade \\n\",\n \"1960 1753634 1846572\\n\",\n \"1970 16263075 17121550\\n\",\n \"1980 18310351 19243452\\n\",\n \"1990 19479454 20420553\\n\",\n \"2000 18229309 19106428\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"births['decade'] = 10 * (births['year'] // 10)\\n\",\n \"births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see that male births outnumber female births in every decade.\\n\",\n \"To see this trend a bit more clearly, we can use the built-in plotting tools in Pandas to visualize the total number of births by year, as shown in the following figure (see [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) for a discussion of plotting with Matplotlib):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"births.pivot_table(\\n\",\n \" 'births', index='year', columns='gender', aggfunc='sum').plot()\\n\",\n \"plt.ylabel('total births per year');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With a simple pivot table and the `plot` method, we can immediately see the annual trend in births by gender. By eye, it appears that over the past 50 years male births have outnumbered female births by around 5%.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Though this doesn't necessarily relate to the pivot table, there are a few more interesting features we can pull out of this dataset using the Pandas tools covered up to this point.\\n\",\n \"We must start by cleaning the data a bit, removing outliers caused by mistyped dates (e.g., June 31st) or missing values (e.g., June 99th).\\n\",\n \"One easy way to remove these all at once is to cut outliers; we'll do this via a robust sigma-clipping operation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"quartiles = np.percentile(births['births'], [25, 50, 75])\\n\",\n \"mu = quartiles[1]\\n\",\n \"sig = 0.74 * (quartiles[2] - quartiles[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This final line is a robust estimate of the sample standard deviation, where the 0.74 comes from the interquartile range of a Gaussian distribution (you can learn more about sigma-clipping operations in a book I coauthored with Željko Ivezić, Andrew J. Connolly, and Alexander Gray: [*Statistics, Data Mining, and Machine Learning in Astronomy*](https://press.princeton.edu/books/hardcover/9780691198309/statistics-data-mining-and-machine-learning-in-astronomy) (Princeton University Press)).\\n\",\n \"\\n\",\n \"With this, we can use the `query` method (discussed further in [High-Performance Pandas: `eval()` and `query()`](03.12-Performance-Eval-and-Query.ipynb)) to filter out rows with births outside these values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next we set the `day` column to integers; previously it had been a string column because some columns in the dataset contained the value `'null'`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# set 'day' column to integer; it originally was a string due to nulls\\n\",\n \"births['day'] = births['day'].astype(int)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we can combine the day, month, and year to create a date index (see [Working with Time Series](03.11-Working-with-Time-Series.ipynb)).\\n\",\n \"This allows us to quickly compute the weekday corresponding to each row:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# create a datetime index from the year, month, day\\n\",\n \"births.index = pd.to_datetime(10000 * births.year +\\n\",\n \" 100 * births.month +\\n\",\n \" births.day, format='%Y%m%d')\\n\",\n \"\\n\",\n \"births['dayofweek'] = births.index.dayofweek\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using this, we can plot births by weekday for several decades (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib as mpl\\n\",\n \"\\n\",\n \"births.pivot_table('births', index='dayofweek',\\n\",\n \" columns='decade', aggfunc='mean').plot()\\n\",\n \"plt.gca().set(xticks=range(7),\\n\",\n \" xticklabels=['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\\n\",\n \"plt.ylabel('mean births by day');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Apparently births are slightly less common on weekends than on weekdays! Note that the 1990s and 2000s are missing because starting in 1989, the CDC data contains only the month of birth.\\n\",\n \"\\n\",\n \"Another interesting view is to plot the mean number of births by the day of the year.\\n\",\n \"Let's first group the data by month and day separately:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" births\\n\",\n \"1 1 4009.225\\n\",\n \" 2 4247.400\\n\",\n \" 3 4500.900\\n\",\n \" 4 4571.350\\n\",\n \" 5 4603.625\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"births_by_date = births.pivot_table('births', \\n\",\n \" [births.index.month, births.index.day])\\n\",\n \"births_by_date.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result is a multi-index over months and days.\\n\",\n \"To make this visualizable, let's turn these months and days into dates by associating them with a dummy year variable (making sure to choose a leap year so February 29th is correctly handled!):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" births\\n\",\n \"2012-01-01 4009.225\\n\",\n \"2012-01-02 4247.400\\n\",\n \"2012-01-03 4500.900\\n\",\n \"2012-01-04 4571.350\\n\",\n \"2012-01-05 4603.625\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from datetime import datetime\\n\",\n \"births_by_date.index = [datetime(2012, month, day)\\n\",\n \" for (month, day) in births_by_date.index]\\n\",\n \"births_by_date.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Focusing on the month and day only, we now have a time series reflecting the average number of births by date of the year.\\n\",\n \"From this, we can use the `plot` method to plot the data. It reveals some interesting trends, as you can see in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot the results\\n\",\n \"fig, ax = plt.subplots(figsize=(12, 4))\\n\",\n \"births_by_date.plot(ax=ax);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In particular, the striking feature of this graph is the dip in birthrate on US holidays (e.g., Independence Day, Labor Day, Thanksgiving, Christmas, New Year's Day), although this likely reflects trends in scheduled/induced births rather than some deep psychosomatic effect on natural births.\\n\",\n \"For more discussion of this trend, see the analysis and links in [Andrew Gelman's blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/) on the subject.\\n\",\n \"We'll return to this figure in [Example:-Effect-of-Holidays-on-US-Births](04.09-Text-and-Annotation.ipynb), where we will use Matplotlib's tools to annotate this plot.\\n\",\n \"\\n\",\n \"Looking at this short example, you can see that many of the Python and Pandas tools we've seen to this point can be combined and used to gain insight from a variety of datasets.\\n\",\n \"We will see some more sophisticated applications of these data manipulations in future chapters!\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 64-bit ('3.9.6')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/03.10-Working-With-Strings.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Vectorized String Operations\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"One strength of Python is its relative ease in handling and manipulating string data.\\n\",\n \"Pandas builds on this and provides a comprehensive set of *vectorized string operations* that are an important part of the type of munging required when working with (read: cleaning up) real-world data.\\n\",\n \"In this chapter, we'll walk through some of the Pandas string operations, and then take a look at using them to partially clean up a very messy dataset of recipes collected from the internet.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Introducing Pandas String Operations\\n\",\n \"\\n\",\n \"We saw in previous chapters how tools like NumPy and Pandas generalize arithmetic operations so that we can easily and quickly perform the same operation on many array elements. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 4, 6, 10, 14, 22, 26])\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"x = np.array([2, 3, 5, 7, 11, 13])\\n\",\n \"x * 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This *vectorization* of operations simplifies the syntax of operating on arrays of data: we no longer have to worry about the size or shape of the array, but just about what operation we want done.\\n\",\n \"For arrays of strings, NumPy does not provide such simple access, and thus you're stuck using a more verbose loop syntax:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Peter', 'Paul', 'Mary', 'Guido']\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = ['peter', 'Paul', 'MARY', 'gUIDO']\\n\",\n \"[s.capitalize() for s in data]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is perhaps sufficient to work with some data, but it will break if there are any missing values, so this approach requires putting in extra checks:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Peter', 'Paul', None, 'Mary', 'Guido']\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = ['peter', 'Paul', None, 'MARY', 'gUIDO']\\n\",\n \"[s if s is None else s.capitalize() for s in data]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This kind of manual approach is not only verbose and inconvenient, it can be error-prone.\\n\",\n \"\\n\",\n \"Pandas includes features to address both this need for vectorized string operations and the need for correctly handling missing data via the `str` attribute of Pandas `Series` and `Index` objects containing strings.\\n\",\n \"So, for example, if we create a Pandas `Series` with this data we can directly call the `str.capitalize` method, which has missing value handling built in:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"lines_to_next_cell\": 2\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 Peter\\n\",\n \"1 Paul\\n\",\n \"2 None\\n\",\n \"3 Mary\\n\",\n \"4 Guido\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"names = pd.Series(data)\\n\",\n \"names.str.capitalize()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Tables of Pandas String Methods\\n\",\n \"\\n\",\n \"If you have a good understanding of string manipulation in Python, most of the Pandas string syntax is intuitive enough that it's probably sufficient to just list the available methods. We'll start with that here, before diving deeper into a few of the subtleties.\\n\",\n \"The examples in this section use the following `Series` object:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"monte = pd.Series(['Graham Chapman', 'John Cleese', 'Terry Gilliam',\\n\",\n \" 'Eric Idle', 'Terry Jones', 'Michael Palin'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Methods Similar to Python String Methods\\n\",\n \"\\n\",\n \"Nearly all of Python's built-in string methods are mirrored by a Pandas vectorized string method. Here is a list of Pandas `str` methods that mirror Python string methods:\\n\",\n \"\\n\",\n \"| | | | |\\n\",\n \"|-----------|----------------|----------------|----------------|\\n\",\n \"|`len()` | `lower()` | `translate()` | `islower()` | \\n\",\n \"|`ljust()` | `upper()` | `startswith()` | `isupper()` | \\n\",\n \"|`rjust()` | `find()` | `endswith()` | `isnumeric()` | \\n\",\n \"|`center()` | `rfind()` | `isalnum()` | `isdecimal()` | \\n\",\n \"|`zfill()` | `index()` | `isalpha()` | `split()` | \\n\",\n \"|`strip()` | `rindex()` | `isdigit()` | `rsplit()` | \\n\",\n \"|`rstrip()` | `capitalize()` | `isspace()` | `partition()` | \\n\",\n \"|`lstrip()` | `swapcase()` | `istitle()` | `rpartition()` |\\n\",\n \"\\n\",\n \"Notice that these have various return values. Some, like `lower`, return a series of strings:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 graham chapman\\n\",\n \"1 john cleese\\n\",\n \"2 terry gilliam\\n\",\n \"3 eric idle\\n\",\n \"4 terry jones\\n\",\n \"5 michael palin\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"monte.str.lower()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But some others return numbers:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 14\\n\",\n \"1 11\\n\",\n \"2 13\\n\",\n \"3 9\\n\",\n \"4 11\\n\",\n \"5 13\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"monte.str.len()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or Boolean values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 False\\n\",\n \"1 False\\n\",\n \"2 True\\n\",\n \"3 False\\n\",\n \"4 True\\n\",\n \"5 False\\n\",\n \"dtype: bool\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"monte.str.startswith('T')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Still others return lists or other compound values for each element:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 [Graham, Chapman]\\n\",\n \"1 [John, Cleese]\\n\",\n \"2 [Terry, Gilliam]\\n\",\n \"3 [Eric, Idle]\\n\",\n \"4 [Terry, Jones]\\n\",\n \"5 [Michael, Palin]\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"monte.str.split()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll see further manipulations of this kind of series-of-lists object as we continue our discussion.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Methods Using Regular Expressions\\n\",\n \"\\n\",\n \"In addition, there are several methods that accept regular expressions (regexps) to examine the content of each string element, and follow some of the API conventions of Python's built-in `re` module:\\n\",\n \"\\n\",\n \"| Method | Description |\\n\",\n \"|-----------|-------------|\\n\",\n \"| `match` | Calls `re.match` on each element, returning a Boolean. |\\n\",\n \"| `extract` | Calls `re.match` on each element, returning matched groups as strings.|\\n\",\n \"| `findall` | Calls `re.findall` on each element |\\n\",\n \"| `replace` | Replaces occurrences of pattern with some other string|\\n\",\n \"| `contains`| Calls `re.search` on each element, returning a boolean |\\n\",\n \"| `count` | Counts occurrences of pattern|\\n\",\n \"| `split` | Equivalent to `str.split`, but accepts regexps |\\n\",\n \"| `rsplit` | Equivalent to `str.rsplit`, but accepts regexps |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With these, we can do a wide range of operations.\\n\",\n \"For example, we can extract the first name from each element by asking for a contiguous group of characters at the beginning of each element:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 Graham\\n\",\n \"1 John\\n\",\n \"2 Terry\\n\",\n \"3 Eric\\n\",\n \"4 Terry\\n\",\n \"5 Michael\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"monte.str.extract('([A-Za-z]+)', expand=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or we can do something more complicated, like finding all names that start and end with a consonant, making use of the start-of-string (`^`) and end-of-string (`$`) regular expression characters:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 [Graham Chapman]\\n\",\n \"1 []\\n\",\n \"2 [Terry Gilliam]\\n\",\n \"3 []\\n\",\n \"4 [Terry Jones]\\n\",\n \"5 [Michael Palin]\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"monte.str.findall(r'^[^AEIOU].*[^aeiou]$')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The ability to concisely apply regular expressions across `Series` or `DataFrame` entries opens up many possibilities for analysis and cleaning of data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Miscellaneous Methods\\n\",\n \"Finally, there are some miscellaneous methods that enable other convenient operations:\\n\",\n \"\\n\",\n \"| Method | Description |\\n\",\n \"|--------|-------------|\\n\",\n \"| `get` | Indexes each element |\\n\",\n \"| `slice` | Slices each element|\\n\",\n \"| `slice_replace` | Replaces slice in each element with the passed value|\\n\",\n \"| `cat` | Concatenates strings|\\n\",\n \"| `repeat` | Repeats values |\\n\",\n \"| `normalize` | Returns Unicode form of strings |\\n\",\n \"| `pad` | Adds whitespace to left, right, or both sides of strings|\\n\",\n \"| `wrap` | Splits long strings into lines with length less than a given width|\\n\",\n \"| `join` | Joins strings in each element of the `Series` with the passed separator|\\n\",\n \"| `get_dummies` | Extracts dummy variables as a `DataFrame` |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Vectorized item access and slicing\\n\",\n \"\\n\",\n \"The `get` and `slice` operations, in particular, enable vectorized element access from each array.\\n\",\n \"For example, we can get a slice of the first three characters of each array using `str.slice(0, 3)`.\\n\",\n \"Note that this behavior is also available through Python's normal indexing syntax; for example, `df.str.slice(0, 3)` is equivalent to `df.str[0:3]`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 Gra\\n\",\n \"1 Joh\\n\",\n \"2 Ter\\n\",\n \"3 Eri\\n\",\n \"4 Ter\\n\",\n \"5 Mic\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"monte.str[0:3]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Indexing via `df.str.get(i)` and `df.str[i]` are likewise similar.\\n\",\n \"\\n\",\n \"These indexing methods also let you access elements of arrays returned by `split`.\\n\",\n \"For example, to extract the last name of each entry, we can combine `split` with `str` indexing:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 Chapman\\n\",\n \"1 Cleese\\n\",\n \"2 Gilliam\\n\",\n \"3 Idle\\n\",\n \"4 Jones\\n\",\n \"5 Palin\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"monte.str.split().str[-1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Indicator variables\\n\",\n \"\\n\",\n \"Another method that requires a bit of extra explanation is the `get_dummies` method.\\n\",\n \"This is useful when your data has a column containing some sort of coded indicator.\\n\",\n \"For example, we might have a dataset that contains information in the form of codes, such as A = \\\"born in America,\\\" B = \\\"born in the United Kingdom,\\\" C = \\\"likes cheese,\\\" D = \\\"likes spam\\\":\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n 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\"\n ],\n \"text/plain\": [\n \" name info\\n\",\n \"0 Graham Chapman B|C|D\\n\",\n \"1 John Cleese B|D\\n\",\n \"2 Terry Gilliam A|C\\n\",\n \"3 Eric Idle B|D\\n\",\n \"4 Terry Jones B|C\\n\",\n \"5 Michael Palin B|C|D\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"full_monte = pd.DataFrame({'name': monte,\\n\",\n \" 'info': ['B|C|D', 'B|D', 'A|C',\\n\",\n \" 'B|D', 'B|C', 'B|C|D']})\\n\",\n \"full_monte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `get_dummies` routine lets us split out these indicator variables into a `DataFrame`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" A B C D\\n\",\n \"0 0 1 1 1\\n\",\n \"1 0 1 0 1\\n\",\n \"2 1 0 1 0\\n\",\n \"3 0 1 0 1\\n\",\n \"4 0 1 1 0\\n\",\n \"5 0 1 1 1\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"full_monte['info'].str.get_dummies('|')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With these operations as building blocks, you can construct an endless range of string processing procedures when cleaning your data.\\n\",\n \"\\n\",\n \"We won't dive further into these methods here, but I encourage you to read through [\\\"Working with Text Data\\\"](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html) in the Pandas online documentation, or to refer to the resources listed in [Further Resources](03.13-Further-Resources.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Recipe Database\\n\",\n \"\\n\",\n \"These vectorized string operations become most useful in the process of cleaning up messy, real-world data.\\n\",\n \"Here I'll walk through an example of that, using an open recipe database compiled from various sources on the web.\\n\",\n \"Our goal will be to parse the recipe data into ingredient lists, so we can quickly find a recipe based on some ingredients we have on hand. The scripts used to compile this can be found at https://github.com/fictivekin/openrecipes, and the link to the most recent version of the database is found there as well.\\n\",\n \"\\n\",\n \"This database is about 30 MB, and can be downloaded and unzipped with these commands:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# repo = \\\"https://raw.githubusercontent.com/jakevdp/open-recipe-data/master\\\"\\n\",\n \"# !cd data && curl -O {repo}/recipeitems.json.gz\\n\",\n \"# !gunzip data/recipeitems.json.gz\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The database is in JSON format, so we will use `pd.read_json` to read it (`lines=True` is required for this dataset because each line of the file is a JSON entry):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(173278, 17)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"recipes = pd.read_json('data/recipeitems.json', lines=True)\\n\",\n \"recipes.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see there are nearly 175,000 recipes, and 17 columns.\\n\",\n \"Let's take a look at one row to see what we have:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"_id {'$oid': '5160756b96cc62079cc2db15'}\\n\",\n \"name Drop Biscuits and Sausage Gravy\\n\",\n \"ingredients Biscuits\\\\n3 cups All-purpose Flour\\\\n2 Tablespo...\\n\",\n \"url http://thepioneerwoman.com/cooking/2013/03/dro...\\n\",\n \"image http://static.thepioneerwoman.com/cooking/file...\\n\",\n \"ts {'$date': 1365276011104}\\n\",\n \"cookTime PT30M\\n\",\n \"source thepioneerwoman\\n\",\n \"recipeYield 12\\n\",\n \"datePublished 2013-03-11\\n\",\n \"prepTime PT10M\\n\",\n \"description Late Saturday afternoon, after Marlboro Man ha...\\n\",\n \"totalTime NaN\\n\",\n \"creator NaN\\n\",\n \"recipeCategory NaN\\n\",\n \"dateModified NaN\\n\",\n \"recipeInstructions NaN\\n\",\n \"Name: 0, dtype: object\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"recipes.iloc[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There is a lot of information there, but much of it is in a very messy form, as is typical of data scraped from the web.\\n\",\n \"In particular, the ingredient list is in string format; we're going to have to carefully extract the information we're interested in.\\n\",\n \"Let's start by taking a closer look at the ingredients:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"count 173278.000000\\n\",\n \"mean 244.617926\\n\",\n \"std 146.705285\\n\",\n \"min 0.000000\\n\",\n \"25% 147.000000\\n\",\n \"50% 221.000000\\n\",\n \"75% 314.000000\\n\",\n \"max 9067.000000\\n\",\n \"Name: ingredients, dtype: float64\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"recipes.ingredients.str.len().describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The ingredient lists average 250 characters long, with a minimum of 0 and a maximum of nearly 10,000 characters!\\n\",\n \"\\n\",\n \"Just out of curiosity, let's see which recipe has the longest ingredient list:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Carrot Pineapple Spice & Brownie Layer Cake with Whipped Cream & Cream Cheese Frosting and Marzipan Carrots'\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"recipes.name[np.argmax(recipes.ingredients.str.len())]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can do other aggregate explorations; for example, we can see how many of the recipes are for breakfast foods (using regular expression syntax to match both lowercase and capital letters):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3524\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"recipes.description.str.contains('[Bb]reakfast').sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or how many of the recipes list cinnamon as an ingredient:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"10526\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"recipes.ingredients.str.contains('[Cc]innamon').sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We could even look to see whether any recipes misspell the ingredient as \\\"cinamon\\\":\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"11\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"recipes.ingredients.str.contains('[Cc]inamon').sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is the type of data exploration that is possible with Pandas string tools.\\n\",\n \"It is data munging like this that Python really excels at.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### A Simple Recipe Recommender\\n\",\n \"\\n\",\n \"Let's go a bit further, and start working on a simple recipe recommendation system: given a list of ingredients, we want to find any recipes that use all those ingredients.\\n\",\n \"While conceptually straightforward, the task is complicated by the heterogeneity of the data: there is no easy operation, for example, to extract a clean list of ingredients from each row.\\n\",\n \"So, we will cheat a bit: we'll start with a list of common ingredients, and simply search to see whether they are in each recipe's ingredient list.\\n\",\n \"For simplicity, let's just stick with herbs and spices for the time being:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"spice_list = ['salt', 'pepper', 'oregano', 'sage', 'parsley',\\n\",\n \" 'rosemary', 'tarragon', 'thyme', 'paprika', 'cumin']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can then build a Boolean `DataFrame` consisting of `True` and `False` values, indicating whether each ingredient appears in the list:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" salt pepper oregano sage parsley rosemary tarragon thyme paprika \\\\\\n\",\n \"0 False False False True False False False False False \\n\",\n \"1 False False False False False False False False False \\n\",\n \"2 True True False False False False False False False \\n\",\n \"3 False False False False False False False False False \\n\",\n \"4 False False False False False False False False False \\n\",\n \"\\n\",\n \" cumin \\n\",\n \"0 False \\n\",\n \"1 False \\n\",\n \"2 True \\n\",\n \"3 False \\n\",\n \"4 False \"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import re\\n\",\n \"spice_df = pd.DataFrame({\\n\",\n \" spice: recipes.ingredients.str.contains(spice, re.IGNORECASE)\\n\",\n \" for spice in spice_list})\\n\",\n \"spice_df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, as an example, let's say we'd like to find a recipe that uses parsley, paprika, and tarragon.\\n\",\n \"We can compute this very quickly using the `query` method of ``DataFrame``s, discussed further in [High-Performance Pandas: `eval()` and `query()`](03.12-Performance-Eval-and-Query.ipynb):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"10\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"selection = spice_df.query('parsley & paprika & tarragon')\\n\",\n \"len(selection)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We find only 10 recipes with this combination. Let's use the index returned by this selection to discover the names of those recipes:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2069 All cremat with a Little Gem, dandelion and wa...\\n\",\n \"74964 Lobster with Thermidor butter\\n\",\n \"93768 Burton's Southern Fried Chicken with White Gravy\\n\",\n \"113926 Mijo's Slow Cooker Shredded Beef\\n\",\n \"137686 Asparagus Soup with Poached Eggs\\n\",\n \"140530 Fried Oyster Po’boys\\n\",\n \"158475 Lamb shank tagine with herb tabbouleh\\n\",\n \"158486 Southern fried chicken in buttermilk\\n\",\n \"163175 Fried Chicken Sliders with Pickles + Slaw\\n\",\n \"165243 Bar Tartine Cauliflower Salad\\n\",\n \"Name: name, dtype: object\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"recipes.name[selection.index]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have narrowed down our recipe selection from 175,000 to 10, we are in a position to make a more informed decision about what we'd like to cook for dinner.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Going Further with Recipes\\n\",\n \"\\n\",\n \"Hopefully this example has given you a bit of a flavor (heh) of the types of data cleaning operations that are efficiently enabled by Pandas string methods.\\n\",\n \"Of course, building a robust recipe recommendation system would require a *lot* more work!\\n\",\n \"Extracting full ingredient lists from each recipe would be an important piece of the task; unfortunately, the wide variety of formats used makes this a relatively time-consuming process.\\n\",\n \"This points to the truism that in data science, cleaning and munging of real-world data often comprises the majority of the work—and Pandas provides the tools that can help you do this efficiently.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/03.11-Working-with-Time-Series.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Working with Time Series\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Pandas was originally developed in the context of financial modeling, so as you might expect, it contains an extensive set of tools for working with dates, times, and time-indexed data.\\n\",\n \"Date and time data comes in a few flavors, which we will discuss here:\\n\",\n \"\\n\",\n \"- *Timestamps* reference particular moments in time (e.g., July 4th, 2021 at 7:00 a.m.).\\n\",\n \"- *Time intervals* and *periods* reference a length of time between a particular beginning and end point; for example, the month of June 2021. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24-hour-long periods comprising days).\\n\",\n \"- *Time deltas* or *durations* reference an exact length of time (e.g., a duration of 22.56 seconds).\\n\",\n \"\\n\",\n \"This chapter will introduce how to work with each of these types of date/time data in Pandas.\\n\",\n \"This is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series.\\n\",\n \"We will start with a brief discussion of tools for dealing with dates and times in Python, before moving more specifically to a discussion of the tools provided by Pandas.\\n\",\n \"After listing some resources that go into more depth, we will review some short examples of working with time series data in Pandas.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Dates and Times in Python\\n\",\n \"\\n\",\n \"The Python world has a number of available representations of dates, times, deltas, and time spans.\\n\",\n \"While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other tools used in Python.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Native Python Dates and Times: datetime and dateutil\\n\",\n \"\\n\",\n \"Python's basic objects for working with dates and times reside in the built-in `datetime` module.\\n\",\n \"Along with the third-party `dateutil` module, you can use this to quickly perform a host of useful functionalities on dates and times.\\n\",\n \"For example, you can manually build a date using the `datetime` type:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.datetime(2021, 7, 4, 0, 0)\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from datetime import datetime\\n\",\n \"datetime(year=2021, month=7, day=4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or, using the `dateutil` module, you can parse dates from a variety of string formats:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.datetime(2021, 7, 4, 0, 0)\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from dateutil import parser\\n\",\n \"date = parser.parse(\\\"4th of July, 2021\\\")\\n\",\n \"date\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once you have a `datetime` object, you can do things like printing the day of the week:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Sunday'\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"date.strftime('%A')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we've used one of the standard string format codes for printing dates (`'%A'`), which you can read about in the [`strftime` section](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) of Python's [`datetime` documentation](https://docs.python.org/3/library/datetime.html).\\n\",\n \"Documentation of other useful date utilities can be found in [``dateutil``'s online documentation](http://labix.org/python-dateutil).\\n\",\n \"A related package to be aware of is [`pytz`](http://pytz.sourceforge.net/), which contains tools for working with the most migraine-inducing element of time series data: time zones.\\n\",\n \"\\n\",\n \"The power of `datetime` and `dateutil` lies in their flexibility and easy syntax: you can use these objects and their built-in methods to easily perform nearly any operation you might be interested in.\\n\",\n \"Where they break down is when you wish to work with large arrays of dates and times:\\n\",\n \"just as lists of Python numerical variables are suboptimal compared to NumPy-style typed numerical arrays, lists of Python `datetime` objects are suboptimal compared to typed arrays of encoded dates.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Typed Arrays of Times: NumPy's datetime64\\n\",\n \"\\n\",\n \"NumPy's `datetime64` dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented compactly and operated on in an efficient manner.\\n\",\n \"The `datetime64` requires a specific input format:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array('2021-07-04', dtype='datetime64[D]')\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"date = np.array('2021-07-04', dtype=np.datetime64)\\n\",\n \"date\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once we have dates in this form, we can quickly do vectorized operations on it:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array(['2021-07-04', '2021-07-05', '2021-07-06', '2021-07-07',\\n\",\n \" '2021-07-08', '2021-07-09', '2021-07-10', '2021-07-11',\\n\",\n \" '2021-07-12', '2021-07-13', '2021-07-14', '2021-07-15'],\\n\",\n \" dtype='datetime64[D]')\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"date + np.arange(12)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because of the uniform type in NumPy `datetime64` arrays, this kind of operation can be accomplished much more quickly than if we were working directly with Python's `datetime` objects, especially as arrays get large\\n\",\n \"(we introduced this type of vectorization in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)).\\n\",\n \"\\n\",\n \"One detail of the `datetime64` and related `timedelta64` objects is that they are built on a *fundamental time unit*.\\n\",\n \"Because the `datetime64` object is limited to 64-bit precision, the range of encodable times is $2^{64}$ times this fundamental unit.\\n\",\n \"In other words, `datetime64` imposes a trade-off between *time resolution* and *maximum time span*.\\n\",\n \"\\n\",\n \"For example, if you want a time resolution of 1 nanosecond, you only have enough information to encode a range of $2^{64}$ nanoseconds, or just under 600 years.\\n\",\n \"NumPy will infer the desired unit from the input; for example, here is a day-based `datetime`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"numpy.datetime64('2021-07-04')\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.datetime64('2021-07-04')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here is a minute-based datetime:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"numpy.datetime64('2021-07-04T12:00')\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.datetime64('2021-07-04 12:00')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can force any desired fundamental unit using one of many format codes; for example, here we'll force a nanosecond-based time:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"numpy.datetime64('2021-07-04T12:59:59.500000000')\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.datetime64('2021-07-04 12:59:59.50', 'ns')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following table, drawn from the NumPy `datetime64` documentation, lists the available format codes along with the relative and absolute time spans that they can encode:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"|Code | Meaning | Time span (relative) | Time span (absolute) |\\n\",\n \"|------|-------------|----------------------|------------------------|\\n\",\n \"| `Y` | Year | ± 9.2e18 years | [9.2e18 BC, 9.2e18 AD] |\\n\",\n \"| `M` | Month | ± 7.6e17 years | [7.6e17 BC, 7.6e17 AD] |\\n\",\n \"| `W` | Week | ± 1.7e17 years | [1.7e17 BC, 1.7e17 AD] |\\n\",\n \"| `D` | Day | ± 2.5e16 years | [2.5e16 BC, 2.5e16 AD] |\\n\",\n \"| `h` | Hour | ± 1.0e15 years | [1.0e15 BC, 1.0e15 AD] |\\n\",\n \"| `m` | Minute | ± 1.7e13 years | [1.7e13 BC, 1.7e13 AD] |\\n\",\n \"| `s` | Second | ± 2.9e12 years | [ 2.9e9 BC, 2.9e9 AD] |\\n\",\n \"| `ms` | Millisecond | ± 2.9e9 years | [ 2.9e6 BC, 2.9e6 AD] |\\n\",\n \"| `us` | Microsecond | ± 2.9e6 years | [290301 BC, 294241 AD] |\\n\",\n \"| `ns` | Nanosecond | ± 292 years | [ 1678 AD, 2262 AD] |\\n\",\n \"| `ps` | Picosecond | ± 106 days | [ 1969 AD, 1970 AD] |\\n\",\n \"| `fs` | Femtosecond | ± 2.6 hours | [ 1969 AD, 1970 AD] |\\n\",\n \"| `as` | Attosecond | ± 9.2 seconds | [ 1969 AD, 1970 AD] |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the types of data we see in the real world, a useful default is `datetime64[ns]`, as it can encode a useful range of modern dates with a suitably fine precision.\\n\",\n \"\\n\",\n \"Finally, note that while the `datetime64` data type addresses some of the deficiencies of the built-in Python `datetime` type, it lacks many of the convenient methods and functions provided by `datetime` and especially `dateutil`.\\n\",\n \"More information can be found in [NumPy's `datetime64` documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dates and Times in Pandas: The Best of Both Worlds\\n\",\n \"\\n\",\n \"Pandas builds upon all the tools just discussed to provide a `Timestamp` object, which combines the ease of use of `datetime` and `dateutil` with the efficient storage and vectorized interface of `numpy.datetime64`.\\n\",\n \"From a group of these `Timestamp` objects, Pandas can construct a `DatetimeIndex` that can be used to index data in a `Series` or `DataFrame`.\\n\",\n \"\\n\",\n \"For example, we can use Pandas tools to repeat the demonstration from earlier.\\n\",\n \"We can parse a flexibly formatted string date and use format codes to output the day of the week, as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Timestamp('2021-07-04 00:00:00')\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"date = pd.to_datetime(\\\"4th of July, 2021\\\")\\n\",\n \"date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Sunday'\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"date.strftime('%A')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Additionally, we can do NumPy-style vectorized operations directly on this same object:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DatetimeIndex(['2021-07-04', '2021-07-05', '2021-07-06', '2021-07-07',\\n\",\n \" '2021-07-08', '2021-07-09', '2021-07-10', '2021-07-11',\\n\",\n \" '2021-07-12', '2021-07-13', '2021-07-14', '2021-07-15'],\\n\",\n \" dtype='datetime64[ns]', freq=None)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"date + pd.to_timedelta(np.arange(12), 'D')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the next section, we will take a closer look at manipulating time series data with the tools provided by Pandas.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Pandas Time Series: Indexing by Time\\n\",\n \"\\n\",\n \"The Pandas time series tools really become useful when you begin to index data by timestamps.\\n\",\n \"For example, we can construct a `Series` object that has time-indexed data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2020-07-04 0\\n\",\n \"2020-08-04 1\\n\",\n \"2021-07-04 2\\n\",\n \"2021-08-04 3\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"index = pd.DatetimeIndex(['2020-07-04', '2020-08-04',\\n\",\n \" '2021-07-04', '2021-08-04'])\\n\",\n \"data = pd.Series([0, 1, 2, 3], index=index)\\n\",\n \"data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And now that we have this data in a `Series`, we can make use of any of the `Series` indexing patterns we discussed in previous chapters, passing values that can be coerced into dates:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2020-07-04 0\\n\",\n \"2020-08-04 1\\n\",\n \"2021-07-04 2\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['2020-07-04':'2021-07-04']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are additional special date-only indexing operations, such as passing a year to obtain a slice of all data from that year:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2021-07-04 2\\n\",\n \"2021-08-04 3\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['2021']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Later, we will see additional examples of the convenience of dates-as-indices.\\n\",\n \"But first, let's take a closer look at the available time series data structures.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Pandas Time Series Data Structures\\n\",\n \"\\n\",\n \"This section will introduce the fundamental Pandas data structures for working with time series data:\\n\",\n \"\\n\",\n \"- For *timestamps*, Pandas provides the `Timestamp` type. As mentioned before, this is essentially a replacement for Python's native `datetime`, but it's based on the more efficient `numpy.datetime64` data type. The associated `Index` structure is `DatetimeIndex`.\\n\",\n \"- For *time periods*, Pandas provides the `Period` type. This encodes a fixed-frequency interval based on `numpy.datetime64`. The associated index structure is `PeriodIndex`.\\n\",\n \"- For *time deltas* or *durations*, Pandas provides the `Timedelta` type. `Timedelta` is a more efficient replacement for Python's native `datetime.timedelta` type, and is based on `numpy.timedelta64`. The associated index structure is `TimedeltaIndex`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The most fundamental of these date/time objects are the `Timestamp` and `DatetimeIndex` objects.\\n\",\n \"While these class objects can be invoked directly, it is more common to use the `pd.to_datetime` function, which can parse a wide variety of formats.\\n\",\n \"Passing a single date to `pd.to_datetime` yields a `Timestamp`; passing a series of dates by default yields a `DatetimeIndex`, as you can see here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DatetimeIndex(['2021-07-03', '2021-07-04', '2021-07-06', '2021-07-07',\\n\",\n \" '2021-07-08'],\\n\",\n \" dtype='datetime64[ns]', freq=None)\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dates = pd.to_datetime([datetime(2021, 7, 3), '4th of July, 2021',\\n\",\n \" '2021-Jul-6', '07-07-2021', '20210708'])\\n\",\n \"dates\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Any `DatetimeIndex` can be converted to a `PeriodIndex` with the `to_period` function, with the addition of a frequency code; here we'll use `'D'` to indicate daily frequency:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"PeriodIndex(['2021-07-03', '2021-07-04', '2021-07-06', '2021-07-07',\\n\",\n \" '2021-07-08'],\\n\",\n \" dtype='period[D]')\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dates.to_period('D')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A `TimedeltaIndex` is created, for example, when a date is subtracted from another:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"TimedeltaIndex(['0 days', '1 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq=None)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dates - dates[0]\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Regular Sequences: pd.date_range\\n\",\n \"\\n\",\n \"To make creation of regular date sequences more convenient, Pandas offers a few functions for this purpose: `pd.date_range` for timestamps, `pd.period_range` for periods, and `pd.timedelta_range` for time deltas.\\n\",\n \"We've seen that Python's `range` and NumPy's `np.arange` take a start point, end point, and optional step size and return a sequence.\\n\",\n \"Similarly, `pd.date_range` accepts a start date, an end date, and an optional frequency code to create a regular sequence of dates:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\\n\",\n \" '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\\n\",\n \" dtype='datetime64[ns]', freq='D')\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.date_range('2015-07-03', '2015-07-10')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Alternatively, the date range can be specified not with a start and end point, but with a start point and a number of periods:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\\n\",\n \" '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\\n\",\n \" dtype='datetime64[ns]', freq='D')\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.date_range('2015-07-03', periods=8)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The spacing can be modified by altering the `freq` argument, which defaults to `D`.\\n\",\n \"For example, here we construct a range of hourly timestamps:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DatetimeIndex(['2015-07-03 00:00:00', '2015-07-03 01:00:00',\\n\",\n \" '2015-07-03 02:00:00', '2015-07-03 03:00:00',\\n\",\n \" '2015-07-03 04:00:00', '2015-07-03 05:00:00',\\n\",\n \" '2015-07-03 06:00:00', '2015-07-03 07:00:00'],\\n\",\n \" dtype='datetime64[ns]', freq='H')\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.date_range('2015-07-03', periods=8, freq='H')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To create regular sequences of `Period` or `Timedelta` values, the similar `pd.period_range` and `pd.timedelta_range` functions are useful.\\n\",\n \"Here are some monthly periods:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"PeriodIndex(['2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12',\\n\",\n \" '2016-01', '2016-02'],\\n\",\n \" dtype='period[M]')\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.period_range('2015-07', periods=8, freq='M')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And a sequence of durations increasing by an hour:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"TimedeltaIndex(['0 days 00:00:00', '0 days 01:00:00', '0 days 02:00:00',\\n\",\n \" '0 days 03:00:00', '0 days 04:00:00', '0 days 05:00:00'],\\n\",\n \" dtype='timedelta64[ns]', freq='H')\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.timedelta_range(0, periods=6, freq='H')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All of these require an understanding of Pandas frequency codes, which are summarized in the next section.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Frequencies and Offsets\\n\",\n \"\\n\",\n \"Fundamental to these Pandas time series tools is the concept of a *frequency* or *date offset*. The following table summarizes the main codes available; as with the `D` (day) and `H` (hour) codes demonstrated in the previous sections, we can use these to specify any desired frequency spacing:\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Code | Description | Code | Description |\\n\",\n \"|------|-------------------|------|----------------------|\\n\",\n \"| `D` | Calendar day | `B` | Business day |\\n\",\n \"| `W` | Weekly | | |\\n\",\n \"| `M` | Month end | `BM` | Business month end |\\n\",\n \"| `Q` | Quarter end | `BQ` | Business quarter end |\\n\",\n \"| `A` | Year end | `BA` | Business year end |\\n\",\n \"| `H` | Hours | `BH` | Business hours |\\n\",\n \"| `T` | Minutes | | |\\n\",\n \"| `S` | Seconds | | |\\n\",\n \"| `L` | Milliseconds | | |\\n\",\n \"| `U` | Microseconds | | |\\n\",\n \"| `N` | Nanoseconds | | |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The monthly, quarterly, and annual frequencies are all marked at the end of the specified period.\\n\",\n \"Adding an `S` suffix to any of these causes them to instead be marked at the beginning:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Code | Description | Code | Description |\\n\",\n \"|-------|-------------------|-------|------------------------|\\n\",\n \"| `MS` | Month start |`BMS` | Business month start |\\n\",\n \"| `QS` | Quarter start |`BQS` | Business quarter start |\\n\",\n \"| `AS` | Year start |`BAS` | Business year start |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Additionally, you can change the month used to mark any quarterly or annual code by adding a three-letter month code as a suffix:\\n\",\n \"\\n\",\n \"- `Q-JAN`, `BQ-FEB`, `QS-MAR`, `BQS-APR`, etc.\\n\",\n \"- `A-JAN`, `BA-FEB`, `AS-MAR`, `BAS-APR`, etc.\\n\",\n \"\\n\",\n \"In the same way, the split point of the weekly frequency can be modified by adding a three-letter weekday code:\\n\",\n \"\\n\",\n \"- `W-SUN`, `W-MON`, `W-TUE`, `W-WED`, etc.\\n\",\n \"\\n\",\n \"On top of this, codes can be combined with numbers to specify other frequencies.\\n\",\n \"For example, for a frequency of 2 hours and 30 minutes, we can combine the hour (`H`) and minute (`T`) codes as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"TimedeltaIndex(['0 days 00:00:00', '0 days 02:30:00', '0 days 05:00:00',\\n\",\n \" '0 days 07:30:00', '0 days 10:00:00', '0 days 12:30:00'],\\n\",\n \" dtype='timedelta64[ns]', freq='150T')\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.timedelta_range(0, periods=6, freq=\\\"2H30T\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the `pd.tseries.offsets` module.\\n\",\n \"For example, we can create a business day offset directly as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DatetimeIndex(['2015-07-01', '2015-07-02', '2015-07-03', '2015-07-06',\\n\",\n \" '2015-07-07', '2015-07-08'],\\n\",\n \" dtype='datetime64[ns]', freq='B')\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from pandas.tseries.offsets import BDay\\n\",\n \"pd.date_range('2015-07-01', periods=6, freq=BDay())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For more discussion of the use of frequencies and offsets, see the [`DateOffset` section](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) of the Pandas documentation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Resampling, Shifting, and Windowing\\n\",\n \"\\n\",\n \"The ability to use dates and times as indices to intuitively organize and access data is an important aspect of the Pandas time series tools.\\n\",\n \"The benefits of indexed data in general (automatic alignment during operations, intuitive data slicing and access, etc.) still apply, and Pandas provides several additional time series–specific operations.\\n\",\n \"\\n\",\n \"We will take a look at a few of those here, using some stock price data as an example.\\n\",\n \"Because Pandas was developed largely in a finance context, it includes some very specific tools for financial data.\\n\",\n \"For example, the accompanying `pandas-datareader` package (installable via `pip install pandas-datareader`) knows how to import data from various online sources.\\n\",\n \"Here we will load part of the S&P 500 price history:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" High Low Open Close Volume \\\\\\n\",\n \"Date \\n\",\n \"2018-01-02 2695.889893 2682.360107 2683.729980 2695.810059 3367250000 \\n\",\n \"2018-01-03 2714.370117 2697.770020 2697.850098 2713.060059 3538660000 \\n\",\n \"2018-01-04 2729.290039 2719.070068 2719.310059 2723.989990 3695260000 \\n\",\n \"2018-01-05 2743.449951 2727.919922 2731.330078 2743.149902 3236620000 \\n\",\n \"2018-01-08 2748.510010 2737.600098 2742.669922 2747.709961 3242650000 \\n\",\n \"\\n\",\n \" Adj Close \\n\",\n \"Date \\n\",\n \"2018-01-02 2695.810059 \\n\",\n \"2018-01-03 2713.060059 \\n\",\n \"2018-01-04 2723.989990 \\n\",\n \"2018-01-05 2743.149902 \\n\",\n \"2018-01-08 2747.709961 \"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from pandas_datareader import data\\n\",\n \"\\n\",\n \"sp500 = data.DataReader('^GSPC', start='2018', end='2022',\\n\",\n \" data_source='yahoo')\\n\",\n \"sp500.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For simplicity, we'll use just the closing price:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"sp500 = sp500['Close']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can visualize this using the ``plot`` method, after the normal Matplotlib setup boilerplate (see [Part 4](04.00-Introduction-To-Matplotlib.ipynb)); the result is shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"sp500.plot();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Resampling and Converting Frequencies\\n\",\n \"\\n\",\n \"One common need when dealing with time series data is resampling at a higher or lower frequency.\\n\",\n \"This can be done using the `resample` method, or the much simpler `asfreq` method.\\n\",\n \"The primary difference between the two is that `resample` is fundamentally a *data aggregation*, while `asfreq` is fundamentally a *data selection*.\\n\",\n \"\\n\",\n \"Let's compare what the two return when we downsample the S&P 500 closing price data.\\n\",\n \"Here we will resample the data at the end of business year; the following figure shows the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sp500.plot(alpha=0.5, style='-')\\n\",\n \"sp500.resample('BA').mean().plot(style=':')\\n\",\n \"sp500.asfreq('BA').plot(style='--');\\n\",\n \"plt.legend(['input', 'resample', 'asfreq'],\\n\",\n \" loc='upper left');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice the difference: at each point, `resample` reports the *average of the previous year*, while `asfreq` reports the *value at the end of the year*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For upsampling, `resample` and `asfreq` are largely equivalent, though `resample` has many more options available.\\n\",\n \"In this case, the default for both methods is to leave the upsampled points empty; that is, filled with NA values.\\n\",\n \"Like the `pd.fillna` function discussed in [Handling Missing Data](03.04-Missing-Values.ipynb), `asfreq` accepts a `method` argument to specify how values are imputed.\\n\",\n \"Here, we will resample the business day data at a daily frequency (i.e., including weekends); the following figure shows the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(2, sharex=True)\\n\",\n \"data = sp500.iloc[:20]\\n\",\n \"\\n\",\n \"data.asfreq('D').plot(ax=ax[0], marker='o')\\n\",\n \"\\n\",\n \"data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o')\\n\",\n \"data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o')\\n\",\n \"ax[1].legend([\\\"back-fill\\\", \\\"forward-fill\\\"]);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because the S&P 500 data only exists for business days, the top panel has gaps representing NA values.\\n\",\n \"The bottom panel shows the differences between two strategies for filling the gaps: forward filling and backward filling.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Time Shifts\\n\",\n \"\\n\",\n \"Another common time series–specific operation is shifting of data in time.\\n\",\n \"For this, Pandas provides the `shift` method, which can be used to shift data by a given number of entries.\\n\",\n \"With time series data sampled at a regular frequency, this can give us a way to explore trends over time.\\n\",\n \"\\n\",\n \"For example, here we resample the data to daily values, and shift by 364 to compute the 1-year return on investment for the S&P 500 over time (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sp500 = sp500.asfreq('D', method='pad')\\n\",\n \"\\n\",\n \"ROI = 100 * (sp500.shift(-365) - sp500) / sp500\\n\",\n \"ROI.plot()\\n\",\n \"plt.ylabel('% Return on Investment after 1 year');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The worst one-year return was around March 2019, with the coronavirus-related market crash exactly a year later. As you might expect, the best one-year return was to be found in March 2020, for those with enough foresight or luck to buy low.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Rolling Windows\\n\",\n \"\\n\",\n \"Calculating rolling statistics is a third type of time series–specific operation implemented by Pandas.\\n\",\n \"This can be accomplished via the `rolling` attribute of `Series` and `DataFrame` objects, which returns a view similar to what we saw with the `groupby` operation (see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)).\\n\",\n \"This rolling view makes available a number of aggregation operations by default.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"For example, we can look at the one-year centered rolling mean and standard deviation of the stock prices (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rolling = sp500.rolling(365, center=True)\\n\",\n \"\\n\",\n \"data = pd.DataFrame({'input': sp500,\\n\",\n \" 'one-year rolling_mean': rolling.mean(),\\n\",\n \" 'one-year rolling_median': rolling.median()})\\n\",\n \"ax = data.plot(style=['-', '--', ':'])\\n\",\n \"ax.lines[0].set_alpha(0.3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As with `groupby` operations, the `aggregate` and `apply` methods can be used for custom rolling computations.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Where to Learn More\\n\",\n \"\\n\",\n \"This chapter has provided only a brief summary of some of the most essential features of time series tools provided by Pandas; for a more complete discussion, you can refer to the [\\\"Time Series/Date Functionality\\\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html) of the Pandas online documentation.\\n\",\n \"\\n\",\n \"Another excellent resource is the book [*Python for Data Analysis*](https://learning.oreilly.com/library/view/python-for-data/9781098104023/) by Wes McKinney (O'Reilly).\\n\",\n \"It is an invaluable resource on the use of Pandas.\\n\",\n \"In particular, this book emphasizes time series tools in the context of business and finance, and focuses much more on particular details of business calendars, time zones, and related topics.\\n\",\n \"\\n\",\n \"As always, you can also use the IPython help functionality to explore and try out further options available to the functions and methods discussed here. I find this often is the best way to learn a new Python tool.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Visualizing Seattle Bicycle Counts\\n\",\n \"\\n\",\n \"As a more involved example of working with time series data, let's take a look at bicycle counts on Seattle's [Fremont Bridge](http://www.openstreetmap.org/#map=17/47.64813/-122.34965).\\n\",\n \"This data comes from an automated bicycle counter installed in late 2012, which has inductive sensors on the east and west sidewalks of the bridge.\\n\",\n \"The hourly bicycle counts can be downloaded from [http://data.seattle.gov](http://data.seattle.gov); the Fremont Bridge Bicycle Counter dataset is available under the Transportation category.\\n\",\n \"\\n\",\n \"The CSV used for this book can be downloaded as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# url = ('https://raw.githubusercontent.com/jakevdp/'\\n\",\n \"# 'bicycle-data/main/FremontBridge.csv')\\n\",\n \"# !curl -O {url}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once this dataset is downloaded, we can use Pandas to read the CSV output into a `DataFrame`.\\n\",\n \"We will specify that we want the `Date` column as an index, and we want these dates to be automatically parsed:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \" Total East West\\n\",\n \"count 147255.000000 147255.000000 147255.000000\\n\",\n \"mean 110.341462 50.077763 60.263699\\n\",\n \"std 140.422051 64.634038 87.252147\\n\",\n \"min 0.000000 0.000000 0.000000\\n\",\n \"25% 14.000000 6.000000 7.000000\\n\",\n \"50% 60.000000 28.000000 30.000000\\n\",\n \"75% 145.000000 68.000000 74.000000\\n\",\n \"max 1097.000000 698.000000 850.000000\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.dropna().describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Visualizing the Data\\n\",\n \"\\n\",\n \"We can gain some insight into the dataset by visualizing it.\\n\",\n \"Let's start by plotting the raw data (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"data.plot()\\n\",\n \"plt.ylabel('Hourly Bicycle Count');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The ~150,000 hourly samples are far too dense for us to make much sense of.\\n\",\n \"We can gain more insight by resampling the data to a coarser grid.\\n\",\n \"Let's resample by week (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"weekly = data.resample('W').sum()\\n\",\n \"weekly.plot(style=['-', ':', '--'])\\n\",\n \"plt.ylabel('Weekly bicycle count');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This reveals some trends: as you might expect, people bicycle more in the summer than in the winter, and even within a particular season the bicycle use varies from week to week (likely dependent on weather; see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), where we explore this further). Further, the effect of the COVID-19 pandemic on commuting patterns is quite clear, starting in early 2020.\\n\",\n \"\\n\",\n \"Another option that comes in handy for aggregating the data is to use a rolling mean, utilizing the `pd.rolling_mean` function.\\n\",\n \"Here we'll examine the 30-day rolling mean of our data, making sure to center the window (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"daily = data.resample('D').sum()\\n\",\n \"daily.rolling(30, center=True).sum().plot(style=['-', ':', '--'])\\n\",\n \"plt.ylabel('mean hourly count');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The jaggedness of the result is due to the hard cutoff of the window.\\n\",\n \"We can get a smoother version of a rolling mean using a window function—for example, a Gaussian window, as shown in the following figure.\\n\",\n \"The following code specifies both the width of the window (here, 50 days) and the width of the Gaussian window (here, 10 days):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"daily.rolling(50, center=True,\\n\",\n \" win_type='gaussian').sum(std=10).plot(style=['-', ':', '--']);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Digging into the Data\\n\",\n \"\\n\",\n \"While these smoothed data views are useful to get an idea of the general trend in the data, they hide much of the structure.\\n\",\n \"For example, we might want to look at the average traffic as a function of the time of day.\\n\",\n \"We can do this using the `groupby` functionality discussed in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"by_time = data.groupby(data.index.time).mean()\\n\",\n \"hourly_ticks = 4 * 60 * 60 * np.arange(6)\\n\",\n \"by_time.plot(xticks=hourly_ticks, style=['-', ':', '--']);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The hourly traffic is a strongly bimodal sequence, with peaks around 8:00 a.m. and 5:00 p.m.\\n\",\n \"This is likely evidence of a strong component of commuter traffic crossing the bridge.\\n\",\n \"There is a directional component as well: according to the data, the east sidewalk is used more during the a.m. commute, and the west sidewalk is used more during the p.m. commute.\\n\",\n \"\\n\",\n \"We also might be curious about how things change based on the day of the week. Again, we can do this with a simple `groupby` (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"by_weekday = data.groupby(data.index.dayofweek).mean()\\n\",\n \"by_weekday.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\\n\",\n \"by_weekday.plot(style=['-', ':', '--']);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This shows a strong distinction between weekday and weekend totals, with around twice as many average riders crossing the bridge on Monday through Friday than on Saturday and Sunday.\\n\",\n \"\\n\",\n \"With this in mind, let's do a compound `groupby` and look at the hourly trends on weekdays versus weekends.\\n\",\n \"We'll start by grouping by flags marking the weekend and the time of day:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"weekend = np.where(data.index.weekday < 5, 'Weekday', 'Weekend')\\n\",\n \"by_time = data.groupby([weekend, data.index.time]).mean()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we'll use some of the Matplotlib tools that will be described in [Multiple Subplots](04.08-Multiple-Subplots.ipynb) to plot two panels side by side, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"fig, ax = plt.subplots(1, 2, figsize=(14, 5))\\n\",\n \"by_time.loc['Weekday'].plot(ax=ax[0], title='Weekdays',\\n\",\n \" xticks=hourly_ticks, style=['-', ':', '--'])\\n\",\n \"by_time.loc['Weekend'].plot(ax=ax[1], title='Weekends',\\n\",\n \" xticks=hourly_ticks, style=['-', ':', '--']);\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result shows a bimodal commuting pattern during the work week, and a unimodal recreational pattern during the weekends.\\n\",\n \"It might be interesting to dig through this data in more detail and examine the effects of weather, temperature, time of year, and other factors on people's commuting patterns; for further discussion, see my blog post [\\\"Is Seattle Really Seeing an Uptick in Cycling?\\\"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/), which uses a subset of this data.\\n\",\n \"We will also revisit this dataset in the context of modeling in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 64-bit ('3.9.6')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/03.12-Performance-Eval-and-Query.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# High-Performance Pandas: eval and query\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we've already seen in previous chapters, the power of the PyData stack is built upon the ability of NumPy and Pandas to push basic operations into lower-level compiled code via an intuitive higher-level syntax: examples are vectorized/broadcasted operations in NumPy, and grouping-type operations in Pandas.\\n\",\n \"While these abstractions are efficient and effective for many common use cases, they often rely on the creation of temporary intermediate objects, which can cause undue overhead in computational time and memory use.\\n\",\n \"\\n\",\n \"To address this, Pandas includes some methods that allow you to directly access C-speed operations without costly allocation of intermediate arrays: `eval` and `query`, which rely on the [NumExpr package](https://github.com/pydata/numexpr).\\n\",\n \"In this chapter I will walk you through their use and give some rules of thumb about when you might think about using them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Motivating query and eval: Compound Expressions\\n\",\n \"\\n\",\n \"We've seen previously that NumPy and Pandas support fast vectorized operations; for example, when adding the elements of two arrays:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.21 ms ± 142 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"rng = np.random.default_rng(42)\\n\",\n \"x = rng.random(1000000)\\n\",\n \"y = rng.random(1000000)\\n\",\n \"%timeit x + y\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As discussed in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb), this is much faster than doing the addition via a Python loop or comprehension:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"263 ms ± 43.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit np.fromiter((xi + yi for xi, yi in zip(x, y)),\\n\",\n \" dtype=x.dtype, count=len(x))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But this abstraction can become less efficient when computing compound expressions.\\n\",\n \"For example, consider the following expression:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"mask = (x > 0.5) & (y < 0.5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because NumPy evaluates each subexpression, this is roughly equivalent to the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"tmp1 = (x > 0.5)\\n\",\n \"tmp2 = (y < 0.5)\\n\",\n \"mask = tmp1 & tmp2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In other words, *every intermediate step is explicitly allocated in memory*. If the `x` and `y` arrays are very large, this can lead to significant memory and computational overhead.\\n\",\n \"The NumExpr library gives you the ability to compute this type of compound expression element by element, without the need to allocate full intermediate arrays.\\n\",\n \"The [NumExpr documentation](https://github.com/pydata/numexpr) has more details, but for the time being it is sufficient to say that the library accepts a *string* giving the NumPy-style expression you'd like to compute:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numexpr\\n\",\n \"mask_numexpr = numexpr.evaluate('(x > 0.5) & (y < 0.5)')\\n\",\n \"np.all(mask == mask_numexpr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The benefit here is that NumExpr evaluates the expression in a way that avoids temporary arrays where possible, and thus can be much more efficient than NumPy, especially for long sequences of computations on large arrays.\\n\",\n \"The Pandas `eval` and `query` tools that we will discuss here are conceptually similar, and are essentially Pandas-specific wrappers of NumExpr functionality.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## pandas.eval for Efficient Operations\\n\",\n \"\\n\",\n \"The `eval` function in Pandas uses string expressions to efficiently compute operations on `DataFrame` objects.\\n\",\n \"For example, consider the following data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"nrows, ncols = 100000, 100\\n\",\n \"df1, df2, df3, df4 = (pd.DataFrame(rng.random((nrows, ncols)))\\n\",\n \" for i in range(4))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To compute the sum of all four ``DataFrame``s using the typical Pandas approach, we can just write the sum:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"73.2 ms ± 6.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit df1 + df2 + df3 + df4\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The same result can be computed via ``pd.eval`` by constructing the expression as a string:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"34 ms ± 4.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\\n\"\n ]\n }\n ],\n \"source\": [\n \"%timeit pd.eval('df1 + df2 + df3 + df4')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `eval` version of this expression is about 50% faster (and uses much less memory), while giving the same result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.allclose(df1 + df2 + df3 + df4,\\n\",\n \" pd.eval('df1 + df2 + df3 + df4'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`pd.eval` supports a wide range of operations.\\n\",\n \"To demonstrate these, we'll use the following integer data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"df1, df2, df3, df4, df5 = (pd.DataFrame(rng.integers(0, 1000, (100, 3)))\\n\",\n \" for i in range(5))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Arithmetic operators\\n\",\n \"`pd.eval` supports all arithmetic operators. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"result1 = -df1 * df2 / (df3 + df4) - df5\\n\",\n \"result2 = pd.eval('-df1 * df2 / (df3 + df4) - df5')\\n\",\n \"np.allclose(result1, result2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Comparison operators\\n\",\n \"`pd.eval` supports all comparison operators, including chained expressions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"result1 = (df1 < df2) & (df2 <= df3) & (df3 != df4)\\n\",\n \"result2 = pd.eval('df1 < df2 <= df3 != df4')\\n\",\n \"np.allclose(result1, result2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Bitwise operators\\n\",\n \"`pd.eval` supports the `&` and `|` bitwise operators:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"result1 = (df1 < 0.5) & (df2 < 0.5) | (df3 < df4)\\n\",\n \"result2 = pd.eval('(df1 < 0.5) & (df2 < 0.5) | (df3 < df4)')\\n\",\n \"np.allclose(result1, result2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In addition, it supports the use of the literal `and` and `or` in Boolean expressions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"result3 = pd.eval('(df1 < 0.5) and (df2 < 0.5) or (df3 < df4)')\\n\",\n \"np.allclose(result1, result3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Object attributes and indices\\n\",\n \"\\n\",\n \"`pd.eval` supports access to object attributes via the `obj.attr` syntax and indexes via the `obj[index]` syntax:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"result1 = df2.T[0] + df3.iloc[1]\\n\",\n \"result2 = pd.eval('df2.T[0] + df3.iloc[1]')\\n\",\n \"np.allclose(result1, result2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Other operations\\n\",\n \"\\n\",\n \"Other operations, such as function calls, conditional statements, loops, and other more involved constructs are currently *not* implemented in `pd.eval`.\\n\",\n \"If you'd like to execute these more complicated types of expressions, you can use the NumExpr library itself.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## DataFrame.eval for Column-Wise Operations\\n\",\n \"\\n\",\n \"Just as Pandas has a top-level `pd.eval` function, `DataFrame` objects have an `eval` method that works in similar ways.\\n\",\n \"The benefit of the `eval` method is that columns can be referred to by name.\\n\",\n \"We'll use this labeled array as an example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" A B C D\\n\",\n \"0 0.850888 0.966709 0.958690 -0.120812\\n\",\n \"1 0.820126 0.385686 0.061402 7.075399\\n\",\n \"2 0.059729 0.831768 0.652259 -1.183638\\n\",\n \"3 0.244774 0.140322 0.041711 2.504142\\n\",\n \"4 0.818205 0.753384 0.578851 0.111982\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.eval('D = (A - B) / C', inplace=True)\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Local Variables in DataFrame.eval\\n\",\n \"\\n\",\n \"The `DataFrame.eval` method supports an additional syntax that lets it work with local Python variables.\\n\",\n \"Consider the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"column_mean = df.mean(1)\\n\",\n \"result1 = df['A'] + column_mean\\n\",\n \"result2 = df.eval('A + @column_mean')\\n\",\n \"np.allclose(result1, result2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `@` character here marks a *variable name* rather than a *column name*, and lets you efficiently evaluate expressions involving the two \\\"namespaces\\\": the namespace of columns, and the namespace of Python objects.\\n\",\n \"Notice that this `@` character is only supported by the `DataFrame.eval` *method*, not by the `pandas.eval` *function*, because the `pandas.eval` function only has access to the one (Python) namespace.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The DataFrame.query Method\\n\",\n \"\\n\",\n \"The `DataFrame` has another method based on evaluated strings, called `query`.\\n\",\n \"Consider the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"result1 = df[(df.A < 0.5) & (df.B < 0.5)]\\n\",\n \"result2 = pd.eval('df[(df.A < 0.5) & (df.B < 0.5)]')\\n\",\n \"np.allclose(result1, result2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As with the example used in our discussion of `DataFrame.eval`, this is an expression involving columns of the `DataFrame`.\\n\",\n \"However, it cannot be expressed using the `DataFrame.eval` syntax!\\n\",\n \"Instead, for this type of filtering operation, you can use the `query` method:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"result2 = df.query('A < 0.5 and B < 0.5')\\n\",\n \"np.allclose(result1, result2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In addition to being a more efficient computation, compared to the masking expression this is much easier to read and understand.\\n\",\n \"Note that the `query` method also accepts the `@` flag to mark local variables:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Cmean = df['C'].mean()\\n\",\n \"result1 = df[(df.A < Cmean) & (df.B < Cmean)]\\n\",\n \"result2 = df.query('A < @Cmean and B < @Cmean')\\n\",\n \"np.allclose(result1, result2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Performance: When to Use These Functions\\n\",\n \"\\n\",\n \"When considering whether to use `eval` and `query`, there are two considerations: *computation time* and *memory use*.\\n\",\n \"Memory use is the most predictable aspect. As already mentioned, every compound expression involving NumPy arrays or Pandas ``DataFrame``s will result in implicit creation of temporary arrays. For example, this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"x = df[(df.A < 0.5) & (df.B < 0.5)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"is roughly equivalent to this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"tmp1 = df.A < 0.5\\n\",\n \"tmp2 = df.B < 0.5\\n\",\n \"tmp3 = tmp1 & tmp2\\n\",\n \"x = df[tmp3]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If the size of the temporary ``DataFrame``s is significant compared to your available system memory (typically several gigabytes), then it's a good idea to use an `eval` or `query` expression.\\n\",\n \"You can check the approximate size of your array in bytes using this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"32000\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.values.nbytes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"On the performance side, `eval` can be faster even when you are not maxing out your system memory.\\n\",\n \"The issue is how your temporary objects compare to the size of the L1 or L2 CPU cache on your system (typically a few megabytes); if they are much bigger, then `eval` can avoid some potentially slow movement of values between the different memory caches.\\n\",\n \"In practice, I find that the difference in computation time between the traditional methods and the `eval`/`query` method is usually not significant—if anything, the traditional method is faster for smaller arrays!\\n\",\n \"The benefit of `eval`/`query` is mainly in the saved memory, and the sometimes cleaner syntax they offer.\\n\",\n \"\\n\",\n \"We've covered most of the details of `eval` and `query` here; for more information on these, you can refer to the Pandas documentation.\\n\",\n \"In particular, different parsers and engines can be specified for running these queries; for details on this, see the discussion within the [\\\"Enhancing Performance\\\" section](https://pandas.pydata.org/pandas-docs/dev/user_guide/enhancingperf.html) of the documentation.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/03.13-Further-Resources.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Further Resources\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"In this part of the book, we've covered many of the basics of using Pandas effectively for data analysis.\\n\",\n \"Still, much has been omitted from our discussion.\\n\",\n \"To learn more about Pandas, I recommend the following resources:\\n\",\n \"\\n\",\n \"- [Pandas online documentation](http://pandas.pydata.org/): This is the go-to source for complete documentation of the package. While the examples in the documentation tend to be based on small generated datasets, the description of the options is complete and generally very useful for understanding the use of various functions.\\n\",\n \"\\n\",\n \"- [*Python for Data Analysis*](https://learning.oreilly.com/library/view/python-for-data/9781098104023/): Written by Wes McKinney (the original creator of Pandas), this book contains much more detail on the Pandas package than we had room for in this chapter. In particular, McKinney takes a deep dive into tools for time series, which were his bread and butter as a financial consultant. The book also has many entertaining examples of applying Pandas to gain insight from real-world datasets.\\n\",\n \"\\n\",\n \"- [*Effective Pandas*](https://leanpub.com/effective-pandas): This short e-book by Pandas developer Tom Augspurger provides a succinct outline of using the full power of the Pandas library in an effective and idiomatic way.\\n\",\n \"\\n\",\n \"- [Pandas on PyVideo](http://pyvideo.org/search?q=pandas): From PyCon to SciPy to PyData, many conferences have featured tutorials by Pandas developers and power users. The PyCon tutorials in particular tend to be given by very well-vetted presenters.\\n\",\n \"\\n\",\n \"Using these resources, combined with the walkthrough given in these chapters, my hope is that you'll be poised to use Pandas to tackle any data analysis problem you come across!\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/04.00-Introduction-To-Matplotlib.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Visualization with Matplotlib\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll now take an in-depth look at the Matplotlib package for visualization in Python.\\n\",\n \"Matplotlib is a multiplatform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.\\n\",\n \"It was conceived by John Hunter in 2002, originally as a patch to IPython for enabling interactive MATLAB-style plotting via `gnuplot` from the IPython command line.\\n\",\n \"IPython's creator, Fernando Perez, was at the time scrambling to finish his PhD, and let John know he wouldn’t have time to review the patch for several months.\\n\",\n \"John took this as a cue to set out on his own, and the Matplotlib package was born, with version 0.1 released in 2003.\\n\",\n \"It received an early boost when it was adopted as the plotting package of choice of the Space Telescope Science Institute (the folks behind the Hubble Telescope), which financially supported Matplotlib’s development and greatly expanded its capabilities.\\n\",\n \"\\n\",\n \"One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends.\\n\",\n \"Matplotlib supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you desire.\\n\",\n \"This cross-platform, everything-to-everyone approach has been one of the great strengths of Matplotlib.\\n\",\n \"It has led to a large user base, which in turn has led to an active developer base and Matplotlib’s powerful tools and ubiquity within the scientific Python world.\\n\",\n \"\\n\",\n \"In recent years, however, the interface and style of Matplotlib have begun to show their age.\\n\",\n \"Newer tools like `ggplot` and `ggvis` in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned.\\n\",\n \"Still, I'm of the opinion that we cannot ignore Matplotlib's strength as a well-tested, cross-platform graphics engine.\\n\",\n \"Recent Matplotlib versions make it relatively easy to set new global plotting styles (see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs—for example, Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), [`ggpy`](http://yhat.github.io/ggpy/), [HoloViews](http://holoviews.org/), and even Pandas itself can be used as wrappers around Matplotlib's API.\\n\",\n \"Even with wrappers like these, it is still often useful to dive into Matplotlib's syntax to adjust the final plot output.\\n\",\n \"For this reason, I believe that Matplotlib itself will remain a vital piece of the data visualization stack, even if new tools mean the community gradually moves away from using the Matplotlib API directly.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# General Matplotlib Tips\\n\",\n \"\\n\",\n \"Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Importing Matplotlib\\n\",\n \"\\n\",\n \"Just as we use the `np` shorthand for NumPy and the `pd` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib as mpl\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `plt` interface is what we will use most often, as you shall see throughout this part of the book.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Setting Styles\\n\",\n \"\\n\",\n \"We will use the `plt.style` directive to choose appropriate aesthetic styles for our figures.\\n\",\n \"Here we will set the `classic` style, which ensures that the plots we create use the classic Matplotlib style:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"plt.style.use('classic')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Throughout this chapter, we will adjust this style as needed.\\n\",\n \"For more information on stylesheets, see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## show or No show? How to Display Your Plots\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A visualization you can't see won't be of much use, but just how you view your Matplotlib plots depends on the context.\\n\",\n \"The best use of Matplotlib differs depending on how you are using it; roughly, the three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in a Jupyter notebook.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Plotting from a Script\\n\",\n \"\\n\",\n \"If you are using Matplotlib from within a script, the function `plt.show` is your friend.\\n\",\n \"`plt.show` starts an event loop, looks for all currently active `Figure` objects, and opens one or more interactive windows that display your figure or figures.\\n\",\n \"\\n\",\n \"So, for example, you may have a file called *myplot.py* containing the following:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"# file: myplot.py \\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"x = np.linspace(0, 10, 100)\\n\",\n \"\\n\",\n \"plt.plot(x, np.sin(x))\\n\",\n \"plt.plot(x, np.cos(x))\\n\",\n \"\\n\",\n \"plt.show()\\n\",\n \"```\\n\",\n \"\\n\",\n \"You can then run this script from the command-line prompt, which will result in a window opening with your figure displayed:\\n\",\n \"\\n\",\n \"```\\n\",\n \"$ python myplot.py\\n\",\n \"```\\n\",\n \"\\n\",\n \"The `plt.show` command does a lot under the hood, as it must interact with your system's interactive graphical backend.\\n\",\n \"The details of this operation can vary greatly from system to system and even installation to installation, but Matplotlib does its best to hide all these details from you.\\n\",\n \"\\n\",\n \"One thing to be aware of: the `plt.show` command should be used *only once* per Python session, and is most often seen at the very end of the script.\\n\",\n \"Multiple `show` commands can lead to unpredictable backend-dependent behavior, and should mostly be avoided.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Plotting from an IPython Shell\\n\",\n \"\\n\",\n \"Matplotlib also works seamlessly within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\\n\",\n \"IPython is built to work well with Matplotlib if you specify Matplotlib mode.\\n\",\n \"To enable this mode, you can use the `%matplotlib` magic command after starting `ipython`:\\n\",\n \"\\n\",\n \"```ipython\\n\",\n \"In [1]: %matplotlib\\n\",\n \"Using matplotlib backend: TkAgg\\n\",\n \"\\n\",\n \"In [2]: import matplotlib.pyplot as plt\\n\",\n \"```\\n\",\n \"\\n\",\n \"At this point, any `plt` plot command will cause a figure window to open, and further commands can be run to update the plot.\\n\",\n \"Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use `plt.draw`.\\n\",\n \"Using `plt.show` in IPython's Matplotlib mode is not required.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Plotting from a Jupyter Notebook\\n\",\n \"\\n\",\n \"The Jupyter notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements, and much more into a single executable document (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\\n\",\n \"\\n\",\n \"Plotting interactively within a Jupyter notebook can be done with the `%matplotlib` command, and works in a similar way to the IPython shell.\\n\",\n \"You also have the option of embedding graphics directly in the notebook, with two possible options:\\n\",\n \"\\n\",\n \"- `%matplotlib inline` will lead to *static* images of your plot embedded in the notebook.\\n\",\n \"- `%matplotlib notebook` will lead to *interactive* plots embedded within the notebook.\\n\",\n \"\\n\",\n \"For this book, we will generally stick with the default, with figures rendered as static images (see the following figure for the result of this basic plotting example):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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nzdCcxvCI1VV1BP/LIjZ+3+ehmTlw8YU8oGwwY4N0bv54v/t9/r1a+9NDVd5aYG7+GlVauVBu2NGx44+d9vuZzvv/DO32OffvCzz/DkSO6k1jP08U/Pl6N1X3oId1J7Ne7N6xa5c03rdNtPLzRU0segOr7fuSRzBtRjzR4hB82/eCZe2hFiqjZ85Mm6U5iPU8X/2nToGVLuPlm3UnsV7gw9Oih7nUY9hq9cbSnuj4uXFDzZB54IPPntqrciotJF9l4eKPPc9nlwQe9s/z7lTxd/MeOVV0+WfXZms/488ifvopju4ce8uab1skSUxKZsm0KD9yWhUrpEjNmqEZUuXKZPzdABPBw/Yf5YdMPvg9mk7Zt1RV0VJTuJNbybPE/cAA2bcreXfqzl8/y3R/e6Si/6y44dQq2bs38uYY1FuxcwK1lbqVKsSq6o1hm7Fi1nENWPVz/YSZtnURiijc2mAgMVItBTpigO4m1PFv8f/pJdXukrz+SFQ/Vf4jJWyeTlJLku2A2CgiA++7z3pvWycZtHsdD9bxzk2n/ftWIys7Y/uDiwQy/d7hnfo/gf10/qam6k1jHs8V/woTsL90cXDyYkFIhLIz1zlY+DzwAEyd6603rVOcSzrF091J61emlO4plJk9Ws3mz04gC1fovlLeQb0JpUK+e2uXrl190J7GOJcVfCNFRCBEjhIgVQgzK4PuPCiGOCyH+TPt4PKPjWCUqCo4dU1PRs+vheg/z4+YfrQ+lSb16asTCr7/qTuJ9N+W7iajnoiiWv5juKJaZNEldPfo7Ibx34zfXxV8IEQh8DXQC6gD3CSHqZPDUn6SUDdI+fDptYtIktSNPYGD2X9urTi+W7FrimV2XhFCtf9P1Y4/yRcrrjmCZ6Gg4elTdOzJUT8L06WplUy+wouXfFIiVUu6WUiYCk4FwC46bI1Kqbo6sDEvLSPECxdk5cKendl26/3417NVs8G5kR24aUV5UqRLUr++dmfNWFP8KwIErvo5Le+xqPYUQm4UQ04QQlSw4b4Z+/12tZ5PZTMQbKV2otHWBHKBKFbWPwULv3MowfExKa7p8pJSeuvGbfg/NC4JsOs8cYJKUMkEI8RTwA9AuoycOHTr0r8/btGlDmzZtsnWiCRPU/yB/W84hM+ldP2FhupMYbrBhg/oD0Lhx7o7z/PznqVumLs80ecaaYJp1766Wej5/Xt1L0yUyMpLIyMhcHSPXq3oKIZoDQ6WUHdK+fgNASvn+dZ4fCJySUhbN4Hu5WtUzORkqVFA3N6tXz/FhPOnkSQgOhoMH1exfwzqHzx/mxMUT3Fb2Nt1RLPPKK1CoELzzTu6OExETwcerP2Zl/5XWBHOALl1UV2pOu5Z9QdeqnuuAGkKIakKIvEA/IOKqYFcusBAGRFtw3musWKG6OEzhv1bJktCihXf6K51kzB9jPLWWT0qKGuJpxSifDrd0YNvxbRw4eyDzJ7tE375qr2y3y3Xxl1ImA88Di1BFfYqUcpsQ4h0hRHonwwtCiG1CiE3AC8CjuT1vRqZMgT59rDve2oNrPbVGS58+3njTOs1P236ib92+umNYZtUqKF367/td51S+oHx0r9Wdn7b9lPuDOUR4OERGqr0N3Mwzm7kkJUH58rB+vWr9W+HhmQ/TpHwTBt4x0JoDanb6NFStqjZ50dlf6SVRx6O4d9y97H9pPwHCG3Mmn3lG/Q4NumbGTs4s272M15a+xoYnN1hzQAfo1k31/2e2v4Fd/HozlxUrVJ+2VYUfoF/dfkzeNtm6A2pWvLja4WvuXN1JvOOnrT/Ru05vzxT+lBS1kFvv3tYds3XV1pQrXM4zc2dAXUX/5PKLGW+8Y4GpU63t8gG4O/huYk7EsO/MPmsPrJHp+rGOlNKTXT4VKsAtt1h3zKCAIObdP89Tc2dCQ9XAkpMndSfJOU8U/6Qktd54L4uXVMkbmJfwkHBmRM+w9sAahYfD8uVw7pzuJO6XnJrMM42f4Y4K3tkjdOpUa1v9XlWkCNxzj6o7buWJ4r9ihWqpWNnlk65XnV5Mi55m/YE1KVZMTdefM0d3EvfLE5iHF5u9iPDIpJKUFLV8gdWNKK/q29fdXT+eKP6+bK20D27PfXXv88y2dGC6foyM/fqr2rClRg3dSdyhSxe1osAJlw4IdP1on6QktU3jhg2+afl70dmzULmyWqu96DVT7Qx/NXCgKv6DB+tO4h69ekHnzvDYY3pz+OVonxUr1KQuU/izrmhRtdy16fox0qWmqi4fX/f3D40cyv6z+317Ehv17Kl+bm7k+uI/bZq5QZUTPXrAzJm6U7iXk66YrbB6tZrYVbOmb88Tdy6O6VEurZYZ6NJFjZA6e1Z3kuxzdfFPSYHZs1UhM7InLAyWLoWL3hl6bZsdJ3fQ8ruWumNYaupUe2709qrTi6lRU31/IpvcdJO6inbj3BlXF/9ff1VjkqtVs+d8XmrtlSgBTZuaZZ5zYnrUdBqUa6A7hmXSu3zsKP7tq7Un5mQMcefifH8ym/TsqSbGuY2ri/+MGfa1+lNlKnVG1PHUWj89erjzTavb9Ojp9KzdU3cMy6xfr8atW7GWT2byBOaha82uzNo+y/cns0loqLqKjo/XnSR7XFv8pVSFq3t3e84XIAKoW6Yus7fPtueENujWTa3yaXb4yrq9Z/ay98xeWlfNwQbRDmVnIwqge63unpo4WbKkO6+iXVv8N2yAAgWgTka7BftIj1o9mLHdO2/am29WP7/ly3UncY8Z0TMIDwknKMCufZB8S0p149+uRhRAx+odGd/DQzuh485RP64t/umtFTsnV3au0ZlV+1ZxPuG8fSf1Mbf2V+oScyKGXnW8MwU2OhouXYJGjew7Z/6g/J7a6B7UVfSCBZCQoDtJ1rmy+Eup/sraPcqnaP6itKzckgWxC+w9sQ91767WJ0lJ0Z3EHf4b+l861eikO4Zl0rtOPbJChTblysFtt8GSJbqTZJ0ri390tBqimNv9RXOiV+1eRB/3yUZkWlSrBhUrwi+/6E5i6GB3l4+Xde+uhp67hSuXd3j3XTh+HD7/3IZQfuDdd+HYMfjiC91JDDvt3QtNmsDhwxDkjVsYWu3eDc2bw6FDEBho77n9ZnkHu0cneF16i8VB7QDDBrNmqWGKugp/cmoy209s13NyHwgOVt0/a9boTpI1riv+e/eqBclaemuCpVZ16kCePPDnn7qTGHaaOVNvI+pY/DGaj2lOYop3xhp36+aeNf5dV/wjIvS2VrxICPWmdVN/pd3GbRrHwXMHdcewzLFjsGkT3H23vgzli5SndqnarNizQl8Ii3Xrpv6ouuEq2nXFf/ZstRuVYa3wcPe0WOyWkJzAwAUDCQywuSPXh+bMUTtR5c+vN0f3Wt2Zud07Kww2aADJyRAVpTtJ5lxV/E+dgnXr1JtWtx0ndzAz2jtv2hYt1I2qvXt1J3GeyL2R1Cldh3KFy+mOYhmnNKLCa4UTERNBqkzVHcUS6VfRbmhIuar4z58PbdtCoUK6k8CFxAu8tvQ1zyz2FhgIXbuarp+MzNo+i261uumOYZn4eIiMVJuQ6FazZE2K5i/K+kPrdUexjCn+PuCU1gpAw3INSUhO8NRoBdPvf61UmcrsmNmeKv5Llqg5MiVK6E6ivNbiNc+0/AFatYI9e+DAAd1Jbsw1xf/yZVi8WN3sdQIhBGEhYcyO8U61vPtutcLjyZO6kzjHuoPrKF6gODVL+niXExs5qREF0L9hf5pVbKY7hmWCgtRVdESE7iQ35priv3w51KundhtyivAQ1V/pFQULQvv2aqVPQwkuHszo0NG6Y1gmJUVtPOKk4u9F6aN+nMw1xd9prRWA1lVbE30imqMXjuqOYpnwcNP1c6XShUrTvFJz3TEs89tvUL48VK2qO4m33XMPrF3r7O0dXVH8U1PVJZTTin/ewLxM7zOdgnkK6o5ima5d1cYUly7pTmL4ghMbUV5UqBDcdZez1/h3RfFfu1bdnKpRQ3eSa7Wr1o4i+YrojmGZUqWgfn1Y4Z15N0YaKU3xt1NYmLOvol1R/J3Y6veysDDn36wysm/7dnVFd/vtupNkbMS6EZ7a4atrV9XyT0rSnSRjrin+YWG6U/iPsDA1AzTVO6Pvsi0pJYmUVG9tcjB7tvp/69S1+wvmKciELRN0x7BM+fKqt2LVKt1JMub44r97t1q+uWlT3Un8R82aakPvjRt1J9FnyrYpPDLrEd0xLOX0RlTXml1Zunspl5Mv645iGSdfRTu++M+Zoy6fAhyeNCklyVMTVZz8prVDxI4I2lZtqzuGZY4dU+vNtGmjO8n1lSpYinpl63lqobf03yMnLgTg8JLq/NZKunY/tuO3A7/pjmEZfy7+CckJLIpdRNeaXXVHscy8eWr4Yb58upPcWGjNUObsmKM7hmXq1lWFf9s23Umu5ejif+aMWshN57KzWdW6SmtPvWmbN4eDB2HfPt1J7Pfzvp+pU7oOZQuX1R3FMnPmOGd2/I2EhYSxIHaBZ9bMEsK5DSlHF/+FC9VYWScs5JYZr7VYAgOhSxc1G9TfRMREEBbigsvNLLp8GZYtc8ZCbpkJKRnCxic3Ipx6VzoHnDrk09HF3y1dPgBNKjTh5MWT7D69W3cUyzi1xeJrZxPOeqr4r1ihlkYpVUp3kswJISheoLjuGJa66y7YsUPtlewkji3+SUmq5d/VJd2uASKArjW7MifGO63/e+9VywGcO6c7ib3GdR9HndJ1dMewjJsaUV6UJw906OC8NbMcW/xXrYLq1dVYWbfoUbsHR+O9s85P4cJqr2QnT1E3bkxK9/T3e1loqPr/4CTCSTdWhBAyPc9LL0HJkvDWW5pD+bkRI1Trf9w43UmMnNi4Efr1g5gY507u8genTqnF9I4ehQIFrD++EAIpZbb+Dzuy5S+lulR1S5ePl3XtCgsWqH1JDfdJb/W7rfAnpiSy7uA63TEsU6IENGyolqZ3CkcW/+ho1edfv77uJEblylCxomr9G+7j1i6fi0kXaf9je+IT43VHsUxoqLMGUDiy+M+dq1qcbmuteJUT+yt9YeS6kRw8d1B3DMscPKi2E2zZUneS7CuWvxhNKjRh6e6luqNYJjRU1Tan9LQ7svi7tbXiVf5Q/C8nX2bQskHkD8qvO4pl5s6Fjh3VaBM38trcmZAQNYjCKWtmOa74nzwJmzdDWxcvqxJ1PIrJWyfrjmGZxo3VbOvYWN1JfGf5nuXUL1ufkgVL6o5imfR1sdwqtGYo83bO89SaWU5qSDmu+M+fD+3aQX4XN8AuJ1/mXyv+pTuGZQIC1Gxfp7xpfWFOzBxCa3rncvPiRVi5UrX83eqWErdQPH9x1h9arzuKZUzxvwEvdPk0LNeQi0kXiTkRozuKZdL7K71ISsmcHXMIDXH5G+8Ky5ZBo0ZQ3OWTZd+8802CAoJ0x7BMy5awd6+6H6ObJcVfCNFRCBEjhIgVQgzK4Pv5hBA/pX3/dyFE1esda8kS1cp0MyGEmu3rof7Ku+9Wi+w5eUPqnPrjyB8UyFOAkJIhuqNYxguNKIAH6z3I7Tc7dOuxHAgKUldjTmhI5br4CyECga+BTkAd4D4hxNVz4wcAp6WU1YFPgQ+vd7yQECjrgcUUu9bsytwdDvg/bJFCheDOO7052ze4eDATe0z0zGJiqamquHih+HuRU7p+rGj5NwVipZS7pZSJwGTg6h13w4Ef0j6fBrQX1/lNc/MNqiu1r9aejYc3cvrSad1RLOOUN63V0ocVesXGjXDTTWoLQcN5OnZU92MuXtSbw4riXwE4cMXXcWmPZfgcKWUycBbIcFiFV1orBfIUYO79cz01dDA01Mz2dQOvdPl4VbFiagTdUs1TGBx3J2XGjKHMnKk+b9OmDW2cvO9cJu6qcpfuCJaqUAGqVIHVq9UytYYzzZkDn32mO4VxI+lX0TldbTUyMpLIyMhcZcj1wm5CiObAUCllh7Sv3wCQUr5/xXMWpT3nNyFEEHAEKC2vOvmVC7sZzjRkiLpcHTZMdxIjIwcPqrX7jx5VNxe94j+r/kPDcg3pVKOT7iiW2LlTNaAOHrRmf3JdC7utA2oIIaoJIfIC/YCrV7CIAB5J+7wXsNxUeXfyUr9/cmoyiSmJumNYKn1Wr5cKP0ChPIWYFjVNdwzL1KgBRYvqne2b6+Kf1of/PLAIiAamSCm3CSHeEUKkX9SMAUoKIWKBl4FrhoMa7nD77Wpzl507dSfJvUWxiwiffPXYBHfLTVeCk4WGmNm+VrNknL+Ucr6UsqaU8hYp5Xtpj/1LShmR9vllKWVvKWV1KWVTKaV39jrMgktJlzyzIXVAgBqR5YXW/5wdc2hfrb3uGJaJj3f/rN7rCS4eTMmCJT21zLMnir9xYw3+24CYk96a7ev24i+lZO6OuZ5a0mHpUmjSRHUneJHXFnpr0QL27YO4OD3nN8XfBm2rtvXU3r7t28OGDXDaxVMY/jjyBwXzFCSklJnV6xahNUNZvGux7hiWCQqCTp30zfY1xd8GYSFhnmqxFCwIrVu7e7av1xZyS01VG4R7ufg3q9iMFY+s0B3DUjqvok3xt0G7au3YdHQTJy+e1B3FMm7v+jmXcI7utbvrjmGZ9evVIm633KI7ie8EBgRSKG8h3TEs1bEjrFql7tfYzRR/G+QPyk/bqm1ZELtAdxTLdOmiWv5JSbqT5MzwDsNpVbmV7hiW8XqXj1cVLaru0+iY7WuKv0363NqH4/HHdcewTIUKEBwMv/6qO4kBpvi7WViYnqvoXM/wtZKZ4esub7+txvwPH647iX/bv1+t3X/kCAQG6k5jZNfu3Wrkz6FDOZ/tq2uGr+GnwsIgIsI5G1L7q7lz1agRfyn8F5Muemq8f3AwlCql9suwkyn+Ro41aACXL0OMd6YwuJK/dfkciz9Gl4ldSElN0R3FMjoGUJjib+SYEOpNG3H1Sk4ONmbjGHac3KE7hmUuXFD3XTp00J3EPlWLVaVc4XKsiVujO4pldPwemeJv5IqbhnymylQGLx/sqT1hFy+G5s3V5i3+xGtzZ+64Q92z2bfPvnOa4m+z3+N+Z9b2WbpjWKZtW9i8GU6c0J0kc+sOrqNkwZIEFw/WHcUyERHeXMgtM2EhYUTEuOiSMxOBgWr4tJ0NKVP8bXY24Swf/fqR7hiWyZ9fLfcwf77uJJmLiIkgrKZ3KmVKivdn9V5P4/KNOX35NLGnYnVHsUz6AAq7mOJvs9ZVWhN1PIqjF47qjmIZu9+0OTVnxxxCQ7xTKX/7DSpWhMqVdSexX4AI4L1273nqpu8998CaNWr4tB1M8bdZvqB8dKjegbk7NK3m5AOdO8OSJZCQoDvJ9e05vYej8Ue5o8IduqNYxl+7fNI91vAxTy3MV7gwtGoFixbZcz5T/DUIqxnG7JjZumNYpkwZqFsXcrmlqE+VLVyWeffPIzDAO4Ph/b34e1FYGMy2qTSY4q9B5xqdidwbycWki7qjWMbON21OFMxTkMblG+uOYZmYGDh/Xu2sZnhHWBgsWGDPmlmm+GtQvEBxFjywwFNDDtPXJzGzfe2Rvl2jyNaEfsPpypdXK7OuWuX7c5nir0nLyi3JG5hXdwzL1KoFBQro3ZDan5guH+8KD7dnAIUp/oYlhIBu3WDWLN1JvO/ECdi0Sc2xMGDQ0kEs37NcdwzLpHeh+voq2hR/wzLh4c7r909KSSI+UcNOGT40b54aFpg/v+4kzlA8f3GmRU3THcMydeuqxtSWLb49jyn+hmWaNYOjR2HPHt1J/mf5nuV0mdhFdwxLzZql/tAaSrda3YiIicAry8ELYU9DyhR/zc4l2DSjwwaBgdC1q7Na/7NjZtOpeifdMSxz8SIsX66WAjCUkFIhFMlXhA2HN+iOYhlT/D1OSkmDbxqw/cR23VEs46SuHyklETERhNfyTjN5yRJo3BhKlNCdxFm6hXTz1JpZrVqpK+i4ON+dwxR/jYQQdKreidnbHVItLXD33bBhA5x0wF71Gw9vpFDeQtQqVUt3FMvMmqVurBt/161WN5bu1rARro8EBamZ875c6M0Uf8261erGzO0zdcewTMGC0K6duimp28ztM+kW0k13DMskJ6tdu0x//7WaVGjCyv4rdcewVHi4b0fPmeKvWeuqrdlxcgeHzh/SHcUy3bo5o+snJTWFXnV66Y5hmdWroVIl/1zILTMBIsBT82YAOnZUi/edOeOb45vir1newLx0rtHZU2uTd+0KS5fCpUt6c7x/9/s0qdBEbwgLmS4f/1K4MLRp47uraFP8HeC+uvd5ap2fUqXUmjNLluhO4h1SmuLvj7p3h5k+6hUWThobK4SQTspj5NwXX6ilHsaO1Z3EGzZvVoV/1y6zno8/OXFCrfVz5IhaPuV6hBBIKbP1zjAtf8MnundXNyftWJ3QH6RP7DKF/8biE+NZvGux7hiW8eVVtCn+hk9UqgTBwbDSWwMwtJkxA3r00J3C+ZJTk+k1pRfnE87rjmIZX3X9mOJv+Iwv+ytv5O3It9l/dr/9J/aRXbvUZX+LFrqTOF/R/EVpVbkV83Y6YKyxRbp1U+P9k5OtPa4p/obP9Oihin9qqn3nPH3pNMN/G07JAiXtO6mPzZihCkCgdzYh86metXsyPXq67hiWqVwZqla1fo1/U/wdZOuxrXz626e6Y1gmJASKFoV16+w759wdc2lbrS2F8hay76Q+Nn069OypO4V7hIWEsXjXYi4laR5rbCFfXEWb4u8gRfMV5d1V75KU4p27pD16qJarXWZun0n3Wt3tO6GPxcXBzp1qvLeRNaULlabRzY1YtMumndBtkF78rRwMaYq/g1QqWokaJWqwYu8K3VEsk1787RjBeyHxAkt3LyUsxDtbXM2cCaGhkCeP7iTu8n93/R9Vi1XVHcMytWurSV9WXkWb4u8wPWv3ZHqUd/orGzZUwz23bvX9uebvnE+LSi0oUcA7S16aUT4507ZaWxqUa6A7hmWEgF69YOpUC4/ppElVZpIX7D69m+ZjmnPo5UMEBnjjDt/LL8NNN8HQob49z+XkyxyPP06lopV8eyKbHD8ONWqokT5m1y5j0yZ143/37mvne5hJXh4QXDyYCkUqsGq/xbf2Nerd29oWy/XkD8rvmcIPanG8Dh1M4TeUevVU99/GjdYczxR/B5rZdyYtK7XUHcMyd9wB585BVJTuJO5iRvkYV7K668d0+xi2eOklKFYMhgzRncQdTp9WY7vj4qBIEd1p3E1KifDIuhgbN0KfPmoE2JX/Sabbx3Asu7p+vGLmTLUrmin8uTNxy0ReWPCC7hiWadhQTZrctCn3xzLF37BFs2ZqUwpfdP3sP7uf05dOW39gjaZMgb59dadwv2YVmzElagrJqRavjaCJENY1pEzxN2wREGD9ULV0g5cPZsKWCdYfWJOTJ9UOTl266E7ifsHFg6lctDKReyN1R7FM+u9RbnvITfF3sGPxxzh47qDuGJbxRddPQnICc3fMpUdt7wyGnzFDjfIp5J0VKrTqe2tfftr6k+4YlmncGBITYcuW3B3HFH8H+3bDt3zwywe6Y1imeXPV9RMdbd0xF8YupH7Z+pQvUt66g2pmunys1btOb2Zun+mZZVPSu36mTMndcXJV/IUQJYQQS4QQO9P+LX6d56UIIf5M+/DOZrU+1rduX6ZGTSUlNUV3FEv4outn0tZJ9Kvbz7oDanbsmJrC36mT7iTeUaVYFVpUasHOUzt1R7FMv34waVLuun5y2/IfBCyTUtYAlqV9nZFLUsoGaR/eWXjFx6qXqE7Fmyry876fdUexTJ8+MHmyNWv9XEi8wILYBfSq0yv3B3OIGTNU4S9YUHcSb4m4L4I6pevojmGZ229XS3znZq2f3Bb/cOCHtM9/ALrl8njGVfre2pfJWyfrjmGZ5s3h0iW1J21unU84z5DWQyhVsFTuD+YQpsvHyAohVOt/ci5KQ64meQkhzkgpi6V9LoDT6V9f9bxk4E8gGfhASjnrOsczk7yusu/MPhqNasThVw6TJ9AbSzu+8YYaq/zhh7qTOMuRI1CrllnLx8ia6Gg1F2T/fggKyv4kr6DMniCEWAqUy+Bbg6/8QkophRDXq9xVpJQHhRDBwHIhxBYp5a6Mnjj0itW/2rRpQxs/X8i8SrEqvNbyNc4nnvfMapX33w9du8L776v7AIYyZYpavtkUfiMzkZGRREZGkpICAwbk7Bi5bfnHAG2klIeFEDcDkVLKkExeMxaYK6WclsH3TMvfT9x2G4wcCa1a6U7iHHfcAe+8o4Z5GkZWfPihWuVz1Cj7l3eIAB5J+/wRYPbVTxBCFBdC5Ev7vBTQEjBLfPm5+++HiRN1p3COHTtg3z5o3153Em+bv3M+S3Yt0R3DMn37qgUAcyK3xf8D4B4hxE7g7rSvEUI0FkKMTntObWC9EGITsALV52+Kv5/r108N+UzyxtDrXJswQf1MgjLtiDVy48zlM3y6xjv7ZFetCjVr5uy1ZlVPQ5sWLeCtt6Bz5+y9buS6kZxPPM9rLV/zTTCbSak2bZk8Wc3eNHwnPjGeCp9UIOb5GMoWLqs7jiW+/BJeeMGs6ul5XvrjmNOunzF/jPHUFn2//65a/I0a6U7ifYXyFiI0JJSftnlnuYecDg02xd9F3lv5Hl+v+1p3DMv06QNz50J8fNZfs+3YNg5fOEz7at7pHB8/Hh544Nqt+QzfePC2Bxm/ebzuGJYpUyZnrzPF30WaVGjCD5t+yPyJLlGmDLRsqWa1ZtWPm37kwdse9Mz+xklJaojnAw/oTuI/2ge3Z//Z/ew4uUN3FK1M8XeR9tXac/DcQbaf2K47imUefRS+/z5rz01JTWH8lvE8XP9hn2ay0+LFqr8/OFh3Ev8RFBDE6gGrqV6iuu4oWpni7yKBAYHcf9v9jNs0TncUy4SGqqUe9u7N/Lmxp2K5rcxt3FrmVp/nssu4cabVr0Nw8WAChH+XPzPax2U2HdlE2OQw9ry4xzNv3uefV11A//qX7iT2OnVKtfh374YS3pi8bWhi9vD1A/XL1adO6TrsO7NPdxTLPPoojB2r1vvxJxMnqhU8TeE3dDAtf0M7KdVyD19/Da1b605jn4YNYdgwtTiXYeSGafkbriTE/1r//uKPP+D0aWjXTncS/3b0wlH+OPyH7hhamOJvOMKDD8LMmXDhgu4k9hgzBvr3N6ua6rbh8AaemvuU7hhamLee4QjlysGdd2a8xeOIdSNYe3Ct/aF85NIltQXfo4/qTmJ0uKUDh84fYsvRXO6G7kKm+BuO8fjjMGrU3x+7nHyZoZFDKVmgpJ5QPjBrllrKoUoV3UmMwIBAHm3wKN/98Z3uKLYzxd/F1h9az/ur3tcdwzJdukBcHPz55/8emxk9k/rl6nNLiVu05bLamDHw2GO6UxjpHmv4GBO2TCAhOUF3FFuZ4u9iFYpU4KPVH3Eh0Rsd5UFB8OSTapOXdKM2juKpRt7pk921S/1x69ZNdxIjXXDxYOqWqcvsmGu2I/E0U/xd7OYiN9O6SmtPbfD++ONqrZtz5yDmRAzRx6MJCwnTHcsyI0aoVr/ZqtFZPrz7Q+qXra87hq3MOH+Xm7djHkN/Hsrax9ciPLIsZK9eagjk8Tpvczn5Mu/f7Y2urYsXoXJlWLcOqlXTncbwkpyM8zfF3+VSUlOo+VVNJvSYQLOKzXTHscSyZfCPf8Cfm1JJSLlMwTwFdUeyxOjREBGhPgzDSmaSlx8KDAhkYNOBTN2WwRhJl2rXDhIT4bfVAZ4p/FLCV1+pdYwMwwlMy98DklKSCAoI8ky3D8Cnn8L69WpvWy/45RcYMACio83ELsN6puXvp/IE5vFU4Qc1AWrBAjh4UHcSa3z9NTz3nCn8brD9xHZSpfdXGTRvRcORiheHhx5Sm1O73eHDsGgRPPKI7iRGVjw08yEWxi7UHcPnTPE3HGXnyZ18s/4bQN30HT0azp/Xmym3RoyAfv2gaFHdSYyseK7Jc3zx+xe6Y/icKf6Go3y25jPizsUBajhk+/ZqRqxbnT8P33wDr7yiO4mRVf3q9mPLsS38eeRP3VF8yhR/jxm9cTSReyN1x8iRU5dOMXHrRJ5r8txfj736qrr5m5ysMVgujBql1uu/xTurU3he/qD8vNTsJT745QPdUXzKFH+PyReYjyGRQ3THyJFRG0YRWjOUm4vc/NdjTZpA1aowbZq+XDmVkACffAKvv647iZFdTzV6imV7lhF7KlZ3FJ8xxd9j7rvtPuLOxbFy30rdUbIlPjGez9Z8xmstX7vme//8p9rxym2jgH/8EerVgwYNdCcxsqtIviJM7DGRovm8e6PGFH+PCQoI4o1Wb/Deqvd0R8mWmdtn0rpqa+qWqXvN9zp3VmvgL12qIVgOpaTARx/BG2/oTmLk1D233EPpQqV1x/AZM8nLgxJTEqn+RXWm9ZlG0wpNdcfJsktJlyiQp0CG35s8GT7/HFavVts+Ot2UKfDZZ/Drr+7Ia7ibmeRlAJA3MC+vtXyN+Tvn646SLdcr/AB9+qgtHufNszFQDqWmwn/+A4MGmcJvOJdp+XuUlNJzs35nzYK334YNG5w9U3bSJNXqX7PGFH/DHqblb/zFa4UfIDwcAgNhxgzdSa4vMREGD4YPPzSF30tW7VvF/rP7dcewlCn+hmsIAe++C//6l7qh6kT//S/Urg1t2uhOYlhp6e6lDF4+WHcMS5nib2jzf8v/jxnR2WvGd+gAJUrAxIk+CpUL587Be+/B+97Ye8a4wqstXmXp7qWemvVrir+fSExJ1B3hb3ae3MnI9SNpUalFtl4nhCqub76pbgA7yfDh6o9TvXq6kxhWK5KvCG/d+RaDlg7SHcUypvj7gUPnD1H769qO2uj9lcWv8HrL1ylXuFy2X3vnnapb5d//tj5XTh0+rJZtfucd3UkMX3my0ZPsOr2LpbtdNOHkBkzx9wPli5SnaYWmfLz6Y91RAFgUu4joE9G8cMcLOT7GsGHw3XcQFWVhsFx48UV46imoUkV3EsNX8gTm4aO7P+L9X5zRr3ch8QLjN4/P8evNUE8/sffMXhqPaszqAaupWbKmthxJKUnU/6Y+H979IaEhobk61hdfqOGfy5bpHVkTEaEWoNu8GfLn15fD8D0pJfFJ8RTOW1h3FAYtHUTcuTjG9xhvhnoa11e1WFWGtB5C/9n9SUnVN1QmPimefnX70bVm11wf69ln4dQp+OknC4Ll0Llzal/eb781hd8fCCEcUfi3n9jO6I2jGXbPsBwfw7T8/UiqTKXdD+0IDwnnpeYv6Y5jidWroXdv1eouWdL+8z//vFq989tv7T+34Z9SUlNo+0Nbutfq/tfvcU5a/qb4+5l9Z/YBUKWYdzqnX3kFYmNVF5Cd3T+rV0OvXrBtm9p20jDs8Hbk26zcv5LFDy4mMCAQMDN8jSyoUqyKpwo/qKGfhw/bu9/vyZPwwANqhI8p/P4rMSWRvWf22na+pJQkfj/4O+O6j/ur8OeUafkbnrB7NzRrBgsXwu23+/ZcKSnQqRPUr69GHRn+a2HsQp6d9yzrnlhHyYIa+h3TmJa/4TgxJ2LoNaUXqTLVp+cJDlYt/759fb/h+1tvqZU7zUxeo2P1jvSs3ZN+0/uRnOquvUZN8fdzqTKV3ad3++TYh88fptOETnSp0YUA4fu3Wt++cM890KMHXL7sm3NMn65W7Zw8GYKCfHMOw13ev1u1At5Y6q6de0zx93Obj26mxZgWlu9Vei7hHJ0nduaxho/Rv2F/S499I19+qUb99O6tVti00vLl8PTT6g9AqVLWHttwr6CAICb3nMz06OmMWDfC8uP76qrZFH8/16BcA95p+w7tf2zPlqNbLDnmxaSL9JzSk2YVmjH4TntXQgwMhHHj1L8PPgjJFl2Jz5kD/fqpwt+okTXHNLyjZMGSLH14KVuObrG0WH/060e+W09ISpnjD6A3sA1IBRrf4HkdgRggFhh0g+dJQ4+JmyfK0h+VlotiF+X6WP/++d/y4ZkPy+SUZAuS5czly1Lee6+UffpIee5c7o41caKUZctKuXatNdkMIzPJKcly6Iqhstpn1WTc2bhMn59WO7NXv7P7Avn3Yl0bCAEir1f8gUBgFxAM5AU2AXWu89xc/cC8ZMWKFbafc+XelbLMsDJy9vbZuTpOUkqSTE1NtShVzn8W8fFSPvaYlNWr56xwX7wo5VtvSVm+vJRbtuQoguV0vC+cyqs/i7izcbLN2Day7di28uC5g1l6TU6Kf666faSU0VLKmEye1hSIlVLullImApOB8Nyc1x9ERkbafs47q9zJqv6raFmpZa6OExQQZOlOYjn9WRQsCGPGqDX2u3SBDz7I+o3gxYvhttsgOhrWroW6dXMUwXI63hdO5ZafxfH441leUuXPI3/SaFQj2ldrz5KHllC+SHmf5bKjz78CcOCKr+PSHjMcqGbJmhmOV5ZXzb9ITElk89HNrIlbY1e0HOvTB9atg5UroWJFtQLn5s3XPi8uTi3T0KWLWqHz889h2jSoYN6tRi68t+o9mo5uyqe/fcq2Y9uu+V26UkjJEGb3m81bd72V60lcmcl0sJoQYimQ0aLrg6WUs62PZDjR/J3zeW7+c1QpVoV9Z/Zx+MJhKt5UkUEtB9GsYjPd8TJVpQrMnw979sD336sCf+kSFCmiPhIT4fhxuPdeNWS0Vy915WAYufVph0+Zs2MO83bM44u1X5CYkkj5IuWZ2XcmFW+q+LfnFshTgDsq3mFLLktm+AohIoFXpZTrM/hec2ColLJD2tdvAEgpr5kiI4Qw03sNwzByQGZzhq8d01TWATWEENWAg0A/4P6Mnpjd8IZhGEbO5KrPXwjRXQgRBzQH5gkhFqU9Xl4IMR9ASpkMPA8sAqKBKVLKbbmLbRiGYeSGoxZ2MwzDMOzhmBm+QoiOQogYIUSsEMJHU9qcTwhRSQixQggRJYTYJoR4UXcm3YQQgUKIP4QQc3Vn0UkIUUwIMU0IsV0IEZ12P80vCSFeSvv92CqEmCSE8Jt91IQQ3wkhjgkhtl7xWAkhxBIhxM60fzNdaNwRxV8IEQh8DXQC6gD3CSHq6E2lTTLwipSyDtAMeM6PfxbpXkR1Gfq7z4GFUspaQH389GcihKgAvICaWFoXNZG0n95UthqLWjXhSoOAZVLKGsCytK9vyBHFHzMR7C9SysNSyo1pn59H/YL77UhzIURFoAswWncWnYQQRYG7gDEAUspEKeUZraH0CgIKCCGCgILAIc15bCOlXAmcuurhcOCHtM9/ALpldhynFH8zESwDQoiqQEPgd81RdPoMeA21fpQ/qwYcB75P6wIbLYQopDuUDlLKg8DHwH7gMHBWSrlYbyrtykopD6d9fgQom9kLnFL8jasIIQoD04F/SCnP6c6jgxCiK3BMSrlBdxYHCAJuB0ZKKRsC8WTh0t6L0vqzw1F/EMsDhYQQD+pN5Rzpa/1k9jynFP+DQKUrvq6Y9phfEkLkQRX+CVLKGbrzaNQSCBNC7EV1BbYTQozXG0mbOCBOSpl+FTgN9cfAH90N7JFSHpdSJgEzgBaaM+l2VAhxM0Dav8cye4FTiv9fE8GEEHlRN28iNGfSQqgV0cYA0VLKT3Tn0UlK+YaUsqKUsirqPbFcSumXLTwp5RHggBAiJO2h9kCUxkg67QeaCSEKpv2+tMdPb35fIQJ4JO3zR4BMl95xxEZ0UspkIUT6RLBA4Ds/ngjWEngI2CKE+DPtsTellPP1RTIcYiAwIa2BtBuwb4s0B5FS/i6EmAZsRI2O+wMYpTeVfYQQk4A2QKm0SbZDgA+AKUKIAcA+oE+mxzGTvAzDMPyPU7p9DMMwDBuZ4m8YhuGHTPE3DMPwQ6b4G4Zh+CFT/A3DMPyQKf6GYRh+yBR/wzAMP2SKv2EYhh/6fxLTSQ2JY8DCAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"x = np.linspace(0, 10, 100)\\n\",\n \"\\n\",\n \"fig = plt.figure()\\n\",\n \"plt.plot(x, np.sin(x), '-')\\n\",\n \"plt.plot(x, np.cos(x), '--');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Saving Figures to File\\n\",\n \"\\n\",\n \"One nice feature of Matplotlib is the ability to save figures in a wide variety of formats.\\n\",\n \"Saving a figure can be done using the `savefig` command.\\n\",\n \"For example, to save the previous figure as a PNG file, we can run this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"fig.savefig('my_figure.png')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We now have a file called *my_figure.png* in the current working directory:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-rw-r--r-- 1 jakevdp staff 26K Feb 1 06:15 my_figure.png\\n\"\n ]\n }\n ],\n \"source\": [\n \"!ls -lh my_figure.png\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To confirm that it contains what we think it contains, let's use the IPython `Image` object to display the contents of this file (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from IPython.display import Image\\n\",\n \"Image('my_figure.png')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In `savefig`, the file format is inferred from the extension of the given filename.\\n\",\n \"Depending on what backends you have installed, many different file formats are available.\\n\",\n \"The list of supported file types can be found for your system by using the following method of the figure canvas object:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'eps': 'Encapsulated Postscript',\\n\",\n \" 'jpg': 'Joint Photographic Experts Group',\\n\",\n \" 'jpeg': 'Joint Photographic Experts Group',\\n\",\n \" 'pdf': 'Portable Document Format',\\n\",\n \" 'pgf': 'PGF code for LaTeX',\\n\",\n \" 'png': 'Portable Network Graphics',\\n\",\n \" 'ps': 'Postscript',\\n\",\n \" 'raw': 'Raw RGBA bitmap',\\n\",\n \" 'rgba': 'Raw RGBA bitmap',\\n\",\n \" 'svg': 'Scalable Vector Graphics',\\n\",\n \" 'svgz': 'Scalable Vector Graphics',\\n\",\n \" 'tif': 'Tagged Image File Format',\\n\",\n \" 'tiff': 'Tagged Image File Format'}\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fig.canvas.get_supported_filetypes()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that when saving your figure, it is not necessary to use `plt.show` or related commands discussed earlier.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Two Interfaces for the Price of One\\n\",\n \"\\n\",\n \"A potentially confusing feature of Matplotlib is its dual interfaces: a convenient MATLAB-style state-based interface, and a more powerful object-oriented interface. I'll quickly highlight the differences between the two here.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### MATLAB-style Interface\\n\",\n \"\\n\",\n \"Matplotlib was originally conceived as a Python alternative for MATLAB users, and much of its syntax reflects that fact.\\n\",\n \"The MATLAB-style tools are contained in the `pyplot` (`plt`) interface.\\n\",\n \"For example, the following code will probably look quite familiar to MATLAB users (the following figure shows the result):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure() # create a plot figure\\n\",\n \"\\n\",\n \"# create the first of two panels and set current axis\\n\",\n \"plt.subplot(2, 1, 1) # (rows, columns, panel number)\\n\",\n \"plt.plot(x, np.sin(x))\\n\",\n \"\\n\",\n \"# create the second panel and set current axis\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(x, np.cos(x));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is important to recognize that this interface is *stateful*: it keeps track of the \\\"current\\\" figure and axes, which are where all `plt` commands are applied.\\n\",\n \"You can get a reference to these using the `plt.gcf` (get current figure) and `plt.gca` (get current axes) routines.\\n\",\n \"\\n\",\n \"While this stateful interface is fast and convenient for simple plots, it is easy to run into problems.\\n\",\n \"For example, once the second panel is created, how can we go back and add something to the first?\\n\",\n \"This is possible within the MATLAB-style interface, but a bit clunky.\\n\",\n \"Fortunately, there is a better way.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Object-oriented interface\\n\",\n \"\\n\",\n \"The object-oriented interface is available for these more complicated situations, and for when you want more control over your figure.\\n\",\n \"Rather than depending on some notion of an \\\"active\\\" figure or axes, in the object-oriented interface the plotting functions are *methods* of explicit `Figure` and `Axes` objects.\\n\",\n \"To re-create the previous plot using this style of plotting, as shown in the following figure, you might do the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# First create a grid of plots\\n\",\n \"# ax will be an array of two Axes objects\\n\",\n \"fig, ax = plt.subplots(2)\\n\",\n \"\\n\",\n \"# Call plot() method on the appropriate object\\n\",\n \"ax[0].plot(x, np.sin(x))\\n\",\n \"ax[1].plot(x, np.cos(x));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For simpler plots, the choice of which style to use is largely a matter of preference, but the object-oriented approach can become a necessity as plots become more complicated.\\n\",\n \"Throughout the following chapters, we will switch between the MATLAB-style and object-oriented interfaces, depending on what is most convenient.\\n\",\n \"In most cases, the difference is as small as switching `plt.plot` to `ax.plot`, but there are a few gotchas that I will highlight as they come up in the following chapters.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 64-bit ('3.9.6')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.01-Simple-Line-Plots.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Simple Line Plots\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$.\\n\",\n \"Here we will take a first look at creating a simple plot of this type.\\n\",\n \"As in all the following chapters, we'll start by setting up the notebook for plotting and importing the packages we will use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For all Matplotlib plots, we start by creating a figure and axes.\\n\",\n \"In their simplest form, this can be done as follows (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"ax = plt.axes()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In Matplotlib, the *figure* (an instance of the class `plt.Figure`) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\\n\",\n \"The *axes* (an instance of the class `plt.Axes`) is what we see above: a bounding box with ticks, grids, and labels, which will eventually contain the plot elements that make up our visualization.\\n\",\n \"Throughout this part of the book, I'll commonly use the variable name `fig` to refer to a figure instance and `ax` to refer to an axes instance or group of axes instances.\\n\",\n \"\\n\",\n \"Once we have created an axes, we can use the `ax.plot` method to plot some data. Let's start with a simple sinusoid, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"ax = plt.axes()\\n\",\n \"\\n\",\n \"x = np.linspace(0, 10, 1000)\\n\",\n \"ax.plot(x, np.sin(x));\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that the semicolon at the end of the last line is intentional: it suppresses the textual representation of the plot from the output.\\n\",\n \"\\n\",\n \"Alternatively, we can use the PyLab interface and let the figure and axes be created for us in the background\\n\",\n \"(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces); as the following figure shows, the result is the same:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, np.sin(x));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we want to create a single figure with multiple lines (see the following figure), we can simply call the `plot` function multiple times:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, np.sin(x))\\n\",\n \"plt.plot(x, np.cos(x));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"That's all there is to plotting simple functions in Matplotlib!\\n\",\n \"We'll now dive into some more details about how to control the appearance of the axes and lines.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Adjusting the Plot: Line Colors and Styles\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first adjustment you might wish to make to a plot is to control the line colors and styles.\\n\",\n \"The `plt.plot` function takes additional arguments that can be used to specify these.\\n\",\n \"To adjust the color, you can use the `color` keyword, which accepts a string argument representing virtually any imaginable color.\\n\",\n \"The color can be specified in a variety of ways; see the following figure for the output of the following examples:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, np.sin(x - 0), color='blue') # specify color by name\\n\",\n \"plt.plot(x, np.sin(x - 1), color='g') # short color code (rgbcmyk)\\n\",\n \"plt.plot(x, np.sin(x - 2), color='0.75') # grayscale between 0 and 1\\n\",\n \"plt.plot(x, np.sin(x - 3), color='#FFDD44') # hex code (RRGGBB, 00 to FF)\\n\",\n \"plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\\n\",\n \"plt.plot(x, np.sin(x - 5), color='chartreuse'); # HTML color names supported\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.\\n\",\n \"\\n\",\n \"Similarly, the line style can be adjusted using the `linestyle` keyword (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, x + 0, linestyle='solid')\\n\",\n \"plt.plot(x, x + 1, linestyle='dashed')\\n\",\n \"plt.plot(x, x + 2, linestyle='dashdot')\\n\",\n \"plt.plot(x, x + 3, linestyle='dotted');\\n\",\n \"\\n\",\n \"# For short, you can use the following codes:\\n\",\n \"plt.plot(x, x + 4, linestyle='-') # solid\\n\",\n \"plt.plot(x, x + 5, linestyle='--') # dashed\\n\",\n \"plt.plot(x, x + 6, linestyle='-.') # dashdot\\n\",\n \"plt.plot(x, x + 7, linestyle=':'); # dotted\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Though it may be less clear to someone reading your code, you can save some keystrokes by combining these `linestyle` and `color` codes into a single non-keyword argument to the `plt.plot` function; the following figure shows the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, x + 0, '-g') # solid green\\n\",\n \"plt.plot(x, x + 1, '--c') # dashed cyan\\n\",\n \"plt.plot(x, x + 2, '-.k') # dashdot black\\n\",\n \"plt.plot(x, x + 3, ':r'); # dotted red\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.\\n\",\n \"\\n\",\n \"There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for details, read through the docstring of the `plt.plot` function using IPython's help tools (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Adjusting the Plot: Axes Limits\\n\",\n \"\\n\",\n \"Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.\\n\",\n \"The most basic way to adjust the limits is to use the `plt.xlim` and `plt.ylim` functions (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, np.sin(x))\\n\",\n \"\\n\",\n \"plt.xlim(-1, 11)\\n\",\n \"plt.ylim(-1.5, 1.5);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, np.sin(x))\\n\",\n \"\\n\",\n \"plt.xlim(10, 0)\\n\",\n \"plt.ylim(1.2, -1.2);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A useful related method is `plt.axis` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*), which allows more qualitative specifications of axis limits. For example, you can automatically tighten the bounds around the current content, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, np.sin(x))\\n\",\n \"plt.axis('tight');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or you can specify that you want an equal axis ratio, such that one unit in `x` is visually equivalent to one unit in `y`, as seen in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, np.sin(x))\\n\",\n \"plt.axis('equal');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Other axis options include `'on'`, `'off'`, `'square'`, `'image'`, and more. For more information on these, refer to the `plt.axis` docstring.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Labeling Plots\\n\",\n \"\\n\",\n \"As the last piece of this chapter, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\\n\",\n \"Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, np.sin(x))\\n\",\n \"plt.title(\\\"A Sine Curve\\\")\\n\",\n \"plt.xlabel(\\\"x\\\")\\n\",\n \"plt.ylabel(\\\"sin(x)\\\");\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The position, size, and style of these labels can be adjusted using optional arguments to the functions, described in the docstrings.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.\\n\",\n \"Again, Matplotlib has a built-in way of quickly creating such a legend; it is done via the (you guessed it) `plt.legend` method.\\n\",\n \"Though there are several valid ways of using this, I find it easiest to specify the label of each line using the `label` keyword of the `plot` function (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, np.sin(x), '-g', label='sin(x)')\\n\",\n \"plt.plot(x, np.cos(x), ':b', label='cos(x)')\\n\",\n \"plt.axis('equal')\\n\",\n \"\\n\",\n \"plt.legend();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As you can see, the `plt.legend` function keeps track of the line style and color, and matches these with the correct label.\\n\",\n \"More information on specifying and formatting plot legends can be found in the `plt.legend` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Matplotlib Gotchas\\n\",\n \"\\n\",\n \"While most `plt` functions translate directly to `ax` methods (`plt.plot` → `ax.plot`, `plt.legend` → `ax.legend`, etc.), this is not the case for all commands.\\n\",\n \"In particular, functions to set limits, labels, and titles are slightly modified.\\n\",\n \"For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\\n\",\n \"\\n\",\n \"- `plt.xlabel` → `ax.set_xlabel`\\n\",\n \"- `plt.ylabel` → `ax.set_ylabel`\\n\",\n \"- `plt.xlim` → `ax.set_xlim`\\n\",\n \"- `plt.ylim` → `ax.set_ylim`\\n\",\n \"- `plt.title` → `ax.set_title`\\n\",\n \"\\n\",\n \"In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the `ax.set` method to set all these properties at once (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.axes()\\n\",\n \"ax.plot(x, np.sin(x))\\n\",\n \"ax.set(xlim=(0, 10), ylim=(-2, 2),\\n\",\n \" xlabel='x', ylabel='sin(x)',\\n\",\n \" title='A Simple Plot');\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.02-Simple-Scatter-Plots.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Simple Scatter Plots\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Another commonly used plot type is the simple scatter plot, a close cousin of the line plot.\\n\",\n \"Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape.\\n\",\n \"We’ll start by setting up the notebook for plotting and importing the packages we will use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Scatter Plots with plt.plot\\n\",\n \"\\n\",\n \"In the previous chapter we looked at using `plt.plot`/`ax.plot` to produce line plots.\\n\",\n \"It turns out that this same function can produce scatter plots as well (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = np.linspace(0, 10, 30)\\n\",\n \"y = np.sin(x)\\n\",\n \"\\n\",\n \"plt.plot(x, y, 'o', color='black');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as `'-'` or `'--'` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of `plt.plot`, or in Matplotlib's [online documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.markers.MarkerStyle.html). Most of the possibilities are fairly intuitive, and a number of the more common ones are demonstrated here (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rng = np.random.default_rng(0)\\n\",\n \"for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\\n\",\n \" plt.plot(rng.random(2), rng.random(2), marker, color='black',\\n\",\n \" label=\\\"marker='{0}'\\\".format(marker))\\n\",\n \"plt.legend(numpoints=1, fontsize=13)\\n\",\n \"plt.xlim(0, 1.8);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, y, '-ok');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Additional keyword arguments to `plt.plot` specify a wide range of properties of the lines and markers, as you can see in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, y, '-p', color='gray',\\n\",\n \" markersize=15, linewidth=4,\\n\",\n \" markerfacecolor='white',\\n\",\n \" markeredgecolor='gray',\\n\",\n \" markeredgewidth=2)\\n\",\n \"plt.ylim(-1.2, 1.2);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These kinds of options make `plt.plot` the primary workhorse for two-dimensional plots in Matplotlib.\\n\",\n \"For a full description of the options available, refer to the [`plt.plot` documentation](https://matplotlib.org/3.5.0/api/_as_gen/matplotlib.pyplot.plot.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Scatter Plots with plt.scatter\\n\",\n \"\\n\",\n \"A second, more powerful method of creating scatter plots is the `plt.scatter` function, which can be used very similarly to the `plt.plot` function (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(x, y, marker='o');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The primary difference of `plt.scatter` from `plt.plot` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\\n\",\n \"\\n\",\n \"Let's show this by creating a random scatter plot with points of many colors and sizes.\\n\",\n \"In order to better see the overlapping results, we'll also use the `alpha` keyword to adjust the transparency level (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rng = np.random.default_rng(0)\\n\",\n \"x = rng.normal(size=100)\\n\",\n \"y = rng.normal(size=100)\\n\",\n \"colors = rng.random(100)\\n\",\n \"sizes = 1000 * rng.random(100)\\n\",\n \"\\n\",\n \"plt.scatter(x, y, c=colors, s=sizes, alpha=0.3)\\n\",\n \"plt.colorbar(); # show color scale\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that the color argument is automatically mapped to a color scale (shown here by the `colorbar` command), and that the size argument is given in pixels.\\n\",\n \"In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.\\n\",\n \"\\n\",\n \"For example, we might use the Iris dataset from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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jtSPsIuBnvQSWO0FZ5q+UJJIMvNUKEPKdubPJW9DiAPmMHLZQEInD9nNh4Ft/ZX2FEEmFUTcG27FGiYYVskXhVFH2adfbx6hggKRVMAiGDwe5haporRxw9QOOKeo7vOUXt4mps00ZKSbLm8qv48Vk4SOkirf1g7gSZBgIgyosT54wn9ysqkMZNCH3tgsoReRIKM5RRuIQsjMujz6xhBHSuuXMDvWcGKOa9VX4+XWC4N8WW92ye9o8jXhljx+O3k0vl6T3dT7wqyvZHbx31nBsf2ELD8jp6T/cz1Jfi1g/e6Esc+1w0nuzvs1D8GQgVoTYh1GqEEvf+qTVlzXcJpR8jS8+Vm5V8poO/sr8C2Xj7WoyQwZ7n99PTnyFZE+fuT26jZV3zjI6zZO0iHv9PH8Iq2QRCxpgLRTAc4L5f30ExX0LTVV9+2OeikVIiSy+CvQ/UpknDNEKJIGUIzLeQwkAEbr+Ell6++L/OKxAhBGuuX8HqrctxbOddOWFVVVHDk9+aXs41/D4LBLcTzHemHY8XQkGqjWDuRGqrEGrd3Nt4meOHcS4Cx3GwzJnrY8w2tmVPqmIphPBX2z6XBdJ8x6tHn0Hi1YuRG16M32dKJvUEuVyOH/zgB7z55psMDQ1RVVXFzTffzIMPPkgkcnUOjzj4+mHeePptXNdl6YbF3PrBG+dEXCw9kGGwe5iKusQYmQPHcXjzmXdofa2N/v5+ih+yue7uTX59u89liTcQ5PDFSQEolWDtQwZuQYixTX8+55jQ2X//+9/nZz/7Gdu2beMTn/gENTU1pNNp9uzZw+/+7u9y77338sgjj1xKW+edruM9vPrDXdQsqkTVVY7vPU0oFuKmB7bM6nmGelM89fWfYVsOiqrw4GffQ3Vj5cj2w28eZd9LB6lvqcVSTN755X7iVTFWbVk+q3b4+FwSyhrtF1NOKYSGRIIzANqiqXe4ipnQ2dfW1vI//+f/HPP4pk2b+MQnPsGLL744p4YtRAa6htAMFc3w3rZkbZyOI12zfp6uY93Ypk3dkhr62gfoONI1ytl3HeshVhFFURUUVSEcD9F9std39j6XJ9Li3WnEA/jzDaZiwkvpHXfcMfL/2WyWgYGBkX8A27ZdfnK575ZIPDRSTw5eSWOi+uL1pSciURPHdVxSfWks0yZZO1ooKVYVo5AtjvxdyJeIV/n17T6XKdMQE5uahVvfvlCY8l3+gz/4A3bv3k08HkdKiRCCJ5988lLYtuBYvG4RSzct4eT+MyiKIJIIc8P91836eZpWNLDj8dtoP9JFw7I6lqwdfXu68fa1dB3rpud0P4N9Q6y9bg3rbrp4nWsfn3lFeA18F8PIUJB3MdTjamFKZ3/ixAl++ctfXgpbFjyqqnLnY7fS3zGIYzlUNiQJhOam7HDZphaWbWoZd1soEuS9v3E3A51DHD16lJu33ziqs9XH53JCqNVItR7pphHKDO9QZQrUJQglOSe2XUlMmRHZtGkTx48fvxS2XBYoikJtczUNy+rmzNFPh1wqT3Y4RyFdJJfKz5sdPj6zgr71nAbOTJB5hDG7BRJXKlOu7KPRKB/+8IcJh8Mjj7388stzapTPxFimxas/epO23ccpZIsMDQ5y+s0uVt+4gpsf3DpqhT/clyKXypOojhNNXp2lsj6XB0JbilQbkE6fJ/07DaTTA2qz989nSqZ09jt37uSNN95A06afRHEchy9/+cucOHECIQRf/epXWbVq1cj2b37zm3zve9+jstKrMPnqV7/KsmXvXq/5amDn02+x91etI2P6stkssiQo5koYQYMb7rsWx3F47v++zMs/fINSrkQ0GeaeT23n+vuuHZE8kFLStusYh988ih7QufaujXMyDWqwe4jdz+7leNsJzLtdNty2BlX1Q04+oxFCh9D7kfnve05cqZmwFFNKF9xeUKoRwQfLzVU+UzFlGKelpWWkAme6PP/88wB8+9vf5vOf/zx/8Rd/MWr7/v37eeKJJ/jWt77Ft771Ld/RT5NcKkfbm8cY7BxEVQTxyiiRRAjpSgZ6hjj4yiFPz373MX7+Ty9glywi8RCZ4Tw/+h/P0HW8Z+RYR3Yf56Xvv06pYJLqS/PTv/8l/Z2Ds2pvdjjH03//S3pP92ObDm88/RbvPOd3O/qMj1CiiPBHQFsKThfS6UZK09PNkRIpS0inG5xu0FYhwh8eGRjiMzVTLtd3797Njh07qKioGHlsqjDO3Xffzfbt2wHo7OwkHh+ddDlw4ADf+MY36OvrY/v27Xz2s5+9CNOvPoZ6UhTzJcyCRawyOvJ4KBokM5ilVDAZ7k2x/6VWkBCr8J5TUaPTe7qfo++coHG516V4eNcxkjVxwjGv67CYL9F+uHNUPf+7pfd0P6W8SX1LDQU7T01VNYfeOMKW92yetXNcbkhpgyyBUADDX5VegFDC3jCPwBDSagVrL8g8IECE4ay08RRzW33GMqWzf/bZZ8nn84TDYXp6eqirm57gkKZp/OEf/iHPPvssf/3Xfz1q2wMPPMDjjz9ONBrld37nd3j++ee58847xxyjtbV1mi9j9ikWi/N6/vHoOzXA8PAw2WwWV/U0cRzHYXg4RSFbYHBwkOPHjzOUGSadyaAOKwgB0pXksjn6BnpHXlP/YB/pviyRnLcyGuwZoqOznUDr7DmfrlO99Pf3IwMOlm3R3t6J67gL7n29kJl/9i660ktQbUNXehDCRkoD02mi6KzElkk0pZ+g2kZAPXXefioFexUlZzmOTF4CO+eHi7ezAtgGuHhNV2cbrzrL/2aXy+X9hIuzdUpn/9//+3/HNE2++MUv8l//639lw4YN/OZv/ua0Dv7EE0/w+7//+3zkIx/hJz/5CeFwGCkln/rUp4jFvLrYbdu2cfDgwXGd/dq105+XOtu0trbO6/nHY/kyi5M7OxGWQnY4RzQZIZVKoboqjS0NLFrewE3bbqBlSQvte7opZAoEwgEKuSJL1y3hrg9uJ1njrYiqItX85H/+EmyJ60haVi7hzvdvG1npzwYrlq8ge6ZI75l+cqk8yUQFd33qdpauXTxr55gNHNfldDrFscFBirZFf3qYO1o2sziRRJtCb0ja7cjiM96gDREEsRQvOuqAmwL5Iji9oNaAUgFi/UgsWkrLm1VKrxeWCN6DEFNPETvLQvyOjodv5+wzka27d++ecJ8pnf1zzz3HD37wAwD++q//mscee2xKZ//DH/6Qnp4ePvvZzxIKhRBCjIh0ZbNZHnzwQZ5++mnC4TA7d+7k4YcfnsoMH7zBJFvv3UwulUdRFVL9GQrZIouWNRGrinLDe69D1VSaVjTwsf/fh3n2n14kM5hlyfpm7v/MjhFHD1C7uIYPfO5eOo50oWoqLRsWz6qjB9ANnfs+fScn9p3m8ME2brrz+gU33OTwQD/PnThG1jQJqBqaotCVydDXeoCIbrB9yVLW1Y6fuHat41D4oTcuT71w7q6KVCJgHvTCEHYRjNpRSUchdFBrvY5s6yhSZiH0oRk5fB+f6TKlsxdCYJomhmFgWdaIVMBk3HPPPfzxH/8xH/vYx7Btmy996Usj4aBHH32UL3zhC3zyk5/EMAxuvvnmq1J64WJZd9NqdF1n9y/3kh3KMdDfz5LVi9h67zUs27hk5Hmrtyxn5bVLMYvWuINHACrrK6isrxjz+GxiBA1WX78CN2otOEe/p7uLZ44doSYcIRk/d6ErBgI0xBMUbYun2g6Rty22NjaN2lc6vVD8EagV46otSul4+uwASg3IojdsI3ATQgRHPVcIAVo90u5GFp9FhB6Y9dfq4zOls3/sscd43/vex6pVqzh+/Di/8Ru/MeVBw+Ewf/VXfzXh9oceeoiHHnpoRoYuJCzTwrHdeRvasXLLMiqbKjj29gnOdJzhrg9sH7VqP4uiKHNqo5SSvvYB+s70EwgFaFpZTyh6ecjMdmcz/Pz4URqiMfQJSkGDmk5DLMZzJ47TEI3RdF6hgTTfwBssPcHrdfq8Ff3Zzk4RBDkMdifoE1SfqXVgH0K6NyOU2UuU+/jANJz9I488wl133cWZM2dobm4eqY2/GrEtm10/38PhN47iOi71S2u59aEbiFfNTJej51Qvrz21C9t02HLPZpZumFkM+50XDvDqD98gPZAhlUrRe3CIOx65mfU3rx55juM4HN9zilR/huqmSpasWzRmdd/fOciZQx1ohsayTUuIxKdfxuY4Di//YCd7XjhANpVHU1WqFlVy76/dyaKVDTN6PfPB212dBFRtQkd/Fl1VCes6uzrbaYqvA0C6GbDbJtRflxJwTnoO/nxEFJzTSG3JuFU4QggkGtLajwjcMWa7j8+7YcLs01e+8hXa2toAqKysZPPmzSOOvrW1la985SuXxsIFxNu/3MeBlw9RUZ+kprmKgc5BfvZPL0w6LepCuk708P/+xt/xwrdf5ZUf7uT/+9zf07br6LT37z3TzytPvk770S4yg1lyqQIdR7p48TuvMtQzDIDrurz0vdd48buvcvDVwzz7rRd56xd7Rx2n+2QvT3395+x54QBvPvMOT/3tz8mlpy+7cHzvKXb9fA+dx3rIp/IM96U4fbCdZ//pBcyiOe3jzAc506S1v4/K0PTuQipCIY4ODZIueQPcpX0c5CT66zIHMjuOs9cAG9zhiU+mVIK5xx+k7TPrTLiy/+IXv8hf/uVfsn//fpYuXUp1dTXpdJrW1lY2bdrE5z//+Uto5vzj2A4HX2ujelEVqur9yCvqknSf6qP3TD8NS6dXkvqrH+zELJjUL/WSfoPdQ/z8n15k1dYV09r/5IEzDHanUBWFSCLslWBKGOoZ5tTBdirqkqT60pzYd5r6pbUIIXAclz0vHmTDbWtG9HzeeX4/wUiAeLlev+dUHyf3n2b9LWumZcfhN46S6k0RjgUxgl5CMdWfYag3RffJPhavaZriCPPHWaetTnOylyK8sr9MqUQ8EPCctZhsOpnJpPrssjiiIHshQuhIrPIxgmO2zyZSWoB6UUNDfC4/JnT2yWSSP/mTPyGbzbJnz56RsYT/6T/9p1E6OVcLUkpc10VRR/9AFSFw7Omvwkq5EkI99+NSdQ2zOP15tnbJAilRzjuGoihICbZ1tvbeBSFGnImieP91nXN2OpaNqp13DFXBMqd/h2KbDq4jR49CFN45ZnKnMx9Ib7bRjBBI3JHiBMnkwzbGObosehcJp9sT/FJiSBEAdRGoDRckbQVI993P87jQBOmC04E0d4G1D7AABanUQuB2hL7SH+13BTMtIbRbb731UtiyoNF0jZb1zZxu7aC6yQtnFXNFNEOjtrlq2sfZeu9mdv98D8N9aVRVITuY472/cfe091+8dhHRigg9p/pRFIFZsJCaoLKxgkWrvFh5siZOVX2SvvZBoskwqf4MLesXEYyccyirtq7ghe++CuWLhJSweM2F5YMTs/yaJRx+4wjDfWlC0SC27aAIQTQZpnZx9bSPMx9EdAMpmXB1PR6ulIT18mpeRPEc5USc97OSFjgdIPO4KNiuQskN4YoghiIJyeNgH0OqjaCdy7kwy+WX0ulG5v8V7CPgDgABvI47CZwEaw9SbUQG70UYN/qr/SuQ2RgRc9Vww3uvIzOYo/d0HxKBEdC562O3j4QxpsPaG1fx6//lozz7v1/Atl3u/cyd3P7BG6e9f8PyOjZv38Dbz+0jPZgFAXWLa7ju7o0jTlbTNe75te3sfnYvg11DbLxjLdfcuWGUY1t53TIQ0PbmUbSAzrV3bphRGeaqrcs5uuckbbuOUcgUUDWV2sXV3PrBG2eU6J0PEsEgzfEEg8U8yeDUK9l0qUhDLEZV+Y5WaC1I88WJLxYi5jlrmQOnE1e65CyNnFVCwWTAtJCkkUBQ1agOh0gYnSAL3kpfW4OYlelNHtI+jcz+PTinQARAqYbzE8RSAgWwT0HuW0h3GIL3+g7/CsN39jMgEg/zvt+6h4GuIWzTpqqxAt2YLHY7Ppu3r2fz9vUXZYOqqmz7yM0s27SEY++cpLOrkzseuJXm1Y2jQiqhaIjbpriIrLx2GSuvvTgRukAowAP/z92svWElZw51EIqGWHHd0jlRzpwLrm9q4nsH9xMPBMsx+fFxpWS4WGRHy7n5vt6wjSavQ1aMLXkVQnjbSy/iYjBYdDBth4BqY5MgpJ+7w7Jdl/Z0mmI4Ql142AvxhD48a69TOgPI3De96iAlOf4dgxBAGJSgF2oqPIUUcUTQv6O/kpjS2e/bt48nn3ySQqEw8tjXvva1OTVqISOEmFWxsItBVVVa1jfTsr6Z1tbWMWMLAdKDGY7vPU16IE11Y+WcdMgaQYM1N6xkzQ2X30jEpckKrqtv5K3uTppi8XGTta6UdKTTbKqtZ/kFJcfCuAmZ/x5SRCZZhbukihaWIwloAhVJ3hktCqgpCqph0J/PYSgqFQGb2QzWS/M1sI9O7OjPRyje89xBKP0caVyDUPw5CFcKUzr7P/mTP+HjH/841dULOw57tWGWLAY6Buk/M4i13Bp1h3Fi/2le+M4rCOGFmo69c5Ldz+7l3l/bvuC6WOcLIQQ7li7DUFV2dXUggGQwhKYolByHrkwGV0q2NjZxx5KWMat/obUgA3dA6UWk2uBJH5SRUoLTQZFFWO5JAqqGIhxyTg0OYx2uAGKGw2DBIh65DtXaB9r08ycTId0smDsBbfo5AKGAEvESufYRhHHNu7bDZ2EwrQTtBz/4wUthi880KWQLPPMPzzPUO0x/Xz9DR7Pc++vbCYQC5NJ5XvzeayRr4qNyCfl0gV/+86945Pffj6b70TvwSi+3tSzl2oYGDvb2crC/j6zp9QjctGgR62pqJ43pK4EbcUUASi94Dl6t8Kpq5DDIIsNFsJwoATVN0U1gy7GllComqjCxZZDThWaixSR1SitS3vGuK2OkfQycLlBmepygl28ovgi+s79imPBXf1azPhaL8bd/+7esX79+JBl12223XRrrfMZl38uHGO5LUbe4Bld36O8YoPX1Nq65cyOnW9txHXdM0jgcD9Fzqp/uk32XRYfrpSQeCHJT82JuavY6mVtbW1nbvGSKvTwU4xqktgJpHwZzF9IdAqcf3Bx5SyfjrKGvkCCopIhpXSgUAYEol2eaMkzKbqboJkHYDBZL1IUluFlQ32XYzenzSj7FDId4C+H1Ech+pLRG3bX4XL5M6Ox/8pOfAJ6zP3XqFKdOndPh9p39/JIZyBA6r4wyGA54lTlAPlNA0yaWADALC7u79XJEKFGEsQWpXwOygDT3gPkyp4sOQU1HFQo5N0TOrEUXBRQcJAIXDVuec+iKsFFkCtwi0joIbgOotYiz+jozpnjWwJntJl1P18cZQlr7QYmB0jhqKpR0B72LGl5jFkoSlLppl7L6XHomdPZnk7Df+973eOSRR0Ye/9//+3/PvVU+k9KwvI4T+04TSYRxHJdcoUDDMq+Dt7qxcqS56kKklMSrouNuu5IpWBYnhoYYLOaR0iu9XJqsIBaYXZE4IVSvBl+tQmIQUC0c1x3puAYFS45NeAocQsogCeMM1boEy0USBCUMEqS2DGFcB0py1PSmquAgbn4dwtgC6pKxK3BR1mySzuhSy4mQFsgUOIPlO4ssFH+JFAqgILV1oFaBfdIr4/R2Aim8/6rVSP36cnPWu78bkG4OaZ/wwmIIEEmEttQfRXiRTOjsf/zjH/Pcc8+xc+dOXn/9dcDTXGlra+OTn/zkJTPQZyxrblhJbjjP/pcPkR7IcPcj21hxzVIAmlY2EKuMMNybIlnrlQVKKRnoHKJxeS1V81xJdCnJWxavnTnN3p5ubOlilEXPLMcFIVlXXcutzUtIBGdZlkCpASFpisdp6+/HUCfOkSiYVOlHMUSawaLCcCnIQLHAqbyLqhRpjsdpipwkVPoFoIG6BNQKENXYrqezI/M/8kb2hd6L0M6Fn4S2CCkS3ipdTCHWJ4tgnwFswPB0fNRlCM2r9JKuDcVnwGkHbT3oq8as4qWbheLTSHsJ/fJODvRl6Myk6erqot3Q2VBbR300OuXqX7oZZOlVsA96PQBCx+tKtpElBalvQBg3+5VCM2TCb+Htt99OTU0Nw8PDPProo4DXlt/c3HzJjPMZH0VRuP6+a7nuPZs41HqI9RvO1exrusa9v34nL3z7VXpO9SEUBem6LFrVyO0fvumquc3OlEp87+B+hooFaiPRMROnHNflyMAAJ4eH+cj6jVTPogSIUKuQ6iLqQv20CXCkizpOKEVgU623oVCgPatiOQ5RPUeJxSSCERzpcio1RL5wguZokQojAEoOIc7mXFRvFquSQMo8svA9ZPAhFL2ss6QuAW0xWK3AJM5eml5DlVCAUDnOHwbN6y3wVDyPeat+pd5b1Stx0EbnfoQSxSLMsZ49nMoc5nD+diJGhKLj0NrfxzvdXTQnEjywcvWEd1XSHUTmv+81mCm1Y9RBpXTA2o+0T0P4wwhlhvmIq5gJnX0ul6O5uZn/8l/+y6jHHWdh655cTaiqOkoj5yzxythI81cxVyKaDI+rd3+lYrsuPzzUStYs0Rgb3xmoikJtNMpwscAPWg/wiU3XENJnLxEpjC0EnB+wqrKK1v5+ksHQmPLNmNqJKnJ0ZRWKtk19NIShmgxaXpmzKhSawoPElAyd2SBONEC1OIFUq8fE8YUIIxUFij9Gqp9CKBUIoSED28sSCVlQJgjhOV3l/9EBF6QNWp03RhFADpa7b5PeBUHGwT6IVCsR4pzTtl3JO11dDJeCNEUyaEYnffZ6QppGTcRbhffmsnz34D4eW7+JiDG6iEDKEjL/Q8BGqOMLCwqhglrvNYsVfgjhj/oJ5GkyobP/whe+gBCCoaEhcrkcK1eu5OjRo1RXV/Pkk09eShtnFcu0sE2bYCR40atcs2ji2M6Ex7Btm+HeNPHK6IykFGZKMV+aMOG6EJq/5ovTqWG6cxkWxae+wCWDITrTadoG+tlcP4tVSupS0NazONqKSxVtA4OoQiFqGChCIHAIi2768goF26IuEiaqFRl2WnCk50AFDjG1G0tGCekKvbk8iUAA3Wk/NxTlPIQIIhFI6wAi4BVRCOMapHUzlF4GV3rJ1vORRa/MUoTx5uYWQEmAseXcqto+7cksnL07EZq33Hd6vDuHMh2ZNIOFPBWhMEVXo1pvpd8eraJaE4nSlcnwevsZ7lq2fNQ2aR0DOTzOiMexCLUKaXd4FyFteoqxVzsTOvvvfOc7AHzuc5/jiSeeIBqNks/n+eIXv3jJjJttDr95lNd+vBvXcahpruaux2+fcVfpOy8cGNGGb1pZz/aP3DIiGwxwqrWdv/+j/0NmMItu6Dz6Bx9g673XzObLwLEdXv23Nzmy+zgDAwPkHzC58YEtoxUor2J2d3YQ0ad/kU2GQuzsaGdjXf2k0gkzQQgFgncjcWiJHqY6WEt7pkRHJo1EElUHcVQHRQRpiiqEtRIpZzE559xAlKAyjMABFBThNV+lSgrVohs5kYNTKsF8G2ncgBCGt/IOP+rpfJqveRU0Iug5d6F4kg+44OYB20vA6jeOTMqSsgBu31hZCBEB+xRSXYQQCq6UnBweImp4vwUXHY0SUbWbC8dm1EQi7O3p5pbmxSN3U94c3jfGlZ+YECWGNN9E+M5+WkzpHbq7u4lGvdu/cDhMX1/fnBs1F/S1D/Dyk2+QrIlTt7iGwc4hXv3RmzM6RsfRLt585m2qGyuoW1xNR1sXu589NxTEdV3+15f+2dOrb6klHA/xz3/6A3pPz+57dvD1Ng6/cZTa5moSdQn2v3KYtt3HZ/Uclysl2+ZUKkVyBknXsK6TLpUYLhamfvIMEMJABN8LwbuJGrCm0mT74ji3LargulpJQ9QgpudR1SB99moyzugVbVAZwpHnLlqGqjFcLGvly9wE59TxBqT0n3tMiSIin4Dwp0FfBSjg9norc6fTK7VUYqBvhsB2hHbeBC6Z9Z5/Yc5B6Hia+155Z8Y0MW171OQvB52I0jPGRk1RcKRLZyZ93nky4A7NKOkqlDg4XUh3+kN3rmambKW87bbb+PjHP86GDRvYu3cvd989fTnehUR6IIMQoBveS66oS9B1fOwXcTKGelLohoZarmNP1o4+RnowS6ovQ0N5MEkoGiTVn6H7ZO+syhT0nuojWhFBKAJFEYRjIXpP97Hmen+FY7mu1xM0wxW6IoRXpTPLCKF6oRR9EzjtqPYRVDcPVj95V6W9GCBkjB9uU8RonRwhBNbZnJmcLHcmvDLKUXYEEMFbkYHrkeYhr3yTNJTeAqUOjOUIMU6SWjqMr8/veoldJ4VUXBxHjpH0kVJFFRP3dZTOz/9JizEHmBaCyeWmfc4ypbP/whe+wP79+zl58iQPPfQQa9ZMb5LRQiMcCyHd8gASRSE7lKOyfmZJy2gyglWyka5EKILscI7mVY3nbQ8TDBtkUzmiiQim6Q0aqahLzuprSdQkONXaQazCu+Mq5EpU1F49CdjJ0BSBHMc3TYWUctqTqy4GIRTQFiPKMW63oCGdNkw3w/mBRAUTXRQQSFRM4JxDlFKijlzEVGCii5Msj0C84FGnG2nu9UoaRxy4BW4HmHmk1gJK9egKGKEyyglLC5wBcLu81bjdDopG2A1Qp1dii+XY8uxFw8WVEyVPxegKKaEy7kVlWkyjh8BnYmd/tpnqz//8z0dWSW1tbTz99NOXZdy+fmktG+9Yx/6XW1EUhWA0wC0PTV9HHmDx2iZWX7+cI28dR1EU4lUxrr//2pHtmqbx8a88wj995dtkh3JICff+2jaaV8/uiL6Nt6+h52QvPaf7GewbYtNNG1hz4+WnPDkXBDWdmkiYTKk07aapkm0T0LQZhX7eNWoDYa0VXVWxXIeIWiCidBNWB8suT2AoWQIiTd6poSSjmA5UhoKADSLEeCtaKR28BqSK8x6TSPMNMF9GSp2ufJAjg0PkLJPGoEFjOE9FuIRu7QWRQBqbz6uyCTHihJ1BsI/iUsKRAtuGvNQQCIJqkabQEYQ4xYC9hiF7OZpikbfG3rW4UoKQNETPSxaLGIgwUhYvmNo1MdLNlSuE/Hr76TChs6+v9+J2y5ZdnN75QkMIwQ33X8uqrcswi9YYobDpoCgKtz98ExtvX4ttOSRr42P07DfetpYv/d/P032il0RtnKbls69DEwgFuO/TOxjqTXH0yBGuv20rquqvbs5yY+Mifnzk8LSd/UAhz63NS8bU4s8lQlsJ4gVaknG6U23UBLtw0TBlhLMradsJoqs5guoQhkxTtKtIBgKgVJW7SNNjD+wOgrZ+tLSBuRPMX9FbiPPCqdMMFQqoQkFVFDKFAK7dzpEhk0XxOIvjWYT5FtLY4uUclChSSYDdCc4JLAl5S0XBxpJBbKkAkqIjMJ0QmrCoNfajUiIn68g4TcDAKBP78znWVNWM+nyEUJHG9Z74mjbN34xMQeCeq6Z35N0yaVMVeJ2099xzD+95z3uorBw/tng58W7rzYUQU4ZlKusrZjT16WJQNZXqxkr6UnHf0V/A0opKYoEAw8XilKv1nGmiCYV1NZd26IpQIkhtHY3BF1BKHWStEIZ2Qd05KiWZwCCDabs0BAcJKIbXLDUO3gBxE2FsOveYfQZKL9NTiPP00aMAVIRCiJHQjA4iTlg3OZkaxnYTLE/mwW4DfYP3FKUanF9hSZWcCYoAVQHTDZXDSgIV0IRCquQCghqjlcHCMtwLJJ1TxSKKENzSvJgLEdpqpPIa0s0iJuoJOPu63DSICEL381TTZcqlzJ/+6Z/iOA5f+tKX+PSnP+1r4/gseAKaxofWrsd2HQbyea+sbxxSxSKpUpEPrV1PfJZ1cqaFsRVddlIfTaAoGjnLwnbPxeElkLbiFByVsK4QNbwyzJFmp/OQ0vKGmQe2j2pIktbbOIR47qSnZRM1Auc5egBBZ7GJgOIS0wUdmRQDRQ2cbqQsC6k5A7hSoWh5c4Y1xcaUEeQFsXJFEcQDARzXpWALDNmB4zq4UpIplehIpwB4bP0mKkNjk8FCiSBCHwSZ80YjToB0B4ESIvTQtEM+PtNI0NbV1bFx40bS6TS/+MUvePrpp69qbZyu4z28+cw7lAolVm1dzsbb1864vv3kgdO8/dx+XMdl/S2rWX39ilG3ovlMgVd/9CYdbZ00LK/j1oduIJLw45IzoSYc4WMbN/PzY0dpT6dQFaU8MFxQtC1Mx6E2HOEDq9fSEJtCN2aOEHIQqa1EdztZmiiStgL050vkLYuytBgRwyARXEJE6QCpAKZXBVN2clKWvNANEoL3oBibR44v3RTYRzmTDpIuFakMjd9TUnDCnMy1sCRyEqRLRzpNVTDslTWqi8A+SlHGcWSaoFqkJKPjavODJKCYoAXIuU3UBbvZnT7NkAn1us4dS1pYVlE5olE07nuiNkL4MWTxWaTTgXfnUT6XLAIWqI2IwD0ItWrmb/pVzJTO/oYbbqCxsZHf/M3f5B//8R+JzdMPYyEw0DXEM//wHJFEGM3QeOPptxFCsOmOddM+RteJHn7xf35FoiqGoir86l93ourqyCxYx3F4+u9/wZs/9Y598PU2BntSPPLF942UfPpMj8pQmMc2bKIvn6O1r5e+vJc0TwYrWV9TOy1RrrlEmntBrQZtEap9mgpxhgpDwXI1JF64RBPS615Vd3iNUNZ+cI4iZRWa0g8yCMatCH0t4sLuWKcLJBwc6ENXLqiquYCcE+NYdiVVRi8hBjAdBUMe9S4scpCCpeKSwHR0NGGhUUKilI8pEUgkgqKboEicvOOyKCb5tfUahyPLWLt27bTfF6HWISIf96qHrNbyxUx4uQp9LUK9POYcLzSmdPbf+MY3+NWvfsX3v/99nnnmGW655RYee+yxS2HbgqP7hFdTH0l4t6CV9UmOvXNiRs7+dGsHgZBBOO6tsuJVUY7tOTXi7HOpPCf2nUbRVBJVMdIDWU4dOEN6MOuXV14kNeEINUuWzrcZY5EZONvlqq9EakvB6UeXWbyKG6M8O7Zi5KIkWQnGbQh9LYPtR6iJbPbKOsc7vCyCEGRNc1Sz03nP4PwLQMkN0llcTNZMsrSqEkPLg9oIxBg0Y2hahLNWaKKISqmsza/gYGDJ8MjxBC6OVNAmCcdMhVDrEWr91E9cQEjpTvh5zDdTOvtrrrmGhoYGamtr+fGPf8yTTz551Tp7PaDjnNd4Y5UsYlUzu9MJhAws0x752yxahCLn4sVnm7Zc28U2bRzbRtXUkWYwn4tDSsdTUgQQgXHFs6QsgrQQlCY4hl0OJQAiOMmg8emicn4dvRAaaBM7NyltsDrB+SGy+FOqghlk7iAyuA2hNo25SxFCRSJRheLtiySoFqjUB4kbKdRynX7BCdFvVpOxYiNxeEW4564DQoLw+he8UwhsGcJmcqkRRUgmcjFS2uCcQtrt3uciAgiteXxd/nlESgluN9I+Xu5aVr1eBG2Fl2SXJaR1HKzd4A4ADpIA6GsQ+sYFdRcy5bf1oYceoqKigrvvvpv/9t/+G3V146vRXQ0sWbeI6sYKuk70oCgqqibYce/Mpnat3LKMtt3H6T7RC0IQCBls2nZOojgUDXHHh2/mp//wHOmBDLGKCLd96EaiyYUXs89bFqdSw2RLJTRFoTocoSken5G+TKZU4sTwEIOFPCFNZ1EiQWM0NmvhFekO4ZQOksq+SskqIIGAFiAe3YIe2OytnJ1TSPPNsvqjQlWwDzd3AIytCG0puBmktZ9CYTeW7V0IAlqIQHgrQl83oiMzY9QGsA6AOrm8spSu9zzzHU/mQARA6ER1C/JvQuG7SO0aZPRTKPoy8pbFQD6PcBwS0qQ2EuFEaZilkT5Cap6SDQN5FRcFAYS0Ak3BU6hhKNgaatQkolSAG/O0dGSOWqNA0TWwqBo1XWs8XCkRQqDieK9x1GuxkeZbYO4acfIIFaSDtN4GAl7Zp3EduGmk3eo1cYE3tUtfc/Hv9wxxrSNgvlrWE9LO6eq7JlI8jyQEMu3ZL+Kg1CCE4iXLrQNI6x2k2owI3j82xDYPTOnsv/nNb5JMJi+BKQufQCjAe3/jbs4c7sQqWdQvrZ1xKWckHuZ9/+49dBzpxnFcGpfXjXTCnmXrPZtpWFZLqj9DvDJK0wKbGVuwLF45c4o9Pd24UqIpnhCWlJJ4IMhtzUtYVzv5iiZTKvHjtkP86swp+nI5hPCEFMOazrrqGj60dj0rq6vpzWXZ29PDieFBHFdSE45wbUMDixPJKevi3dJbdA08RXsmQ6oUBeWs6FaR+MDPWBR7loaIjqIkQY2PqC1aLt7Aj8JTSDdNxoTTWZeunD4y8UnIYRqjz7Ao9itisbsRxtYZX6CEvh5pvTPpc6R0PMVK6x1PehjNkzcQAkcWQDE8pUrrRayhVt7KforXehJezF/abE0OIF2VlbHjIBW6895PXimLqwHkbU8Wuik8QDJo4yhNaGoQ9LVe2aXdRFDvo1B0iKrdFNxKSnLi733JdqgNg6ImEca1QHv5tZjI4o/BPl52jGOdtpQmlJ5DFp4Epap8MShfXJxTSPNVpLYcEXjPlOWZ7wa39DqUfgVKBUK7oClSAWkdAet1T9ffuGGULULoUF7RS6cHmf8XCD/qzR6YR6Z09r6jH40RNFi+ueVdHSMUDbHi2sljyE0rGmhasbCcPHh16d89uI+hwvhDQfKWxVNtraRKRW4ep5YavJLHv3j9VQ729RAzAjTGzt0NFG2LPX09tA70cVNTM2nTxFBVksEghiroyWX519YD1EWiPLRm3YQlk27pbY53f4/jKYNIoJZE6IKYtdTIFfYx4BaojF+HpiwatVkoMaTbSzp/gJ6cQspZTyJ4ruFJIuktWLRnTK6p/TE1FSAC18/ovRRqPVJpQLoDCGWCyhLzLbDe9kb/CcOTHx51UVFBiVJyDAqlMzSIr7M09u8p4n13bGU9K8I/4kRKMpiDiK6M0TQLKJKG0DCWo1BygixNZsCNeAlR4Y0jNJznURWdkiMJqYO4roolxzrbs9pEcaMIxvaRFa2UEln8OVinRqZfjY8Ddkd5FCGgX3PeRTTuhVXsdqTznbIDnX2H75p7PUevNowbqpPlBjMvn1EC66zK6NjvolBrygNZfgiRjyLE3EmeT8XCzCRcBRSyBXKp8ZULFzJPH20jVSzSEIuPu7IO6zqN8QQvnT7J8aHBMdtdKfn7t3bR2tdLXTRGPBgcFfYJajp1kSgD+TzfPrCPiK5TF40S0DQMVaUiFGJRPMFwscC/tu6nZNtjziHdYboGnuJYyiAeiqEpY5OTlXo7EUPQX4oznG0dU9ct3TT5wmHOZEMEdZW6QBejRMkQhHWDWDDMO70KmcwvkM7oTtHpIEL3AwrSHRr7OmQezLfLo/kUb4U7jliZLSWDBRNTxqnQB1kWfPHcNgIgbQzVQFMEBdubi3t+50FMyyKlg4NKSDcwbbuscln+fLUlCLWeioCFEIKCoxJUyuWeZVwJRdvGdhxaYja6WgXB9547idsF1mGYJOEqpQRzH+B4E7HcfpCj3xchBEKtAZlHFn82yTt7cXh3Fi+CWje+o5cu2MdARM99JtIE58yExxRKJcgBpH1y1u2dCROu7F9++eUJd7rttsnj1I7j8OUvf5kTJ04ghOCrX/0qq1atGtn+3HPP8T/+x/9A0zQefvhhPvKRj1yE6eMjy/HC8ShkCxx+8xiFbJFFqxpmXbNmOhTzJV754RucPtiOlJK6llru+PBNo0I5juNw5K0TDHYNUVGXYNWW5WPKLvs7Bzm+5yRnzpyhvqrxklTq9OVynBoepik++Sg4TVFIBIO83n6GZRWjb9XPpIZ5p6eTqnB4wjCM6diY5eaitoF+bgqPvUOoiURpT6c43N/HpguGjjjmAToyGaKBmgsaiDxUUSKkDGLKCAFNMFwskrBOoQeS5x2kncGChaEGcDAIKcOoFHEIXnAsBUMzaE9nWRs7gFDvmPS9uRChVHh68/mnvLpyEQYR98TIzFZPEkAY3vBxMf77XrAsQKIoKq7UqTf2cqx0PyUZp1I7TmtqCbWB09RFNIaKEgmYtlO23yUayoMIUhnUCagWg2aMYFBFlykQSYTQkYEdaPJZqoN95GyNkl3CdjJY7rn4fTKoUhUoYGhJiH4BRTuvucvcU06MTxLqkmnv38hgFsObeTtejF6p9sI6zsCs1ttL6zhgTbwCl8NAcZT2kKftfwapLh0zRvHcc6Jg7SpLTM8PEzr7n/zkJxPuNJWzf/755wH49re/zc6dO/mLv/gLvv71rwNgWRZf+9rX+P73v08oFOKjH/0oO3bsoLq6+mLsH8X+V1rZ9fM9xCqi3PWx20fF00uFEk///S9J9acxAjoHXjnEHY/czKotyyc54uzz6o/eoPX1NnpP9yNdSTaVwy5ZvP9z9438EF790ZscfvMooUiQg9ki3Sf72P6RW0a293cM8OO/exZFUejt6+Oprp/xvn93z6yra17I/t6eaevHJAJBOtJp+vI5asLnkssvnDyB5bgEtIkjiKliEYEgpGscGxrimvoGguOMDKwMhdnZ2cHGuvpzpYnSIZV5jZQZIR4c/4cXUs6uFgWKgLwMki+1EzfWeFLA2JTMdtKmRtjwjiERhJRBsu7YKUphXaczZ9CS30XYuHnG1SRCqYTIx8Fp95KXzulykm8fYIBax4RVLUDOMlHLTsYhSFAZpko7xKC9CkWm6MhHyDsraQ6foSqYI6SHcWQQW0JIzVOh6wghkDgU3EryTois6VKh94w4XqFEkKH7UEq7iCnHiWiSmExjEUAgCagOmmKAdi1EPoZyXqhGUAL7EChTVKY4nYxWsAyB04vUzDHO17NXQ9oHEertM3q/J8V+x3PMk9p4wecrNE8KWg6CGF/KXChxpNOJdAcvWYL5Qib8xX3ta18b9/He3t4pD3r33Xezfft2ADo7O4mftxI8duwYixcvJpHwHPGWLVt48803uf/++2di9xhS/Wl2Pv021Y2VZIeyvP7j3dz36ztGtncd72W4N019i/dhlAomu5/de0mdfSFb4NSBdvraB9A0FUVT6D3VTygaYrB7mKqGCnKpHEffOkH9klqEIkjUxDmx9xRb7t5EvFzmeeC1NjRdo6IugSlKIOHwm8e46cEtc2p/VzZD1Jh+zFEILxF7vrM/nRqetIMSIGdZaKpAQaFg2xQde1xnH9Z1OtJpirZ9bn6sLFGy88hJyiI1CrjynA2qUCnZZztTAwhhYbvOqFWoREVX8hOoCgukUCnZJcKyWK7amBlCqF64RPN0b1y3ANYePHc+8Wtxyonxs5EqF08qOKgMoYkitisRCEpuiKPZlWikWRzNUh/OIwBDKSBQyLnV5Tp5BUVxKDrSS1KPsjEIwduQ8joU8zABt4uAthwIgLYUAnegjCNipojSudc4GTLtJWRHTqh4uQppenc3Y960kDeAZTZxhydX0ZSFiT9f15wiMC7Ole7OA1MmaP/qr/6Kf/mXf8GyLIrFIi0tLZOu+kcOrGn84R/+Ic8++yx//dd/PfJ4Npsd1YUbiUTIZrPjHqO1tXU6rwGAdH+Wgf5+HM2mkClQcPKj9u881kN/fz8y4N2+2qZNMV+a8BzFYnFG558OxVyJvv5+0sMptICOoigUs0UG+vtpO9xGcjhOPl2gr78PW7NGnM3QwDCHDh0mVul9CdtPn6F/YJCiW8CyLdLDWYKnNBKtk5fwvVu6uropOjahSVbl5zNQLHD02DFKkZ6R93NgaJBcLodmjY21n6VQKGC7LooQlByb3t4+ChMMAx8oFjh0+BBB1bNJUMAlRTarQ3H8wRlGMI/QSljlkLPpuhRkiV6zF1fmcBybXC5PsaRAOSegKSUKtk6qkBr3mFnbZMiQ9A0cwh0ncTlTBCUaIzk0UcKdaFXvSoqFApZlw0j/h0TTbNLpYXqzfcTiWUqlwMjrMF0N06xhKBdE4JIw+qgM9lJyVCj3F1iuS8F2GXaHKdrdE9hXBSLJcOmh8x4dLv8bjVUq0t/fj+1OXq0U0tIIHOR5K2dFZCnYvbhyrI9QRB7HLZK2Zud3WiwW6evtxZFhJnKNIS2FwEYy2mkrIkvJGcB2J76gacoAKfMItjv+d2imts7UP035q33uued46aWX+NM//VN+/dd/na9+9avTPvgTTzzB7//+7/ORj3yEn/zkJ4TDYaLRKLncucRkLpebUIJhJi3WUkrIqhx45TDVVRHu+dR2ahadi+UtWdRC74FBLNMmEDRI57Pc9aEbJzxHa2vrjM4/Xbr3DhDUQvSe6sN1JcvWL6VxeR033O7JFEspGWzLcOZQB5FEmFw6z8Yb17P15utGNHiSgUqe/vtfENJD9A71kkwk2P7+O6htfvehsMloN3Ra+/uoiUyv5t9Jp9i8Zi110ejI+7m+kKXnxHES0YnrjouqymAhjxCCmDBobmg4t3I//3m2Tci22Lx+w0iSV0qbgb5n6bCLxIPjO11DzRNWc5hn68Utk3hEQwnW4Eidwb48ldEIhuMQKs+y1YWL4ySJ6p7dZ9UezxmTpa46QqRi87jxXilNpH3CW627KUAp141vhvIc19HPd5CDteCkQBm/rj1fKBAKhci4LpqieBO6cFCFTiC6mKpQEwk9hlEQBHVPd15aFhXRGImyTk5IcQhqwyjn185bFsmwIBmtR+jjJ1SlmwKlkobw1L+RQ61FqqurQKmdtLtUWgPg9MHZChspQUpigaZxK12kfQoENCg7QZY8DR1tTVk6YvK80ni0trZSU7cMZHHCKh9p9oI7BBeOT3RdYnr9pE1U0pHUhDfMSo5hIv+0e/fuCfeZ0tnX1NRgGAa5XI4lS5ZgWVOPAPvhD39IT08Pn/3sZwmFQgghRhzV8uXLOXXqFMPDw4TDYXbt2sVnPvOZKY85FUIIbrz/OjZvWz9qdOBZwrEQD3z2Pex94SD5TIFrdmxg1dZLG68HuP1DN2EVbcKxoKfTUpvgrsdvG5EpFkKw/dFb2PvSQfraB1l+zVI23TFabK1hWR33f+YuWl8/gqObvOeRHXPu6AHW19byTk/XtJ6bNU1qwhFqL7gw3NmyjOdPHsd0HDRFIWuWGCwUsBxPUTEeCBLWNQYKULItliYrODk8xEAhj5QQ0Q0WJxNUBEMM5vNsW9IyqppHCI1E9Hqigz/FkWHUcZxL0a0goXrVE44rsZwc7dkE3YO9ICGby1JQdTSZwXRUVCGw3RL7ByFne7NdvSayMPFAANt1qQwU6Cys5tXTXoVQxDDYVFvHyqpqAhyD4rOAVa7iCAISnDNIuw1EEkIPkrLiHOzr5VB/P5bjsDm+ks3xVnRho0wQllKAiKGTK0siKLLIsBXip6cipMxh7mtyCKoS0xEYqooA4sFzjrMkzzrFc9IJLhALiHKuYAJkFvTtE28//6kEQV0GbieMU1s/gtpYjomfpeDVuV/g6KW0wG4F66g3N1eJlmvxbTBfQ5qvIPXNiMC2mXc569dA6efABHdnagO4PcB532vp4JXATvzapJvz1ErnKV4P03D29fX1I8nUP//zPyedHmdgwgXcc889/PEf/zEf+9jHsG2bL33pSzz77LPk83keffRR/uiP/ojPfOYzSCl5+OGHZ7UrNxieWKo2Xhnjtg/NbDrVbBNNRnjfb93DcG8Kx3GprE+OUc3UDZ0td2+e4AgejcvraVxeT2trK/Utl6YluyEaozEaYyCfoyo88erelZKBfJ6H1qwdU33RkqxgbXUtb3d1UrRtHCnRVW+QhpSSgXwOFyhZJlnLJmeadGYyBDUNRRGkzRJvdXUhgI21dawdR4deC2xmUexFDg4USIbG2mnLIEU3iSrSdGctkoZJSTSSCJRXtyUTk3p0erw8hWpTcgPYRAjr3utxpEtPNktvLocmJI2RHLuGI+i6S1DXKdg2Pz9+lP2dv+S+RcdJRpaMXZ2W/5ZOmjPdf8fT7deQd6tIBoMEdZ223Aaa9J8RUgaJBKsITJDrCOsGOdOkYJUIiAIHhlaQsZOoquDg0GJWRPfRmQ0RDwSojURHXQAdaZB3qwkqg9gyTNG2iRsCXY16F6FxkNIEDK+7eJoI4zpk4dgUT0p4F0OZ96qSZAn09aOeIqXtlaO6vaA2IfTzF2wGqOFyx/E7SJmD4Htn5PCFvhxZ8uQlxt1PqfTOI21GRj/KnGfLZOeRKTDuG/N7uJRM+S785//8n+nu7ua+++7jySef5M///M+nPGg4HOav/uqvJty+Y8cOduzYMeH2K53pDEBZiAgheHDVGv5l/156c1mqw5Ex0gim49CdzbC1sZHVVWPvNjRF4ZF1G3jtzGmypknUMNCE4v0IysfKmyZp06QiFCKo6aiKgqZ6rf2uFJiOd0GRSG/E3YV2qlU0VL6HnPkUpzIQD4bH2DloNaFanVQGTFR9GXlntEa8QwJbaSKhHSFva3SZy86b/+qVXOoqDBWyVAWGyXErifC5oSKGqlIdyNFi7GFXT4QtjS6VE6gMnEjZnBwucn3lbo6W3o97NmatNtBr38qy4LOki4MkQpUY4/QMaEJgKALcNBk7xp70TSPCZ0POYnT1OFGtSLoEKyvHhhAyTgNhZQDbKSFQqY8IUJeNG3KR0vFWtoG7Z9YgpC4CdTHS6Rylt38+QihIfSOYb3rOXKkZXeII3lAVtxdEDPSNEx9HaQS7DWk2zKjZTYgQ0rgFSi8i1aYx74EQKlJb5t1ZUAGUvO+t1jzhMaWbAhG94MJ06ZnS2Q8NDfEP//APnDx5kpUrV1JTM35p0eWA67p0n+jFKllUNlSMkSnwmZpEMMjjGzfx7PFjHB8aREVgaBqulJQcm4CqsaNlGVsaxwpznWV/Tw/bW5ZxdHCA06lhhsvTi87K+kYNg6gRIBkMsKGujt5cjv58HglEdZ31NbXURiL053Ps7upge8vY0Zlq8BaW1zsE9WfpyGTJ2FFUxfu629JBOsMYVJIIKRRkHBUTh3MrbxWT3rxCSFSStjVct0Te0c+r25dUBvKEIgVO5lZzsNDCugtuMir1IyhKgKAe4kBfL7c1L+HCt6Rg2xwdGiQeqMRQ+4mpHaSclpHtR0sPYKhZmrTXKJQGMILJCypTJJadQyGFUCr4Rdd7aUtXISiMzLJ9xbmW9zbvYagAZ9Ip6qMxokagfAGUZCyNbGkxLZHj1Ic1dH3tGE0bKAvFuX1g3IjQrxn3s50IIRQIPoAsPIm0u7x8xXjVOSIE2hKvvl6tBjmEpFx44A6VG7PqQN88avTi2PMJpFID5ptI49qZre6NrUiZAfMtpFo79o5MbQI3A84xIASBrYhxmt2AcrOejQg9Om7e4VIy5Tvw+c9/nvvvv58Pf/jD7N69mz/4gz/g7/7u7y6FbbOK67o8/+1XOLHvNKqqoGgK9396B7WLL9+L13wRDwR5eO16Bgt5jgwOkC4W0RSVhlhsyuEUg4U8J1PDLIrHaU4kyJol2tNpsqUSuqpSH43RmU4zXCpiuy4F2+baBq+2/cKGuepwhLe7u7h50eIxdftCKOihbSxpWEV9cjfD2XfImUVAENJV2oab6SzdSX++hoTWTo1+kABpJAJHz+LKSl7tXU5G7iBAmqWR02ysymBLCUh0RaUjv5RXe5PYoo50KcfySnvEDpUSFepxim6SgKoyVCwwVCyMGSDSnc0gAEUITDdCjX5wlLN3MTiYf5S8UUWt9jIRI+tp3KOiihK4OYqORm9pGSftD5FMrOTWkEnWNJFlO5PBZQyxlMXJF8kV+6mORujIFnHLN0WVQYWliSRJYxOKogMGuL1IEcKL41vlksMoBO5DMcZfUU+FUMIQ/jCy9DJY+5C45TLHsvqnm/dWyfr1EP1dhDuAtPZ5nbQIvBLP9aCvmJaMsBABpOwH5xRo019VC6FAYAdSqQTzdaTTX06SG4DrvRdKAsT6kb+lDI04cyldL2wj8yAqEaFHFsSglWld7h5//HEA1qxZwzPPPDOnBs0VnUe7ObHvNPUtNQghyA7nePnJN/jQ7z4w36ZdtlSGwtzYNLNyz55cDjjntKNGgDXV5y64Ukr29nYTN7zEZ282x4py6OHCOwVdVXFdSX8+P2FXr1AbCEYfpC5yF7jeuS03wP89sYemeAIHGLRXMWivwBBZFGHTNTCAE6njeK6XZDCIRYhf9cUJRBsJq145p2WH2N0/RMYpEtY9x5M1zRFnbyhZEHJEMlgVgqHCWGffm82NVBrZMkxY7SuXH567YLoYHDffyxuD1/K+ljSLjT0gUxTyBQKx9TzT2UyRxRia190bMQwiF/RDFGUFR4rvI1s4xqq6LGsqB3Gk9EJTSqSs8LnaGzDu9CGtgyOSvV4IYi2ozVPXyk+BEAFE8C6kcTPSPgr2CcDLAWAsRmirEGcrXZQIQjvXPe3mfwRCm5levNCRTi9iBs7es1MgjGu9sJJzGmkdOidxrNYitLUItQrpZrwBK+ZuJAOMVGipSz0ROHXRu37PZospnf2yZcv4t3/7N2688UYOHDhAMpnkxIkTACxdugAHQkxAqWCiKMqIwwhFg6T6M/Ns1dWH647blVR2cAqScyt4IQTOBM8/i4IN1hHc4rDXsCIMUOoQ2vJzTgMvFovqOVpXWqOqJs8eyZRxkFB0SgipXDCpVWC5AYrnHdOV7qiwzPn5A4FzvnQMihA44+QXHOmOknRQZZGkegQFgU2AjNOAWw4vmTLCkNxIS8UjAPT0tFLZspY+83USwanj5xKVYbuelLKGulgcTVrlJKM+yoEKtQahbpvyeO8GoYS9wejnDUefGpuZy3mVm7IuEm/GwDKENjZUCCCUGCJwA9LY6o2IlBZCCS0oTf6zTOnsjx8/zvHjx/ne97438thXvvIVhBCX1fDxyoYKhIBCtkggbNDXPsjqreN/gD5zR6g8BxYgqAxSqR0jqZ5AFTZSCvJuJW2ROCm7DssVRPTxnZiCSbV2iEXJPcSpAiuGJz/sgtyHLD2H1NYhAjcgRrRWPAxVQxMKdrk+fTwMVUGWvbUrJQjGPDek6QwVip7qMA41gR7q9WFUYaJIC0NkKVAFCGzXJaRp5eHnNiAQQiOk66SKBRLaABX6UeJqJ5X6sfJ7JHFkiA5zC53mVqT0VuyulFiOg1W+EMYDAYq2Pb3uZgkhTfOSq/OowHhRiAgwfpPXeHjJ5D4w38J1TgHCq/U3NoDSOCuVMVKa5eElu7xzSYkUBlJfXx5eMvcl0dNlSmf/rW99i0wmQ0dHB83NzUSm2VCz0KioTfCeT27j5Sd3ku3MseLapdzw3uvm26yrjkXxBGHNpkl7jkqjC0dqlNxEOWwh0UWeuxva6cnvY+fAZqrDq9nf201XJosrJclQkPVVEa5Jvo7q9lOkkkiwZUziU0oH7FakcwRCD48ab6cIwcbaevb19lAXHT9JnwgEMVTNyxtYFjXhCEcHBxgseNOuqsNhqsMR2tNDNAXPsKLqKMtjIVx0pFQQwiGuthNW+hlylqAJnZpAL5TexnP2IEWIJZFaBpx3qA32IHDJu9Xk3XOZXoUSiwOv0GS8RqFwN6dTDTxz9Aim49Df38/bdomqYIj9fb1TOvuibXuqpLHJG476cjn29XZzangYF0llMMzm+nqWJJKo09RGmguEvhppH5zWc6XdBfZhT69GvxFvIpgE+wjS2u9p5Yfun7AyaDq41nEoPg2YXnWQ4mk0ebpGe5HWW0htFSJ4z7wnZ2Eazv5nP/sZX//613Ech/vu8+pEf/u3f/tS2DbrLFrVyGN/+MFJlTF95hZdsbi3cS9d6TPknAtFxQSWjCDUIAW7nesSr/CT9mHac8myTLGgPd1Ls7aPU65LQG/gusaaMY4eyjosap0XUy38K4Q/OkqAalNdPbu7Oydc3auKwpJEgkP9ffRmcxwfHsRx3ZEadUdKgqrCjoYTLA91EA4sIu+epzwqvVxAhXaUSuUdmpIaARq81enZyhC3SEK8SDDUT9apRFcYMxTEJUDerSFTHGJr4t94O2WQDF7vNVDlcpRsh/19vRzu7yes69REJuj8lJLeXJYdLcsmvJsp2TY/O3aEw/396KpKPBBAEQpd2QxHWweoCIb4wJq1o7SOLinqYhARpCx4YbkJkPaZcmmkBuri0cNHylPBpJsuDxV5BKHOXP3WtY5A8Udlzf/Rq3dveEmddxdnHUPKH0Log/OqZQ/TCID94z/+I9/97ndJJpP89m//Nr/4xS8uhV1ziu/o5w9ZfIElMZtQYBFDhQKOHB2Tl0hMxyFv6XTmNO5pPERjVCUWCBALGFxfM0RTJM+RYW+QSVVo4h89eDFVpEQWXxr1eE0kwm3Ni+lIp7EnyAs0RGN0ZjL05LMEVY3KUJhEMEgiGKQyFGJT8gy1geO052OAMabmP+dUkLdcYnqWpF7uPD+/BNBNocg0uhYhrKYo2Aa2HL0CdKVksFAgY2loaiW3Vv6MiHZOoCwWCLAkWcGq6ip2trfTlU6XQ0XnKNk27ek066prua5hrGoneJOqnmo7xJGBAZriceqiUUK6TlDTRmYIWK7Dd/bvZbCQH/cYc40QKgS2gTPgrZ7HQbpDZUcfBlzQVo5/LCUOIoos/BDpjq/NNRHSHYbiT7xZtJNcdIQQCK0e7E6vAmmemXJlr6oqhmGMJMxCU/y4fHwmQrppsA+iaPVsrhecHBriVGp4jLPVVRVLulRFqgiIfuoC7bSmFiOEy4roCYbNCHXRCEXb4u3uLm5aNHFDC+B1PdrHke7wqPj9zYsWoykqvzp9EgkkA0F0VaVg23Sm03RlvLLI5ckKMpZF3rJGVsVCmmysPE1R1qAIQUDTyJhF5KikrKQlGSZqVKAIy2tGEnFGZjC6HYCKJgAthC51hopWOVrvIfBW5XWRKFIRqKKXOu1tzlijdfNbkpVYjkvetujIpEeOISQEdJ07lrRwfWPThGGYw/39HBsaZHEiOe52gGQwRH8+x/MnT/Dw2vUTPm8uUfQ1uDIHpeeRIgoiMXrxZh335AtEqVyLP/GcB6FEkXYWaR9GGNNXjJWWF0oSIjjFM8uotV5Yx7hp0t6AuWZKZ79lyxZ+7/d+j56eHr7yla+wcePF1dj6+EjrEF5iUkEFlldWsiRZwVDxfG2cAK+dOYOhqIQ0HU1Uc0fDAFm5lurAIPVRhYydRCDQFZWDfX1sbWyaVGdfCAUpFKTVigjcfN7jghuaFrG2uobD/X0c6O+jWHboty9p4am2VmqjUWojUWzHIW2WRiZjLY+nqI0YpKwoqVKRom1zx5IWUqUijuvN5a0I5NDsIIgab+KS3QH0gBL0asplCoiAEkUjSLVaIhGKU3C8yU+qomC7Dl2ZDGp5YKzpRlkU2Em7deuY19mSrGCwUOCx9RsZLhVxpSSk6yyKxUc6asf9XKTkjY4zVIamdkSVoTDHhwYZKhSomKeFn2JsQSo1SPMNb4DJ2WomWQD3NKjLQVs6vSHfahLMXchpNolJaYK5e0YaN0KoSFyk3YYwpneeuWBKZ//FL36Rl156ibVr17J8+XLuvPPOS2GXz5WIc9xLZJ2HpghqwqOdzPHhwZFkoy11kkaWurCkLlhCEee6WA1VJWuV6MpmaI5PMalLRME5Ddw8ZlMsEGBr0yK2NnkDN1pbW2mqruEvXn+V6rJtmqqOcoar4kex3HJdu27Qnk6hCEHdeTFzaZ0CdC90I2pA0z0JALUWrGPgRr2/R3YAXTHRtXMJ1FOp4VEXMpswYaUXXYwNPeiqiuO6lBxnVO/CVAwXiwwUClNOIANGZCfOpFPz5uwBhLYYoS1GuoOeUiYO0m4HqSD0GWj2iKAX+pHTlB12hwB75vF3EfUGrc+js58yZt/T00NjYyM7duzg2WefnXWNd5+rCFksl0dOjO26XiL0PAcnpUAVNrpi4soLtEqkGBmxNylC9YS1ponpONiuM0oP53y082xRhMBxJY57QR29tC54vRqIMEJtKMv4jleLPTqk5bhyjK6Pd6SJX4vlTuP9uOD5M0ljaYpCwZ5a/fZSIJRKhL4aoa9DaI2gXEzVywxq8aXFOE0a0+DcvID5Ykpn/3u/93v09/fzl3/5l9x666386Z/+6aWwy+dKRBggJ2+S0hQFVSgXNCBJXFQsV0cRox2qxCWs9iKLO5HFF5DFV5H2UeSFDk86MEX5myzXr0spMVQFTbnQjnPYUkcR7sh+iiJGQi3nXq9elr8d2ctrrXd6vZADFw5wcb0LotvvTWByBzEUc0ziVwA2E68spzs68vznj/8qx8dxXULawmsaAs2bbDVjJONfeMdBaDCjd+ssDrDAtXGEEFx//fX87d/+LQ888ADf/e53L4VdPnNM1jQ5OtDPULGIEIL6aJSlyYpJZ8O+a9Rl4LzG+VrgjpSkikUs10FBEAsEaEkmOTk8TCIYRBUWttTJ22GyahyVc86z2mhna8UJalQVxxK4UkEIiWbvBxFEamtBvw6hqGUZ2jVjTJJS0pnNsKe7i0P9fV71y8AAt2gKjdEY3dkMteMMWukr1lIf6CLvRMhZFoti8bGaQEoNOB2ew5fD5f8veKqNbs4T05LSkyuQDt7Abde7SHgan1QHbIYkOKIKW4bQyFOSMSwZAwqjTme7LorifZYzoSIYoiIQJGeaY2QWxnu/XAlNU9TqzwtqLQh3RqXVUpaAoKd1w9QjV71YvYaU1sy6ZN0cGFun//w5YMpftm3b/Nmf/Rlbt27l9ddfn9bwEp+FS8m2eeHkCfb39SClJKBpSAm7Om10ReWW5sVsaWwaN3TwbhH6WmTpVaR0kQjOpFKcGB7CdBzvxlh4a6agpmK6Do6UJI0sbZnVuKj0l6oxpYEqLFrCJ2kMHkdVogwUguQtsyxfIAhqBokghOVbnqCXcQ9gI/R1Y96Lnx87yqGBPgKq5mm9Kwpkc5wYHsLFuxAkgkECF6xke0r1rGc/UnrzajeMN5NBqQQUL1ZL0buzUOpAKF5VjtvtOX8nC7jecO8LkoqGJlFEiqDSRcGtRhdFjhXfM0o75yz9uRyb6+sJznDVLYTghkXN/PTI4Smd/WChwNJkBVXh+asqmQihVCLVxZ6mzwRa/GNwB8G4ddqqmEIYSGOzp6k/zYYsKR0QILRV07Npjpjyfu9rX/sazc3N/OZv/iaDg4M88cQTl8IunzmgZNv84NAB9vf1UBeJ0hRPUB2OUBOJ0BRPkAyFeO7EcV44eWJMrfZsIJQE6KuQTi/7enpo7e9DV1WSwRCJYIhEIETcCGK5krCmkSmmsB1JZ8GrDXdROZFbxrJIG02hE+TsCGlLkCqdq9eXUpIzS3Smi/SXgrhOB5R+BmrLqKYq23V5+kgbhwf6aIrFqYlERvIEihBUhyNsqW+kPhLjQF8veWt0TNeWOoeGl6DRy4ba2olXutIFN+uFF5R6z9F7bwYoTV7Y5mx35zixYAVB1IhQsHUiSg8Chy7r2jHPGy4WCGgaWxtn3iAEsLqqmsWJJF2ZiYcTpUtFHNdl+wLWxBLGDeBmvSEnUyDdvCesps9s/KjQNwBu+a5gGrh9oG2YcNThpWLKy1lLSwstLS0AvPe9751re3zmkNfbz9CRTtM0QeWKoaosSiR4o6OdJYkEy8cZdPFuEYEdnBw8SqbQTmWongsdnCIEMSPAiooIuWKKn3Wupz1vEi4vVgeyUT7Q0IUrdSSeWNmFZYUKnhrmUKGIIQzi+mnQVox6ztHBAY4M9rMonpjwll9VVR5YtZqfHztCTyaDrmoj8XDLdRjUm7mhMcJN9VnAYkzc1+kBbFAry7F7Uc4dlO0dkfe1gHg5gWxz4c8youtIWcK2TTqLDeSdCFr5mpG3TIYKBaJGgEfWbyAemGbt9wUYqspDa9byk7Y2jg8PElA1EsEgAijYFpmSSdTQeWzDpvnroJ0GQluCDNwOpZeQat2EVTPSzYLMIEIPT69E8/xzKJXIwH1Q+jFSqZm03l46PaDUIIJ3TPicS8UcBmh9FhIl2+at7k5qJ2inP4siBMlgkJ0d7XPi7G0Z4Oed17AhbhNR+7BdozwH1fNemigQULIEFY223N18asutnEqlONjXg+1Krq9MUx2pY7iYx1ALSALjpssURaDikDWLRAJVqOZuMM6Nenyzo51kMDRlbFdVFO5YspSSbbMsmeR0OgUIllZUcPOixUQNDWnuAvMNJBbefFnVG1tn7QE0MG70wgpu57kYPoDMgLLM+68c9Bb3Ttar/T6LtEHmiGqQ13cQFwql1Bl67Sr6iwViEu5ZtoKVVdXjDmWfCUFN50Nr19FVzmGcTqVwpKQiGOLOlmW0JCsmnVWwUFACN+KKkDdt6qxuzdnkvMyVtfnjiPBHLkoqAUAx1uIKFYo/9aSNRcVIw5QnwDboVfjoSxHB+y8PbRyfK4PTqRS2407aXHOWeCBAZybNcLFAMji7tdRn0imylsYZ6z0MOP1UakdIqqcAT1my5CY4U7qJtLMIS7gcGxriw+s28NAaL97upp+gVIxxOqcT11XiWgGvJkdBSoEQngIlAgp2iK5SiEi4ioT5CvBpwBug0pPLTniHcyERw2C4WGRrUzP3rxqb5BWBG5HGtUj7hDcow/WkbtGWgLb6nJ65ugqpLees6iXFV0APA4rnHJx2r47bhZGKD6GBtgr0dUTUKlqCvXyqKkZJu4EjbW1cs379rMp/CCFojMWnFEtb6CjGJqS+xlOktPeAmwYUT19e31zW5n93om6KvgqpNSOto95ErJFh6Qpo6zwJZ6Vuwciz+M7+KiFvmYgLSwMn4Kw0Rt6yZt3ZFyyLs/HpvFtD3qyhnZtQsJGooxKPIc1mqDi64gQ3heOqWI4gQwV5J0FAKRJUiijCxXUVLKlTcMI4UsV0LIq2QkI7N7ugYNsz/gEKPNXICbcLA6GvBn01ANJuR7qnxgyu8P5WvWlG4ryQjlrl/bO7QV/nvUfC8PRXlPM+A2GgijxRwyCoqgvGkSxEhDAQxhowxl6gZ+8cIYSxEYyNuO7Zctowyjizgucb39lfJaiKAjNMuqrvcuUzHsq4Nd0K7jh1466U49igIYT05GWQuFKh4IQpOBNVh0hUhVHNTdpFvC4hBMo0L5beDp5K58SUbZCSUR1NShC0JZNMN5L4P9uFg3TzSLsNzF1e6SwACq62CmFsnjXd/NnA/9ZcJdSEI9NuBbEcB00oJIMXl+ybjh3TqYVOFYtsabxApVFbhmG1EdA0SrYzaQzZcV10RSWqFb0h0WUSQW/Y9mTDSy48DkgqjCzS7vMeFInJB1OIOCCR0hnXcQsBUsTwuirL77MseR22k3UZu0UwLl6D3Wd2kFIizTfBfBWQoCQRytlZyY4nvGe1euWZoQfHDNCZD3xnf5VQF41SH4mSLpWIByZPFg3kc1zftOhdN1j1ZLPs6eniyOAAXT09rDKLXNfQSFM0xnCxOKm2iuO62NJlQ20dA/k8vbny8BL9BhrET6mLRDkxNIyjKNiOi+k4SLwRf7qqoqsKpuNQHQ4TVIoQPFdJFtR0NtfVs7dn4uEl5+NaR7mn4QxB842y6JbX8CTVBoRxM0JrGbOPUCJIbR3YbaBOoFOjLoFyA5h3okI5hDM+C6Ve+2rHc/QvgvkmKA1javS9WQreQkC6g2Xd/EdHlf7OB76zv4q4Y0kL3zmwj4CqTujIU6Uiuqqxqa5+3O3TwZWSF0+eYFdnB4amUhEMkTcClByHZ462ISUUHRtDVcdt4nGlpCOTZn1NLc+dOM7xoSGEYES2997aKlqi/UQMg75cDrUssaAIBaQXW8+ZLlHDoCWheTo0xo2jzrG5roF3ursoWNakVSxJsYeK8C6WxFcj1NE/Vm8AxveQwR0o40jkCmMz0t4/cbelWgO2XtZlkV4ydqILA5TrtdeNmq3rc+mR1j4vbKM0TZnkFUol0h1C5p+EyCfmdYDJ/M0Y87nkLElW8P7Vaxko5OnOZkbpyBdtm45MGseVfORd1GsDvHrmFG90ttMQi1EbiaKrKqoQRA2DpniCWCCAgmC4WKQ9nSJnmtiutzrvyWbpKjv606kUnZkMi+JxFsUTNMUTLIon2J/9AB0Zi5iW8o4lwJYOtutil8saw4ZOZdDGdVMQ/V0UZfTrqQqHeWj1OgYLBYaLhXGbyIR9lKTyJkuq1hENjl2VCSUOaj0Un0Pap8ZuV+shsB2cbk8a98LtQgVjI7jDnnSCsXnCFnzp9Huho8Dt0/gEfOYKKR0wXysPLpme+xRKBciUVxk0j/gr+6uMNdU1VIcj7O/t5p3ubq/zVHqDwLctbmFtTe30BldPQNY0eaOjnaZYfMJBGYmgJ2+wua6e2miMtzo7GCoUMFSVjbV13sjArg5Mx6ZuHF0aoTXwndMf4t7af2VFPE/BDTBYMnAcr74+YVjE9AK2NHim54N8sHbjuKuaZZWVfHTjJl46dYL2VBohQFNUegsFnNQwd9QcZlXVKpKhSQZgCB2pxJHm6whtyZjtirEFF90btoHt1duLACBB5suhm/Vep60sIGUAIc7Wa7ue9K7Me/NNQx+Y1+EXPoBzBmR20qEo4yLiYL2J1FfPW8LWd/ZXIdXhMNtblnFL8xKKtoVAENb1WRkm3TbQjywP3pjchgj7e3v5rSVL2Vg7OuGYM00O9PWO0oY/n4JlcSob5n9lHuX+lmE2J96mKdJ1tlSfohvjaHEb3ea1nMy6nE4Ns6xi/HhpUyzORzdspi+foyOdpmBZdEu4fUMDlRiTJ2HPIuLgdCDdwXHjsl7N90qvHtt6B9wUXs13NSJ4txe7x0JaR7x6bbeTkaCV2uJNUVIXTZ649bkkSGs/TDKKcCKEEvXq8OUQiPmJ3fvO/irGUNVZ74jszKSn1cmpqyq2lGTN0pgJSWdr6ye6YBRsy+sFUILsG16Fqd2KRhZNKeG6OiZRRjpy1Qx9+dyEzv4sNeHIiAxAazZHVchBFqZ5my6El7h10xNOMDq/Hnt8NISxCalvBIplWQV9QXRe+pyHTHPxUsWirIU0P/jO3mfBo4scusgDElfqjKc9bhPFdudXaGo6SGmWE7ICRHCcpisBhC5uPoaPzyT4zn6BIaXpJXKco+UBF0HQViD0ZZfFKq8xFudwf/+UI+u8Wn5B1Bj7miqCIQQuIdFJrd5GTOtAShXhVejToIXpHU7Qka8jMUUvgO24FyfcJWJcODVqIrzkrgRlfIkBKSW4PUhzL/nCHkzHRgABPUQwdCNCX3vJ6rClm/bqv639Xi4ADbRmhH4NqI3vWkLgikckgGHOn8kwfeS5Mtt5wHf2CwQpJdJ6C0qvAhaIMJ4iogOlI8iShjRuRhhbpvWDzJom7ekUlusSVDWaE/EZ65xfDKuqqnnh5HEc1yVrmnRkUvTm8qTTaWpsi8WJJHWRCP35HDeUZ74eHxokb1moikJDNEoioLG9/ihWaQ+GUkHeqeH8pa6uFril5jC9hWMYkUcmdMlF2yZsGCxOJGf+QpRaUGqRbnZqaVqZ9mLq44RwpLSQhWcZyOzmTDpPfzE0InMs5DAN0R/THHuBWPwehH7dnCXvpHSQ5iteySCKN6xDVAKuN7TbPuy95tCDXvWIz7gIfT3SPjTj/aSb8YbZiPl7b31nvwDwmjReAvONsnDShdUwSaS0oPQCUqYhsGNCp1C0LV48eZIDfT04UpbTfAJNEWxtaOLm5sUzHls3E6KGwfWNi/g/e9+h6NjoqkpY0wlqKrZ0OdDXw75eyeqqGlwp+btdb1Asr3Q9xRzJ7bWHWZvs5a1iPdmSJHrB25G3dYqyltpwmoTxC87Y92PL0XF/y3HozWZ5cNXqi3q9QgikcTMUnkSK4ITDLaS0PKlc4/5xtjnIwk851reTo6kAESNJInjuOBJJX8GiM1Nik/1v1Fa4iMD1M7Z1KqR0kcVfgLUP1IYLQkcqCE/d1GsA+g6EH1sQHZ8LErUZRBQpi5NKG49BZsC4fV6lE3xnvwCQ9hEo7QS1acKKCyF0pNoE1ltIpQFhjO20LNk23z94gJ5slrpodFSC03ZdXu84w1CxwAMrV89K5c1EZC0TTVUQ5ZkcnjwCI0KOqhCcHh7il7bNsspKqs9LEkeUdoR9gLf769lUV8fxoWH68znvPSgfIqTp3Ny8GFUI2voOYtivkXbvIKCq2K7LUKGAIyV3L1vO+tqLlxZQ9BW47h2eNrqSHKV7LqX0VvRuGoJ3IbTFY/aXVisdQ29wNBWgIhRGXBCI96qgDBxN453eHDeqPydZuQSh1l60zeMhrcNg7S1/vyb+3L0GoAFk4ScQfnzBaLosJIRQvUVA6WdIpXFaFVLSHfJ6JPRll8DCiZl1Z29ZFl/60pfo6OjANE1+67d+i7vuumtk+ze/+U2+973vUVnp3fJ+9atfZdmy+X0T5hMpJZivg1I55RdHCAUpqsDcidTXjPnh7u7qoDubGVe6V1MUmmJxWvv7WFVZxZqa2XUoZ+nJZtnX08MtzUvImiXa02l6s1mKjkNcUVhfW4ehqLxy+iSxQGBMNVCd3oomKjCLDu2pNFsaGshZJumSiZSSkK6RDIRGtMOubdjEYP4Ev+jJMZDXCGga1zc1sa6mdkyVz8WgBG7EVarBfL0sYXvWAUovxh28b9z6eiklVvE1jg0LksGxjv58VKEQ0oOcTKXZHNuPUHe8a7vPtwNrZ/n7NfUFXihVSLsD3B6vYcxnDELfiJSDXpnsOHIJ5yPdAUBBhD84r92zMAfO/t/+7d9IJpP82Z/9GcPDwzz00EOjnP3+/ft54okn2LBhw2yf+vLE7QG3b9pDFIQS9pyO2zVK3Mt0HHZ3dlIzyXASIQQVwRA7O9tZXV0zJyu3vT3dBDQVRQjigSDraoKsq6mlq6uLhoYG7znd3cQDQfoL+VFyBQGRIqL2knNqiAWgK5dhpV1FRDeI6OP/UAK6Tn0kzsfXRVGM62b99QAo+nLQlyOdPu92HMpCaJMMd3G7GMp3UXQDhKbxPgc1nb5CgFxuN9HALTMLEUyG2w3OIEJrnPq5ZxEG0tyLCPnOfjyEEGBsQxIB8xUkLigViHL9vTe8ZMgTtlPrEFeqENp9993HvffeC3irCvWClduBAwf4xje+QV9fH9u3b+ezn/3sbJtweeEOe/NJL2a/85z9YCGP6U6uAgkQCwToSKfJWda76pSdiGNDg5Nq4Esp6clliQcDpIslMmZpxNnrIocrFTi7DpaQLpUITiXIphjg9M7aa5gIodYAk2jXnI87TLpYIqBOvxxUolKwTaJuFtTZcvbDM99HiXlTtXwmRAiBCFyP1Nd7Ydizw0sEgAojEscNCyYcNuvOPhLxSpKy2Sz/4T/8Bz7/+c+P2v7AAw/w+OOPE41G+Z3f+R2ef/557rzzznGP1draOtvmTZtisXhJzh9QjxPT+7Hc6cfQNWWAnHWEoqOM2NlbKDDQ34/I5qbcf7BYoPXQIaLvcozdeHT29BDTtDE5Acuy6OrqQkpJNpuFUomMZdLbq+IEvdVyRaCHqmSWrOnZlbNNevv6cNKZMec5H0VksF2NjDU7n9dsfPYB9TjpUppMXmKp0/uZ5SyTQd1meOgQjpx6JOR07Ayox4jq/dgz+H4JLIQwGSotnPfzUnDxdhrArXiD4108tyqAVPnf7HMxts5Jgrarq4vPfe5zPP7447zvfe8beVxKyac+9SliMS/RtW3bNg4ePDihs1+7dmZT32eT1tbWS3J+aRvIwmFPNGu6+zguNcF1CH3ViJ2NxSJvFvPUx2KTriQsx0EpFNi8fv2czBNdZZfImxaxC2SUzw/jVNsmCgJpWTTV14/U5EcUhVgwgup4OQdZLHjbp6ill44K+iqU4Ox8XrPx2UtbR+l6gyERnFJS+ixusUBNpaCyesO0bvunY6e0dWShbWbfLzcDSpT68MJ5Py8Fl4udMLGtu3fvnnCfWS/J6O/v59Of/jT/8T/+Rz784Q+P2pbNZnnwwQfJ5XJIKdm5c6cfu1cX4Y2pGzvybrzBUlI6nJ2leT6JYJCWRJLh4uTt2IOFPNfU18/Z4Ojr6htJlUojf7uuS8YskbUsipYFwJJEklSpSEBVRzVFFdxKXKmhYGE6DiFNJzkt9U0Toa2Y7Zfy7lAaqQxHcMf5XMfDkS5hpUgstKjcuDNLqE2AMu73a0JkBrRNs2eDz4Jg1lf2f/u3f0s6neZv/uZv+Ju/+RsAHnnkEQqFAo8++ihf+MIX+OQnP4lhGNx8881s27Zttk24rBAigNSvAettUOsp2Dad6TSnUykvBq+oNCcSNMXihHQN3H7QN4yrfnjz4sX88749hHR93Dh31vTa9DfXNczZ61leUUlE1xkqFOhMp9nf10uqVKBkmoT7+1hakWRddQ2W6w0WUc67C3HR6bPWUK3up9+McE19A1OFO6Wb95Qk1RkkIC8BQgkTDV9HXeg5Bk19wgTzWTKlEmsqBFrgxlkeIB5C6pvKNfZTl6FKaQEqQl8+azb4LAxm3dl/+ctf5stf/vKE2x966CEeeuih2T7tZY0wtiDtNlKFLnZ1F3Cl61WgKAaOdDkxPMTJ4SG21EdIBoMI44Zxj9MUi/OB1Wv5SdshJFAVCqMp3sSmwUKegKbxyPoNU0oZvBsCmsaDK1fze8/+lI50iqgRIBEIUZSg6SptA/0c6u/jg6vXETEMOtIpEsEQIU3DkZK2fB2B6F7WV4epn2KKlJQ2uAMQev+CbPMXgS2sqNzLW90pCjaEtPEdfrpUpCZUpCGxck5qsb3vVyvSTXsa/BPgVZF0Q+Cu2asG8lkwLLxfyFWIUKLk1Pext2eQSmOIyoBAK0+nV4VCZUBQHRhmf28fWeV9k/5gV1dV85lrt3LLosUUbZv+vBcy27F0OZ++ZgtNsYn3nS1ePnOKkmPTEIsjhKBoWxQdB8txiRoBaiIR9vZ089Caddy3YhWGojCQz5MzTdbVLmf94n/HopjuTfiZYEi6lEVwuiBwB4q+es5f08UglEoi8Ue5rj5BRM0wVPReo+k4mI5NulQiVczSFMmzvnYpemRuarGFkkCEPgzYSKe7vHo/h5QS6Q6D0wnGbQj92lm3wWf+8TtoFwitgzZvp+9kdWKIGr2VsNKHlAoCF4swvdaNHE5Xog3Y3DJFr1AiGOTm5sXc3Dy2q3OuyVsWzx47Sn0kRsQwsByHom2TyWZIxuOENB0hBO2pYXa2n+H9a9ayYZwuV+l8FFn6JTidSNSyVpAATE8gTkQh+CCKsbATakJbRCTxKbYEXyGdO0BPLkvOlijAoohGTSROOHwHwrhxTgeTCLUOwh9HWnvAfMdz+ALOztNFXYII3j9ug5jPlYHv7BcI73R3EQ8mGHKqGXKWY4gcCjYuKqb09NkTIYe3u7q4pXnh/iBb+3rJmCWay+Jj3vBvFbdYJHxe3DoaCPLiqRO8f834zlqoNYjwY0inD2kd9HIVuCCiCH0tqM2XzTAPoVYjwh8gEbyTeOJEuTFLBZFE6EsvWchEKHFE4HakcaN3VyRLIFSvIWieh2H7zD2+s18g5C2Tav2sbKqCKceO49MVhYJtI6VcMI0aF5K3rEmlAc5iqApZa+xc1gsRag1CvTKS+EKJe402822HMMBfwV91+DH7BULUCGA6zqTPMR2HqKEvWEcPnuqlRE4Yaz9LybGJG34S0MfnUuE7+wXCtQ2NDBUKkz5noJDnmvqFVWJ4IWtrakgEg+TKNfUlxyZVKpK1LLJmaeQikCuZ3Ll06XyaeslxXG8e7sG+Xlr7eunJZufbJJ+rCD+Ms0BYU1XN62dOky4ViY/TSJQ1SxiKyvqaWtKlIgf7+jiTHqars4tsPMaa6poxXavzQVDTuX/FKv7pnbdRFUHB9pp5SqUSKemiCgVNFdSEI9y2gHMPs4mUkn29Pbx65jQZ08SbMODJNddHotyxpIUlSX9giM/c4q/sFwgRw+DD6zZgOy4d6RR5y8JxXQqWRWcmTdG2eXjdelr7+/jG7l28euYUg4UiKdPkpdMn+cZbb/JOd9d8vwwArm9cRFjX6MvncKVLQFMJKAqaUMhZJVKFEtc3NhGZAyG2hYaUkhdOnuCnR9oIaCqL4nEWxRM0xRMsiifI2xbfObCPQ/19822qzxWOv7K/gP6OAfo7Bunq6WXF8hXoxtyP8jtLXTTKr11zHYcH+nm7u5OBQp6IbrBtcQtramo5NjjA8yeP0xiLj0xfyuk6DbE4luPws6NHCKgqa+dIq346FG2Lf2s7xF3LVtCVzbC/t4fBfJ6ibRHVdTbW1LG+to5UqcRbXZ1c37Ro6oNexhwZHGBnRzuL4vFxB8bEA0ECqsZPjhymLhKd04Y3n6sb39mfxxs/fZt9vzqIEIL+/n6Gjma479N3EklczHDhiyNiGFzX0Mh1DaNj86bj8OLpk9RFouOO2dNVlZpIhBdOnWBVVfWcTqKajKODg5Rsm+pwmFggwIrKKkq2TVd3N82NjehlTZ6QrvNGZwfXNjTO6ZjE+WZnxxkqQqFJP4+ApiGAA3093La45ZLZ5nN1ceX+ymZIX/sA+37VSm1zNXVLaqhsrCA7lOOdFw7Mt2kAtKdTlGybwCTa7iFdJ2tadGTSl9Cy0bzV1TlK3EwRgpCuE9K0EUcPnoPLWyad82jrXDOQz9OTy01L9bI6HOGtsgS0j89c4Dv7Mv0dAwgBinruLUnUxjl9sH0erTpHplSaVv26QJIzp65fnyumNWykjBDnErhXIkXbntZnBt6dmenaU5bf+vhcLL6zLxOOjY2VmgWTaMWlC+FMhq6q0171ndXVmQ90RcFx3ek9WYK2gHsG3i2qcrbmZmpc6U1mv5JDWj7zix+zL9OwvJ5YVYz+jkGSNXEKmSIUBDe/f+t8mwZAY3koiSvlKFng83FcFyEEDbGx3beXipVVVezt6aYuOrkNrpSAnHRm7qVCStfT4HFOgiwQ1vqQdhTUpmmrabpScjo1zMnhYUzHJqzrLIon0Muqo1PND0gVi7QkKuYt1+Jz5eM7+zJGQOe9n9nBOy/8/9u796CozvOB499ddhcWWK4LBhUUjHgNTaQ/L7+YaNBptJiaaIxAo40mqbGmNjbJBDNOmjRJU9OxnXZGFGOrCcnEBKpNpp3aaWIamibe6xW8EfAnF0UQWBb2fs7vD3QVFSQMuofs8/lLzrvseTi883h49z3Pc5TTR6sJjTBxf959JI/oWSPwmy0mzEx6fDwVTRe4rYtEeq7NzpiExJvSW7an7ki8jX21tfgUpdvE1djezoj4hB53cbpZVO8ZVOcneLznaXH6cCk6HL4mXK3VmIxxqKHT0RuHdvseJxsb2VFVgc3pwmQIIUSnx6sofFV9hlani0aHg5HWrnvXqqpKq8vFzOHpffzTCXGZJPsrRERHcPfs8dw9ezzl5eWaSfSXTEsdRpPTQbWthYTwCP+HtU6vl4a2NgZaLEwdGtinUhMiIsgcOIg9tdUMslx/u2GL04kOuDvl1lflvJLiqUBxbOPrZh9VNg+K2vGBcmurjtNeN0MtFxgWUwzhs9Ebr5+Ij9Sf428nj2MNj2Bw9LUdphoMbeyqrsao1zMs7tq+sqqqUtNqY6TVypCLxeOEuBkk2fcjESYT88fcwYGzZ9lbW0Ojo50Gp4Mwj4epQ1PJGHBbt7t1bpUpQ4aiA/bW1WDQ6YkOC8Ot+GhxOml1u4gKDSVnbAZx5ptX0vdGVKUVnH+jvNFHTauH6LCwy8tjThcWUxiVNicOr5E7Ev6GGpJ4TV/YhvZ2tp86yW2Rli6XaazhEUxKTmZvbQ36i9ciwmhEpaNpidvnY2R8AvffPrzL5Tkh+kLgM4P4RsIMRiYOTua7Awdhd7s4ceIEmWPv0NRab4hez32paWQMuI0j9eeoaLqAV1GIM5uZljqMoTExnbZhBoLqKafFaaemVSHWHAZX7ZrR63TEhoVxts1BskUhNrQMXej/dnrNoXN1GPS6G67Hx5nDGWVNJCNxAA5fR0MZvU7HKGsC3xmQxIAbdOQSoi9Isu+nDHo9MWFmLEaTphL9leLDw5kyNJUpQ1MpLy9n1ChtNBpRVRU8+6myhRBq0HF1or9MR5jRSGULxJr/i2qa4K+h7/b5OHjuLNbwnu3WsoaHc6bVxpPjtPGBvwg+2swSQtxUHd2uGp0+wgzdl8MIMxi44PKC6gbV6T/u8HhQVLXHWyXNRiMtTufFXUhC3HqS7EUQ6tj//o1WyP0t/C5+2YvldUnzIpAk2YsgZARdFInhetpv0C2r3eMhwWzs6IF7RftAs8GIUR/S4yde7W43CeHh8iGsCBhJ9iLo6HQ6MH2XFAt4fD7ULu65VVTcXi9Do1QwZnZ6wMoYEkJm0kAa29t6dM4Wp4Px3/IKn0LbJNmLoKQzpGMJtZAWE0qTw4FP7VziwacqNDkcDIk2ER0Wgc448pr3GJOY2FHf52JXrq7YXE4iTCaGxUpTbxE4kuxFUNLpw9GFP8TtsaGMtRpxeDw0Ox00uxy0et04PG5GxxsZEWtCZ34Qnf7a7ZExYWZmjxjFBYeDZqfjmtpFiqrS0N6Gy+tjzsgxmngGQgQvmX0iaOlCBkJ4HoNCSkmKqKTV7cKr6mg1+UgZEIreOARd6L3oQgZ0+R5psXHk3pHB51WV/nLNIfqOcgk6dKTFxjJlSCrx4YF7gEwIkGQvgpwuJAFd+Fx0ygWivbWgOmhtriPEMhmdvmfLLoMsUeTd8R3Ot7VR22rD7fNhNhoZHBVFTJh0nhLaIMleCECnj0Nn6kjuLl95jxP9lRIiIkiI0EZJbCGuJmv2QggRBCTZCyFEEJBk34+5fT68Pe0KJYQIarJm3894FYWTjQ3srqnmfHsbDQ2NjPG5+Z+BgxkeF6/ZomhCiMCSZN+PuLxePj5+jK+bLxBnNjMoKhp9WzvtHi8fHS9nZLyV7w8fEfDywUII7enzZO/xeHjxxRepqanB7XazdOlSpk2b5h/fsWMHa9euxWAwMHfuXB555JG+DuFb6/PTlZxuaSLlqo5GUaGhWEwmjjc2EB1mDni3KiGE9vR5sv/444+JiYnhN7/5Dc3NzTz44IP+ZO/xeHjjjTcoKSnBbDaTm5tLVlYWVqu1r8P41rG73RyuP9dl/9mORuNR/LeulomDB9+wdK8QIrj0+QLvjBkz+NnPfgZ0NIkIuWJJoaKigpSUFKKjozGZTGRmZrJnz56+DuFb6YytBUVVu12TN+j1+FSFapvtFkYmhOgP+vzOPuLiQyV2u53ly5fzzDPP+MfsdjsWi6XTa+12e5fvVV5e3tfh9ZjT6Qzo+a92oqWZxoZGQtraOx33eDzU1dX5v25wODh+6hQeS/2tDrFbWrue3ekvsUqcfau/xAm9i/WmfEBbV1fHsmXLyMvL44EHHvAfj4yMpK3tcknYtra2Tsn/aoFsY6elNnoAhsZGTig+kqKiOh2vq6sjKSnJ/7XS0sLo9HRSY2JvdYjd0tr17E5/iVXi7Fv9JU7oOtZ9+/Z1+T19vozT0NDA4sWLef7553n44Yc7jQ0bNozTp0/T3NyM2+1m79693HXXXX0dwrdSSnQ0Br0OTzfNMtw+H6FGI4MtUV2+RggRnPr8zn79+vXYbDYKCgooKCgAYN68eTgcDubPn09+fj6PP/44qqoyd+5cBgzouqKguCzUYGDSoGRK/6+qY8vlVR2PfIrC2dZWpqcNk62XQohr9HmyX7VqFatWrepyPCsri6ysrL4+bVAYPziZNq+HfXW1hIUYiAoNxe3zUW+34/L5mJSczLikgYEOUwihQfJQVT+i1+nIGprGaGsiB8/VccbWgk9VGZ2QSMaA2xgQeW2DDSGEAEn2/U7HfnoLSRc/2C4vL2fUsNsDHJUQQuukkIoQQgQBSfZCCBEEJNkLIUQQ0KmqqgY6iOvp7uEAIYQQ15eZmXnd45pN9kIIIfqOLOMIIUQQkGQvhBBBQPbZA42NjcyZM4c//elPDBs2zH988+bNFBcXExcXB8Arr7xCWlpaoMLkoYceIvLig1ODBw/mjTfe8I99+OGHbNmyBYPBwNKlS7nvvvsCFWa3cb722mvs37/fXx21oKCg22J4N1NhYSE7duzA4/GQm5vLvHnz/GNaarLTXZxamqNbt25l27ZtALhcLsrLy/nPf/5D1MXifVqZozeKUytz1OPxkJ+fT01NDXq9nldffbVTfvrGc1QNcm63W/3JT36ifu9731NPnTrVaezZZ59VDx8+HKDIOnM6ners2bOvO1ZfX6/OmjVLdblcqs1m8/87ELqLU1VVNScnR21sbLx1AXVh586d6pIlS1Sfz6fa7Xb1D3/4g3/M7Xar06dPV5ubm1WXy6XOmTNHPX/+vObiVFVtzdErvfzyy+qWLVv8X2tpjl7p6jhVVTtz9J///Ke6fPlyVVVV9YsvvlCffvpp/1hv5mjQL+OsXr2anJwcEhMTrxk7evQoGzZsIDc3l8LCwgBEd9mxY8dwOBwsXryYhQsXcuDAAf/YoUOHuOuuuzCZTFgsFlJSUjh27Jjm4lQUhdOnT/PSSy+Rk5NDSUlJQGIE+OKLL0hPT2fZsmU89dRTTJ061T+mpSY73cUJ2pqjlxw+fJhTp04xf/58/zEtzdFLrhenluZoamoqPp8PRVGw2+0YDJcXYnozR4N6GWfr1q3ExcVxzz33sGHDhmvGs7OzycvLIzIykqeffprPPvssYH96hoWF8fjjjzNv3jyqqqp48skn2b59OwaD4Rs3hQlUnO3t7Tz66KMsWrQIn8/HwoULGTt2LCNHjrzlcTY1NVFbW8v69euprq5m6dKlbN++HZ1Op6nr2V2coK05eklhYSHLli3rdExL1/SS68WppTkaHh5OTU0NM2fOpKmpifXr1/vHenM9g/rO/s9//jNffvklCxYsoLy8nBdeeIHz588DHS0Vf/SjHxEXF4fJZGLKlCmUlZUFLNbU1FR+8IMfoNPpSE1NJSYmxh/rN20KE6g4zWYzCxcuxGw2ExkZycSJEwN2dxcTE8PkyZMxmUykpaURGhrKhQsXAG1dz+7i1NocBbDZbFRWVjJx4sROx7V0TaHrOLU0Rzdv3szkyZP5xz/+wUcffUR+fj4ulwvo3fUM6mT/3nvv8e6771JUVMSoUaNYvXo1CQkJQMf/nLNmzaKtrQ1VVdm1axdjx44NWKwlJSX8+te/BuDcuXPY7XZ/rBkZGezbtw+Xy0VraysVFRWkp6drLs6qqipyc3Px+Xx4PB7279/PmDFjAhJnZmYm//73v1FVlXPnzuFwOIiJiQG01WSnuzi1NkcB9uzZw6RJk645rqU5Cl3HqaU5GhUV5U/g0dHReL1efBebF/VmjspDVRctWLCAl19+mbKyMtrb25k/fz5/+ctfKCoqwmQyMWnSJJYvXx6w+NxuNytXrqS2thadTsdzzz3HwYMHSUlJYdq0aXz44Yd88MEHqKrKkiVLuP/++zUZ58aNG/n73/+O0Whk9uzZ5ObmBiROgDfffJNdu3ahqiorVqygubnZ/7u/tNNBvdhk54c//KEm49TSHAXYuHEjBoOBxx57DIBNmzZpbo7eKE6tzNG2tjZefPFFzp8/j8fjYeHChQC9nqOS7IUQIggE9TKOEEIEC0n2QggRBCTZCyFEEJBkL4QQQUCSvRBCBAFJ9iKo5OfnU1paesNjvVVbW8uOHTuAju28FRUV3b6+qqqKNWvW9Pp877//Pl999VWvv18ED0n2QvShnTt3sn///h6/fvXq1SxatKjX55s3bx7r1q3zP2wjRFeCujaO0K7KykpWrlyJwWBAURTWrFlDUlISa9asYe/evSiKwmOPPcbMmTNZsGABqampVFZWoqoqv/vd74iLi+Oll17i7Nmz1NfXk5WVxYoVK7o9p8fj4Re/+AWnT59GURSeeeYZJkyYwAMPPMD48eM5fvw4Op2OgoICIiMjeeWVVzhy5AhWq5WamhrWrl3Lhg0bcDqd/qcZ165dS0NDAw6Hg9/+9rckJyf7z/f111+jqqq/PHFBQQGffPIJPp+P3NxcJk+ezIoVK0hKSqK6uprs7GxOnjxJWVkZU6dO5ec//zkGg4HRo0fzr3/9i2nTpt28X4jo9+TOXmjSl19+SUZGBps2beKnP/0pra2tfP7551RXV/P+++/zzjvvsH79emw2GwDjxo2jqKiImTNnUlhYSF1dHXfeeSd//OMfKSkpYcuWLTc8Z3FxMbGxsbz33nsUFBTwy1/+Euh4kjE7O5t3332XxMRESktL+fTTT2lubqakpIRf/epX1NXVodfr+fGPf8ysWbP8iXfKlCm888473HvvvWzfvr3T+fbs2cOIESMAKCsro7S0lOLiYoqLi6mqqkJVVc6cOcPrr79OYWEhv//978nPz6e4uLhTNcYRI0awe/fuPrnu4ttL7uyFJj388MO89dZbPPHEE1gsFlasWMGJEyc4evQoCxYsAMDr9VJTUwPgL2g1btw4duzYQUxMDIcPH2bnzp1ERkbidrtveM4TJ06wb98+Dh065H//S0XHRo8eDUBSUhIul4uamhruvPNOAOLi4rpsGHKpVo3VaqWhoaHTWFNTE/Hx8UDHXzIZGRmEhIQQEhJCfn4+1dXVJCcnY7FYMJlMWK1Wf12cS1UvARISEti5c+cNfz4R3OTOXmjSp59+SmZmJm+//TYzZsxg48aNpKWlMWHCBIqKinj77beZOXOmf1nkyJEjAOzfv5/bb7+drVu3YrFYWLNmDYsXL8bpdHKjyiBpaWlkZ2dTVFTEW2+9xYwZM66bXAGGDx/ur9Xf0tJCVVUVAHq9HkVRevQzxsfH+/8ySUtLo6ysDEVR8Hg8LFq0CLfbfc15r8dms/mXgoToitzZC00aO3YsL7zwAuvWrUNRFFauXMno0aPZvXs3eXl5tLe3M336dH/7w23btrF582bMZjNvvvkmDQ0NPPvssxw4cACTycSQIUOor6/v9pw5OTmsWrWKRx99FLvdTl5eHnr99e+Hpk6dSmlpKTk5OVitVsLCwjAajaSnp7Nu3boeVUocP348r7/+OgCjRo3innvuITc3F0VRyM3NxWQy9ehaHTx4kLvvvrtHrxXBSwqhiX7vUsXSK/tz3mwVFRUcO3aM7OxsmpqamDVrFp999lmPE/QlTz31FK+99hpWq7VXcXi9XhYtWsTmzZsJCQnp1XuI4CDLOEL0QlJSEn/961955JFHeOKJJ3juuee+caIHeP7559m0aVOv4/jggw9YsmSJJHpxQ3JnL4QQQUDu7IUQIghIshdCiCAgyV4IIYKAJHshhAgCkuyFECIISLIXQogg8P+wYNRO73LQvwAAAABJRU5ErkJggg==\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.datasets import load_iris\\n\",\n \"iris = load_iris()\\n\",\n \"features = iris.data.T\\n\",\n \"\\n\",\n \"plt.scatter(features[0], features[1], alpha=0.4,\\n\",\n \" s=100*features[3], c=iris.target, cmap='viridis')\\n\",\n \"plt.xlabel(iris.feature_names[0])\\n\",\n \"plt.ylabel(iris.feature_names[1]);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A full-color version of this plot is available in the [online version](http://github.com/jakevdp/PythonDataScienceHandbook) of the book.\\n\",\n \"\\n\",\n \"We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data:\\n\",\n \"the (*x*, *y*) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\\n\",\n \"Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## plot Versus scatter: A Note on Efficiency\\n\",\n \"\\n\",\n \"Aside from the different features available in `plt.plot` and `plt.scatter`, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, `plt.plot` can be noticeably more efficient than `plt.scatter`.\\n\",\n \"The reason is that `plt.scatter` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\\n\",\n \"With `plt.plot`, on the other hand, the markers for each point are guaranteed to be identical, so the work of determining the appearance of the points is done only once for the entire set of data.\\n\",\n \"For large datasets, this difference can lead to vastly different performance, and for this reason, `plt.plot` should be preferred over `plt.scatter` for large datasets.\"\n ]\n }\n ],\n \"metadata\": {\n \"jupytext\": {\n \"encoding\": \"# -*- coding: utf-8 -*-\",\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.03-Errorbars.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Visualizing Uncertainties\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For any scientific measurement, accurate accounting of uncertainties is nearly as important, if not more so, as accurate reporting of the number itself.\\n\",\n \"For example, imagine that I am using some astrophysical observations to estimate the Hubble Constant, the local measurement of the expansion rate of the Universe.\\n\",\n \"I know that the current literature suggests a value of around 70 (km/s)/Mpc, and I measure a value of 74 (km/s)/Mpc with my method. Are the values consistent? The only correct answer, given this information, is this: there is no way to know.\\n\",\n \"\\n\",\n \"Suppose I augment this information with reported uncertainties: the current literature suggests a value of 70 ± 2.5 (km/s)/Mpc, and my method has measured a value of 74 ± 5 (km/s)/Mpc. Now are the values consistent? That is a question that can be quantitatively answered.\\n\",\n \"\\n\",\n \"In visualization of data and results, showing these errors effectively can make a plot convey much more complete information.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Basic Errorbars\\n\",\n \"\\n\",\n \"One standard way to visualize uncertainties is using an errorbar. A basic errorbar can be created with a single Matplotlib function call, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = np.linspace(0, 10, 50)\\n\",\n \"dy = 0.8\\n\",\n \"y = np.sin(x) + dy * np.random.randn(50)\\n\",\n \"\\n\",\n \"plt.errorbar(x, y, yerr=dy, fmt='.k');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here the `fmt` is a format code controlling the appearance of lines and points, and it has the same syntax as the shorthand used in `plt.plot`, outlined in the previous chapter and earlier in this chapter.\\n\",\n \"\\n\",\n \"In addition to these basic options, the `errorbar` function has many options to fine-tune the outputs.\\n\",\n \"Using these additional options you can easily customize the aesthetics of your errorbar plot.\\n\",\n \"I often find it helpful, especially in crowded plots, to make the errorbars lighter than the points themselves (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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FajEyJqJB+KERWWZGKKR8RTU1NmDVrVmCldW72h9M8H1dccQXOnDkz4f+DPtbITEb2sFVIP3hJxZSPKLdplKA49cifeOIJZY81E87CaDwje9giF8B0GkThRcrH7QVJnepyZdXYFuuRuz12vD7eTDkL043X+1WrgO0mSLg53dXt4C6W8hE9YNwGMp3qcb0eZek21eXH8WbKdKI68WO/apUS8arKQbcUg1PKZ8mSJUZM0m46P443FS+8m86P/apVwPaKbge3Uz71nXfe0eqLJ6z8ON5UvntNTU1N7sckfuxXBmyofXA7KXSxTrcvnrDy43hT+cK7KcPc82eg9GO/MmBD7YPbDR2/eNwyofLBj+PNlOlEVZY//4gf+1Wri45eEakqUVGQ9yP0Q7GLOoA++8+v482P6URNS2tUwo/9yoD9fybMlWvKF8+o/HI8k+bzkDmIKsj9rXtaQzav4wgDtmFM+OIZlV+OV2w+j3w6l7CV22vVrRyVKldRDvv9999HW1ubrLYQFeXnfB5BVjKUe1FOt3JUqpxwwN6wYQMee+wxZLNZme0hcuR0UeeHP/xhwf+v5GKrDpUMrAoKH+GAPWfOHHR3d0tsCplKVmWHjvN5eCkMVUE0Xskc9t69e7F79+5xj23cuBFLlizB4cOHPWsYqaHSlECxPOucOXNcL0/mfB66M60qyI/0k+5VLRHbtm3RFx8+fBh79uzBtm3bJjxnWdaEXk+50uk0YrGYaLNyMplM7vdoNCr9/2W2SWSbnZblx3aU26aFCxdiYGBgwv/V1tbi7bffRiwWc70dsh4v9Vwhlb7nY/ezjP20b98+bNu2LfdF1draiqamJtft8pKsz7NXSr2HTrz6/KVSKTQ0NBR8ztMqkXg8LvS6/v5+4deO1dfX56otbv9fZptEttlpWX5sR7ltcroN2ODgIGKxGOLxuOvtkPV4qecKqfQ9H7ufZeyneDyOxYsX5/7OrwoK8lgYJevz7IVkMonu7u5xZ4Dd3d2YNWtWyTM0rz5/lmU5PseRjuQp5llJZbpV2lQUsH/xi18UTIcQjTJl2D+ZSbdKG/awSar8+mXOaUEq0+0MkCMdfab7VepSCtUtmzT6ksyiW6UNA7bPVB6IEUamf4FScbrNv8OATYELMmjyC5R0OgNkDpsC5+UwcNnzZ5t6txTSg9E9bH6ows2L2eycvlR4rJEfjA7YPN0NNz/vHM5jjfzAlAgZS7caW6JSjO5hm46n4cXV1dXhxIkTBR8n0hF72BrTYc7mIHGUJXklqJtBs4dNxtKtxtYLPAuTL8hbszFgj2HKwW3Cdsi6uazKNbZ+7CeefclX6mJ2TU0NTp8+jenTp0tfNwP2GKYc3Lpvh2k3lx179/exdN9PYVXqYvaMGTPwxRdfeLJ/mcMm5eg25WUpw8PDuR/SX5ATRjFgk3JYjkcqC/JiNgM2KadUD4bDwylIQU4ZzBw2KafUlJfM/VLQgrqYzYBNyhEtx2OPm0zHgE1KEunBhLXnzS+q8GDAJtJcWL+owogXHYmINMGATUSkCaZEyBfMsxJVTihgf/3112hvb8e5c+cwMjKChx56CD/72c9kt40MwjwrUeWEAvZzzz2HX/7yl1i+fDn++9//oq2tDa+//rrsthER+UKXM0ChgL18+XJMmjQJAJDJZHD55ZdLbRQRkZ90OQOM2LZtF/uHvXv3Yvfu3eMe27hxI+rr6zE8PIzf/e53WLduHW688cZx/2NZ1oTx9uVKp9OIxWJCr9UVt3miTCaT+z0ajQqvR9ZyZKx/ZGSE+9kQxY6rSrY5lUqhoaGh4HMlA7aTY8eO4YEHHsCDDz6I+fPnT3jesizHlZbS39+PeDwu9FpdcZsn6uvry/1eydBfWcuRsf5oNMr9bIhix1Ul21wsdgqlRP7zn/9g9erVePzxx/HTn/5UqFFEROSOUMB+7LHHcOHChdxkPFOmTMHTTz8ttWFERDSeUMBmcCYi8h9HOhIRaYIjHcl4utTYEpXCgE3G06XGlqgUpkSIPJRMJtHY2Ij6+no0NjZi3759QTeJNMaATeSRZDKJFStWYGBgALZtY2BgAF1dXUgmk0E3jTTFgE3kkY6OjnH3pQQujYDr6OgIqEUkUxA3g2YOm8gjn332mavHSS9BXBthD5vII3V1da4eJyqFAZuUFcQpp0w9PT0TJkCLxWK5EcJEbjElQsrSvRwvkUgAANrb2zE4OIiZM2eitbU19ziRWwzYRB5KJBK4/vrrc38HMb0rmYMpESIiTTBgExFpggGbiEgTDNhERJpgwCYi0gQDNhGRJhiwiYg0wYBNRKQJBmwiIk0wYBMRaYIBm4hIEwzYRESaEJr8KZVKoa2tDWfPnsX3vvc9bNmyRfuZ1YiIVCfUw37llVdw7bXXIplM4rbbbsOuXbtkt4uIiPII9bCXL1+OTCYDADh16hSmTp0qtVFERDRRxLZtu9g/7N27F7t37x732MaNG1FfX497770X//73v/Hcc88hHo+P+x/LsibcbaNc6XQasVhM6LW64jaba7RzAwAjIyOh2OaxwrKfx6pkm1OpFBoaGgo+VzJgl3L8+HH8/ve/x4EDB8Y9blmW40pL6e/vn/AFYDpus7n6+vpyv0ej0VBs81hh2c9jVbLNxWKnUA57586deOONNwAAkydP5l00iIh8IJTDvvPOO7F27Vq8+uqryGQy2Lhxo+x2ERFRHqGAPX36dDz77LOy20JkpLF3ff/iiy8CbAnpjjfhJfLY2DEKDNhUCY50JCLSBAM2EZEmGLCJiDTBgE1EpAkGbCIiTTBgExFpggGbiEgTDNhERJqoePInJ5ZlebFYIiLjeTZbHxER+YMpESIiTTBgExFpQqmAnc1m0dXVhbvvvhstLS04ceJE0E3y3MjICNrb29Hc3IylS5figw8+CLpJvjhz5gzmz5+P48ePB90U3+zcuRN33303fvvb32Lv3r1BN8dTIyMjaGtrw7Jly9Dc3Gz8fv7444/R0tICADhx4gTuueceNDc349FHH0U2m5W2HqUC9oEDB3DhwgW8/PLLaGtrw+bNm4NukufeeustTJs2DS+99BKeeeYZ/OlPfwq6SZ4bGRlBV1dXqG4bdfjwYfzrX//CX//6V7zwwgsYHBwMukme+uijj3Dx4kXs2bMHK1euxOOPPx50kzyza9cudHZ24ttvvwUAbNq0CWvWrMFLL70E27aldsKUCtiWZWHevHkAgBtuuGHcrZVMdcstt2D16tUAANu2Q3H3ni1btmDZsmW46qqrgm6Kb/7+979j9uzZWLlyJf7whz9gwYIFQTfJUz/5yU+QyWSQzWZx7tw5XHaZuTM519XV4cknn8z9ffToUdx4440AgF/96lc4dOiQtHUp9S6eO3cOU6ZMyf0djUZx8eJFo3f25MmTAVza9lWrVmHNmjXBNshjr732Gq688krMmzcPvb29QTfHN19++SVOnTqFHTt24OTJk7j//vvx7rvvIhKJBN00T1RXV+Pzzz/H4sWL8eWXX2LHjh1BN8kzixYtwsmTJ3N/27ad26+TJ0/G119/LW1dSvWwp0yZgvPnz+f+zmazRgfrUQMDA7j33ntx++2349Zbbw26OZ569dVXcejQIbS0tKC/vx9r167F8PBw0M3y3LRp0zB37lxMmjQJV199NS6//HKjb2bw/PPPY+7cuXjvvffw5ptv4qGHHsqlDExXVfVdWD1//jymTp0qb9nSliTBnDlzcPDgQQDAkSNHMHv27IBb5L3Tp0/jvvvuQ3t7O5YuXRp0czyXTCbx4osv4oUXXkA8HseWLVvG3ULLVA0NDfjb3/4G27YxNDSEb775BtOmTQu6WZ6ZOnUqvv/97wMAfvCDH+DixYvIZDIBt8of11xzDQ4fPgwAOHjwIH7+859LW7ZS3debb74Z//jHP7Bs2TLYth2Km/vu2LEDZ8+exfbt27F9+3YAly5ihOmCXBj8+te/xj//+U8sXboUtm2jq6vL6OsVy5cvx7p169Dc3IyRkRG0traiuro66Gb5Yu3atXjkkUfw5z//GVdffTUWLVokbdkc6UhEpAmlUiJEROSMAZuISBMM2EREmmDAJiLSBAM2EZEmGLCJiDTBgE1EpAkGbCIiTfwPB+Fq5t46wVcAAAAASUVORK5CYII=\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.errorbar(x, y, yerr=dy, fmt='o', color='black',\\n\",\n \" ecolor='lightgray', elinewidth=3, capsize=0);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In addition to these options, you can also specify horizontal errorbars, one-sided errorbars, and many other variants.\\n\",\n \"For more information on the options available, refer to the docstring of `plt.errorbar`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Continuous Errors\\n\",\n \"\\n\",\n \"In some situations it is desirable to show errorbars on continuous quantities.\\n\",\n \"Though Matplotlib does not have a built-in convenience routine for this type of application, it's relatively easy to combine primitives like `plt.plot` and `plt.fill_between` for a useful result.\\n\",\n \"\\n\",\n \"Here we'll perform a simple *Gaussian process regression*, using the Scikit-Learn API (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb) for details).\\n\",\n \"This is a method of fitting a very flexible nonparametric function to data with a continuous measure of the uncertainty.\\n\",\n \"We won't delve into the details of Gaussian process regression at this point, but will focus instead on how you might visualize such a continuous error measurement:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.gaussian_process import GaussianProcessRegressor\\n\",\n \"\\n\",\n \"# define the model and draw some data\\n\",\n \"model = lambda x: x * np.sin(x)\\n\",\n \"xdata = np.array([1, 3, 5, 6, 8])\\n\",\n \"ydata = model(xdata)\\n\",\n \"\\n\",\n \"# Compute the Gaussian process fit\\n\",\n \"gp = GaussianProcessRegressor()\\n\",\n \"gp.fit(xdata[:, np.newaxis], ydata)\\n\",\n \"\\n\",\n \"xfit = np.linspace(0, 10, 1000)\\n\",\n \"yfit, dyfit = gp.predict(xfit[:, np.newaxis], return_std=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We now have `xfit`, `yfit`, and `dyfit`, which sample the continuous fit to our data.\\n\",\n \"We could pass these to the `plt.errorbar` function as in the previous section, but we don't really want to plot 1,000 points with 1,000 errorbars.\\n\",\n \"Instead, we can use the `plt.fill_between` function with a light color to visualize this continuous error (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Visualize the result\\n\",\n \"plt.plot(xdata, ydata, 'or')\\n\",\n \"plt.plot(xfit, yfit, '-', color='gray')\\n\",\n \"plt.fill_between(xfit, yfit - dyfit, yfit + dyfit,\\n\",\n \" color='gray', alpha=0.2)\\n\",\n \"plt.xlim(0, 10);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Take a look at the `fill_between` call signature: we pass an x value, then the lower *y*-bound, then the upper *y*-bound, and the result is that the area between these regions is filled.\\n\",\n \"\\n\",\n \"The resulting figure gives an intuitive view into what the Gaussian process regression algorithm is doing: in regions near a measured data point, the model is strongly constrained, and this is reflected in the small model uncertainties.\\n\",\n \"In regions far from a measured data point, the model is not strongly constrained, and the model uncertainties increase.\\n\",\n \"\\n\",\n \"For more information on the options available in `plt.fill_between` (and the closely related `plt.fill` function), see the function docstring or the Matplotlib documentation.\\n\",\n \"\\n\",\n \"Finally, if this seems a bit too low-level for your taste, refer to [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), where we discuss the Seaborn package, which has a more streamlined API for visualizing this type of continuous errorbar.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.04-Density-and-Contour-Plots.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Density and Contour Plots\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sometimes it is useful to display three-dimensional data in two dimensions using contours or color-coded regions.\\n\",\n \"There are three Matplotlib functions that can be helpful for this task: `plt.contour` for contour plots, `plt.contourf` for filled contour plots, and `plt.imshow` for showing images.\\n\",\n \"This chapter looks at several examples of using these. We'll start by setting up the notebook for plotting and importing the functions we will use: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-white')\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Visualizing a Three-Dimensional Function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Our first example demonstrates a contour plot using a function $z = f(x, y)$, using the following particular choice for $f$ (we've seen this before in [Computation on Arrays: Broadcasting](02.05-Computation-on-arrays-broadcasting.ipynb), when we used it as a motivating example for array broadcasting):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def f(x, y):\\n\",\n \" return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A contour plot can be created with the `plt.contour` function.\\n\",\n \"It takes three arguments: a grid of *x* values, a grid of *y* values, and a grid of *z* values.\\n\",\n \"The *x* and *y* values represent positions on the plot, and the *z* values will be represented by the contour levels.\\n\",\n \"Perhaps the most straightforward way to prepare such data is to use the `np.meshgrid` function, which builds two-dimensional grids from one-dimensional arrays:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"x = np.linspace(0, 5, 50)\\n\",\n \"y = np.linspace(0, 5, 40)\\n\",\n \"\\n\",\n \"X, Y = np.meshgrid(x, y)\\n\",\n \"Z = f(X, Y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's look at this with a standard line-only contour plot (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.contour(X, Y, Z, colors='black');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that when a single color is used, negative values are represented by dashed lines and positive values by solid lines.\\n\",\n \"Alternatively, the lines can be color-coded by specifying a colormap with the `cmap` argument.\\n\",\n \"Here we'll also specify that we want more lines to be drawn, at 20 equally spaced intervals within the data range, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.contour(X, Y, Z, 20, cmap='RdGy');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we chose the `RdGy` (short for *Red–Gray*) colormap, which is a good choice for divergent data: (i.e., data with positive and negative variation around zero).\\n\",\n \"Matplotlib has a wide range of colormaps available, which you can easily browse in IPython by doing a tab completion on the `plt.cm` module:\\n\",\n \"```\\n\",\n \"plt.cm.\\n\",\n \"```\\n\",\n \"\\n\",\n \"Our plot is looking nicer, but the spaces between the lines may be a bit distracting.\\n\",\n \"We can change this by switching to a filled contour plot using the `plt.contourf` function, which uses largely the same syntax as `plt.contour`.\\n\",\n \"\\n\",\n \"Additionally, we'll add a `plt.colorbar` command, which creates an additional axis with labeled color information for the plot (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.contourf(X, Y, Z, 20, cmap='RdGy')\\n\",\n \"plt.colorbar();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The colorbar makes it clear that the black regions are \\\"peaks,\\\" while the red regions are \\\"valleys.\\\"\\n\",\n \"\\n\",\n \"One potential issue with this plot is that it is a bit splotchy: the color steps are discrete rather than continuous, which is not always what is desired.\\n\",\n \"This could be remedied by setting the number of contours to a very high number, but this results in a rather inefficient plot: Matplotlib must render a new polygon for each step in the level.\\n\",\n \"A better way to generate a smooth representation is to use the `plt.imshow` function, which offers the `interpolation` argument to generate a smooth two-dimensional representation of the data (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower', cmap='RdGy',\\n\",\n \" interpolation='gaussian', aspect='equal')\\n\",\n \"plt.colorbar();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are a few potential gotchas with `plt.imshow`, however:\\n\",\n \"\\n\",\n \"- It doesn't accept an *x* and *y* grid, so you must manually specify the *extent* [*xmin*, *xmax*, *ymin*, *ymax*] of the image on the plot.\\n\",\n \"- By default it follows the standard image array definition where the origin is in the upper left, not in the lower left as in most contour plots. This must be changed when showing gridded data.\\n\",\n \"- It will automatically adjust the axis aspect ratio to match the input data; this can be changed with the `aspect` argument.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, it can sometimes be useful to combine contour plots and image plots.\\n\",\n \"For example, here we'll use a partially transparent background image (with transparency set via the `alpha` parameter) and overplot contours with labels on the contours themselves, using the `plt.clabel` function (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"contours = plt.contour(X, Y, Z, 3, colors='black')\\n\",\n \"plt.clabel(contours, inline=True, fontsize=8)\\n\",\n \"\\n\",\n \"plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',\\n\",\n \" cmap='RdGy', alpha=0.5)\\n\",\n \"plt.colorbar();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The combination of these three functions—`plt.contour`, `plt.contourf`, and `plt.imshow`—gives nearly limitless possibilities for displaying this sort of three-dimensional data within a two-dimensional plot.\\n\",\n \"For more information on the options available in these functions, refer to their docstrings.\\n\",\n \"If you are interested in three-dimensional visualizations of this type of data, see [Three-dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"encoding\": \"# -*- coding: utf-8 -*-\",\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.05-Histograms-and-Binnings.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Histograms, Binnings, and Density\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A simple histogram can be a great first step in understanding a dataset.\\n\",\n \"Earlier, we saw a preview of Matplotlib's histogram function (discussed in [Comparisons, Masks, and Boolean Logic](02.06-Boolean-Arrays-and-Masks.ipynb)), which creates a basic histogram in one line, once the normal boilerplate imports are done (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-white')\\n\",\n \"\\n\",\n \"rng = np.random.default_rng(1701)\\n\",\n \"data = rng.normal(size=1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(data);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `hist` function has many options to tune both the calculation and the display; \\n\",\n \"here's an example of a more customized histogram, shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(data, bins=30, density=True, alpha=0.5,\\n\",\n \" histtype='stepfilled', color='steelblue',\\n\",\n \" edgecolor='none');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `plt.hist` docstring has more information on other available customization options.\\n\",\n \"I find this combination of `histtype='stepfilled'` along with some transparency `alpha` to be helpful when comparing histograms of several distributions (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x1 = rng.normal(0, 0.8, 1000)\\n\",\n \"x2 = rng.normal(-2, 1, 1000)\\n\",\n \"x3 = rng.normal(3, 2, 1000)\\n\",\n \"\\n\",\n \"kwargs = dict(histtype='stepfilled', alpha=0.3, density=True, bins=40)\\n\",\n \"\\n\",\n \"plt.hist(x1, **kwargs)\\n\",\n \"plt.hist(x2, **kwargs)\\n\",\n \"plt.hist(x3, **kwargs);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you are interested in computing, but not displaying, the histogram (that is, counting the number of points in a given bin), you can use the `np.histogram` function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 23 241 491 224 21]\\n\"\n ]\n }\n ],\n \"source\": [\n \"counts, bin_edges = np.histogram(data, bins=5)\\n\",\n \"print(counts)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Two-Dimensional Histograms and Binnings\\n\",\n \"\\n\",\n \"Just as we create histograms in one dimension by dividing the number line into bins, we can also create histograms in two dimensions by dividing points among two-dimensional bins.\\n\",\n \"We'll take a brief look at several ways to do this here.\\n\",\n \"We'll start by defining some data—an `x` and `y` array drawn from a multivariate Gaussian distribution:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"mean = [0, 0]\\n\",\n \"cov = [[1, 1], [1, 2]]\\n\",\n \"x, y = rng.multivariate_normal(mean, cov, 10000).T\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### plt.hist2d: Two-dimensional histogram\\n\",\n \"\\n\",\n \"One straightforward way to plot a two-dimensional histogram is to use Matplotlib's `plt.hist2d` function (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist2d(x, y, bins=30)\\n\",\n \"cb = plt.colorbar()\\n\",\n \"cb.set_label('counts in bin')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just like `plt.hist`, `plt.hist2d` has a number of extra options to fine-tune the plot and the binning, which are nicely outlined in the function docstring.\\n\",\n \"Further, just as `plt.hist` has a counterpart in `np.histogram`, `plt.hist2d` has a counterpart in `np.histogram2d`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(30, 30)\\n\"\n ]\n }\n ],\n \"source\": [\n \"counts, xedges, yedges = np.histogram2d(x, y, bins=30)\\n\",\n \"print(counts.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the generalization of this histogram binning when there are more than two dimensions, see the `np.histogramdd` function.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### plt.hexbin: Hexagonal binnings\\n\",\n \"\\n\",\n \"The two-dimensional histogram creates a tesselation of squares across the axes.\\n\",\n \"Another natural shape for such a tesselation is the regular hexagon.\\n\",\n \"For this purpose, Matplotlib provides the `plt.hexbin` routine, which represents a two-dimensional dataset binned within a grid of hexagons (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hexbin(x, y, gridsize=30)\\n\",\n \"cb = plt.colorbar(label='count in bin')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`plt.hexbin` has a number of additional options, including the ability to specify weights for each point and to change the output in each bin to any NumPy aggregate (mean of weights, standard deviation of weights, etc.).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Kernel density estimation\\n\",\n \"\\n\",\n \"Another common method for estimating and representing densities in multiple dimensions is *kernel density estimation* (KDE).\\n\",\n \"This will be discussed more fully in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb), but for now I'll simply mention that KDE can be thought of as a way to \\\"smear out\\\" the points in space and add up the result to obtain a smooth function.\\n\",\n \"One extremely quick and simple KDE implementation exists in the `scipy.stats` package.\\n\",\n \"Here is a quick example of using KDE (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from scipy.stats import gaussian_kde\\n\",\n \"\\n\",\n \"# fit an array of size [Ndim, Nsamples]\\n\",\n \"data = np.vstack([x, y])\\n\",\n \"kde = gaussian_kde(data)\\n\",\n \"\\n\",\n \"# evaluate on a regular grid\\n\",\n \"xgrid = np.linspace(-3.5, 3.5, 40)\\n\",\n \"ygrid = np.linspace(-6, 6, 40)\\n\",\n \"Xgrid, Ygrid = np.meshgrid(xgrid, ygrid)\\n\",\n \"Z = kde.evaluate(np.vstack([Xgrid.ravel(), Ygrid.ravel()]))\\n\",\n \"\\n\",\n \"# Plot the result as an image\\n\",\n \"plt.imshow(Z.reshape(Xgrid.shape),\\n\",\n \" origin='lower', aspect='auto',\\n\",\n \" extent=[-3.5, 3.5, -6, 6])\\n\",\n \"cb = plt.colorbar()\\n\",\n \"cb.set_label(\\\"density\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"KDE has a smoothing length that effectively slides the knob between detail and smoothness (one example of the ubiquitous bias–variance trade-off).\\n\",\n \"The literature on choosing an appropriate smoothing length is vast; `gaussian_kde` uses a rule of thumb to attempt to find a nearly optimal smoothing length for the input data.\\n\",\n \"\\n\",\n \"Other KDE implementations are available within the SciPy ecosystem, each with its own strengths and weaknesses; see, for example, `sklearn.neighbors.KernelDensity` and `statsmodels.nonparametric.KDEMultivariate`.\\n\",\n \"For visualizations based on KDE, using Matplotlib tends to be overly verbose.\\n\",\n \"The Seaborn library, discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb), provides a much more compact API for creating KDE-based visualizations.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"encoding\": \"# -*- coding: utf-8 -*-\",\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.06-Customizing-Legends.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Customizing Plot Legends\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Plot legends give meaning to a visualization, assigning meaning to the various plot elements.\\n\",\n \"We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib.\\n\",\n \"\\n\",\n \"The simplest legend can be created with the `plt.legend` command, which automatically creates a legend for any labeled plot elements (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = np.linspace(0, 10, 1000)\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"ax.plot(x, np.sin(x), '-b', label='Sine')\\n\",\n \"ax.plot(x, np.cos(x), '--r', label='Cosine')\\n\",\n \"ax.axis('equal')\\n\",\n \"leg = ax.legend()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But there are many ways we might want to customize such a legend.\\n\",\n \"For example, we can specify the location and turn on the frame (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ax.legend(loc='upper left', frameon=True)\\n\",\n \"fig\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can use the ``ncol`` command to specify the number of columns in the legend, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ax.legend(loc='lower center', ncol=2)\\n\",\n \"fig\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And we can use a rounded box (`fancybox`) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ax.legend(frameon=True, fancybox=True, framealpha=1,\\n\",\n \" shadow=True, borderpad=1)\\n\",\n \"fig\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For more information on available legend options, see the `plt.legend` docstring.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Choosing Elements for the Legend\\n\",\n \"\\n\",\n \"As we have already seen, by default the legend includes all labeled elements from the plot.\\n\",\n \"If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by `plot` commands.\\n\",\n \"`plt.plot` is able to create multiple lines at once, and returns a list of created line instances.\\n\",\n \"Passing any of these to `plt.legend` will tell it which to identify, along with the labels we'd like to specify (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))\\n\",\n \"lines = plt.plot(x, y)\\n\",\n \"\\n\",\n \"# lines is a list of plt.Line2D instances\\n\",\n \"plt.legend(lines[:2], ['first', 'second'], frameon=True);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x, y[:, 0], label='first')\\n\",\n \"plt.plot(x, y[:, 1], label='second')\\n\",\n \"plt.plot(x, y[:, 2:])\\n\",\n \"plt.legend(frameon=True);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that the legend ignores all elements without a `label` attribute set.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Legend for Size of Points\\n\",\n \"\\n\",\n \"Sometimes the legend defaults are not sufficient for the given visualization.\\n\",\n \"For example, perhaps you're using the size of points to mark certain features of the data, and want to create a legend reflecting this.\\n\",\n \"Here is an example where we'll use the size of points to indicate populations of California cities.\\n\",\n \"We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Uncomment to download the data\\n\",\n \"# url = ('https://raw.githubusercontent.com/jakevdp/PythonDataScienceHandbook/'\\n\",\n \"# 'master/notebooks/data/california_cities.csv')\\n\",\n \"# !cd data && curl -O {url}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"cities = pd.read_csv('data/california_cities.csv')\\n\",\n \"\\n\",\n \"# Extract the data we're interested in\\n\",\n \"lat, lon = cities['latd'], cities['longd']\\n\",\n \"population, area = cities['population_total'], cities['area_total_km2']\\n\",\n \"\\n\",\n \"# Scatter the points, using size and color but no label\\n\",\n \"plt.scatter(lon, lat, label=None,\\n\",\n \" c=np.log10(population), cmap='viridis',\\n\",\n \" s=area, linewidth=0, alpha=0.5)\\n\",\n \"plt.axis('equal')\\n\",\n \"plt.xlabel('longitude')\\n\",\n \"plt.ylabel('latitude')\\n\",\n \"plt.colorbar(label='log$_{10}$(population)')\\n\",\n \"plt.clim(3, 7)\\n\",\n \"\\n\",\n \"# Here we create a legend:\\n\",\n \"# we'll plot empty lists with the desired size and label\\n\",\n \"for area in [100, 300, 500]:\\n\",\n \" plt.scatter([], [], c='k', alpha=0.3, s=area,\\n\",\n \" label=str(area) + ' km$^2$')\\n\",\n \"plt.legend(scatterpoints=1, frameon=False, labelspacing=1, title='City Area')\\n\",\n \"\\n\",\n \"plt.title('California Cities: Area and Population');\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The legend will always reference some object that is on the plot, so if we'd like to display a particular shape we need to plot it.\\n\",\n \"In this case, the objects we want (gray circles) are not on the plot, so we fake them by plotting empty lists.\\n\",\n \"Recall that the legend only lists plot elements that have a label specified.\\n\",\n \"\\n\",\n \"By plotting empty lists, we create labeled plot objects that are picked up by the legend, and now our legend tells us some useful information.\\n\",\n \"This strategy can be useful for creating more sophisticated visualizations.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Multiple Legends\\n\",\n \"\\n\",\n \"Sometimes when designing a plot you'd like to add multiple legends to the same axes.\\n\",\n \"Unfortunately, Matplotlib does not make this easy: via the standard `legend` interface, it is only possible to create a single legend for the entire plot.\\n\",\n \"If you try to create a second legend using `plt.legend` or `ax.legend`, it will simply override the first one.\\n\",\n \"We can work around this by creating a new legend artist from scratch (`Artist` is the base class Matplotlib uses for visual attributes), and then using the lower-level `ax.add_artist` method to manually add the second artist to the plot (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots()\\n\",\n \"\\n\",\n \"lines = []\\n\",\n \"styles = ['-', '--', '-.', ':']\\n\",\n \"x = np.linspace(0, 10, 1000)\\n\",\n \"\\n\",\n \"for i in range(4):\\n\",\n \" lines += ax.plot(x, np.sin(x - i * np.pi / 2),\\n\",\n \" styles[i], color='black')\\n\",\n \"ax.axis('equal')\\n\",\n \"\\n\",\n \"# Specify the lines and labels of the first legend\\n\",\n \"ax.legend(lines[:2], ['line A', 'line B'], loc='upper right')\\n\",\n \"\\n\",\n \"# Create the second legend and add the artist manually\\n\",\n \"from matplotlib.legend import Legend\\n\",\n \"leg = Legend(ax, lines[2:], ['line C', 'line D'], loc='lower right')\\n\",\n \"ax.add_artist(leg);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is a peek into the low-level artist objects that comprise any Matplotlib plot.\\n\",\n \"If you examine the source code of `ax.legend` (recall that you can do this with within the Jupyter notebook using `ax.legend??`) you'll see that the function simply consists of some logic to create a suitable `Legend` artist, which is then saved in the `legend_` attribute and added to the figure when the plot is drawn.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.07-Customizing-Colorbars.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Customizing Colorbars\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Plot legends identify discrete labels of discrete points.\\n\",\n \"For continuous labels based on the color of points, lines, or regions, a labeled colorbar can be a great tool.\\n\",\n \"In Matplotlib, a colorbar is drawn as a separate axes that can provide a key for the meaning of colors in a plot.\\n\",\n \"Because the book is printed in black and white, this chapter has an accompanying [online supplement](https://github.com/jakevdp/PythonDataScienceHandbook) where you can view the figures in full color.\\n\",\n \"We'll start by setting up the notebook for plotting and importing the functions we will use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-white')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we have seen several times already, the simplest colorbar can be created with the `plt.colorbar` function (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = np.linspace(0, 10, 1000)\\n\",\n \"I = np.sin(x) * np.cos(x[:, np.newaxis])\\n\",\n \"\\n\",\n \"plt.imshow(I)\\n\",\n \"plt.colorbar();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll now discuss a few ideas for customizing these colorbars and using them effectively in various situations.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Customizing Colorbars\\n\",\n \"\\n\",\n \"The colormap can be specified using the `cmap` argument to the plotting function that is creating the visualization (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(I, cmap='Blues');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The names of available colormaps are in the `plt.cm` namespace; using IPython's tab completion feature will give you a full list of built-in possibilities:\\n\",\n \"\\n\",\n \"```\\n\",\n \"plt.cm.\\n\",\n \"```\\n\",\n \"But being *able* to choose a colormap is just the first step: more important is how to *decide* among the possibilities!\\n\",\n \"The choice turns out to be much more subtle than you might initially expect.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Choosing the Colormap\\n\",\n \"\\n\",\n \"A full treatment of color choice within visualizations is beyond the scope of this book, but for entertaining reading on this subject and others, see the article [\\\"Ten Simple Rules for Better Figures\\\"](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833) by Nicholas Rougier, Michael Droettboom, and Philip Bourne.\\n\",\n \"Matplotlib's online documentation also has an [interesting discussion](https://matplotlib.org/stable/tutorials/colors/colormaps.html) of colormap choice.\\n\",\n \"\\n\",\n \"Broadly, you should be aware of three different categories of colormaps:\\n\",\n \"\\n\",\n \"- *Sequential colormaps*: These are made up of one continuous sequence of colors (e.g., `binary` or `viridis`).\\n\",\n \"- *Divergent colormaps*: These usually contain two distinct colors, which show positive and negative deviations from a mean (e.g., `RdBu` or `PuOr`).\\n\",\n \"- *Qualitative colormaps*: These mix colors with no particular sequence (e.g., `rainbow` or `jet`).\\n\",\n \"\\n\",\n \"The `jet` colormap, which was the default in Matplotlib prior to version 2.0, is an example of a qualitative colormap.\\n\",\n \"Its status as the default was quite unfortunate, because qualitative maps are often a poor choice for representing quantitative data.\\n\",\n \"Among the problems is the fact that qualitative maps usually do not display any uniform progression in brightness as the scale increases.\\n\",\n \"\\n\",\n \"We can see this by converting the `jet` colorbar into black and white (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from matplotlib.colors import LinearSegmentedColormap\\n\",\n \"\\n\",\n \"def grayscale_cmap(cmap):\\n\",\n \" \\\"\\\"\\\"Return a grayscale version of the given colormap\\\"\\\"\\\"\\n\",\n \" cmap = plt.cm.get_cmap(cmap)\\n\",\n \" colors = cmap(np.arange(cmap.N))\\n\",\n \" \\n\",\n \" # Convert RGBA to perceived grayscale luminance\\n\",\n \" # cf. http://alienryderflex.com/hsp.html\\n\",\n \" RGB_weight = [0.299, 0.587, 0.114]\\n\",\n \" luminance = np.sqrt(np.dot(colors[:, :3] ** 2, RGB_weight))\\n\",\n \" colors[:, :3] = luminance[:, np.newaxis]\\n\",\n \" \\n\",\n \" return LinearSegmentedColormap.from_list(\\n\",\n \" cmap.name + \\\"_gray\\\", colors, cmap.N)\\n\",\n \" \\n\",\n \"\\n\",\n \"def view_colormap(cmap):\\n\",\n \" \\\"\\\"\\\"Plot a colormap with its grayscale equivalent\\\"\\\"\\\"\\n\",\n \" cmap = plt.cm.get_cmap(cmap)\\n\",\n \" colors = cmap(np.arange(cmap.N))\\n\",\n \" \\n\",\n \" cmap = grayscale_cmap(cmap)\\n\",\n \" grayscale = cmap(np.arange(cmap.N))\\n\",\n \" \\n\",\n \" fig, ax = plt.subplots(2, figsize=(6, 2),\\n\",\n \" subplot_kw=dict(xticks=[], yticks=[]))\\n\",\n \" ax[0].imshow([colors], extent=[0, 10, 0, 1])\\n\",\n \" ax[1].imshow([grayscale], extent=[0, 10, 0, 1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAV0AAABrCAYAAADU3FGzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAHR0lEQVR4nO3bvW4cORqF4UOyjAl2MLkSp7qCCR3OVS8mmsl0BQqcOBEcL3aBgYrkBEWW2GzWT0urbyHv+wBE8b/Y1d1Htgy7nLMAADb8//oAAPD/hNAFAEOELgAYInQBwBChCwCGpq2B+/v7nyT9KulJUjQ7EQB8bEHSnaSHx8fHv/rBzdDVErh/vNepAOAH90XSn33nXug+SdK3b79pnn/R8puIT+UaynUq9bZMzZzQzOnn+2787J71NyLu8rT9tkHXR/XdeGj6+mPXPjc4zmjN6L7W5xjeN5d6kkKSQpT3WWGKcj4t9RDlQ5QPWd4nBRfllBSU5JXkFBWUFbT0+1L3ivLK8kryiuv8lzXLHmfXtG1Xrsua9K5rQlnj1jWxzMmn1yz1dk0sZxmsyUk+ZYUY5VJSiMvb45OkWXJJUtby98u5qUdJqbmmjb65Gz+a3/eN1py5Z/4v7HHDOXNZk5IU03Kd56W+lmbbeoS522qrHZtjxa7M3ZGem/a/pkm/f/4slQzt7YVulKR5/ofm+Wct39xPug7D6Q3t166RrkI3l9IPu1L6/wNS+73GgXX08+M1Py/ee4/RnjV0Q3wJ3ZCUp3L1S5+mMu6TnF9CJysplwCtHylXAsStAfoSdKFp+9Juw66fc9R+zR6vv69v9nAKcmUPV9qXP+eWH0KujOfm8ecS3Hn9oXN135TkU1KIUT6+hG4o32bXhkyfGH34jvrOrtma06fQmT36FDraw5e6a67td7R+n+v3tO/PJXRruMblOsel1NCddRmafTtqCcw+UPfWnNmjGP5aln9IAwBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYInQBwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADA07YwFSZqmf5eql/SpXGt7KvW2TM2c0Mzp5/tu/Oye9eeEuz5t6KbV4q6nK5eSSjsNxuttUlPavWN3v/4MozNNzfp+TjvePxI3eCz9muF9c6knKSQpRHmfFaYo59NSD1E+RPmQ5X2Sd1FOaS3LC82SorKSsrKyotLaX+ekMp6U1rnLHl5ZSVFRUV5ZXkleUUGp1C/brlyDYhl7vzWhrHHrmljm5J01WUFZTnl97Mtb5OTX4te3oX37XHbyyStEJ5eSQlzeHp8kzZJL6+OW5qYeLx71Zb3tm7vxo/l932jNmXvmN+7R1uszqKVyLyWXz/y61ElzlqKTYiqleRz1CHN3+6127Wvb7R7tkZ+b9n+mNVaDBvZC906SPn/+586UDyxreXK4Wf1OoHJavkp7Xye8Sf1DxcdyJ+lr37n3KXmQ9EXSk5YABwAcC1oC92E06HLOo34AwDv4eH9gB4APjNAFAEOELgAYInQBwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADA0bQ3c39//JOlXSU+SotmJAOBjC5LuJD08Pj7+1Q9uhq6WwP3jvU4FAD+4L5L+7Dv3QvdJkqZpkvfLbyGccxfFey/n3MVY7atF0lXfmbG9e53Vnq2/9ufYu3d7zv68bbuvj8b6awjhak07Z7RPv6a2+z231o/O0tZHcs6Hz/vMnPZ9uHVO39e2t+qj+f11a2zrfv3nZqtv1F/rOeeL0vallHavt/TVdu2TdNHuS7vH6Eztebf2GL2+fqydM6qPrq22r76e9uyj9qh+Zv7RnJyzYoxr//Pzs75//y6VDO3thW5sPyh7QfiavrfscdYtoXsU/qPQPQq0vXobkKOxUWlDdtTu99xaf9Q38lFD9yhY98aOPj97fUf1rVB6bRhs9Z1tnwnurXPeUkavve2r9b1rX2+f016p4Th6vlvzz87x3l88k2L4a1n+IQ0ADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFCFwAMEboAYIjQBQBDhC4AGCJ0AcAQoQsAhghdADBE6AKAIUIXAAwRugBgiNAFAEOELgAYInQBwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIamnbEgSTnntTjnLoqki6tz7mqeJHnvr9YejfWlzrvF6IztWfvXsVXac/bnbdt9fTTWX0MIV2vaOaN9+jW13e+5tX50lrY+knM+fN5n5rTvw61z+r62vVUfze+vW2Nb99v7/I/2Hc1pv1f1udV6Smn3ektfbdc+SRftvrR7jM7Unndrj9Hr68faOaP66Npq++rrac8+ao/qZ+Yfzck5K8a49s/zXI8Wrg6u/dC9k9RuAAA4707S175zL3QfJH2R9CQpvtOhAOBHE7QE7sNo0J39KyEA4O34hzQAMEToAoAhQhcADBG6AGDobwmgt+TBW0QxAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"view_colormap('jet')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice the bright stripes in the grayscale image.\\n\",\n \"Even in full color, this uneven brightness means that the eye will be drawn to certain portions of the color range, which will potentially emphasize unimportant parts of the dataset.\\n\",\n \"It's better to use a colormap such as `viridis` (the default as of Matplotlib 2.0), which is specifically constructed to have an even brightness variation across the range; thus, it not only plays well with our color perception, but also will translate well to grayscale printing (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"view_colormap('viridis')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For other situations, such as showing positive and negative deviations from some mean, dual-color colorbars such as `RdBu` (*Red–Blue*) are helpful. However, as you can see in the following figure, it's important to note that the positive/negative information will be lost upon translation to grayscale!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAV0AAABrCAYAAADU3FGzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAIW0lEQVR4nO3dv2/kxhnG8ecdCrgkxfVKkVZ9AJf319udAFduVLhJo/6aWEH4vinmB2cpcskNVuPz+fsBiOXMvDNDck/PySvZtogQAGCM9HtfAAD8mRC6ADAQoQsAAxG6ADAQoQsAAz3sDTw9PX2S9IOkV0nzsCsCgD+2SdKjpOeXl5e39eBu6CoH7o8fdVUA8J37Iumndee10H2VpH/+601/m/PnEGamyaRkJpOUTJrM8phy/2S5LnXjtTbV/tbO8yaz3N7aQ1JKJqtHWSzljfJeUx3Li1gy2ZTbli4PWR7PayZZrS8XlM8ls9TWzXNyrVIq66ZlvqUyP8mm/ImNpZSvIaUynvuUrKuv6yx9bZ0rfUpJMpMsX4NWfSp9rbbsUx5OaU/5DVC9pqnNy/fbt7t92z5lr/oumRSltiwgmSlqrbrrk0r/sr5Uaru59YiLvrxn/e3yqEcsr14Gcjvejbe+i7mxXxOSR1601ZTDI1r9HEttRMgV8sg1CmmOaGN1rkfIuz3qeu11c4265+W4e36dPRTK7TlC7mWfVrfU1mP25dpmr/vnvtnLnLpXbUe/Ru5X9zzqIS/PxPN9q8yR93Xl3Jd7U7mPqHMjFGWOLvrVnbsiXHLP1+Jz6StjPivqWKkLL3NUzru+CJcit+V1nXmZ0+1Rx6f53/r7f35pGXpL6M6S9Jf/Sn+d1YWoWkCebnchu7SXoO1DN3Wh2+aFyephJpMpKX+BmlkO6I2AXR8tqFMN6i50py4AS0D2oZumLlhbWPbn5XUqIdWH7lZdW6vU1YCdunpb1uj3XcKwhG4JSGuhW9tLUFo3J7dL6NZ2H7otlNf79Ht3odsCdh26aQnddWhuhm5a1ebzd6Fbg1jli3kVnN7a26HrW0HcBWofjH3ovgtmXYbk3M5XYVT6+9B1aQnDsl4fqBch+W6NrX2WsJzrul3o9uE7r0J39iV0l/Hl+pfxJVyXdje3D12v15nfEOsCVRGl3YVuPW8hms9Xb0p7c2PdX8+9C8kuZGuAttCtfe5LaPbtfjzWc2aFdYFspV5zuS6/yNA1fpAGAAMRugAwEKELAAMRugBwTwf/DTFCFwDuya4PE7oAMBChCwB3FAefLxC6AHBHdvD5AqELAAMRugBwR8FvLwDAOMZvLwDAOAff6BK6AHBPB9/oEroAcFd8pgsA3w5CFwDuin85AgDGOfj1BUIXAO6I39MFgIGMjxcAYCA+XgCAbwehCwD3dPChLqELAHfFxwsAMI7xnS4ADMR3ugDwzSB0AWAgQhcABiJ0AWCghytjkyT99iAly+lsJk2W26b8OtWxrm2lr7XLeV0nj0VbZ7Ire5S5Vg+FJFOSSTJZmFLkV/P8AbbJZFb6VodMsmRKbrKUZCm3bc6bWrLcZ0lpyu08J9cqpbx2Sst8S7kuJdmU/x6zlGRTqbO6T8p7tPq6ztLX1rnSp5TKw8jXoFWfSl+rLftItdZkacoPVvWapjYv32/f7vZt+5S96rtkUpTasoBkpqi16q5PKv3L+lKp7ebWIy768p7158NRj1hevQzkdrwbb30Xc2O/JiSPvGirKYdHtPo5ltqIkCvkkWsU0hzRxupcj5B3e9T12uvmGnXPy3H3/Dp7KJTbc4Tcyz6tbqmtx+zLtc1e9899s5c5da/ajn6N3K/uedRDXp6Jl/85eZkj7+vKuS/3pnIfUedGKMqc9gZ79G+GcoHL3MsDmktfGfNZ8qVt7gp3Rbikct71RVkv3CX3cq3zMidC4XM+L+OT/3aRobeE7qMk/fyPT1dK7iV2zk+aywHgT6P8Ff0te5T067rzWug+S/oi6VVEGgCcNSkH7vPWoMXRf4cMAHA3/CANAAYidAFgIEIXAAYidAFgIEIXAAYidAFgIEIXAAYidAFgIEIXAAYidAFgIEIXAAYidAFgIEIXAAYidAFgIEIXAAYidAFgIEIXAAYidAFgIEIXAAYidAFgIEIXAAYidAFgoIe9gaenp0+SfpD0KmkedkUA8Mc2SXqU9Pzy8vK2HtwNXeXA/fGjrgoAvnNfJP207rwWuq+S9PnzZz08PMjM3h0ppXYu6aJdx6/1b61ztMde3bXrOFr31r6+v7+XvnZ973tr1/5ae1R35v739jv7zG85ev0e/V57Y1vzzqyxFhHt9ej83vV7fe7+bqzv25q3Hj+z5ta82u77+z53v7imM/OP2vX8lvupNVv9e8/p6BmefR7rfkmn6s7sM8+z3t7epJKha9dCd5akaZo0TdPmF+rZ9i3zjmqPQlTaDt0z17Z1fq/xvfqj9rX+M8/j7PjRXy7XQvfa661jt6zRf7H9Xud7x1Y4bPXdY3wdfuuQWPedqb/2urfOrfezFXj/z3Pdq/noVzN7dx/9nxHtfCzLD9IAYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGInQBYCBCFwAGergyNknSPM8yM5mZ3L2dm5lSSu1c0kW7jl/r31pnfaz32Ku7dh1H697a1/f399LXru99b+3aX2uP6s7c/95+Z5/5LUev36Pfa29sa96ZNdYior0end+7fq/P3d+N9X1b89bjZ9bcmlfbfX/f5+4X13Rm/lG7nt9yP7Vmq3/vOR09w7PPY90v6VTd2X2KaevP67XQfZSkr1+/XikBAOx4lPTruvNa6D5L+iLpVdL8QRcFAN+bSTlwn7cGrf/HKADAx+IHaQAwEKELAAMRugAwEKELAAP9D+NJB0/2pLaoAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"view_colormap('RdBu')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll see examples of using some of these colormaps as we continue.\\n\",\n \"\\n\",\n \"There are a large number of colormaps available in Matplotlib; to see a list of them, you can use IPython to explore the `plt.cm` submodule. For a more principled approach to colors in Python, you can refer to the tools and documentation within the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Color Limits and Extensions\\n\",\n \"\\n\",\n \"Matplotlib allows for a large range of colorbar customization.\\n\",\n \"The colorbar itself is simply an instance of `plt.Axes`, so all of the axes and tick formatting tricks we've seen so far are applicable.\\n\",\n \"The colorbar has some interesting flexibility: for example, we can narrow the color limits and indicate the out-of-bounds values with a triangular arrow at the top and bottom by setting the `extend` property.\\n\",\n \"This might come in handy, for example, if displaying an image that is subject to noise (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# make noise in 1% of the image pixels\\n\",\n \"speckles = (np.random.random(I.shape) < 0.01)\\n\",\n \"I[speckles] = np.random.normal(0, 3, np.count_nonzero(speckles))\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10, 3.5))\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 1)\\n\",\n \"plt.imshow(I, cmap='RdBu')\\n\",\n \"plt.colorbar()\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 2)\\n\",\n \"plt.imshow(I, cmap='RdBu')\\n\",\n \"plt.colorbar(extend='both')\\n\",\n \"plt.clim(-1, 1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that in the left panel, the default color limits respond to the noisy pixels, and the range of the noise completely washes out the pattern we are interested in.\\n\",\n \"In the right panel, we manually set the color limits and add extensions to indicate values that are above or below those limits.\\n\",\n \"The result is a much more useful visualization of our data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Discrete Colorbars\\n\",\n \"\\n\",\n \"Colormaps are by default continuous, but sometimes you'd like to represent discrete values.\\n\",\n \"The easiest way to do this is to use the `plt.cm.get_cmap` function and pass the name of a suitable colormap along with the number of desired bins (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(I, cmap=plt.cm.get_cmap('Blues', 6))\\n\",\n \"plt.colorbar(extend='both')\\n\",\n \"plt.clim(-1, 1);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The discrete version of a colormap can be used just like any other colormap.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Handwritten Digits\\n\",\n \"\\n\",\n \"As an example of where this can be applied, let's look at an interesting visualization of some handwritten digits from the digits dataset, included in Scikit-Learn; it consists of nearly 2,000 $8 \\\\times 8$ thumbnails showing various handwritten digits.\\n\",\n \"\\n\",\n \"For now, let's start by downloading the digits dataset and visualizing several of the example images with `plt.imshow` (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# load images of the digits 0 through 5 and visualize several of them\\n\",\n \"from sklearn.datasets import load_digits\\n\",\n \"digits = load_digits(n_class=6)\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(8, 8, figsize=(6, 6))\\n\",\n \"for i, axi in enumerate(ax.flat):\\n\",\n \" axi.imshow(digits.images[i], cmap='binary')\\n\",\n \" axi.set(xticks=[], yticks=[])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because each digit is defined by the hue of its 64 pixels, we can consider each digit to be a point lying in 64-dimensional space: each dimension represents the brightness of one pixel.\\n\",\n \"Visualizing such high-dimensional data can be difficult, but one way to approach this task is to use a *dimensionality reduction* technique such as manifold learning to reduce the dimensionality of the data while maintaining the relationships of interest.\\n\",\n \"Dimensionality reduction is an example of unsupervised machine learning, and we will discuss it in more detail in [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb).\\n\",\n \"\\n\",\n \"Deferring the discussion of these details, let's take a look at a two-dimensional manifold learning projection of the digits data (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) for details):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# project the digits into 2 dimensions using Isomap\\n\",\n \"from sklearn.manifold import Isomap\\n\",\n \"iso = Isomap(n_components=2, n_neighbors=15)\\n\",\n \"projection = iso.fit_transform(digits.data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll use our discrete colormap to view the results, setting the `ticks` and `clim` to improve the aesthetics of the resulting colorbar (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the results\\n\",\n \"plt.scatter(projection[:, 0], projection[:, 1], lw=0.1,\\n\",\n \" c=digits.target, cmap=plt.cm.get_cmap('plasma', 6))\\n\",\n \"plt.colorbar(ticks=range(6), label='digit value')\\n\",\n \"plt.clim(-0.5, 5.5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The projection also gives us some insights on the relationships within the dataset: for example, the ranges of 2 and 3 nearly overlap in this projection, indicating that some handwritten 2s and 3s are difficult to distinguish, and may be more likely to be confused by an automated classification algorithm.\\n\",\n \"Other values, like 0 and 1, are more distantly separated, and may be less likely to be confused.\\n\",\n \"\\n\",\n \"We'll return to manifold learning and digit classification in [Part 5](05.00-Machine-Learning.ipynb).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.08-Multiple-Subplots.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Multiple Subplots\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sometimes it is helpful to compare different views of data side by side.\\n\",\n \"To this end, Matplotlib has the concept of *subplots*: groups of smaller axes that can exist together within a single figure.\\n\",\n \"These subplots might be insets, grids of plots, or other more complicated layouts.\\n\",\n \"In this chapter we'll explore four routines for creating subplots in Matplotlib. We'll start by importing the packages we will use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-white')\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## plt.axes: Subplots by Hand\\n\",\n \"\\n\",\n \"The most basic method of creating an axes is to use the `plt.axes` function.\\n\",\n \"As we've seen previously, by default this creates a standard axes object that fills the entire figure.\\n\",\n \"`plt.axes` also takes an optional argument that is a list of four numbers in the figure coordinate system (`[left, bottom, width, height]`), which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.\\n\",\n \"\\n\",\n \"For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure). The following figure shows the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax1 = plt.axes() # standard axes\\n\",\n \"ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The equivalent of this command within the object-oriented interface is `fig.add_axes`. Let's use this to create two vertically stacked axes, as seen in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4],\\n\",\n \" xticklabels=[], ylim=(-1.2, 1.2))\\n\",\n \"ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4],\\n\",\n \" ylim=(-1.2, 1.2))\\n\",\n \"\\n\",\n \"x = np.linspace(0, 10)\\n\",\n \"ax1.plot(np.sin(x))\\n\",\n \"ax2.plot(np.cos(x));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We now have two axes (the top with no tick labels) that are just touching: the bottom of the upper panel (at position 0.5) matches the top of the lower panel (at position 0.1 + 0.4).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## plt.subplot: Simple Grids of Subplots\\n\",\n \"\\n\",\n \"Aligned columns or rows of subplots are a common enough need that Matplotlib has several convenience routines that make them easy to create.\\n\",\n \"The lowest level of these is `plt.subplot`, which creates a single subplot within a grid.\\n\",\n \"As you can see, this command takes three integer arguments—the number of rows, the number of columns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for i in range(1, 7):\\n\",\n \" plt.subplot(2, 3, i)\\n\",\n \" plt.text(0.5, 0.5, str((2, 3, i)),\\n\",\n \" fontsize=18, ha='center')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The command `plt.subplots_adjust` can be used to adjust the spacing between these plots.\\n\",\n \"The following code uses the equivalent object-oriented command, `fig.add_subplot`; the following figure shows the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"fig.subplots_adjust(hspace=0.4, wspace=0.4)\\n\",\n \"for i in range(1, 7):\\n\",\n \" ax = fig.add_subplot(2, 3, i)\\n\",\n \" ax.text(0.5, 0.5, str((2, 3, i)),\\n\",\n \" fontsize=18, ha='center')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we've used the `hspace` and `wspace` arguments of `plt.subplots_adjust`, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## plt.subplots: The Whole Grid in One Go\\n\",\n \"\\n\",\n \"The approach just described quickly becomes tedious when creating a large grid of subplots, especially if you'd like to hide the x- and y-axis labels on the inner plots.\\n\",\n \"For this purpose, `plt.subplots` is the easier tool to use (note the `s` at the end of `subplots`). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array.\\n\",\n \"The arguments are the number of rows and number of columns, along with optional keywords `sharex` and `sharey`, which allow you to specify the relationships between different axes.\\n\",\n \"\\n\",\n \"Let's create a $2 \\\\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By specifying `sharex` and `sharey`, we've automatically removed inner labels on the grid to make the plot cleaner.\\n\",\n \"The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# axes are in a two-dimensional array, indexed by [row, col]\\n\",\n \"for i in range(2):\\n\",\n \" for j in range(3):\\n\",\n \" ax[i, j].text(0.5, 0.5, str((i, j)),\\n\",\n \" fontsize=18, ha='center')\\n\",\n \"fig\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In comparison to `plt.subplot`, `plt.subplots` is more consistent with Python's conventional zero-based indexing, whereas `plt.subplot` uses MATLAB-style one-based indexing.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## plt.GridSpec: More Complicated Arrangements\\n\",\n \"\\n\",\n \"To go beyond a regular grid to subplots that span multiple rows and columns, `plt.GridSpec` is the best tool.\\n\",\n \"`plt.GridSpec` does not create a plot by itself; it is rather a convenient interface that is recognized by the `plt.subplot` command.\\n\",\n \"For example, a `GridSpec` for a grid of two rows and three columns with some specified width and height space looks like this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From this we can specify subplot locations and extents using the familiar Python slicing syntax (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.subplot(grid[0, 0])\\n\",\n \"plt.subplot(grid[0, 1:])\\n\",\n \"plt.subplot(grid[1, :2])\\n\",\n \"plt.subplot(grid[1, 2]);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This type of flexible grid alignment has a wide range of uses.\\n\",\n \"I most often use it when creating multiaxes histogram plots like the ones shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Create some normally distributed data\\n\",\n \"mean = [0, 0]\\n\",\n \"cov = [[1, 1], [1, 2]]\\n\",\n \"rng = np.random.default_rng(1701)\\n\",\n \"x, y = rng.multivariate_normal(mean, cov, 3000).T\\n\",\n \"\\n\",\n \"# Set up the axes with GridSpec\\n\",\n \"fig = plt.figure(figsize=(6, 6))\\n\",\n \"grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\\n\",\n \"main_ax = fig.add_subplot(grid[:-1, 1:])\\n\",\n \"y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\\n\",\n \"x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\\n\",\n \"\\n\",\n \"# Scatter points on the main axes\\n\",\n \"main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)\\n\",\n \"\\n\",\n \"# Histogram on the attached axes\\n\",\n \"x_hist.hist(x, 40, histtype='stepfilled',\\n\",\n \" orientation='vertical', color='gray')\\n\",\n \"x_hist.invert_yaxis()\\n\",\n \"\\n\",\n \"y_hist.hist(y, 40, histtype='stepfilled',\\n\",\n \" orientation='horizontal', color='gray')\\n\",\n \"y_hist.invert_xaxis()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"encoding\": \"# -*- coding: utf-8 -*-\",\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.09-Text-and-Annotation.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Text and Annotation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Creating a good visualization involves guiding the reader so that the figure tells a story.\\n\",\n \"In some cases, this story can be told in an entirely visual manner, without the need for added text, but in others, small textual cues and labels are necessary.\\n\",\n \"Perhaps the most basic types of annotations you will use are axes labels and titles, but the options go beyond this.\\n\",\n \"Let's take a look at some data and how we might visualize and annotate it to help convey interesting information. We'll start by setting up the notebook for plotting and importing the functions we will use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib as mpl\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Effect of Holidays on US Births\\n\",\n \"\\n\",\n \"Let's return to some data we worked with earlier, in [Example: Birthrate Data](03.09-Pivot-Tables.ipynb#Example:-Birthrate-Data), where we generated a plot of average births over the course of the calendar year. We'll start with the same cleaning procedure we used there, and plot the results (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# shell command to download the data:\\n\",\n \"# !cd data && curl -O \\\\\\n\",\n \"# https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from datetime import datetime\\n\",\n \"\\n\",\n \"births = pd.read_csv('data/births.csv')\\n\",\n \"\\n\",\n \"quartiles = np.percentile(births['births'], [25, 50, 75])\\n\",\n \"mu, sig = quartiles[1], 0.74 * (quartiles[2] - quartiles[0])\\n\",\n \"births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')\\n\",\n \"\\n\",\n \"births['day'] = births['day'].astype(int)\\n\",\n \"\\n\",\n \"births.index = pd.to_datetime(10000 * births.year +\\n\",\n \" 100 * births.month +\\n\",\n \" births.day, format='%Y%m%d')\\n\",\n \"births_by_date = births.pivot_table('births',\\n\",\n \" [births.index.month, births.index.day])\\n\",\n \"births_by_date.index = [datetime(2012, month, day)\\n\",\n \" for (month, day) in births_by_date.index]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(figsize=(12, 4))\\n\",\n \"births_by_date.plot(ax=ax);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When we're visualizing data like this, it is often useful to annotate certain features of the plot to draw the reader's attention.\\n\",\n \"This can be done manually with the `plt.text`/`ax.text` functions, which will place text at a particular *x*/*y* value (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(figsize=(12, 4))\\n\",\n \"births_by_date.plot(ax=ax)\\n\",\n \"\\n\",\n \"# Add labels to the plot\\n\",\n \"style = dict(size=10, color='gray')\\n\",\n \"\\n\",\n \"ax.text('2012-1-1', 3950, \\\"New Year's Day\\\", **style)\\n\",\n \"ax.text('2012-7-4', 4250, \\\"Independence Day\\\", ha='center', **style)\\n\",\n \"ax.text('2012-9-4', 4850, \\\"Labor Day\\\", ha='center', **style)\\n\",\n \"ax.text('2012-10-31', 4600, \\\"Halloween\\\", ha='right', **style)\\n\",\n \"ax.text('2012-11-25', 4450, \\\"Thanksgiving\\\", ha='center', **style)\\n\",\n \"ax.text('2012-12-25', 3850, \\\"Christmas \\\", ha='right', **style)\\n\",\n \"\\n\",\n \"# Label the axes\\n\",\n \"ax.set(title='USA births by day of year (1969-1988)',\\n\",\n \" ylabel='average daily births')\\n\",\n \"\\n\",\n \"# Format the x-axis with centered month labels\\n\",\n \"ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\\n\",\n \"ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\\n\",\n \"ax.xaxis.set_major_formatter(plt.NullFormatter())\\n\",\n \"ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `ax.text` method takes an *x* position, a *y* position, a string, and then optional keywords specifying the color, size, style, alignment, and other properties of the text.\\n\",\n \"Here we used `ha='right'` and `ha='center'`, where `ha` is short for *horizontal alignment*.\\n\",\n \"See the docstrings of `plt.text` and `mpl.text.Text` for more information on the available options.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Transforms and Text Position\\n\",\n \"\\n\",\n \"In the previous example, we anchored our text annotations to data locations. Sometimes it's preferable to anchor the text to a fixed position on the axes or figure, independent of the data. In Matplotlib, this is done by modifying the *transform*.\\n\",\n \"\\n\",\n \"Matplotlib makes use of a few different coordinate systems: a data point at $(x, y) = (1, 1)$ corresponds to a certain location on the axes or figure, which in turn corresponds to a particular pixel on the screen.\\n\",\n \"Mathematically, transforming between such coordinate systems is relatively straightforward, and Matplotlib has a well-developed set of tools that it uses internally to perform these transforms (these tools can be explored in the `matplotlib.transforms` submodule).\\n\",\n \"\\n\",\n \"A typical user rarely needs to worry about the details of the transforms, but it is helpful knowledge to have when considering the placement of text on a figure. There are three predefined transforms that can be useful in this situation:\\n\",\n \"\\n\",\n \"- `ax.transData`: Transform associated with data coordinates\\n\",\n \"- `ax.transAxes`: Transform associated with the axes (in units of axes dimensions)\\n\",\n \"- `fig.transFigure`: Transform associated with the figure (in units of figure dimensions)\\n\",\n \"\\n\",\n \"Let's look at an example of drawing text at various locations using these transforms (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(facecolor='lightgray')\\n\",\n \"ax.axis([0, 10, 0, 10])\\n\",\n \"\\n\",\n \"# transform=ax.transData is the default, but we'll specify it anyway\\n\",\n \"ax.text(1, 5, \\\". Data: (1, 5)\\\", transform=ax.transData)\\n\",\n \"ax.text(0.5, 0.1, \\\". Axes: (0.5, 0.1)\\\", transform=ax.transAxes)\\n\",\n \"ax.text(0.2, 0.2, \\\". Figure: (0.2, 0.2)\\\", transform=fig.transFigure);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Matplotlib's default text alignment is such that the \\\".\\\" at the beginning of each string will approximately mark the specified coordinate location.\\n\",\n \"\\n\",\n \"The `transData` coordinates give the usual data coordinates associated with the x- and y-axis labels.\\n\",\n \"The `transAxes` coordinates give the location from the bottom-left corner of the axes (here the white box), as a fraction of the total axes size.\\n\",\n \"The `transFigure` coordinates are similar, but specify the position from the bottom-left corner of the figure (here the gray box) as a fraction of the total figure size.\\n\",\n \"\\n\",\n \"Notice now that if we change the axes limits, it is only the `transData` coordinates that will be affected, while the others remain stationary (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ax.set_xlim(0, 2)\\n\",\n \"ax.set_ylim(-6, 6)\\n\",\n \"fig\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This behavior can be seen more clearly by changing the axes limits interactively: if you are executing this code in a notebook, you can make that happen by changing `%matplotlib inline` to `%matplotlib notebook` and using each plot's menu to interact with the plot.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Arrows and Annotation\\n\",\n \"\\n\",\n \"Along with tickmarks and text, another useful annotation mark is the simple arrow.\\n\",\n \"\\n\",\n \"While there is a `plt.arrow` function available, I wouldn't suggest using it: the arrows it creates are SVG objects that will be subject to the varying aspect ratio of your plots, making it tricky to get them right.\\n\",\n \"Instead, I'd suggest using the `plt.annotate` function, which creates some text and an arrow and allows the arrows to be very flexibly specified.\\n\",\n \"\\n\",\n \"Here is a demonstration of `annotate` with several of its options (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots()\\n\",\n \"\\n\",\n \"x = np.linspace(0, 20, 1000)\\n\",\n \"ax.plot(x, np.cos(x))\\n\",\n \"ax.axis('equal')\\n\",\n \"\\n\",\n \"ax.annotate('local maximum', xy=(6.28, 1), xytext=(10, 4),\\n\",\n \" arrowprops=dict(facecolor='black', shrink=0.05))\\n\",\n \"\\n\",\n \"ax.annotate('local minimum', xy=(5 * np.pi, -1), xytext=(2, -6),\\n\",\n \" arrowprops=dict(arrowstyle=\\\"->\\\",\\n\",\n \" connectionstyle=\\\"angle3,angleA=0,angleB=-90\\\"));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The arrow style is controlled through the `arrowprops` dictionary, which has numerous options available.\\n\",\n \"These options are well documented in Matplotlib's online documentation, so rather than repeating them here it is probably more useful to show some examples.\\n\",\n \"Let's demonstrate several of the possible options using the birthrate plot from before (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(figsize=(12, 4))\\n\",\n \"births_by_date.plot(ax=ax)\\n\",\n \"\\n\",\n \"# Add labels to the plot\\n\",\n \"ax.annotate(\\\"New Year's Day\\\", xy=('2012-1-1', 4100), xycoords='data',\\n\",\n \" xytext=(50, -30), textcoords='offset points',\\n\",\n \" arrowprops=dict(arrowstyle=\\\"->\\\",\\n\",\n \" connectionstyle=\\\"arc3,rad=-0.2\\\"))\\n\",\n \"\\n\",\n \"ax.annotate(\\\"Independence Day\\\", xy=('2012-7-4', 4250), xycoords='data',\\n\",\n \" bbox=dict(boxstyle=\\\"round\\\", fc=\\\"none\\\", ec=\\\"gray\\\"),\\n\",\n \" xytext=(10, -40), textcoords='offset points', ha='center',\\n\",\n \" arrowprops=dict(arrowstyle=\\\"->\\\"))\\n\",\n \"\\n\",\n \"ax.annotate('Labor Day Weekend', xy=('2012-9-4', 4850), xycoords='data',\\n\",\n \" ha='center', xytext=(0, -20), textcoords='offset points')\\n\",\n \"ax.annotate('', xy=('2012-9-1', 4850), xytext=('2012-9-7', 4850),\\n\",\n \" xycoords='data', textcoords='data',\\n\",\n \" arrowprops={'arrowstyle': '|-|,widthA=0.2,widthB=0.2', })\\n\",\n \"\\n\",\n \"ax.annotate('Halloween', xy=('2012-10-31', 4600), xycoords='data',\\n\",\n \" xytext=(-80, -40), textcoords='offset points',\\n\",\n \" arrowprops=dict(arrowstyle=\\\"fancy\\\",\\n\",\n \" fc=\\\"0.6\\\", ec=\\\"none\\\",\\n\",\n \" connectionstyle=\\\"angle3,angleA=0,angleB=-90\\\"))\\n\",\n \"\\n\",\n \"ax.annotate('Thanksgiving', xy=('2012-11-25', 4500), xycoords='data',\\n\",\n \" xytext=(-120, -60), textcoords='offset points',\\n\",\n \" bbox=dict(boxstyle=\\\"round4,pad=.5\\\", fc=\\\"0.9\\\"),\\n\",\n \" arrowprops=dict(arrowstyle=\\\"->\\\",\\n\",\n \" connectionstyle=\\\"angle,angleA=0,angleB=80,rad=20\\\"))\\n\",\n \"\\n\",\n \"\\n\",\n \"ax.annotate('Christmas', xy=('2012-12-25', 3850), xycoords='data',\\n\",\n \" xytext=(-30, 0), textcoords='offset points',\\n\",\n \" size=13, ha='right', va=\\\"center\\\",\\n\",\n \" bbox=dict(boxstyle=\\\"round\\\", alpha=0.1),\\n\",\n \" arrowprops=dict(arrowstyle=\\\"wedge,tail_width=0.5\\\", alpha=0.1));\\n\",\n \"\\n\",\n \"# Label the axes\\n\",\n \"ax.set(title='USA births by day of year (1969-1988)',\\n\",\n \" ylabel='average daily births')\\n\",\n \"\\n\",\n \"# Format the x-axis with centered month labels\\n\",\n \"ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\\n\",\n \"ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\\n\",\n \"ax.xaxis.set_major_formatter(plt.NullFormatter())\\n\",\n \"ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));\\n\",\n \"\\n\",\n \"ax.set_ylim(3600, 5400);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The variety of options make `annotate` powerful and flexible: you can create nearly any arrow style you wish.\\n\",\n \"Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time-consuming when producing publication-quality graphics!\\n\",\n \"Finally, I'll note that the preceding mix of styles is by no means best practice for presenting data, but rather is included as a demonstration of some of the available options.\\n\",\n \"\\n\",\n \"More discussion and examples of available arrow and annotation styles can be found in the Matplotlib [Annotations tutorial](https://matplotlib.org/stable/tutorials/text/annotations.html).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 64-bit ('3.9.6')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.10-Customizing-Ticks.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Customizing Ticks\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Matplotlib's default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot. This chapter will give several examples of adjusting the tick locations and formatting for the particular plot type you're interested in.\\n\",\n \"\\n\",\n \"Before we go into examples, however, let's talk a bit more about the object hierarchy of Matplotlib plots.\\n\",\n \"Matplotlib aims to have a Python object representing everything that appears on the plot: for example, recall that the `Figure` is the bounding box within which plot elements appear.\\n\",\n \"Each Matplotlib object can also act as a container of subobjects: for example, each `Figure` can contain one or more `Axes` objects, each of which in turn contains other objects representing plot contents.\\n\",\n \"\\n\",\n \"The tickmarks are no exception. Each axes has attributes `xaxis` and `yaxis`, which in turn have attributes that contain all the properties of the lines, ticks, and labels that make up the axes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Major and Minor Ticks\\n\",\n \"\\n\",\n \"Within each axes, there is the concept of a *major* tickmark, and a *minor* tickmark. As the names imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller. By default, Matplotlib rarely makes use of minor ticks, but one place you can see them is within logarithmic plots (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('classic')\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.axes(xscale='log', yscale='log')\\n\",\n \"ax.set(xlim=(1, 1E3), ylim=(1, 1E3))\\n\",\n \"ax.grid(True);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this chart each major tick shows a large tickmark, label, and gridline, while each minor tick shows a smaller tickmark with no label or gridline.\\n\",\n \"\\n\",\n \"These tick properties—locations and labels, that is—can be customized by setting the `formatter` and `locator` objects of each axis. Let's examine these for the x-axis of the just-shown plot:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(ax.xaxis.get_major_locator())\\n\",\n \"print(ax.xaxis.get_minor_locator())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(ax.xaxis.get_major_formatter())\\n\",\n \"print(ax.xaxis.get_minor_formatter())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see that both major and minor tick labels have their locations specified by a `LogLocator` (which makes sense for a logarithmic plot). Minor ticks, though, have their labels formatted by a `NullFormatter`: this says that no labels will be shown.\\n\",\n \"\\n\",\n \"We'll now look at a few examples of setting these locators and formatters for various plots.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Hiding Ticks or Labels\\n\",\n \"\\n\",\n \"Perhaps the most common tick/label formatting operation is the act of hiding ticks or labels.\\n\",\n \"This can be done using `plt.NullLocator` and `plt.NullFormatter`, as shown here (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.axes()\\n\",\n \"rng = np.random.default_rng(1701)\\n\",\n \"ax.plot(rng.random(50))\\n\",\n \"ax.grid()\\n\",\n \"\\n\",\n \"ax.yaxis.set_major_locator(plt.NullLocator())\\n\",\n \"ax.xaxis.set_major_formatter(plt.NullFormatter())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We've removed the labels (but kept the ticks/gridlines) from the x-axis, and removed the ticks (and thus the labels and gridlines as well) from the y-axis.\\n\",\n \"Having no ticks at all can be useful in many situations—for example, when you want to show a grid of images.\\n\",\n \"For instance, consider the following figure, which includes images of different faces, an example often used in supervised machine learning problems (see, for example, [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(5, 5, figsize=(5, 5))\\n\",\n \"fig.subplots_adjust(hspace=0, wspace=0)\\n\",\n \"\\n\",\n \"# Get some face data from Scikit-Learn\\n\",\n \"from sklearn.datasets import fetch_olivetti_faces\\n\",\n \"faces = fetch_olivetti_faces().images\\n\",\n \"\\n\",\n \"for i in range(5):\\n\",\n \" for j in range(5):\\n\",\n \" ax[i, j].xaxis.set_major_locator(plt.NullLocator())\\n\",\n \" ax[i, j].yaxis.set_major_locator(plt.NullLocator())\\n\",\n \" ax[i, j].imshow(faces[10 * i + j], cmap='binary_r')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each image is shown in its own axes, and we've set the tick locators to null because the tick values (pixel numbers in this case) do not convey relevant information for this particular visualization.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reducing or Increasing the Number of Ticks\\n\",\n \"\\n\",\n \"One common problem with the default settings is that smaller subplots can end up with crowded labels.\\n\",\n \"We can see this in the plot grid shown here (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(4, 4, sharex=True, sharey=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Particularly for the x-axis ticks, the numbers nearly overlap, making them quite difficult to decipher.\\n\",\n \"One way to adjust this is with `plt.MaxNLocator`, which allows us to specify the maximum number of ticks that will be displayed.\\n\",\n \"Given this maximum number, Matplotlib will use internal logic to choose the particular tick locations (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# For every axis, set the x and y major locator\\n\",\n \"for axi in ax.flat:\\n\",\n \" axi.xaxis.set_major_locator(plt.MaxNLocator(3))\\n\",\n \" axi.yaxis.set_major_locator(plt.MaxNLocator(3))\\n\",\n \"fig\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This makes things much cleaner. If you want even more control over the locations of regularly spaced ticks, you might also use `plt.MultipleLocator`, which we'll discuss in the following section.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Fancy Tick Formats\\n\",\n \"\\n\",\n \"Matplotlib's default tick formatting can leave a lot to be desired: it works well as a broad default, but sometimes you'd like to do something different.\\n\",\n \"Consider this plot of a sine and a cosine curve (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot a sine and cosine curve\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"x = np.linspace(0, 3 * np.pi, 1000)\\n\",\n \"ax.plot(x, np.sin(x), lw=3, label='Sine')\\n\",\n \"ax.plot(x, np.cos(x), lw=3, label='Cosine')\\n\",\n \"\\n\",\n \"# Set up grid, legend, and limits\\n\",\n \"ax.grid(True)\\n\",\n \"ax.legend(frameon=False)\\n\",\n \"ax.axis('equal')\\n\",\n \"ax.set_xlim(0, 3 * np.pi);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are a couple of changes we might like to make here. First, it's more natural for this data to space the ticks and gridlines in multiples of $\\\\pi$. We can do this by setting a `MultipleLocator`, which locates ticks at a multiple of the number we provide. For good measure, we'll add both major and minor ticks in multiples of $\\\\pi/2$ and $\\\\pi/4$ (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))\\n\",\n \"ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4))\\n\",\n \"fig\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But now these tick labels look a little bit silly: we can see that they are multiples of $\\\\pi$, but the decimal representation does not immediately convey this.\\n\",\n \"To fix this, we can change the tick formatter. There's no built-in formatter for what we want to do, so we'll instead use `plt.FuncFormatter`, which accepts a user-defined function giving fine-grained control over the tick outputs (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def format_func(value, tick_number):\\n\",\n \" # find number of multiples of pi/2\\n\",\n \" N = int(np.round(2 * value / np.pi))\\n\",\n \" if N == 0:\\n\",\n \" return \\\"0\\\"\\n\",\n \" elif N == 1:\\n\",\n \" return r\\\"$\\\\pi/2$\\\"\\n\",\n \" elif N == 2:\\n\",\n \" return r\\\"$\\\\pi$\\\"\\n\",\n \" elif N % 2 > 0:\\n\",\n \" return rf\\\"${N}\\\\pi/2$\\\"\\n\",\n \" else:\\n\",\n \" return rf\\\"${N // 2}\\\\pi$\\\"\\n\",\n \"\\n\",\n \"ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))\\n\",\n \"fig\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is much better! Notice that we've made use of Matplotlib's LaTeX support, specified by enclosing the string within dollar signs. This is very convenient for display of mathematical symbols and formulae: in this case, `\\\"$\\\\pi$\\\"` is rendered as the Greek character $\\\\pi$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Summary of Formatters and Locators\\n\",\n \"\\n\",\n \"We've seen a couple of the available formatters and locators; I'll conclude this chapter by briefly listing all of the built-in locator and formatter options. For more information on any of these, refer to the docstrings or to the Matplotlib online documentation.\\n\",\n \"Each of the following is available in the `plt` namespace:\\n\",\n \"\\n\",\n \"Locator class | Description\\n\",\n \"-------------------|-------------\\n\",\n \"`NullLocator` | No ticks\\n\",\n \"`FixedLocator` | Tick locations are fixed\\n\",\n \"`IndexLocator` | Locator for index plots (e.g., where `x = range(len(y)))`\\n\",\n \"`LinearLocator` | Evenly spaced ticks from min to max\\n\",\n \"`LogLocator` | Logarithmically spaced ticks from min to max\\n\",\n \"`MultipleLocator` | Ticks and range are a multiple of base\\n\",\n \"`MaxNLocator` | Finds up to a max number of ticks at nice locations\\n\",\n \"`AutoLocator` | (Default) `MaxNLocator` with simple defaults\\n\",\n \"`AutoMinorLocator` | Locator for minor ticks\\n\",\n \"\\n\",\n \"Formatter class | Description\\n\",\n \"--------------------|---------------\\n\",\n \"`NullFormatter` | No labels on the ticks\\n\",\n \"`IndexFormatter` | Set the strings from a list of labels\\n\",\n \"`FixedFormatter` | Set the strings manually for the labels\\n\",\n \"`FuncFormatter` | User-defined function sets the labels\\n\",\n \"`FormatStrFormatter`| Use a format string for each value\\n\",\n \"`ScalarFormatter` | Default formatter for scalar values\\n\",\n \"`LogFormatter` | Default formatter for log axes\\n\",\n \"\\n\",\n \"We'll see further examples of these throughout the remainder of the book.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"encoding\": \"# -*- coding: utf-8 -*-\",\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.11-Settings-and-Stylesheets.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Customizing Matplotlib: Configurations and Stylesheets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"While many of the topics covered in previous chapters involve adjusting the style of plot elements one by one, Matplotlib also offers mechanisms to adjust the overall style of a chart all at once. In this chapter we'll walk through some of Matplotlib's runtime configuration (*rc*) options, and take a look at the *stylesheets* feature, which contains some nice sets of default configurations.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Plot Customization by Hand\\n\",\n \"\\n\",\n \"Throughout this part of the book, you've seen how it is possible to tweak individual plot settings to end up with something that looks a little nicer than the default.\\n\",\n \"It's also possible to do these customizations for each individual plot.\\n\",\n \"For example, here is a fairly drab default histogram, shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('classic')\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = np.random.randn(1000)\\n\",\n \"plt.hist(x);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can adjust this by hand to make it a much more visually pleasing plot, as you can see in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# use a gray background\\n\",\n \"fig = plt.figure(facecolor='white')\\n\",\n \"ax = plt.axes(facecolor='#E6E6E6')\\n\",\n \"ax.set_axisbelow(True)\\n\",\n \"\\n\",\n \"# draw solid white gridlines\\n\",\n \"plt.grid(color='w', linestyle='solid')\\n\",\n \"\\n\",\n \"# hide axis spines\\n\",\n \"for spine in ax.spines.values():\\n\",\n \" spine.set_visible(False)\\n\",\n \" \\n\",\n \"# hide top and right ticks\\n\",\n \"ax.xaxis.tick_bottom()\\n\",\n \"ax.yaxis.tick_left()\\n\",\n \"\\n\",\n \"# lighten ticks and labels\\n\",\n \"ax.tick_params(colors='gray', direction='out')\\n\",\n \"for tick in ax.get_xticklabels():\\n\",\n \" tick.set_color('gray')\\n\",\n \"for tick in ax.get_yticklabels():\\n\",\n \" tick.set_color('gray')\\n\",\n \" \\n\",\n \"# control face and edge color of histogram\\n\",\n \"ax.hist(x, edgecolor='#E6E6E6', color='#EE6666');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This looks better, and you may recognize the look as inspired by that of the R language's `ggplot` visualization package.\\n\",\n \"But this took a whole lot of effort!\\n\",\n \"We definitely do not want to have to do all that tweaking each time we create a plot.\\n\",\n \"Fortunately, there is a way to adjust these defaults once in a way that will work for all plots.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Changing the Defaults: rcParams\\n\",\n \"\\n\",\n \"Each time Matplotlib loads, it defines a runtime configuration containing the default styles for every plot element you create.\\n\",\n \"This configuration can be adjusted at any time using the `plt.rc` convenience routine.\\n\",\n \"Let's see how we can modify the rc parameters so that our default plot will look similar to what we did before.\\n\",\n \"\\n\",\n \"We can use the `plt.rc` function to change some of these settings:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from matplotlib import cycler\\n\",\n \"colors = cycler('color',\\n\",\n \" ['#EE6666', '#3388BB', '#9988DD',\\n\",\n \" '#EECC55', '#88BB44', '#FFBBBB'])\\n\",\n \"plt.rc('figure', facecolor='white')\\n\",\n \"plt.rc('axes', facecolor='#E6E6E6', edgecolor='none',\\n\",\n \" axisbelow=True, grid=True, prop_cycle=colors)\\n\",\n \"plt.rc('grid', color='w', linestyle='solid')\\n\",\n \"plt.rc('xtick', direction='out', color='gray')\\n\",\n \"plt.rc('ytick', direction='out', color='gray')\\n\",\n \"plt.rc('patch', edgecolor='#E6E6E6')\\n\",\n \"plt.rc('lines', linewidth=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With these settings defined, we can now create a plot and see our settings in action (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(x);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's see what simple line plots look like with these rc parameters (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for i in range(4):\\n\",\n \" plt.plot(np.random.rand(10))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For charts viewed onscreen rather than printed, I find this much more aesthetically pleasing than the default styling.\\n\",\n \"If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes!\\n\",\n \"Optionally, these settings can be saved in a *.matplotlibrc* file, which you can read about in the [Matplotlib documentation](https://matplotlib.org/stable/tutorials/introductory/customizing.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Stylesheets\\n\",\n \"\\n\",\n \"A newer mechanism for adjusting overall chart styles is via Matplotlib's `style` module, which includes a number of default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the *.matplotlibrc* files mentioned earlier, but must be named with a *.mplstyle* extension.\\n\",\n \"\\n\",\n \"Even if you don't go as far as creating your own style, you may find what you're looking for in the built-in stylesheets.\\n\",\n \"`plt.style.available` contains a list of the available styles—here I'll list only the first five for brevity:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background']\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"plt.style.available[:5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The standard way to switch to a stylesheet is to call `style.use`:\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"plt.style.use('stylename')\\n\",\n \"```\\n\",\n \"\\n\",\n \"But keep in mind that this will change the style for the rest of the Python session!\\n\",\n \"Alternatively, you can use the style context manager, which sets a style temporarily:\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"with plt.style.context('stylename'):\\n\",\n \" make_a_plot()\\n\",\n \"```\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To demonstrate these styles, let's create a function that will make two basic types of plot:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"def hist_and_lines():\\n\",\n \" np.random.seed(0)\\n\",\n \" fig, ax = plt.subplots(1, 2, figsize=(11, 4))\\n\",\n \" ax[0].hist(np.random.randn(1000))\\n\",\n \" for i in range(3):\\n\",\n \" ax[1].plot(np.random.rand(10))\\n\",\n \" ax[1].legend(['a', 'b', 'c'], loc='lower left')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll use this to explore how these plots look using the various built-in styles.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Default Style\\n\",\n \"\\n\",\n \"Matplotlib's `default` style was updated in the version 2.0 release; let's look at this first (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with plt.style.context('default'):\\n\",\n \" hist_and_lines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### FiveThiryEight Style\\n\",\n \"\\n\",\n \"The `fivethirtyeight` style mimics the graphics found on the popular [FiveThirtyEight website](https://fivethirtyeight.com).\\n\",\n \"As you can see in the following figure, it is typified by bold colors, thick lines, and transparent axes:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with plt.style.context('fivethirtyeight'):\\n\",\n \" hist_and_lines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### ggplot Style\\n\",\n \"\\n\",\n \"The `ggplot` package in the R language is a popular visualization tool among data scientists.\\n\",\n \"Matplotlib's `ggplot` style mimics the default styles from that package (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with plt.style.context('ggplot'):\\n\",\n \" hist_and_lines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Bayesian Methods for Hackers Style\\n\",\n \"\\n\",\n \"There is a neat short online book called [*Probabilistic Programming and Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) by Cameron Davidson-Pilon that features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually appealing style throughout the book.\\n\",\n \"This style is reproduced in the ``bmh`` stylesheet (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with plt.style.context('bmh'):\\n\",\n \" hist_and_lines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dark Background Style\\n\",\n \"\\n\",\n \"For figures used within presentations, it is often useful to have a dark rather than light background.\\n\",\n \"The `dark_background` style provides this (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with plt.style.context('dark_background'):\\n\",\n \" hist_and_lines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Grayscale Style\\n\",\n \"\\n\",\n \"Sometimes you might find yourself preparing figures for a print publication that does not accept color figures.\\n\",\n \"For this, the `grayscale` style (see the following figure) can be useful:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with plt.style.context('grayscale'):\\n\",\n \" hist_and_lines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Seaborn Style\\n\",\n \"\\n\",\n \"Matplotlib also has several stylesheets inspired by the Seaborn library (discussed more fully in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).\\n\",\n \"I've found these settings to be very nice, and tend to use them as defaults in my own data exploration (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with plt.style.context('seaborn-whitegrid'):\\n\",\n \" hist_and_lines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Take some time to explore the built-in options and find one that appeals to you!\\n\",\n \"Throughout this book, I will generally use one or more of these style conventions when creating plots.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"encoding\": \"# -*- coding: utf-8 -*-\",\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.12-Three-Dimensional-Plotting.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Three-Dimensional Plotting in Matplotlib\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Matplotlib was initially designed with only two-dimensional plotting in mind.\\n\",\n \"Around the time of the 1.0 release, some three-dimensional plotting utilities were built on top of Matplotlib's two-dimensional display, and the result is a convenient (if somewhat limited) set of tools for three-dimensional data visualization.\\n\",\n \"Three-dimensional plots are enabled by importing the `mplot3d` toolkit, included with the main Matplotlib installation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from mpl_toolkits import mplot3d\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once this submodule is imported, a three-dimensional axes can be created by passing the keyword `projection='3d'` to any of the normal axes creation routines, as shown here (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"ax = plt.axes(projection='3d')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With this three-dimensional axes enabled, we can now plot a variety of three-dimensional plot types. \\n\",\n \"Three-dimensional plotting is one of the functionalities that benefits immensely from viewing figures interactively rather than statically, in the notebook; recall that to use interactive figures, you can use `%matplotlib notebook` rather than `%matplotlib inline` when running this code.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Three-Dimensional Points and Lines\\n\",\n \"\\n\",\n \"The most basic three-dimensional plot is a line or collection of scatter plots created from sets of (x, y, z) triples.\\n\",\n \"In analogy with the more common two-dimensional plots discussed earlier, these can be created using the `ax.plot3D` and `ax.scatter3D` functions.\\n\",\n \"The call signature for these is nearly identical to that of their two-dimensional counterparts, so you can refer to [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) and [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) for more information on controlling the output.\\n\",\n \"Here we'll plot a trigonometric spiral, along with some points drawn randomly near the line (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.axes(projection='3d')\\n\",\n \"\\n\",\n \"# Data for a three-dimensional line\\n\",\n \"zline = np.linspace(0, 15, 1000)\\n\",\n \"xline = np.sin(zline)\\n\",\n \"yline = np.cos(zline)\\n\",\n \"ax.plot3D(xline, yline, zline, 'gray')\\n\",\n \"\\n\",\n \"# Data for three-dimensional scattered points\\n\",\n \"zdata = 15 * np.random.random(100)\\n\",\n \"xdata = np.sin(zdata) + 0.1 * np.random.randn(100)\\n\",\n \"ydata = np.cos(zdata) + 0.1 * np.random.randn(100)\\n\",\n \"ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that scatter points have their transparency adjusted to give a sense of depth on the page.\\n\",\n \"While the three-dimensional effect is sometimes difficult to see within a static image, an interactive view can lead to some nice intuition about the layout of the points.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Three-Dimensional Contour Plots\\n\",\n \"\\n\",\n \"Analogous to the contour plots we explored in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb), `mplot3d` contains tools to create three-dimensional relief plots using the same inputs.\\n\",\n \"Like `ax.contour`, `ax.contour3D` requires all the input data to be in the form of two-dimensional regular grids, with the *z* data evaluated at each point.\\n\",\n \"Here we'll show a three-dimensional contour diagram of a three-dimensional sinusoidal function (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def f(x, y):\\n\",\n \" return np.sin(np.sqrt(x ** 2 + y ** 2))\\n\",\n \"\\n\",\n \"x = np.linspace(-6, 6, 30)\\n\",\n \"y = np.linspace(-6, 6, 30)\\n\",\n \"\\n\",\n \"X, Y = np.meshgrid(x, y)\\n\",\n \"Z = f(X, Y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"ax = plt.axes(projection='3d')\\n\",\n \"ax.contour3D(X, Y, Z, 40, cmap='binary')\\n\",\n \"ax.set_xlabel('x')\\n\",\n \"ax.set_ylabel('y')\\n\",\n \"ax.set_zlabel('z');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sometimes the default viewing angle is not optimal, in which case we can use the `view_init` method to set the elevation and azimuthal angles. In the following example, visualized in the following figure, we'll use an elevation of 60 degrees (that is, 60 degrees above the x-y plane) and an azimuth of 35 degrees (that is, rotated 35 degrees counter-clockwise about the z-axis):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ax.view_init(60, 35)\\n\",\n \"fig\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Again, note that this type of rotation can be accomplished interactively by clicking and dragging when using one of Matplotlib's interactive backends.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Wireframes and Surface Plots\\n\",\n \"\\n\",\n \"Two other types of three-dimensional plots that work on gridded data are wireframes and surface plots.\\n\",\n \"These take a grid of values and project it onto the specified three-dimensional surface, and can make the resulting three-dimensional forms quite easy to visualize.\\n\",\n \"Here's an example of using a wireframe (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"ax = plt.axes(projection='3d')\\n\",\n \"ax.plot_wireframe(X, Y, Z)\\n\",\n \"ax.set_title('wireframe');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A surface plot is like a wireframe plot, but each face of the wireframe is a filled polygon.\\n\",\n \"Adding a colormap to the filled polygons can aid perception of the topology of the surface being visualized, as you can see in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.axes(projection='3d')\\n\",\n \"ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\\n\",\n \" cmap='viridis', edgecolor='none')\\n\",\n \"ax.set_title('surface');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Though the grid of values for a surface plot needs to be two-dimensional, it need not be rectilinear.\\n\",\n \"Here is an example of creating a partial polar grid, which when used with the `surface3D` plot can give us a slice into the function we're visualizing (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"r = np.linspace(0, 6, 20)\\n\",\n \"theta = np.linspace(-0.9 * np.pi, 0.8 * np.pi, 40)\\n\",\n \"r, theta = np.meshgrid(r, theta)\\n\",\n \"\\n\",\n \"X = r * np.sin(theta)\\n\",\n \"Y = r * np.cos(theta)\\n\",\n \"Z = f(X, Y)\\n\",\n \"\\n\",\n \"ax = plt.axes(projection='3d')\\n\",\n \"ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\\n\",\n \" cmap='viridis', edgecolor='none');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Surface Triangulations\\n\",\n \"\\n\",\n \"For some applications, the evenly sampled grids required by the preceding routines are too restrictive.\\n\",\n \"In these situations, triangulation-based plots can come in handy.\\n\",\n \"What if rather than an even draw from a Cartesian or a polar grid, we instead have a set of random draws?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"theta = 2 * np.pi * np.random.random(1000)\\n\",\n \"r = 6 * np.random.random(1000)\\n\",\n \"x = np.ravel(r * np.sin(theta))\\n\",\n \"y = np.ravel(r * np.cos(theta))\\n\",\n \"z = f(x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We could create a scatter plot of the points to get an idea of the surface we're sampling from, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.axes(projection='3d')\\n\",\n \"ax.scatter(x, y, z, c=z, cmap='viridis', linewidth=0.5);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This point cloud leaves a lot to be desired.\\n\",\n \"The function that will help us in this case is `ax.plot_trisurf`, which creates a surface by first finding a set of triangles formed between adjacent points (remember that `x`, `y`, and `z` here are one-dimensional arrays); the following figure shows the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.axes(projection='3d')\\n\",\n \"ax.plot_trisurf(x, y, z,\\n\",\n \" cmap='viridis', edgecolor='none');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result is certainly not as clean as when it is plotted with a grid, but the flexibility of such a triangulation allows for some really interesting three-dimensional plots.\\n\",\n \"For example, it is actually possible to plot a three-dimensional Möbius strip using this, as we'll see next.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Visualizing a Möbius Strip\\n\",\n \"\\n\",\n \"A Möbius strip is similar to a strip of paper glued into a loop with a half-twist, resulting in an object with only a single side!\\n\",\n \"Here we will visualize such an object using Matplotlib's three-dimensional tools.\\n\",\n \"The key to creating the Möbius strip is to think about its parametrization: it's a two-dimensional strip, so we need two intrinsic dimensions. Let's call them $\\\\theta$, which ranges from $0$ to $2\\\\pi$ around the loop, and $w$, which ranges from –1 to 1 across the width of the strip:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"theta = np.linspace(0, 2 * np.pi, 30)\\n\",\n \"w = np.linspace(-0.25, 0.25, 8)\\n\",\n \"w, theta = np.meshgrid(w, theta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now from this parametrization, we must determine the (*x*, *y*, *z*) positions of the embedded strip.\\n\",\n \"\\n\",\n \"Thinking about it, we might realize that there are two rotations happening: one is the position of the loop about its center (what we've called $\\\\theta$), while the other is the twisting of the strip about its axis (we'll call this $\\\\phi$). For a Möbius strip, we must have the strip make half a twist during a full loop, or $\\\\Delta\\\\phi = \\\\Delta\\\\theta/2$:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"phi = 0.5 * theta\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we use our recollection of trigonometry to derive the three-dimensional embedding.\\n\",\n \"We'll define $r$, the distance of each point from the center, and use this to find the embedded $(x, y, z)$ coordinates:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# radius in x-y plane\\n\",\n \"r = 1 + w * np.cos(phi)\\n\",\n \"\\n\",\n \"x = np.ravel(r * np.cos(theta))\\n\",\n \"y = np.ravel(r * np.sin(theta))\\n\",\n \"z = np.ravel(w * np.sin(phi))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, to plot the object, we must make sure the triangulation is correct. The best way to do this is to define the triangulation *within the underlying parametrization*, and then let Matplotlib project this triangulation into the three-dimensional space of the Möbius strip.\\n\",\n \"This can be accomplished as follows (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# triangulate in the underlying parametrization\\n\",\n \"from matplotlib.tri import Triangulation\\n\",\n \"tri = Triangulation(np.ravel(w), np.ravel(theta))\\n\",\n \"\\n\",\n \"ax = plt.axes(projection='3d')\\n\",\n \"ax.plot_trisurf(x, y, z, triangles=tri.triangles,\\n\",\n \" cmap='Greys', linewidths=0.2);\\n\",\n \"\\n\",\n \"ax.set_xlim(-1, 1); ax.set_ylim(-1, 1); ax.set_zlim(-1, 1)\\n\",\n \"ax.axis('off');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Combining all of these techniques, it is possible to create and display a wide variety of three-dimensional objects and patterns in Matplotlib.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"encoding\": \"# -*- coding: utf-8 -*-\",\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/04.14-Visualization-With-Seaborn.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Visualization with Seaborn\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Matplotlib has been at the core of scientific visualization in Python for decades, but even avid users will admit it often leaves much to be desired.\\n\",\n \"There are several complaints about Matplotlib that often come up:\\n\",\n \"\\n\",\n \"- A common early complaint, which is now outdated: prior to version 2.0, Matplotlib's color and style defaults were at times poor and looked dated.\\n\",\n \"- Matplotlib's API is relatively low-level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\\n\",\n \"- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas `DataFrame` objects. In order to visualize data from a `DataFrame`, you must extract each `Series` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the `DataFrame` labels in a plot.\\n\",\n \"\\n\",\n \"An answer to these problems is [Seaborn](http://seaborn.pydata.org/). Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas.\\n\",\n \"\\n\",\n \"To be fair, the Matplotlib team has adapted to the changing landscape: it added the `plt.style` tools discussed in [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb), and Matplotlib is starting to handle Pandas data more seamlessly.\\n\",\n \"But for all the reasons just discussed, Seaborn remains a useful add-on.\\n\",\n \"\\n\",\n \"By convention, Seaborn is often imported as `sns`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"sns.set() # seaborn's method to set its chart style\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exploring Seaborn Plots\\n\",\n \"\\n\",\n \"The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.\\n\",\n \"\\n\",\n \"Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood), but the Seaborn API is much more convenient.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Histograms, KDE, and Densities\\n\",\n \"\\n\",\n \"Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.\\n\",\n \"We have seen that this is relatively straightforward in Matplotlib (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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HwIyIiUFJSgoKCAqxbtw5r165FbGwsioqK0NraOunYAwcO4PLly8jIyEBLSwtee+01f+UmIqIp8uoEY2ZmJjIzM12mVVVVuS139OhRl8dRUVG4ePHiNOIREZG/8NYKRESCYOETEQmChU9EJAgWPhGRIFj4RESCYOETEQmChU9EJAgWPhGRIFj4RESCYOETEQmChU9EJAgWPhGRIFj4RESC8M9HBJFwglXDgG30AzF0kJG01LdPvAoPUeMLfwYjogmx8Mk3tmH0fvw+AEByyOj88rFPq3k6bY0fQxHRZHhKh4hIECx8IiJBsPCJiATBwiciEgQLn4hIECx8IiJBsPCJiATBwiciEgQLn4hIECx8IiJB8NYKTwg7gGHJ4TLN3DMI67hp/qKDDMkhAwDkGdkCEfkbC/8JMSw50PjRZy7TQkL0GBgYnpHtJS3VO++f82xU+Ixsg4j8i6d0iIgEwcInIhIEC5+ISBBeFX59fT0yMjKQmpqK6upqt/l3797Fhg0bkJaWhv3798NmswEA6urqsHLlSmRnZyM7OxsVFRX+TU9ERF5TfNHWbDajoqICtbW1CAoKQl5eHhISErBs2TLnMnv27MHhw4cRFxeHffv24fLly8jPz0draytKS0uxdu3aGd0JIiJSpniE39TUhMTERISHh8NgMCAtLQ0NDQ3O+R0dHbBarYiLiwMA5OTkOOe3trairq4OWVlZ2L17N3p7e2dmL4iISJFi4VssFhiNRudjk8kEs9k84Xyj0eicbzQasXPnTly/fh2RkZE4dOiQP7MTEdEUKJ7SkWX3y2pUKpVX8ysrK53TCgsLkZKSMqVwixeHTmn52WQ0hgU6ggtzzyBCQtw/SNzTNH/QaTUI0o0+fVQqlfNrX/g6dux21erpZRhronWNnebNPvsr09j1eLPdiebrtBqfnw/BwToYFxl8GuvJXPv9mch8yDmVjIrPxoiICLS0tDgfWywWmEwml/kPHz50Pu7q6oLJZEJfXx+uXr2KzZs3Axj9w6DVTu3J393dD4dj7l3HaTSGoaurL9AxXFglh9tFVjN54ZVk02NEGn1xXpZl59e+8HXs2O06HNPLMJandQXptC7TvNlnf2Uaux6l7Y7POZZks/v8fLBaJb895+fi748n8yHn+IxqtWrSA2XFUzpJSUlobm5GT08PhoaG0NjYiOTkZOf8qKgo6PV63Lp1C8DoO3OSk5NhMBhw/vx53L59GwBw6dIlpKam+rxjREQ0PV4d4ZeUlKCgoACSJCE3NxexsbEoKipCcXExYmJicPz4cZSVlWFgYADPPfccCgoKoNFocPLkSZSXl8NqtWLJkiU4duzYbOwTERF54NU5lszMTGRmZrpMq6qqcn4dHR2NX//6127j4uPjce3atWlGJCIif+CVtkREguDdMokEsfgpPZKW+jZ2Afqgsf/fu/O0eljlmXn3F80sFj6RIDSyDZ0fvevTWMMz4dCpRwt/wQurAA0Lfz7iKR0iIkHwCJ+IFKlUKucnnNkdMgYdvn+S2lQ/iU2vU0Pj89ZoLBY+ESmyO2T8reMxACDymX40/bXb53VN9YLA7yUugUHHkxH+wO8iEZEgWPhERIJg4RMRCYKFT0QkCBY+EZEgWPhERIJg4RMRCYKFT0QkCBY+EZEgWPhERIJg4RMRCYKFT0QkCBY+EZEgWPhERILg7ZH9zA5geAr3+vYX2cvl4paEwKCxTXt7i0K16Jz2WohoNrHw/WxYcqDxo89mfbv/+W9f9Wo5g8b3j7kby5SaMe11ENHsYuET0ZymVqswGID/NfcNjMz6NmcaC5+I5rQRmwN/+O/PZ327Wf/x9SfuRU4WPhFNyeKn9Eha6vt4nVYDyabHoF2LTz4b8F8wUsTCJ6Ip0cjTex0oSKfFiGRDZGKaH1ORN560/7EQEdEEWPhERIJg4RMRCYKFT0QkCK9etK2vr8eZM2cgSRI2b96MTZs2ucy/e/cuysrK0N/fj/j4eBw8eBBarRb379/Hnj170N3djWeffRbHjx9HSEjIjOzIk07pCtmF6n4kLdW7TPvnuyHG4hWyROJSLHyz2YyKigrU1tYiKCgIeXl5SEhIwLJly5zL7NmzB4cPH0ZcXBz27duHy5cvIz8/HwcPHkR+fj7WrFmDyspKnD59Gnv27JnRHQJm/vYG5p5BWCdYv7e3OJgqpStkg6PC0dnx2GXaP98NMRavkCXyjsMhT/h7PtP0OjU0M7BexcJvampCYmIiwsPDAQBpaWloaGjAj370IwBAR0cHrFYr4uLiAAA5OTl488038dJLL+HmzZuorKx0Tv/+978/pcJXq1VT3J1RwzYHmu50+DTWG4Z/0WNwaNjjvKTnn0GoQef3bep0OgSHhk04X/svIQgOtY8bo4V6XOGrtdpJ1+OtsevxtG1f1jNVY7frr/2aaF3jv5fe7HMgvteefub+yOPP7/U/M+p0OoQalAtVq1HPyO+UErtjZntkMv/+wjPQab074z62J5U6UyXL8qQHpWfPnsXg4CBKSkoAAFeuXMGdO3fw05/+FADw8ccf49ixY/jVr34FAPj888+xZcsWXLx4Ebm5ufjTn/4EALDZbIiLi8Nf/vIXr3aCiIj8S/FPiKe/ByqVSnG+0jgiIppdioUfERGBhw8fOh9bLBaYTKYJ53d1dcFkMmHRokXo7++H3W53mU5ERIGhWPhJSUlobm5GT08PhoaG0NjYiOTkZOf8qKgo6PV63Lp1CwBQV1eH5ORk6HQ6xMfH48aNGy7TiYgoMBTP4QOjb8s8e/YsJElCbm4uioqKUFRUhOLiYsTExKC9vR1lZWUYGBjAc889h9dffx1BQUHo6OhAaWkpuru7ERkZiRMnTmDBggWzsV9ERDSOV4VPRETzH6+0JSISBAufiEgQLHwiIkGw8ImIBMHC95HFYsGWLVuwbt065OXl4csvvwx0pAm1tbVhxYoVgY4xoVu3bmHDhg3Izs7GK6+8go6OwFzO7kl9fT0yMjKQmpqK6urqQMeZ0KlTp7BmzRqsWbMGx44dC3ScSb3xxhsoLS0NdIwJ/eEPf0BOTg5Wr16Nw4cPBzrOhK5fv+78mb/xxhveDZLJJ6+88opcU1Mjy7Is19TUyLt27QpsoAkMDg7KL7/8svyNb3wj0FEm9OKLL8p3796VZVmWr1y5Im/bti3AiUY9ePBAfvHFF+VHjx7JAwMDcmZmpnzv3r1Ax3Lz4Ycfyi+//LI8PDwsj4yMyAUFBXJjY2OgY3nU1NQkJyQkyHv37g10FI+++OILeeXKlXJnZ6c8MjIib9y4Uf7ggw8CHcvN4OCg/K1vfUvu7u6WJUmSc3Nz5Q8//FBxHI/wfdDT04P29nbk5eUBADZs2IDXXnstsKEmcPToUWzevDnQMSY0MjKCXbt2ITo6GgCwfPlydHbOjRs4j71xoMFgcN44cK4xGo0oLS1FUFAQdDodli5divv37wc6lpvHjx+joqIC27ZtC3SUCf3+979HRkYGnn76aeh0OlRUVOD5558PdCw3drsdDocDQ0NDsNlssNls0Ov1iuNY+D74+9//jq985Sv42c9+hqysLBQXF0Onm/27+Sl5//33YbVasXr16kBHmVBQUBCys7MBAA6HA6dOnUJKSkqAU42yWCwwGo3OxyaTCWazOYCJPPv617/uvFvtZ599hhs3buC73/1uYEN58JOf/AQlJSV46qmnAh1lQp9//jnsdjteffVVZGVloaamZk5eLBoaGopdu3YhPT0dycnJiIqKwje/+U3FcV59AIrIfve73+H11193mfbVr34VbW1t2LlzJ/bv348rV66gtLQUFy9enDMZv/a1r6G/vx8XLlwISCZPJsp54cIFjIyMoLS0FDabDVu3bg1QQlfyPLsB4L1797B161bs3bsXS5YsCXQcF1euXEFkZCS+/e1vo7a2NtBxJmS329HS0oKLFy/CYDBgx44duHbtGnJycgIdzUV7ezuuXr2KP/7xjwgLC8Pu3bvx9ttvo7CwcNJxvNLWB1988QXWr1/vvH/Q0NAQEhMTcfv27QAn+39XrlzB2bNnnZ8w1t7ejujoaFRXVyM0NDTA6VwNDAxg+/btCA8Px/HjxxEUFBToSACAa9euoaWlBUeOHAEAVFZWQpZl52dBzCW3bt1CcXEx9u3bhzVr1gQ6jpsf/vCH6OrqgkajQW9vLwYHB7Fu3Trs27cv0NFcnDx5Ev39/SgrKwMAVFdX4969eygvLw9ssHHOnz+P7u5u7N27FwDwwQcfoKamBufOnZt84Ay/tvDESk9Pd76Y89vf/lbeuHFjgBNNbi6/aLt9+3a5rKxMdjgcgY7i4p8v2nZ3d8uDg4NyVlaWfPv27UDHcnP//n05ISFBbmpqCnQUr1y9enXOvmj7ySefyGlpaXJvb69ss9nkrVu3ypcvXw50LDd//vOf5aysLHlgYEB2OBzyj3/8Y/nNN99UHMdTOj46deoUDhw4gJ///OcIDQ3F0aNHAx1pXmpra8P777+PZcuWYd26dQBGz5VXVVUFNhhGb/1dUlKCgoIC540DY2NjAx3Lzdtvv43h4WGX52BeXh42btwYwFTz0/PPP4/CwkLk5+dDkiR85zvfwYYNGwIdy83KlSvR1taGnJwc6HQ6xMTEYMuWLYrjeEqHiEgQfJcOEZEgWPhERIJg4RMRCYKFT0QkCBY+EZEgWPhERIJg4RMRCYKFT0QkiP8F4xAe+rBA2CUAAAAASUVORK5CYII=\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\\n\",\n \"data = pd.DataFrame(data, columns=['x', 'y'])\\n\",\n \"\\n\",\n \"for col in 'xy':\\n\",\n \" plt.hist(data[col], density=True, alpha=0.5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Rather than just providing a histogram as a visual output, we can get a smooth estimate of the distribution using kernel density estimation (introduced in [Density and Contour Plots](04.04-Density-and-Contour-Plots.ipynb)), which Seaborn does with ``sns.kdeplot`` (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.kdeplot(data=data, shade=True);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we pass `x` and `y` columns to `kdeplot`, we instead get a two-dimensional visualization of the joint density (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.kdeplot(data=data, x='x', y='y');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see the joint distribution and the marginal distributions together using `sns.jointplot`, which we'll explore further later in this chapter.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Pair Plots\\n\",\n \"\\n\",\n \"When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. These are very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\\n\",\n \"\\n\",\n \"We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three Iris species:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.pairplot(iris, hue='species', height=2.5);\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Faceted Histograms\\n\",\n \"\\n\",\n \"Sometimes the best way to view data is via histograms of subsets, as shown in the following figure. Seaborn's `FacetGrid` makes this simple.\\n\",\n \"We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:[^1]\\n\",\n \"\\n\",\n \"[^1]: The restaurant staff data used in this section divides employees into two sexes: female and male. Biological sex\\n\",\n \"isn’t binary, but the following discussion and visualizations are limited by this data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" total_bill tip sex smoker day time size\\n\",\n \"0 16.99 1.01 Female No Sun Dinner 2\\n\",\n \"1 10.34 1.66 Male No Sun Dinner 3\\n\",\n \"2 21.01 3.50 Male No Sun Dinner 3\\n\",\n \"3 23.68 3.31 Male No Sun Dinner 2\\n\",\n \"4 24.59 3.61 Female No Sun Dinner 4\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tips = sns.load_dataset('tips')\\n\",\n \"tips.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\\n\",\n \"\\n\",\n \"grid = sns.FacetGrid(tips, row=\\\"sex\\\", col=\\\"time\\\", margin_titles=True)\\n\",\n \"grid.map(plt.hist, \\\"tip_pct\\\", bins=np.linspace(0, 40, 15));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The faceted chart gives us some quick insights into the dataset: for example, we see that it contains far more data on male servers during the dinner hour than other categories, and typical tip amounts appear to range from approximately 10% to 20%, with some outliers on either end.\\n\",\n \"\\n\",\n \"### Categorical Plots\\n\",\n \"\\n\",\n \"Categorical plots can be useful for this kind of visualization as well. These allow you to view the distribution of a parameter within bins defined by any other parameter, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with sns.axes_style(style='ticks'):\\n\",\n \" g = sns.catplot(x=\\\"day\\\", y=\\\"total_bill\\\", hue=\\\"sex\\\", data=tips, kind=\\\"box\\\")\\n\",\n \" g.set_axis_labels(\\\"Day\\\", \\\"Total Bill\\\");\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Joint Distributions\\n\",\n \"\\n\",\n \"Similar to the pair plot we saw earlier, we can use `sns.jointplot` to show the joint distribution between different datasets, along with the associated marginal distributions (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with sns.axes_style('white'):\\n\",\n \" sns.jointplot(x=\\\"total_bill\\\", y=\\\"tip\\\", data=tips, kind='hex')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The joint plot can even do some automatic kernel density estimation and regression, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.jointplot(x=\\\"total_bill\\\", y=\\\"tip\\\", data=tips, kind='reg');\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Bar Plots\\n\",\n \"\\n\",\n \"Time series can be plotted using `sns.factorplot`. In the following example, we'll use the Planets dataset that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb); see the following figure for the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with sns.axes_style('white'):\\n\",\n \" g = sns.catplot(x=\\\"year\\\", data=planets, aspect=2,\\n\",\n \" kind=\\\"count\\\", color='steelblue')\\n\",\n \" g.set_xticklabels(step=5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can learn more by looking at the *method* of discovery of each of these planets (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with sns.axes_style('white'):\\n\",\n \" g = sns.catplot(x=\\\"year\\\", data=planets, aspect=4.0, kind='count',\\n\",\n \" hue='method', order=range(2001, 2015))\\n\",\n \" g.set_ylabels('Number of Planets Discovered')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For more information on plotting with Seaborn, see the [Seaborn documentation](http://seaborn.pydata.org/), and particularly the [example gallery](https://seaborn.pydata.org/examples/index.html).\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Exploring Marathon Finishing Times\\n\",\n \"\\n\",\n \"Here we'll look at using Seaborn to help visualize and understand finishing results from a marathon.\\n\",\n \"I've scraped the data from sources on the web, aggregated it and removed any identifying information, and put it on GitHub, where it can be downloaded\\n\",\n \"(if you are interested in using Python for web scraping, I would recommend [*Web Scraping with Python*](http://shop.oreilly.com/product/0636920034391.do) by Ryan Mitchell, also from O'Reilly).\\n\",\n \"We will start by downloading the data and loading it into Pandas:[^2]\\n\",\n \"\\n\",\n \"[^2]: The marathon data used in this section divides runners into two genders: men and women. While gender is a\\n\",\n \"spectrum, the following discussion and visualizations use this binary because they depend on the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# url = ('https://raw.githubusercontent.com/jakevdp/'\\n\",\n \"# 'marathon-data/master/marathon-data.csv')\\n\",\n \"# !cd data && curl -O {url}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" age gender split final\\n\",\n \"0 33 M 01:05:38 02:08:51\\n\",\n \"1 32 M 01:06:26 02:09:28\\n\",\n \"2 31 M 01:06:49 02:10:42\\n\",\n \"3 38 M 01:06:16 02:13:45\\n\",\n \"4 31 M 01:06:32 02:13:59\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.read_csv('data/marathon-data.csv')\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice that Pandas loaded the time columns as Python strings (type `object`); we can see this by looking at the `dtypes` attribute of the `DataFrame`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"age int64\\n\",\n \"gender object\\n\",\n \"split object\\n\",\n \"final object\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.dtypes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's fix this by providing a converter for the times:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"
0 days 01:06:26
\\n\",\n \"
0 days 02:09:28
\\n\",\n \"
3986.0
\\n\",\n \"
7768.0
\\n\",\n \"
-0.026262
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
2
\\n\",\n \"
31
\\n\",\n \"
M
\\n\",\n \"
0 days 01:06:49
\\n\",\n \"
0 days 02:10:42
\\n\",\n \"
4009.0
\\n\",\n \"
7842.0
\\n\",\n \"
-0.022443
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
3
\\n\",\n \"
38
\\n\",\n \"
M
\\n\",\n \"
0 days 01:06:16
\\n\",\n \"
0 days 02:13:45
\\n\",\n \"
3976.0
\\n\",\n \"
8025.0
\\n\",\n \"
0.009097
\\n\",\n \"
\\n\",\n \"
\\n\",\n \"
4
\\n\",\n \"
31
\\n\",\n \"
M
\\n\",\n \"
0 days 01:06:32
\\n\",\n \"
0 days 02:13:59
\\n\",\n \"
3992.0
\\n\",\n \"
8039.0
\\n\",\n \"
0.006842
\\n\",\n \"
\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" age gender split final split_sec final_sec \\\\\\n\",\n \"0 33 M 0 days 01:05:38 0 days 02:08:51 3938.0 7731.0 \\n\",\n \"1 32 M 0 days 01:06:26 0 days 02:09:28 3986.0 7768.0 \\n\",\n \"2 31 M 0 days 01:06:49 0 days 02:10:42 4009.0 7842.0 \\n\",\n \"3 38 M 0 days 01:06:16 0 days 02:13:45 3976.0 8025.0 \\n\",\n \"4 31 M 0 days 01:06:32 0 days 02:13:59 3992.0 8039.0 \\n\",\n \"\\n\",\n \" split_frac \\n\",\n \"0 -0.018756 \\n\",\n \"1 -0.026262 \\n\",\n \"2 -0.022443 \\n\",\n \"3 0.009097 \\n\",\n \"4 0.006842 \"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['split_frac'] = 1 - 2 * data['split_sec'] / data['final_sec']\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Where this split difference is less than zero, the person negative-split the race by that fraction.\\n\",\n \"Let's do a distribution plot of this split fraction (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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3eAb2eQUvu4ZACEZFCGLhERAph4BIRKYSBS5ogCAJnBpDm8aIZyfLWW2thNveAzeb0+L4EQUDBznIEG3m6kraxh0uyBAUFISgoyOP7ae7Z2uodsNd7PtyJPIldBpJl8+ZNMJkCMWFCssf2wZ4t+Zou9XAzMzNbtaWnp3d7MaQdu3fvxI4dOzy+H/ZsyZd02HXIzc1FZWUlysrKcOHCBVe70+nE999/7/HiiIh8SYeBO3nyZBw/fhzHjh1zPVcMAHQ6He655x6PF0dE5Es6DNxhw4Zh2LBhGDNmDPr166dUTUREPqlLVyP++c9/IiMjA5cuXWqxUMf27ds9VhgRka/pUuC+/PLLSE5ORmRkJCefEwBg3boNfv8gQSJ3dSlw9Xo9Zs2a5elaiIh8Wpemhd155504duyYp2shDfnv/16Ht99+W+0yVMff+MgdXerhnj59GsnJybj11lsRGBjoaucYrv/65JOPYTCISE6eqnYpqgk2iijcexy/emAQFyGnLulS4C5YsMDTdRBpkr3BoXYJpCFdCty77rrL03UQEfm8LgXuqFGjIAgCJElyjVmZzWZ88sknHi2OiMiXdClwjx496nrtcDiwe/fuFm3kf4xGIwwGLipD5A63l2fU6/WYMGECPv30U0/UQxrxn//5Lt5//321yyDSlC51US5evOh6LUkSDh8+jMuXL3uqJiIin+T2GC4A9OnTB1lZWR4tjLzb2rX5CA42YNq0OWqXQqQZbo/hEgHA558fgMEgMnCJ3NClwG1qasK6devwySefwOl0YuzYsXjqqacgirxoQkTUVV26aPbaa6/hs88+w4wZMzBr1ix89dVXWLlypadrIz/FJ/SSr+pSF/Wvf/0r/vSnP0Gv1wMAHnjgAUyaNKnNR+8Q3Yzm55gBwKzxkSpXQ9S9uhS4kiS5whYADAZDi6/J/9xyyy0IDPTMOWCr5+2y5Ju6FLgRERF45ZVXMG3aNADA+++/z9t9/dxrr63merhEburSGG5ubi4uX76MJ554Ao8//jhqa2uRk5Pj6dqIiHxKh4F79epVvPjii/jss8+wYsUKlJaW4u6774ZOp4PJZFKqRvJCb775GpYvX652GUSa0mHgvvnmm7DZbC2e0Lt06VJcvnwZq1ev9nhx5L2+/voQysrK1C6DSFM6DNyPP/4Yr732Gvr06eNqCwsLw8qVK/HnP/9Z9k737NmDpKQkxMXF4T/+4z8AAKWlpUhISEBMTAxWrVrl2ra8vBzJycmIjY1FVlYWnE6n7P0SEampw8DV6/UwGo2t2k0mEwwGg6wdnj59Grm5ucjPz8f27dtx5MgR7Nu3D5mZmcjPz0dJSQkOHz6Mffv2AQAyMjKQk5ODXbt2QZIkFBYWytoveb8b599yKi75mg4DNyAgADabrVW7zWaT3dP86KOPMH78ePTr1w96vR6rVq1CUFAQBg4ciAEDBkAURSQkJMBqtaKiogINDQ2IiooCACQlJcFqtcraL3m35vm3m/f8HQAQYtRj26cnVK6KqHt1GLgTJ05EdnY26urqXG11dXXIzs5GTEyMrB2eOnUKjY2NmD17NiZNmoRNmzahqqoKZrPZtY3FYkFlZWWrdrPZjMrKSln7pe4VFtYP/fv379bPtNU7YK//6R/yOj6+hnxMh/NwZ8yYgdzcXIwdOxZ33nknmpqa8N133yEhIQFPP/20rB02Njbiyy+/xIYNGxAcHIz58+cjKCio1XbXr052Y7s7+vTx/GwKs7mHx/fhbd59t/uf2KvXixD1OugbJYh6HURR7LCts/c9/TkAoBdF9O3rnzN2/PG8v56c4+8wcAMCArB06VL85je/wZEjRxAQEIBhw4YhLCxMdpF9+/bF6NGjERoaCgB4+OGHYbVaodPpXNtUVVXBYrEgLCwMNTU1rvbq6mpYLBa39nf+vA1NTZ57oqo/T/7vjmO//h9Qh8MJp05w/d8pCh22dfa+pz8HRhEOpxM1NTa/e2qvP5/3QPvH31kId+nGh9tuuw0xMTF45JFHbipsAeDBBx/E/v37cfnyZTQ2NuKvf/0r4uLicOLECddww44dOxAdHY3w8HAEBga6ph8VFxcjOjr6pvZP3WPlylfw0ksv3dRnNI/bFuws52I15BcUX19x+PDhmDNnDlJSUuBwODB27FhMmTIFd9xxB9LT03HlyhWMGzcOcXFxAIC8vDxkZ2fDbrcjMjISqampSpdMbTh2rPymnmnWPCOB6yaQP1FlQdvJkydj8uTJLdpGjx6Nbdu2tdo2IiICRUVFSpVGCmju2QYbuZ4y+Ree8eRRbV38bO7Z+tmwJ5H7T+0l6ipBEPDBx/+44WYGAdtLW8+v5RAu+QP2cEmWgQNvR1BQ53cb2hscrsBt7uneOL+WNzmQv2DgkiwvvbS0S1ODggNFrC85AqDjJzjwJgfyBwxc8jhbvQMhRj3WlxzxyQtlN/bgidrDMVyS5eWXc7Bw4UK3vufGW3d9QXMPnnOJqSt8r7tBijh16uRNzcP1JZxLTF3FHi4RkUIYuERECmHgEhEphINwJMvgwUMQFKRXuwwiTWHgkiwLF2b6/RJ9RO7ikAIRkUIYuCRLZmYG0tPT233/xgdCEhGHFEimyspz7c7D5fKLRG1jD5c8whfvKiO6WQxcIiKFMHCJiBTCQTaSZfjwKAQHB6pdBpGmMHBJlmeffZ7zcIncxCEFom7CqXDUGQYuyfL88+mYO3eu2mV4jeYF1rkuLnWEQwoky8WLF7ke7g24Li51hj1cIiKFMHCJiBTCwCXqZhzDpfYwcEmWkSNHY+zYsWqX4XVCjHoU7j3O0KU28aoHyZKWNp/zcNthb+DFM2obe7hERAph4JIsTz89F9OmTVO7DCJN4ZACydLQ0ICmJp4+RO5gD5eISCEMXOp2vEJP1DYGLnUrQRCwvfSE2mUQeSUOwpEs0dEPwGQytvleHadFEbWJgUuyzJgxm/Nwidyk2pDC73//eyxatAgAUF5ejuTkZMTGxiIrKwtO57WHD549exZTp05FXFwc5s2bB7vdrla5REQ3TZXAPXDgAD788EPX1xkZGcjJycGuXbsgSRIKCwsBAEuWLEFKSgqsViuGDh2K/Px8NcqlNsyePR2TJ09WuwwiTVE8cC9evIhVq1bhqaeeAgBUVFSgoaEBUVFRAICkpCRYrVY4HA4cPHgQsbGxLdqJiLRK8cB96aWXsGDBAvTs2RMAUFVVBbPZ7HrfbDajsrIStbW1MJlMEEWxRTsRkVYpetHsgw8+QP/+/TF69Ghs2bIFACBJUqvtBEFot91dffqY3C/UTWZzD4/vw9s0P+2hrWMXRRF6vQhRr2vxWt8odWub2p9z47E2bxcgCOjb1/Pnndr88by/npzjVzRwS0pKUF1djcTERFy6dAl1dXUQBAE1NTWubaqrq2GxWBAaGgqbzYbGxkbodDpXu7vOn7ehqal1eHcXf71Sf/WqEwaD2OrYBUGA0+mEw+GEUyfAKQqu193dpvbnwCi2ONbm/wsBQE2Nrc1Og6/w1/O+WXvH31kIKxq469evd73esmULvvjiCyxfvhwTJ05EWVkZ7rvvPhQXFyM6Ohp6vR4jRoxASUkJEhISXO3kHWJi4mEyBapdBpGmeMU83Ly8PGRnZ8NutyMyMhKpqakAgNzcXCxatAhr1qxB//798frrr6tcKTX79a9T/L6XQ+Qu1QI3KSkJSUlJAICIiAgUFRW12iY8PBwbNmxQujTqgvr6etTXtzx9BEHgOgpEHfCKHi5pzzPPpMFgELFmzbVhIkEQULCzHMFGnlJE7eHiNdRtbPUO2OudapdB5LUYuERECmHgEhEphIFLRKQQXuEgWSZNegw9erS9Hi79dFekL9/8QO5j4JIsiYlJnIfbjuBAEetLjgAAZsYPYeiSCwOXZKmtrYVO5wCgV7sUr2Sr51MvqDWO4ZIsL7zwLNLS0tQug0hTGLhERAph4BIRKYSBS0SkEAYuEZFCOEuBZHn88Smch0vkJgYuyRIbO57zcIncxMAlWc6d+wFXr16GwdBT7VK8WnvP5yP/xDFckiUrayGee+45tcvwaiFGPQr3Huei7OTCwCXyIHsD7zijnzBwiYgUwsAlIlIIA5eISCGcpUCypKbOQs+eQWqXQaQpDFySZdy4hzgPl8hNDFyS5eTJ73HpUgh69QpTuxQizWDgkixLl+bCYBCxZs16tUsh0gxeNCMiUggDl4hIIQxcIiKFMHDppgmCwPUCiLqAF81Ilrlz56FXryAIgoCCneUINvJUIuoM/5aQLKNGjYHZ3AM1NTbY6h3gCoTta+79c5lG4pACyXL0aDkOHz6sdhleLzhQxPqSIyjYWc5hF2IPl+R59dVXYDCIePvtArVL8Xq2ei7RSNewh0tEpBAGLhGRQhi4REQKYeASKYTzlUmVi2ZvvfUWdu7cCQAYN24cFi5ciNLSUixfvhxXrlxBfHw8FixYAAAoLy9HdnY2bDYbRowYgSVLlkAUea1PbenpC3DLLcFql6EZIUY91pccAQDMjB/CKWJ+SvEebmlpKfbv348PP/wQxcXF+Pbbb7Fjxw5kZmYiPz8fJSUlOHz4MPbt2wcAyMjIQE5ODnbt2gVJklBYWKh0ydSGqKh78a//+q9ql6EptnoHZyz4OcUD12w2Y9GiRTAYDNDr9fjZz36GkydPYuDAgRgwYABEUURCQgKsVisqKirQ0NCAqKgoAEBSUhKsVqvSJVMbDh36Gw4ePKh2GUSaovjv5nfeeafr9cmTJ1FSUoLp06fDbDa72i0WCyorK1FVVdWi3Ww2o7Ky0q399eljuvmiO2E29/D4PrzNO++sBgAUFRVBrxch6nXQN0oQ9TqIouhqu/51W+/fTJvanwPA7c8GgL59PX9OKsEfz/vryTl+1QZDjx8/jt/85jd48cUXIYoiTpw40eJ9QRDaHOdy96LD+fM2NDV5brzMXx8zc/WqEwaDiPPn7XA4nHDqBNf/nWLbr7u7Te3PgVGE0+l067MBoKbGpvkxXH8975u1d/ydhbAqsxTKysowc+ZMPP/883jssccQFhaGmpoa1/tVVVWwWCyt2qurq2GxWNQomdqxvfRE5xsREQAVAveHH37A008/jby8PEyYMAEAMHz4cJw4cQKnTp1CY2MjduzYgejoaISHhyMwMBBlZWUAgOLiYkRHRytdMnWgroEXgYi6SvEhhXXr1uHKlStYsWKFq+2JJ57AihUrkJ6ejitXrmDcuHGIi4sDAOTl5SE7Oxt2ux2RkZFITU1VumQiom6heOBmZ2cjOzu7zfe2bdvWqi0iIgJFRUWeLovclJGRid69g/FJOXu47mrv+gT5Pt5pRrJERAzB0KFD1S5Dc0KMehTuPc47zvwUb9kiWT77rBS9egUB8O+pQXLYOe7ttxi4JMu7766BwSDioV9nqV0KkWZwSIGISCEMXCIihTBwiYgUwsAlIlIIL5qRLDk5S9C7dwh2f+W/99MTuYs9XJLl9tvvwKBBg9QuQ7P49Af/xB4uybJv3x707BkEwNzpttRScKDoevrDrPGRkCSJd575CfZwSZb33luPtWvXql2GZtnqHZAkYH3JERTsLGdv10+wh0ukIj5yx7+wh0tEpBAGLhGRQhi4REQK4RguybJs2UqEhoZga2mV2qUQaQYDl2Tp3//W/3v6LAO3O3BRcv/AIQVymyAIWLziXTybtVrtUnxC86LkAQEBnB7m49jDJVm+Ki2BTheAiKj71S7FJ0iS5LoZYmb8EPZ2fRQDl8hLcE6u7+OQAhGRQhi4REQKYeASESmEY7gkyyOPP48epkC1yyDSFAYuuaV5HVdjcE8EhxjVLscnNU8N40wF38PApS4TBAEFO8sRbBTx90N7EWTU477Rj6pdlk8JMepbrJXb1NSkckXUnTiGS26x1Ttgr3fi74c+xuGDf1G7HJ/UvFZu4d7jvBHCxzBwibyUvYHzcn0NA5eISCEMXCIvxodN+hYGLnUZ/+Irq/lhkwU7y7mwjY/gLAXqEkEQsL30hOvruKmZ6Ml5uB5nq3e0mLnAhW20jYFLnWr+tbbuuos4oj4QegPn4SqFC9v4BgYudej6ubfXO3JwF4yBIkaNm6hSZf6JC5VrG8dwqVPNc2+v9/23pTj29X6VKvJPNy5UzjFd7WEPl0hDmhcqDzaKEAQBv3pgECRJ4u3AGqGJHu727dsxfvx4PProo9i4caPa5fiF5h4Ue1Hep/k3DnuDA4IgICAgAAU7y1vMZrjx58afpXfw+h5uZWUlVq1ahS1btsBgMOCJJ57AyJEjMWjQILVL04zOxv2u/4vY3FvaceAkai7Wtxq7Je/RPG0s2Ci6ZjNsLz2Bmov1AK6txdD887xxlgPHgtXh9X+bSktLMWrUKNxyyy0AgNjYWFitVjzzzDNd+v6AAPf+VZfTC9DpvPcXBUEQ8Je/ncbD9w5o8y+YIAj4fwdOwmjQAQLw8L0DAACmID2uOhoRFChC1AUgKFBs8fq2W/shQBcAS+9gV1v9FWer7a5/3db7N9Om9ueEBBsQKAaoevwXLje0amv+ue38/BSMBh0CDTr0DDEA+Olc7eic6CpvPu+7Q2d/Nu5mCwAIkpf/M/fOO++grq4OCxYsAAB88MEH+Oabb7B06VKVKyMico/X/xPVXq+MiEhrvD5ww8LCUFNT4/q6qqoKFotFxYqIiOTx+sAdM2YMDhw4gAsXLqC+vh67d+9GdHS02mUREbnN6y+ahYWFYcGCBUhNTYXD4cDkyZNx9913q10WEZHbvP6iGRGRr/D6IQUiIl/BwCUiUggDl4hIIQxcIiKFMHDddPbsWUydOhVxcXGYN28e7HZ7q22qqqowe/ZsJCYm4rHHHsOBAwdUqLT7dLZ4UHl5OZKTkxEbG4usrCw4nc42PkW7Ojv+P//5z0hMTMSkSZMwf/58XLp0SYUqPaOrC0d9/PHHeOihhxSsTBmdHf/333+P6dOnY9KkSZg9e3bnP3uJ3JKWlibt2LFDkiRJeuutt6SVK1e22ub555+XNmzYIEmSJH333XfSmDFjJKfTqWid3eXcuXPSgw8+KNXW1kp2u11KSEiQjh8/3mKbCRMmSF999ZUkSZK0ePFiaePGjSpU6hmdHf+PP/4ojR07Vjp37pwkSZL0xhtvSEuXLlWr3G7VlZ+9JElSdXW1FBcXJz344IMqVOk5nR1/U1OTFBMTI+3bt0+SJEl69dVX28yD67GH6waHw4GDBw8iNjYWAJCUlASr1dpqu5iYGCQkJAAABg4ciCtXrqCurk7RWrvL9YsHBQcHuxYPalZRUYGGhgZERUUBaP/PRKs6O36Hw4Hf/e53CAsLAwAMHjwYP/zwg1rldqvOjr1ZdnZ2lxeT0pLOjv/bb79FcHCw60asp556ClOnTu3wMxm4bqitrYXJZIIoXrtfxGw2o7KystV2MTEx6NWrFwBg3bp1GDJkCHr06KFord2lqqoKZrPZ9bXFYmlxzDe+396fiVZ1dvy9e/fGI488AgBoaGjA2rVrXV9rXWfHDgDvvfceIiMjMXz4cKXL87jOjv+f//wn+vbtixdffBEJCQnIzc1FcHBwh5/p9XeaqWXnzp1Yvnx5i7bbb7+91XYdLaRTUFCAzZs34/333+/u8hQjdbJ4UGfva11Xj+/HH3/E/PnzERERgccee0yJ0jyus2P/+9//jt27d6OgoADnzp1TsjRFdHb8TqcTX3zxBd5//30MGzYMb7zxBlasWIEVK1a0+5kM3HbEx8cjPj6+RZvD4cDIkSPR2NgInU6H6urqdhfSWblyJfbt24eNGzeiX79+SpTsEWFhYfjyyy9dX9+4eNCNiwt19GeiRZ0df3Pb7NmzMWrUKGRmZipdosd0duxWqxXV1dVITk6Gw+FAVVUVUlJSsGnTJjXK7XadHb/ZbMbAgQMxbNgwAMDEiRPx7LPPdviZHFJwg16vx4gRI1BSUgIAKC4ubnMhnYKCAnz++ef44x//qOmwBTpfPCg8PByBgYEoKysD0P6fiVZ1dvyNjY146qmnEB8fj6ysLJ/q3Xd27M8++yx27dqFrVu3Yu3atbBYLD4TtkDnx3/PPffgwoULOHr0KABgz549+PnPf97xh3rqCp+vOnPmjDRt2jQpPj5eevLJJ6WLFy9KkiRJmzZtkt544w2pqalJGjFihPTAAw9IkyZNcv3XfBVbi7Zt2yZNmDBBiomJkdauXStJkiTNmTNH+uabbyRJkqTy8nIpOTlZiouLk/793/9dunLliprldruOjn/37t3S4MGDW/ysMzMzVa64+3T2s292+vRpn5ulIEmdH/+hQ4ek5ORkafz48dKTTz4p1dTUdPh5XLyGiEghHFIgIlIIA5eISCEMXCIihTBwiYgUwsAlIlIIA5f8wqJFi7Bu3ToAQGJiIi5fvowff/wRqampnX5vY2Mj5s2bh9jYWE3fNUjq451m5He2bt0KADhz5gz+93//t9PtKysrsX//fhw6dAg6nc7T5ZEPY+CSJtntdixevBinTp1CQEAAfv7zn2PChAnIy8tDWFgYTp8+DaPRiBUrVuBnP/tZi+8dPHgwDhw4gMWLF6OhoQGJiYnYsmVLm2Fqs9kwZ84cOJ1OJCUlYfXq1Rg/fjwefvhhHD16FHl5eTh27Bg2b94Mh8OBS5cuYe7cuUhJSQEAvPPOO/jwww8hiiIGDhyIFStWaHYhI7p5HFIgTfroo49gt9uxdetWFBUVAbjWYz1y5AiefPJJbN++HUlJScjIyGj3M5YvXw6j0YitW7e223M1mUxYu3ata7t/+Zd/gcPhwIMPPohdu3bhjjvuwAcffIC1a9eiuLgYq1atwquvvgoA+Mtf/oItW7Zg8+bN2LFjB2677TYOSfg59nBJk+677z6sWrUK06dPx5gxYzBjxgxcuHABERERGDFiBAAgOTkZL7/8Mmpra7t9/837CAkJwdtvv419+/bh5MmTOHr0qGvt4wMHDiAuLs61VOfixYu7vQ7SFvZwSZMGDBiAjz76CGlpabDZbJg1axZqa2tb9VQlSfLIuGvzuqfnzp3DL3/5S1RUVOC+++7Db3/7W9c2Op2uxWI2ly9fxpkzZ7q9FtIOBi5p0qZNm7B48WLcf//9yMjIwP3334+NGzfi6NGjrtWbNm/ejHvvvRc9e/Zs8zNEUURjY2Ob65521eHDhxEaGor58+fj3/7t37B3714A12Y2jBkzBh999BFsNhsAYPXq1SgoKJC9L9I+DimQJv3yl7/EF198gfHjxyMoKAi33norUlNTcfr0abzxxhuoqKhAaGgoVq5c2e5nmM1mREZGIj4+Hn/84x/Ru3dvt+sYO3YsioqKEBcXh6CgINx9990IDQ3FqVOnMG7cOPzjH//AlClTAACDBg3C0qVLZR8zaR9XCyOf8fnnn2Pp0qXYsWOH2qUQtYk9XCIAKSkpbT7yHgA2btwIk8mkcEXki9jDJSJSCC+aEREphIFLRKQQBi4RkUIYuERECmHgEhEphIFLRKSQ/w8SUlW3410NMQAAAABJRU5ErkJggg==\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.displot(data['split_frac'], kde=False)\\n\",\n \"plt.axvline(0, color=\\\"k\\\", linestyle=\\\"--\\\");\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"251\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sum(data.split_frac < 0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Out of nearly 40,000 participants, there were only 250 people who negative-split their marathon.\\n\",\n \"\\n\",\n \"Let's see whether there is any correlation between this split fraction and other variables. We'll do this using a `PairGrid`, which draws plots of all these correlations (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"g = sns.PairGrid(data, vars=['age', 'split_sec', 'final_sec', 'split_frac'],\\n\",\n \" hue='gender', palette='RdBu_r')\\n\",\n \"g.map(plt.scatter, alpha=0.8)\\n\",\n \"g.add_legend();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: faster runners tend to have closer to even splits on their marathon time. Let's zoom in on the histogram of split fractions separated by gender, shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.kdeplot(data.split_frac[data.gender=='M'], label='men', shade=True)\\n\",\n \"sns.kdeplot(data.split_frac[data.gender=='W'], label='women', shade=True)\\n\",\n \"plt.xlabel('split_frac');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The interesting thing here is that there are many more men than women who are running close to an even split!\\n\",\n \"It almost looks like a bimodal distribution among the men and women. Let's see if we can suss out what's going on by looking at the distributions as a function of age.\\n\",\n \"\\n\",\n \"A nice way to compare distributions is to use a *violin plot*, shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ],\n \"text/plain\": [\n \" age gender split final split_sec final_sec \\\\\\n\",\n \"0 33 M 0 days 01:05:38 0 days 02:08:51 3938.0 7731.0 \\n\",\n \"1 32 M 0 days 01:06:26 0 days 02:09:28 3986.0 7768.0 \\n\",\n \"2 31 M 0 days 01:06:49 0 days 02:10:42 4009.0 7842.0 \\n\",\n \"3 38 M 0 days 01:06:16 0 days 02:13:45 3976.0 8025.0 \\n\",\n \"4 31 M 0 days 01:06:32 0 days 02:13:59 3992.0 8039.0 \\n\",\n \"\\n\",\n \" split_frac age_dec \\n\",\n \"0 -0.018756 30 \\n\",\n \"1 -0.026262 30 \\n\",\n \"2 -0.022443 30 \\n\",\n \"3 0.009097 30 \\n\",\n \"4 0.006842 30 \"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['age_dec'] = data.age.map(lambda age: 10 * (age // 10))\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"men = (data.gender == 'M')\\n\",\n \"women = (data.gender == 'W')\\n\",\n \"\\n\",\n \"with sns.axes_style(style=None):\\n\",\n \" sns.violinplot(x=\\\"age_dec\\\", y=\\\"split_frac\\\", hue=\\\"gender\\\", data=data,\\n\",\n \" split=True, inner=\\\"quartile\\\",\\n\",\n \" palette=[\\\"lightblue\\\", \\\"lightpink\\\"]);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see where the distributions among men and women differ: the split distributions of men in their 20s to 50s show a pronounced overdensity toward lower splits when compared to women of the same age (or of any age, for that matter).\\n\",\n \"\\n\",\n \"Also surprisingly, it appears that the 80-year-old women seem to outperform *everyone* in terms of their split time, although this is likely a small number effect, as there are only a handful of runners in that range:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"7\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"(data.age > 80).sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Back to the men with negative splits: who are these runners? Does this split fraction correlate with finishing quickly? We can plot this very easily. We'll use `regplot`, which will automatically fit a linear regression model to the data (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"g = sns.lmplot(x='final_sec', y='split_frac', col='gender', data=data,\\n\",\n \" markers=\\\".\\\", scatter_kws=dict(color='c'))\\n\",\n \"g.map(plt.axhline, y=0.0, color=\\\"k\\\", ls=\\\":\\\");\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Apparently, among both men and women, the people with fast splits tend to be faster runners who are finishing within ~15,000 seconds, or about 4 hours. People slower than that are much less likely to have a fast second split.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/04.15-Further-Resources.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Further Resources\\n\",\n \"\\n\",\n \"A single part of a book can never hope to cover all the available features and plot types available in Matplotlib.\\n\",\n \"As with other packages we've seen, liberal use of IPython's tab completion and help functions (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very helpful when exploring Matplotlib's API.\\n\",\n \"In addition, Matplotlib’s [online documentation](http://matplotlib.org/) can be a helpful reference.\\n\",\n \"See in particular the [Matplotlib gallery](https://matplotlib.org/stable/gallery/), which shows thumbnails of hundreds of different plot types, each one linked to a page with the Python code snippet used to generate it.\\n\",\n \"This allows you to visually inspect and learn about a wide range of different plotting styles and visualization techniques.\\n\",\n \"\\n\",\n \"For a book-length treatment of Matplotlib, I would recommend *Interactive Applications Using Matplotlib* (Packt), written by Matplotlib core developer Ben Root.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Other Python Visualization Libraries\\n\",\n \"\\n\",\n \"Although Matplotlib is the most prominent Python visualization library, there are other more modern tools that are worth exploring as well.\\n\",\n \"I'll mention a few of them briefly here:\\n\",\n \"\\n\",\n \"- [Bokeh](http://bokeh.pydata.org) is a JavaScript visualization library with a Python frontend that creates highly interactive visualizations capable of handling very large and/or streaming datasets.\\n\",\n \"- [Plotly](http://plot.ly) is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. It is actively developed and provides a wide range of interactive chart types.\\n\",\n \"- [HoloViews](https://holoviews.org/) is a more declarative, unified API for generating charts in a variety of backends, including Bokeh and Matplotlib.\\n\",\n \"- [Vega](https://vega.github.io/) and [Vega-Lite](https://vega.github.io/vega-lite) are declarative graphics representations, and are the product of years of research into how to think about data visualization and interaction. The reference rendering implementation is JavaScript, and the [Altair package](https://altair-viz.github.io/) provides a Python API to generate these charts.\\n\",\n \"\\n\",\n \"The visualization landscape in the Python world is constantly evolving, and I expect that this list may be out of date by the time this book is published.\\n\",\n \"Additionally, because Python is used in so many domains, you'll find many other visualization tools built for more specific use cases.\\n\",\n \"It can be hard to keep track of all of them, but a good resource for learning about this wide variety of visualization tools is https://pyviz.org/, an open, community-driven site containing tutorials and examples of many different visualization tools.\"\n ]\n }\n ],\n \"metadata\": {\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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{
"path": "notebooks/05.00-Machine-Learning.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Machine Learning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This final part is an introduction to the very broad topic of machine learning, mainly via Python's [Scikit-Learn](http://scikit-learn.org) package.\\n\",\n \"You can think of machine learning as a class of algorithms that allow a program to detect particular patterns in a dataset, and thus \\\"learn\\\" from the data to draw inferences from it.\\n\",\n \"This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here.\\n\",\n \"Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in [Further Machine Learning Resources](05.15-Learning-More.ipynb)).\\n\",\n \"Rather, the goals here are:\\n\",\n \"\\n\",\n \"- To introduce the fundamental vocabulary and concepts of machine learning\\n\",\n \"- To introduce the Scikit-Learn API and show some examples of its use\\n\",\n \"- To take a deeper dive into the details of several of the more important classical machine learning approaches, and develop an intuition into how they work and when and where they are applicable\\n\",\n \"\\n\",\n \"Much of this material is drawn from the Scikit-Learn tutorials and workshops I have given on several occasions at PyCon, SciPy, PyData, and other conferences.\\n\",\n \"Any clarity in the following pages is likely due to the many workshop participants and co-instructors who have given me valuable feedback on this material over the years!\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/05.01-What-Is-Machine-Learning.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# What Is Machine Learning?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Before we take a look at the details of several machine learning methods, let's start by looking at what machine learning is, and what it isn't.\\n\",\n \"Machine learning is often categorized as a subfield of artificial intelligence, but I find that categorization can be misleading.\\n\",\n \"The study of machine learning certainly arose from research in this context, but in the data science application of machine learning methods, it's more helpful to think of machine learning as a means of *building models of data*.\\n\",\n \"\\n\",\n \"In this context, \\\"learning\\\" enters the fray when we give these models *tunable parameters* that can be adapted to observed data; in this way the program can be considered to be \\\"learning\\\" from the data.\\n\",\n \"Once these models have been fit to previously seen data, they can be used to predict and understand aspects of newly observed data.\\n\",\n \"I'll leave to the reader the more philosophical digression regarding the extent to which this type of mathematical, model-based \\\"learning\\\" is similar to the \\\"learning\\\" exhibited by the human brain.\\n\",\n \"\\n\",\n \"Understanding the problem setting in machine learning is essential to using these tools effectively, and so we will start with some broad categorizations of the types of approaches we'll discuss here.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Categories of Machine Learning\\n\",\n \"\\n\",\n \"Machine learning can be categorized into two main types: supervised learning and unsupervised learning.\\n\",\n \"\\n\",\n \"*Supervised learning* involves somehow modeling the relationship between measured features of data and some labels associated with the data; once this model is determined, it can be used to apply labels to new, unknown data.\\n\",\n \"This is sometimes further subdivided into classification tasks and regression tasks: in *classification*, the labels are discrete categories, while in *regression*, the labels are continuous quantities.\\n\",\n \"You will see examples of both types of supervised learning in the following section.\\n\",\n \"\\n\",\n \"*Unsupervised learning* involves modeling the features of a dataset without reference to any label.\\n\",\n \"These models include tasks such as *clustering* and *dimensionality reduction.*\\n\",\n \"Clustering algorithms identify distinct groups of data, while dimensionality reduction algorithms search for more succinct representations of the data.\\n\",\n \"You will also see examples of both types of unsupervised learning in the following section.\\n\",\n \"\\n\",\n \"In addition, there are so-called *semi-supervised learning* methods, which fall somewhere between supervised learning and unsupervised learning.\\n\",\n \"Semi-supervised learning methods are often useful when only incomplete labels are available.\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Qualitative Examples of Machine Learning Applications\\n\",\n \"\\n\",\n \"To make these ideas more concrete, let's take a look at a few very simple examples of a machine learning task.\\n\",\n \"These examples are meant to give an intuitive, non-quantitative overview of the types of machine learning tasks we will be looking at in this part of the book.\\n\",\n \"In later chapters, we will go into more depth regarding the particular models and how they are used.\\n\",\n \"For a preview of these more technical aspects, you can find the Python source that generates the following figures in the online [appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Classification: Predicting Discrete Labels\\n\",\n \"\\n\",\n \"We will first take a look at a simple classification task, in which we are given a set of labeled points and want to use these to classify some unlabeled points.\\n\",\n \"\\n\",\n \"Imagine that we have the data shown in this figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This data is two-dimensional: that is, we have two *features* for each point, represented by the (x,y) positions of the points on the plane.\\n\",\n \"In addition, we have one of two *class labels* for each point, here represented by the colors of the points.\\n\",\n \"From these features and labels, we would like to create a model that will let us decide whether a new point should be labeled \\\"blue\\\" or \\\"red.\\\"\\n\",\n \"\\n\",\n \"There are a number of possible models for such a classification task, but we will start with a very simple one. We will make the assumption that the two groups can be separated by drawing a straight line through the plane between them, such that points on each side of the line all fall in the same group.\\n\",\n \"Here the *model* is a quantitative version of the statement \\\"a straight line separates the classes,\\\" while the *model parameters* are the particular numbers describing the location and orientation of that line for our data.\\n\",\n \"The optimal values for these model parameters are learned from the data (this is the \\\"learning\\\" in machine learning), which is often called *training the model*.\\n\",\n \"\\n\",\n \"See the following figure shows a visual representation of what the trained model looks like for this data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now that this model has been trained, it can be generalized to new, unlabeled data.\\n\",\n \"In other words, we can take a new set of data, draw this line through it, and assign labels to the new points based on this model (see the following figure).\\n\",\n \"This stage is usually called *prediction*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This is the basic idea of a classification task in machine learning, where \\\"classification\\\" indicates that the data has discrete class labels.\\n\",\n \"At first glance this may seem trivial: it's easy to look at our data and draw such a discriminatory line to accomplish this classification.\\n\",\n \"A benefit of the machine learning approach, however, is that it can generalize to much larger datasets in many more dimensions.\\n\",\n \"\\n\",\n \"For example, this is similar to the task of automated spam detection for email. In this case, we might use the following features and labels:\\n\",\n \"\\n\",\n \"- *feature 1*, *feature 2*, etc. $\\\\to$ normalized counts of important words or phrases (\\\"Viagra\\\", \\\"Extended warranty\\\", etc.)\\n\",\n \"- *label* $\\\\to$ \\\"spam\\\" or \\\"not spam\\\"\\n\",\n \"\\n\",\n \"For the training set, these labels might be determined by individual inspection of a small representative sample of emails; for the remaining emails, the label would be determined using the model.\\n\",\n \"For a suitably trained classification algorithm with enough well-constructed features (typically thousands or millions of words or phrases), this type of approach can be very effective.\\n\",\n \"We will see an example of such text-based classification in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb).\\n\",\n \"\\n\",\n \"Some important classification algorithms that we will discuss in more detail are Gaussian naive Bayes (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), and random forest classification (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Regression: Predicting Continuous Labels\\n\",\n \"\\n\",\n \"In contrast with the discrete labels of a classification algorithm, we will next look at a simple regression task in which the labels are continuous quantities.\\n\",\n \"\\n\",\n \"Consider the data shown in the following figure, which consists of a set of points each with a continuous label.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"As with the classification example, we have two-dimensional data: that is, there are two features describing each data point.\\n\",\n \"The color of each point represents the continuous label for that point.\\n\",\n \"\\n\",\n \"There are a number of possible regression models we might use for this type of data, but here we will use a simple linear regression model to predict the points.\\n\",\n \"This simple model assumes that if we treat the label as a third spatial dimension, we can fit a plane to the data.\\n\",\n \"This is a higher-level generalization of the well-known problem of fitting a line to data with two coordinates.\\n\",\n \"\\n\",\n \"We can visualize this setup as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Notice that the *feature 1–feature 2* plane here is the same as in the two-dimensional plot in Figure 37-4; in this case, however, we have represented the labels by both color and three-dimensional axis position.\\n\",\n \"From this view, it seems reasonable that fitting a plane through this three-dimensional data would allow us to predict the expected label for any set of input parameters.\\n\",\n \"Returning to the two-dimensional projection, when we fit such a plane we get the result shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This plane of fit gives us what we need to predict labels for new points.\\n\",\n \"Visually, we find the results shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"As with the classification example, this task may seem trivial in a low number of dimensions.\\n\",\n \"But the power of these methods is that they can be straightforwardly applied and evaluated in the case of data with many, many features.\\n\",\n \"\\n\",\n \"For example, this is similar to the task of computing the distance to galaxies observed through a telescope—in this case, we might use the following features and labels:\\n\",\n \"\\n\",\n \"- *feature 1*, *feature 2*, etc. $\\\\to$ brightness of each galaxy at one of several wavelengths or colors\\n\",\n \"- *label* $\\\\to$ distance or redshift of the galaxy\\n\",\n \"\\n\",\n \"The distances for a small number of these galaxies might be determined through an independent set of (typically more expensive or complex) observations.\\n\",\n \"Distances to remaining galaxies could then be estimated using a suitable regression model, without the need to employ the more expensive observation across the entire set.\\n\",\n \"In astronomy circles, this is known as the \\\"photometric redshift\\\" problem.\\n\",\n \"\\n\",\n \"Some important regression algorithms that we will discuss are linear regression (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)), support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), and random forest regression (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Clustering: Inferring Labels on Unlabeled Data\\n\",\n \"\\n\",\n \"The classification and regression illustrations we just saw are examples of supervised learning algorithms, in which we are trying to build a model that will predict labels for new data.\\n\",\n \"Unsupervised learning involves models that describe data without reference to any known labels.\\n\",\n \"\\n\",\n \"One common case of unsupervised learning is \\\"clustering,\\\" in which data is automatically assigned to some number of discrete groups.\\n\",\n \"For example, we might have some two-dimensional data like that shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"By eye, it is clear that each of these points is part of a distinct group.\\n\",\n \"Given this input, a clustering model will use the intrinsic structure of the data to determine which points are related.\\n\",\n \"Using the very fast and intuitive *k*-means algorithm (see [In Depth: K-Means Clustering](05.11-K-Means.ipynb)), we find the clusters shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"*k*-means fits a model consisting of *k* cluster centers; the optimal centers are assumed to be those that minimize the distance of each point from its assigned center.\\n\",\n \"Again, this might seem like a trivial exercise in two dimensions, but as our data becomes larger and more complex such clustering algorithms can continue to be employed to extract useful information from the dataset.\\n\",\n \"\\n\",\n \"We will discuss the *k*-means algorithm in more depth in [In Depth: K-Means Clustering](05.11-K-Means.ipynb).\\n\",\n \"Other important clustering algorithms include Gaussian mixture models (see [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb)) and spectral clustering (see [Scikit-Learn's clustering documentation](http://scikit-learn.org/stable/modules/clustering.html)).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Dimensionality Reduction: Inferring Structure of Unlabeled Data\\n\",\n \"\\n\",\n \"Dimensionality reduction is another example of an unsupervised algorithm, in which labels or other information are inferred from the structure of the dataset itself.\\n\",\n \"Dimensionality reduction is a bit more abstract than the examples we looked at before, but generally it seeks to pull out some low-dimensional representation of data that in some way preserves relevant qualities of the full dataset.\\n\",\n \"Different dimensionality reduction routines measure these relevant qualities in different ways, as we will see in [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb).\\n\",\n \"\\n\",\n \"As an example of this, consider the data shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Visually, it is clear that there is some structure in this data: it is drawn from a one-dimensional line that is arranged in a spiral within this two-dimensional space.\\n\",\n \"In a sense, you could say that this data is \\\"intrinsically\\\" only one-dimensional, though this one-dimensional data is embedded in two-dimensional space.\\n\",\n \"A suitable dimensionality reduction model in this case would be sensitive to this nonlinear embedded structure and be able to detect this lower-dimensionality representation.\\n\",\n \"\\n\",\n \"The following figure shows a visualization of the results of the Isomap algorithm, a manifold learning algorithm that does exactly this.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Notice that the colors (which represent the extracted one-dimensional latent variable) change uniformly along the spiral, which indicates that the algorithm did in fact detect the structure we saw by eye.\\n\",\n \"As with the previous examples, the power of dimensionality reduction algorithms becomes clearer in higher-dimensional cases.\\n\",\n \"For example, we might wish to visualize important relationships within a dataset that has 100 or 1,000 features.\\n\",\n \"Visualizing 1,000-dimensional data is a challenge, and one way we can make this more manageable is to use a dimensionality reduction technique to reduce the data to 2 or 3 dimensions.\\n\",\n \"\\n\",\n \"Some important dimensionality reduction algorithms that we will discuss are principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) and various manifold learning algorithms, including Isomap and locally linear embedding (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Summary\\n\",\n \"\\n\",\n \"Here we have seen a few simple examples of some of the basic types of machine learning approaches.\\n\",\n \"Needless to say, there are a number of important practical details that we have glossed over, but this chapter was designed to give you a basic idea of what types of problems machine learning approaches can solve.\\n\",\n \"\\n\",\n \"In short, we saw the following:\\n\",\n \"\\n\",\n \"- *Supervised learning*: Models that can predict labels based on labeled training data\\n\",\n \"\\n\",\n \" - *Classification*: Models that predict labels as two or more discrete categories\\n\",\n \" - *Regression*: Models that predict continuous labels\\n\",\n \" \\n\",\n \"- *Unsupervised learning*: Models that identify structure in unlabeled data\\n\",\n \"\\n\",\n \" - *Clustering*: Models that detect and identify distinct groups in the data\\n\",\n \" - *Dimensionality reduction*: Models that detect and identify lower-dimensional structure in higher-dimensional data\\n\",\n \" \\n\",\n \"In the following sections we will go into much greater depth within these categories, and see some more interesting examples of where these concepts can be useful.\\n\",\n \"\\n\",\n \"All of the figures in the preceding discussion are generated based on actual machine learning computations; the code behind them can be found in [Appendix: Figure Code](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Introducing Scikit-Learn\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"There are several Python libraries that provide solid implementations of a range of machine learning algorithms.\\n\",\n \"One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms.\\n\",\n \"Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation.\\n\",\n \"A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is straightforward.\\n\",\n \"\\n\",\n \"This chapter provides an overview of the Scikit-Learn API. A solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches in the following chapters.\\n\",\n \"\\n\",\n \"We will start by covering data representation in Scikit-Learn, then delve into the Estimator API, and finally go through a more interesting example of using these tools for exploring a set of images of handwritten digits.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Data Representation in Scikit-Learn\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented.\\n\",\n \"The best way to think about data within Scikit-Learn is in terms of *tables*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"A basic table is a two-dimensional grid of data, in which the rows represent individual elements of the dataset, and the columns represent quantities related to each of these elements.\\n\",\n \"For example, consider the [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), famously analyzed by Ronald Fisher in 1936.\\n\",\n \"We can download this dataset in the form of a Pandas `DataFrame` using the [Seaborn](http://seaborn.pydata.org/) library, and take a look at the first few items:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" sepal_length sepal_width petal_length petal_width species\\n\",\n \"0 5.1 3.5 1.4 0.2 setosa\\n\",\n \"1 4.9 3.0 1.4 0.2 setosa\\n\",\n \"2 4.7 3.2 1.3 0.2 setosa\\n\",\n \"3 4.6 3.1 1.5 0.2 setosa\\n\",\n \"4 5.0 3.6 1.4 0.2 setosa\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"iris = sns.load_dataset('iris')\\n\",\n \"iris.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Here each row of the data refers to a single observed flower, and the number of rows is the total number of flowers in the dataset.\\n\",\n \"In general, we will refer to the rows of the matrix as *samples*, and the number of rows as `n_samples`.\\n\",\n \"\\n\",\n \"Likewise, each column of the data refers to a particular quantitative piece of information that describes each sample.\\n\",\n \"In general, we will refer to the columns of the matrix as *features*, and the number of columns as `n_features`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### The Features Matrix\\n\",\n \"\\n\",\n \"The table layout makes clear that the information can be thought of as a two-dimensional numerical array or matrix, which we will call the *features matrix*.\\n\",\n \"By convention, this matrix is often stored in a variable named `X`.\\n\",\n \"The features matrix is assumed to be two-dimensional, with shape `[n_samples, n_features]`, and is most often contained in a NumPy array or a Pandas `DataFrame`, though some Scikit-Learn models also accept SciPy sparse matrices.\\n\",\n \"\\n\",\n \"The samples (i.e., rows) always refer to the individual objects described by the dataset.\\n\",\n \"For example, a sample might represent a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements.\\n\",\n \"\\n\",\n \"The features (i.e., columns) always refer to the distinct observations that describe each sample in a quantitative manner.\\n\",\n \"Features are often real-valued, but may be Boolean or discrete-valued in some cases.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### The Target Array\\n\",\n \"\\n\",\n \"In addition to the feature matrix `X`, we also generally work with a *label* or *target* array, which by convention we will usually call `y`.\\n\",\n \"The target array is usually one-dimensional, with length `n_samples`, and is generally contained in a NumPy array or Pandas `Series`.\\n\",\n \"The target array may have continuous numerical values, or discrete classes/labels.\\n\",\n \"While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, `[n_samples, n_targets]` target array, we will primarily be working with the common case of a one-dimensional target array.\\n\",\n \"\\n\",\n \"A common point of confusion is how the target array differs from the other feature columns. The distinguishing characteristic of the target array is that it is usually the quantity we want to *predict from the features*: in statistical terms, it is the dependent variable.\\n\",\n \"For example, given the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the `species` column would be considered the target array.\\n\",\n \"\\n\",\n \"With this target array in mind, we can use Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import seaborn as sns\\n\",\n \"sns.pairplot(iris, hue='species', height=1.5);\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"For use in Scikit-Learn, we will extract the features matrix and target array from the `DataFrame`, which we can do using some of the Pandas `DataFrame` operations discussed in [Part 3](03.00-Introduction-to-Pandas.ipynb):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(150, 4)\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_iris = iris.drop('species', axis=1)\\n\",\n \"X_iris.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(150,)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_iris = iris['species']\\n\",\n \"y_iris.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"To summarize, the expected layout of features and target values is visualized in the following figure.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Features-and-Labels-Grid)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With this data properly formatted, we can move on to consider Scikit-Learn's Estimator API.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## The Estimator API\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\\n\",\n \"\\n\",\n \"- *Consistency*: All objects share a common interface drawn from a limited set of methods, with consistent documentation.\\n\",\n \"\\n\",\n \"- *Inspection*: All specified parameter values are exposed as public attributes.\\n\",\n \"\\n\",\n \"- *Limited object hierarchy*: Only algorithms are represented by Python classes; datasets are represented\\n\",\n \" in standard formats (NumPy arrays, Pandas `DataFrame` objects, SciPy sparse matrices) and parameter\\n\",\n \" names use standard Python strings.\\n\",\n \"\\n\",\n \"- *Composition*: Many machine learning tasks can be expressed as sequences of more fundamental algorithms,\\n\",\n \" and Scikit-Learn makes use of this wherever possible.\\n\",\n \"\\n\",\n \"- *Sensible defaults*: When models require user-specified parameters, the library defines an appropriate default value.\\n\",\n \"\\n\",\n \"In practice, these principles make Scikit-Learn very easy to use, once the basic principles are understood.\\n\",\n \"Every machine learning algorithm in Scikit-Learn is implemented via the Estimator API, which provides a consistent interface for a wide range of machine learning applications.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Basics of the API\\n\",\n \"\\n\",\n \"Most commonly, the steps in using the Scikit-Learn Estimator API are as follows:\\n\",\n \"\\n\",\n \"1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn.\\n\",\n \"2. Choose model hyperparameters by instantiating this class with desired values.\\n\",\n \"3. Arrange data into a features matrix and target vector, as outlined earlier in this chapter.\\n\",\n \"4. Fit the model to your data by calling the `fit` method of the model instance.\\n\",\n \"5. Apply the model to new data:\\n\",\n \" - For supervised learning, often we predict labels for unknown data using the `predict` method.\\n\",\n \" - For unsupervised learning, we often transform or infer properties of the data using the `transform` or `predict` method.\\n\",\n \"\\n\",\n \"We will now step through several simple examples of applying supervised and unsupervised learning methods.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Supervised Learning Example: Simple Linear Regression\\n\",\n \"\\n\",\n \"As an example of this process, let's consider a simple linear regression—that is, the common case of fitting a line to $(x, y)$ data.\\n\",\n \"We will use the following simple data for our regression example (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"rng = np.random.RandomState(42)\\n\",\n \"x = 10 * rng.rand(50)\\n\",\n \"y = 2 * x - 1 + rng.randn(50)\\n\",\n \"plt.scatter(x, y);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With this data in place, we can use the recipe outlined earlier. Let's walk through the process: \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"#### 1. Choose a class of model\\n\",\n \"\\n\",\n \"In Scikit-Learn, every class of model is represented by a Python class.\\n\",\n \"So, for example, if we would like to compute a simple `LinearRegression` model, we can import the linear regression class:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Note that other more general linear regression models exist as well; you can read more about them in the [`sklearn.linear_model` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"#### 2. Choose model hyperparameters\\n\",\n \"\\n\",\n \"An important point is that *a class of model is not the same as an instance of a model*.\\n\",\n \"\\n\",\n \"Once we have decided on our model class, there are still some options open to us.\\n\",\n \"Depending on the model class we are working with, we might need to answer one or more questions like the following:\\n\",\n \"\\n\",\n \"- Would we like to fit for the offset (i.e., *y*-intercept)?\\n\",\n \"- Would we like the model to be normalized?\\n\",\n \"- Would we like to preprocess our features to add model flexibility?\\n\",\n \"- What degree of regularization would we like to use in our model?\\n\",\n \"- How many model components would we like to use?\\n\",\n \"\\n\",\n \"These are examples of the important choices that must be made *once the model class is selected*.\\n\",\n \"These choices are often represented as *hyperparameters*, or parameters that must be set before the model is fit to data.\\n\",\n \"In Scikit-Learn, hyperparameters are chosen by passing values at model instantiation.\\n\",\n \"We will explore how you can quantitatively choose hyperparameters in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb).\\n\",\n \"\\n\",\n \"For our linear regression example, we can instantiate the `LinearRegression` class and specify that we would like to fit the intercept using the `fit_intercept` hyperparameter:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression()\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = LinearRegression(fit_intercept=True)\\n\",\n \"model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values.\\n\",\n \"In particular, we have not yet applied the model to any data: the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"#### 3. Arrange data into a features matrix and target vector\\n\",\n \"\\n\",\n \"Previously we examined the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array.\\n\",\n \"Here our target variable `y` is already in the correct form (a length-`n_samples` array), but we need to massage the data `x` to make it a matrix of size `[n_samples, n_features]`.\\n\",\n \"In this case, this amounts to a simple reshaping of the one-dimensional array:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(50, 1)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X = x[:, np.newaxis]\\n\",\n \"X.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"#### 4. Fit the model to the data\\n\",\n \"\\n\",\n \"Now it is time to apply our model to the data.\\n\",\n \"This can be done with the `fit` method of the model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression()\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This `fit` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\\n\",\n \"In Scikit-Learn, by convention all model parameters that were learned during the `fit` process have trailing underscores; for example in this linear model, we have the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1.9776566])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.coef_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"-0.9033107255311146\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.intercept_\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"These two parameters represent the slope and intercept of the simple linear fit to the data.\\n\",\n \"Comparing the results to the data definition, we see that they are close to the values used to generate the data: a slope of 2 and intercept of –1.\\n\",\n \"\\n\",\n \"One question that frequently comes up regards the uncertainty in such internal model parameters.\\n\",\n \"In general, Scikit-Learn does not provide tools to draw conclusions from internal model parameters themselves: interpreting model parameters is much more a *statistical modeling* question than a *machine learning* question.\\n\",\n \"Machine learning instead focuses on what the model *predicts*.\\n\",\n \"If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [`statsmodels` Python package](http://statsmodels.sourceforge.net/).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"#### 5. Predict labels for unknown data\\n\",\n \"\\n\",\n \"Once the model is trained, the main task of supervised machine learning is to evaluate it based on what it says about new data that was not part of the training set.\\n\",\n \"In Scikit-Learn, this can be done using the `predict` method.\\n\",\n \"For the sake of this example, our \\\"new data\\\" will be a grid of *x* values, and we will ask what *y* values the model predicts:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"xfit = np.linspace(-1, 11)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"As before, we need to coerce these *x* values into a `[n_samples, n_features]` features matrix, after which we can feed it to the model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"Xfit = xfit[:, np.newaxis]\\n\",\n \"yfit = model.predict(Xfit)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Finally, let's visualize the results by plotting first the raw data, and then this model fit (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(x, y)\\n\",\n \"plt.plot(xfit, yfit);\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Supervised Learning Example: Iris Classification\\n\",\n \"\\n\",\n \"Let's take a look at another example of this process, using the Iris dataset we discussed earlier.\\n\",\n \"Our question will be this: given a model trained on a portion of the Iris data, how well can we predict the remaining labels?\\n\",\n \"\\n\",\n \"For this task, we will use a simple generative model known as *Gaussian naive Bayes*, which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) for more details).\\n\",\n \"Because it is so fast and has no hyperparameters to choose, Gaussian naive Bayes is often a good model to use as a baseline classification, before exploring whether improvements can be found through more sophisticated models.\\n\",\n \"\\n\",\n \"We would like to evaluate the model on data it has not seen before, so we will split the data into a *training set* and a *testing set*.\\n\",\n \"This could be done by hand, but it is more convenient to use the `train_test_split` utility function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,\\n\",\n \" random_state=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With the data arranged, we can follow our recipe to predict the labels:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.naive_bayes import GaussianNB # 1. choose model class\\n\",\n \"model = GaussianNB() # 2. instantiate model\\n\",\n \"model.fit(Xtrain, ytrain) # 3. fit model to data\\n\",\n \"y_model = model.predict(Xtest) # 4. predict on new data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.9736842105263158\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import accuracy_score\\n\",\n \"accuracy_score(ytest, y_model)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Unsupervised Learning Example: Iris Dimensionality\\n\",\n \"\\n\",\n \"As an example of an unsupervised learning problem, let's take a look at reducing the dimensionality of the Iris data so as to more easily visualize it.\\n\",\n \"Recall that the Iris data is four-dimensional: there are four features recorded for each sample.\\n\",\n \"\\n\",\n \"The task of dimensionality reduction centers around determining whether there is a suitable lower-dimensional representation that retains the essential features of the data.\\n\",\n \"Often dimensionality reduction is used as an aid to visualizing data: after all, it is much easier to plot data in two dimensions than in four dimensions or more!\\n\",\n \"\\n\",\n \"Here we will use *principal component analysis* (PCA; see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), which is a fast linear dimensionality reduction technique.\\n\",\n \"We will ask the model to return two components—that is, a two-dimensional representation of the data.\\n\",\n \"\\n\",\n \"Following the sequence of steps outlined earlier, we have:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.decomposition import PCA # 1. Choose the model class\\n\",\n \"model = PCA(n_components=2) # 2. Instantiate the model\\n\",\n \"model.fit(X_iris) # 3. Fit to data\\n\",\n \"X_2D = model.transform(X_iris) # 4. Transform the data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now let's plot the results. A quick way to do this is to insert the results into the original Iris `DataFrame`, and use Seaborn's `lmplot` to show the results (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"iris['PCA1'] = X_2D[:, 0]\\n\",\n \"iris['PCA2'] = X_2D[:, 1]\\n\",\n \"sns.lmplot(x=\\\"PCA1\\\", y=\\\"PCA2\\\", hue='species', data=iris, fit_reg=False);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\\n\",\n \"This suggests to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Unsupervised Learning Example: Iris Clustering\\n\",\n \"\\n\",\n \"Let's next look at applying clustering to the Iris data.\\n\",\n \"A clustering algorithm attempts to find distinct groups of data without reference to any labels.\\n\",\n \"Here we will use a powerful clustering method called a *Gaussian mixture model* (GMM), discussed in more detail in [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb).\\n\",\n \"A GMM attempts to model the data as a collection of Gaussian blobs.\\n\",\n \"\\n\",\n \"We can fit the Gaussian mixture model as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.mixture import GaussianMixture # 1. Choose the model class\\n\",\n \"model = GaussianMixture(n_components=3,\\n\",\n \" covariance_type='full') # 2. Instantiate the model\\n\",\n \"model.fit(X_iris) # 3. Fit to data\\n\",\n \"y_gmm = model.predict(X_iris) # 4. Determine labels\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"iris['cluster'] = y_gmm\\n\",\n \"sns.lmplot(x=\\\"PCA1\\\", y=\\\"PCA2\\\", data=iris, hue='species',\\n\",\n \" col='cluster', fit_reg=False);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying labels: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*.\\n\",\n \"This means that even without an expert to tell us the species labels of the individual flowers, the measurements of these flowers are distinct enough that we could *automatically* identify the presence of these different groups of species with a simple clustering algorithm!\\n\",\n \"This sort of algorithm might further give experts in the field clues as to the relationships between the samples they are observing.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Application: Exploring Handwritten Digits\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of handwritten digits.\\n\",\n \"In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of preformatted digits, which is built into the library.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Loading and Visualizing the Digits Data\\n\",\n \"\\n\",\n \"We can use Scikit-Learn's data access interface to take a look at this data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1797, 8, 8)\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.datasets import load_digits\\n\",\n \"digits = load_digits()\\n\",\n \"digits.images.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The images data is a three-dimensional array: 1,797 samples each consisting of an 8 × 8 grid of pixels.\\n\",\n \"Let's visualize the first hundred of these (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"fig, axes = plt.subplots(10, 10, figsize=(8, 8),\\n\",\n \" subplot_kw={'xticks':[], 'yticks':[]},\\n\",\n \" gridspec_kw=dict(hspace=0.1, wspace=0.1))\\n\",\n \"\\n\",\n \"for i, ax in enumerate(axes.flat):\\n\",\n \" ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\\n\",\n \" ax.text(0.05, 0.05, str(digits.target[i]),\\n\",\n \" transform=ax.transAxes, color='green')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"In order to work with this data within Scikit-Learn, we need a two-dimensional, `[n_samples, n_features]` representation.\\n\",\n \"We can accomplish this by treating each pixel in the image as a feature: that is, by flattening out the pixel arrays so that we have a length-64 array of pixel values representing each digit.\\n\",\n \"Additionally, we need the target array, which gives the previously determined label for each digit.\\n\",\n \"These two quantities are built into the digits dataset under the `data` and `target` attributes, respectively:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1797, 64)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X = digits.data\\n\",\n \"X.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1797,)\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y = digits.target\\n\",\n \"y.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We see here that there are 1,797 samples and 64 features.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Unsupervised Learning Example: Dimensionality Reduction\\n\",\n \"\\n\",\n \"We'd like to visualize our points within the 64-dimensional parameter space, but it's difficult to effectively visualize points in such a high-dimensional space.\\n\",\n \"Instead, we'll reduce the number of dimensions, using an unsupervised method.\\n\",\n \"Here, we'll make use of a manifold learning algorithm called Isomap (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)) and transform the data to two dimensions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1797, 2)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.manifold import Isomap\\n\",\n \"iso = Isomap(n_components=2)\\n\",\n \"iso.fit(digits.data)\\n\",\n \"data_projected = iso.transform(digits.data)\\n\",\n \"print(data_projected.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We see that the projected data is now two-dimensional.\\n\",\n \"Let's plot this data to see if we can learn anything from its structure (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\\n\",\n \" edgecolor='none', alpha=0.5,\\n\",\n \" cmap=plt.cm.get_cmap('viridis', 10))\\n\",\n \"plt.colorbar(label='digit label', ticks=range(10))\\n\",\n \"plt.clim(-0.5, 9.5);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros and ones have very little overlap in the parameter space.\\n\",\n \"Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\\n\",\n \"On the other hand, there seems to be a more or less continuous spectrum between ones and fours: we can understand this by realizing that some people draw ones with \\\"hats\\\" on them, which causes them to look similar to fours.\\n\",\n \"\\n\",\n \"Overall, however, despite some mixing at the edges, the different groups appear to be fairly well localized in the parameter space: this suggests that even a very straightforward supervised classification algorithm should perform suitably on the full high-dimensional dataset.\\n\",\n \"Let's give it a try.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Classification on Digits\\n\",\n \"\\n\",\n \"Let's apply a classification algorithm to the digits data.\\n\",\n \"As we did with the Iris data previously, we will split the data into training and testing sets and fit a Gaussian naive Bayes model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.naive_bayes import GaussianNB\\n\",\n \"model = GaussianNB()\\n\",\n \"model.fit(Xtrain, ytrain)\\n\",\n \"y_model = model.predict(Xtest)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now that we have the model's predictions, we can gauge its accuracy by comparing the true values of the test set to the predictions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.8333333333333334\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import accuracy_score\\n\",\n \"accuracy_score(ytest, y_model)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With even this very simple model, we find about 83% accuracy for classification of the digits!\\n\",\n \"However, this single number doesn't tell us where we've gone wrong. One nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"\\n\",\n \"mat = confusion_matrix(ytest, y_model)\\n\",\n \"\\n\",\n \"sns.heatmap(mat, square=True, annot=True, cbar=False, cmap='Blues')\\n\",\n \"plt.xlabel('predicted value')\\n\",\n \"plt.ylabel('true value');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This shows us where the mislabeled points tend to be: for example, many of the twos here are misclassified as either ones or eights.\\n\",\n \"\\n\",\n \"Another way to gain intuition into the characteristics of the model is to plot the inputs again, with their predicted labels.\\n\",\n \"We'll use green for correct labels and red for incorrect labels; see the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(10, 10, figsize=(8, 8),\\n\",\n \" subplot_kw={'xticks':[], 'yticks':[]},\\n\",\n \" gridspec_kw=dict(hspace=0.1, wspace=0.1))\\n\",\n \"\\n\",\n \"test_images = Xtest.reshape(-1, 8, 8)\\n\",\n \"\\n\",\n \"for i, ax in enumerate(axes.flat):\\n\",\n \" ax.imshow(test_images[i], cmap='binary', interpolation='nearest')\\n\",\n \" ax.text(0.05, 0.05, str(y_model[i]),\\n\",\n \" transform=ax.transAxes,\\n\",\n \" color='green' if (ytest[i] == y_model[i]) else 'red')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Examining this subset of the data can give us some insight into where the algorithm might be not performing optimally.\\n\",\n \"To go beyond our 83% classification success rate, we might switch to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)), or another classification approach.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Summary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"In this chapter we covered the essential features of the Scikit-Learn data representation and the Estimator API.\\n\",\n \"Regardless of the type of estimator used, the same import/instantiate/fit/predict pattern holds.\\n\",\n \"Armed with this information about the Estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data.\\n\",\n \"\\n\",\n \"In the next chapter, we will explore perhaps the most important topic in machine learning: how to select and validate your model.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Hyperparameters and Model Validation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"In the previous chapter, we saw the basic recipe for applying a supervised machine learning model:\\n\",\n \"\\n\",\n \"1. Choose a class of model.\\n\",\n \"2. Choose model hyperparameters.\\n\",\n \"3. Fit the model to the training data.\\n\",\n \"4. Use the model to predict labels for new data.\\n\",\n \"\\n\",\n \"The first two pieces of this—the choice of model and choice of hyperparameters—are perhaps the most important part of using these tools and techniques effectively.\\n\",\n \"In order to make informed choices, we need a way to *validate* that our model and our hyperparameters are a good fit to the data.\\n\",\n \"While this may sound simple, there are some pitfalls that you must avoid to do this effectively.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Thinking About Model Validation\\n\",\n \"\\n\",\n \"In principle, model validation is very simple: after choosing a model and its hyperparameters, we can estimate how effective it is by applying it to some of the training data and comparing the predictions to the known values.\\n\",\n \"\\n\",\n \"This section will first show a naive approach to model validation and why it\\n\",\n \"fails, before exploring the use of holdout sets and cross-validation for more robust\\n\",\n \"model evaluation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Model Validation the Wrong Way\\n\",\n \"\\n\",\n \"Let's start with the naive approach to validation using the Iris dataset, which we saw in the previous chapter.\\n\",\n \"We will start by loading the data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.datasets import load_iris\\n\",\n \"iris = load_iris()\\n\",\n \"X = iris.data\\n\",\n \"y = iris.target\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Next, we choose a model and hyperparameters. Here we'll use a *k*-nearest neighbors classifier with `n_neighbors=1`.\\n\",\n \"This is a very simple and intuitive model that says \\\"the label of an unknown point is the same as the label of its closest training point\\\":\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n \"model = KNeighborsClassifier(n_neighbors=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Then we train the model, and use it to predict labels for data whose labels we already know:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"model.fit(X, y)\\n\",\n \"y_model = model.predict(X)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Finally, we compute the fraction of correctly labeled points:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.0\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import accuracy_score\\n\",\n \"accuracy_score(y, y_model)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We see an accuracy score of 1.0, which indicates that 100% of points were correctly labeled by our model!\\n\",\n \"But is this truly measuring the expected accuracy? Have we really come upon a model that we expect to be correct 100% of the time?\\n\",\n \"\\n\",\n \"As you may have gathered, the answer is no.\\n\",\n \"In fact, this approach contains a fundamental flaw: *it trains and evaluates the model on the same data*.\\n\",\n \"Furthermore, this nearest neighbor model is an *instance-based* estimator that simply stores the training data, and predicts labels by comparing new data to these stored points: except in contrived cases, it will get 100% accuracy every time!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Model Validation the Right Way: Holdout Sets\\n\",\n \"\\n\",\n \"So what can be done?\\n\",\n \"A better sense of a model's performance can be found by using what's known as a *holdout set*: that is, we hold back some subset of the data from the training of the model, and then use this holdout set to check the model's performance.\\n\",\n \"This splitting can be done using the `train_test_split` utility in Scikit-Learn:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.9066666666666666\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"# split the data with 50% in each set\\n\",\n \"X1, X2, y1, y2 = train_test_split(X, y, random_state=0,\\n\",\n \" train_size=0.5)\\n\",\n \"\\n\",\n \"# fit the model on one set of data\\n\",\n \"model.fit(X1, y1)\\n\",\n \"\\n\",\n \"# evaluate the model on the second set of data\\n\",\n \"y2_model = model.predict(X2)\\n\",\n \"accuracy_score(y2, y2_model)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We see here a more reasonable result: the one-nearest-neighbor classifier is about 90% accurate on this holdout set.\\n\",\n \"The holdout set is similar to unknown data, because the model has not \\\"seen\\\" it before.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Model Validation via Cross-Validation\\n\",\n \"\\n\",\n \"One disadvantage of using a holdout set for model validation is that we have lost a portion of our data to the model training.\\n\",\n \"In the preceding case, half the dataset does not contribute to the training of the model!\\n\",\n \"This is not optimal, especially if the initial set of training data is small.\\n\",\n \"\\n\",\n \"One way to address this is to use *cross-validation*; that is, to do a sequence of fits where each subset of the data is used both as a training set and as a validation set.\\n\",\n \"Visually, it might look something like the following figure:\\n\",\n \"\\n\",\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#2-Fold-Cross-Validation)\\n\",\n \"\\n\",\n \"Here we do two validation trials, alternately using each half of the data as a holdout set.\\n\",\n \"Using the split data from earlier, we could implement it like this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(0.96, 0.9066666666666666)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y2_model = model.fit(X1, y1).predict(X2)\\n\",\n \"y1_model = model.fit(X2, y2).predict(X1)\\n\",\n \"accuracy_score(y1, y1_model), accuracy_score(y2, y2_model)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"What comes out are two accuracy scores, which we could combine (by, say, taking the mean) to get a better measure of the global model performance.\\n\",\n \"This particular form of cross-validation is a *two-fold cross-validation*—that is, one in which we have split the data into two sets and used each in turn as a validation set.\\n\",\n \"\\n\",\n \"We could expand on this idea to use even more trials, and more folds in the data—for example, the following figure shows a visual depiction of five-fold cross-validation.\\n\",\n \"\\n\",\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#5-Fold-Cross-Validation)\\n\",\n \"\\n\",\n \"Here we split the data into five groups, and use each of them in turn to evaluate the model fit on the other four-fifths of the data.\\n\",\n \"This would be rather tedious to do by hand, but we can use Scikit-Learn's `cross_val_score` convenience routine to do it succinctly:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.96666667, 0.96666667, 0.93333333, 0.93333333, 1. ])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import cross_val_score\\n\",\n \"cross_val_score(model, X, y, cv=5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Repeating the validation across different subsets of the data gives us an even better idea of the performance of the algorithm.\\n\",\n \"\\n\",\n \"Scikit-Learn implements a number of cross-validation schemes that are useful in particular situations; these are implemented via iterators in the `model_selection` module.\\n\",\n \"For example, we might wish to go to the extreme case in which our number of folds is equal to the number of data points: that is, we train on all points but one in each trial.\\n\",\n \"This type of cross-validation is known as *leave-one-out* cross validation, and can be used as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\n \" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\n \" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\n \" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\n \" 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1.,\\n\",\n \" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\n \" 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\\n\",\n \" 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1.,\\n\",\n \" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import LeaveOneOut\\n\",\n \"scores = cross_val_score(model, X, y, cv=LeaveOneOut())\\n\",\n \"scores\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Because we have 150 samples, the leave-one-out cross-validation yields scores for 150 trials, and each score indicates either a successful (1.0) or an unsuccessful (0.0) prediction.\\n\",\n \"Taking the mean of these gives an estimate of the error rate:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.96\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"scores.mean()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Other cross-validation schemes can be used similarly.\\n\",\n \"For a description of what is available in Scikit-Learn, use IPython to explore the ``sklearn.model_selection`` submodule, or take a look at Scikit-Learn's [cross-validation documentation](http://scikit-learn.org/stable/modules/cross_validation.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Selecting the Best Model\\n\",\n \"\\n\",\n \"Now that we've explored the basics of validation and cross-validation, we will go into a little more depth regarding model selection and selection of hyperparameters.\\n\",\n \"These issues are some of the most important aspects of the practice of machine learning, but I find that this information is often glossed over in introductory machine learning tutorials.\\n\",\n \"\\n\",\n \"Of core importance is the following question: *if our estimator is underperforming, how should we move forward?*\\n\",\n \"There are several possible answers:\\n\",\n \"\\n\",\n \"- Use a more complicated/more flexible model.\\n\",\n \"- Use a less complicated/less flexible model.\\n\",\n \"- Gather more training samples.\\n\",\n \"- Gather more data to add features to each sample.\\n\",\n \"\\n\",\n \"The answer to this question is often counterintuitive.\\n\",\n \"In particular, sometimes using a more complicated model will give worse results, and adding more training samples may not improve your results!\\n\",\n \"The ability to determine what steps will improve your model is what separates the successful machine learning practitioners from the unsuccessful.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### The Bias-Variance Trade-off\\n\",\n \"\\n\",\n \"Fundamentally, finding \\\"the best model\\\" is about finding a sweet spot in the trade-off between *bias* and *variance*.\\n\",\n \"Consider the following figure, which presents two regression fits to the same dataset.\\n\",\n \"\\n\",\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff)\\n\",\n \"\\n\",\n \"It is clear that neither of these models is a particularly good fit to the data, but they fail in different ways.\\n\",\n \"\\n\",\n \"The model on the left attempts to find a straight-line fit through the data.\\n\",\n \"Because in this case a straight line cannot accurately split the data, the straight-line model will never be able to describe this dataset well.\\n\",\n \"Such a model is said to *underfit* the data: that is, it does not have enough flexibility to suitably account for all the features in the data. Another way of saying this is that the model has high bias.\\n\",\n \"\\n\",\n \"The model on the right attempts to fit a high-order polynomial through the data.\\n\",\n \"Here the model fit has enough flexibility to nearly perfectly account for the fine features in the data, but even though it very accurately describes the training data, its precise form seems to be more reflective of the particular noise properties of the data than of the intrinsic properties of whatever process generated that data.\\n\",\n \"Such a model is said to *overfit* the data: that is, it has so much flexibility that the model ends up accounting for random errors as well as the underlying data distribution. Another way of saying this is that the model has high variance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"To look at this in another light, consider what happens if we use these two models to predict the *y*-values for some new data.\\n\",\n \"In the plots in the following figure, the red/lighter points indicate data that is omitted from the training set.\\n\",\n \"\\n\",\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff-Metrics)\\n\",\n \"\\n\",\n \"The score here is the $R^2$ score, or [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination), which measures how well a model performs relative to a simple mean of the target values. $R^2=1$ indicates a perfect match, $R^2=0$ indicates the model does no better than simply taking the mean of the data, and negative values mean even worse models.\\n\",\n \"From the scores associated with these two models, we can make an observation that holds more generally:\\n\",\n \"\\n\",\n \"- For high-bias models, the performance of the model on the validation set is similar to the performance on the training set.\\n\",\n \"- For high-variance models, the performance of the model on the validation set is far worse than the performance on the training set.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"If we imagine that we have some ability to tune the model complexity, we would expect the training score and validation score to behave as illustrated in the following figure:\\n\",\n \"\\n\",\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Validation-Curve)\\n\",\n \"\\n\",\n \"The diagram shown here is often called a *validation curve*, and we see the following features:\\n\",\n \"\\n\",\n \"- The training score is everywhere higher than the validation score. This is generally the case: the model will be a better fit to data it has seen than to data it has not seen.\\n\",\n \"- For very low model complexity (a high-bias model), the training data is underfit, which means that the model is a poor predictor both for the training data and for any previously unseen data.\\n\",\n \"- For very high model complexity (a high-variance model), the training data is overfit, which means that the model predicts the training data very well, but fails for any previously unseen data.\\n\",\n \"- For some intermediate value, the validation curve has a maximum. This level of complexity indicates a suitable trade-off between bias and variance.\\n\",\n \"\\n\",\n \"The means of tuning the model complexity varies from model to model; when we discuss individual models in depth in later chapters, we will see how each model allows for such tuning.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Validation Curves in Scikit-Learn\\n\",\n \"\\n\",\n \"Let's look at an example of using cross-validation to compute the validation curve for a class of models.\\n\",\n \"Here we will use a *polynomial regression* model: this is a generalized linear model in which the degree of the polynomial is a tunable parameter.\\n\",\n \"For example, a degree-1 polynomial fits a straight line to the data; for model parameters $a$ and $b$:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"y = ax + b\\n\",\n \"$$\\n\",\n \"\\n\",\n \"A degree-3 polynomial fits a cubic curve to the data; for model parameters $a, b, c, d$:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"y = ax^3 + bx^2 + cx + d\\n\",\n \"$$\\n\",\n \"\\n\",\n \"We can generalize this to any number of polynomial features.\\n\",\n \"In Scikit-Learn, we can implement this with a linear regression classifier combined with the polynomial preprocessor.\\n\",\n \"We will use a *pipeline* to string these operations together (we will discuss polynomial features and pipelines more fully in [Feature Engineering](05.04-Feature-Engineering.ipynb)):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.preprocessing import PolynomialFeatures\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"from sklearn.pipeline import make_pipeline\\n\",\n \"\\n\",\n \"def PolynomialRegression(degree=2, **kwargs):\\n\",\n \" return make_pipeline(PolynomialFeatures(degree),\\n\",\n \" LinearRegression(**kwargs))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now let's create some data to which we will fit our model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"def make_data(N, err=1.0, rseed=1):\\n\",\n \" # randomly sample the data\\n\",\n \" rng = np.random.RandomState(rseed)\\n\",\n \" X = rng.rand(N, 1) ** 2\\n\",\n \" y = 10 - 1. / (X.ravel() + 0.1)\\n\",\n \" if err > 0:\\n\",\n \" y += err * rng.randn(N)\\n\",\n \" return X, y\\n\",\n \"\\n\",\n \"X, y = make_data(40)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We can now visualize our data, along with polynomial fits of several degrees (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"\\n\",\n \"X_test = np.linspace(-0.1, 1.1, 500)[:, None]\\n\",\n \"\\n\",\n \"plt.scatter(X.ravel(), y, color='black')\\n\",\n \"axis = plt.axis()\\n\",\n \"for degree in [1, 3, 5]:\\n\",\n \" y_test = PolynomialRegression(degree).fit(X, y).predict(X_test)\\n\",\n \" plt.plot(X_test.ravel(), y_test, label='degree={0}'.format(degree))\\n\",\n \"plt.xlim(-0.1, 1.0)\\n\",\n \"plt.ylim(-2, 12)\\n\",\n \"plt.legend(loc='best');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The knob controlling model complexity in this case is the degree of the polynomial, which can be any nonnegative integer.\\n\",\n \"A useful question to answer is this: what degree of polynomial provides a suitable trade-off between bias (underfitting) and variance (overfitting)?\\n\",\n \"\\n\",\n \"We can make progress in this by visualizing the validation curve for this particular data and model; this can be done straightforwardly using the ``validation_curve`` convenience routine provided by Scikit-Learn.\\n\",\n \"Given a model, data, parameter name, and a range to explore, this function will automatically compute both the training score and the validation score across the range (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import validation_curve\\n\",\n \"degree = np.arange(0, 21)\\n\",\n \"train_score, val_score = validation_curve(\\n\",\n \" PolynomialRegression(), X, y,\\n\",\n \" param_name='polynomialfeatures__degree',\\n\",\n \" param_range=degree, cv=7)\\n\",\n \"\\n\",\n \"plt.plot(degree, np.median(train_score, 1),\\n\",\n \" color='blue', label='training score')\\n\",\n \"plt.plot(degree, np.median(val_score, 1),\\n\",\n \" color='red', label='validation score')\\n\",\n \"plt.legend(loc='best')\\n\",\n \"plt.ylim(0, 1)\\n\",\n \"plt.xlabel('degree')\\n\",\n \"plt.ylabel('score');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This shows precisely the qualitative behavior we expect: the training score is everywhere higher than the validation score, the training score is monotonically improving with increased model complexity, and the validation score reaches a maximum before dropping off as the model becomes overfit.\\n\",\n \"\\n\",\n \"From the validation curve, we can determine that the optimal trade-off between bias and variance is found for a third-order polynomial. We can compute and display this fit over the original data as follows (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(X.ravel(), y)\\n\",\n \"lim = plt.axis()\\n\",\n \"y_test = PolynomialRegression(3).fit(X, y).predict(X_test)\\n\",\n \"plt.plot(X_test.ravel(), y_test);\\n\",\n \"plt.axis(lim);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Notice that finding this optimal model did not actually require us to compute the training score, but examining the relationship between the training score and validation score can give us useful insight into the performance of the model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Learning Curves\\n\",\n \"\\n\",\n \"One important aspect of model complexity is that the optimal model will generally depend on the size of your training data.\\n\",\n \"For example, let's generate a new dataset with five times as many points (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X2, y2 = make_data(200)\\n\",\n \"plt.scatter(X2.ravel(), y2);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now let's duplicate the preceding code to plot the validation curve for this larger dataset; for reference, we'll overplot the previous results as well (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"degree = np.arange(21)\\n\",\n \"train_score2, val_score2 = validation_curve(\\n\",\n \" PolynomialRegression(), X2, y2,\\n\",\n \" param_name='polynomialfeatures__degree',\\n\",\n \" param_range=degree, cv=7)\\n\",\n \"\\n\",\n \"plt.plot(degree, np.median(train_score2, 1),\\n\",\n \" color='blue', label='training score')\\n\",\n \"plt.plot(degree, np.median(val_score2, 1),\\n\",\n \" color='red', label='validation score')\\n\",\n \"plt.plot(degree, np.median(train_score, 1),\\n\",\n \" color='blue', alpha=0.3, linestyle='dashed')\\n\",\n \"plt.plot(degree, np.median(val_score, 1),\\n\",\n \" color='red', alpha=0.3, linestyle='dashed')\\n\",\n \"plt.legend(loc='lower center')\\n\",\n \"plt.ylim(0, 1)\\n\",\n \"plt.xlabel('degree')\\n\",\n \"plt.ylabel('score');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The solid lines show the new results, while the fainter dashed lines show the results on the previous smaller dataset.\\n\",\n \"It is clear from the validation curve that the larger dataset can support a much more complicated model: the peak here is probably around a degree of 6, but even a degree-20 model is not seriously overfitting the data—the validation and training scores remain very close.\\n\",\n \"\\n\",\n \"So, the behavior of the validation curve has not one but two important inputs: the model complexity and the number of training points.\\n\",\n \"We can gain further insight by exploring the behavior of the model as a function of the number of training points, which we can do by using increasingly larger subsets of the data to fit our model.\\n\",\n \"A plot of the training/validation score with respect to the size of the training set is sometimes known as a *learning curve.*\\n\",\n \"\\n\",\n \"The general behavior we would expect from a learning curve is this:\\n\",\n \"\\n\",\n \"- A model of a given complexity will *overfit* a small dataset: this means the training score will be relatively high, while the validation score will be relatively low.\\n\",\n \"- A model of a given complexity will *underfit* a large dataset: this means that the training score will decrease, but the validation score will increase.\\n\",\n \"- A model will never, except by chance, give a better score to the validation set than the training set: this means the curves should keep getting closer together but never cross.\\n\",\n \"\\n\",\n \"With these features in mind, we would expect a learning curve to look qualitatively like that shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Learning-Curve)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The notable feature of the learning curve is the convergence to a particular score as the number of training samples grows.\\n\",\n \"In particular, once you have enough points that a particular model has converged, *adding more training data will not help you!*\\n\",\n \"The only way to increase model performance in this case is to use another (often more complex) model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Learning Curves in Scikit-Learn\\n\",\n \"\\n\",\n \"Scikit-Learn offers a convenient utility for computing such learning curves from your models; here we will compute a learning curve for our original dataset with a second-order polynomial model and a ninth-order polynomial (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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nbIyML5rEpidtdEHxlZpa+lGe/213ysLazDXsrbd9nn5X+6hJWVJDXW/CB8r3JC18u77boaGnMGKlpU7OfEQAAAOwoKkq67DJjGTHC2LZvnxFc+JZZs6Tc3AaSjFAgLs5/qV/fmHC16PaylthY40tYZKR5z72i8vKMNnvh3gZFex4Uvp6X5x88FA0iCl8vz+uSnn5QSUm1KuW5eL3K7w3AsAx/Xq8RaBT97lba5d27Dyk2toHf8S+8zsyUDh/23+Z0VqyHSlmKBhgOh/F45xK2RUQUfG5979PYWKlRI+O6731T+OxGZzv7UVn7y6ylYi+HvXg8RsJ2+LD/cuhQ8W3Hjxe8Uc+Hw1HQ1al6denoUSPZfvtt6eabK/VpAQAAACVq3Fi69VZjkYwvNRs3blfnzhfStb2Q8PCCL23BzuEI7uAokBwOI6SrVs04xfLZpKcfU1JSg3N+HF9PmKIBx5kzRpDh6wVUeClpW2nbfds8Hv/AoehS0r7zGQ5WEWlppe+z9D9B48aVPWFOebdFRBSkWiUFD77tR4+WnHKFhRkzXdevbyyXXWa8eQt3TSs8lqnouKaS9kdH+ydJO3dKt98u9e8vDR8uTZtm3AYAAACoKtHRUkKCh6ACCCDfHCI1a5pdibVZ+p+h558P3H37urHVry+1aCFdcYUxVs63rfBSu7aRpgZS69bSunXS6NFG97v//ld6/32pbdvAPi4AAAAAAFZj6bDCN4NreSfMKWsSndhY/wAiJsbsZ1dcdLQxQ3P37tLQocY5sV97TfrjH82uDAAAAACAqmPpsCIsrKCLTCjp10/atEm64w5pyBBp1SppzhxjTBEAAAAAAHYXZnYBKFmTJsZpXJ55RnrnHeNUUt99Z3ZVAAAAAAAEHmGFhUVESBMmSCtXSidOSJdfLr3++rme+xcAAAAAgOBCWBEEuneXNm+WrrtOGjZMGjTICC8AAAAAALAjS89ZgQL160v/+pc0fbr09NPShg3SokVGbwsAAFA+8+fP97t+8cUX67LLLlNubq4WLlxY7PYdOnRQhw4d5HK5lJKSUmx/586ddckll+jkyZP68MMPi+2/6qqrdOGFF+ro0aP66KOPiu3v2rWrWrZsqYMHD2rFihXF9vfo0UNNmjTR3r17tWrVqmL7e/furcTERO3atUtr1qwptv+mm25S3bp1tX37dn3xxRfF9vfv318JCQnasmWLNmzYUGz/oEGDVL16dW3atEmbNm0qtn/w4MGKjIzUN998o61btxbbP3ToUEnS+vXrtWPHDr99kZGRGjx4sCRp9erV2r17t9/+6tWra9CgQZKklStXat++fX774+PjNWDAAEnSihUrdPDgQb/9derUUb9+/SRJy5cv16+//uq3PzExUb1795YkLVmyRKdOnfLb37hxY/Xs2VOSlJKSIpfL5be/RYsW6tatmyRp4cKFys3N9dvftm1bXX311ZKKv+8ka7/3XC6XYmNjee/JOu89l8ulr776SpK933tScP2798033+QfFx+7vfcKC8R779JLLy12Ox96VgSRsDDpySeltWuNoSDXXGOEFx6P2ZUBAAAAAFB5HF6vNWdASEtLU3JystllWNbx49K990pLlkg33mhMwlmvXmAfMz09XUlJSYF9EJwTjok1cVysh2NScXb4f9kOz8GO+HxaD8fEejgm1sRxqbiy/m9mGEiQqlVL+uADY8LNxx6TOnSQFi405rUIBh6P5HaH3mlpEfxyc42w8NdfpWPHCpbC10+fbqDWraXatUteataUwsPNfiYAAACAdRFWBDGHw5hw8+qrpdtuMybifPZZaezYwH4RcrulkyeN5dSpgsulLSXd5vRp474aNJCaNSt9SUgI3POoTHl50pEj0qFD0sGDxrrwZd/a45Fat5batvVfEhON42kGj8eo76efpL17jecSEVF5S7Vq1gulPB4pM9N4b546dfbwofB133u3JGFhRpCYk5NQ5u0cDiOwKC3MKLokJEjR0cZr6Vuio43X16z3DQAAABBIhBU28JvfGBNuPvywNH689Pnn0t//LjVqVPy2OTnnHiz4ll9/bS2nUyoyz0qJqlWT4uONL1m+JTHR/3pYmPHleM8eadMmadkyKTvb/35q1iw7zKhXLzBf1rxe40v7sWMlhw5Ftx09WvLcITExxvNu0MAIKbxe6ccfpRUrjGPhExsrtWnjH2D4rteqVbHn4nZL+/YZr/NPPxlr3+ILKArXEgiRkcZzrIwlMrIgaDh9uvzrwpczM8uuNzzcPyxo2FC65JLiIUKdOv7X4+ON93V6+g61aZOkEyf8A4+yll27jPXx4+WfhyYsrCC4KBxiFA01il6OjjZeR1+gVJ7LZ9uXl2d8fnNyCpai10vaVtpt4uOLh3o1a1b0nQgAAIBgQVhhE7Gx0vz5Ru+KBx80AoyOHYsHDllZZ7+v6tX9Q4WEBKlpU8nrzVSzZrWUkFA8iCi8xMcbX4bOlccjHT7s/2Xat+zaJX32WfFftWNijNqaNZOaNDGCi9xc4wtPaeuy9hVelzabS3R0QQDRvLl0xRUF131r3+XY2JLDlLw8IyTYsaNg+fFH6ZtvpMWL/b+s1q1bPMBo29YIPyTjmO7d6x9EFL68b1/xL7+JicZr1rmzNHCgcbl5c+O1jIoyAo6KLrm5xpKdbYQDJS179/pfdzpLf93LKzLSeA/Gx0txcca6Xj2pVauC64XXcXHFg4e4OCMIqIiICOPY1a17bn/n8RihSuHeHKdOGa9jVlbBUtb1wpePHy/5tr5j5HYb78eqFB5uvM98S3S0//WoKOMzUfSzUK9e8QCjbVvj2MbEVO1zAAAAQGARVtjMnXcaX54ffdQIJ+rUkVq2LD1YKBo8xMcbX/ZKkp5+UElJFfyZvwxhYcaX6MRE4zkU5fVKJ06UHGbs2SN9/71xu6go4zmUtI6LK3u/b+27XLNm8SAiPr7ivTnCw41woHlz6be/9d+XnW2EMz/+6B9mfPKJEUgVlpDQRidPFn8dGzc2AoiuXQuCCF9vlKZNjV/Xrcjjkc6cKTnYcDqNdXZ2yaGDb30+QZmVhIUZ77uaNY3PblXw9STyhReFg4ySrpe0LyKi5OChpCCivMPUcnKMz0Lhz8GOHUbPpHnzCm7ncBhhZUlBRrNmRm0AAAAILjThbOjCC6V//9vsKiqfw2EMiahVy5hQ1K6io6WkJGMp6vRpaefOgi9tW7ee1kUX1coPIpo3N4b/lBY4WV1YmFSjhrE0aGB2NaHD4SgYzmElUVFSu3bGUtTp08UDvR07jImGCwd4kZFGz4vExMZq1874fDRqZAR6vsuVEUBWBY/HeG7HjxuLb9hQ0cu+61lZxUOjiqwTE81+BQAAQCixWNMUQFni4ozhPR07GtcD3dsFsKq4OKlTJ2MpzOs15pApGmJs3Rqp7783htYUFRtbcohR+Hr9+uc/cXFurjHXj9NZ9lI4iCgpgDhxouxhUtWqFQS6tWsboV9urhHs/PprwfwgJa3d7rM/jw0bzu/5AwAAnA/CCgCAbTgcxtwW9epJ11xTsD09fbeSkpKUlSUdOGDM5bJ/v7EUvvzZZ9IvvxT/8h4eLl1wQUGIkZho3MYXNJQVRuTmlr/+yEj/wKFBA6Nnie964X2+y76lIvN2eDzFJz0tvC46+TEAAECgEVYAAEJGtWrGXCBlzQfim+y3tEBjyxZp1SojWPANW/ItjRoZkxQX3V6eJT7eWJsxJMV3Zpmy5rNJS6u6egAAgM14vcYvRtu2GUt6urR9uzRlSql/QlgBAEAhhSf77dzZ7GoAAACCSG6ulJFhhBG+UMIXUBQ+tWN8fMkTkxVCWAEAAAAAAMrv1Cn/XhK+dUaG/3jaRo2MMwfcdZex9s2e3rCh0Z20jK6blg4r5hc5T+PFF1+syy67TLm5uVq4cGGx23fo0EEdOnSQy+VSSkpKsf2dO3fWJZdcopMnT+rDDz8stv+qq67ShRdeqKNHj+qjjz4qtr9r165q2bKlDh48qBUrVhTb36NHDzVp0kR79+7VqlWriu3v3bu3EhMTtWvXLq1Zs6bY/ptuukl169bV9u3b9cUXXxTb379/fyUkJGjLli3aUMJMZ4MGDVL16tW1adMmbdq0qdj+wYMHKzIyUt988422bt1abP/QoUMlSevXr9eOHTv89kVGRqrT/2ayW716tXbv3u23v3r16ho0aJAkaeXKldq3b5/f/vj4eA0YMECStGLFCh08eNBvf506ddSvXz9J0vLly/VrkVnwEhMT1bt3b0nSkiVLdOrUKb/9jRs3Vs+ePSVJKSkpcrlcfvtbtGihbt26SZIWLlyo3CKDyNu2baurr75aUvH3nWTd957L5dJXX31l+/fe4MGDJQXPe893XCT7vvd8guW9t3PnzvxjUpjd3nuFVfZ779JLLy12GwAAYFGrV0vTpxs9HcLCjGCg8LqkbWfb5/VKP/1kBBMHDhQ8VkSE1KaNdNFF0sCBRhiRlGScpjIu7ryfgqXDCgAAAAAAUE4ej/Tii9LTTxtjWps0MbZ5vf7r893WpInUq1dBINGunTEZWGRkpT8Vh9db1onQzJOWlqbk5GSzy0Ah6enpSkpKMrsMFMIxsSaOi/VwTCrODv8v2+E52BGfT+vhmFgPx8SaLHdcjh0zhlt89JF0223SW29VqGdDVSjr/2Z6VgAAAAAAEMy++Ua69VZjeMacOdKDD5pzirFKRFgBIDBcLiPV/ekn43yIMTEF50b0LWfbFh5esRp8Xdbc7uJLXl7B5fBwY4mIKH3tG6tnZV6vMS4xK0s6c8ZYl3Q5N9c4v2ZsrLHExRVcjoqy/vMEAACAweuVXn1VGjnSGPbx3/9Kl19udlWVgrACQOVxu6WVK6WFC6UPP5SczordX0RE8QAjMtI/aDhbEFGZzhZo/G/dMi+vIGwJDzeCjrLWZ9vn8RQPHkoLIzyeij3HiIiC4KKspXDAUaOG8R+l220EIUWX89kuSdHRxhIV5b8u7XIZ22L27jV+aThz5twW3+tbdMnOLnh/VqtmPFZZl8uz3+uVcnKMJTvbf12ey0W35eUVnyyrtMmzyrPvuecq/hkCAACV5/Rp6f77pfffl/r0kRYskOrUMbuqSkNYAaBivF7pq6+MgGLRIunIEalmTekPf5AGD5Y6dza+OPm+TBf9cl3WtpK25+QYXxJLW3zBQXlv4wsDCoccpa3LeZusEycUXaOGsc3jKXudnV36Pt9lh8M/sImPLx7ilHS5tG0REUbPl8zM8i/79xffdi5THkVGGo8bGVl8KWm712uMu/R98S76hTw7+5zCqObluVFMTPHF95rVqiVdcEHB9uho4/ELv7d9l0+elA4fLr49O9t4P5/PVFEOh38QUziMKXrZ10MmPLzkSbJKmzTL7S59X0VDMAAAULm2bJFuuUX68Udp8mRp1CjjxwUbIawAcH62bZP+/ndjycgwviz162cEFDfeaFz3iY01r04THEhPV4KVJlsKBK/X+OLtCy4cjtLDh/DwwAwt8XiKBxglhRrZ2fp59241vfDCkgMJX/hQFcNffD1QSgo5fIFE0QAiOrriQ6IqQxnnQQcAAFXo3XelP//Z+AFr1SrpuuvMriggCCsAlN+BA0Y3s4ULpW+/NdLb7t2lZ56R+veXEhLMrhBVxeEw5r2oXl2qX9+cGsLCCnqOnIUzPd04vZbZCoc6IRbiAQCACsrKkh55xDjLR7du0nvvSQ0bml1VwBBWACjbiRPSkiVGQPHZZ8Yvw507SzNnGqdEsvE/kAAAAIAlZGQYwz42bZKeekoaP97ozWpj9n52gJ1kZ0tHj/otNbdvl1q3NrqA+ZaEhILLkZHn91hZWdK//mUEFP/8p/HYrVpJzz4r3XGHdOGFlfvcAAAAAJTsww+lP/3J6FW6fLl0001mV1QlCCsAM+TlGZMHFg4fjhwpFkb4LadPF7ubs/Zp8E3GWDTEKLr49kVFSStWSKmpxiSB9esb4+EGD5Yuu4xTWgIAAABVJTdXGj1aeukloy2ekiI1b252VVWGsALmOHbMmKytbl1jGEG9etaYQO5svF7jdJynThnhQWnr0vadOGEED8ePl35GgNhY43XxLRde6H/dt9Srpx8PHVKbCy4w7rvwcvJk8W2+7bt3+1/Pyyv++AMGGAFF9+62714GAAAAWM6+fdLtt0vr1kkPPSTNmOE/gX0I4FsIqtb330uzZ0t/+5txJgGf8HDjV/yGDcteEhONX/8rIjvbCAuOHTv7cvJk8RCiPKcdDA83eirExRWsa9UyktB69UoOH+rWNc6LHBNT7qfidjgqNiTDd0YHX3iRmSm1a2dMmggAAACg6n36qTH0OivLmNz+ttvMrsgUhBUIPLdbWrbMCCk+/9z4Mv7HP0q33mp8+f/lF/9l/35pwwbp8OGSg4E6dUoOMurVk1yuswcQTmfptYaFGaFC7drGumZNqWlT/9ChPOtq1YJjyEThMzokJppdDQAAABC68vKkiROl55+XLrpI+uAD44fEEEVYgcD59Vdp7lzp1Veln3+WmjWTXnxRuuceIww4G7fbCCyKhhmFl23bpIMHjfFchUVGGqFG7drG0qyZ1LFjwfWiiy+giI83AgsAAAAAqCpHjhjDsD/9VLrzTuM7VI0aZldlKsIKVL7Nm41eFAsXGl2XuneXXn5Z6tfv3OaliIiQLrjAWMri8Rg9Jo4cMeZbqF3b6CkQDD0bAAAAAIS2b7+Vbr7Z+KH2rbeMH3f5LkNYgUridktLl0p/+Yu0dq0RFtx1l/Tww9IllwT2scPCCuZ8AAAAAIBg8d570t13G0Pa162TkpPNrsgyCCtQMUePGunfq68aM9a2aCFNn2584GrVMrs6AAAAALCevDzpqaekadOkLl2M+Snq1ze7KkshrMD52bjRGOrx978bZ9fo2VN65RWpb9/gOAUpAAAAAJjh+HHjtKSffCI9+KA0c2bFz3hoQ4QVKL/cXCklxRjqsW6dMdTj7ruNoR4XXWR2dQAAAABgbVu3Sr//vXECgrfeku691+yKLIuwAuWzaJFajxghHToktWwpvfSS9Kc/Gaf2BAAAAACUbelSacgQ46QAn38uXX212RVZGmEFzu7ECem++5TXqJEi586VbryRoR4AAAAAUB4ejzR+vPT889Lll0tLlkiNGpldleWFBeJOPR6Pxo4dq9tuu01DhgzRnj17/Pa//fbbGjBggAYOHKhPP/00ECWgMr3+unT6tA5MnCjddBNBBQCgytG2AAAEpVOnpAEDjKBi6FBp9WqCinIKSM+KlStXKicnR4sWLdKmTZs0ZcoUvfbaa5KkU6dOacGCBfrkk0905swZ3XzzzerVq1cgykBlyMqSZs2SfvtbZTMvBQDAJLQtAABB58cfjfkpduyQXn5ZGj5ccjjMripoBCSsSEtLU5cuXSRJHTp00JYtW/L3xcTE6IILLtCZM2d05swZOThY1vbOO8Y8FaNHm10JACCE0bYAAASVFSukP/zB6JX+ySdS9+5mVxR0AhJWZGZmKjY2Nv96eHi43G63IiKMh2vYsKH69u2rvLw8/fnPfy71ftLT0wNRHsorL0+tJk1S3qWX6qcGDZSVlcUxsRiOiTVxXKyHYxL8aFvYF59P6+GYWA/HxJpKPC5er2q//bbqz5yp7DZttG/2bOU2bChx/M5ZQMKK2NhYOZ3O/Osejye/MbFmzRodPnxYq1atkiTdc8896tSpk9q3b1/sfpKSkgJRHsorJUXau1eaOVNJF12k9PR0jonFcEysieNiPRyTiktLSzP18Wlb2BefT+vhmFgPx8Saih0Xl8s4Fel770mDBqna22+rdY0a5hUYBMpqXwRkgs1OnTppzZo1kqRNmzapbdu2+fsSEhJUrVo1RUVFKTo6WnFxcTp16lQgykBFeL3S1KlS27bSzTebXQ0AIMTRtgAAWNqePdI110jvvy+98IKxJqiokID0rOjVq5fWrVun22+/XV6vV5MnT9a8efPUtGlT9ejRQ+vXr9egQYMUFhamTp066ZprrglEGaiIVaukb7+V3nqLs38AAExH2wIAYFmffy7dequUmyt99JHUp4/ZFdlCQMKKsLAwjR8/3m9bq1at8i8/8sgjeuSRRwLx0KgsU6ZIDRtKQ4aYXQkAALQtAADW4/VKc+ZIjz4qtWkjLV0qXXih2VXZRkDCCgS5DRuMnhUvvihFR5tdDQAAAABYS3a2Gj77rLRkidSvn/Tuu1JCgtlV2UpA5qxAkJs61figlTGbOgAAAACErN/9TjWXLJGeecboUUFQUekIK+Dvxx+l1FRp2DApPt7sagAAAADAWrZtkz75RIcfeUSaMEEK42t1IPCqwt/06VJUlDRihNmVAAAAAID1pKZKkk72729yIfZGWIECv/wizZ8vDR0qJSaaXQ0AAAAAWE9qqnTllXI3aGB2JbZGWIECL78sud3SE0+YXQkAAAAAWM+uXdLGjdLAgWZXYnuEFTCcPCm99pp0yy1S69ZmVwMAAAAA1rNkibEmrAg4wgoYXn9dOnVKGjXK7EoAAAAAwJpSU6WOHaUWLcyuxPYIKyBlZUmzZkm9ekmdOpldDQAAAABYz7590pdf0quiikSYXQAs4N13pYMHpb/9zexKAAAAAMCaPvzQWBNWVAl6VoS6vDzpxRel5GSpe3ezqwEAAAAAa0pNlS66SGrXzuxKQgJhRaj78ENp505p9GjJ4TC7GgAAAACwnsOHpbVr6VVRhQgrQpnXK02ZIrVpI/Xvb3Y1AAAAAGBNS5dKHg9hRRVizopQ9p//SGlp0ptvSuHhZlcDAAAAANaUmiq1aiW1b292JSGDnhWhbOpUKTFRGjLE7EoAAAAAwJqOHzd+6B04kKHzVYiwIlSlpUmffio9+qhUrZrZ1QAAAACANS1bJrndDAGpYoQVoerFF6X4eOmBB8yuBAAAAACsKzVVatJEuuwysysJKYQVoWjnTumDD6Rhw6SEBLOrAQAAAABrOn1a+uQTacAAhoBUMcKKUDR9uhQRIY0YYXYlAAAAAGBd//ynlJ3NEBATEFaEmoMHpfnzpaFDpYYNza4GAAAAAKwrNVVq0EC6+mqzKwk5hBWh5uWXpZwc6YknzK4EAAAAAKzL5ZL+9S+pf38pPNzsakIOYUUoOXVKeu016ZZbpDZtzK4GAAAAAKzr44+NwIIhIKYgrAglb7whnTwpjRpldiUAAAAAYG2pqVLt2lK3bmZXEpIIK0JFdrY0c6bUo4eUnGx2NQAAAABgXdnZ0vLl0u9/L0VGml1NSIowuwBUkXfflX75RVqwwOxKAAAAAMDaVq0yhtEzBMQ09KwIBXl50rRpUqdORs8KAAAAAEDpUlOl+HipZ0+zKwlZ9KwIBUuXSjt2SIsWSQ6H2dUAAAAAgHW53dI//iHddJMUHW12NSGLnhV25/VKU6dKrVrRhQkAAAAAzmb1aunXX/n+ZDJ6Vtjd559L33wjvf465wYGAAAAgLNJTZWqV5d69za7kpBGzwq7mzJFatBAuususysBAAAAAGvzeKQPP5RuvNEILGAawgo727hR+uQT6dFHpWrVzK4GAAAAAKxt/Xrp4EGGgFgAYYWdTZ0qxcVJDzxgdiUAAAAAYH2pqVJUlNS3r9mVhDzCCrvKyJAWL5aGDZNq1jS7GgAAAACwNq9XWrJE+u1vjdOWwlSEFXY1Y4YUEWEMAQEAAAAAlG3DBunnnxkCYhGEFXaUmyvNmyf98Y9Sw4ZmVwMAAAAA1peaavzg+7vfmV0JRFhhT6dPS1lZ0m9+Y3YlAAAAAGB9Xq8RVlx/vVS7ttnVQIQV9uR0GmtOtQMAAAAAZ/f999LOndKAAWZXgv8hrLAjX1hRo4a5dQAAAABAMEhNlRwO6eabza4E/0NYYUcul7GmZwUAAAAAnF1qqnTttVJiotmV4H8IK+yInhUAAAAAUD7bt0tbt3IWEIshrLAjX88KwgoAAAAAKFtqqrFmvgpLIaywIybYBAAAAIDySU2VLr9catLE7EpQCGGFHTEMBAAAAADObvdu6dtvGQJiQYQVdsQEmwAAAABwdkuWGGvCCsshrLAjelYAAAAAwNktWSL95jdSq1ZmV4IiCCvsiDkrAAAAAKBsBw5I69fTq8KiCCvsyOWSIiONBQAAAABQ3IcfGmvCCksirLAjp5MhIAAAAABQltRUqV076aKLzK4EJSCssCOXiyEgAAAAAFCaI0ek1avpVWFhhBV2RM8KAAAAACjdP/4heTyEFRZGWGFHhBUAAAAAULrUVKlFC6lDB7MrQSkIK+yIYSAAAAAAULITJ6RVq4xeFQ6H2dWgFIQVdkTPCgAAAAAo2fLlUm4uQ0AsjrDCjlwuwgoAAAAAKElqqtSokXT55WZXgjIQVtiR08kwEAAAAAAoKjNT+vhjacAAKYyvw1bG0bEjhoEAAAAAQHH/+peUlcUQkCBAWGFHTLAJAAAAAMWlpkr160vXXmt2JTiLiEDcqcfj0bhx47R9+3ZFRUVp4sSJatasWf7+1atX65VXXpHX69XFF1+s5557Tg5mYa0cXi89KwAAtkPbAgBQYWfOSP/8pzR4sBQebnY1OIuA9KxYuXKlcnJytGjRIj3++OOaMmVK/r7MzExNmzZNr7/+uhYvXqxGjRrp+PHjgSgjNOXmSnl5hBUAAFuhbQEAqLBPPjF+2GUISFAISFiRlpamLl26SJI6dOigLVu25O/buHGj2rZtq6lTp+qOO+5Q3bp1Vbt27UCUEZqcTmPNMBAAgI3QtgAAVFhqqlSrlnT99WZXgnIIyDCQzMxMxcbG5l8PDw+X2+1WRESEjh8/rq+++kpLly5V9erVNXjwYHXo0EEtWrQIRCmhxxdW0LMCAGAjtC0AABWSkyMtWybdfLMUGWl2NSiHgIQVsbGxcvq+NMsYZxoRYTxUzZo1demll6pevXqSpM6dOys9Pb3EBkV6enogyrO1qJ9+UitJ+48f16lKfv2ysrI4JhbDMbEmjov1cEyCH20L++LzaT0cE+vhmFRcjbVr1fTkSe29/HJlVtJryXEJrICEFZ06ddJnn32mPn36aNOmTWrbtm3+vosvvlg7duzQsWPHFB8fr82bN2vQoEEl3k9SUlIgyrO3rCxJUqO2bdWokl+/9PR0jonFcEysieNiPRyTiktLSzP18Wlb2BefT+vhmFhPyB2TgwelpUuN3uLx8VJCQvF1VNS53edLL0mxsWpy991StWqVUmbIHZcAKKt9EZCwolevXlq3bp1uv/12eb1eTZ48WfPmzVPTpk3Vo0cPPf7447r33nslSb179/ZrcKCCXC5jzTAQAICN0LYAgBDy+OPS3/9e9m2qVSs9yChp29Kl0k03VVpQgcALSFgRFham8ePH+21r1apV/uW+ffuqb9++gXhoMMEmAMCGaFsAQIjYu1dKSZEeekh67DHp5Enp1Cn/dWmXMzL8t3m9/vd9223mPCecl4CEFTARE2wCAAAACFZz5kgej/TEE1Lz5ud/P16vlJlZEFzk5krt21damQg8wgq78Q0DoWcFAAAAgGCSmSm98YY0cGDFggpJcjikuDhjQVAKM7sAVDJ6VgAAAAAIRvPnGz0hRo40uxJYAGGF3RBWAAAAAAg2eXnSrFnSVVdJV15pdjWwAMIKu2EYCAAAAIBgs3y5MUHmY4+ZXQksgrDCbpxOKSLi3M87DAAAAABmeeklqVkzqX9/syuBRRBW2I3LxRAQAAAAAMFjwwZp7VppxAjjh1dAhBX243QyBAQAAABA8Jg50zhrxz33mF0JLISwwm6cTnpWAAAAAAgOe/dKKSnSffdJ8fFmVwMLIaywG5eLnhUAAAAAgsOcOZLHIw0fbnYlsBjCCruhZwUAAACAYJCZKb3xhjRwoNS8udnVwGIIK+yGCTYBAAAABIP586WTJ6WRI82uBBZEWGE3TLAJAAAAwOry8qRZs6SrrpKuvNLsamBBhBV2wzAQAAAAAFa3fLmUkSE99pjZlcCiCCvshgk2AQAAAFjdSy9JzZpJ/fubXQksirDCbuhZAQAAAMDKNmyQ1q6VRoyQIiLMrgYWRVhhN0ywCQAAAMDKZs6U4uKke+4xuxJYGGGFneTmGgvDQAAAAABY0d69UkqKdN99Uny82dXAwsodVmRmZmrbtm1yuVyBrAcV4XQaa3pWAACCAG0LAAhBc+ZIHo80fLjZlcDiyjVAaMWKFXr99deVl5en3r17y+Fw6MEHHwx0bThXvsYePSsAABZH2wIAQlBmpvTGG9LAgVLz5mZXA4srV8+K+fPnKyUlRTVr1tSDDz6olStXBrounA96VgAAggRtCwAIQfPnSydPSiNHml0JgkC5worw8HBFRUXJ4XDI4XAoJiYm0HXhfBBWAACCBG0LAAgxeXnSrFnSVVdJV15pdjUIAuUKK5KTk/X444/r0KFDGjt2rC699NJA14XzwTAQAECQoG0BACFm+XIpI0N67DGzK0GQKNecFffdd582btyopKQktWzZUt27dw90XTgf9KwAAAQJ2hYAEGJeeklq1kzq39/sShAkyhVW3H///XrvvffUtWvXQNeDivD1rCCsAABYHG0LAAghGzZIa9cagUVEub6CAuULKxISEvTOO++oRYsWCgszRo5ce+21AS0M58HXs4JhIAAAi6NtAQAhZOZMKS5OuucesytBEClXWFGrVi1t27ZN27Zty99Gg8KCGAYCAAgStC0AIETs3SulpEjDh0vx8WZXgyBSrrDihRde0I4dO7Rz5061aNFCSUlJga4L54MJNgEAQYK2BQCEiDlzJI9HeuQRsytBkClXWPHuu+/qo48+Uvv27fX222/rxhtv1D104bEeelYAAIIEbQsACAGZmdIbb0gDB0rNm5tdDYJMucKKjz76SAsXLlRERIRyc3N1++2306CwIpdLCg+XoqLMrgQAgDLRtgCAEDB/vnTypDRypNmVIAiFledGXq9XEf+btTUyMlKRkZEBLQrnyek0hoA4HGZXAgBAmWhbAIDN5eVJs2ZJV10lXXml2dUgCJWrZ0VycrIeeeQRJScnKy0tTR07dgx0XTgfTidDQAAAQYG2BQDY3PLlUkaG9MILZleCIFWusGLUqFH6/PPPlZGRoYEDB6pbt26Brgvnw+Vick0AQFCgbQEANvfSS1KzZlL//mZXgiBVrmEg//nPf/Tdd9/pnnvu0YIFC/Tf//430HXhfNCzAgAQJGhbAICNbdggrV0rjRghRZTr93GgmHKFFbNnz9af/vQnSdKsWbM0Z86cgBaF8+RyEVYAAIICbQsAsLGZM6W4OImJk1EB5QorIiIiFBcXJ0mKi4tTWFi5/gxVzTfBJgAAFkfbAgBsau9eKSVFuvdeKT7e7GoQxMrVJ6d9+/Z6/PHH1aFDB33//fe66KKLAl0XzofTKdWubXYVAACcFW0LALCpOXMkj0d65BGzK0GQK9fPGPfdd59atWqlM2fOKC0tTQMGDAh0XTgfTLAJAAgStC0AwIYyM6U33pAGDpSaNze7GgS5coUVTzzxhDp16qQtW7Zo5MiReoHTz1gTE2wCAIIEbQsAsKH586WTJ6WRI82uBDZQrrDC4XDosssu0+nTp9W3b1/GlVoVE2wCAIIEbQsAsJm8PGnWLOmqq6QrrzS7GthAuVoGbrdb06ZNU3Jysr788kvl5uYGui6cDybYBAAECdoWAGAzy5dLGRnSY4+ZXQlsolxhxQsvvKAmTZro/vvv17FjxzR16tRA14Vz5XZLOTn0rAAABAXaFgBgMy+9JDVrJvXvb3YlsIlynQ2kefPmav6/CVL69OkTyHpwvlwuY01YAQAIArQtAMBGNmyQ1q41AouIcn3FBM6KAaJ24XQaa4aBAAAAAKhKf/mLFBcn3XOP2ZXARggr7MIXVtCzAgAAAEBV+vFH6YorpPh4syuBjRBW2IVvGAg9KwAAAABUNc7qhErGO8ou6FkBAAAAALAJwgq7YIJNAAAAAIBNEFbYBRNsAgAAAABsgrDCLhgGAgAAAACwCcIKu2CCTQAAAACATRBW2AU9KwAAAAAANkFYYRdMsAkAAAAAsAnCCrtwOiWHQ4qONrsSAAAAAAAqhLDCLpxOo1eFw2F2JQAAAAAAVAhhhV24XEyuCQAAAACwBcIKu/D1rAAAAAAAIMgFJKzweDwaO3asbrvtNg0ZMkR79uwp8Tb33nuv3nvvvUCUEHpcLsIKAIBt0bYAACC0BCSsWLlypXJycrRo0SI9/vjjmjJlSrHbzJo1S6dOnQrEw4cmp5NhIAAA26JtAQBAaAlIWJGWlqYuXbpIkjp06KAtW7b47V+xYoUcDkf+bVAJGAYCALAx2hYAAISWgIQVmZmZio2Nzb8eHh4ut9stSdqxY4c++ugjjRgxIhAPHbqYYBMAYGO0LQAACC0RgbjT2NhYOZ3O/Osej0cREcZDLV26VIcOHdJdd92l/fv3KzIyUo0aNVLXrl2L3U96enogyrOllsePK7t+fe0P4GuWlZXFMbEYjok1cVysh2MS/Ghb2BefT+vhmFiP1Y9JszNn5AkP114L1xgIVj8uwS4gYUWnTp302WefqU+fPtq0aZPatm2bv+/JJ5/Mvzx79mzVrVu3xMaEJCUlJQWiPHtyuxXdsKHiA/iapaenc0wshmNiTRwX6+GYVFxaWpqpj0/bwr74fFoPx8R6LH9MYmKkGjWsXWMAWP64BIGy2hcBCSt69eqldevW6fbbb5fX69XkyZM1b948NW3aVD169AjEQ4IJNgEANkbbAgCA0BKQsCIsLEzjx4/329aqVatitxs+fHggHj40McEmAMDGaFsAABBaAjLBJqpYXp6UnU1YAQAAAACwBcIKO3C5jDXDQAAAAAAANkBYYQe+2dHpWQEAAAAAsAHCCjugZwUAAAAAwEYIK+yAnhUAAAAAABshrLADX88KwgoAAAAAgA0QVtiBr2cFw0AAAAAAADZAWGEHDAMBAAAAANgIYYUdMMEmAAAAAMBGCCvsgJ4VAAAAAAAbIaywAybYBAAAAADYCGGFHTDBJgAAAADARggr7MAXVsTEmFsHAAAAAACVgLDCDlwuo1eFw2F2JQAAAAAAVBhhhR04ncxXAQAAAACwDcIKO3C5CCsAAAAAALZBWGEHTieTawIAAAAAbIOwwg4YBgIAAAAAsBHCCjtgGAgAAAAAwEYIK+yAYSAAAAAAABshrLADhoEAAAAAAGyEsMIOXC56VgAAAAAAbIOwwg7oWQEAAAAAsBHCCjtggk0AAAAAgI0QVgQ7j0c6c4ZhIAAAAAAA2yCsCHYul7GmZwUAAAAAwCYIK4KdL6ygZwUAAAAAwCYIK4Kd02ms6VkBAAAAALAJwopgxzAQAAAAAIDNEFYEO1/PCoaBAAAAAABsgrAi2DEMBAAAAABgM4QVwY4JNgEAAAAANkNYEezoWQEAAAAAsBnCimDHBJsAAAAAAJshrAh2TLAJAAAAALAZwopgxzAQAAAAAIDNEFYEO98wkJgYc+sAAAAAAKCSEFYEO6fTCCrCOJQAAAAAAHvgG26wc7kYAgIAAAAAsBXCimDndDK5JgAAAADAVggrgp3TSc8KAAAAAICtEFYEO4aBAAAAAABshrAi2DEMBAAAAABgM4QVwY5hIAAAAAAAmyGsCHYuFz0rAAAAAAC2QlgR7OhZAQAAAACwGcKKYMcEmwAAAAAAmyGsCHZMsAkAAAAAsBnCimDm8dCzAgAAAABgO4QVwSwry1jTswIAAAAAYCOEFcHM6TTW9KwAAAAAANgIYUUwc7mMNWEFAAAAAMBGCCuCma9nBcNAAAAAAJjF6zW7AtgQYUUwYxgIAAAAACtwOMyuADZDWBHMGAYCAAAAALAhwopgxjAQAAAAAIANEVYEM3pWAAAAAABsiLAimNGzAgAAAABgQxGBuFOPx6Nx48Zp+/btioqK0sSJE9WsWbP8/fPnz9c///lPSVK3bt308MMPB6IM+2OCTQBAiKBtAQBAaAlIz4qVK1cqJydHixYt0uOPP64pU6bk79u7d6+WLVum999/XykpKfrvf/+rbdu2BaIM+2MYCAAgRNC2AAAgtASkZ0VaWpq6dOkiSerQoYO2bNmSvy8xMVFz585VeHi4JMntdis6OjoQZdifr2dFTIy5dQAAEGC0LQAACC0BCSsyMzMVGxubfz08PFxut1sRERGKjIxU7dq15fV69eKLL+qiiy5SixYtSryf9PT0QJRnG/X27lXt6Ght37GjSh4vKyuLY2IxHBNr4rhYD8ck+NG2sC8+n9bDMbEeqx+T5mfOKC8yUnstXGMgWP24BLuAhBWxsbFy+n71lzHONCKi4KGys7M1ZswY1ahRQ88991yp95OUlBSI8uyjWjWpRo0qe53S09M5JhbDMbEmjov1cEwqLi0tzdTHp21hX3w+rYdjYj2WPyYxMVX6vcQqLH9cgkBZ7YuAzFnRqVMnrVmzRpK0adMmtW3bNn+f1+vVgw8+qAsvvFDjx4/P77KJ8+B0Ml8FACAk0LYAACC0BKRnRa9evbRu3Trdfvvt8nq9mjx5subNm6emTZvK4/Ho66+/Vk5OjtauXStJGjlypDp27BiIUuzN5SKsAACEBNoWAACEloCEFWFhYRo/frzftlatWuVf/v777wPxsKHH6ZSqVze7CgAAAo62BQAAoSUgw0BQRRgGAgAAAACwIcKKYOZy0bMCAAAAAGA7hBXBjJ4VAAAAAAAbIqwIZkywCQAAAACwIcKKYMYEmwAAAAAAGyKsCGYMAwEAAAAA2BBhRbDyehkGAgAAAACwJcKKYJWVZQQWDAMBAAAAANgMYUWwcrmMNT0rAAAAAAA2Q1gRrJxOY03PCgAAAACAzRBWBCtfWEHPCgAAAACAzRBWBCuGgQAAAAAAbIqwIlgxDAQAAAAAYFOEFcGKnhUAAAAAAJsirAhW9KwAAAAAANgUYUWwYoJNAAAAAIBNEVYEK4aBAAAAAABsirAiWDEMBAAAAABgU4QVwYqwAgAAAABgU4QVwcrlkqKipIgIsysBAAAAAKBSEVYEK6eT+SoAAAAAALZEWBGsXC7CCgAAAACALRFWBCunk/kqAAAAAAC2RFgRrBgGAgAAAACwKcKKYMUwEAAAAACATRFWBCuGgQAAAAAAbIqwIljRswIAAAAAYFOEFcGKnhUAAAAAAJsirAhWTLAJAOcsOztbixcvLvftlyxZolWrVpW6/80339R3331XGaUhRPGeBACgZBFmF4DzxDAQADhnR44c0eLFi3XrrbeW6/YDBgwoc//9999fGWUhhPGeBACgZIQVwcjrZRgIAJyH119/XTt37tScOXPk9Xq1ceNGuVwuTZo0SUuXLtWWLVt04sQJtWvXTi+88IJmz56tunXrqmXLlnrrrbcUGRmpffv2qU+fPho2bJhGjx6tPn366OjRo1q9erWysrL0888/67777tOAAQP03Xff6fnnn1eNGjVUp04dRUdHa8qUKfn17N69W0899ZQiIiLk8Xg0Y8YMJSYmasKECfruu++Um5ur4cOHq2fPnpoyZYrS0tIkSTfddJPuuusujR49WidOnNCJEyf0xhtvaO7cudqwYYM8Ho+GDh2qG2+80ayXGuXEexIAgJIRVgSjnBzJ46FnBYCgtmCB9PbblXufd98t3Xln6fsfeOAB7dixQw8//LBmz56tli1b6plnnlFmZqbi4+M1b948eTwe9e3bV4cOHfL72wMHDmjZsmXKyclRly5dNGzYML/9mZmZ+utf/6qffvpJDzzwgAYMGKDnnntOL774otq0aaOZM2cWu8/169erffv2+r//+z9t2LBBp0+f1pYtW3T8+HF98MEHOnnypObNm6fw8HDt27dPKSkpcrvduuOOO3TllVdKkq688koNHTpUq1ev1r59+/Tee+8pOztbgwYN0jXXXKP4+PjKeXFDAO9J3pMAAOtgzopg5HQaa3pWAECFtGjRQpIUHR2tY8eOaeTIkRo7dqxcLpdyc3P9btu2bVtFRESoevXqqlatWrH7ateunSSpYcOGysnJkSQdPnxYbdq0kSQlJycX+5tbbrlF8fHxuvfee7Vw4UKFh4dr9+7d6tChgyQpISFBjz76qDIyMtS5c2c5HA5FRkbqN7/5jTIyMvyew44dO7R161YNGTJE9957r9xut/bv318JrxKqEu9JAAAM9KwIRr6wgp4VAILYnXeW/YtzIISFhcnj8fhdl6Q1a9bol19+0axZs3Ts2DF9+umn8nq9fn/rcDjKvO+S9icmJmrnzp1q3bq1Nm/eXGz/qlWrlJycrIcfflgfffSR5s6dqx49emjFihWSpNOnT+vRRx/VH//4Ry1ZskRDhw5Vbm6uNm7cqP79+/s9bsuWLXXFFVdowoQJ8ng8evXVV9WkSZNzeHXAe5L3JADAOggrgpHLZawJKwDgnNSpU0e5ubmaNm2a3y/R7du316uvvqrBgwfL4XCoSZMmOnz4cIUf77nnntOYMWNUvXp1RUZGqkGDBn77L7nkEo0aNUqvvfaaPB6PnnrqKV100UX64osv9Ic//EF5eXl66KGH1K1bN3399de67bbblJubq969e+viiy/2u6/u3bvr66+/1h133CGXy6WePXsqNja2ws8BgcV7EgCAkjm8RWN6i0hLSyuxeyIkffutlJwsLV0q/f73Vfaw6enpSkpKqrLHw9lxTKyJ42I9Zh2ThQsX6sYbb1Tt2rU1c+ZMRUZG6uGHH67yOiqDHf5ftsNzqCgrvif5N9N6OCbWY/ljcuWVUs2a0v96YYUKyx+XIFDW/830rAhG9KwAgKBQp04d3X333apevbri4uL8zroAmIH3JAAgWBBWBCMm2ASAoNC7d2/17t3b7DKAfLwnAQDBgrOBBCMm2AQAAAAA2BhhRTBiGAgAAAAAwMYIK4IRw0AAAAAAADZGWBGMGAYCAAAAALAxwopg5BsGQs8KAAiYIUOGKCMjQ0uWLNGqVauK7b/mmmvK/PtPP/1Uhw4d0pEjRzRu3LgAVYlQwnsSABBKCCuCkdMpRUYaCwAgoAYMGKAePXqc898tWLBAmZmZqlevHl8MUal4TwIAQgGnLg1GLhdDQADgPDz88MO68847dfnll+v777/Xq6++qmnTpunpp5/W6dOndfjwYd1xxx2644478v9m9uzZqlu3rgYNGqRnn31WO3fuVJMmTZSTkyNJ2rFjh6ZMmaK8vDwdP35c48aN06lTp5Senq5Ro0Zp2rRpGjVqlFJSUrRu3TrNmjVL0dHRqlmzpiZPnqz09HS99dZbioyM1L59+9SnTx8NGzbMr+6ZM2fqq6++ktvt1m9/+1vdf//92rx5syZPniyPx6MGDRpo+vTp2rVrlyZMmKDw8HBFR0drwoQJ8ng8GjZsmGrWrKmuXbuqa9eumjhxoiTl1xAXF1d1BwF+eE92VePGjTV58mRJvCcBAAUIK4KR08kQEADBb8EC6e23K/c+775buvPOUnffeuut+vDDD3X55ZdryZIlGjRokPbs2aO+ffvqt7/9rQ4dOqQhQ4b4fTH0+fTTT5Wdna2UlBQdOHBAH3/8sSRp586dGjVqlC688EItX75cS5Ys0cSJE5WUlKRx48Yp8n+94Lxer5599lm99957atCggd555x299tpruu6663TgwAEtW7ZMOTk56tKlS7EvhsuXL9eCBQtUv359LVmyRJI0duxYvfTSS2rVqpUWL16sjIwMPfvss5o0aZKSkpK0cuVKTZkyRU8++aSOHDmi1NRURUVFadCgQZo8ebJat26txYsXa+7cuXrssccq6wgEN96Tprwn+/Xrp5kzZ/KeBAD4IawIRk4nPSsA4Dx06dJF06ZN04kTJ7RhwwY988wzOnr0qN555x198sknio2NldvtLvFvf/rpJ7Vv316SdMEFF6hhw4aSpPr16+vVV19VtWrV5HQ6FRsbW+LfHz9+XLGxsWrQoIEk6bLLLtNLL72k6667Tm3btlVERIQiIiJUrVq1Yn87bdo0zZgxQ0ePHlWXLl0kSUePHlWrVq0kGV94Jenw4cNKSkrKv/8ZM2ZIkho3bqyoqChJUkZGhp5//nlJUm5urpo3b35uLyIqFe9Jad++fbwnAQDFEFYEI4aBALCDO+8s8xfnQAgLC1Pv3r01btw49ezZU+Hh4Xr77bfVoUMH3XHHHfryyy+1evXqEv+2devW+uc//6m77rpLhw4d0qFDhyRJkyZN0vTp09WqVSv95S9/0f79+yVJDodDXq83/+9r1aqlzMxMHT58WPXr19fXX3+d/6XM4XCUWnNOTo5WrFihl156SZLUp08f9e3bV/Xr19dPP/2k5s2b680331SLFi1Uv359bdu2Te3atdM333yTf/9hYQVTVLVo0UJTp07VBRdcoLS0NB05cuS8X0/b4T1pynuyUaNGvCcBAMUQVgQjhoEAwHkbOHCgevbsmd9l/vrrr9fEiRP1r3/9S3FxcQoPD88f+19Yjx49tG7dOt1666264IILVKtWLUnS7373O40YMULx8fFKTEzU8ePHJUkdO3bUk08+qQkTJkgyvvxNnDhRw4cPl8PhUEJCgl544QX9+OOPZdYbFRWlhIQEDRo0SNWqVdM111yjCy64QM8//7zGjBmjsLAw1atXT0OHDlWjRo00YcIEeb1ehYeH588DUNi4ceM0atQoud1uORwOTZo0qUKvJyou1N+Tf/7zn3lPAgCKcXgLR+wWkpaWpuTkZLPLsKarr5ZiY6VPPqnSh01PT8/vyglr4JhYE8fFejgmFWeH/5ft8BzsiM+n9XBMrMfyx+TKK6WaNaUVK8yupEpZ/rgEgbL+b+bUpcGInhUAAAAAABsjrAhGTLAJAAAAALAxwopgxASbAAAAAAAbI6wIRgwDAQAAAADYGGFFMKJnBQAAAADAxggrgk1OjuR207MCAAAAAGBbhBXBxuk01vSsAAAAAADYVEDCCo/Ho7Fjx+q2227TkCFDtGfPHr/9KSkpGjBggAYNGqTPPvssECXYl8tlrAkrAAAhhLYFAAChJSIQd7py5Url5ORo0aJF2rRpk6ZMmaLXXntNknTkyBG9++67Sk1NVXZ2tu644w5dc801ioqKCkQp9uPrWcEwEABACKFtAQBAaAlIz4q0tDR16dJFktShQwdt2bIlf993332njh07KioqSnFxcWratKm2bdsWiDLsiZ4VAIAQRNsCAIDQEpCeFZmZmYqNjc2/Hh4eLrfbrYiICGVmZiouLi5/X40aNZSZmVni/aSlpQWivOC3YYOxNuH14ZhYD8fEmjgu1sMxCW60LeyN42I9HBPrsfQxeeUVY23lGgPE0sclyAUkrIiNjZXTN1xBxjjTiIiIEvc5nU6/BoZPcnJyIEoDAABBiLYFAAChJSDDQDp16qQ1a9ZIkjZt2qS2bdvm72vfvr3S0tKUnZ2t06dPKyMjw28/AABAUbQtAAAILQ6v1+ut7Dv1eDwaN26cduzYIa/Xq8mTJ2vNmjVq2rSpevTooZSUFC1atEher1d//vOfdcMNN1R2CQAAwEZoWwAAEFoCElacj82bN2v69Ol69913tWfPHo0ePVoOh0Nt2rTRc889p7CwgHQCwVkUPi4//PCD/vznP6t58+aSpD/84Q/q06ePuQWGkNzcXI0ZM0b79+9XTk6Ohg0bptatW/NZMVlJx6Vhw4Z8VkyUl5enZ555Rrt375bD4dDzzz+v6OhoPishivaF9dC2sBbaF9ZD28KaaF9UvYDMWXGu3nrrLS1btkwxMTGSpBdeeEGPPvqorrjiCo0dO1arVq1Sr169TK4y9BQ9Llu3btWf/vQn3X333SZXFpqWLVummjVratq0aTpx4oRuvvlmtWvXjs+KyUo6Lg899BCfFRN99tlnkqT3339fX331lWbOnCmv18tnJQTRvrAe2hbWQ/vCemhbWBPti6pnidinadOmmj17dv71rVu36vLLL5ckde3aVevXrzertJBW9Lhs2bJFn3/+uQYPHqwxY8aUOtM6AqN3794aMWKEJMnr9So8PJzPigWUdFz4rJirZ8+emjBhgiTpwIEDio+P57MSomhfWA9tC+uhfWE9tC2sifZF1bNEWHHDDTfkz+gtGR9Kh8MhyTj92OnTp80qLaQVPS7t27fXk08+qYULF6pJkyZ6xXeKIlSJGjVqKDY2VpmZmXrkkUf06KOP8lmxgJKOC58V80VERGjUqFGaMGGC+vXrx2clRNG+sB7aFtZD+8J6aFtYF+2LqmWJsKKowuN8nE6n4uPjTawGPr169dIll1ySf/mHH34wuaLQ88svv+jOO+/U73//e/Xr14/PikUUPS58Vqxh6tSp+vjjj/Xss88qOzs7fzufldDFv5nWw7+X1kD7wnpoW1gX7YuqY8mw4qKLLtJXX30lSVqzZo06d+5sckWQpHvuuUffffedJOmLL77QxRdfbHJFoeXo0aO6++679X//93+65ZZbJPFZsYKSjgufFXMtXbpUb7zxhiQpJiZGDodDl1xyCZ8V8G+mBfHvpfloX1gPbQtron1R9SxzNpB9+/Zp5MiRSklJ0e7du/Xss88qNzdXLVu21MSJExUeHm52iSGp8HHZunWrJkyYoMjISNWtW1cTJkxQbGys2SWGjIkTJ+rf//63WrZsmb/t6aef1sSJE/msmKik4/Loo49q2rRpfFZM4nK59NRTT+no0aNyu92677771KpVK/5fCVG0L6yHtoW10L6wHtoW1kT7oupZJqwAAAAAAACQLDoMBAAAAAAAhC7CCgAAAAAAYCmEFQAAAAAAwFIIKwAAAAAAgKUQVgAAAAAAAEshrABCUHZ2thYvXlzu2y9ZskSrVq0qdf+bb76Zf+7vypKenq45c+ZU6n0CAIDAoX0BoDJx6lIgBBU+xz0AAEBloH0BoDJFmF0AgKr3+uuva+fOnZozZ468Xq82btwol8ulSZMmaenSpdqyZYtOnDihdu3a6YUXXtDs2bNVt25dtWzZUm+99ZYiIyO1b98+9enTR8OGDdPo0aPVp08fHT16VKtXr1ZWVpZ+/vln3XfffRowYIC+++47Pf/886pRo4bq1Kmj6OhoTZkyJb+e3bt366mnnlJERIQ8Ho9mzJihn3/+We+//75GjhypMWPGSJKcTqd27dqlL774Qp9//rnmz5+vsLAwJScn64knnjDr5QQAAKJ9AaByMQwECEEPPPCAWrdurYcffliS1LJlS73//vtq0KCB4uPjNW/ePKWmpmrTpk06dOiQ398eOHBAs2fP1qJFizR37txi952Zmak33nhDr732mt58801J0nPPPacpU6ZowYIFatq0abG/Wb9+vdq3b6958+Zp+PDhOn36dP6+Jk2a6N1339Vf//pX1axZUy+//LKys7M1e/ZszZ8/X++9954OHTqkdevWVeZLBAAAzhHtCwCVibACgFq0aCFJio6O1rFjxzRy5EiNHTtWLpdLubm5frdt27atIiIiVL16dVWrVq3YfbVr106S1LBhQ+Xk5EiSDh8+rDZt2kiSkpOTi/3NLbfcovj4eN17771auHChwsPD/fa73W499thj+t3vfqdu3brp559/1rFjx3T//fdryJAhysjI0M8//1zxFwIAAFQa2hcAKoJhIEAICgsLk8fj8bsuSWvWrNEvv/yiWbNm6dixY/r0009VdFobh8NR5n2XtD8xMVE7d+5U69attXnz5mL7V61apeTkZD388MP66KOPNHfuXN18882SJK/Xq6efflodO3bM39a4cWM1bNhQb7/9tiIjI7VkyRIlJSWdy0sAAAAqGe0LAJWJsAIIQXXq1FFubq6mTZvm9+tF+/bt9eqrr2rw4MFyOBxq0qSJDh8+XOHHe+655zRmzBhVr15dkZGRatCggd/+Sy65RKNGjdJrr70mj8ejp556SpmZmZKkFStW6JNPPtGhQ4e0evXq/PsbOnSohgwZory8PDVq1Eg33nhjhesEAADnj/YFgMrE2UAABNzChQt14403qnbt2po5c6YiIyPzx7MCAACcD9oXgL3RswJAwNWpU0d33323qlevrri4OL+ZugEAAM4H7QvA3uhZAQAAAAAALIWzgQAAAAAAAEshrAAAAAAAAJZCWAEAAAAAACyFsAIAAAAAAFgKYQUAAAAAALAUwgoAAAAAAGAp/w80UHDadp4CjgAAAABJRU5ErkJggg==\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import learning_curve\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(1, 2, figsize=(16, 6))\\n\",\n \"fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\\n\",\n \"\\n\",\n \"for i, degree in enumerate([2, 9]):\\n\",\n \" N, train_lc, val_lc = learning_curve(\\n\",\n \" PolynomialRegression(degree), X, y, cv=7,\\n\",\n \" train_sizes=np.linspace(0.3, 1, 25))\\n\",\n \"\\n\",\n \" ax[i].plot(N, np.mean(train_lc, 1),\\n\",\n \" color='blue', label='training score')\\n\",\n \" ax[i].plot(N, np.mean(val_lc, 1),\\n\",\n \" color='red', label='validation score')\\n\",\n \" ax[i].hlines(np.mean([train_lc[-1], val_lc[-1]]), N[0],\\n\",\n \" N[-1], color='gray', linestyle='dashed')\\n\",\n \"\\n\",\n \" ax[i].set_ylim(0, 1)\\n\",\n \" ax[i].set_xlim(N[0], N[-1])\\n\",\n \" ax[i].set_xlabel('training size')\\n\",\n \" ax[i].set_ylabel('score')\\n\",\n \" ax[i].set_title('degree = {0}'.format(degree), size=14)\\n\",\n \" ax[i].legend(loc='best')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This is a valuable diagnostic, because it gives us a visual depiction of how our model responds to increasing amounts of training data.\\n\",\n \"In particular, when the learning curve has already converged (i.e., when the training and validation curves are already close to each other) *adding more training data will not significantly improve the fit!*\\n\",\n \"This situation is seen in the left panel, with the learning curve for the degree-2 model.\\n\",\n \"\\n\",\n \"The only way to increase the converged score is to use a different (usually more complicated) model.\\n\",\n \"We see this in the right panel: by moving to a much more complicated model, we increase the score of convergence (indicated by the dashed line), but at the expense of higher model variance (indicated by the difference between the training and validation scores).\\n\",\n \"If we were to add even more data points, the learning curve for the more complicated model would eventually converge.\\n\",\n \"\\n\",\n \"Plotting a learning curve for your particular choice of model and dataset can help you to make this type of decision about how to move forward in improving your analysis.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Validation in Practice: Grid Search\\n\",\n \"\\n\",\n \"The preceding discussion is meant to give you some intuition into the trade-off between bias and variance, and its dependence on model complexity and training set size.\\n\",\n \"In practice, models generally have more than one knob to turn, meaning plots of validation and learning curves change from lines to multidimensional surfaces.\\n\",\n \"In these cases, such visualizations are difficult, and we would rather simply find the particular model that maximizes the validation score.\\n\",\n \"\\n\",\n \"Scikit-Learn provides some tools to make this kind of search more convenient: here we'll consider the use of grid search to find the optimal polynomial model.\\n\",\n \"We will explore a two-dimensional grid of model features, namely the polynomial degree and the flag telling us whether to fit the intercept.\\n\",\n \"This can be set up using Scikit-Learn's `GridSearchCV` meta-estimator:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import GridSearchCV\\n\",\n \"\\n\",\n \"param_grid = {'polynomialfeatures__degree': np.arange(21),\\n\",\n \" 'linearregression__fit_intercept': [True, False]}\\n\",\n \"\\n\",\n \"grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Notice that like a normal estimator, this has not yet been applied to any data.\\n\",\n \"Calling the ``fit`` method will fit the model at each grid point, keeping track of the scores along the way:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"grid.fit(X, y);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now that the model is fit, we can ask for the best parameters as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'linearregression__fit_intercept': False, 'polynomialfeatures__degree': 4}\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"grid.best_params_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Finally, if we wish, we can use the best model and show the fit to our data using code from before (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"model = grid.best_estimator_\\n\",\n \"\\n\",\n \"plt.scatter(X.ravel(), y)\\n\",\n \"lim = plt.axis()\\n\",\n \"y_test = model.fit(X, y).predict(X_test)\\n\",\n \"plt.plot(X_test.ravel(), y_test);\\n\",\n \"plt.axis(lim);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Other options in `GridSearchCV` include the ability to specify a custom scoring function, to parallelize the computations, to do randomized searches, and more.\\n\",\n \"For more information, see the examples in [In-Depth: Kernel Density Estimation](05.13-Kernel-Density-Estimation.ipynb) and [Feature Engineering: Working with Images](05.14-Image-Features.ipynb), or refer to Scikit-Learn's [grid search documentation](http://Scikit-Learn.org/stable/modules/grid_search.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Summary\\n\",\n \"\\n\",\n \"In this chapter we began to explore the concept of model validation and hyperparameter optimization, focusing on intuitive aspects of the bias–variance trade-off and how it comes into play when fitting models to data.\\n\",\n \"In particular, we found that the use of a validation set or cross-validation approach is vital when tuning parameters in order to avoid overfitting for more complex/flexible models.\\n\",\n \"\\n\",\n \"In later chapters, we will discuss the details of particularly useful models, what tuning is available for these models, and how these free parameters affect model complexity.\\n\",\n \"Keep the lessons of this chapter in mind as you read on and learn about these machine learning approaches!\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 64-bit ('3.9.6')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/05.04-Feature-Engineering.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feature Engineering\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The previous chapters outlined the fundamental ideas of machine learning, but all of the examples assumed that you have numerical data in a tidy, `[n_samples, n_features]` format.\\n\",\n \"In the real world, data rarely comes in such a form.\\n\",\n \"With this in mind, one of the more important steps in using machine learning in practice is *feature engineering*: that is, taking whatever information you have about your problem and turning it into numbers that you can use to build your feature matrix.\\n\",\n \"\\n\",\n \"In this chapter, we will cover a few common examples of feature engineering tasks: we'll look at features for representing categorical data, text, and images.\\n\",\n \"Additionally, we will discuss derived features for increasing model complexity and imputation of missing data.\\n\",\n \"This process is commonly referred to as vectorization, as it involves converting arbitrary data into well-behaved vectors.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Categorical Features\\n\",\n \"\\n\",\n \"One common type of nonnumerical data is *categorical* data.\\n\",\n \"For example, imagine you are exploring some data on housing prices, and along with numerical features like \\\"price\\\" and \\\"rooms,\\\" you also have \\\"neighborhood\\\" information.\\n\",\n \"For example, your data might look something like this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"data = [\\n\",\n \" {'price': 850000, 'rooms': 4, 'neighborhood': 'Queen Anne'},\\n\",\n \" {'price': 700000, 'rooms': 3, 'neighborhood': 'Fremont'},\\n\",\n \" {'price': 650000, 'rooms': 3, 'neighborhood': 'Wallingford'},\\n\",\n \" {'price': 600000, 'rooms': 2, 'neighborhood': 'Fremont'}\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"You might be tempted to encode this data with a straightforward numerical mapping:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"{'Queen Anne': 1, 'Fremont': 2, 'Wallingford': 3};\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"But it turns out that this is not generally a useful approach in Scikit-Learn. The package's models make the fundamental assumption that numerical features reflect algebraic quantities, so such a mapping would imply, for example, that *Queen Anne < Fremont < Wallingford*, or even that *Wallingford–Queen Anne = Fremont*, which (niche demographic jokes aside) does not make much sense.\\n\",\n \"\\n\",\n \"In this case, one proven technique is to use *one-hot encoding*, which effectively creates extra columns indicating the presence or absence of a category with a value of 1 or 0, respectively.\\n\",\n \"When your data takes the form of a list of dictionaries, Scikit-Learn's ``DictVectorizer`` will do this for you:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0, 1, 0, 850000, 4],\\n\",\n \" [ 1, 0, 0, 700000, 3],\\n\",\n \" [ 0, 0, 1, 650000, 3],\\n\",\n \" [ 1, 0, 0, 600000, 2]])\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction import DictVectorizer\\n\",\n \"vec = DictVectorizer(sparse=False, dtype=int)\\n\",\n \"vec.fit_transform(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Notice that the `neighborhood` column has been expanded into three separate columns representing the three neighborhood labels, and that each row has a 1 in the column associated with its neighborhood.\\n\",\n \"With these categorical features thus encoded, you can proceed as normal with fitting a Scikit-Learn model.\\n\",\n \"\\n\",\n \"To see the meaning of each column, you can inspect the feature names:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array(['neighborhood=Fremont', 'neighborhood=Queen Anne',\\n\",\n \" 'neighborhood=Wallingford', 'price', 'rooms'], dtype=object)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vec.get_feature_names_out()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"There is one clear disadvantage of this approach: if your category has many possible values, this can *greatly* increase the size of your dataset.\\n\",\n \"However, because the encoded data contains mostly zeros, a sparse output can be a very efficient solution:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"<4x5 sparse matrix of type ''\\n\",\n \"\\twith 12 stored elements in Compressed Sparse Row format>\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vec = DictVectorizer(sparse=True, dtype=int)\\n\",\n \"vec.fit_transform(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Nearly all of the Scikit-Learn estimators accept such sparse inputs when fitting and evaluating models. `sklearn.preprocessing.OneHotEncoder` and `sklearn.feature_extraction.FeatureHasher` are two additional tools that Scikit-Learn includes to support this type of encoding.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Text Features\\n\",\n \"\\n\",\n \"Another common need in feature engineering is to convert text to a set of representative numerical values.\\n\",\n \"For example, most automatic mining of social media data relies on some form of encoding the text as numbers.\\n\",\n \"One of the simplest methods of encoding this type of data is by *word counts*: you take each snippet of text, count the occurrences of each word within it, and put the results in a table.\\n\",\n \"\\n\",\n \"For example, consider the following set of three phrases:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"sample = ['problem of evil',\\n\",\n \" 'evil queen',\\n\",\n \" 'horizon problem']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"For a vectorization of this data based on word count, we could construct individual columns representing the words \\\"problem,\\\" \\\"of,\\\" \\\"evil,\\\" and so on.\\n\",\n \"While doing this by hand would be possible for this simple example, the tedium can be avoided by using Scikit-Learn's `CountVectorizer`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"<3x5 sparse matrix of type ''\\n\",\n \"\\twith 7 stored elements in Compressed Sparse Row format>\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction.text import CountVectorizer\\n\",\n \"\\n\",\n \"vec = CountVectorizer()\\n\",\n \"X = vec.fit_transform(sample)\\n\",\n \"X\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The result is a sparse matrix recording the number of times each word appears; it is easier to inspect if we convert this to a `DataFrame` with labeled columns:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" evil horizon of problem queen\\n\",\n \"0 1 0 1 1 0\\n\",\n \"1 1 0 0 0 1\\n\",\n \"2 0 1 0 1 0\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"pd.DataFrame(X.toarray(), columns=vec.get_feature_names_out())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"There are some issues with using a simple raw word count, however: it can lead to features that put too much weight on words that appear very frequently, and this can be suboptimal in some classification algorithms.\\n\",\n \"One approach to fix this is known as *term frequency–inverse document frequency* (*TF–IDF*), which weights the word counts by a measure of how often they appear in the documents.\\n\",\n \"The syntax for computing these features is similar to the previous example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" evil horizon of problem queen\\n\",\n \"0 0.517856 0.000000 0.680919 0.517856 0.000000\\n\",\n \"1 0.605349 0.000000 0.000000 0.000000 0.795961\\n\",\n \"2 0.000000 0.795961 0.000000 0.605349 0.000000\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.feature_extraction.text import TfidfVectorizer\\n\",\n \"vec = TfidfVectorizer()\\n\",\n \"X = vec.fit_transform(sample)\\n\",\n \"pd.DataFrame(X.toarray(), columns=vec.get_feature_names_out())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"For an example of using TF-IDF in a classification problem, see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Image Features\\n\",\n \"\\n\",\n \"Another common need is to suitably encode images for machine learning analysis.\\n\",\n \"The simplest approach is what we used for the digits data in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb): simply using the pixel values themselves.\\n\",\n \"But depending on the application, such an approach may not be optimal.\\n\",\n \"\\n\",\n \"A comprehensive summary of feature extraction techniques for images is well beyond the scope of this chapter, but you can find excellent implementations of many of the standard approaches in the [Scikit-Image project](http://scikit-image.org).\\n\",\n \"For one example of using Scikit-Learn and Scikit-Image together, see [Feature Engineering: Working with Images](05.14-Image-Features.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Derived Features\\n\",\n \"\\n\",\n \"Another useful type of feature is one that is mathematically derived from some input features.\\n\",\n \"We saw an example of this in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) when we constructed *polynomial features* from our input data.\\n\",\n \"We saw that we could convert a linear regression into a polynomial regression not by changing the model, but by transforming the input!\\n\",\n \"\\n\",\n \"For example, this data clearly cannot be well described by a straight line (see Figure 40-1):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"x = np.array([1, 2, 3, 4, 5])\\n\",\n \"y = np.array([4, 2, 1, 3, 7])\\n\",\n \"plt.scatter(x, y);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We can still fit a line to the data using `LinearRegression` and get the optimal result, as shown in Figure 40-2:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"X = x[:, np.newaxis]\\n\",\n \"model = LinearRegression().fit(X, y)\\n\",\n \"yfit = model.predict(X)\\n\",\n \"plt.scatter(x, y)\\n\",\n \"plt.plot(x, yfit);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"But it's clear that we need a more sophisticated model to describe the relationship between $x$ and $y$.\\n\",\n \"\\n\",\n \"One approach to this is to transform the data, adding extra columns of features to drive more flexibility in the model.\\n\",\n \"For example, we can add polynomial features to the data this way:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 1. 1. 1.]\\n\",\n \" [ 2. 4. 8.]\\n\",\n \" [ 3. 9. 27.]\\n\",\n \" [ 4. 16. 64.]\\n\",\n \" [ 5. 25. 125.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.preprocessing import PolynomialFeatures\\n\",\n \"poly = PolynomialFeatures(degree=3, include_bias=False)\\n\",\n \"X2 = poly.fit_transform(X)\\n\",\n \"print(X2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The derived feature matrix has one column representing $x$, a second column representing $x^2$, and a third column representing $x^3$.\\n\",\n \"Computing a linear regression on this expanded input gives a much closer fit to our data, as you can see in Figure 40-3:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"model = LinearRegression().fit(X2, y)\\n\",\n \"yfit = model.predict(X2)\\n\",\n \"plt.scatter(x, y)\\n\",\n \"plt.plot(x, yfit);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This idea of improving a model not by changing the model, but by transforming the inputs, is fundamental to many of the more powerful machine learning methods.\\n\",\n \"We'll explore this idea further in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) in the context of *basis function regression*.\\n\",\n \"More generally, this is one motivational path to the powerful set of techniques known as *kernel methods*, which we will explore in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Imputation of Missing Data\\n\",\n \"\\n\",\n \"Another common need in feature engineering is handling of missing data.\\n\",\n \"We discussed the handling of missing data in `DataFrame` objects in [Handling Missing Data](03.04-Missing-Values.ipynb), and saw that `NaN` is often is used to mark missing values.\\n\",\n \"For example, we might have a dataset that looks like this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from numpy import nan\\n\",\n \"X = np.array([[ nan, 0, 3 ],\\n\",\n \" [ 3, 7, 9 ],\\n\",\n \" [ 3, 5, 2 ],\\n\",\n \" [ 4, nan, 6 ],\\n\",\n \" [ 8, 8, 1 ]])\\n\",\n \"y = np.array([14, 16, -1, 8, -5])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"When applying a typical machine learning model to such data, we will need to first replace the missing values with some appropriate fill value.\\n\",\n \"This is known as *imputation* of missing values, and strategies range from simple (e.g., replacing missing values with the mean of the column) to sophisticated (e.g., using matrix completion or a robust model to handle such data).\\n\",\n \"\\n\",\n \"The sophisticated approaches tend to be very application-specific, and we won't dive into them here.\\n\",\n \"For a baseline imputation approach using the mean, median, or most frequent value, Scikit-Learn provides the `SimpleImputer` class:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[4.5, 0. , 3. ],\\n\",\n \" [3. , 7. , 9. ],\\n\",\n \" [3. , 5. , 2. ],\\n\",\n \" [4. , 5. , 6. ],\\n\",\n \" [8. , 8. , 1. ]])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.impute import SimpleImputer\\n\",\n \"imp = SimpleImputer(strategy='mean')\\n\",\n \"X2 = imp.fit_transform(X)\\n\",\n \"X2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We see that in the resulting data, the two missing values have been replaced with the mean of the remaining values in the column. This imputed data can then be fed directly into, for example, a `LinearRegression` estimator:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([13.14869292, 14.3784627 , -1.15539732, 10.96606197, -5.33782027])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = LinearRegression().fit(X2, y)\\n\",\n \"model.predict(X2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Feature Pipelines\\n\",\n \"\\n\",\n \"With any of the preceding examples, it can quickly become tedious to do the transformations by hand, especially if you wish to string together multiple steps.\\n\",\n \"For example, we might want a processing pipeline that looks something like this:\\n\",\n \"\\n\",\n \"1. Impute missing values using the mean.\\n\",\n \"2. Transform features to quadratic.\\n\",\n \"3. Fit a linear regression model.\\n\",\n \"\\n\",\n \"To streamline this type of processing pipeline, Scikit-Learn provides a ``Pipeline`` object, which can be used as follows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.pipeline import make_pipeline\\n\",\n \"\\n\",\n \"model = make_pipeline(SimpleImputer(strategy='mean'),\\n\",\n \" PolynomialFeatures(degree=2),\\n\",\n \" LinearRegression())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"This pipeline looks and acts like a standard Scikit-Learn object, and will apply all the specified steps to any input data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[14 16 -1 8 -5]\\n\",\n \"[14. 16. -1. 8. -5.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"model.fit(X, y) # X with missing values, from above\\n\",\n \"print(y)\\n\",\n \"print(model.predict(X))\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"All the steps of the model are applied automatically.\\n\",\n \"Notice that for simplicity, in this demonstration we've applied the model to the data it was trained on; this is why it was able to perfectly predict the result (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for further discussion of this).\\n\",\n \"\\n\",\n \"For some examples of Scikit-Learn pipelines in action, see the following chapter on naive Bayes classification, as well as [In Depth: Linear Regression](05.06-Linear-Regression.ipynb) and [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb).\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
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"path": "notebooks/05.05-Naive-Bayes.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# In Depth: Naive Bayes Classification\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The previous four chapters have given a general overview of the concepts of machine learning.\\n\",\n \"In this chapter and the ones that follow, we will be taking a\\n\",\n \"closer look first at four algorithms for supervised learning,\\n\",\n \"and then at four algorithms for unsupervised learning.\\n\",\n \"We start here with our first supervised method, naive Bayes classification.\\n\",\n \"\\n\",\n \"Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets.\\n\",\n \"Because they are so fast and have so few tunable parameters, they end up being useful as a quick-and-dirty baseline for a classification problem.\\n\",\n \"This chapter will provide an intuitive explanation of how naive Bayes classifiers work, followed by a few examples of them in action on some datasets.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Bayesian Classification\\n\",\n \"\\n\",\n \"Naive Bayes classifiers are built on Bayesian classification methods.\\n\",\n \"These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities.\\n\",\n \"In Bayesian classification, we're interested in finding the probability of a label $L$ given some observed features, which we can write as $P(L~|~{\\\\rm features})$.\\n\",\n \"Bayes's theorem tells us how to express this in terms of quantities we can compute more directly:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"P(L~|~{\\\\rm features}) = \\\\frac{P({\\\\rm features}~|~L)P(L)}{P({\\\\rm features})}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"If we are trying to decide between two labels—let's call them $L_1$ and $L_2$—then one way to make this decision is to compute the ratio of the posterior probabilities for each label:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\frac{P(L_1~|~{\\\\rm features})}{P(L_2~|~{\\\\rm features})} = \\\\frac{P({\\\\rm features}~|~L_1)}{P({\\\\rm features}~|~L_2)}\\\\frac{P(L_1)}{P(L_2)}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"All we need now is some model by which we can compute $P({\\\\rm features}~|~L_i)$ for each label.\\n\",\n \"Such a model is called a *generative model* because it specifies the hypothetical random process that generates the data.\\n\",\n \"Specifying this generative model for each label is the main piece of the training of such a Bayesian classifier.\\n\",\n \"The general version of such a training step is a very difficult task, but we can make it simpler through the use of some simplifying assumptions about the form of this model.\\n\",\n \"\\n\",\n \"This is where the \\\"naive\\\" in \\\"naive Bayes\\\" comes in: if we make very naive assumptions about the generative model for each label, we can find a rough approximation of the generative model for each class, and then proceed with the Bayesian classification.\\n\",\n \"Different types of naive Bayes classifiers rest on different naive assumptions about the data, and we will examine a few of these in the following sections.\\n\",\n \"\\n\",\n \"We begin with the standard imports:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"plt.style.use('seaborn-whitegrid')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Gaussian Naive Bayes\\n\",\n \"\\n\",\n \"Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes.\\n\",\n \"With this classifier, the assumption is that *data from each label is drawn from a simple Gaussian distribution*.\\n\",\n \"Imagine that we have the following data, shown in Figure 41-1:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.datasets import make_blobs\\n\",\n \"X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The simplest Gaussian model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions.\\n\",\n \"This model can be fit by computing the mean and standard deviation of the points within each label, which is all we need to define such a distribution.\\n\",\n \"The result of this naive Gaussian assumption is shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Gaussian-Naive-Bayes)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses.\\n\",\n \"With this generative model in place for each class, we have a simple recipe to compute the likelihood $P({\\\\rm features}~|~L_1)$ for any data point, and thus we can quickly compute the posterior ratio and determine which label is the most probable for a given point.\\n\",\n \"\\n\",\n \"This procedure is implemented in Scikit-Learn's `sklearn.naive_bayes.GaussianNB` estimator:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.naive_bayes import GaussianNB\\n\",\n \"model = GaussianNB()\\n\",\n \"model.fit(X, y);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's generate some new data and predict the label:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"rng = np.random.RandomState(0)\\n\",\n \"Xnew = [-6, -14] + [14, 18] * rng.rand(2000, 2)\\n\",\n \"ynew = model.predict(Xnew)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now we can plot this new data to get an idea of where the decision boundary is (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu')\\n\",\n \"lim = plt.axis()\\n\",\n \"plt.scatter(Xnew[:, 0], Xnew[:, 1], c=ynew, s=20, cmap='RdBu', alpha=0.1)\\n\",\n \"plt.axis(lim);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We see a slightly curved boundary in the classifications—in general, the boundary produced by a Gaussian naive Bayes model will be quadratic.\\n\",\n \"\\n\",\n \"A nice aspect of this Bayesian formalism is that it naturally allows for probabilistic classification, which we can compute using the `predict_proba` method:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0.89, 0.11],\\n\",\n \" [1. , 0. ],\\n\",\n \" [1. , 0. ],\\n\",\n \" [1. , 0. ],\\n\",\n \" [1. , 0. ],\\n\",\n \" [1. , 0. ],\\n\",\n \" [0. , 1. ],\\n\",\n \" [0.15, 0.85]])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"yprob = model.predict_proba(Xnew)\\n\",\n \"yprob[-8:].round(2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The columns give the posterior probabilities of the first and second labels, respectively.\\n\",\n \"If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a good place to start.\\n\",\n \"\\n\",\n \"Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results.\\n\",\n \"Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a reliable method.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Multinomial Naive Bayes\\n\",\n \"\\n\",\n \"The Gaussian assumption just described is by no means the only simple assumption that could be used to specify the generative distribution for each label.\\n\",\n \"Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.\\n\",\n \"The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates.\\n\",\n \"\\n\",\n \"The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model it with a best-fit multinomial distribution.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Example: Classifying Text\\n\",\n \"\\n\",\n \"One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified.\\n\",\n \"We discussed the extraction of such features from text in [Feature Engineering](05.04-Feature-Engineering.ipynb); here we will use the sparse word count features from the 20 Newsgroups corpus made available through Scikit-Learn to show how we might classify these short documents into categories.\\n\",\n \"\\n\",\n \"Let's download the data and take a look at the target names:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['alt.atheism',\\n\",\n \" 'comp.graphics',\\n\",\n \" 'comp.os.ms-windows.misc',\\n\",\n \" 'comp.sys.ibm.pc.hardware',\\n\",\n \" 'comp.sys.mac.hardware',\\n\",\n \" 'comp.windows.x',\\n\",\n \" 'misc.forsale',\\n\",\n \" 'rec.autos',\\n\",\n \" 'rec.motorcycles',\\n\",\n \" 'rec.sport.baseball',\\n\",\n \" 'rec.sport.hockey',\\n\",\n \" 'sci.crypt',\\n\",\n \" 'sci.electronics',\\n\",\n \" 'sci.med',\\n\",\n \" 'sci.space',\\n\",\n \" 'soc.religion.christian',\\n\",\n \" 'talk.politics.guns',\\n\",\n \" 'talk.politics.mideast',\\n\",\n \" 'talk.politics.misc',\\n\",\n \" 'talk.religion.misc']\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.datasets import fetch_20newsgroups\\n\",\n \"\\n\",\n \"data = fetch_20newsgroups()\\n\",\n \"data.target_names\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"For simplicity here, we will select just a few of these categories and download the training and testing sets:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"categories = ['talk.religion.misc', 'soc.religion.christian',\\n\",\n \" 'sci.space', 'comp.graphics']\\n\",\n \"train = fetch_20newsgroups(subset='train', categories=categories)\\n\",\n \"test = fetch_20newsgroups(subset='test', categories=categories)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Here is a representative entry from the data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Subject: Federal Hearing\\n\",\n \"Originator: dmcgee@uluhe\\n\",\n \"Organization: School of Ocean and Earth Science and Technology\\n\",\n \"Distribution: usa\\n\",\n \"Lines: 10\\n\",\n \"\\n\",\n \"\\n\",\n \"Fact or rumor....? Madalyn Murray O'Hare an atheist who eliminated the\\n\",\n \"use of the bible reading and prayer in public schools 15 years ago is now\\n\",\n \"going to appear before the FCC with a petition to stop the reading of the\\n\",\n \"Gospel on the airways of America. And she is also campaigning to remove\\n\",\n \"Christmas programs, songs, etc from the public schools. If it is true\\n\",\n \"then mail to Federal Communications Commission 1919 H Street Washington DC\\n\",\n \"20054 expressing your opposition to her request. Reference Petition number\\n\",\n \"\\n\",\n \"2493.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(train.data[5][48:])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers.\\n\",\n \"For this we will use the TF-IDF vectorizer (introduced in [Feature Engineering](05.04-Feature-Engineering.ipynb)), and create a pipeline that attaches it to a multinomial naive Bayes classifier:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.feature_extraction.text import TfidfVectorizer\\n\",\n \"from sklearn.naive_bayes import MultinomialNB\\n\",\n \"from sklearn.pipeline import make_pipeline\\n\",\n \"\\n\",\n \"model = make_pipeline(TfidfVectorizer(), MultinomialNB())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With this pipeline, we can apply the model to the training data and predict labels for the test data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model.fit(train.data, train.target)\\n\",\n \"labels = model.predict(test.data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator.\\n\",\n \"For example, let's take a look at the confusion matrix between the true and predicted labels for the test data (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"mat = confusion_matrix(test.target, labels)\\n\",\n \"sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\\n\",\n \" xticklabels=train.target_names, yticklabels=train.target_names,\\n\",\n \" cmap='Blues')\\n\",\n \"plt.xlabel('true label')\\n\",\n \"plt.ylabel('predicted label');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Evidently, even this very simple classifier can successfully separate space discussions from computer discussions, but it gets confused between discussions about religion and discussions about Christianity.\\n\",\n \"This is perhaps to be expected!\\n\",\n \"\\n\",\n \"The cool thing here is that we now have the tools to determine the category for *any* string, using the `predict` method of this pipeline.\\n\",\n \"Here's a utility function that will return the prediction for a single string:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def predict_category(s, train=train, model=model):\\n\",\n \" pred = model.predict([s])\\n\",\n \" return train.target_names[pred[0]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Let's try it out:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'sci.space'\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predict_category('sending a payload to the ISS')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'soc.religion.christian'\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predict_category('discussing the existence of God')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'comp.graphics'\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predict_category('determining the screen resolution')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Remember that this is nothing more sophisticated than a simple probability model for the (weighted) frequency of each word in the string; nevertheless, the result is striking.\\n\",\n \"Even a very naive algorithm, when used carefully and trained on a large set of high-dimensional data, can be surprisingly effective.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## When to Use Naive Bayes\\n\",\n \"\\n\",\n \"Because naive Bayes classifiers make such stringent assumptions about data, they will generally not perform as well as more complicated models.\\n\",\n \"That said, they have several advantages:\\n\",\n \"\\n\",\n \"- They are fast for both training and prediction.\\n\",\n \"- They provide straightforward probabilistic prediction.\\n\",\n \"- They are often easily interpretable.\\n\",\n \"- They have few (if any) tunable parameters.\\n\",\n \"\\n\",\n \"These advantages mean a naive Bayes classifier is often a good choice as an initial baseline classification.\\n\",\n \"If it performs suitably, then congratulations: you have a very fast, very interpretable classifier for your problem.\\n\",\n \"If it does not perform well, then you can begin exploring more sophisticated models, with some baseline knowledge of how well they should perform.\\n\",\n \"\\n\",\n \"Naive Bayes classifiers tend to perform especially well in the following situations:\\n\",\n \"\\n\",\n \"- When the naive assumptions actually match the data (very rare in practice)\\n\",\n \"- For very well-separated categories, when model complexity is less important\\n\",\n \"- For very high-dimensional data, when model complexity is less important\\n\",\n \"\\n\",\n \"The last two points seem distinct, but they actually are related: as the dimensionality of a dataset grows, it is much less likely for any two points to be found close together (after all, they must be close in *every single dimension* to be close overall).\\n\",\n \"This means that clusters in high dimensions tend to be more separated, on average, than clusters in low dimensions, assuming the new dimensions actually add information.\\n\",\n \"For this reason, simplistic classifiers like the ones discussed here tend to work as well or better than more complicated classifiers as the dimensionality grows: once you have enough data, even a simple model can be very powerful.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/05.06-Linear-Regression.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# In Depth: Linear Regression\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Just as naive Bayes (discussed in [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) is a good starting point for classification tasks, linear regression models are a good starting point for regression tasks.\\n\",\n \"Such models are popular because they can be fit quickly and are straightforward to interpret.\\n\",\n \"You are already familiar with the simplest form of linear regression model (i.e., fitting a straight line to two-dimensional data), but such models can be extended to model more complicated data behavior.\\n\",\n \"\\n\",\n \"In this chapter we will start with a quick walkthrough of the mathematics behind this well-known problem, before moving on to see how linear models can be generalized to account for more complicated patterns in data.\\n\",\n \"\\n\",\n \"We begin with the standard imports:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Simple Linear Regression\\n\",\n \"\\n\",\n \"We will start with the most familiar linear regression, a straight-line fit to data.\\n\",\n \"A straight-line fit is a model of the form:\\n\",\n \"$$\\n\",\n \"y = ax + b\\n\",\n \"$$\\n\",\n \"where $a$ is commonly known as the *slope*, and $b$ is commonly known as the *intercept*.\\n\",\n \"\\n\",\n \"Consider the following data, which is scattered about a line with a slope of 2 and an intercept of –5 (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rng = np.random.RandomState(1)\\n\",\n \"x = 10 * rng.rand(50)\\n\",\n \"y = 2 * x - 5 + rng.randn(50)\\n\",\n \"plt.scatter(x, y);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We can use Scikit-Learn's `LinearRegression` estimator to fit this data and construct the best-fit line, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"model = LinearRegression(fit_intercept=True)\\n\",\n \"\\n\",\n \"model.fit(x[:, np.newaxis], y)\\n\",\n \"\\n\",\n \"xfit = np.linspace(0, 10, 1000)\\n\",\n \"yfit = model.predict(xfit[:, np.newaxis])\\n\",\n \"\\n\",\n \"plt.scatter(x, y)\\n\",\n \"plt.plot(xfit, yfit);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The slope and intercept of the data are contained in the model's fit parameters, which in Scikit-Learn are always marked by a trailing underscore.\\n\",\n \"Here the relevant parameters are `coef_` and `intercept_`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model slope: 2.0272088103606953\\n\",\n \"Model intercept: -4.998577085553204\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Model slope: \\\", model.coef_[0])\\n\",\n \"print(\\\"Model intercept:\\\", model.intercept_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We see that the results are very close to the values used to generate the data, as we might hope.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The `LinearRegression` estimator is much more capable than this, however—in addition to simple straight-line fits, it can also handle multidimensional linear models of the form:\\n\",\n \"$$\\n\",\n \"y = a_0 + a_1 x_1 + a_2 x_2 + \\\\cdots\\n\",\n \"$$\\n\",\n \"where there are multiple $x$ values.\\n\",\n \"Geometrically, this is akin to fitting a plane to points in three dimensions, or fitting a hyperplane to points in higher dimensions.\\n\",\n \"\\n\",\n \"The multidimensional nature of such regressions makes them more difficult to visualize, but we can see one of these fits in action by building some example data, using NumPy's matrix multiplication operator:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.50000000000001\\n\",\n \"[ 1.5 -2. 1. ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"rng = np.random.RandomState(1)\\n\",\n \"X = 10 * rng.rand(100, 3)\\n\",\n \"y = 0.5 + np.dot(X, [1.5, -2., 1.])\\n\",\n \"\\n\",\n \"model.fit(X, y)\\n\",\n \"print(model.intercept_)\\n\",\n \"print(model.coef_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Here the $y$ data is constructed from a linear combination of three random $x$ values, and the linear regression recovers the coefficients used to construct the data.\\n\",\n \"\\n\",\n \"In this way, we can use the single `LinearRegression` estimator to fit lines, planes, or hyperplanes to our data.\\n\",\n \"It still appears that this approach would be limited to strictly linear relationships between variables, but it turns out we can relax this as well.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Basis Function Regression\\n\",\n \"\\n\",\n \"One trick you can use to adapt linear regression to nonlinear relationships between variables is to transform the data according to *basis functions*.\\n\",\n \"We have seen one version of this before, in the `PolynomialRegression` pipeline used in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) and [Feature Engineering](05.04-Feature-Engineering.ipynb).\\n\",\n \"The idea is to take our multidimensional linear model:\\n\",\n \"$$\\n\",\n \"y = a_0 + a_1 x_1 + a_2 x_2 + a_3 x_3 + \\\\cdots\\n\",\n \"$$\\n\",\n \"and build the $x_1, x_2, x_3,$ and so on from our single-dimensional input $x$.\\n\",\n \"That is, we let $x_n = f_n(x)$, where $f_n()$ is some function that transforms our data.\\n\",\n \"\\n\",\n \"For example, if $f_n(x) = x^n$, our model becomes a polynomial regression:\\n\",\n \"$$\\n\",\n \"y = a_0 + a_1 x + a_2 x^2 + a_3 x^3 + \\\\cdots\\n\",\n \"$$\\n\",\n \"Notice that this is *still a linear model*—the linearity refers to the fact that the coefficients $a_n$ never multiply or divide each other.\\n\",\n \"What we have effectively done is taken our one-dimensional $x$ values and projected them into a higher dimension, so that a linear fit can fit more complicated relationships between $x$ and $y$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Polynomial Basis Functions\\n\",\n \"\\n\",\n \"This polynomial projection is useful enough that it is built into Scikit-Learn, using the `PolynomialFeatures` transformer:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 2., 4., 8.],\\n\",\n \" [ 3., 9., 27.],\\n\",\n \" [ 4., 16., 64.]])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.preprocessing import PolynomialFeatures\\n\",\n \"x = np.array([2, 3, 4])\\n\",\n \"poly = PolynomialFeatures(3, include_bias=False)\\n\",\n \"poly.fit_transform(x[:, None])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We see here that the transformer has converted our one-dimensional array into a three-dimensional array, where each column contains the exponentiated value.\\n\",\n \"This new, higher-dimensional data representation can then be plugged into a linear regression.\\n\",\n \"\\n\",\n \"As we saw in [Feature Engineering](05.04-Feature-Engineering.ipynb), the cleanest way to accomplish this is to use a pipeline.\\n\",\n \"Let's make a 7th-degree polynomial model in this way:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.pipeline import make_pipeline\\n\",\n \"poly_model = make_pipeline(PolynomialFeatures(7),\\n\",\n \" LinearRegression())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With this transform in place, we can use the linear model to fit much more complicated relationships between $x$ and $y$. \\n\",\n \"For example, here is a sine wave with noise (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rng = np.random.RandomState(1)\\n\",\n \"x = 10 * rng.rand(50)\\n\",\n \"y = np.sin(x) + 0.1 * rng.randn(50)\\n\",\n \"\\n\",\n \"poly_model.fit(x[:, np.newaxis], y)\\n\",\n \"yfit = poly_model.predict(xfit[:, np.newaxis])\\n\",\n \"\\n\",\n \"plt.scatter(x, y)\\n\",\n \"plt.plot(xfit, yfit);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Our linear model, through the use of seventh-order polynomial basis functions, can provide an excellent fit to this nonlinear data!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Gaussian Basis Functions\\n\",\n \"\\n\",\n \"Of course, other basis functions are possible.\\n\",\n \"For example, one useful pattern is to fit a model that is not a sum of polynomial bases, but a sum of Gaussian bases.\\n\",\n \"The result might look something like the following figure:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The shaded regions in the plot are the scaled basis functions, and when added together they reproduce the smooth curve through the data.\\n\",\n \"These Gaussian basis functions are not built into Scikit-Learn, but we can write a custom transformer that will create them, as shown here and illustrated in the following figure (Scikit-Learn transformers are implemented as Python classes; reading Scikit-Learn's source is a good way to see how they can be created):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.base import BaseEstimator, TransformerMixin\\n\",\n \"\\n\",\n \"class GaussianFeatures(BaseEstimator, TransformerMixin):\\n\",\n \" \\\"\\\"\\\"Uniformly spaced Gaussian features for one-dimensional input\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, N, width_factor=2.0):\\n\",\n \" self.N = N\\n\",\n \" self.width_factor = width_factor\\n\",\n \" \\n\",\n \" @staticmethod\\n\",\n \" def _gauss_basis(x, y, width, axis=None):\\n\",\n \" arg = (x - y) / width\\n\",\n \" return np.exp(-0.5 * np.sum(arg ** 2, axis))\\n\",\n \" \\n\",\n \" def fit(self, X, y=None):\\n\",\n \" # create N centers spread along the data range\\n\",\n \" self.centers_ = np.linspace(X.min(), X.max(), self.N)\\n\",\n \" self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0])\\n\",\n \" return self\\n\",\n \" \\n\",\n \" def transform(self, X):\\n\",\n \" return self._gauss_basis(X[:, :, np.newaxis], self.centers_,\\n\",\n \" self.width_, axis=1)\\n\",\n \" \\n\",\n \"gauss_model = make_pipeline(GaussianFeatures(20),\\n\",\n \" LinearRegression())\\n\",\n \"gauss_model.fit(x[:, np.newaxis], y)\\n\",\n \"yfit = gauss_model.predict(xfit[:, np.newaxis])\\n\",\n \"\\n\",\n \"plt.scatter(x, y)\\n\",\n \"plt.plot(xfit, yfit)\\n\",\n \"plt.xlim(0, 10);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"I've included this example just to make clear that there is nothing magic about polynomial basis functions: if you have some sort of intuition into the generating process of your data that makes you think one basis or another might be appropriate, you can use that instead.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Regularization\\n\",\n \"\\n\",\n \"The introduction of basis functions into our linear regression makes the model much more flexible, but it also can very quickly lead to overfitting (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this).\\n\",\n \"For example, the following figure shows what happens if we use a large number of Gaussian basis functions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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9KOk8f6kL6sOf1xdjmNiseeP8wUmKV2PJ790Gej2ZkJsJid+DAOerfESxl9c59jYfFqXrclxLjPmAIzaHSeoxL0aKqufc+T91nxL7OnIPN3WrzCKm4zzStxMOFZE+38xGvA/3fvytGg8GCV5dkd6mc4LvLRsQgUi7Bj8W1XA9FsC40OGfsw2LdzOi1SlxsMnndj4WPHA4GhRdbMT45us8Zb/f7vW3FHWqdWy4Azr46E1L6XmneW1sUoeBtoK9obMP7P1/AHVcMw9jkaK6HE1ARUgmuGhmHH0/XUJllkFxsNkEuFSPWzb7AKTGRsDkYVPejoynfVDQaoTfbkDU4ymPfJdY1lyR0+fmuKcN7PCZcChzYhoLn18/BnVcNx+ELjThZ2dzr7yR7+IDydDsf8TbQ/3vfeYgA/GH6SK6HEhTTMxNR3mBESZ2B66EIUlWzCYOiFG7rv5NjnG/wikbhpm9Ota8Kzhqscc2EPaUqdhV1/WbJrmZNiooImzUo7jw8Kx0xKjke33oUbRb3C64YhsENYwf1+L+HywdXoPAy0Bstdmz9tRw3jR8sqE/dzmZkOGdRlL4JjqpmEwZFu99zlH1N6ZqEeUE2r0CHJz4+CgD4n/cOu0ooHR6+PXbOvTMMgy+OVuKy4TE49NS1vbbi5lqMSo4NC8bjdHUr7v/v4S6rne0OBtuOXcQNr+zFpn2lUMjEkEk6gv1N4weH5f/JV7ws6fjuVBX0ZhsWX5bK9VCCZmhsJEYmqPBjcQ3umTqC6+EITlWLCROHat3exwb6Sg+7jvFZ95rxi51KI72pWvnlfCNOV+ux9tbwW2zozozMRKybPw6rPj2O2S/vwU0TBsNuZ/DdqWpcaGhDWrwKLy2agFsmDIFUIkaz0YpHtxzBx4crcO3oJMweI4wGabyc0X9yWIdkrRKXj4jleihBNSMzEYdKG2C00J6YgcQwDKpaTBjsYUavlEugUUhRI8AcfX+3wZSIRF1SGP/aVwJtpKzLloHhbvFlqfjw91ciJUaJN/eU4D8HypCsVWLj7Tn4/rHpmJ+T4trnNlopw8bbczA+RYsVHx0VzCY0vJvRNxut2H+2Dr+blib4vtMzMhPwr32lOFBSh5mXUH/0QGlss8JicyApyn2gB4CkKAVqWoXxJu+sr20wgY49EpRyCUxWu6vY4Wh5E747VY0HZoziXRvtySNiseUPV4JhGDgYZ2M1TxQyCV5cOAE3vLIHa78uxEuLJoZuoEHCu0C/53Qt7A4G1432rb8Gn0weEQulzFlmSYE+cNi6cU85egBI1EQIsuqmr/RM58WFta1mXPvSbjzw3mGsvOESPPfVKcSrI/CH6fxdsS0SiSDxYn44KlGNu6eMwJt7SrDvTB1qW81hv6q+N7xL3ewsqkFMpAwTh/bd5IjvXGWWxdTNMpCqWpyBrrdAnxSlQHWL8Gb0K2ZnIkLa9W3vqcIkQROBjbfnoKKxDXe98wtqWkx4/Y4caBTCWbPSm6ExkWAA1LSa+92+OdzwakZvdzDYVVyDmZmJvX71EpIZmQnYUVSD0joD0hKoyVkgVDU7A/igXlI3iVERqG01g2GYfrfgDWfzspNxurrV1Qc+uY9Z6pRR8djx+AwcrWjCpGExbrcLFAJ33Sv/b/e5Ho/jaw8cXgX6I+VNaGqz4ppLhJ+2Yc3MSsKfPz+Jb05U4YFrRnE9HEGoajZCJHLOWD1J1ChgsTvQ1GZFjJtFVXzGdkXd+fh0ryYPg6IVGBQtjOoTdzx1r/S0YRHXrR58wavUzcESZ++XKaPiOR5J6CRrlbh0WAy+OFLJ9VAEo6rFhAR1RK+NtJKinB8C1a3Cy9OzKaneLkYPJJ4qkTwtIOO61YMvfA70DocDq1evxuLFi5Gbm4uysrIu969Zswbz589Hbm4ucnNz0dra6vdgD5bUIzNJ43bZupDNnTgExdWtKKrqu1Ur6dvFXhZLsRI1zvtrBJinr24xQRMhhSqCV1/og8bTDN3OMD3KTfm6YtbnQP/DDz/AYrFgy5YtePzxx7F+/fou9588eRKbNm3C5s2bsXnzZmg0/nWWtNodyC9rxOVpwq6dd+fGcYMhEYuQV0Cz+kCobTUjsZe0DdBpRi/AypuqZhOS+vigG0g8zdDZ1g5D2s+VUiYJy1YP3vD5Iz0/Px/Tpk0DAEycOBEnTpxw3edwOFBWVobVq1ejrq4OCxYswIIFC3oco7Cw0Ovn+++RBrRZ7PjPgTJ8e0yHO3NiMDNNGG2JTSZTn+dicrISHxwqxQ1D7ZAHoHd3uPLmXPirprkNwzS9v/7YfusnS8oxVsXN/rHBOhel1Y3QyMRBP8+BFMzXxbJxarz6kwlme0dlW4REhGXj1MhUtOBf84bg1QO12FmiR6qkyatNUMKNz4Fer9dDre64kCORSGCz2SCVStHW1oY77rgDd911F+x2O5YvX46xY8fikksu6XKMrKwsr54rr0CHLcdLXT/XGGz458EGJA/h14YinhQWFvZ5Lh6UJeD2TYdwxhyNBZNSen0sn3lzLvzBMAxazKVIS05EVtYlvT42Ul4OSaQ2qOPpTbDORWteJcakxnL2//JFMF8XWVlA8pDe94y9S5GEb04fwAW7FvPGcxtz8vPz+/07Pgd6tVoNg6Gjs6LD4YBU6jycUqnE8uXLoVQ6vxJdccUVKCoq6hHovbVhe3GPDYz5Wubkq6tGxiE9UY2395fitpxkQZX8hVKL0Qabg0GcF9d54tRy1AtkCTzL4WBQ02rutbR0IOprF7pLh8UgWavEZx720A13PucAcnJysGfPHgDAkSNHkJGR4brv/PnzWLp0Kex2O6xWKw4fPowxY8b4PEhPu/3wsczJVyKRCL+7Og0nK1vwzYkqrofDW/UGZ+COU3sR6FURqDdYgj2kkKo3WGBzMFRx009isQi3TByCfWfrePnh73Ogv+666yCXy7FkyRKsW7cOq1atwttvv40dO3Zg5MiRmDt3LhYtWoTc3FzMnTsX6enpPg+SvTDWHR/LnPxxW04KMpLUWP9Nkcf+2qR3bOCOVfW98CdeLUedXmiB3vsPOtLVLROGwO5g8PXxi1wPpd98Tt2IxWI899xzXW4bObJjE5B7770X9957r+8j6+SGsYPxzk/nu9zG1zInf0jEIvzllrFYtukgVn9+EhsWjKcUTj/Vtwdur1I3qggcq+h9dyK+aXB90FGg769LBmmQnqjGF0crkXvlcK6H0y+8KKSVS8WQiEVI0kTgYrOJ182F/HXlyDg8dM0ovLrzLGQSMZ6ek+Wqh7bYHCiqasHR8ibU6S2IU8sxLT0BI+J7boA9ULGBzqvUjVqOBoMFDgcjmE6pjQYrAAr0vhCJRLhlwhC8+P1p6JqMvNr0iBeB/kh5E8YmR+PzB6ZwPZSw8Oh1GTDbHXhjdwm+OlaJ0YOjoDfbcKZGD0t7WSBLJHKmfC4bFoNXd571WFUwULD5VW8CXZw6AjYHg2ajcNogNBi8//+Tnm5uD/RfHa3k1TamYR/obXYHjlc0Y/FlQ7keStgQiURYdUMWfjN6ELb8cgGldQbEqSMwZVQ8JqRoMWFoNJK1SlQ0GvHfg2V4c28JPsmvAFu3xPbyADDggn29wQJNhBQR0r77qce3z/rrDWYBBXrnjD4mUhj/n1AbHq9Camwk/v5dMdZ/U8SbSVPYB/ozNXoYrXZMGBrN9VDCzqRhMZg0zHO75qGxkVh1YxY+zq/oUT0y0MpTWQ0GC2K9vBDJdmqs01swSiB99BrbLIhSSHvt80M8yyvQ4WKTEdb2cm++TJrC/q99tLwJAAZE//lgafBQIjiQylNZ9Qaz12kLNo9fL6DKm3qDhdI2ftiwvdgV5FnspCmchX+gr2hClEKK4XGRXA+FtzyVoQ608lTAGbTjvCitBOB6HFuSKASNBotg0lBc6G0rxnAW9oG+4EITJgzVUhmhH9xt+qyQigdceSrg/HbjTWklAMREyiASQVC19P35/5Oe+DppCttAn1egw5XrdqCoqhVHLjTxcvuucDEvOxnr5o/rUg52+xXDAABT1u/EiJXbMGX9TsGfY4Zh+pWjl0rEiIkUVhuEBoOFLsT6wd2kiQ9resLyYmz3HV9azTZeXPAIZ2wvD5vdgd+8vAfbjl3EewfLXF0a+XJRyR/96XPDilPJBZOjZxgGDW3ef9CRntj3xobtxdA1GSEWAX+9dWzYv2fCckbvaceXcL/gwQdSiRiP/SYDVS0mV5BnCf0c+7L8P04tR51AZvQGix0WmwOxNKP3y7zsZOxfOROvLJkIBwNe7KMbloGerxc8+KL7oqrOhHyO+9PnhhWnivBYtcQ3je3/D0rdBMb1YwchTiXHfw6c53oofQrLQM/XCx58kFegw58+O+HxfiGf4/70uWHFquSC6WDZYnIulopSyjgeiTBESCVYOjkVO4pqcKG+jevh9CosA/2K2ZlQSLsOjQ8XPPjAXVqMJfRz3J8+N6xYlRzNRiusds/fgvii2egM9NEU6APmjiuGQSwSYfPB81wPpVdhGejnZSfjjvaqEKBj78Zwv+DBB72lZoR+jn3p88J+KDS28X9W32J0traOUoZlDQYvDYpW4Iaxg/DBz+Wu1Fg4CstADwBKuQQSsQinnpuN/StnCjoAhZLHtFi0QvDnuE7vfZ8bFvuhIIQ8fQvN6IPioZnpMFhseHNvCddD8ShsA/2R8iZkJGkQKafZRyC5qwMGgOmZCRyMJrT6U0PPcgV6AZRYUo4+ODIHaXDLhCF4Z/951LSYuB6OW2EZ6BmGwdHyJkykRmYB13nxlAjOmXyKVomfztXDJoA8dG8afOjz0tEGgf+BvtlohVgEqGnyFHCPXZcBO8Pgua9OcT0Ut8LyL15aZ0CLyYYJKVquhyJI3TdC/v5UNX73n1/xWYEOCy8VbjvoOr0ZKTH965nE5uiFkrrRKGSC2UQlnAyLU+Gha0bhxe9PY35ONWZeksT1kLoIyxn90YomAMDEVC2n4xgors1KxNjkKPxz11nXrD6vQCe49gi+9HmJiZRDJBLOjJ7y88Hz++lpyEzSYMVHx1AdZimcsAz0Ry40QSmTYFSCmuuhDAgikQhXjIhDWX0bRv3pG0z8y3dY8fFR6JqMYNDRHoHPwb6/fW5YErEIWqXMVbHDZy0mG1XcBFGEVIL/vT0bRqsdD71fEFap0LAM9D+fb0TOMC2ktDlCSOQV6PDfg2Wun5uMVljt/Ou53Rtf+tywYlVyQaRuaEYffKMSNVh76zj8fL4Bfwuj90vYRdLmNiuKqloweXgc10MZMDZsL+7R98YdPrdH8KXPDStOFSGIxmYtRiuiFBTog21edjKWXzkMb+5x7ukcDsIu0P9a1gCGASaPiOV6KAOGtwGcz+0RGnzoc8OiGT3pr6fnjMakYTF44uNjOF3dyvVwwi/Q/1zaALlEjGy6EBsy3gRwvrdHqPOhzw0rVk2BnvSPXCrGxttzECmX4r7/5sPkoe1IqIRdoD9U2oAJQ6OhcLOohwSHp0VU6ggJRBBGCwpf+tyw4lRyNLRZYO+2VyifmKx2mG0OWiwVQklRCry8eAJKag34311nOR1LWF2Cf/9QGY60bwY+Zf1OrJidyevgwhedN1OobDJicLQCAGCxO7Dr/12DBE3499vuiy99blixKjkYBmhqsyCOB73H3Wk1tfe5UYTVW17wpqUnYH52Ml7ffQ7zspMxkqNKwrCZ0ecV6PDMFyddPwuhpI9P2M0UStfPwU+rZuGduyej1WTDEx8fBcPwdybL8qXPDUsI/W5a29sfaOhibMg9NScLMokYL39/mrMxhE2g37C9WHAlfXyWkaTBUzdmYVdxLd7ef57r4fjNlxp6lhDaIOjNzhm9OoJm9KEWr47A3VNG4KtjF1Fcxc2FWZ8DvcPhwOrVq7F48WLk5uairKysy/1bt27F/PnzsWjRIuzatavP4+loV6mws/zKYbg2KxHrvynCCV0z18Pxiy99blhCmNGzgV5FgZ4T90wdAYVMjLf3l3Ly/D4H+h9++AEWiwVbtmzB448/jvXr17vuq62txebNm/Hhhx/iX//6F1566SVYLL2/STxVQ/C5pI+v2PYHaau+xgldC5RyCf74QQEM7cGCj+r0ZtfMvL/i278J8HpG356j11COnhMxKjluzU7GZwU6TvrW+xzo8/PzMW3aNADAxIkTceJEx/Z0x44dQ3Z2NuRyOTQaDVJTU1FUVNTr8VLjejab4ntJHx/lFeiw6tPjrvYHVS0mGC12lNYZ8Gynayh840ufG1aMAFoVGyyUuuHa8iuHw2xz4EsOFlH5/FfX6/VQqzuuIEskEthsNkilUuj1emg0Gtd9KpUKer2+xzEKCwsBAI1GG07qmjFxsAKVLTbUGmxIUElxZ04MMhUtKCxs8XWYvGAymVzngmtrv7rQY6tBi92BSJkIH+VXIF1twbThwascCMa5YBgG9XozYG71+dhquRhnK6pQWBi6bzWBPBdnzztTb5UXSmCs5V+wD6f3iD9GxMjx/k9ncZk2tClpn//iarUaBoPB9bPD4YBUKnV7n8Fg6BL4Wfd+fhErZmfiVFULrA4GLy27HGkDsJFZYWEhsrKyuB4GAKDW4H6XnDYrg3HJ0dh0uBnLZmYHbWYYjHPR3GaFnSlFeupgZGWl+XSMhKhqMHJVSP9OgTwXP1afA1CPnHGjeblGJZzeI/5YUiPHum+KoExIxfB4lU/HyM/P7/fv+Jy6ycnJwZ49ewAAR44cQUZGhuu+8ePHIz8/H2azGa2trTh37lyX+1m6JiOe/PgYNu0twW05KQMyyIcbT9dEkrVKPD9vLGr1ZrzyA3dlYr7wp88Ni+9tEPRmKyRiESKkYVNoNyDdMnEIRCLg8yOhTd/4/Fe/7rrrIJfLsWTJEqxbtw6rVq3C22+/jR07diAhIQG5ublYtmwZ7rzzTjz66KOIiHB/Icxsd0AEEZ66kf+f1kLgbpUse61k4lAtFuSk4N2fynCxmT/VUK5VsT5ejAX4H+gNZjvUEVKIRLTpCJcGRysxKTUG352qCunz+vz9WywW47nnnuty28iRI13/XrRoERYtWuTVsewM43PpGwms7qtkh2iVXVYo/3FWOj4r0GHjrnN4ft5YLofqtXpXQzPfX2NxKrlr1TYftZpsdCE2TAyOVuDLYxcxfOU2JHd7fwVLWPzlk6mEMqx032qws6GxkVh4aQq2/FKOB2eOQlKUIsSj6z+2xbC/qZtGgwUMw/ByVmwwU6APB3kFOnx3qtr1M9sBAEBQgz3nCTsqoeSf+6aPhNXhwPuHLnA9FK/40+eGFauSw+Zg0GLk51oCvdkGVQT/LsIKzYbtxTB32/shFB0AOA30QuiKOBANi1NhRkYC3v/5AixebFjCtXqD731uWOy3gTqebimoN9ugpj43nPO00j/YHQA4DfT7V86kIM9Ty68cjtpWM7afDO1FJV/U633vc8NiNyzh6wVZvdkGNc3oOeepqi3YHQA4T90QfpqekYBkrRKfHK7geih98mdVLIv9fb5uKaini7FhobeqtmCiQE98IhaLcMvEIdh7pg51+vBOZ9QbLD5tIdgZ3xubOS/GUuqGa/Oyk7Fu/jhXAYpMIgpJ+poCPfHZrdnJsDsYfHU0PDZA9qReb/Z7Rt8R6MP7Q80dhmGgt1DqJlywez88cm06bA4G12QmBv05KdATn2UkaZA1OAqfh3GgZxgGjW0Wv0orAUAhk0AdIeVlB8s2ix0MA6ipc2VYmToqHgwDHCipC/pzUaAnfpkzbhAKLjShpsXE9VDcajHZYLUHZkEeX1fHUi/68DRhqBYquQT7zlKgJ2FO0r54aPLaHZiyfmfYbf1Yr/e/zw2L74GeLsaGF5lEjMvT4rD/bH3Qn4sCPfFZXoEOr+w44/o5HPf5ZQNzfAA29Y5TyXlZdcNuOkKBPvxMGRWP0jqDxx32uvuh06ra/qBAT3y2YXsxTBys8uuPOr3/Dc1YfJ3RG2hGH7amjooHAOz3Mn3zlY+bllCgJz7japVff7Cln/GBSN2onYGeYZi+HxxGWilHH7YyktSIV0d4HehPVvq2CRMFeuIzrlb59QebaokJwMXYOJUcFrvDlfPmC3ZGT/vFhh+RSIRhsZH48mglhq/c1ut1Lr3ZhrO1PXfq8wYFeuIzd6v8FFJxWDWpqzeYoY2UQSbx/6XO1zYIVHUTvvIKdDima4Kj/Utib9e5Tuia4euXSQr0xGfdV/kBwCPXZYRV/6J6vf/tD1iuNgg8DfSUow8/G7YXw2rvGr09Xec6XtHs8/PQX574he1dX97Qhml/2wV5AGbOgVSnNyMuABU3QKfVsTyrvDFa7BCLQNsIhqH+XOc6WtHk894d9JcnATE0NhJp8SrsPVPL9VC6qDdYAnIhFugI9PU8a4PQZrEjUk7bCIaj/lznOlbRjPEp0T49DwV6EjDT0uNxsKQBZpud66G4OPvcBGZGzy664lvqps1ig1JOfW7CkbfdLJvaLLjQ0IbxKVqfnocCPQmYaekJMFrtyC9r5HooAACb3YHGNmtAVsUCQKRcCqVMwrvUjXNGT4E+HLHXuQa3b8mpUUjddrP85bzzPZWdqvXpeSjQk4C5YmQcpGIR9p4Jfu8ObzS0sXvFBmZGD/Bz0RSbuiHhaV52Mg48NQsTh2oxIl7ltpjhp3N1iJCKKdAT7qkjpMhJjcGe0+GRp2dr6OMDVHUDONM3fEzd0Iw+/F0/dhCOVTS7bYdw4Fw9Lhse6/N2mBToSUBNS4/HycoWVzMxLrGBnmb0lLrhg9ljBgEAvj3RdXvOOr0ZRVWtuGpUnM/HpkBPAmpaRgIAhKT1al/Y6phA5egBfgZ6IwV6XhgRr8LowVH4JL/C1WYjr0CHa1/cDQB4e995nxsGUqAnATUuORrRSllY5OnrXKmbwM3o41Ry3pZXkvC37PJUnLrYgoLyJuQV6LDq0+NoMloBALV6M1Z9etyn41KgJwElEYswdVQ89p6p5bz5V73eDKlYhChl4IJcrCoCJqsDbRb+9Luh8kr+mJedDHWEFG/uLsGG7cUwWruWKnf/2VsU6EnATUuPR3WLGWdqfGvAFCj1eucWgoFcKORqg8CjEss2ix0qCvS8oI6Q4vdXp+Hbk1Ve96j3BgV6EnBT0509trlO39QbArdYitWxSTg/Ar3DwcBotUNJqRve+P3VaZg8Ijagx/Tpr28ymbBixQrU19dDpVLhhRdeQGxs14Hdf//9aGxshEwmQ0REBDZt2hSQAZPwlxLT0Q7hnqkjOBtHnd7/TcG7i1XzK9CbbM6NweliLH8oZBL85+7JWPt1Ibb8XA6zvWNzn+6raL3lU6D/4IMPkJGRgYceegjbtm3Dxo0b8fTTT3d5TFlZGbZt20b9NQaoaenx2PJrOcw2u8+1v/6qbTUjLV4V0GPyrYNlm8WZ06VAzy8KmQTPzR2LnNQYbNhejMomI4Zolc7WCI6qvg/QjU+pm/z8fEybNg0AcPXVV+PAgQNd7q+rq0NLSwvuu+8+LF26FLt27fLlaQiPTUtPgMnqQP55btohMAyD2lYzEqICm7pha/LDYZ2AN4yuQE+pGz6al52M/StnonT9HOxfOdPnFuB9/vU/+ugjvPvuu11ui4uLg0ajAQCoVCq0trZ2ud9qteLuu+/G8uXL0dzcjKVLl2L8+PGIi+ta8F9YWOjToIXGZDIJ7lzE2ByQiIDPDhYhxur9Qo9AnYsWkx0WuwNoaw7ouWUYBgqpCEVlF1FYGNxZfSDOxflG5xgbai6isLC1j0eHLyG+R0Kpz0C/cOFCLFy4sMttDz74IAwGAwDAYDAgKiqqy/3x8fFYsmQJpFIp4uLikJWVhdLS0h6BPisry9/xC0JhYaEgz8WkAy041WDr1/8tUOeiqKoFQBnGpaciK2uI38frbIi2BhZJZND/ZoE4F8YLjQAqkJ42DFmZiYEZGAeE+h7xRX5+fr9/x6fUTU5ODnbvdq7W2rNnDyZNmtTl/p9++gkPP/wwAOcHwZkzZ5CWlubLUxEeu7q9HUIdB2mOmhbncya1dwUMpEHRClS1mAJ+3GBgUzcqSt0MaD4F+qVLl+LMmTNYunQptmzZggcffBAA8Le//Q3Hjh3D9OnTMXz4cCxatAj33HMPHnvssR5VOUT4ZrTPIHcW1oT8uWtanYE+URPYHD0ADIpSoKq570CfV6DDlPU7MXzlNqT/6WsseuMAbJ0qKEKBLsYSwMeqG6VSiVdffbXH7U888YTr33/60598HxURhDFDopASo8Q3Jy5i0WVDQ/rc1e0z7kRN4Gf0SdEKVLeY4HAwEIvdV5Wxy9fZlYxWO4OfSxvwdN4JrL9tfMDH5Am7gpdWxg5stGCKBI1IJML1YwZh/9l6tJisIX3u2lYzNAppUALc4GgFbA6m1xJLd8vXAeBTH5tS+Ypm9ASgQE+C7IZxg2CxO7CrKLTpm5pWU1DSNkBH3r+39I2nTZ8tNgcaQ1iD30bllQQU6EmQZQ+NQaImokeP7WCrbjEHJW0DOGf0AHq9IOtp02cA+DWEWy22mZ2pG5rRD2wU6ElQicUi3DhuMHYU1aCpLXQz2ZpWE5ICvFiKNSiq70C/YnYmIqRd314KqRgiEXBC1xyUcbnTZrVDJhFBJqG3+kBGf30SdIsuHQqLzYHPQpSfZhjGOaMPQmkl4FwdK5OIoGv03F1wXnYy5ndaxZisVWL9beMxKkGNk5WhC/RG6kVP4GPVDSH9MXpIFCakROPDn8vx26uGB73/UYvRBovNEbQcvUQsQkpMJMob23p9nNnuQKxKjvynr3X9n38srsGh0oagjMsd2i+WADSjJyGyZHIqiqtbQxLkqlvbSyuDNKMHgKGxkShv6D3QHyppwOUjYrt8sI1MUONis8m1kCnYDBY7lVYSCvQkNG7NTka8OgKv7TwT9OdiN2xI1gYv0KfGKnGhl0Bf3tAGXZMRV6R1bfsxrL2bZm+/G0hGi51WxRIK9CQ0FDIJ7puehv1n67H3TG1Qn4stbeyt8sVfw2JVaGqzotnofn3AwZJ6AOgR6IfHRQIAztcbgja2zmgbQQJQoCchdMcVwzAiXoWn804Edc/VyiYjpGJR0MorAWfqBoDH9M3BkgbEquRIT1R3uX1YrHNGXxayQG+nHD2hQE9CRyGTYO2t41De0IZHtxxx9X1he8KMWLkNd358AXl+VufoGo0YFK2AxEN7gkBIbQ/0nlIwB0vqMXl4bI8WCdGRMmgjZSFL3VCgJwAFehJiV46Mw59vGo3tJ6tx1zu/4F/7SrDq0+PQNRnBAKgx2LDq0+N+BfvKJlNQ0zYAkNqegimr7xmwO/Lz7hv5DY5WetUULRCovJIAVF5JOHDXlBFQyiR45ouTbjcQN1rt2LC92OfddHRNxoBvrtydOkKKQVEKnKnpuZkHW1l0xUj3G64Mjlagsik0gZ7KKwlAM3rCkSWTU/Hjihke7/fUK6YvdgeDqhYThgSx4oaVMUiD09U9A/3BknrERMqQkahx+3uh7GdP5ZUEoEBPODQ4WolkDymWeLVvi51qWk2wO5igp24AIDNJjTPVetgdTJfbD5XW4/IRcR5bGA+OUqDBYIHJTXfLQLI7GFhsDiqvJBToCbdWzM6EUtZ1ximCM+XgS2UKmzMfGhMZiOH1ymC2w2xzYORTX2PK+p3IK9DhfJ0B5Q1GXOkhbQMAg9s/hIKdp2crmyh1QyjQE07Ny07GuvnjkKxVQgQgUSXFUzdmQSoR4+EPj/SYLfeF/XAY0b4wKVjyCnT45HCF62ddkxGrPj2OV3c4F4TNyEzw+LveNEULBLZFMaVuCH2nI5ybl53suvDq3AQ6DfEaOR7dchRPfnwMB0rqUdlkxBCtEitmZ/Z6kba0rg0yicjVSjhYNmwvhtnWdVtAo9WObccvYkS8CsPiPH/QxGvkAIB6fXC7eRpp0xHSjmb0JCzNm5iMUYlqfHy4wlV6yc6aeyu9PF9nwNDYSEiD3JbX08Vis82B6RmeZ/MAEKdyXn+oNwR303R2h6vuqTEy8FCgJ2FJJBK57V/Pll56cr7egBG9zKYDpbeLvdpIWa+/GxMpg0gE1AV7Rt8e6BUU6Ac8CvQkbHlKbbibTecV6HDVuh0oqmrFwdJ6v1fX9sXdRWTW6z+e6/X5pRIxYiLlqNcHd0ZvstCMnjhRoCdhy9OsOVrZdcacV6DDqk+Po7K9isVgtvu9urYv7EVkiZve+iabo9dvHQAQp5IHPUdvstGMnjhRoCdha8XsTEjdlKIbLLYuQXzD9mJXmoLVV4onEOZlJ8PBuK8K6mvBV5xaHvwcvcV5sZiqbggFehK25mUnI8LNbNRqZ7oEcU9B1dfVtf3h6VtHXwu24tQRwa+6oYuxpB0FehK28gp0MHjYialzEPc12AaCu1y9UibBitmZvf5evEqOuiDn6OliLGFRoCdhq7fUS+fKFl+DbSB0X/CVrFVi3fxxfTZki1NHoMXk3Ns2WEy0YIq0owVTJGz1lnrpnBqfl50Mi82BJz45BsAZbPtaWBVInRd8eStO7Vw01WCwYFCQFnexvXQUUprPDXQU6EnYGqJVuvZ/7a77Fn4j23dyev2OHFw/dnDQx+YvdtFUnd4ctEBvtNohk4iCvniMhD+/XgHff/89Hn/8cbf3bd26FfPnz8eiRYuwa9cuf56GDFArZmfC0x5R3fPvh0qde7ReOjy4fegDJVblnNE3ulkUFihGq53y8wSAHzP6NWvWYN++fcjKyupxX21tLTZv3oxPPvkEZrMZy5Ytw5QpUyCXy/0aLBlY5mUn49eyBrx38AI6FzFKRKIe+fdDJQ1IT1T73N441NhrDE1t7jcXDwST1U4VNwSAHzP6nJwcPPvss27vO3bsGLKzsyGXy6HRaJCamoqioiJfn4oMYGvmjcPLiye6LnaqI6RgwOCqTm2ADWYbDpbUY8qoeO4G2k/a9kVf3VNQgWS00IyeOPU5o//oo4/w7rvvdrlt7dq1uPHGG3Ho0CG3v6PX66HRdOyuo1KpoNfrezyusLCwv+MVJJPJROeinbtzkakANs115t11LVbc+1k5Xt12GLdPjAEA7C7Vw2xzYLTGzJvzaGnfGP1MmQ6F0e43Cvf3dVHb0ASxw8qbc9Ibeo/4p89Av3DhQixcuLBfB1Wr1TAYOjaNMBgMXQI/y13aZyBytualcwH0fS6yAPym2IJPC+vwPzdkY3C0An/+8QCGRCtw2/RsSDzs6hSOFLILiNDEePz/+vu6kB5oRTRjFcRri94jHfLz8/v9O0G5HD9+/Hjk5+fDbDajtbUV586dQ0ZGRjCeigxAf75pNBwMg4c+KMD/7jqLX8sa8YfpI3kV5AFAq5S77dAZKEarHUoZVdyQAJdXvv3220hNTcWsWbOQm5uLZcuWgWEYPProo4iI4MdFMhL+hsZG4sWFE/HIlgLklzViekYCbr88leth9Zs2Uhb0i7FxKiqAIH4G+ssvvxyXX3656+e77rrL9e9FixZh0aJF/hyeEI/mjB+MScNiUFKnx+ThsbysFY9SyoJ/MVZLF2MJLZgiPDYoWhG0xUahoFXKcKHB/YXYQDDZqLySOPFvGkSIQAQ7dWO0OKCgPjcEFOgJ4Ux0kFM3tGCKsCjQE8IRbaQcRqvd1XwskBiGaW+BQG9xQoGeEM6wWyK2BGFWb7UzsDsYmtETABToCeFMdBDbINCmI6QzCvSEcMTV2CwIgd5spU1HSAcK9IRwRKNwBvpWU/Bm9JS6IQAFekI4o1E4l7G0mmwBPzalbkhnFOgJ4YgmIoiB3kIzetKBAj0hHOlI3dCMngQXBXpCOKKQiSERi6A3B+NirLPfPV2MJQAFekI4IxKJoFFIgzyjp7c4oUBPCKc0Cin0lKMnQUaBnhAOqSNkaAnijJ4CPQEo0BPCKWfqJvA5erZ/DnWvJAAFekI4pYmQQm8O/IzeRDN60gkFekI4FMyLsRKxCDIe7rxFAo9eBYRwSK0IzozeaHHQbJ64UKAnhEMahQytJisYhgnocZ296CnQEycK9IRwSKOQwmpnYLY5Anpck9UOpZze3sSJXgmEcChY/W5MVjsUUprREycK9IRwKFitio1WO7U/IC4U6AnhkLp9Rh/oC7JGC+XoSQcK9IRwKFg96U1WO1XdEBcK9IRwKFitio0U6EknFOgJ4VDHjD6wOXqT1UGdK4kLvRII4RAb6AOeo6eLsaQTCvSEcEgVrPJKuhhLOpH688vff/89vv32W7z44os97luzZg0OHz4MlUoFANi4cSM0Go0/T0eI4MgkYihlkuCUV1KgJ+18DvRr1qzBvn37kJWV5fb+kydPYtOmTYiNjfV5cIQMBIHud2O1O2BzMDSjJy4ixscmG19//TViY2OxZcsWvPzyy13uczgcmDp1KnJyclBXV4cFCxZgwYIFXR6Tn5/v+6gJIWQAmzRpUr8e3+eM/qOPPsK7777b5ba1a9fixhtvxKFDh9z+TltbG+644w7cddddsNvtWL58OcaOHYtLLrnE54ESQgjxTZ+BfuHChVi4cGG/DqpUKrF8+XIolUoAwBVXXIGioqIugZ4QQkhoBKXq5vz581i6dCnsdjusVisOHz6MMWPGBOOpCCGE9MGvqpvu3n77baSmpmLWrFmYO3cuFi1aBJlMhrlz5yI9PT2QT0UIIcRLPl+M9ZXD4cCzzz6L4uJiyOVyrFmzBsOGDQvlEMKG1WrFU089BZ1OB4vFgvvvvx+zZs3ielicqq+vx/z58/Hvf/8bI0eO5Ho4nHnjjTewc+dOWK1WLF26tN/pU6GwWq1YuXIldDodxGIxnn/++QH5ujh69Cj+/ve/Y/PmzSgrK8PKlSshEomQnp6OZ555BmJx78mZkC+Y+uGHH2CxWLBlyxY8/vjjWL9+faiHEDa++OILaLVavP/++9i0aROef/55rofEKavVitWrV0OhUHA9FE4dOnQIBQUF+OCDD7B582ZUVVVxPSTO7N69GzabDR9++CEeeOAB/OMf/+B6SCH31ltv4emnn4bZbAYArFu3Do888gjef/99MAyDHTt29HmMkAf6/Px8TJs2DQAwceJEnDhxItRDCBvXX389Hn74YQAAwzCQSAZ23fMLL7yAJUuWIDExkeuhcGrfvn3IyMjAAw88gPvuuw8zZszgekicGTFiBOx2OxwOB/R6PaTSgGabeSE1NRWvvfaa6+eTJ09i8uTJAICrr74aP/30U5/HCPlZ0+v1UKvVrp8lEglsNtuA/AOyq4b1ej3++Mc/4pFHHuF2QBz69NNPERsbi2nTpuHNN9/kejicamxsRGVlJV5//XVUVFTg/vvvx7fffguRSMT10EIuMjISOp0ON9xwAxobG/H6669zPaSQmz17NioqKlw/Mwzjei2oVCq0trb2eYyQz+jVajUMBoPrZ4fDMSCDPOvixYtYvnw55s6di5tvvpnr4XDmk08+wU8//YTc3FwUFhbiySefRG1tLdfD4oRWq8XUqVMhl8uRlpaGiIgINDQ0cD0sTrzzzjuYOnUqtm/fjs8//xwrV650pTAGqs75eIPBgKioqL5/J5gDcicnJwd79uwBABw5cgQZGRmhHkLYqKurw913340VK1b0WDk80Lz33nv473//i82bNyMrKwsvvPACEhISuB4WJyZNmoS9e/eCYRhUV1fDaDRCq9VyPSxOREVFuXpkRUdHw2azwW63czwqbo0ePdq1WHXPnj249NJL+/ydkE+lr7vuOuzfvx9LliwBwzBYu3ZtqIcQNl5//XW0tLRg48aN2LhxIwDnhZeBfjFyoLvmmmvwyy+/YMGCBWAYBqtXrx6w129++9vf4qmnnsKyZctgtVrx6KOPIjIykuthcerJJ5/En//8Z7z00ktIS0vD7Nmz+/ydkJdXEkIICS3qR08IIQJHgZ4QQgSOAj0hhAgcBXpCCBE4CvSEECJwFOgJIUTgKNATQojA/X9fgslr3mJI+AAAAABJRU5ErkJggg==\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"model = make_pipeline(GaussianFeatures(30),\\n\",\n \" LinearRegression())\\n\",\n \"model.fit(x[:, np.newaxis], y)\\n\",\n \"\\n\",\n \"plt.scatter(x, y)\\n\",\n \"plt.plot(xfit, model.predict(xfit[:, np.newaxis]))\\n\",\n \"\\n\",\n \"plt.xlim(0, 10)\\n\",\n \"plt.ylim(-1.5, 1.5);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With the data projected to the 30-dimensional basis, the model has far too much flexibility and goes to extreme values between locations where it is constrained by data.\\n\",\n \"We can see the reason for this if we plot the coefficients of the Gaussian bases with respect to their locations, as shown in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def basis_plot(model, title=None):\\n\",\n \" fig, ax = plt.subplots(2, sharex=True)\\n\",\n \" model.fit(x[:, np.newaxis], y)\\n\",\n \" ax[0].scatter(x, y)\\n\",\n \" ax[0].plot(xfit, model.predict(xfit[:, np.newaxis]))\\n\",\n \" ax[0].set(xlabel='x', ylabel='y', ylim=(-1.5, 1.5))\\n\",\n \" \\n\",\n \" if title:\\n\",\n \" ax[0].set_title(title)\\n\",\n \"\\n\",\n \" ax[1].plot(model.steps[0][1].centers_,\\n\",\n \" model.steps[1][1].coef_)\\n\",\n \" ax[1].set(xlabel='basis location',\\n\",\n \" ylabel='coefficient',\\n\",\n \" xlim=(0, 10))\\n\",\n \" \\n\",\n \"model = make_pipeline(GaussianFeatures(30), LinearRegression())\\n\",\n \"basis_plot(model)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The lower panel of this figure shows the amplitude of the basis function at each location.\\n\",\n \"This is typical overfitting behavior when basis functions overlap: the coefficients of adjacent basis functions blow up and cancel each other out.\\n\",\n \"We know that such behavior is problematic, and it would be nice if we could limit such spikes explicitly in the model by penalizing large values of the model parameters.\\n\",\n \"Such a penalty is known as *regularization*, and comes in several forms.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Ridge Regression ($L_2$ Regularization)\\n\",\n \"\\n\",\n \"Perhaps the most common form of regularization is known as *ridge regression* or $L_2$ *regularization* (sometimes also called *Tikhonov regularization*).\\n\",\n \"This proceeds by penalizing the sum of squares (2-norms) of the model coefficients $\\\\theta_n$. In this case, the penalty on the model fit would be: \\n\",\n \"$$\\n\",\n \"P = \\\\alpha\\\\sum_{n=1}^N \\\\theta_n^2\\n\",\n \"$$\\n\",\n \"where $\\\\alpha$ is a free parameter that controls the strength of the penalty.\\n\",\n \"This type of penalized model is built into Scikit-Learn with the `Ridge` estimator (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import Ridge\\n\",\n \"model = make_pipeline(GaussianFeatures(30), Ridge(alpha=0.1))\\n\",\n \"basis_plot(model, title='Ridge Regression')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The $\\\\alpha$ parameter is essentially a knob controlling the complexity of the resulting model.\\n\",\n \"In the limit $\\\\alpha \\\\to 0$, we recover the standard linear regression result; in the limit $\\\\alpha \\\\to \\\\infty$, all model responses will be suppressed.\\n\",\n \"One advantage of ridge regression in particular is that it can be computed very efficiently—at hardly more computational cost than the original linear regression model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"### Lasso Regression ($L_1$ Regularization)\\n\",\n \"\\n\",\n \"Another common type of regularization is known as *lasso regression* or *L~1~ regularization* involves penalizing the sum of absolute values (1-norms) of regression coefficients:\\n\",\n \"$$\\n\",\n \"P = \\\\alpha\\\\sum_{n=1}^N |\\\\theta_n|\\n\",\n \"$$\\n\",\n \"Though this is conceptually very similar to ridge regression, the results can differ surprisingly. For example, due to its construction, lasso regression tends to favor *sparse models* where possible: that is, it preferentially sets many model coefficients to exactly zero.\\n\",\n \"\\n\",\n \"We can see this behavior if we duplicate the previous example using L1-normalized coefficients (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import Lasso\\n\",\n \"model = make_pipeline(GaussianFeatures(30), Lasso(alpha=0.001, max_iter=2000))\\n\",\n \"basis_plot(model, title='Lasso Regression')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With the lasso regression penalty, the majority of the coefficients are exactly zero, with the functional behavior being modeled by a small subset of the available basis functions.\\n\",\n \"As with ridge regularization, the $\\\\alpha$ parameter tunes the strength of the penalty and should be determined via, for example, cross-validation (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) for a discussion of this).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"## Example: Predicting Bicycle Traffic\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"As an example, let's take a look at whether we can predict the number of bicycle trips across Seattle's Fremont Bridge based on weather, season, and other factors.\\n\",\n \"We already saw this data in [Working With Time Series](03.11-Working-with-Time-Series.ipynb), but here we will join the bike data with another dataset and try to determine the extent to which weather and seasonal factors—temperature, precipitation, and daylight hours—affect the volume of bicycle traffic through this corridor.\\n\",\n \"Fortunately, the National Oceanic and Atmospheric Administration (NOAA) makes its daily [weather station data](http://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND) available—I used station ID USW00024233—and we can easily use Pandas to join the two data sources.\\n\",\n \"We will perform a simple linear regression to relate weather and other information to bicycle counts, in order to estimate how a change in any one of these parameters affects the number of riders on a given day.\\n\",\n \"\\n\",\n \"In particular, this is an example of how the tools of Scikit-Learn can be used in a statistical modeling framework, in which the parameters of the model are assumed to have interpretable meaning.\\n\",\n \"As discussed previously, this is not a standard approach within machine learning, but such interpretation is possible for some models.\\n\",\n \"\\n\",\n \"Let's start by loading the two datasets, indexing by date:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# url = 'https://raw.githubusercontent.com/jakevdp/bicycle-data/main'\\n\",\n \"# !curl -O {url}/FremontBridge.csv\\n\",\n \"# !curl -O {url}/SeattleWeather.csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"counts = pd.read_csv('FremontBridge.csv',\\n\",\n \" index_col='Date', parse_dates=True)\\n\",\n \"weather = pd.read_csv('SeattleWeather.csv',\\n\",\n \" index_col='DATE', parse_dates=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For simplicity, let's look at data prior to 2020 in order to avoid the effects of the COVID-19 pandemic, which significantly affected commuting patterns in Seattle:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"counts = counts[counts.index < \\\"2020-01-01\\\"]\\n\",\n \"weather = weather[weather.index < \\\"2020-01-01\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Next we will compute the total daily bicycle traffic, and put this in its own `DataFrame`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"daily = counts.resample('d').sum()\\n\",\n \"daily['Total'] = daily.sum(axis=1)\\n\",\n \"daily = daily[['Total']] # remove other columns\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We saw previously that the patterns of use generally vary from day to day. Let's account for this in our data by adding binary columns that indicate the day of the week:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true,\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\\n\",\n \"for i in range(7):\\n\",\n \" daily[days[i]] = (daily.index.dayofweek == i).astype(float)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Similarly, we might expect riders to behave differently on holidays; let's add an indicator of this as well:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from pandas.tseries.holiday import USFederalHolidayCalendar\\n\",\n \"cal = USFederalHolidayCalendar()\\n\",\n \"holidays = cal.holidays('2012', '2020')\\n\",\n \"daily = daily.join(pd.Series(1, index=holidays, name='holiday'))\\n\",\n \"daily['holiday'].fillna(0, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We also might suspect that the hours of daylight would affect how many people ride. Let's use the standard astronomical calculation to add this information (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(8.0, 17.0)\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def hours_of_daylight(date, axis=23.44, latitude=47.61):\\n\",\n \" \\\"\\\"\\\"Compute the hours of daylight for the given date\\\"\\\"\\\"\\n\",\n \" days = (date - pd.datetime(2000, 12, 21)).days\\n\",\n \" m = (1. - np.tan(np.radians(latitude))\\n\",\n \" * np.tan(np.radians(axis) * np.cos(days * 2 * np.pi / 365.25)))\\n\",\n \" return 24. * np.degrees(np.arccos(1 - np.clip(m, 0, 2))) / 180.\\n\",\n \"\\n\",\n \"daily['daylight_hrs'] = list(map(hours_of_daylight, daily.index))\\n\",\n \"daily[['daylight_hrs']].plot()\\n\",\n \"plt.ylim(8, 17)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"We can also add the average temperature and total precipitation to the data.\\n\",\n \"In addition to the inches of precipitation, let's add a flag that indicates whether a day is dry (has zero precipitation):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"weather['Temp (F)'] = 0.5 * (weather['TMIN'] + weather['TMAX'])\\n\",\n \"weather['Rainfall (in)'] = weather['PRCP']\\n\",\n \"weather['dry day'] = (weather['PRCP'] == 0).astype(int)\\n\",\n \"\\n\",\n \"daily = daily.join(weather[['Rainfall (in)', 'Temp (F)', 'dry day']])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Finally, let's add a counter that increases from day 1, and measures how many years have passed.\\n\",\n \"This will let us measure any observed annual increase or decrease in daily crossings:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"daily['annual'] = (daily.index - daily.index[0]).days / 365.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Now our data is in order, and we can take a look at it:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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Mon
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Wed
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holiday
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Rainfall (in)
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2012-10-03
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14084.0
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0.0
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0.0
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1.0
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0.0
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0.0
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0.0
\\n\",\n \"
0.0
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0.0
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11.277359
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0.0
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56.0
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1
\\n\",\n \"
0.000000
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2012-10-04
\\n\",\n \"
13900.0
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0.0
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0.0
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0.0
\\n\",\n \"
1.0
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0.0
\\n\",\n \"
0.0
\\n\",\n \"
0.0
\\n\",\n \"
0.0
\\n\",\n \"
11.219142
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0.0
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56.5
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1
\\n\",\n \"
0.002740
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2012-10-05
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12592.0
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0.0
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0.0
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0.0
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0.0
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1.0
\\n\",\n \"
0.0
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0.0
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0.0
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11.161038
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0.0
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59.5
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1
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0.005479
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2012-10-06
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8024.0
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0.0
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0.0
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0.0
\\n\",\n \"
0.0
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0.0
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1.0
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0.0
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0.0
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11.103056
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0.0
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60.5
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1
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0.008219
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2012-10-07
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8568.0
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0.0
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0.0
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0.0
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0.0
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0.0
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0.0
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1.0
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0.0
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11.045208
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0.0
\\n\",\n \"
60.5
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1
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0.010959
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\\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" Total Mon Tue Wed Thu Fri Sat Sun holiday daylight_hrs \\\\\\n\",\n \"Date \\n\",\n \"2012-10-03 14084.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 11.277359 \\n\",\n \"2012-10-04 13900.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 11.219142 \\n\",\n \"2012-10-05 12592.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 11.161038 \\n\",\n \"2012-10-06 8024.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 11.103056 \\n\",\n \"2012-10-07 8568.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 11.045208 \\n\",\n \"\\n\",\n \" Rainfall (in) Temp (F) dry day annual \\n\",\n \"Date \\n\",\n \"2012-10-03 0.0 56.0 1 0.000000 \\n\",\n \"2012-10-04 0.0 56.5 1 0.002740 \\n\",\n \"2012-10-05 0.0 59.5 1 0.005479 \\n\",\n \"2012-10-06 0.0 60.5 1 0.008219 \\n\",\n \"2012-10-07 0.0 60.5 1 0.010959 \"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"daily.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With this in place, we can choose the columns to use, and fit a linear regression model to our data.\\n\",\n \"We will set `fit_intercept=False`, because the daily flags essentially operate as their own day-specific intercepts:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Drop any rows with null values\\n\",\n \"daily.dropna(axis=0, how='any', inplace=True)\\n\",\n \"\\n\",\n \"column_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun',\\n\",\n \" 'holiday', 'daylight_hrs', 'Rainfall (in)',\\n\",\n \" 'dry day', 'Temp (F)', 'annual']\\n\",\n \"X = daily[column_names]\\n\",\n \"y = daily['Total']\\n\",\n \"\\n\",\n \"model = LinearRegression(fit_intercept=False)\\n\",\n \"model.fit(X, y)\\n\",\n \"daily['predicted'] = model.predict(X)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"Finally, we can compare the total and predicted bicycle traffic visually (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"daily[['Total', 'predicted']].plot(alpha=0.5);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"From the fact that the data and model predictions don't line up exactly, it is evident that we have missed some key features.\\n\",\n \"Either our features are not complete (i.e., people decide whether to ride to work based on more than just these features), or there are some nonlinear relationships that we have failed to take into account (e.g., perhaps people ride less at both high and low temperatures).\\n\",\n \"Nevertheless, our rough approximation is enough to give us some insights, and we can take a look at the coefficients of the linear model to estimate how much each feature contributes to the daily bicycle count:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Mon -3309.953439\\n\",\n \"Tue -2860.625060\\n\",\n \"Wed -2962.889892\\n\",\n \"Thu -3480.656444\\n\",\n \"Fri -4836.064503\\n\",\n \"Sat -10436.802843\\n\",\n \"Sun -10795.195718\\n\",\n \"holiday -5006.995232\\n\",\n \"daylight_hrs 409.146368\\n\",\n \"Rainfall (in) -2789.860745\\n\",\n \"dry day 2111.069565\\n\",\n \"Temp (F) 179.026296\\n\",\n \"annual 324.437749\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"params = pd.Series(model.coef_, index=X.columns)\\n\",\n \"params\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"These numbers are difficult to interpret without some measure of their uncertainty.\\n\",\n \"We can compute these uncertainties quickly using bootstrap resamplings of the data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.utils import resample\\n\",\n \"np.random.seed(1)\\n\",\n \"err = np.std([model.fit(*resample(X, y)).coef_\\n\",\n \" for i in range(1000)], 0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"With these errors estimated, let's again look at the results:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false,\n \"deletable\": true,\n \"editable\": true,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" effect uncertainty\\n\",\n \"Mon -3310.0 265.0\\n\",\n \"Tue -2861.0 274.0\\n\",\n \"Wed -2963.0 268.0\\n\",\n \"Thu -3481.0 268.0\\n\",\n \"Fri -4836.0 261.0\\n\",\n \"Sat -10437.0 259.0\\n\",\n \"Sun -10795.0 267.0\\n\",\n \"holiday -5007.0 401.0\\n\",\n \"daylight_hrs 409.0 26.0\\n\",\n \"Rainfall (in) -2790.0 186.0\\n\",\n \"dry day 2111.0 101.0\\n\",\n \"Temp (F) 179.0 7.0\\n\",\n \"annual 324.0 22.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pd.DataFrame({'effect': params.round(0),\\n\",\n \" 'uncertainty': err.round(0)}))\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"deletable\": true,\n \"editable\": true\n },\n \"source\": [\n \"The `effect` column here, roughly speaking, shows how the number of riders is affected by a change of the feature in question.\\n\",\n \"For example, there is a clear divide when it comes to the day of the week: there are thousands fewer riders on weekends than on weekdays.\\n\",\n \"We also see that for each additional hour of daylight, 409 ± 26 more people choose to ride; a temperature increase of one degree Fahrenheit encourages 179 ± 7 people to grab their bicycle; a dry day means an average of 2,111 ± 101 more riders,\\n\",\n \"and every inch of rainfall leads 2,790 ± 186 riders to choose another mode of transport.\\n\",\n \"Once all these effects are accounted for, we see a modest increase of 324 ± 22 new daily riders each year.\\n\",\n \"\\n\",\n \"Our simple model is almost certainly missing some relevant information. For example, as mentioned earlier, nonlinear effects (such as effects of precipitation *and* cold temperature) and nonlinear trends within each variable (such as disinclination to ride at very cold and very hot temperatures) cannot be accounted for in a simple linear model.\\n\",\n \"Additionally, we have thrown away some of the finer-grained information (such as the difference between a rainy morning and a rainy afternoon), and we have ignored correlations between days (such as the possible effect of a rainy Tuesday on Wednesday's numbers, or the effect of an unexpected sunny day after a streak of rainy days).\\n\",\n \"These are all potentially interesting effects, and you now have the tools to begin exploring them if you wish!\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 64-bit ('3.9.6')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"513788764cd0ec0f97313d5418a13e1ea666d16d72f976a8acadce25a5af2ffc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/05.07-Support-Vector-Machines.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# In Depth: Support Vector Machines\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression.\\n\",\n \"In this chapter, we will explore the intuition behind SVMs and their use in classification problems.\\n\",\n \"\\n\",\n \"We begin with the standard imports:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\\n\",\n \"from scipy import stats\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Motivating Support Vector Machines\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As part of our discussion of Bayesian classification (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)), we learned about a simple kind of model that describes the distribution of each underlying class, and experimented with using it to probabilistically determine labels for new points.\\n\",\n \"That was an example of *generative classification*; here we will consider instead *discriminative classification*. That is, rather than modeling each class, we will simply find a line or curve (in two dimensions) or manifold (in multiple dimensions) that divides the classes from each other.\\n\",\n \"\\n\",\n \"As an example of this, consider the simple case of a classification task in which the two classes of points are well separated (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.datasets import make_blobs\\n\",\n \"X, y = make_blobs(n_samples=50, centers=2,\\n\",\n \" random_state=0, cluster_std=0.60)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A linear discriminative classifier would attempt to draw a straight line separating the two sets of data, and thereby create a model for classification.\\n\",\n \"For two-dimensional data like that shown here, this is a task we could do by hand.\\n\",\n \"But immediately we see a problem: there is more than one possible dividing line that can perfectly discriminate between the two classes!\\n\",\n \"\\n\",\n \"We can draw some of them as follows; the following figure shows the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"xfit = np.linspace(-1, 3.5)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\\n\",\n \"plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)\\n\",\n \"\\n\",\n \"for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:\\n\",\n \" plt.plot(xfit, m * xfit + b, '-k')\\n\",\n \"\\n\",\n \"plt.xlim(-1, 3.5);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These are three *very* different separators which, nevertheless, perfectly discriminate between these samples.\\n\",\n \"Depending on which you choose, a new data point (e.g., the one marked by the \\\"X\\\" in this plot) will be assigned a different label!\\n\",\n \"Evidently our simple intuition of \\\"drawing a line between classes\\\" is not good enough, and we need to think a bit more deeply.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Support Vector Machines: Maximizing the Margin\\n\",\n \"\\n\",\n \"Support vector machines offer one way to improve on this.\\n\",\n \"The intuition is this: rather than simply drawing a zero-width line between the classes, we can draw around each line a *margin* of some width, up to the nearest point.\\n\",\n \"Here is an example of how this might look (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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FHYPlyFLsd08UXi0XLUMMQBuHq2BG++UbEon/6CXr2FAuaRvH3v4vj/MYbpxqDxcSIhcnMTOP2I/HA7XZ7ZIfoqxtDweFwsGrVKvLy8li/fj0pKSnMnDmTGTNm0KNHD9xuNwf9dbNshKCEu76+nscee4wEo1qAnk6YTOILee+9HJ03j84ZGTB8uKjuay2sXds0D9pqFXcaTaVLF5H6Nn8+24qL6d+/f9O30RL06SMeRhMXJ3q7PPOMKNtv1070ZGkNd2WtEKfTic1mw2azAWA2m4kz4FgfO3aM/Px8Fi1axNGjR+nTpw9//vOfmTBhAkm+7r6bQVDC/dxzzzFx4kT+/e9/G7LT05KePSmfMoXO0SpGgUhPFxcgXx53fLx4LS5OZIX07ClGx/Xt29JWth06d4bRoyNtRZtErW6sqqqi7mTapVG51xs2bGDevHmsWrUKh8PBVVddxezZsxk6dKjhmSeNCvc777xDeno6l112WUDhLi4uNtSwUKmrq5M2BUljdiVefTVnvf02Zh+TZdxmMzs//ZS4w4dxJyZSf9ZZ4oUQP2c0Hav47dtJ2LIFS1ISxQ5H4OyPFiaajpNKNNqkCvbWrVtRdA5IqIJdX1/PJ598wuuvv8769etJTExk3Lhx3HLLLfTs2ROA48eP+7WpuZiURn578uTJmEwmTCYTxcXF9OzZk5dffpmOHTtq7ykoKCA3N7fZRoSD4ii81Y5GmyAIuxRFpP69886p/GyzWSyavfaaqBpsaZtagvJy+OUvRezdZMIFxMTGimKca6+NrG0niYrj5EU02eR0OrVmT+Xl5aSnpxMTKPU0SMrKyliyZAkLFy7k4MGD9OjRg5kzZzJx4kRSGutgeRI1xt0c7WzU416yZIn2/6lTp/LEE094iLbkNMBkElNtbrgBXnxRtGrNzYU5c0RWRSRxu8Xib3296Jfib9pPcxg7Fn74QStz177uY8bApk1igpAk6lC9a5vNRu3Ju0S1qjFU0d66dSvz5s3jnXfewW63c9lll/Hss88ybNgwQy4IwSKbH0iCw2QSE2QCTZFpad57D2bPFncB6i3v00/DPfeEvu0dO0QnQF/tauvrxQXs738PfT8Sw3C73dTW1mKz2Rr0vg4Fl8vFRx99xLx58/j222+xWq1MmDCBmTNn0q9fvyZvT1EUXC4XTn+VwkHQJOFevHhxs3ckkRjK11/DxIkNs10efljkzc+aFdr2N20SC66+JsbX14usD0lUoIZDqqurDe19XVFRwRtvvMGCBQvYv38/Xbt25ZFHHmHy5MmkBVk4pygKbrcbt9uN0+nE5XJpAxRCiXFLj1vSOnn0Ud8piuqsyxkz/PfXDobOnf03tjKZRNvaaERRxDi5558XDb/69IEHH4RRoyJtmaH4C4cYkc63Y8cO5s+fz/Lly6mtreWiiy7iySefZPjw4cQGaL2gKIom1KpH7dIVqalrhWazWU7AkZym/O9//l87cUI0vTrzzOZv/+KLRTXjyRxfD6xW+PWvm7/tcPL738Orr55aRD58GG6+WVSmPv54ZG0zAP0oMIfDYZh37Xa7+fTTT8nLy+Pzzz8nPj6eMWPGMGvWLAYOHOjzd1SRVgXa5XI1yFhRRdrX70nhlpx+WK3+i4JcLt+l403BbBaDEq66SoRGamtRzGZMCQmiVcHQoaFtPxxs2yaGJXuHd6qr4dlnRRVv9+6RsS1EfI0Ci4uLC1mwq6qqePPNN5k/fz4lJSV07tyZBx98kClTptBBnXpE4JAHeHrT/lC3ASJ3PCEhgaqqqmbZLYVb0jqZNg3+9a+Gi4cmk+irkpoa+j5yc0Vp+7x58NVXnLBaSZ0zR/QriUaWLhUXGV8oipimc//9LWtTCCiKQn19PTabjZqTF2mjwiElJSXMnz+fZcuWYbPZOP/885kzZw6jRo0iNjYWRVE8PGm9N+0d8mgMt9uNoiiYTCbi4+O13ifS45aEn9paMSX+lVdEKGLQIHjySVG+HwkefVR4xAcPnho8YLEIT/vVV43bT4cOonz/oYc4VFxMapTkJ/ukqsp/T3OHw3fYJwpRmz1VVlYamh2iKApffPEFeXl5fPzxx8TGxjJ69GhmzpzJ4MGDtbh0XV1dg5BHY960r32pD7PZTEJCgqHTcaRwSxrH4RChgU2bTonk99+LkWx/+xvcfnvL25SWJsalvfyyyDF3OESZ+O9+J0annY4MGybuDnwJdFJSdIZ3dOhHgRmZHVJdXc1bb73Fq6++yu7du+nQoQP33HMPt9xyC+np6VqTKdUr1j+ainc4xGKxBFzQbC5SuCWNs2KFGFGmH6kFp8aKTZkSeky5ObRvLzImHnyw5fcdjYwYIWLYO3d6hkwsFtHF8PLLI2ebH9RwSHV1tSaeRoVD9u7dy4IFC1i6dCknTpzgvPPO4y9/+QsjR47EcrJtgeoRG+HNq4JtsViwWCxhnYwjhVvSOPn5/keRxcTAZ5+1uXSzVklMjJhONH06fPyxqCJ1OOC660TXwUj3NNehKAp2u53KykocDodW1RiKgKqFLevWrWP+/PmsXbsWk8nEtddey/Tp0znvvPNITEw0LFzhKxxixIJpMEjhbku43aJfdFmZmFZzsslNyHh72t74qi6URIaMDFi9WozM279feOC67IhI43K5qK2tDbn3tXfOtM1m491332XRokVs27aNtLQ0br/9diZPnsyZJ9NCa2pqDBFVvWCHMxwSCCncbYV160RvDbX82+EQMc9lyyA5ObRt33QTFBT4H3ochbfgpz2dOolHKKjphbt2iZF1d9zR7MIjNTuk+uSdW1PDIao37Z3lcfjwYd544w3efPNNysvL6devH3/+85+54YYbDJ8f0NLhkEBI4W4L7Nsnsju8F6U++USUha9eHdr2b7tNVOLZ7Z7jyhITxZc5IyO07Uuij1dfhfvuE7Fyp1OcS3/7G7z9toilB4EaDqmqqsJ+cp5mMN51YznTAOvXryc/P58PP/wQRVG4+uqrmT59OkOGDDE0VBHJcEggpHC3Bf7f//MdrqirE1+4khLo1av522/fXnTJu+MOMQ0nNlYseD3wgFwYbIuUlMC993qGyNRBxuPGwZEjvueonkRRFGw2W1DhkKaUiTscDtasWUN+fj6bNm0iJSWFW2+9lWnTptGtWzejPr2HXepiaXx8fIuHQwIRPZZIms+XX/qPM1ssop90KMINIsVu9WqorBSPzp0bDvaVtA0WLAjcp2XlSpg8ucFL6igwRVGoqKjwGQ7Ri7Qq1I2ViR87dow33niDN954g9LSUs4++2yeeuopxowZQ6LB2Uze4ZC4uLgWbdcaLPKb1xY44wz/rymKsaGMlBTxkLRd9u3z7wjY7aL/yUnUcIja7MlkMqEoCnFxcVpcWu9Nu91u7T2NFbZs3LiR/Px83n//ferr67niiiu49dZbufTSS8MWDlGrGy0WS8TDIYGQwt0WuOsukf7lK2XPaoVLL215myStlwsuELn7vhaj4+Nh4ECt93VVVZVW3RgTE6N5zzU1NQ2aLpnN5kZzpuvr6/nwww/Jz8+nqKiIpKQkJk2axLRp0+gV6l2jF9EeDglE67BSEphrrxWLkMuWnRJvi0U83npL5PdKJMEyZQr88Y8NnzebcZ59NtVDhlB18KAWi1b7euhFWo1tB5t1UVZWxptvvsmSJUs4fPgw3bt355FHHmHcuHG0a9fOkI+log+HxMXFYbFYWiwccvz4cYqKiigsLGTTpk0888wzzdqOFO62gMkkSp3HjxfpW4cPCy/7t7+FHj0ibZ2ktdG+PXz6qSjcqanBrSjUDhhA1XXXUTNuHK5jxzze7qtMPFjBLi4uJj8/n3fffReHw8Gll17K008/zRVXXGFoqp16UVEvKC0RDnE4HGzZsoXCwkJNrHfv3g2IY3buuec2e9uGCXddXZ12K6TeDkVzjKjNYTKJlMBINX2StBlcLhfOAQOwb9uGbds2qgG31YrJag25lweIRcxPPvmE/Px8vv/+e6xWK2PHjmX69On07dvX0M+i964BrFaroc2e9PvZt28fhYWF2mPTpk1aGmSnTp3Izc1l0qRJZGdnk5WVRWJiIgcPHmzW/gwT7tLS0gbPmc1mYmJitH9jYmKIjY31EHcp9BJJ5FB7hahd8RwOBw6HA6fTKcIfaWmi2ZMB/TwqKipYvnw5r7/+OgcOHKBr1648+OCDTJgwgfbt2xv0iQS+wiF2u92QHigg+nivX7+ewsJCCgoKKCoq4tjJO5GEhAQGDx7MrbfeSm5uLjk5OXTt2rXB8YuKtq7eVzH11kRdUXY4HAFnrKkirgq8/uEt8kY0hTntsNlg+XLYvRv69hVhlUg0hpJEDDXLo76+HofDgd1ux6HLHlG/q/rFRiO+Zzt27GDRokWsXLmS2tpahgwZwh//+EeGDRtm6GKgr+wQtfc1ENIdwvbt2zWRLiwsZOfOnZqe9enThyuuuEIT6f79+xt2gfBH2GLc6kEK9mCpB9zpdFJfX+/xB/CFKuC+hF5NN3I6ndKbB5Hnff31Ije3ulqUwN9zD3z0EQwZEmnrJGHC7XZTX19PfX295k3rvTz1+6M6Vmr81wjBdrvdfPbZZ+Tn5/PNN99gsVi44YYbtGZPRmJ07+tDhw55hDzWr1+vzbVMT08nOzub0aNHk5ubS1ZWFqlGDO1oIlGzONkUcVWvdOptni9v3u12c1iXb6r33r1DNt6PNiXyJ06Izn36cnj1/8OHi0EEklaP3umx2+3Y7XacuqEK6h2r6gmqw3bV705TJroEoqqqijfeeINly5axd+9eOnfuzO9+9zsmTpxIenp6SNv2xrv3dXx8fJOzQ2pqati4caNHyEONO8fFxTFw4EBuueUWcnJyyMnJoWfPnlGhD1Ej3E0hGG/eZDJ5nKTeKUuBPHrV69CLvT+hj3pvfulS/1VwLpfoPXH++S1rkyQkVMGqr6/H7XZz7NixBs6L6px4n5uqd11/sl+3eh6HSklJCYsWLeLtt9+murqanJwcfve73zF8+HDDwwb+RoEF83s7duzQPOmioiK2bt2qpTX26NGDIUOGaCI9YMAAwxtVGYVhwn333XfTsWNHOnTo0OCRkZFBfHy8UbtqMkZ7897oPXlV5NX/6x+NbScsbNvmf6iuzSZmKkrhjmrUZkuqh1xXV6d5mup52lh4w+l0YrfbPfKrjRge8PXXX7Nw4UI+//xz4uLiGDVqFBMnTuSCCy4Iadu+9tXUcEhpaalHKl5hYaE2nLddu3ZkZ2dz9913k52dTU5Ojsdw4GinUeF2uVw88sgjlJSUYDKZePLJJznnnHMavO/bb7+ltLSUOj+9m1NSUujQoYMm7hkZGT5FvkOHDqSmpkasXWJzY/Pqwo76s7/3HjhwIOAirHfGTcjefN++YhHSl3gnJYXew0RiKOqdoSq0dXV1WshDFS39SC/17tDftsIRDqmpqeE///kP+fn5/PTTT9oosEmTJtGxY0dtsK8RBBsOsdvtbN68WRPogoIC9u7dCwjHqn///owYMYJf/OIX5OTk0Ldv34hpjBE0KtyfffYZAMuWLeP777/nb3/7Gy+//HKD9/3www+AmO9WWlpKaWkpx44do6ysTPtZfezatYtvv/2W8vJynyIXGxvrV9h9PW90o5mm0FRvXi0LNmoRttGwzeTJMGeOb4NiYkS3t5MnuKTlUbM49AuI3mXiTV1o02eOqNswQqT279/PokWLWLFiBZWVlQwaNIi//vWvjBw50vA76kC9rxVFYc+ePVqGR2FhIVu2bNE+75lnnkl2djbTp08nNzeXQYMGkZSURGlpaavyqgPRqHBfffXVXHHFFQAcPHiQlAANhkwmE8nJySQnJ9MziOkrLpeL48ePU1pa6iHwquAfO3aM0tJSfv75Z0pLS7Um7N4kJSX5FPPu3bs3eD49PT1i3b6aWrjQWNhGzZ7R40vgY9aswXTPPZhrajAfP47ZZMJcX49p9eqA7TklxqL+HfULiPoWpuoFOpS0tXCEQ77//nsWLlzIJ598gslkYsSIEUyfPp2cnJwW6X194sQJvv32W4/Y9PHjxwFITEwkMzOT2bNna7HpLl26GGZTtGJSggy8zpkzh7Vr1/Liiy9yqVfTooKCAuLj48O+SFdbW8vx48cbPMrKyrRHeXm59rz+S6FiMplIS0sjPT3d45GRkdHg/xkZGYbOqHM6nWFtYhMwT76+nrh9+4ix2XCnpODo1g1OLhrV19d7LCD5+7wtuQhbV1cXdQtDTbFJ/7fw93+VUI6r0+kMiyNSV1fHmjVrWLJkCTt27CA1NZXx48czYcIEOnfuHPB31QntzaG+vp6dO3eyceNGNm3axKZNmygpKQHEcerduzeDBw9m8ODBDBo0iD59+gT9nQr396+pqJ0Vc3Nzm/y7QQs3iL64EyZM4P333/cITxQUFPisDIokR48eJS4uzsOL14drVA9f9e5PnDjhczsJCQk+wzO+FmIzMjICrqBH062a3psvKysjLS2tSYuw/mLzRqVUFhcX079//5C2YTSBbFKzPPSFLd4500ammrrdbhwOB1VVVcTHxxu27YMHD7JkyRKWLVtGRUUF/fr149Zbb+WXv/xl0BetmpqaoMKXbrebAwcOsGHDBjZv3sz69evZtGmTtk7WoUMHzYvOzc0lMzMz4B1/Y0TT9w/E5z948GCzhLvRy8/KlSs5cuQId9xxB9aTvQpaQ1DfbDaTlpZGWlpaUP0P7HZ7g3CNd4z+2LFjFBcXc+zYMS2dypvU1FQPIdcLfHx8PL169dJ+bt++fcQudvpFWHVCSSDUW1j9vL9gUiqDEfqoT6n0IpicafWzGo2aXeJ0OrVjFup+FEWhoKCA/Px8PvroIxRF4ZprrmH69OlceOGFhv1tbDYbGzduZMOGDRQVFbFx40atTDw+Pp5BgwYxbdo0TazPOuusVnVetCSNCve1117LQw89xOTJk3E6nTz88MM+r7zqieurCU1rOPjx8fGceeaZ2kToQCiKQmVlZYNFV+8Y/Y4dO1i3bh3l5eU+txMXFxcwu8b7EcnQQXNj8/pF2EB497Vxu91UV1f7FfmWOqfUi5U6BODo0aMNPo+/nGkjbVC9eXUQgREett1u5/333yc/P5/NmzeTkpLCjBkzmDJlSsijwFwuF7t27WL9+vXaY9euXdpdSK9evRg6dCjZ2dnk5uZy3nnnYbFYQtrn6USjwp2YmMg//vGPRjeUmpqqndzek5i90U+/AN9iH82YTCbat29P+/btOfvssxt9f319PcePH2fnzp04nU6fmTZqtk2glMrk5GSfYRpf4p+ent6qUiqhYV8bfxe8cPa10ZeJq960PmdajSm3xHmqhkP02SFGePFHjx7VRoGVlZXRt29fnn76aW688cZmZ2gdO3ZME2g1y0NNJmjfvj2DBw9mxIgRXHDBBeTm5hpeRXm6YVikPjk52efzalqP90Mv7vrRRoFord58XFwcZ5xxBjExMY3G2BRFoaamxqcHr/+5pKSEH374gePHj/vsMmY2m4P25iN9DH39LfWVr94Y1dfGbDZr56NaTagPQXiXiQfKmTYSfThEtd+Iv9HGjRtZuHAha9aswel0cuWVVzJ9+nQuueSSJm2/rq6OzZs3s2HDBk2s1TLx2NhYzj33XMaMGcPgwYPJysri3HPPjfpRYK2NsC+xNiXepy9mUR/6uKq32Ptri6j/YrdGbz4pKYmkpCR6BDEEweVyUV5e3sB716dTlpaWUlRURGlpKTZ9zxIdVqu1UYFXPf309PSIrs43N2yjNlJS7wz1pdP6u0B9KELvvYfznFK3rXr4RoVDvEeBJScnM3nyZKZOnRpUyq7b7ebnn39m/fr1mlBv27ZNu6h07dqV7OxsbrvtNjIzM7XF28TExFY1CqylCVSoFwxRdVT1X5Rg8CXyqvdeWVlJQkKCxzTpYPbd2rx51YsPdrW8trZWE3XVi9+zZw81NTXazwcPHmTTpk2Ulpb6PW5qumQggVcf7dq1a/FjqT8X9Bd7FdWh8LZLH7bxfg5oUBXoLfTquesdj/d3TumzUdSLhxFefVlZGcuWLWPJkiUcOXKEHj168OijjzJ27NiAo8DKy8s9RHrDhg1UVlYC4q560KBBzJ49m8zMTLKysujYsWOD3tc2m40kWR/ggV6jVEK5qEWVcDeVQN68GipQ0WdBeIdtnE5ng7BNY03OW6vQW61WunXr5rH45C9NSlEUTpw44TelUvXs1UwbfymVFovFZzzeX0plc6rwfP0tVbzFNRCN/S3155r+S6ifv+hre+rzqh3qnaQv71r/3qaydetW8vPzWbVqFQ6Hg8suu4w//elPDB06tIFD5HA42LZtG0VFRZpQ79mzBxDfn3POOYeRI0eSnZ1NZmYmZ599tvb59VlGJpPnKDB/hXKnC3qNUVELitSmWGpjrAMHDjRrH61auJuCXmCbG7bx9uD0Qh+w+OXkvtXttaawTWpqKqmpqfTp06fR9zscDo4fP95A4L1j9Dt27KC0tFQb6+RNSkoKaWlpnHHGGXTs2LGBZ5+RkUFGRgZpaWkkJSU1uO00KszQGN4x+cZQzyF9Vor+vAi0D1+L+Sr19fV8/PHH5Ofn88MPP2C1Whk/fjxTp07VUmEVRYzWUkV6w4YNHmXinTp1Iisri5tvvpmsrCwGDhzo02vWO0AxMTFYLJawZtREO/oLmIrJZNLK9PXdC408RqeNcDeVUMM2voRe/cO11bCNxWKhc+fOjVbWgTheNpvNbz+bAwcOUFlZ2Whfm5iYGNLT0z0EPdDDarWG46M3+lm97wIaO6/0sXn9v3oqKipYsWIFy5Yt49ChQ3Tt2pU//OEPjB07lpiYGDZt2sQHH3yg5U6rZeIJCQkMHDiQadOmkZWVRWZmJl26dAl4jnmHQ1pyMno0of4tvUMeSUlJmki3xIVMCrdBBLMIe+zYMbp06eLXm/fn0fvyxvRZFN5C3xpE3mQy0a5dO9q1a0fv3r2159XPW15eTmJioiZ2TqeTEydONGhxoD7U5/fs2UNZWZnfDnWJiYkeIq/G6vUP9fW0tLSQxMk77NaUv0ug9+7YsYMlS5awatUq6urquOCCC5g6dSrx8fFs3ryZSZMmUVJSoolL7969ufzyy7VS8b59+xIXF+dx/qjH2fv80fcOaUrv67aAv5BHfHy8FhqK1PGQwh0BjPDm/WXbNMebjxT6BUQ1Nq33ZNSeF+pFsVOnTnTq1CmobdfU1DQQ+dLSUo+mZvv379c80UB9bVRBT01NpVOnTg28e/VnNQfaO3xmxHF2uVx8+eWXLFq0iO+++47Y2Fh69+5NbGwsmzdv1rpzpqWlMXjwYEaNGkVmZiaDBg3yWSauDy/5q7VQbVfP0/r6eo8GVuB7ETaUbIlIoXem9Par4Q5VpFsqh78xpHC3AloipdK7hN87Pc6IsI3+jkJNydN/Ru+LWSieTGJiIomJiUFVALrdbioqKnwKvV7wi4uL+eabb7Rm/N4kJCQ0aFKm9+j1z6WlpQU1Gebo0aO88sorrFmzhhMnTmjHxOl0UlJSQv/+/Rk3bhyDBw8mMzOTbt26BfX38fceVbT0ue7NWYStqqrymVLp7y6xpfHlTcfExGC1Wj0WEKNBpH0hhbuN0RxvvrKyks6dOwcM2QSqhPXet37BTb8t/e9G03xPs9msdYYMtAirNk+y2+2ayKs9bVRxVx/Hjh1j27ZtHD9+3G9fm/bt23sIelpaGmazGZvNxr59+9i5c6dH3n1GRgZDhgwhKyuLwYMH079/f61fdyh4l+97e5VNXYRV36ePiwdz3nh778GmVAbz+VQHRv1bqAuIautY1ZtuLUjhPs1RvxDBfvn1+fL6FDy1NFutQNR/YX3dRqvZNd4Vj9Eg5I0RHx9Pp06dyMjI0LI2AnmwNptNE3Z9TP7gwYOUlJRw4MABtmzZok0S90VsbCyxsbGUlJRQWVnJ5s2btWlRHTt2bNCaOJiUSn04RN8uwAiCFdnGvHnveLv6nF7YfTUq01fTmkwmzXtWO3hGS8ijuUjhljQJdSHL32it2NhYbVqJPobqnUfv72c93ilW3v+PxBdPjcX7sssX6iKsxWKhoqKCgwcPsnHjRjZu3Kjl8MbExNCxY0diY2OpqqoiJSWFkSNHMmTIEK23jSr6qkdfUlJCWVlZwL423guves9ejc2roh/pvjbe//eHt9DrzyMVfZ8adf6rum21k6MRfW0iiRRuSUBCGa3VVIHVfwntdruWuqeP1XsvIHnHVgPtO5TYvHoHoU/rDPQ59uzZown0hg0b2L59u3ab3qVLF20Bcd++fXz11VccPnyYQYMGMXXqVIYPHx5UpzxFUaiqqtKKpMrLyxuIvJppU1RURHl5uc8MJTWlMph0Sv0ibEvjq6pVvVv0Humn/x21nYB69+OdkaXfdsijAlsIKdwSDTXsEa7RWo3hHccMtHgXyJtXP0sgb957v97/N5lM2vHwd+uuUlFRwaZNmzSR3rhxo1ZFmpiYyMCBA5k+fTqZmZkMHjyYkpISFi9ezLx584iJiWH48OFMmTKFzMzMJqcMJiYmkpKSwllnnRXwOKnbrays9AjZ6EM46mLsvn37KCsr81sBmZiYGFDYk5KS6Nq1qxbKaW4M3p83reaQq15zIJp7TjV1VKD63YiNjfU5WEQW4EgMQc00UePRR44c8VhEU72LaK2KC8Wb1//sLfD63Gvv+DuI6tAdO3Z4iPTPP/+s2dKnTx+uvvpqMjMzPcrEa2trWb16NbNnz2bHjh2kpaVxxx13MHHiRM444wzjDozu84Jnn3OgSX1t6urqPNInfT0OHTrE5s2bKSsr85mKqk+p9OfVq43L0tPTPbqMenvT4Q5lNPec0l/c9bF1b7y9+VDCU1K4TxP0zYy8R2upIh6tIm0Ejd3Wqq1d1VCI6pkdOHBAa7ZUVFREcXGxVqrfoUMHBg0axI033sjgwYMZOHBgg/bGhw4dYunSpaxYsYITJ05w7rnnMnfuXK6//nrDJ6NDw3S+UP6eCQkJzRoucvDgQaqrqz28ePX/27ZtCzgqMD4+PujmZRkZGREdvtCUUIkvb76xfkiBkMLdBlFPDqfTqcWlA43WUn8+HdH3vq6urm7QZ7q0tBQQgtK/f38mT55MVlYWWVlZnHnmmQ1un1WP68cff2TRokWsXbsWRVG4+uqrmTp1qsdk9GALVYLNzFA91EgssplMp4aLdOnSxSMO7ivk4XQ6qayspKKiokHuvP6xffv2gH1t2rdv30DMfTUv69ChQ0QLg3x581K4T2NUb1nN8rDb7Q2aGEVzyCMSKIpCXV0dW7Zs0WYfbtiwgZ07d2rHrVevXlx66aVaL49+/frhdDp9LszpMxZWr15Nfn4+W7ZsoX379sycOZMpU6bQtWtXj/3r/1X/73277f1/78+gRxVsvT2RQLXLO/NGjf/qQx7BTsFRFIXq6upGm5ft2rWL7777juPHj/s8ZrGxsQ26UwYaNhKJvjbBIoW7lRFotJZ669bac1TDweHDhykoKOCHH36gqKiITZs2af1MUlNTyczMZOTIkVpsOjU1tcE2/LUTOHr0KEuWLGHp0qXaKLC5c+cyevTogELf1PQ37wXYmJgY6urqSEhICGkRtrkEurjEx8d7DIYOBZPJRHJyMsnJyfTq1avR97tcLi02rxf5vXv3UlNTowl+SUkJx44d85s/n5SU5Lf9sHeb4vT09Ba9a5XCHcWoCx+BponrR2tJBDU1NWzatImCggIKCwspLCzURmvFxcXRv39/xo4dq3nTPXv2bJaAbdiwQRsF5nK5uOqqq5g+fTq/+MUvDC1k0Yca1EwStaDEbrcHDEsEWoSFwLfr/lLmfKWD6rMpHA5HWOL3waLmxXfs2NHjeX9956urqz2mRvkK2ezZs4cff/wx4KhAtUtlMJOkQk2plMIdJahfJu8FRP2XJNzTxFsjbrebn376icLCQgoKCigqKmLr1q3arXq3bt3Iysri1ltv1fpMhyIqDodDGwW2fv16kpOTmTp1KlOnTg1q1FxT0ItwTExMUKPAmrtg5iutUt/PRp+lohdpfYzfV/plazhXmzIqUO1c6WvYt/6xYcMGSktL/fa1sVqtZGRksHLlymbZLIU7QniXird0znRrpbS0lMLCQoqKirR/1dFa7dq1IzMzkzvvvJNBgwYxePBgOnbsaEhVYGlpKYsWLWLFihUcPXqUnj178vjjj3PTTTf5HZTdXPQecTh7X3vnrOu9evVCoU5tUePT6nvUqkVffW3U6trGwjbeOdat4VxXJ2tlZGRw7rnnNvr+urq6Bh68vrdNc5HC3QLoT/SKigptAVFFLiD6xm63s2XLFs2bLiws9Bit1b9/f0aPHk1OTg5ZWVmcddZZmuAZlVmxZcsWFi5cyOrVq7VRYH/+85+5/PLLDS0T9w6H6EeBGY0quP5Ga6lTbQJ9vkCef1lZGV27dvXZ10bfxlfv1Rs3XKQc+AbYDcQBuUAOkZK6hIQEunbt6rE4reJ2u7UQXlORwh0G/JWJu91ubDabFGkfKIrC3r17NYH+3//+x7Zt27TRWl26dCEnJ0dLqcvMzCQxMdFjMrqiKIYIttPpZO3atSxcuJAff/wRq9XKhAkTGD9+PAMHDjTi42p4h0OMHgWm95BVTCZTgz7T4UghVIW/pUYFKkopJtMHmEx1gBuTyYbJ9AUm0wZMptsM/WyRplHhrq+v5+GHH+bAgQM4HA7uuusuhg0b1hK2tQrUnOlgysQbK7k9nThx4gTr16/38KbVW0er1cqAAQOYNWsWubm55OTk0KVLF+131bUAfd8JI0IJ5eXlLF++nMWLF3Po0CG6devGww8/zPjx40lJSfE7Vac5GB8OsQNFKMpPuFzJKMoAoDtgishoraaifj9CGxX4G1yuMlyuVFyuFFyuFNzuZJxOE7AJOMtni93gvfnooVHhXrVqFampqfzlL3+hoqKCG2+88bQVbn2ZuH4BUUWGPHzjdDopLi7WBLqoqIidO3dqr/ft25drr72WnJwccnJy6NevHxUVFQ0yAFwul9Y2FjAk1Qxg+/bt5Ofn8+6771JXV8dFF13E448/zlVXXWVobNlXOCSU0VfqturrjwPvAS7M5iri4wtISHiDuLgziYt7DbM5chke4cK7iAx2AWuBhhdXRQFFGUxl5VI6derUrFGBYju+h4tE4rveqHCPGDGC4cOHA2i3c6cLgcrEIXBnvNMVRVE4ePCgloZXWFjIxo0btVzZjIwMcnJyuOmmm8jNzSUrK8vnaC399tRFXP3YrFCPucvl4rPPPmPhwoV8++23xMfHM3r0aG699dagFp2aghHhEO+0PhWTyUS7di9jsawlLu4wMTEVnNpsIjAP+I2RHydKqcKfnJlMYDKVaSGixvD25v0twjZ3VKARemFSgqwDtdls3HXXXUyYMIFf/vKXHq8VFBRErNWjP9TChGDxLnTw9X8I7aA7nc6Qp5WEg1DsqqmpYfPmzVp3vE2bNnH06FFAhADOO+88LcNj0KBBQY/WUnsmG01lZSUrV65k6dKl7N+/n86dOzNx4kTGjh3rs+hGjzoDM1SaWnjj/Tv6/7tcBxkwYBRmswNf2O092b17TTMtbR5N/e4ZgclUxznnXILZ3LCYxu02U1V1Hbt3P2W4XYHkM5jX7HY7ubm5Td5vUN/WQ4cO8etf/5pbbrmlgWir9O/fv8k7DyfFxcV+bfIuE1dT8rxzpo2+DfJXABBpgrXL5XKxY8cOLRWvsLCQbdu2aXchPXv25LLLLiM7O5ucnBwGDBjQ5JxpNRxis9mIj483bNHsp59+YvHixbz99tvU1NRw/vnn8+CDD3LNNdcEfdFSR5c1hr6zYDCTwH1leai/m5CQoHnovi5kP/2062QoxLdwx8fbWvy7Gei7F17+APwF73CJ2ZxA+/bPkZBgirhOeS/Cbtq0qVnbafSMLS0tZcaMGTz22GNcfPHFzdpJpAlUJg4yZ9ofR48e9Qh5rF+/XpuB2L59e3JychgxYgQ5OTlkZ2eTkZHR7H2pISn9oIJQPW63282XX37JwoUL+eqrr7BYLIwaNYpbb701rNkhamqdr3CIv5BHXFwciYmJWtw72POxvr4b0HBC/SmymveBWiWPAW7grwhpcwEZwGLgPKA4cqadpKmLsP5oVLhfeeUVKisreemll3jppZcAmDdvXovfCgWLGhNVc6b1o7VAlon7o7a2ls2bN3uUie/fvx8QObvnnXceY8eOJScnh9zcXHr37h3yyacoisfdjlHxa5vNxjvvvMOiRYsoKSmhY8eO3HvvvUyaNMnwOx59dkhsbKzWo8P7dV/TxC0WS1A504H3bwXuAl6m4cJcIkLMThfMwFPAHEQWSTIwAGh7Dlmjwv3II4/wyCOPtIQtTSZQmbiaMy3LxBuiKAq7d+/2yJnesWOHdoHr2rUrOTk5zJo1i5ycHAYNGmRopzR9Zg4Ylx2yZ88eFi9ezIoVK7DZbGRmZvK3v/2NESNGGN63WR8OUb1ktfzbu7gq/DnTzwLHgaWIohMTwtt8CbjMwP20FpKAiyJtRFiJvpWyADSlTFzmTJ/i+PHjHnHpoqIiKioqANGnYcCAAfzqV7/S0vE6deoUFjvUNQUjs0MURWHdunXk5+fz6aefEhMTw8iRI7XeJEajL/RRhyLDqTamkcmZjgUWAE8jqgatwDCExy1pi0StcHvnTNfV1cky8SBwOBxs3bpVa7hUWFjI7t27AXFhO/fccxk1apQm0ueccw7l5eVhWzRVPVC73W5oOKS2tpaVK1eSn5/Pzp07SU9P51e/+hWTJ082fBSYvvJQdQjUlqXx8fGaSIeSk20MXYEJEdy/pKWIGuFWszxUkW7KNPHTFUVR2Ldvn+ZFFxQUsGnTJm1aSKdOncjNzWXSpElkZ2eTlZVleEMkf6ijwNShDkaFQw4ePMjrr7/Om2++SUVFBeeddx7PPfccv/zlLw1rJaqed/rYdFxcHA6Hgw4dOng0XZLnoyQSRES4VS+ssdFa8kvhSVVVVYMycXW0VkJCApmZmdx2223aAqI6Wqsl8Q6HGLGCrihiFFh+fj7//e9/URSFa6+9lltvvZXzzz/fkHCLdw9rk0mUirdr14527dphsVjYtm0b7du3D2lfXntGxKX/BhwCBgEPc3rGpSVNIezCLcvEm4fT6WT79u1aXLqgoMBjtFafPn248sorNZHu379/xGL66oVYrSw1Khxit9t57733WLRokTYKbNasWUyZMiWoAbb+bNWLNOBhr9lsxmq1kpKSQnx8fBjPSQWYDrwDVJ987gDwJULIbw/TfiVtAcOFW58zrXrTsky8cQ4fPuzhSW/YsEFrapSWlkZ2drbWwjQ7O7vRKr+WQA2H6LNDjKh2PHLkCAsXLuStt97i+PHj9O3blz/96U+MHj26Sdkt3oMCVPTzD9XPYTabSUxMJDk5uYUugN/gKdoqNcC9wM2Akd69pC1hmHCXl5f7HK3l2QhGAmJU0saNGz2yPPSjtQYOHMikSZO0BcTmjtYKF/rJ6GBc7+uioiLy8/P54IMPcLlcDBs2jOnTp3PxxRcHXSbuLdKqo6Ceh/osELfbTUxMDGlpaVitdcTELACKgN7ATMDYiTaeLMRXQyRBDLAamBzG/UtaM4YJd3V1tcyZ9oHb7WbXrl0UFBSwbt06iouLKS4u1rIUunfvzpAhQzSRHjBgQFQWN4UrHOJwOPjggw/Iz89nw4YNJCcnM23aNMaNGxew2ZN+AVFF7yj4GlSrH2hhsVho164dCQkJmEzrgJGAE6gFLMALiKKW6SF9Pv+cQIRLfOECbGHarxM4BqQg8p0lrRHDhFsKtkA/WqugoID169dro7WSk5PJycnh7rvvJjs7m+zs7AYDTaMNfYGTkb2vS0tLWbp0KUuWLOHYsWP06tWLJ554gptuuomkpCSP3tfeIQ+10EXNpVaF2l9vGbd7Oy7XRqCCpKTtJCX1Iy7uXkwmK6KP9fWI7nIq6hrMncBQoGfIn7chI4APaBgqUTF6gdINPH/yUYe4OIxEXJy6BPg9STQSNemArZG6uroGo7X27t0LiDhq//79ufHGGzVvOjU1NWzFLUbjcrk8Ql9GpfNt3ryZhQsX8v777+NwOLj88st57rnnuOyyy7Ttq0LtPbXFO+QRyFE4VWq+CbP5a1JSPiMpaQMxMTYgAZHNcQXwP/yHLNxAHjA35M/dkEmIcnRVRFUSEBeL8wze3++BV/H8rO8DFyJ6eLRMmqjEGKRwB4miKPz8888eWR5btmzRioLOPPNMsrOzufXWW7Uy8aQkz1tRNXUvWlH7vKhNuIwKhzidTv773/+ycOFCrQXwzTffzLRp0+jVq5e2b71QA1rfD9WGYOPcajgkLs5NaupDWK2bMJn0fZPrgC0IwfLdNF/gAH5q4qcNlkTgO+AW4EcgHuH9TwBeMXhfpQjPus7reSeiVD4f+LXB+5SEEyncfqioqGhQJq4frZWZmcntt9+uedP60VqtDUVRPPq8GDkK7M0332Tx4sUcPnyYs846i4cffphx48ZphUBqRkdsbKzWbMlsNuNwOJpUUKM2ugd02SHvYzL9hBAon7/VyFatQHbQNjSds4CvgP3AYeBsIC0M+/kCEbf3Fm4QHvhbSOFuXUjhRrQULS4u1jzpoqIidu3aBYhb9HPOOYfhw4drIn3uuedG5UCEpqL2vgaRM23kKLCFCxfy7rvvYrfbufjii3niiScYOnSoR7hDH5tuDvrOe2azmZSUFBITE3V/Gyf+FwCDIQaYEcLvB0u3k49w0Vh6o7ENuCThp/WrTxNRFIUDBw54eNL60VodOnQgNzeX8ePHk5OTQ1ZWFu3atYuw1cbhaxQYhN772ul08umnn5Kfn893331HQkICN954I9OnT2fAgAFNDnkE8xlApE+2b98eq9Xq46JzBdBwOGxgYhBhDDOwCoi+wRdNZxj+7zqSEGmHzyPCKRVADvA4cHlLGCdpBm1euG02G+vXr9fCHgUFBdporfj4eAYNGsTUqVM1b/qss85qk9kxvrJDQolfqxkeJ06cYMWKFSxZsoT9+/dz5pln8vDDD2u9r408lvpwiNVqJTk5GYvFEmAfHYFfIWLGwUxoT0BULF4C3HDy57ZAEmIyzAN4HocEoD9CsDcgUiEBPkXE3/+NzCWPTtqUcKujtQoLC1m3bh1bt25l+/btWq5v7969ufzyyz1GaxndpznaMGIyuq/Clt27d7N48WLeeecdamtrGTJkCI8//jgjR440NIykD4eIwbjtSEpKasI+XkCkuz2LyI1WEFkc3lNjYoE+wN9pi433xQWsB8KT3oKoyrzz5HO/5ZRoq9QgBjSMQyycSqKJVi3cR44caTBaq7pa5MWmpKSQm5vLddddp5WJp6enR9jiliGUyeiBysRNJhNfffUVCxYs4IsvvsBisTBmzBhmzZrFoEGDwvIZFEUhNjY2QDikMUyIVLj7ERkUycAPCI/ahQghxCAE7CPapmirjDr50DMM/7nkJsTC5rXhNErSDFqNcNfW1nqUiRcWFnLgwAFAFP8MGDCACRMmaCGPlJSUqC9uMZrmjAILJme6pqaGpUuXsmDBAnbv3s0ZZ5zBnDlzmDJliuHHWBVsdbKM2pnPZKoAtgGdEX2nm4qZU/Hqy4AjwIeIrnwDgV/QtkXbHw2nonviKxNFEmmiUrjdbje7d+/2yPLYunWrtiDVrVs3cnNztXS8gQMHNmg+FO0500YS7Cgwf2Xi4DtnuqSkhAULFrB06VJsNhs5OTm89NJLXH/99YaHmNTeIWo4pLKy8uRwh1rgNuBNRPaDA7gAWIJIp2suFoTX3RbYCqxAhDeuPvnQX4RciBCRr6/7aGA9vgXcgbigSaKNqBDusrIybfGwqKjIY7RWcnIyWVlZLTJaq7XR2CgwfWzaV5m4KvB2u13LmVYUha+++op58+bx8ccfExMTww033KDNnzQSfbGMdzhEbbolYqyfIjw/1ftbh5gpuJPTezyXwhlnPA2sRGTPOBFzJs9FHLO9wH3AZwjhvhAxAV0vxrNPPmfHM689EdFoqy1k1bQ9Wly4HQ4Hmzdv9gh5/Pzzz4DwFPv168f111+viXTfvn1ld0Ed/kaBqa95e9PBlolXV1fz9ttvk5eXx44dO8jIyODee+9l+vTpdO7c2fDPoN49JSQkkJyc7Kf39VaE6HjfrruASkTZ+kxDbWtdLCU1dSWe3rIN2IyowPwaz/j1d8A1iFj+pSefSwe+R9zVfMepYcP3c3pNiG9dhFW4FUVh7969HiK9adMm7Zb+jDPOIDc3l8mTJ5Obm0tmZmaDMnGJQA2H1NfXawUnqhethkD0faaDzZnet28fL730Ev/5z3+oqKhg0KBB/OMf/2D06NGGdylUhz2bTCaSk5NJSkpqpPf11wFeswFrOL2F+1nMZl8hDjtCnH1RA9wDFOie6wV8jiiNLwe6IzNJohtDhbuystJjtFZRUZEWa7ZarQwePJiZM2dq3nQkRmu1NtTxbt69r9WydH2ZeLDHUlEUvvvuO/Ly8vjggw8wmUxcd911zJo1iwsvvNDQv4k+HKL2vk5MTAwyOyQJkfHhCxOQapidrZN9zfy9TYhuiN6FZR2QoZHWgWHCffnll7Nr1y7N++vbty9XXXWVNlqrX79+ERut1ZpQhU4/aFedLK4PezRHXOvq6vjPf/7D/Pnz2bx5M2lpafz617/mhhtuCEs6nyrYanZI00eBXU/DfGuVRMLXK7u10ANR6dhUQmkDIIkGghbuDRs28MILL7B48WKfr/fo0YMxY8ZoOdPGDlVtm6hhDrfbrf3rdDpxuVzExMRgtVq1PueheMGHDh0iPz+fRYsWcfz4cfr168cLL7zATTfdRGJioqEZOPrqxqSkpBBHgbVHFMTch2fFXxIiG+J0H6r7EG73bX7CJYHIpKG3HSpliArMt+nZ0wncgYiby9BnOAhKuOfNm8eqVasCzvtbvHixDHs0guqF6hcQ1XCHvvdGYmKiIf08CgoKyMvLY/Xq1bhcLkaMGMHMmTO55JJLwhIOcbvdxMbGkpqaitVqNWhR+XagH/AnxKJbF4SQT6Jl865PkJS0DhEHvojGGzc1FwUR2/8ZUcl5Ef4/5wTKyz8kI+NNREZJMH1ZEoF/GGCnnn3A+YgF4zqETMwB/oVY8JROnNEEJdzdu3fnn//8J3/4wx/CbU+bQV+qraKGPBISEoiNjaW+vp7q6mrq6+sxmUyN9N0IDofDwapVq8jLy2P9+vWkpKQwa9YsbrvtNnr0MHaGol6w1XCIGAVmtKBeTuQaHinAI8D/0bVrLKKQJwYxlGC8wfvajphKc0z33JmISTm9fbzfxNGjfyAj44/A24h5mavwX1RzEWKC/EWGWSy4C+Fx68NaNUAJYgjFXwzen8Sk6OuaA7B//37uv/9+li9f3uC1goKCZsQvw4vT6Wyx1qu+DqH+WKj/V/Ol9WXl3u9tLqWlpSxfvpzly5dTWlpKr169mDx5MjfccAOJiYFznZt6rLzt9v7XCOrq6qJi9mZGxjw6dHilQTjC7U5g79551NbmGrIfk8lOnz7DiIkpx2Q6dT4pihmnsyO7dq3Fl5/leZxcdO9+G1brZszmUymUbncCR4/+mvJy4zNwTKYazjnnYsxm396+09menTu/NXy/zSFazik9NTU15OY2/RwyTNmM7gQXKqWlpScr74zHlzcdExNDfHw8FosFi8WiZXuo73c4HOzatYuUlBTAuBmdGzZs0EJZDoeDYcOGMWvWLIYOHRp0X49gjpV372s1nS9cOfbFxcX0798/LNsOnnpgAb48WLO5jp49FwJTDNrX64hKRU8nwGRyExdXQ//+PwE3NvithsfpG0RBzb8QvVn6YTY/TufOozE4Hf8kRxF3Ib6Jja2Ngr+jIDrOKU8KCgoaf5MPoqJyMppRFxC9e3lYLBZNqOPi4nym47ndbmpra6mqqtIyRIwQ7Pr6etasWUNeXh4//PADSUlJTJ06lRkzZnD22WeHtG1vPEeBxWnx62i6SIePffjvYw2iWZVRFOB/snsVoiz9xiC2YwEeOvloCTog0jKP+Hk9q4XsOL2Qwu2F6lXqwx+xsbEkJSURHx+vpeUFEi6n00l1dTU2m82jzDzU7JCysjKWLFnCwoULOXjwID169OCpp55i4sSJmidvFL5HgcWdJoKtkkLgBT8jj3kXTs2d9MYKRGubBzOiVezvadjzPBF4upnbLQNeBJad/HkConBI5plDE4S7W7duPuPbrRl9Kp4q1GazmYSEBE2kVW86mG05HA5sNps2TUetYAyVrVu3kpeXxzvvvENdXR2XX345zz77LMOGDTM0VOFrFFg4wyHRTwdgCCL84D2fUh26YBRTEALoj5sN3JfR3ImouPwTEHsynTUW+CfNawl7CMhFhHrUC9lfgHmIO5PmdIdsW5w2Hrd3zjScyvKIj48nPj5eK3BpilepD4eo5dxGhENcLhcfffQReXl5rFu3DqvVyoQJE5gxYwb9+vULadveqP1PoLFRYKcjCxBZGNWc6pmSiGjk9DsD93MmokHUrznVMCoO8RVdAGQYuC+jMQEPIzzi79i//wA9ekyk+bMsH0Bk1ujDVHZEKubvET1qTm/arHDrG/GrxMbGYrVatXS8UG79nU4nNTU12Gw2j0nloQp2RUUFb7zxBq+99hr79u2ja9euPProo9xyyy2kpRk7AVwNh5hMpiBHgZ2O9EE0u/oXdXVvkpCQhuiPMgXfo81ciOED+xAC3B24mOAKXm5DXCT+H6L3+EDgN0DfED9DS5EEDKOmppjmi7aCaFHra23BBbyDuPs5vZ2KNiHc/nKmVU9av4AY6n58hUOMKOXfsWMHeXl5rFixgtraWi666CKeeOIJhg8fHvZRYJWVlWRkRLNHF2k6AU9SUjKxkawEdbJOBae8czNCxB5EdNtr7KLYH5ERYjQHED3MjyDCPzcSndPd3QReV3CefESj7S1HqxNuXyEPELf4SUlJmkifasQfOm63m7q6OqqqqnA4HIZ51263m08++YS8vDy++OIL4uPjtVFgAwcONMR2leB6X0uazzHEGLAqr+fdCBF/HhEz/3UL2wViWPJ9CG/WjvD+7wO+wndhTySJAc5DzMX0xTmc7qINrUC4/eVMW61WjywPb2/aiNt9l8ulZYeo4RAjMiuqqqp48803mT9/PiUlJXTu3JkHH3yQKVOmGJ577t37+tQoMBkOMZZ5BE4drAGeRFQZtuRt/npEb219T/MqRMz+eoRARtu58CxiMdZXlsqfW96cKCSqhNtfyMNisZCQkKBleYQzy0FdqLPZbNTUiBPHqHDI3r17+fvf/86yZcuw2Wycf/75zJkzh1GjRhneOdF7FFjTJqNL/FODKDrpiGcDpW9ofH5jFcIzPyM8pvnkH4jCHm/ciAk5hYgMjmjiesRdwj2cuhjGIMr1b4yQTdFFxL7J+pCHKjAgFhATExO1LA+jKgyDsaeuro7Kykqtd4gR+1YUhS+//JJ58+bxySefEBsby+jRo5k5cybZ2dkGWX9qX6pgy+wQo6mhc+fHgPcRHrMLmIhIeUsGup183jttUI/75Htbku34b40bg+gnEm3CDTAV0UisCBHiySZ8jb1aHy0m3L68aX3OtHeZeEvhcrmoqamhqqrK0OyQ6upqVqxYwfz589m5cycdOnTgjjvu4K677uKMM4z1uIIfBSZpHgowgvbtv8fTe10KFAPfItqYvk7D23sVMyKnuaXbnJ4H/A/f4u0CfFXa1iHuDtLxP8iiJYhFDIaWeBMW4Q6lTLwlUMMh1dXVVFeLmXxGhkPUyegnTpxg8ODB/POf/+SGG26gqqrK0Bi2vroxuFFgkuaxDijEbPYOOdgRMeLPgSsROcZ/oWHIxAKkIfpVtzT3AG/Q0KYYxMKk/q6vDPgtIuUOREx5DiKvWt61RROGCbeasaBtuIll4i2BGg5Rs0OMDIesW7eO+fPn8+GHH2IymRg1ahSzZ8/m/PPP17ZfVeWdcdC8felHgaWmpjZhFJikeXyMf0/aBnyIEO4ngesQceUfT77WAZiMqLI0Ng8/OAYh0gt/hViErEVklaQDq3Xvq0WkCe7lVDqeHXgK2I8ICUmiBcOEOz4+3mMBMZqExOVyadWNasGJEYJdW1vLf/7zH/Ly8ti6dSvp6en85je/Yfr06XTtamxZrjGjwCTNIx7xVfGVXxyDZyHOEISHG03cBowC3kQsjuae/Fn/9X8TOEzDz1iDyJj5IxCW9oKSZmBoW9doQ80OMToccujQIRYuXMjixYs5fvw4/fv35//+7/8YM2ZMwClBzcHYUWCS5jEG4Xn6Em4Lxg9UCAedEGEQf6xApAj6wgJ8CtxitFGSZtLm8sMURcFut+N2uzly5Iih4ZAff/xRGwWmKArDhw9n1qxZ/OIXv2hFo8AkTedcYAZu9wKvgQqJiMwHY4ulIkNjzkCbk4pWTZv5a7jdbi07RF8hGKqg2u12bRTYhg0bSElJYfbs2a18FJik6fyTQ4fOomvXfEQcuBti0W5GZM0yjCnAJ/juCV5P87r8ScJFqxduNTvEZhMnnBoOCbX39dGjR7XJ6MeOHaNv3748++yzjB8/nqQkY1O6vMMhanaIFOxowkRl5Q107Ton0oaEiRuBF4ANeFZZJiLazaa2vEkSv7RK4VbDITabjbo6cZIZlbWyfv168vLyePfdd6mvr2fYsGHMnj2boUOHGh4Okb2vJdFDLPAZoqT8FUSjrHOAJ4BxEbNK4ptWJdzeo8CMKpapr6/n/fffJy8vjx9//JHk5GSmT5/OjBkz6N3b2CY8p/coMEl0Y0Uswj4VaUMkjdAqhNvfKLBQKS0t1UaBHTp0iF69ejF37lxuvvlm2rULpn9y8KjdDJ1O52k8CkwikRhB1Ap3OEeBbdmyRRsFZrfbGTp0KM8//zzDhg0zNP/cOxxiMpno0qWLDIdIJAE5CryFGId2EXAV0dfBMLJEnXCHaxSY0+nURoF9++23WK1Wbr75ZmbOnMm5555rkPUC/fQdi8VCamoqCQkJnDhxQoq2RBIQtXe4CVG5mQj0QMTfO0bQrugiaoRbDYdUV1cbPgpsyZIlvPbaa+zfv59u3brx2GOPccstt5CammqM8SfRZ4fIUWASSVP5HjHHU5/VYgN2IIqcPm9BWxREhs0exCJtoMlHLU9EhTuco8B++uknnn/+eW0U2MUXX8xTTz3FtddeG7ZRYGazWfa+lkiazQv47mlejxD13bTMxJ6fECPo9iBaGjgRPV9WEi1l/xFRF+9RYEaFQ9xuNx9//DF5eXl8+eWXxMfHc9NNNzFr1iwGDBhgkPUC7+wQ2ftaIgmVrQhP1xfxwC7CL9x1wCWIni763uoFiEZiW4iGToktKtzhHAW2bNky5s+fz88//0yXLl24++67uf3228M6CkyGQyQSIzkbId6+cCBi3eFG7dniPRDDieiS+ClwdQvYEZhGhdvtdvPEE0+wfft2LBYLc+fObVKpdzhHge3evZv58+ezbNkyqqurueCCC3jwwQcZNWoUJ06cMFS05SgwyemFG/gv8BqiGOd6YDqQEsZ93o8ou/duoRsDDED0jAk3X+O77B9EGOdHWoVwf/zxxzgcDt58803Wr1/Ps88+y8svN94QPpyjwD7//HPy8vL45JNPiIuLC/soMF+T0SWStosLGItn75KvgbnAd0CvMO33CuAh4E8nbahH9A5P5dRwh3BzBqLhlq9OkPGIPuaRp1HhLigo4LLLLgMgKyuLzZs3B3y/r3BIOEaBdezYkd/97ndMnz6dTp06hbRtb+RkdMnpzSLE8Ah9m9caRPx3MmIiULh4BDHL83VEnPlyRFvd0AvugmMaYoqRL+F2Ey3l/40Kt81mIzn51IDTmJgYnE5ngzCBw+EIyyiwPXv2aKPAKisrPUaBxcfHh7x9PXIUmKRpKCcfbe0O7B/47s3tRgzv3Y/ojhgu+iB6pEQCdd9PIUIjCiJUY0HkmLcSjzs5OVkTY0DrEe3Njh07tP8bEQ754YcfeP311/nss88wm81cc801TJ48maysLEwmE1VVVQFHgTmdTkpLS4Pal7fd6vaNpq6ujuLiYsO3GyrRaFc022Sx/EynTs+TnPwV4Ka2djBHjz5AbW1OxGwykj59DuLPX3G5Ytmz53vsdv/fj2j820FT7LoBq7U7aWmLsVj2YLefw/Hj07Db+yGGQ0eeRoU7JyeHzz77jOuuu47169dzzjnn+Hxfhw4dDBkF9s4775CXl0dxcTHp6encfffdTJ8+nTPPPLNJ2yotLQ24OKkPh7TUKLDi4mL694+uRH6ITrui1yYLYnhCJWrqWmLiBnr2nA28j0gZa2mbjD5O5wMf+HwlJsZJ797XEGiRMhr/dtBUu/oj/s5gtYLBtXoaBQUFzfq9RoX7mmuu4ZtvvmHixIkoisIzzzzTrB0F4sCBAyxcuJDXX3+d8vJyzjvvPDkKTBKlPAZU0TDfuBb4DSLPt7XzR+ALGmZ3WIGphDezRBIMjQq32WzmqaeMb/OohkPmzZvHmjVrUBSFESNGMGvWLC6++OKwjALTT0aXo8AkzWM1DXN8VXYhFtRae0+NS4CXgbsQEuFG5DFfD7wYQbskKi2eiGy323n33XfJy8tj48aNtG/fnttvv53bbruN7t27G7ovvWBbLBZSUlLkZHRJiPir7APRGCnQ662JaYiUwP8iUgIvJXxpgJKm0mLCfeTIEW0UWGlpKX379uW5555j3LhxYRkFpop2YmKiHAUmMZBRwHJ8e929ENPU2wpJiFQ8SbQRduEuKioiLy+PVatW4XQ6ufrqq5k9ezaXXXZZWEeBmUwmOnfuLMMhEoN5ClhDwzi3FfhnRCySnH6ERbjVUWDz5s2joKCA5ORkbr31VmbMmEGvXsbebnmHQ9TJ6LL3tSQ89EVUD96HKFJRgBxEZ7uhEbRLcjphqHCXlpby+uuvs3DhQg4fPkzv3r2ZO3cuEydO9CjiMQJ9dogcBSZpWfoDHyIW7Ny0XFWfRCIwTLjvvfdeVq5cid1u58orr+SFF17gqquuCusoMDkZXRJZZJMxSWQw7Mx77733mDRpEjNmzPBbpNNc1HCI2+32GAUmmz1JJJLTEcOEu7CwUI4Ck0gkkhbAMOFu3769IdvxFQ5JTEyUva8lEonkJFGjhnIUmEQikQRHxIVbhkMkEomkaURMuOUoMIlEImkeLaqUchSYRCKRhE6LCLccBSaRSCTGEVbhVuPXMhwikUgkxmG4ivrqfZ2YmCjDIRKJRGIQhgm3dzgkOTlZ9r6WSCSSMGCYcLtcLjkKTCKRSFoAw4S7S5custmTRCKRtACGBZ6laEskEknLIFcMJRKJpJUhhVsikUhaGVK4JRKJpJUhhVsikUhaGVK4JRKJpJUhhVsikUhaGVK4JRKJpJVhUhRFCXUjBQUFRtgikUgkpx25ublN/h1DhFsikUgkLYcMlUgkEkkrQwq3RCKRtDJCajK1du1aPvzwQ/761782eG358uUsW7aM2NhY7rrrLq688spQdtUodXV1PPDAA5SVlZGUlMRzzz1Henq6x3vuuusuysvLiYuLIz4+nry8vLDY4na7eeKJJ9i+fTsWi4W5c+fSo0cP7fWWPjbB2DR37lwKCwtJSkoC4KWXXqJdu3Zhtwtgw4YNvPDCCyxevNjj+U8//ZR//etfxMbGMnbsWCZMmNAi9gSyaeHChaxYsUI7t5588kl69+4ddnvq6+t5+OGHOXDgAA6Hg7vuuothw4Zpr0fiWDVmUySOlcvl4pFHHqGkpASTycSTTz7JOeeco70eiePUmE3NOk5KM3n66aeV4cOHK/fee2+D144ePapcf/31it1uVyorK7X/h5MFCxYoL774oqIoirJ69Wrl6aefbvCekSNHKm63O6x2KIqifPTRR8qcOXMURVGUoqIi5c4779Rei8SxacwmRVGUiRMnKmVlZWG3w5t///vfyvXXX6+MHz/e43mHw6FcffXVSkVFhWK325WbbrpJOXbsWERtUhRF+d3vfqds2rSpRezQ89Zbbylz585VFEVRysvLlaFDh2qvRepYBbJJUSJzrNauXas8+OCDiqIoynfffedxnkfqOAWySVGad5yaHSrJycnhiSee8Pnaxo0byc7OxmKx0K5dO7p37862bduau6ugKCgo4LLLLgPg8ssv59tvv/V4vbS0lMrKSu68804mTZrEZ5991iK2ZGVlsXnzZu21SBybxmxyu93s2bOHxx57jIkTJ/LWW2+F3R6V7t27889//rPB8z/99BPdu3enffv2WCwWcnNz+eGHHyJqE8CWLVv497//zaRJk3j11VdbxB6AESNGcM899wBo06VUInWsAtkEkTlWV199NU8//TQABw8eJCUlRXstUscpkE3QvOPUaKhkxYoV5Ofnezz3zDPPcN111/H999/7/B2bzeZxm52UlITNZgvKoGDwZVNGRoa2z6SkJKqqqjxer6+vZ8aMGUybNo0TJ04wadIkBg8eTEZGhmF2qdhsNpKTk7WfY2JicDqdxMbGhv3YNMemmpoapkyZwm233YbL5WLatGkMHDiQfv36hd2u4cOHs3//fp/2RuI4BbIJYNSoUdxyyy0kJyfzm9/8hs8++6xFQl1qCMtms3H33Xdz7733aq9F6lgFsgkid6xiY2OZM2cOa9eu5cUXX9Sej+Q55c8maN5xatTjHj9+PKtXr/Z4DB48OODvJCcnU11drf1cXV1taLzUl03t2rXT9lldXd3gqtahQwcmTpxIbGwsGRkZ9O/fn5KSEsNs0uP9+d1utzYkOdzHpjk2Wa1Wpk2bhtVqJTk5mYsuuqhF7gICEanjFAhFUZg+fTrp6elYLBaGDh3K1q1bW2z/hw4dYtq0aYwePZpf/vKX2vORPFb+bIr0sXruuef46KOPePTRR6mpqQEif075sqm5xyksWSWDBw+moKAAu91OVVUVP/30k0cwPhzk5OTwxRdfAPDll182SGpft26ddltXXV3Nzp07w7ZQkpOTw5dffgnA+vXrPT57JI5NYzb9/PPPTJo0CZfLRX19PYWFhQwYMCDsNgXi7LPPZs+ePVRUVOBwOPjxxx/Jzs6OqE02m43rr7+e6upqFEXh+++/Z+DAgS2y79LSUmbMmMEDDzzAuHHjPF6L1LEKZFOkjtXKlSu1cIPVasVkMmmDyiN1nALZ1NzjZOiU99dee43u3bszbNgwpk6dyi233IKiKNx3333Ex8cbuasGTJo0iTlz5jBp0iTi4uK0TJfnn3+eESNGMHToUL7++msmTJiA2Wzm/vvvb5B1YhTXXHMN33zzDRMnTkRRFJ555pmIHptgbBo9ejQTJkwgLi6O0aNH07dv37Db5Iv33nuPmpoabr75Zh588EFmzpyJoiiMHTuWM844I+I23XfffUybNg2LxcLFF1/M0KFDW8SGV155hcrKSl566SVeeuklQNx51tbWRuxYNWZTJI7Vtddey0MPPcTkyZNxOp08/PDDrF27NqLnVGM2Nec4ycpJiUQiaWXIAhyJRCJpZUjhlkgkklaGFG6JRCJpZUjhlkgkklaGFG6JRCJpZUjhlkgkklaGFG6JRCJpZUjhlkgkklbG/wcvaL9KkqA1agAAAABJRU5ErkJggg==\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"xfit = np.linspace(-1, 3.5)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\\n\",\n \"\\n\",\n \"for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:\\n\",\n \" yfit = m * xfit + b\\n\",\n \" plt.plot(xfit, yfit, '-k')\\n\",\n \" plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',\\n\",\n \" color='lightgray', alpha=0.5)\\n\",\n \"\\n\",\n \"plt.xlim(-1, 3.5);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The line that maximizes this margin is the one we will choose as the optimal model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Fitting a Support Vector Machine\\n\",\n \"\\n\",\n \"Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier (`SVC`) to train an SVM model on this data.\\n\",\n \"For the time being, we will use a linear kernel and set the ``C`` parameter to a very large number (we'll discuss the meaning of these in more depth momentarily):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SVC(C=10000000000.0, kernel='linear')\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.svm import SVC # \\\"Support vector classifier\\\"\\n\",\n \"model = SVC(kernel='linear', C=1E10)\\n\",\n \"model.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To better visualize what's happening here, let's create a quick convenience function that will plot SVM decision boundaries for us (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_svc_decision_function(model, ax=None, plot_support=True):\\n\",\n \" \\\"\\\"\\\"Plot the decision function for a 2D SVC\\\"\\\"\\\"\\n\",\n \" if ax is None:\\n\",\n \" ax = plt.gca()\\n\",\n \" xlim = ax.get_xlim()\\n\",\n \" ylim = ax.get_ylim()\\n\",\n \" \\n\",\n \" # create grid to evaluate model\\n\",\n \" x = np.linspace(xlim[0], xlim[1], 30)\\n\",\n \" y = np.linspace(ylim[0], ylim[1], 30)\\n\",\n \" Y, X = np.meshgrid(y, x)\\n\",\n \" xy = np.vstack([X.ravel(), Y.ravel()]).T\\n\",\n \" P = model.decision_function(xy).reshape(X.shape)\\n\",\n \" \\n\",\n \" # plot decision boundary and margins\\n\",\n \" ax.contour(X, Y, P, colors='k',\\n\",\n \" levels=[-1, 0, 1], alpha=0.5,\\n\",\n \" linestyles=['--', '-', '--'])\\n\",\n \" \\n\",\n \" # plot support vectors\\n\",\n \" if plot_support:\\n\",\n \" ax.scatter(model.support_vectors_[:, 0],\\n\",\n \" model.support_vectors_[:, 1],\\n\",\n \" s=300, linewidth=1, edgecolors='black',\\n\",\n \" facecolors='none');\\n\",\n \" ax.set_xlim(xlim)\\n\",\n \" ax.set_ylim(ylim)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\\n\",\n \"plot_svc_decision_function(model);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is the dividing line that maximizes the margin between the two sets of points.\\n\",\n \"Notice that a few of the training points just touch the margin: they are circled in the following figure.\\n\",\n \"These points are the pivotal elements of this fit; they are known as the *support vectors*, and give the algorithm its name.\\n\",\n \"In Scikit-Learn, the identities of these points are stored in the `support_vectors_` attribute of the classifier:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0.44359863, 3.11530945],\\n\",\n \" [2.33812285, 3.43116792],\\n\",\n \" [2.06156753, 1.96918596]])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.support_vectors_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A key to this classifier's success is that for the fit, only the positions of the support vectors matter; any points further from the margin that are on the correct side do not modify the fit.\\n\",\n \"Technically, this is because these points do not contribute to the loss function used to fit the model, so their position and number do not matter so long as they do not cross the margin.\\n\",\n \"\\n\",\n \"We can see this, for example, if we plot the model learned from the first 60 points and first 120 points of this dataset (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def plot_svm(N=10, ax=None):\\n\",\n \" X, y = make_blobs(n_samples=200, centers=2,\\n\",\n \" random_state=0, cluster_std=0.60)\\n\",\n \" X = X[:N]\\n\",\n \" y = y[:N]\\n\",\n \" model = SVC(kernel='linear', C=1E10)\\n\",\n \" model.fit(X, y)\\n\",\n \" \\n\",\n \" ax = ax or plt.gca()\\n\",\n \" ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\\n\",\n \" ax.set_xlim(-1, 4)\\n\",\n \" ax.set_ylim(-1, 6)\\n\",\n \" plot_svc_decision_function(model, ax)\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(1, 2, figsize=(16, 6))\\n\",\n \"fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\\n\",\n \"for axi, N in zip(ax, [60, 120]):\\n\",\n \" plot_svm(N, axi)\\n\",\n \" axi.set_title('N = {0}'.format(N))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the left panel, we see the model and the support vectors for 60 training points.\\n\",\n \"In the right panel, we have doubled the number of training points, but the model has not changed: the three support vectors in the left panel are the same as the support vectors in the right panel.\\n\",\n \"This insensitivity to the exact behavior of distant points is one of the strengths of the SVM model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you are running this notebook live, you can use IPython's interactive widgets to view this feature of the SVM model interactively:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"ba29c3a8344b463a8848a4a7bfec4da8\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"interactive(children=(IntSlider(value=10, description='N', max=200, min=10), Output()), _dom_classes=('widget-…\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from ipywidgets import interact, fixed\\n\",\n \"interact(plot_svm, N=(10, 200), ax=fixed(None));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Beyond Linear Boundaries: Kernel SVM\\n\",\n \"\\n\",\n \"Where SVM can become quite powerful is when it is combined with *kernels*.\\n\",\n \"We have seen a version of kernels before, in the basis function regressions of [In Depth: Linear Regression](05.06-Linear-Regression.ipynb).\\n\",\n \"There we projected our data into a higher-dimensional space defined by polynomials and Gaussian basis functions, and thereby were able to fit for nonlinear relationships with a linear classifier.\\n\",\n \"\\n\",\n \"In SVM models, we can use a version of the same idea.\\n\",\n \"To motivate the need for kernels, let's look at some data that is not linearly separable (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.datasets import make_circles\\n\",\n \"X, y = make_circles(100, factor=.1, noise=.1)\\n\",\n \"\\n\",\n \"clf = SVC(kernel='linear').fit(X, y)\\n\",\n \"\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\\n\",\n \"plot_svc_decision_function(clf, plot_support=False);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is clear that no linear discrimination will *ever* be able to separate this data.\\n\",\n \"But we can draw a lesson from the basis function regressions in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), and think about how we might project the data into a higher dimension such that a linear separator *would* be sufficient.\\n\",\n \"For example, one simple projection we could use would be to compute a *radial basis function* (RBF) centered on the middle clump:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"r = np.exp(-(X ** 2).sum(1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can visualize this extra data dimension using a three-dimensional plot, as seen in the following figure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from mpl_toolkits import mplot3d\\n\",\n \"\\n\",\n \"ax = plt.subplot(projection='3d')\\n\",\n \"ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='autumn')\\n\",\n \"ax.view_init(elev=20, azim=30)\\n\",\n \"ax.set_xlabel('x')\\n\",\n \"ax.set_ylabel('y')\\n\",\n \"ax.set_zlabel('r');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see that with this additional dimension, the data becomes trivially linearly separable, by drawing a separating plane at, say, *r*=0.7.\\n\",\n \"\\n\",\n \"In this case we had to choose and carefully tune our projection: if we had not centered our radial basis function in the right location, we would not have seen such clean, linearly separable results.\\n\",\n \"In general, the need to make such a choice is a problem: we would like to somehow automatically find the best basis functions to use.\\n\",\n \"\\n\",\n \"One strategy to this end is to compute a basis function centered at *every* point in the dataset, and let the SVM algorithm sift through the results.\\n\",\n \"This type of basis function transformation is known as a *kernel transformation*, as it is based on a similarity relationship (or kernel) between each pair of points.\\n\",\n \"\\n\",\n \"A potential problem with this strategy—projecting $N$ points into $N$ dimensions—is that it might become very computationally intensive as $N$ grows large.\\n\",\n \"However, because of a neat little procedure known as the [*kernel trick*](https://en.wikipedia.org/wiki/Kernel_trick), a fit on kernel-transformed data can be done implicitly—that is, without ever building the full $N$-dimensional representation of the kernel projection.\\n\",\n \"This kernel trick is built into the SVM, and is one of the reasons the method is so powerful.\\n\",\n \"\\n\",\n \"In Scikit-Learn, we can apply kernelized SVM simply by changing our linear kernel to an RBF kernel, using the `kernel` model hyperparameter:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SVC(C=1000000.0)\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"clf = SVC(kernel='rbf', C=1E6)\\n\",\n \"clf.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's use our previously defined function to visualize the fit and identify the support vectors (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\\n\",\n \"plot_svc_decision_function(clf)\\n\",\n \"plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\\n\",\n \" s=300, lw=1, facecolors='none');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using this kernelized support vector machine, we learn a suitable nonlinear decision boundary.\\n\",\n \"This kernel transformation strategy is used often in machine learning to turn fast linear methods into fast nonlinear methods, especially for models in which the kernel trick can be used.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tuning the SVM: Softening Margins\\n\",\n \"\\n\",\n \"Our discussion thus far has centered around very clean datasets, in which a perfect decision boundary exists.\\n\",\n \"But what if your data has some amount of overlap?\\n\",\n \"For example, you may have data like this (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X, y = make_blobs(n_samples=100, centers=2,\\n\",\n \" random_state=0, cluster_std=1.2)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To handle this case, the SVM implementation has a bit of a fudge factor that \\\"softens\\\" the margin: that is, it allows some of the points to creep into the margin if that allows a better fit.\\n\",\n \"The hardness of the margin is controlled by a tuning parameter, most often known as `C`.\\n\",\n \"For a very large `C`, the margin is hard, and points cannot lie in it.\\n\",\n \"For a smaller `C`, the margin is softer and can grow to encompass some points.\\n\",\n \"\\n\",\n \"The plot shown in the following figure gives a visual picture of how a changing `C` affects the final fit via the softening of the margin:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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27oVMn2yDPf//BgAGqCFfXru4bmxBCCHGnzp+HZs0gJgYSE0GrhXnzVOvMjz923Jpb5L5HHoGffoLk5JuX/fgjHDgAhw7ZdCwToiCTzAYh8qMNG2D6dHUQMhrVZYmJcPUq9Ozp3rEVJS+84DibJDkZnnpKtlQIIYQoWB5+GC5cUHMKAJNJHec+/1yy9lzl+HFYtco20AAq2+HiRbW4JEQhIcEGIfKjGTMgKcn+cpMJTp5UUW+R9zZvdn7dmTNw/brrxiKEEELcjbNnYdcuyMiwvy4pSWVTiry3fr3zDJLERPjhB9eOR4g8JMEGIfKj06edX+flpQoVirznmc1OM7NZ0hyFEEIUHOfPg4+P8+vPnnXdWIoyHx+1fcUZnc51YxEij0mwQYj8qE4d5wei1FRVqFDkvV69wMPD8XWNG0NAgGvHI4QQQuRUVJSaQzhTo4brxlKUdeniOLsEwN9f1XMQopCQYIMQ+dEzz4Cvr/3lXl7QtClUrOj6MRVFU6dCSIhthoNWq4IMs2a5b1xCCCHEnQoPV4WNHWU36HSqTpHIexER8Mor9hkMOh20aaN+hCgkJNggRH5Uty7MmQN+furE1ttbRbvvuQe++cbdoys6ypSBffvgscegWDEIDoaHHlJdKmrXdvfohBBCiDuzYIHKzPP3vzm38PWFd9+F++5z9+iKjhdfVO1H69dX87yKFeHtt2HlSukIIgoVaX0phNkMW7eqoknFikGPHuqk0t0GDoTu3dWBJyYGmjRR7arkIORapUqpwM+cOe4eiRBCiIIkLk4dw69fVyf4+eEYHhgImzapoPnmzerfPXtCaKh7x1UU9eihfoQoxCTYIIq2q1fh/vvhn39UyyFvbxg9Gj77TLWHcrfgYBgyxN2jKDjOnYOPPlKVnkNDYdQoePBBd49KCCFEUbNkCYwYoer+pKWpbZBVqsBvv+WPE/uGDdWPuDWzGX75RW2fvHwZWrSA8eOhQgV3j0yIfE+CDaJo69ULDh9WgQZQEwKA4cOhVi21bUEUDLt3Q9u2qviV5XPculUFk6ZMce/YhBBCFB3796tAQ3LyzcvS0lTb6t694Y8/3DY0cYfMZhg6FFasuNmS/MABmD9fBSDCw907PiHyOanZIIquf/5RWycsgYbMUlPhgw9cPyaRM2Yz9O0LCQk3Aw2gJga//UbgmjXuG5sQQoiiZdo022ORRXo67NgBJ0+6fkwiZ9assQ00gPock5LUvMNodN/YhCgAJNggiq5jx9S2CUeMRlUYUBQM+/dDdLTj65KSCPnqK9eORwghRNG1f7/zk1AfHzX/EAXDnDm2gYbMkpPR7d3r2vEIUcBIsEEUXaVLO58MaDSyF68guXbNtj1lFp7Xr7twMEIIIYq07OYP6elq/iEKhsuXnV+n0eARG+uyoQhREEmwQeSOtDTVkvGZZ+Cdd1ShvvyuTh3V2tBRZWg/P5gwweVDEjlUu7ba+uKIpyeGBg1cOx4hhBC547//4K234Nln4bvvICPD3SO6tQkTQKezv1yjgfLlpXVyQdKqlfMs2LQ0UmrUcO14hChgJNgg7t5//0FUFAwbpuocvPIKVK4MM2e6e2TZ02jghx8gLEz1mQa1Ou7nB889J/2mC5LwcOjXT312WXl7EzN0qOvHJIQQ4u68/z5UqwavvabqIAwZouYX58+7e2TZa90ann5aHZM8PNRl/v7qWPXDD24dmrhD48Y5Djb4+kKHDqSXKuX6MQlRgEg3CnH3unWDCxfAZFL/thRFmjQJ7r03f7dWqlIFTp+GZctg40aIiFBBE4lUFzyffqpWvL79Vu2JNZvVRG/pUtIiI909OiGEEHdiyxZ49VVISbl5WUICGAzQsyfs3Om+sd2ON96A/v1h4UK4ckUFIPr3d5zxIPKv0qVVu9LevdXvn0aj5rmdOsHixXDmjLtHKES+JsEGYSs5WW2H2LMHSpWCgQMhuxO1/ftVVWVLoCGzlBSYPl31ms7P/P3hscfUT3534gS89x5s2ABBQTBqFAwa5DzFryjx8VG/a++9p35/9Xpo1kytKh054u7RCSFE0Xb8OHz1FcTHqxPvLl1urvo7Mm2abetIC6NRtZA8dgyqVs2z4eaKmjVVdkZ+ZzLB8uXw8ceqRkHjxmrBqE4dd48sf7j3Xjh7Fv78U9WIqlMHypZ196iEKBAk2CBu+vtvNQFITobERHXy9uqrqhJv48aO7/Pvv84L85lMUnE5N23fDh06qCCOZc/q+PGwaBH8/rsEHCxKllQ/Qggh8ofnn4ePPlLFETMyYP589T29ebPz+/zzj8pQc8TbW80/8nuwoSAwm+Hhh+Hnn292XTh9Wm33WLoUund37/jyC61WLWAIIe6I1GwQiskEnTvD1asq0ACq4F5KCowejffp0zdve+GCShszm1WtBmfFmjw8ZDtCbjGb4ZFH1GeT+f02GGDvXhVwEEIIIfKbVatUDafk5JvHr4QEOHUKHn305u1SUlSmZFyc+nf16o4LOINKY4+KyttxFxVr19oGGkDNCQ0Gld1q2RorhBA5IMEGoWzaBDExjlcR0tMJWbpU3aZGDXWAr1YNypW7mcboKBXS2xueeirvx14UHDqk9nw6kpQEs2e7djxCCCHE7XjvPdsTWYv0dNi4Ec/z51UxxbAwqFsXihdXNRmGDXNc9NfTE+rVg0qV8nzoRcKnnzr+fCzWrXPdWIQQhY4EG4Ty77/O0xUzMvA9eFBlPhw5olYfkpPV/rVBg2DMGLV3LTBQ3d7PT1XpnTFDTQjE3YuPd75dBW6uBAkhhBD5SebMyKx8fIh86SW1XTMpSWXvpaaqlfYnnoApU9R8wtdX3T4gACpUUIWARe6IiXF+ndks8wshxF25q5oNPXv2JCAgAIDSpUvz1ltv5cqghBtUquQ8XdHLC88rV1RKXVYGg+p/ffw4rF4Nu3ap1YmHHlKrEyJ33HOP81RGT09o29a14xFCiDwic4tCpnJlOHfO8XUpKej++sv++JaergoVRkSorRXLl6uT4nvvVV0AtLJWlms6dIAdOxwX40xPV++5EELkUI6DDampqZjNZr788svcHI9wl+bNVXDAYLDvLOHlhefVq87ve+6cinx366Z+RO4LCoLRo9XqT9agj48PPPece8YlhBC5SOYWhdCkSepkNuuxy9tbbck8ftzx/RIT4ccfVb2iCRPyfJhF1uOPq60uKSm2Ga6+vvDAA1C+vNuGJoQo+HIcGj569CjJyckMGzaMQYMGsW/fvlwclnA5jQbWrFFtLv39b16m0UDfvpizS+E3m6UTgiu8+y6MHKkmAMHBKp20bFn1uVWu7O7RCSHEXZO5RSHUsSO8+KI6dnl5qcs0GrXlsl07zNllKeh0rhljURYaClu2qJpcOp2aX/j6Qt+++b91uRAi39OYzc426mfv2LFj7N+/n759+/Lvv/8yYsQIfv31VzxvnJTu2bMHnQsPEikpKfha9vSJHPM+cYLy/fqhSUtDeyPDweTri8nTE4/kZDRGo83tzUBy/fr8t3ixG0ZbeNzJ7682IQGfY8cwBQSQWrWq8+0vwkq+H/KWvL95KzffX4PBQIMGDXLlsfLCreYWIPOLgqrEa68RvHIl2vR0QM0fzD4+aDIy7OYWACY/P85+8gkGSeO/K3fy++t96hQeMTGkVayIsVixPB5Z4SDfD3lL3t+85Yr5RY63UVSoUIFy5cqh0WioUKECer2e6OhoIiMjrbepXr16Th/+jh05csSlz1doPf64Ks6UKQalTUkBLy80Pj5qX6WldZVWi8bfH93ChfLe36U7/v1t3PjWt8nIgK1bVYuxhg2hRImcD7CAk++HvCXvb97Kzfd3z549ufI4eeV25hYg84sC5+BB1QLzRqABQANoUlMxabVo/PxsawbodGjbtqXckCESUL9Ld/T7e7u3u3AB9uwBvR6aNXPckayIkO+HvCXvb95yxfwix9soVqxYwdtvvw3A5cuXSUxMJDw8PKcPJ/KDa9dg506HXSm06ekqbX/ECFUAUq9XRSB374batV0/VpG9tWvVlphu3dR+1/LlYehQm4meEELkNzK3KKQWLXJa5Njs46OOTw0bqvpEUVHwzjvw/fcSaMhvUlPVnCIqCh59FLp0gZIlYcMGd49MCJFP5TizoU+fPrzwwgs8/PDDaDQa3nzzTZs0x1yXmAg//ABXrqg+zK1by0EotyUmqs4GzroepKTArFnqR+Rfhw+rHuVZi3F9/bUqJjlnjnvGJYQQt+DyuQWoVfcNG9Q+9e7di3QWWJ6JiQEHWyUANOnpULEifPKJiwcl7tjjj8PKlWo+mJKiLktIgK5d4a+/oEoV945PCJHv5PgI7u3tzbRp03JzLM6tWgUPP6yCC2lpqhhh6dKwbp2KqIrcUaqUmmw5aHFpBjS3k7ov3O+dd9TqQ1bJyfDFF6pVaUiI68clhBC34NK5RUoK9O6tAg0mk0oFHz8eXnoJ/vc/14yhqGjTRrWvTEy0v87LC5o2df2YxJ25ckV9hpYgQ2ZpaTBtGsyd6/pxCSHytfzfqPjkSejfH5KS1EEqLU39+c8/Kn1L5B5PT3j5ZYfVn82+vjB5shsGJe7Y1q1OV5Dw8VGZD0IIUdQ99RSsX68CsampKtCekgJvvqlaLorc06eP6nKQdW+/tzepUVESbCgIDh5UcwhHMjJg82bXjkcIUSDk/2DDzJmO95kbjao3819/uX5Mhdn48WpFx99f7Z0MDITwcM6//75MBgqK0FDn16WnZ3+9EEIUBUlJKtPL0SqtwQBTp7p+TIWZry9s367qMvj53Wyv2L49Z+bNk22xBUGxYs4XMkDV8xJCiCzyf7Dhr7+cF7Xz8ICjR107nsJOo1H9sK9cgZ9/Vqs+Fy+S2Latu0cmbte4cSpY5EjZsrdfbVoIIQqr8+ezr6B//LjrxlJUlCkDf/6pVsh//BFOnICff8YUHOzukYnbUbeu84CCv7+aewghRBb5P9gQFQXabIZZqpTrxlKU6HTQooVahSjCLY0KpEcegVatbAMOPj4qU2XpUveNSwgh8ouIiOy780iRyLwTFQX33Sfzt4JGo1E1GwIDVe00C39/uP9+tVVGCCGyyP/BhrFjVaqdI8HB6oRYCHGTh4daNfr8c2jfXgWMnnkGjhxRKxNCCFHU6fXQqZMqTpiVvz88/bTLhyREvteoEfz9N0yYAA0aQMeO8OWX8O232S8MCiGKrDzuJ5UL6tdXhQlfekntFUtPVxMBb2+V5l9UvtzMZli7VrWGunQJmjeHJ5+EChXcPTKRH3l4qFUGWWkQQgjH5s+HZs3g4kVVeFqjUVl9XbvCY4+5e3Suc+2aaon800/q9T/2GPTt6zgQI0Tp0qrrlRBC3Ib8H2wAtSrbrRt89hlcuAD33gsDB6q08KLAbFa9jZcuVUWtAPbtg3nz1OSgdWt3jk7ciePH4ZtvVFGytm3VZyeFsYQQwvXCwlR3nu+/V4sX/v5qG1rTpkXne/nECTWnSkq6WSxzxw6YPRt+/9159wGRv6Snww8/wN69aovQww9D8eLuHpUQQhSQYANAlSpFN5K6bp1toAHUgSU9Xa0+XLpUcOsqGAyqv3lAgLtHkrsOHoTXXlP92319VXAsLk5VP8/IUD8zZqhijevW2d/fbIaNG9X+yIwM6N4dOncuuJ+zEELkR15e6jjat6+7R+IegwfD9evqOGyRlAR79sCsWao9aEFkMkF8vJpbeBacqe4tGY3w6adq/nDlClSrBsOHwyuvQEKC+vH1hRdeUAGjIUPsH+P6dVi0SAUmKlSAYcNU8WghhMgDRWQPQgE3d65toCGz1FT44w+XDidX7N2r6m0EB0NICNSpo07MC4MtW9RK0cqV6qB+8SJ88IGaIKSkqOABqLTdAwdg1Cjb+6enQ5cuKpV3zhyVwfLggyqTZ+pUFbQQQggh7salSyqokDnQYJGcrIINBY3RqI6TYWFqZT8oCEaOVCfhBZ3ZDL17w7PPqizJ2FjV3WPECDXPsLzGlBT1M3asqq+Q2Z9/QrlyquvYokXwxhtQvrzaslwQ55JCiHxPgg0FwaVLzq/TaNR+y4Lk4EFViXrr1pur/AcOqBPs9evdPbq7YzarVQKDQf3dIiPD9t8Wqanw3XdoExNvXjZtmjroJyXdvI/JpB7z1VehVq3sfydux4ULqq2sBC6EEKJoun7dtquAo+sLmhEj4M031djT0lTQ5IsvoGXLm4H+gmrDBrW1xWCwvdxsdjy/SEuDjz+2/XeXLiooYXkMk0nd96+/VPbkK6/c3RgzMtQc7+hRx2MSQhQ5EmwoCO67z/m+ybQ0FZEuSF54wf5gCWpSMGGCy4eTq/79F86du7P7eHnheeWKOjBfuaLSI5OTHd/WaFQrGDlNbT17VrXFjIpS9SJKlFApmJa9ukIIIYqGChUcZzVYNGjgurHkhtOn1ZbTrPOL1FQ4eVLVuCrIFi92nuXqSEaGqkkCak6xZEn27V5TUuD99+2zIW7X/PmqXkSzZqoLVvnyqrC5EKJIk2BDQTBmjOOq0D4+6oSxUiWXD+mu/P6784j30aMFe7U9JeXO6yqkp6PbsUNVeC5bFi5fzv72RiN89132kwZHkpLU9o6tW9U44+PVn199Bf3739ljCSGEKNh8fVVXK53O/jqdTmXSFSRr1zov7JmYCCtWuHY8ue1OAg2g5iIVKlDqqafUdtXHH7/1dpL0dNU2+059+SWMH68yShIT1VjPnIEePdTWDSFEkSXBhoKgVCn47TcoWRICA9UeRF9faN9edTYoaG7VrrQgtzOtXDn7tNSsfHygVi2Kv/ee2tqQmnp79zOZbv+2FkuWqECO0Wh7eXKymqQdP35njyeEEKJge+MNGDpUzSmCgm7+LFyoVqgLEg+P7LuIFPRCkd263VkxbW9vWLeOgHXr1HzhdraRZGSoDMs7YTbD8887z1h96aU7ezwhRKFSgM/qiph771Up8KtXq6I+R46olMCC2MWhSxfnAYW6dVVApaDy9FTFqRytFHl5qeCCZcITEKDqLxw/jvZOtzFERqo2bXfil1+cr4xotbBp0509nhBCiILNwwNmzlSr0IsXw7ffQnQ09Ovn7pHduc6d7YPpFgEBBT+Dr29fCA+3z3T18VHBIsvcyddX/fTvD7GxaO+kVoW/v9pqeScuXYKYGOfXb9t2Z48nhChUCniYt4jRaqF5c3eP4u699ZbK1EhIsN0vqtPZFjMqqEaNUqsrL72kovoZGao91fz5ajLwzTdqBaB9e/Xvdu3u7PF1Opg8+c77wAcFOb9Oq73z4IUQQojCITxcrZwXZKVKwRNPqC4amVfZfX1V/YkOHdw3ttzg46O2JDz2mJpDeXmp4Mrw4aoo5urVsHu3qsX08MOqWPWdbL2wzAMeeujOxuXn5zzIA+r9F0IUWRJsEK5XqRLs2qUKRf70kzpItWkDb79d8IpdOjNypJoA/PuvOhCXLHnzuswphfv2ZV+xOSBAFQH18lIrUBkZ8Nprjntn38rQoarWg6PJR0aGyjgRQgghCqp334Xq1WHKFPjvPyhWTLWAfOGFgr1F0yIiAlatUrURrl5VARZLJmXfvurHwlGtLwtfX9V6PCZGzVEyMlTB0O+/d5yZmR29XmXfbtliP5/x8oJHH72zxxNCFCqF4JtXFEiVK6tiTSkpqiDR2rX5J9CwbBnUqKH2O0ZGqm0RaWl3/jgeHqrrQ+ZAQ1a1azvPKPD1VfsgY2Phhx/U+3XlCjz33J2PBVQx0S5d7J9Pp4MPP8w+8+F2SJsrIYQQ7qTRqBX9U6fUQkZ0tArQO+vo5UpXrqjMx+BgdXxv0wa2b8/ZY4WEqHlUdoGBQYOyz1g8dkxtyV22TGVMHDqU84Ljc+aoTM3MdTF8fFSWxd2203TW2lMIUSBIsEGIzF5/XaUoHjmigiCXLqlgQ6dO2bcIyymtFj79FFPWNENvb3WQHjtWrTq0awf33393Wx00GtUWbNYsVRujRAn1mL/8onqT50R6unp/IiLUawkIgDJl4IEH4OefZYIghBBCXL0K9erBggWqE1RqKvzxh9pOmVftIR98EGrXxpQ10KLTqcKgwcFqQaRzZ7jnnrt7rho14MABldFZqpRqezlxosreDAvL2WMeOqTmEl5eKogRFqbqXE2ceOctxoUQbiPBBiEsrlxRWzmyVlROTlbbPtasyZvn7d6dM59+Ci1bqpUAvV5tw9i9W/09N2m1arXjr7/g4kU1ybnTYlAWZjP07KmCDdHR6rKkJDUJWL1a7fscPFgCDkIIIYq2999XAYesLasNBtWSMi+Ok56esH49V4cPh+LF1fyidm1VCPTZZ3P/+cqVg9mz1Rzg9GlVW6pYsZw91oED0LSpmksYjWqx59o1OHwYZsxQW2V27szV4Qsh8oYEG4R7mEwqfXDNmpsnqu62erXz1liJiapidx5JbthQdYNISVF7MT/6CEJD8+z5csX27WplJjnZ8fVJSapGxC+/uHRYQgghirArV+DXX9XWgLzISMyJr75yvh3z4kW1AJAXfH25NnasytJMSYH9+9UiQX73zDNq3uVIerq6rl8/WcwQogCQYINwvQ0bVB2Djh3V6nfZsir1LmvE39XS07OfmKxZo1Ie76S6c2H23XeO+2pnlpSkVjqEEEKIvJSerrZBliun2j526AClS8PGje4emSrA6Examuo09s03rhtPfmYyqXnirVy7Bnv35v14hBB3RYINwrWOHYOuXeHyZdX6Mi5ORduXLlUtq9zp/vuzDzYYjbB1qyrwJG5/xejKlbwdhxBCCDFmjJpLpKSouUVCgsoaeOAB+Ocf946te3fnmZOgxjxkiNomIG4vY8HDQwUchBD5mgQbhGu9844qjJSVwQBffKHaMLlLuXKqN3V21Z1TUtTqgzvHmV90737rFlne3nDffa4ZjxBCiKLp2jVVi8DRtr60NHjvPdePKbMXXrh1gee0NJg+3TXjyc+0WpXpcSspKaoGhRAiX5Ngg3CtzZtVhoAjPj6q+rA7zZunKh1rNM5v4+MD//7rsiHlW61aQcOGqoWXM97e7s9YEUIIUbgdOOC8vWVGhqqJ5E7lyqk6R7VqOb9NRkbe1W4oaKZNy34xw88PevdWXbWEEG5hNBqJiYnh9OnTXLp0yentssnpEiIPZFeZOCND9Y52Jw8PePVVWLUK9uxxfJu0NDnAgQrI/Por/O9/MHeuyk4xm9WEz9tbtcH85hs1yRJCCCHySkhI9nURctoVITdVr64yOFu2dFzvSKNRLSMFNGoE69fDU0/Bjh1q26ZWq4IMRiN06waffebuUQpRqKWnpxMbG0tcXBxarZaKFSsCsGTJEi5fvkxCQgLmG1ue6tWrR+nSpR0+jgQbhGuNHav2JDoqsliiRPZRf1d65hkYMcJ+nB4ecO+9qsClUFkN06apFNWkJJXKeuCA6qTRtKmaHAghhBB5qU4dCA93PLfw91dzj/ygXj1VtPKff+zrEvj5wfjx7hlXftSkCWzbprZLGI2qGOT169CgAZQq5e7RCVHgpaSkEB0djUajwWg0UrNmTQC+/fZbTp48iSFTULRMmTLWYENgYCD+/v4EBwej1+vR6/WEhobyj5PaOBJsEK41YIAq4LR5881Jgbe3Wg1fujT77Quu1L8//PYbLF+u9oCaTGqlvlgxWLLE3aPLf7RaCAxUP3e6MmM2w/nz6s/SpfPP74AQQoiCQaOBr7+Gdu1U9qGlzaS/v6ob1L+/e8dnodHADz+o7IbkZDUP8vBQ86Cnn5YaR45Ytmq2bHnn9zUYVJHQ8HAICsrdcQmRj5nNZgwGA7GxscTGxpKcnEzDhg0B+OWXX9i/fz+pqalER0cTHh5OUFCQNdhQrFgxvL290ev1NgEFi+7du9/RWCTYIFzL0xN++kml18+eDbGxqp3khAlQpoy7R3eTRqNS9EaOhEWL1Dg7dYK+fbOvUSDuzO+/qwriZ8+q97x4cZg5E7p0ubPHuXJFrX6ULi3ZFEIIURQ1bqwyJ6dPVyn4xYqp7lF9+qgT+vyiWjU4fVoVtNy0SR33hg2De+5x98gKj9RUFbxZuFB99hkZauvF3Ll3tl03LU0thoSEQKaTLSHczWQykZCQQFxcnHWrQ/PmzdFqtWzYsIFt27aRnp5uvb1Wq6V+/fpotVoiIiKoU6cOer2eq1ev0qBBA5tgQps2bXJ1rBJsEK7n4aFWGfLLSoMzGo1K42vSJO+eIyMDPv2Uiu+/r9p01akDL7+sii8Wdlu2wIMP2u5d/fdfFdD5/nvVI/1Wdu+Gxx+Hv/9WQYbgYNXxZNCg3Bnj0aMqkFG9uloZEUIIkX+VLVswOjoEBKhASF620j5+HKZMofLPP6sMj4ED1RbR/FC/Iq/16gUbNth2J/nhBxWM2r8/+zakoLZtvP46zJihMlszMqBNG5g/P3e2cBgMqhior6/aWiOLJCKLjIwM4uPjrYGE2NhYmjRpgk6nY8eOHaxZswZTlhb0derUISgoiMjISBo1amSTlRAcHIz2xu+ZJcMB4MiRI5TK421JEmwoSDIy4IMP1IE0OlrVDZg0CUaPli+qgshkUpH2TZvwsZxwr1unKlbPnAlDh7p3fHlt4kTHRbKSk+HZZ1Xth+wcPaoO/omJtvcdPVptyRg8OOdj+/tv6NdPrT55ealVkt69VbcSP7+cP64QQuRHR47A88+ror+ggr3vvAM1arh3XCJn9u5VixbJyXgajapd97Rpahvo3r2FO+Bw4AD88Yd9G9S0NDhzRmXX9uiR/WOMG6eyWjPPUX77TWXPHD2qtozmhNkMU6fCW2+pgIfJpAJBCxbAAw/k7DFFgZSWlmYNIlgCCvXq1SM0NJRDhw7x7bffWosvAmg0GqpUqYJOpyMyMpLmzZvbbHMIDg7Gy8sLgGrVqlGtWjV3vTQ7EmwoKMxmlQr42283v/zOnlUnbPv2qZMgUbD88ota3c96wm0wqHaR/frdui93QWU2qwrTzvz9t5ooZHdiP3my42CFwaD+XwwcmLMg3LVr0KKF2jqTuYDXt9+q/bUrV975YwohRH51+LAq6JuYePM77+ef1Qnbtm2S3l8QDR9uG4gHFTS/dAnefhvefdc943KFdeucdyZJTLx1sOHSJbX9IjXV9nKjUc0LFi3KecHRDz5Q73/muUtiosro3LBBBTNEgWc2m0lJSbHJSoiNjaVmzZqUKVOG//77j4ULF9rcx8PDg7JlyxIaGkqJEiVo1aqVTTAhKCgIjxvbwcqWLUvZsmXd8dJyRIINBcWOHbaBBguDQe37e+45qFLFPWMTObNggf1kwMLDA9auhZ49XTsmV/L0vFnEKyuN5tZ7bNeuVasCjiQmwqlTUKnSnY9r/nxV/yFrpfCUFLXqd+oU3KjIK4QQBd4zz9gGGkD9PTFR7Xv/7Tf3jU3cuUuXVMDekdRUdbJcmIMNPj7O5w8aDeh02d9/0yZVsDNrsAHUnPu773IWbEhPhylTHHdMSU6G115Ti1Ai3zObzSQlJdlkJcTGxhIVFUW1atW4fv06H330kc19vLy8iIiIoEyZMoSFhdG+fXubrITAwEA0Nwqkh4WF0bp1aze8srwhwYaC4ttv7VPCLIxG+PFHlXoucubUKZUyunatWk0fPlztpbzVQeluOAs0gJroOfu8CwONBrp3VwftrAEDjQbatlUH++xkd73JlPNCnr/95vy99/SEP/+UYIMQonAwmVSh3qzBVYsNG9QcIz8VWCxIzGbV1Wr6dLhwQdVleuEFaNYs754zOTn7z8vRSXRh8uCDKoDmiJ+f6oqWHR+f7K/P6bzwzBkVcHDEbFZbaEW+kTkjwRJQKFmyJA0bNiQjI4P333/f5va+vr4EBwcDEBQURMeOHW1qJvj5+VmDCf7+/rRo0cLlr8ldJNhQUGRkOJ8MmM1qMiByZs8eaN1arVxbUu9eegk+/1x9+efVVoZu3WDrVsdbAdLTVSp/YfbuuyrdMSHh5vvu4aHe7w8/vPX9BwyAjz92nB1RoYLqTJET2e1ltRShFEKIwsLZ3MJyXXbXC+fMZhgy5OYWPIBz51SnjLysy1S2rDqOOppbaDRqvlOYlSqlsn0/+MA2i0Cng65db130u31753Nqf/+c14MKCHC+vcNyvXCZ69evc/36dZvMhJCQEGtGwdy5czFk+j/k7++P342tvV5eXnTv3p2AgABrQMEnU5DK09OTpk2buvT15GcSbCgounVT6d2OVsO9vFRbxqLk6FGVibBxo1rhDg1VB9G6deHJJ+9sS8ngwfbva3IynDihTmaffz5Xh27zvG++qVYZMh/YdDpVjLAA7cfKkQoVVL2RKVPUZMxsVr/nr7wCUVG3vv/zz8OyZapYqiXgoNGolYu7qWHy2GMqldFRqiOoiYgQQhQGWq2q17B1q+PrmzS5deX+wsRohK++Usf+K1dUW0qNBoKCVIC7f//bz5rbutU20ADqOGcwqDT83r3V4+Y2Dw9VgPDJJ+0DDn5+qstCYTd5MtSurYoxnjoFJUrAU0+p7lU3Vped8vdXgYqnn7Z9//z8oH79WxeXdKZ4cTVH3bnTPoDn66syakWuiYmJ4dq1azaZCd7e3nTv3h2A5cuXc/HiRUAVXwwKCsIz03ddt27d8Pb2Jjg42Kb4okX9+vVd92IKuLs6gly7do1evXqxYMECom7n5EDkXJs26ktq9261Am/h56eqRtep47ahudzGjapqb9aTdFC1LRYuVFkJffve+rH+/RdOnnR8XXKyCvDkVbAhKEiNd9AgTNu3o/XxUa/n8ccd76c8ckQV8ipZUk0Ob3XALAjKloVPP1U/dyosTLWOevNNVWE7NVX9P7FMMnKqQwe1+vHTTzcniVqtmgx88cWtUyyFEHdN5hcuNG2a2rrm6MR02jT3jMkdTCZVJ2n9+pvf/f/9d/P6bdvgvffUn7eT4bZwoePsAlABnJ9/hocfvvtxO/LYY+r1vPACxuRkPMxmKFNGzWmyHh9TU2+2iWzaVJ2YFwZ9+qifnBg5EsqXV4GZQ4cgJEQFiMaPv7vg24IFagtNcvLNRRI/P7U109nWD+FQTEwMly9fttnukJGRwaOPPgrAmjVrOHbsGKCKLwYHB1OyZEnr/Tt06IBGo0Gv1xMYGGgtvmhRvXp1172YQi7H/2PS09N55ZVX8M3pvmhxZzQaVU9g0iT1ZZWerk5+xo1TRWWKCpMJHnnE+QE8I0P9DB6sThozTwjS09WXu05380Q9MVFlhmQO4GSWXV2F3FC2LPzxByc2b6ZKsWLq4JZ128bVqyqSvnevGqvJpFL9f/yxaAWZHAkPV3thc7OvukajVrZWrFDbOS5fVhWiJ02S91sIF5D5hYs1aaJq1UyYoAK4oL7rPvxQnXwWFatWqZNuZ1ltSUkq4/F//1PbICzMZnWdj486Rltcv+58C4rRqLYQ5qURI2DoUP799VeiatZU2YRZff21up1lTpSaqjo5zZ5dtDJaHOnYUf3kpho11KLRBx+oBQ1fXxUYGj48b2uEFUDXr1/n/Pnz7Nu3j1OnThEbG0t8fDyPP/44Hh4e/Pnnn+zcuRPAmoEQEhKC2WxGo9HQunVrWrRogV6vJyAgwFovwaKCo/8PIk/k+JvknXfeoX///nyakxVJkTN+fvDRR+pLKj5enUgXtaJNe/dCXNytb6fVqvTFYcPUHsnx49VEwmxW+/neekutKFSunP1juGhvozEsDBxFUc1m6NwZ9u9XwRJL4cLERDW206dBr3fJGIsUrVa1Hu3Xz90jEaLIkfmFGzRrptK7LQH2orh//NNPb73AkJamMic//vhmYPrFF+H8+ZuFjz/8UM0zOnZUi0SOghdmMzRvnicvw4anJ2lRUY4DDdu3qzlS1sWbr75S88ssBfBELilVSmUMFaWsIQdiY2P577//7Do6DB48mODgYA4fPszvv/9OdHQ0ZcuWRa/XExISQlpaGn5+fjRp0oR69eoRHBxsU3zRIjIy0k2vTGSVo2DDd999R7FixWjZsqVMBtzB0zP7InaFWULC7QVYUlPVXv5r16BhQ5UdYNly8d9/KoocGwujR6vK0FOm2B9wfX1VoUh32rVLbZ9wVME4LU2laT71lOvHJYQQeUDmF25WFIMMFrGxt3e7pCSVYTh3ripEmHnu8P33sGWLWr1+9FGVeZqcbNt1ydcXWraEmjVzcfA58MYbjrNEDQaV2TB5sqy2ixxLSEjg+PHj1iCC5c8+ffpQpkwZzp49y8qVKwEICAhAr9dTsmRJTDf+r9SpU4cqVapw8eJF6jjIKg0NDXXp6xE5pzGb77zM8COPPIJGo0Gj0XDkyBHKly/P7NmzCQ8Pt95mz5496Fz4JZWSkiIpl3kov7y/2vh4KrdqhfYWrZuMOh3nZ8zAb/9+QufNQ+ugY4HR359/tm7F7OVF6Ny5hM2fj1mrRWM0khESwsW33sLQqFFevRQbzt5f/bJlFH/nHaevN759e85n6eUr7OWX39/CSt7fvJWb76/BYKBBgwa58lh5QeYXRU9+eX9DP/mEsPnzbzm/SCtVilM//0zl5s3xcJC1YPLx4eqIEVwbMwbP8+cp9eyz+B49itnLC01aGgn338/FyZMx36hsn9ecvb+VW7TAMybG4X2M/v78t2QJqXdSbLuIyi+/v65i2aaQkpLC8ePHSUhIsPlp2bIllStX5vz586xcuRKtVktAQAABAQEEBQVRp04dwsPDSU1NJTk5mYCAAJvCjFkVtffX1Vwxv8hRsCGzgQMH8tprr9kVcNqzZ49LJzRHjhyRYh55KF+9v08+CZ995rxug4eHqn1w/DjUqqUyAxwJClJdByypjMnJqhCQTqf21bmwAKPT9/ebb9R+Pkd7Oz08YNQo272jwqF89ftbCMn7m7dy8/119bH5bsj8omjIN+/vlStQtWr2GQ46nVr1r1gRunRRW1odqVlTzScs/vsPLl1SnZbCwnJ12Lfi9P2tVg1uFNCz4+urimdnKqgnHMs3v7+5wGw2YzQa8fT0JD09nd27d9tkJcTGxtKyZUuaN29ObGwsM2bMwNPT09r+MTg4mHr16lGmTBkyMjJITEwkKCgIrVab4zEVpvc3P3LF/KKIV38R+YLJpA7yOt3ttYGaPl1tJZk9W51wW4IOlpWuqChV5VmrVT/ZyXy9nx+4KJPhtnXt6vw6b29V2EkIIYQQ9pKSVJ2niIhbFzyMiIDNm1V7y5MnVV2F1FRV9NHXV21nfOEFGDRIdaTITta5R7ly6ic/GTdOFT7OunCj1UK9ehJoKITMZjNpaWn4+PhgNpvZunWrXc2EevXq8cADD6DValm7dq21+KJer6dMmTKUuNGtJCgoiGeffRZ/f3+7egkAnp6e6KWmmCAXgg1ffvllboxDFFXz58PLL6uVBJNJZRnMnZt94UYPD1Uk87XX1MpBcLC6/9mzUKWK6oNs8eijqnWRo24TWq2q55Cf+fnBsmWqjWd6uvrRaNTlzzwj3RGEEIWWzC9Ejl2+rGoy/fKLOtb7+Khj5osvZr8IUauWmlccO6ZqPkVGqg4dGo1qrWw5eWrUyPnj+PmpuUd+N3IkrFypWnBbtoP4+amFmwLyf89oNPL7779z9OhREhISCAgIoFKlSnTq1Cnb1PzCymg0kpKSgv+NrmZ//vknly9ftgYU4uPjqVy5Mv3790ej0bBjxw6MRiPBwcGEhYURFRVl7dLg4eHBpEmT8PHxcRhMsGyPEOJWit7/RJF/fPihOvBnjqpv3KjacB06dOuoelCQqqCdndGjVQbExYu2RRZ1OtXZI3ObqvzqgQdUN4oPP4Q9e9TqyBNP3Pq1CyGEEEVNUpKaR1y4cPO4n5ysulBdunR7Ww+rVr35d0edHLy81DF59GjbOYyXl8qQGDny7l6DK3h5wZo18MMPMG+e6sTRrZvKmMznRcivXr3KggULmDNnDqGhodx7770EBgYSHR3NihUrGDVqFI8//jgjRowoVF0J0tPTSUpKsmYM7Nq1izNnzlizEhISEoiMjOTxxx8H4NChQ8TFxREcHEypUqWoWbMmJTPNrcePH59tUEZqJYjcIMEG4R6pqSqjIWv6nsmkJgrTp8N779398wQHw+7dKqjx1Vcqw+Gee+DNN9VJfEFRqZJqtSWEEEII55YsUR2osnZxMhhUvaeXXoIbqeB3ZdAgCA1V84tDh1RWwIABan4RHHz3j+8Knp7Qu7f6KSC2bNlCnz59eOCBB/j6669p5GD764EDB5g9eza1a9dm0aJFdO7c2Q0jvXMpKSnEx8cTEREBwP79+zl27Jg1mJCUlIS/vz/PPfccAP/99x/nzp1Dr9dToUIF9Ho9YZlqggwbNizbeglFMftDuJ78lgn32LfPeQHGtDSV2pcbwQaA8HAVtZ83T+3BdGHhx3zDZII//oAzZ9RWk6ZNi+b7IIQQonD79tub2wKy8vJSx8L+/XPnubp0UT9FdW4BqubW77+rv99/v5pz5ZGNGzfSt29fFi9eTIcOHZzernbt2syePZuBAwfSq1cv5s2bR7du3fJsXLfDbDZjMBiIjY2lRIkSeHh48Pfff7N//35rzYSUG1t+X3rpJTw9PYmOjuby5cvo9XqqVq2KXq9Hr9dbO0L07t3b4RYHi7spzChEbpFgg3APLy91cM7u+rxQFCcDBw9C586qarbJpN6DkiVV+mT58u4enRBCCJF7vL2zvz4v5hdFcW5hNquCmR9+qDIkNBq1WPT00zB1aq6/J2fOnKFfv34sW7aMtm3b3tZ9mjVrxo8//kjXrl35448/qFGjRq6OKTOTyURiYiKxsbEUL14cHx8fTp48yfbt263BhPQb2TZPPvkkxYoVIykpidjYWIKDgylXrpy1o4NF+/btad++vdPnzC7QIER+IcEG4R516qiUQ0ctHX19YfBg14+pMEpKgtatIWsv7RMn1OUnT6qCm65w7RqsW6cmIO3a5fs9oUIIIQqgQYNgwwbH2Q0ZGWr1Xdy9OXNU/YusBbg//FDVucjlblkzZ87k0UcftQ80mM2wcyf8849aQGne3CbQ0bhxY8aPH8/06dOZN29ejp/faDQSFxdHXFwc4eHhBAQEcO7cOVauXMnatWuJi4vDZDIBMGTIEMqXL09GRgYGg4GIiAgqV65sDSZYCjg2atTI4TYQIQoTCTYI9/DwUNsa+vdXhZssvL3VqvuYMe4bW2GydKlaacjKZFIBiDVr8r52hdkMr7wC779/c0UpPV213Hr11aK5IiSEECJv9Oyp6j7t22c7v9DpVD2F22mxLW5tyhTHAR2DAd54I1eDDcnJySxcuJDt27fbXvHffypz88yZm3OJsDDVhaR6devNhg8fTrVq1XjvvfectmNMS0uz1kYoVqwYoaGhXLt2je+//57Y2FgSExMx38jI7dmzJ3Xq1MHDwwOTyUTp0qWpVauWtUVk8eLFAahatSpVMxcbFaIIkmCDcJ/u3dXJ7ssvq6i0n59akXjlFZkM5JZdu1SFaUcMBjhwIO+DDfPmqValKSm2KyDvv686awwdmrfPL4QQoujw9IT161Xdp1mzVGvs6tVVu+zu3d09usIhNVV19nDm3DmVRZJLBQhXrFhBgwYNqFSp0s0LjUZo1Uq1Pb+RUQCoAEirVioQ4ecHQEREBPfffz8zZ86kT58+BAUFUbJkSQwGA4sXLyY2NhZDpoLl7dq1o2XLlnh7e+Pp6UlUVJQ1KyFzMCEyMpLevXtTPVNgQwhhS4INwr1atlTFmkTeKF1a9RdPTbW/ztcXbhww89TkyfZdR0BNCF5/XYINQgghcpevr1rIePlld4+kcPL2Vu+xo2M7qCySXNyiuW/fPtq1a2d74S+/qAxNkwkzkATEAnFmM35JSVRcvhzzoEHMnTuX69evk5KSwooVK8jIyKBhw4aULFkSX19f/P39KVmypDWQEBwcbO3oEBgYyOACvK334sWLXL58mfT0dGvHCulAIVxNfuOEKExiYmDVKnUif999qvbFm286vq3ZnPftrlJT4eJF59efOZOrqx9CCCGEyGVmM+zYAXv2qHaf3bqp+cWCBfaLGT4+8NhjubpFMjY2lrJly/Lff/8RFxeHh4cHNffvh8REPgfOARmZbl/FYKDirl1oBg8mMjKScuXKYTKZMBgMjBgxgmI3akZptVoeeeSRXBtnfpCWlsZ3333HrFmzOHz4MKVLl8bLy4tr166Rnp7O448/zogRI4iMjHT3UEURITN8IQqLmTPhuefUaoLRqA70rVqpVNKJE1WdhIwMtSLh4aHqOeT1dhVvbzXxyLxvNrNcXv0QQgghRC66ehU6doRjx9TcwstLbVv49FPYsgVOn765XdPfHypVUt0o7kBGRgbx8fHExsYSGxuL0Wi0Fk78+uuvOXz4MOfPn+f69esAFC9enJoREeDnR1mDgZKAHgi2/Gmp/wU8+OCDN17GVUqUKEGpUqXu+i3Jr3777TcGDx5M9erVGT9+PN27d8crU/eV/fv3M3v2bGrUqMHw4cN5++238ZA5mMhjEmwQojBYt04VXMxaFfqPP6BECbUaMWsWHD8OtWurApwVKuT9uDQaVYdj4UL7QpU+PmoLhRSIFEIIIfKnnj1VC+0bbRut84wRI2D7dvj7b7V4ATBggLp9lvajaWlp1vaPsbGxJCUl0bp1awBWrVrFnj17bG6v0+mswYYKFSpQu3Ztrl69yqOPPnqzPWRiIkyYgMMmmFotDBxoc9GRI0coXbr03bwT+dry5ct54oknWL58Oa1atXJ4mzp16jBnzhymTp1Knz59GDBgAF999ZUEHESekmCDEIXBm2863juZkgLLlsGMGfDxxy4fFgDvvAObN6tiTZbK1f7+Kthxh6sfQgghhHCRY8fUYoUl0JBZWhrMmoV59mxSHnzwZjBh715iY2Np164dXl5erF+/nk2bNtnc1cPDg+bNm+Pl5UXFihUJDAxEr9dbAwlBmbIuGzduTKlSpbjnnnsoXrw4gYGB6oqQEPjiC7WdIyNDjdHDQwU6ZsyAMmUyDTWNhQsXsn79+rx4l9xu8+bNjBs3jt9//53atWvf8vahoaH8+uuvPPDAAzz99NN8+OGHLhilKKok2CBEYXD4sPPrvL3h33+hTh2XDcdGcDDs3QvLl8NXX6lMhkcegT59VHaDUBOla9fUtpYb1bOFEEIIt/r7b8yeniQCcdwowHjjz5YZGQTt3cvOnTtZvXq1zd28vLxo0qQJISEhVKhQAW9vb2sBRr1eT0BAAJobWY01a9a85TBKlSpF27ZtWbx4MaNHj755Rd++ULcufPKJ6q5VtSqMGwdZHnPlypXUqFGj0HaNeO655/jkk08cBxoSEtTCU1iYTSapj48P3377LVWqVGHcuHFUrlzZhSMWRYkEG4QoDCIj4fJlx9elprqm60R2fHxUSmOWtMYiz2RS2R3TpqnPyWyGXr1U/Y0bBayEEEKIvGIymYiPj7ducbD82ahRIyJLleKI0cjyLPfxA2oDQeXLU758eTp27GjTGtLPz88aTKhQoQIVcmHb5hNPPMHgwYPp1auXtfUkAJUrq0wGJ+Li4nj99deZWkgzKffs2cOlS5fo1auX7RXHjsHo0aquhlYL4eHw1lvw6KPWm+j1eoYNG8acOXOYNm2ai0cuigoJNghRGDz9tDqoWLYpWHh6QosWqm6DyH9Gj4bFi223wKxYAbt3q1UaX1/3jU0IIcQtGY1GEhIS8PPzwycfZuuZTCauXbtmE0iIjY2ldu3aVKpUiYsXLzJv3jyb+wQEBFC1alUiGzWiVEQED/z7r00BRh9QBZ6ffJLixYvbnvznkVatWjFo0CC6du3K6tWrre0psxMfH0+PHj1o164dPXr0yPMxusPs2bMZNWqUbd2Fs2ehSROIj1eLGADnzsHIkapg94gR1puOHDmSRo0a8cYbb6DT6Vw8elEUSLBBiMLgkUdgzRr4/nt14mo2Q0CA2tO4aJG7RyccOXdO7TfN2jYsPV21C12xwmYFQgghRP6QlJTEV199xezZszlw4AD+/v4kJycTHBzMoEGDGDVqlMvS0s1mM9HR0dYggiWgULlyZerWrUtycjIfZ6rZpNFoCAoKsmYbhIaG0q1bN5uaCZ6Z2lEH//QTje+7T6XiGww36yI8/bRazHCh119/nbS0NJo1a8YHH3xA586dHRY3NJlMrFu3jmeffZamTZsyY8YMa6ZFYbNmzRpefPFF2wvffffmXDAzg0EVEx861NpyvEKFClSqVIndu3dz3333uWjUoiiRYIMQhYFWC19+Cdu2qeBCXBw88AD06yer4/nVunWqhVjWYAOoKtvffCPBBiGEyEdMJhNTp05lxowZtGzZkrfffpt27drh4eGB2Wzm1KlTfPrppzRv3pyGDRsyf/58St5owXg3Ll68aA0mWAIKJUuWtJ4czp07F6PRCKjii8HBwdbOCzqdjp49e1q3OAQFBaHVaq2P7evrS4MGDZw/ec2aqr3lokWq2HOJEvDYY6qzlYtpNBrefvttGjRowOTJkxk3bhwjR46kadOmBAUFkZCQwJ49e5gzZw5+fn489dRTDB48+LYDDWazmWvXrhETE4OHhwdhYWGq80U+FhMTQ3h4uO2FP/zguKgnqBpRhw6pWhc3REREEBMTk3eDFEWaBBuEKCw0GmjeXP2I/C9T72uHsrQOE0II4T5Go5FBgwZx+vRp9u7dS7ly5Wyu12g0REVF8c477/D666/z7rvv0rRpU3777TeqVKmS7WNfvHiRq1ev2tRN0Ov1dOnSBYCvv/6a2NhYAGuxxYiICOvz9uvXD39/f4KDg22KL1qur3O3BaKDglThxXHj7u5xcknfvn3p27cvu3btYt68efz666/ExcURFBREVFQUCxcupFmzZrcdZEhMTGTJkiXMmjWL//77j/DwcIxGI1euXKFp06aMGTOGbt262WR85BdarRaTyWR7YXbzC5PJ7nqTySTtL0WeyX//a4QQoijo2FGtMDgSECDFNIUQIh+ZMGECly9fZt26dfjdomuQr68vr7zyCpGRkXTu3Jnvv/8eo9FoE0zw9PRkwIABAKxevZozZ84A4Ofnh16vJzQ01Pp4PXv2tAYZMhdftKhatWouv9qCoVGjRjRq1OiuHuPjjz/m1VdfpVWrVrz//vu0a9fOmvmRmprKt99+y7Rp03jyySeZP38+HTt2zI2h55qwsDDOnz9vm4HxyCNqK4WjzEm9HmrUsLno/Pnzt1UDQ4ickGCDEEK4Q2govPyy6kaRuUCkn59Kb7yxoiWEEMK9du7cyY8//siBAwccBhouXbrE+fPnbWompKSkMGbMGP7++29r7QBQxRcttREsHnjgAbRaLXq9Hm8HWW1ZsyjE3TObzUyaNImffvqJnTt3UqlSJbvb+Pj4MGDAAAYMGMAff/xB//79+eCDD6xBovygZ8+efPnll7z11ls3L5wwARYuVF3KMm+n8PODTz+1aYF54MABrly5QsOGDV03aFGkSLBBFH5XrsBrr8HSpZCWpgoaTZkCdxkNF+Kuvfiiatv1+utw4oRacRg7FiZOVEW4hBBCuI3ZbEaj0fDBBx/QvXt3duzYYZOd8ETlyvhMmcLB/fvZ6u2NtmFDgrp0ITgsjMjISIxGI8899xw1atRg7ty5lC5d2mEqfgnpGOVy06dP55dffmHr1q2EhITc8vatW7dm/fr1tGnThoiICNq3b++CUd7aqFGjaNmyJa+99trNbijFisHevfDqq8QuWcLl5GRS7rmH4BdeoHSHDjYnf7Nnz+bxxx/H61ZbO4XIIQk2iMLt6lWoVw+io29Gd9euVX2HV62Ctm3dOz6RcyaTShH09bWJ0hc4ffuqHyGEEC5jvlGpX6PREBMTw7Fjx+w6Ojz22GNotVp+/vlnRo8ezc6dO62FFovv3YvxlVcgOZl7gcbp6QT++SfapCTYutVad6dkyZLcf//9rF69mtGjR7vxFQuLa9eu8cYbb7Bv3z7HgYa0NFV4O0tgqEaNGixcuJAnn3ySw4cP54sOF1WqVKFOnTrMnz+fsWPHAqoGw+9//cWsCxdYbzZTonx5fNPTuf7UU5jGj2fEiBGMGDECk8nEsmXLOHz4sJtfhSjMJNggCrd331UBh6xVeQ0GePxx+Oefgn2iWhTFx8Pzz8Pnn6sJQUQEvPQSjB4tn6UQWcTHxxMTE4PBYCApKcmmCr0QhZnJZMJsNuPh4UF8fDz79++3yUqIjY3loYceolKlSly5coU1a9bg4+NjDSaULVsWT09PfvnlF9q2bcsrr7yCv7+/OsFMSoJRoyA5GYBAy5OmpsKRI6p1caZU+0GDBvHhhx9KsCGfWLhwId27d7ffnrJhAzzzDOzfr4IN7dvDjBmQqSZG586dee6559i4cSOtW7d26bid+fDDD2ndujWVK1cmIiKC/v374+fnx5gxY1iyZAn+/v7W2x46dIg5c+ZQs2ZNPDw8eP7553OlY4oQzkiwQRRuS5aoE1JHLlxQ7ZwqVnTtmETOWbbBHD9+s/DRxYvw3HPqs3zvPfeOT4g8YjabSU5OJikpiaSkJEqVKoWXlxenT5/myJEj1sstQYUnnngCX19fduzYwdatW62P061bNze+CiHuXFpaGitXrmTLli1cv34db29vihcvTp8+fbjnnnvw9vbGYDCwc+dOm0BCfHw8Xbp0oX79+iQlJbFu3Tp0Oh16vZ6wsDAqVapEYKAKE0RFRTFp0iR8fX3tVqujo6MpV64cAQEBNy9cv95u1dsqKUkFwzMFG0qVKsXVq1dz+60ROWAymZg9ezZfffWV7RVr1kDPntYAEiaTuqxxY7UlISoKUJkwY8aMYdasWfkm2FC9enVWrFhB9+7dycjI4NNPP6V///4OMy9q1arF6NGjWbt2LSaTiQ0bNjBhwoSbWzCEyGUSbBCFm7Nq/6Ci1s76EIv86dtvVVAha4VlgwFmzlRBhxvtwIRjly5dYt26dcTExKDVagkNDeX++++3qXwuXCc5OZnLly9bgwSWgEHTpk0JCQnh0KFDfP755wQGBlrTvgHGjBlDREQEV65c4eDBg+h0Ovz9/QkNDaVMmTLW29arV4+oqCj8/f3R6XQcP37cXS9ViDty7tw5Zs6cyRdffEGVKlWoUaMGISEhJCQksHv3bmbNmkXp0qV58cUX6d69Oxs3brQWXyxdujTBwcEUL14cgOLFi/Piiy86LL4I4OXl5XTPusO2gLeaO2RZ5PDw8MBoNN7eCxd56tixY5jNZho3bnzzQrNZ1UuyBBoyX56YqOp+ffml9eKHH36YiRMnumbAtyk8PBytVmv9P3HmzBmGDRtGeHg4AOnp6axatYpZs2Zx6NAhXn/9dR577DH69evHY489xpdffnlX20LMZjObN2/m+PHjJCYmEhAQQJUqVWjZsmW+2G4i3EeCDaJwe+ABdYBwdJDX6cBB9WGRj339tTrwO+LpCb/9plo+FURms9oi4udn3eubew9tZtOmTcyaNYu1a9fSrl07ihcvjtls5sKFC4waNYoePXowZswY2wmYuG0mk8maeWAwGChWrBhBQUHExMSwfft2u2BCjx49qFy5MmfPnrVbYfPz86NWrVqEhISg1+uJioqiatWq+Pv7W3/0ej0AjRs3pkmTJk7HFRYWJi3NRL6UkpJCWqaT8nXr1nHt2jViY2M5evQon332GU2bNmXdunVUr16dt99+G51OZ93mMGnSJE6dOsWHH37IypUr+fzzz22zDzLRarVOAw23EhISwp49e2wvvO8+51mTOh307m1zUXR0NMWKFcvR84vcFRMTQ4kSJWxPgC9dgvPnHd/BZFI1vjIJCQkhPT2dlJSUPBzpnfnf//7H//73P55++mlrMK58+fKACqYlJibSpEkTxo4dS+/eva2ZDEuWLKFWrVps376dZs2a3fHzxsbG8sUXXzB79mw8PT1p3LgxgYGBJCQk8OGHH5Kens7o0aMZPHiw9bglihYJNojC7aWX1Gp4QoLt5TqdSrmXiv8Fy62i4wU1ev7ZZ/DKK6qQqUYD3brBxx9DZORdP3RKSgpDhgzhr7/+Yty4cXz66ae2/bhRE+GFCxfSt29fOnfuzMyZMx1WTC+KjEYjZ8+etdumULVqVSpVqkRMTAzz588nOTnZJvOgW7duNGjQgLS0NA4dOmQNEoSFheHv7289KSpdujSDBw+2Zib4+fnZrKKWLl2a1q1bU716dYfjkxUjkR+ZzWaSkpJISUmxBrs2bdpkbQ8ZGxtLamoqUVFR1pZ7x48fx2g0kpyczMKFC5kwYQKDBw+m4o2tjs8//7zD3/fBgwczaNAg+vfvz/fff5/r311t27bl2WefJSkp6ebe97AwGDMG5syxbV3s6QkhITBkiM1jLF++nA4dOuTquETOZf6uBtRxN+tlWa938Bj55fv3/PnzbNiwgc8//xyAhg0bsmDBAj6bOpWksWNJ++kngjUaPE6fVtt8MgXe/Pz8GDt2LLNnz77jYMO2bdvo1asXbdq0Yd68ebRo0cLmPTGbzWzdupVZs2bx1ltv8d133+UooCHyr4yMDOLi4rKtByWzSVG4RUWpqtCjRsGuXSq4EBYG77xjs59SFBD9+8PvvzvObkhPh/vvd/2Y7tY778DkybYT1h9+gO3b4fBh1Q4zh9LS0ujWrRshISHs378fX19fh7cLDw9n4sSJjBo1ir59+zJgwACWLVtWqIoJZs088PX1pUSJEphMJlavXm0TSEhKSqJBgwa0a9eOtLQ06wTOws/Pj9DQUCpVqoSfnx81atSwblOwBBUsqaslSpRg0qRJTsel0+moUKFCXr50IXKdyWQiISGBpKQka3G5bdu2cfLkSWvNhIyMDMLCwhg3bhwAFy5cIDY2Fr1eT7ly5dDr9URERJB+Y0vC6NGjycjIoEaNGnzwwQcMHTrU5jmdndj5+PiwePFiunbtyuTJk5k8eXKuvtZy5crRvHlzli5dyvDhw29e8d57atve22+rrX0mE3TsCLNnQ6C1ZCSxsbF88803HDlyJFfHlR8lJibyzTffcOrUKRITEwkMDKR69er07NnT6fHH1UJDQ7l06ZJtsKB4cShXTtWDysrDAx580OaimJgYvL29802dg3nz5jFgwABrDRIA4uPRNG5MwKVLN7cUX7wIEyaohY0XX7TedMiQIbzxxhtER0dbj123smnTJnr37s2iRYvo3Lmzw9toNBpatGhBixYt+PXXX+nRowfffPMNrVq1yulLFS6WmppqUwvH8qfl74k35uMNGjRwWmhUgg0id50/D8uWqQ4QTZpA167Oiyi5yj33qIBDbCykpKiDSj6JRos71KuXOjn/+2/bug06nTqA3uZBMt9ISrIPNICaGFy/DvPmqToUOfTkk0+i0+lYunSp/Z5jB4KCgvjxxx+5//77eeWVV5gyZUqOnzuvZZ4onj17lvj4eJuAQbFixWjatCkAH330EdevX7dZzapduza9evVCq9Vy7NgxvL29rZkH5cqVo1SpUgD4+voyaNAgaxBBp9PZBGH8/Pzo2rWrC1+5EHnPaDQSFxdHXFwc5cuXR6PRsGfPHg4ePGgtvmgymfDy8uLFF19Eo9EQFxdHcnIyERERVK5cGb1eb7N1oH///g6fK/NJ+I8//kiJEiXsAg2YTCrQ/Mcf6kT+oYdsijt7eXkxa9Ys7r33Xl544QX8/Pxy9f0YM2YMkyZNYvDgwTdrO2i1MGmS6l5w5QoEBYGDbRzz5s2jU6dOlChRIlfHlJ8cO3aMmTNnsmTJEu677z7q169P6dKlSUhI4PPPP2f8+PEMHTqU0aNHW1P73aVKlSp4enrabhvQaGDWLJVVmLlug1arft9ee83mMZYsWZKviu2uXr2a999/3/bCefPg2jX72mUGA0ydCk8+af19LVasGK1atWLDhg3069fvls/333//0bdvX5YuXUr79u1va4ydOnVi6dKl9OvXjx07drj990DcLDx94cIFuyCC5c/kLHVMPD09CQ4OJjg4mCpVqqDX6wkODqZEiRKcO3fO4fNIsEHkHkuBPrNZnQgGBkJoKGzeDKVLu3t0d7VCLPIJLy/YtAlefhnmz1cZDuXKqe0yjz3m7tHduW3bnAfjkpNVjYocBhvOnTvH8uXL+ffff+0DDXFxsGWLWrFp2RIytcXy8fFh2bJl1KxZk+eee85uy0VeyZx5kJSUhNlstqZPb9myhQsXLtgEE8LDw60nJKtWreLKlSuAWknx8/OjSpUq1se+5557AGyyDzLvHX366aedjkuj0VjHIURhkZaWZp1Qli9fHi8vLw4dOsSOHTuIjY0lMTHRGpybNGkSfn5+pKamYjQaKVOmjHWCmfn/kbPVzTsxa9Ys+/aQMTHQurUqDpyYqI4Dkyerk/xMAVHLlozly5czePDgux5LZh06dODjjz9m1KhRzJ8/3zbLwtMTnKzo/f7777z//vts2rQpV8eTnyxZsoSnnnqKUaNGsX//fsqUKWN3m3/++Ye5c+fSqFGjbFfCXUGr1Vq7Sdik9LdrB2vXqmPuzp0q0NC1q8pgyXRibDabmTVrFvPmzXP94J24du2atRiq1fLl9gUvLTw91RygUyfrRcWLF+fatWu39XwfffQRgwcPdhxoOHAATp1SwcDatW2uateuHUOGDOGjjz7igw8+uK3nEjlnNptJSEiwy0rI/Of58+dtslm8vb3R6/Xo9Xq773q9Xn+z/a8DEmwQeWvnThXhz1wsJyFBRVB79IDdu902NFHIBATA9Onqx2gs2HU3bjX2u3htn376KY888ghBQUE3LzSb1ST97bdv7tnMyFDZIjdSnQFKlixJhw4dWLRoEU888USOnt9kMlkzAC5fvszVq1dt6h6YTCbrytCKFSs4fPiwTeZBSEgI48ePB+DixYtcuXLFuj2hXLlyNgfHnj174uHhgU6ns8s8AGjTpk2OXoMQBZHZbCYlJcU6oSxTpgz+/v6cOHGC9evXExsbiyFTNtXo0aOtJyqenp5ERUVZJ5bBwcHWlfxmzZrl6X7rkydPcvDgQXr16mV7xaBBcOzYzYKM6enqZ8YMaNoUunSx3nTMmDG8/fbbuR5s0Gq1fP3113To0IFBgwYxb968W24LWLFiBWPGjGHFihVUrVo1V8eTX3zxxRe8/PLLbNiwgZo1azq9XeXKlXn//ffp1asXPXv2ZOHChTzwwAMuHKmtIUOGMHnyZE6fPm27ja1FC7WF0WRS2Q4OTqpWrVqFl5cXLVq0cOGIs6fVau3rUNwqqzjL/MJsNt/W1kmDwcAXX3zBrl27bK84c0Zlhpw4oZ47I0MVYf/xR7UodMOoUaNo1KgRU6ZMQafT3fL5hHNGo5H4+HingYS4uDi7LjiWIrthYWFERUVRsWJF6tSpY/3Od9T+925JsEHkjunTbQMNFkYjHDmi0t5r1HD9uEThVpADDQDNmjkvSqXTqUl2DqSnpzNv3jzWrVtne8XcufDuu+r/aub/r5MmQalSqsf4DWPGjGHUqFGMGzcOjUZjl3lgMBioVq0aHh4e/PPPPxw8eNAm8yAlJYWXXnoJrVbLzp07rdXcLZkHmYMglStXJjQ01CbzIHNV+b59+2b7eiNzoZCmEAWFpfiiZUJZsmRJQkJCOH/+PD/88ANxcXGkZtpmNmDAAKpUqYKXlxc6nY6SJUtaV6qCg4OtWx1q1apFrVq13PWy+Oeff6hTp47tPvjLl9X2CUedH5KSVKA0U7ChUaNG/PPPP3kyvoCAAH7//XeGDx9OuXLleOyxxxg5ciTlMp1IJScns3z5cmbNmkV0dDRr1qyhXr16eTIed9u9ezcTJ07kjz/+cFrANqtmzZrx/fff0717d/7880+ioqLyeJSOhYSEMHnyZB544AE2b95s37HHyUn3gQMHGD58OMuXL883xSFB1aG4cOGCbVDrkUdg3z77bZqggilZgiXnz5+/rSKmy5cv595777UN0mRkqCzJ8+dtu78dPqw6t5w4oTKSgAoVKnDvvfeybNkyhg0bdicvs8hJT093WishNjaWhIQEuyBTYGAger2ekiVLUqNGDZvver1eb9eV58iRI7f9/zenJNggcsfRo+rLyxEvLzh5UoIN+c3Ro7Bunfp8unXLlc4H4g75+sK0aareROYJgbe3OvnPUtH8dp0/fx4vLy9qZP4/ZzbD669bn8fyv1ULxBkMnH3uOQylSlmDCUlJSZw+fZrk5GQOHDjAmjVr7A5qTz/9NEFBQSQlJXHt2jWbzAN/f39rdkOLFi1o0qSJ08yDOnXq5Oh1ClEYWYovWiaWERERREZGcv36dRYvXkxcXBwZmfZhd+3alYYNG+Lr60tISAgVKlSwmVxaTqTKlStnc2Kc31gKCto4e1Z9T2au0ZPZyZM2/7S03MsrOp2Or776iuPHjzNnzhwaNGhAeHg4wcHBJCcnc/bsWe69915efvllOnfufFu1cgqqadOm8cILL9ifqKSlwc8/q5XuypVV0cxM70PTpk0ZPnw4n3zyiVtT6Z944gmuXLlC8+bN+eGHH6hWrVq2t//tt9949NFHmTlzJq1bt3bNIG/Tgw8+yOLFi22z+IYMgY8+UtuPMgfrLN3YMtU1uXTpElu3bmXJkiW3fK5du3bRsWNH2wtXrVJ1prK2mTca1eWrVqmaWzd07NiRPXv2FPlgQ+YMNEfBhKSkJJvba7VagoKC0Ov1VKxY0S6QEBQUlC87ieW/EYmCqXp1tU/LUcAhPV11hRD5Q3q66sTx00/q31qtKhQ0caJKsReuNWIElCih6k4cOqTqJwwZoj6LTLUUbsVkMmEwGDAYDPz999/4+voSHx9PUFAQFy5cYMvatSRFR5MEGIBkYChQFvgXWHnyJPzyizXzwN/fn+DgYGJjYylVqhT33XefXccFSxu4unXrZhsZDwkJyfHbI7JnNBptumjc6u/3F8SOLYWMpVWYZUJpmTimpqYye/Zsa/FFi5YtWxIZGYmfnx8lSpSgatWqNhNMy/+v0NBQHn74YXe9rLsWEBBgHygoU8Zx1qRFlrlFQkKCfcAiD1SpUoUPPviAqVOncvr0aWJjY/Hz86NkyZL2e+cLoUuXLrF69Wpmz55te8XOndC5s5pnpKWpwHlgoFrYyHQyP3LkSBo2bOj2VPo33niD0qVLWwPiY8aMoVOnTtYgUXJyMl9//bU1U2Xp0qW0bdvWbeN1ZtiwYVStWpX333//5vFWp4MdO1Rb7c8/V/VOataEN96w667x2Wef0bdvX5saLM7ExcXZ327XLvsW8xYJCer3IlOwISQkhNjY2Nt+fQWR2WzGYDBk28khJct3m6X4ol6vp1q1aja1EoKDgwkMDCyQXcIk2CByx4QJql1f1nQtDw91gJGshvzjxRfVqkPWCdwHH6gD0UMPuWdcRVm3buonE5PJhCkjA09PT1JTU/nnn3/sTiAbNmxIVFQUZ8+e5bPPPrPe9+rVq1y7do2zZ89Ss2ZNMjIyiI6LQwdEAP43fixT8irAmIAA/J97Dj8/P+vB7H//+x9+fn6EhIQ4LPolcp/RaLT5nA0GA4cPH+bChQsOgwdZJysWWq3Wmkni7+9PZGSk7I91kdTUVJvJpU6ns+5n/+ijj4iJibG5fZ06dahYsSLe3t5UqFCBwMBAmxUrS5FWX1/fW24pKsgqV67MgQMHSEtLu5nqW7w4tG8Pv/1mv5XC318FyTPZtWsXlStXtrksPT2dH374gYULF/Lvv/+SlJREUFAQtWrVYtSoUbRs2TLHKfGW1rdFzeeff06fPn1sTzrj41X76fj4m5elpqqT3DZtVKbDjVT68uXL07RpU5YvX86QHGbw5ZaRI0cyaNAgvv76a15//XUeeughihUrhtFo5Pr167Rt25bXXnuNjh075ttMlYiICLp06cJHH33Eq6++evOK4GD48EP140RcXBxz5szhhx9+uK3n8vX1tT/uhIerDCRHxyMfH7tOYcnJybneMcbVTCYTiYmJDjs4WP60tPW18PHxsX6vlytXzi6YkF3xxYJMgg0idzRurPZOOupGcZtfYMIF0tJgzhzHFYqTklQ7JAk25AlL5kHmE8WQkBBKlSpFSkoKP/zwg83JZHJyMu3bt6dFixYYDAZWrFgBYJN5YDngh4SE0Lp1a+uJpdFo5PPPP7fWMihbtixjx49Xha++/dauFZafjw9+w4fbZFJcunQJo9FoW2BS3DFL8CBrAMFZ9oGj4EF0dDTFixe3fr46nY7IyEi7TJPMf3dW5MlSO0PkjKX4YuYJplarpXHjxoBqc3j+/Hmb+1SsWNEabKhZs6bN6pVer7euxGs0Gh7MsuJYlERFRVGrVi2+++472zaZixbZd6Pw8FCLHFnazs6ePZvHH38cUBkk77zzDrNmzaJSpUqMHDmS2rVro9PpiI+PZ/PmzYwcORIPDw9eeOEFHnnkEde92ALu8OHD9llSS5bYp9GDmhMmJalFjh49rBe3bNmSw4cP5+1Ab5Ofnx9DhgxhyJAhxMXFcf36dTw9PSlWrFiBCdJOnTqVZs2aUbNmTfr06XNb90lJSaF379706NGD+vXr39Z9SpUqxbFjx2wv7N9fLWQ5otFAloyr48ePU9JJB5f8InP7X0dZCXFxcTYZaKC2Wen1epv2v5m/629VVLawynGwwWg08tJLL3H69Gk0Gg2vv/66TasxUQSNG6cKzC1bBlevQpMmaiKQD/cPFVmXLzuvrQF2+1+FcyaTifT0dGsxs7///puEhASbE8gyZcrQrFkzzGYzU6ZMsTswNW7cmFKlSuHl5cXVq1fx9/cnIiLCesJYtmxZAIKCghgzZgz+/v42mQcWAQEBdntIO3TowIoVK6wTb0Dt39y+Xf3/tGQh+ftD2bKqnkMm8+fPZ8CAAfl2JcddHAUPsvt7qpO95pbMA8tnXbJkSYdBA51Ox9mzZ6lbt26hXPHIyt1zC7PZbF2tskwuT5w4Yd0mtHTpUo4fP25zn4iICGuwoXbt2lSvXt1mkpm52Gm7du1c9loKojFjxjBjxgzbYEOxYqrQ3W+/wcaNEBQE/fqp1nqZnDx5kl27dvHtt99iMBjo27cvaWlp/Prrr9b2t5nVrVuXcePGsWHDBkaPHs3+/ft55513isT/s7sVFxdnH4jet08FFRwxGFSx8EzBhqCgIE7mwzlH5myigqRcuXL89NNPPPDAA1y8eJHRo0dnu3///PnzPPTQQ5QtW5YZM2bc9vM8+uijtGjRgqlTp94s5hoZqQrFP/OMym4wmdQWXV9feP99m9awqampLFq0iM2bN+f0peaK9PR0p7US4uLi7IovajQaa9ZZ6dKlqVWrll0GWtbii0LJ8Vnghg0bAFi2bBk7duxg+vTp9nu3RNFTqpT6shH5U7FijlceLLKkuhUlmTMPLCeMnp6e1qJRP//8M1euXLHJPKhSpQoDBgwAYPXq1SQkJNhkHkRERADqINW2bVu8vb1tTiItkzUPDw/Gjh3rdGweHh7Wx7pdY8aM4ZlnnmHEiBE3J88REaouxOefq6CgpycMHKiqVmdKaczIyGDu3LmsWrXqjp6zIMrIyLitjIPcDB5kl3ngSHR0dJE5AcrruYXRaLQWX7RMLOPj4+natSsajYZVq1axd+9em/sYDAbMZjMajYbatWvbFWDMnA7cpEmTXBtrUdS9e3eef/55Fi5cyNChQ29eodWqQoNZC9PdkJ6ezpgxYxg9ejTe3t707NkTvV7P559/bm3d6Yjlu3nbtm107NiRyZMn26ahC4cCAgLsitdRoYLzVHo/PzU/zCQpKcla90fkjnr16rF582YGDx7Me++9x8iRIxk+fLi1jojZbGbjxo3MmjWL3377jSeffJJXX331juoAVK5cmbp167JixQrbbKBRo9Qi44wZqgNd9eoq+yhLxsSKFSuoU6dOngaRs7b/dZSdYMiy7Vur1Vq/0ytWrGiXlRAUFCSLLzmU42BD+/btrStpFy5ckFRbIQoCf3/o3RtWrLDf/6rTwfjx7hlXHjAajaSkpFgnM6dPn7YGCywnj5ZJKag9qGfOnLF5jMjISGuwITk5GY1GY5POnjkAMHToUHx8fBxmHgAu78ndtm1b0tLS+Oqrr2wnBIGB8MQT6seJmTNnUq5cOerWrZv3A81lmYMHtxNAcEfwQDh3t3OLjIwMh5PLzp074+fnx6ZNm9i4caPNfQICArj//vvx9fWlZs2aREZG2qxYnTp1yvrZurM1ZFHg6enJqlWraNWqFX5+frYZDk6kpqYyaNAgvLy8eOWVV5g7dy5xcXGsWLEi20BDZqGhofz00080bNiQTp06SdDoFipUqMD+/fttjy2DB2dfZLp3b5t/7t+/n0aNGuXRCIuuSpUqsXXrVvbu3cvs2bOJiorC09MTX19fYmNjiYqKYsyYMcyfPz/H527jxo1j4sSJdO3a1TYLpF49+OILp/eLi4tjypQpvP322zl6XgtL+19nWQmxsbF2x3YvLy/r93pkZKRNrQRLBlpBLL5YENxVfrunpyeTJk3it99+46OPPsqtMYmi7MoV+OQT1SlBp4Phw9VeL0lNyj2ffKLSHc+cUftfNRr1Xrdpk+0JqLtZqu5nrWtgSV/+888/+fvvv60nksnJyfj6+vL8888Dqif44cOH0Wg01hPE0NBQ6+M3btyYe+65x+5k0uJWeyAtverzC61Wy9dff0379u0JCwuzb1XlxNdff827777L1q1b83iEtyevggd6vV6CB/nU7cwtjh8/bjOxbNOmDWFhYezfv98mI0ej0Vjbs/r5+VG1alVryqtlopk51TgqKooo6Z7kVlWrVuW3336jS5cubNy4kSeeeMJhEcaMjAx++ukn3nrrLcqWLcs333yDh4cHM2fOZM6cOfYpzevWqa1k//2nVluffhoyBY9KlCjBhAkT+OSTTyTYcAtDhgyhefPmTJ48+eY+9MhIWLAAhg1TGZRpaSrTwcMDVq60qQl07do1Vq1axfTp0930Cgq/+vXrM2/ePObMmUN8fDzJycnWTKy7PbZ17dqVNWvW0KNHD3788cfb6gCTmJhIz549adu2Ld27d8/2tpnb/x47dozo6Gi7wEJGltpTvr6+1gBC+fLl7dpC6nQ6Oaa7icactXF6DkRHR9OvXz9+/vlnayGVPXv2uLSoSkpKSpEtvOEKrnh/vU+fpvzDD6NJSUF7Y9Xd5OdHSpUqnPn8c8yWvWGFkMt/f9PTCfzjDwLWr8fs7U18164YGjZUgQcXsWQe+Pr64uHhwdWrVzl//jzJyckkJyeTkpKCwWCga9eu+Pj4sG3bNrv0ZlCVpL28vNi7dy///fcffn5+Nj+1atUiNTUVo9GIVqvFx8enSEWv9+7dy/jx4xk8eDAPPfSQ00nB9evX+fLLL/n++++ZPXs2VatWve3nuJPf34yMDFJSUkhOTrYWRMz698w/aVkzcG7QarU2n7Ovry9+fn7odDrr3zP/+Pj4FNiJRm5+PxgMBho0aJArj5XXHM0tQM0vFixYAKjAREBAAG3btqVkyZLExsZy6dIlAgMDCQwMzJXVKplf5C1n7+/Vq1dZunQp33zzDRUrVqRt27YEBweTkZHBuXPn+OGHH4iMjKR///506dIFrVbLjh07mDp1Kj/88IPN//fw99+n2NKlaJKT0QAmDw/w8uLCW2+RkCkQGxsbS8eOHfn1118LTcvevPr9HTFiBN26dbM7cfS8eBH9t9/i/e+/pFSpQlzv3hgzBfYBFixYwIkTJ3jzzTdzfVyuVlS/H4xGI5MnT+bQoUO88MILNGjQwOEx1mw2s3fvXt58801q1qzJq6++aq2Nk5CQ4PAnMTHRWuMq40ZXLj8/P4KCgqzf7Vl/fArxOUJecsX8IsfBhu+//57Lly8zcuRIEhMT6d69O7/88ot1wHv27HHphObIkSPZ9nkXd8cl7++996pevFl/Jf38VF/gQlwLojD8/ppMJoerzTVq1CAwMJATJ06wadMmm8wDULUFIiIi2LFjB6tXr7bJPNDpdPTp04eAgADOnj3LpUuX7FaibydKXxje37tx/PhxXnnlFdasWUO/fv3o378/ERERmM1mLly4wOLFi1m1ahUPPvggb7zxxh21uczIyOCvv/6idOnSd515kF13hax/L0qZB7n5++vqY/OdutXcAtRrKF68OMHBwQQEBOT570FR//7Ia7d6f9PS0vj+++/ZvHkz169fx9vbm+LFi/PQQw/ZbfV67LHHuOeee5gwYcLNC//6C5o3d9yFSadTGZWZVt0feeQRWrZsyahRo+7yleUPefX7+8svv/DEE0/w559/En4H9Z5Onz5N8+bN+fHHH2nYsGGuj8vVivL3g9lsZv78+UybNg0vLy/GjBlDo0aN8PHx4eLFi2zfvp0vv/yS1NRUunTpQsOGDYmLiyMxMdFh8cWsWxv0ej2XL1+mUaNGt70lStwZV8wvcryNokOHDtZWQRkZGbz44otFMrIncsmFC7B/v32gAdQEYfbsQh1syI8s2xa8vb3x8fEhMTGRw4cP2508tm3blvLly/PPP/+wdOlSu8cJCwsjMDAQrVaLh4cHJUqUcLhVoW7dutxzzz34+vo6XIksU6bMHZ0Ei5uqVKnCsmXLuHTpEvPnz+ell14iJiYGrVZLaGgo3bt3Z/r06YSGhpKRkUFcXNxtb11ITU0lOjrabrKZNXgQEhIiwQNxS7c7tyhdurQbRifcwdvbm379+tGvX79b3vbChQv0zlIbgM8+s69RZOHhAT/+aNOar3Llyly8ePFuhlwkPPDAA2zdupUuXbrwyy+/EBYWdsv7nDlzhk6dOvHiiy8WikBDUZS1/e8999zD9OnT2bp1K5999hlTpkwhOTkZHx8fihUrRrNmzahUqRJ6vR4vLy8qVapkE0wIDg7OtvhiWlqaBBoKuBwHG3Q6HR9++GFujkUUEUajkdWrVzN37lwOHTpEfHw8/t7elE9PZxjwEOCX9U5xca4faCFjSVvLesJYunRpSpcuTVxcHN9++6318pQbFaW7du1Kw4YNSUhIsMs88Pf3t0anIyMj6dKli93JpKVSe8WKFamYpVVZZpIClzcyMjJsPvNu3brRrl07u+DB4sWLrcEDRyzBA8tnGxISYv37lStXqFWrls3vRUHetlCYXL9+nejoaOvnnN8XBWRuIe5GfHw8K1as4IUXXuDKlStkZGQQkpJCa6ORMUDdrHdIT4fr120u8vX1JSYmxkUjLtgsLZ2bNm3K1KlT6dGjh8P2f8nJySxbtoyXX36Zp59+mnHjxrlhtOJ2WOaKjjo4WP6edXujl5cXkZGRTJgwwS47ITg4mMDAQJkPFGF3VSBSiDthNpuZO3cu77zzDuHh4dZ+2kFBQRhiYjhQty5zjEaeA0YArwHeoOoI3CgCKG6yZB4ABAYGYjab2bFjh10woUaNGjRt2pSUlBSmTZtm9zitWrWidOnSeHl52WQeWE4ky5UrB6he8hMnTnS6bSEoKEgqS7uAJXhwO1sWcho8cJR9kF3w4MiRI9auHSJvpKWl2Xy2FSpUwMvLi5MnT3Lw4EG7z/7pp5/Gz8+PPXv2sGXLFuvjdOvWzY2vQoi8cf78eZ599ll27tyJTqdjzpw5lC1bFk9PT659/DHfvfsu3dLTKQu8CbSy3FGrtZtfxMbGotfrXfsCCiiNRsNbb71F06ZNmT59OuPHj2f48OHUq1ePgIAA4uPj+fPPP/n8889p0qQJixYtom3btu4edpFmMpmIj493GkzIrvhiSEiIXevf3Co6KQovCTYIlzCZTIwdO5bt27ezfPly+5PS8HDKPfss3T74gFMGA08BnYHvgUA/P3jlFdcP2g0SExNJTEy0OXEMCAiwtltbsmQJMTExNpkHtWvXplevXmg0GtatW0dGRobNiaIl/czX15euXbvanUxaMg90Oh2DBw92OjYPDw+XFn0tKu4keJCUlOS0YKLl88mN4IHIW5ZsII1GQ1JSEufPn7f7nFu1akWxYsU4cOAAn332mV2xOkutk9jYWE6fPm39fMPDw23+nzZo0IBq1apZrz906JBLX6sQee3w4cN07tyZoUOH8uKLL3LhwgWaNm1qvb74xInUmDOH569d4zugH/AB8Ii3N9xzD2RK5zebzaxevZr333/f5a+jIOvevTvdu3fn8OHDLFiwgC+//JKEhASCgoKoVq0aO3fuzDazUeQey1ZIR+0gY2NjSUhIsBZftAgICCA4OJgSJUpQrVo1u04Oknkq7oYEG4RLTJo0iUOHDrFp0ybnfX1ffx0SE6k4Zw7feXvzeHIyfc1mVn3+OV4FsA2VJfMgLS3N2mLx4MGDXLlyxeakQq/XW9t6ffHFF0RHR9s8ToUKFazBBj8/PyIjI21OGiMiIqy3ffrpp53ufddoNLJH0gXyInhgqXngKGhg+bcED9zHbDaTnp5u8/lGRESg1+u5du0amzZtsvnMk5KS6Nu3L1WrVuX8+fN89dVX1sfy9PTE39+fhg0bUqxYMcLCwrjnnnuoXr263e8DqGBCdgUfQ0JCCk1VfSGyOnfuHJ06deLNN99k4MCBXL58mWrVqvHee+/dzE4ICoLNm/Hs0YN+589TS6OhXWIiwdWr03X1apvH27ZtG8nJybRr1871L6YQqFmzpsMMSpF7UlNTHW5tsPyZmJhoc3tL+1+9Xk+5cuXsshKytv8VIrfJb5fIc5s2bWLFihXs2bPHeaABVDrj9Onw8st47NzJXC8vOr/1FrMuXGC864abLYPBQHx8vM1JRUZGBi1atADgt99+48iRI9Y2fgB6vd5aGXvfvn2cPn0anU5nPXEICAiwPn779u0xmUw2K9SWzAOAXr16ZTu+zLcVuSM9Pd3pFoU7DR5kLZiYXdcFCR64j9lsxmw2o9VqycjI4NSpU3afdY0aNahatSrXrl1j9uzZdmmnllonGRkZ/Pvvv9bPNzw8HH9/f+uJUJkyZRgxYoRNJlLmz71kyZI0b968yFY7FyI7o0ePZtSoUQwcOBCA4sWL07lzZxYuXMhTTz1184bVq8PRo7B/PzUuXOD7lBS6PP44/3l745/p8WbOnMno0aOLVHtkkX+YzWaSk5OdZiXExcVZO3lZeHh4WIMHlStXtquZEBgY6LT4ohCuIMEGkec++eQTnn76aYoVK2Z7xY4d8M47cOAAlCkDTz0F3bpBsWLQqROewGu+vgwdOpQnnngi1w/+RqPRZhVSq9Vy+vRp64lF5pPKsWPHotVqWbduHXv27LF5HC8vL5o3b45Go8Hf35+SJUvanDQGBgZab9u/f388PT3tTiKPHDkCQNWqVXP1NQp7lhXoWwUNLH+/3eBBsWLFsu22IMED93GUeeDv70+pUqUwmUz88MMPdp//vffeS/v27cnIyHCYeWDphuDv70/jxo3tPnPL913x4sVtT3qy8PPzo1SpUnn7BghRCJ0+fZrt27fz9ddf21w+afx47m/fnpazZtFQq4XOnVU3qzJloG5dqFuXJkCLRYv46quvGDFiBKAyC//8809mz57t+hcjigRL8UVnhRfj4uLs5hze3t7WwEGZMmXstji4ov2vEHdDgg0iT128eJG1a9fy6aef2l7x+ecwdqxqa2k2w8mTsGsXDBkCM2dab9asWTP8/PxYv3497du3v+Xzpaamcv36dbsTxnvvvRd/f3/279/Pxo0bbTIPAJ555hkCAwM5c+YMW7dutcksiIyMJCMjA29vb+rXr0+lSpVsTioyb1to1qxZtuOT9j25L2vwwNHfT5w4oQqR3tjW4kjWbQuhoaESPMinzGYzRqPRmvp56tQp4uLibD77sLAwWrZsCcAHH3xAQkKCzWNYap1otVrOnz+Pt7e3TeaBpTCqj48Pw4cPt372WTMPfH196dChg4teuRDCYu7cuQwePNi2llBSEnUef5x5aWl0PXGCb4Hmp0/DwoWwdSvc2JIIqu7JpEmTGD58OAsXLuTFF19k/fr1UhxS5JjRaCQhIcHpFoe4uDiMRqPNffz8/NDr9YSGhhIVFWXXyUGKL4qCToINIk+tWLGCBx98kODg4JsXxsXBmDEq0JBZUpKaEAwZAg0bWjMP+vXrx6JFi2jfvj3Xrl1j3759dieUvXr1onTp0hw9epSVK1faPKxWq6VGjRrWk4WSJUva7Xm3FL9p0aIF9913n9Mv9lKlSskqZB67neBB5r/fKvPA8vmWLVtWggf5lNlsJi0tzeazhZuZPhs2bLArohgZGcljjz0GwK+//sqVK1cAFdDLXBgVoEmTJtbMI8vnnnlLV3Zt2DQajTWLQQiRf3z11Vf8+uuvthfOmAHHj/NgWhq+QA+gc3o6Y9PTaTx4MJpMmYlt2rTh3Llz3HfffVy8eJGNGzdKdqHIVnp6urVjgyV4cOjQIbZv305cXBzx8fHWAsAWgYGBBAcHU7JkSapXr26zzSE4OFiKL4pCT4INIk9dunSJSpUqWf+dkZFB9KJFGDQakoAkwABUBUoDl5OT+fqppzB06WLNPDh+/Dhnz54FVA/tzJkHluCB5cSifPnyPPTQQ04zDypVqmQznqxkX1vuy4vggU6nIzQ01ObkMWsAydvb2/q5HzlyRPa8u5Bl24Kl3/qFCxe4fPmyzedsMpno3bs3AMuXL7duJbLQ6/XWiX9sbCwGg4GAgACKFy+OTqcjLCzMett+/frh6emJTqdz2OPdUlNFCFE4mM1mLl26RFRUlO0V8+fDjblDR+Ao8DnwCBD411/c06cP/uHhxMXFsXXrVlJTU2nRogWvvvoqvr6+rn0RIt9JTU3NdotD1uKLWq2W5ORkqlSpQvny5e2yEqT4ohASbBB3ISMjw3qi6OvrS0hICGlpaTaV13ft2oWfnx87duygSZMmJCQkMHf1akhNtT6OFghGBRt8zWZKmUzo6tSxnjTu27ePRYsWASqY8PLLLztdgbZ8uYu846rgQda/Zw4eCNdylHlgMBioXbs2Hh4eHD16lD179th8/kajkZdffhmtVsvevXvZvXs3cDPzIHMtk1q1alG6dGmbzzxz4dSePXtmO77MgQchROFn2UpltzUxy3apUOAZ4Clgs68v/zZsSFJQEEFBQUycOJFJkybRqlUrCTQUAWazGYPB4LTwYmxsrM32WlA1eizzyipVqtgFE4KCgjh27JgsZgiRDQk2CDuXLl0iMTHR5sQiISGB6tWrYzabmTlzJomJiaRmChg0adKEzp07o9Vq2bZtm/WkITAwEJPJZA0ABAYG8tBjj+G/YQP+RiM6wBewnEIG+/vTe9gwVdDphpMnT1pPJuRkM/dJ8KDosQQPPD098fDwICYmhn///dfus+7evTtBQUFs27aN3377ze5xoqKiCAoKsgYiMmce+Pv7YzKZ0Gq13HfffbRo0cJp5kHNmjVd8bKFEIWEVqslICCA69evW1tLA9C8OaxapWpBZb490Mrbm1ZPPw2ZvoNiYmKkRkMhYTabrfUSnAUU0tPTbe7j4+NjU3wxaycHf39/macIcZck2FCIZc48MJvNlCxZElB9pK9evWpzUlGyZEn69OkDwJIlS2yKqWm1WmtldY1GQ/ny5a0V2S0nkOHh4YCKAmfOPKhatSpPPvmkNR3a09OT6r17w0cfqW4UmQIWaLUQGAgPP2zzOn766adbFl4s7BISEjh37hyJiYkEBgZSunRpm5XfzBytQGcXQMh68LWwpKVnLpiYNWggwYP8wRI8yPr5VqxYEb1ez/nz59mwYYNdy9Zhw4ZRtmxZzp07x48//gioyteWz9Xyu1G+fHnuv/9+u8/c8jtYu3btbFd2sm15K4QQOdCsWTN+/vlnBg0adPPCV1+F338Hg8H2xjodPP+8TaDh3LlznDx5klqZikYWNWazmXPnznH16lUAihUrRtmyZfPlsdxoNBIfH+80mBAfH29XfFGn06HX6wkPD6dy5crWIIIloJB5m60QIm9IsKEAunbtGtevX7c5YfTw8KBNmzYAfPPNN5w4ccIm8yAyMpKRI0cCcPToUa5fv249cQgJCbEGIkClLFuCCTqdDl9fX44ePWq9vlu3btmOL/MXd5s2bcjIyGDz5s3cd999N2/0008waBCsXg2+vpCWBjVrwvLlalJww9WrV1m1ahXTp0/P2ZtVgJnNZnbt2sXHH3/Mjz/+aO2OkJCQwLVr12jRogX3338/JUqUsDnBzC54kPlkMSwsDJ1OZ11tDg4OJiAgwHobCR64jyV4AGrlJSUlhb///tthe8bKlStz9uxZFixYYPc4ffr0Qa/XW3t3BwYGUrx4cWuwwJJxVLVqVSZMmGBXWNFCCqMKIfKbMWPGMHXqVNtgQ/368N13MHSo2lKh1UJGBkycCJMm2dx/3rx5DBgwwGngvjBLTEzkq6++YtasWVy8eJESJUqg0Wi4fPkyISEhjBkzhoEDB971ttSMjAyA26pbYCm+6KyTQ0JCgk3xxYSEBA4dOsT169dJT08nMDCQ8uXL88gjj1C/fn2Cg4MdZtIJIVxLgg1ulJGRYXPyYClcePjwYU6cOGFzUmEymay92tevX8/hw4etj6PVagkPD7cGG0qXLk1gYKDNCmTmlcVhw4ZlO66KFSvm2mvUaDSMGTOGjz76yDbYEBgIK1fCxYtw4gRERoKDwo3z5s2jR48etmmSBZyjzIPDhw9z9uxZ6+UXLlxg9uzZxMbG0qBBA0aOHGnT3isxMZH9+/czZcoUIiIiGD9+POXKlcu220Lm4MHVq1dZuHAh8+fP59SpU4D6rKpXr87jjz/OwIEDpUJyLnKWeRAWFkbZsmVJSUnhm2++sclCycjIoH379rRo0YKUlBSHmQeWiVxoaCgdOnSwyzixTKJLly5t7SXviI+Pj3zeQogC5YEHHmDcuHHs3LmTxo0b37yiY0c4dw727VNdr+rWBX9/m/umpKQwb948h9vDCrvPP/+cZ555hvvuu4/33nuPdu3aodVqAXWs2rhxI7NmzeLll19mypQpjB079rYf22w2s3XrVmbNmsUvv/xiLagYEBBA586dGTBgAJUrV3bYEtLShchCq9USFBSEXq+nYsWK1qyEEydO8MUXX7B161YGDBhAixYtCAwMJCkpiX379jFo0CDuueceJkyYQNeuXXPvjRNC5IgEG/JAfHw8V65csVuF7NSpEz4+PmzdupVNmzbZZB4AvPjii3h7e3Px4kVOnDhhk3ng7++P2WxGo9Fw33330aRJE5u2fplXoJs2berql5ytIUOG8PHHHzN37lxrdoVVZKT6cWDz5s1Mnz6dTZs2uWCUOWc5ibzdmgeOMg+io6OJjIzE39+f5ORkPvjgAzp06MCjjz5KUFCQ0+CByWTitddeY9q0aWzcuPGWLfoMBgNPPfUUy5cv58EHH+SLL76gfv36eHt7k5KSwrZt25g9ezYvv/wyw4cPZ+rUqQ5Xuos6S/AgIyMD/xuT2H379pGQkGDzmZcvX56WLVsC8Pbbb9u1xGrcuDFly5bFy8uLlJQUu8yDcuXKAWobQnaZB/7+/kV+q5EQomjx8PDgjTfeYMCAAWzfvt26nRNQGQ316zu8n8lkYvDgwbRp06bI1Yt59913mTNnDps3b6ZGjRp212s0Glq3bk3r1q05efIkXbp04dKlS7zxxhvZPq7ZbGbt2rU89dRTGAwGHnzwQT755BNAdRO6cOECu3fvZvDgwXh7e/PAAw9Yt/np9XoiIyPtii8GBgZagyAWc+bM4fXXX+eVV15h6dKlNoWGAR566CFee+01vvvuOyZMmMDWrVuZOnWq3eMIIVxHgg3ZsGQeZK5roNPpOH/+PLt377Y7iXzssceIiIjgyJEjrF692vo4Hh4e6HQ6UlJS8PHxISIignr16tmdPFraLrZv35727ds7HVfx4sXz/LXnpqCgIFavXk2rVq1IT0/Ptqe9xdq1a3n00UdZvHgx1apVc8Eob8qN4AHgsK6Fs3oHZ86coXbt2sTFxdG0aVNefvllxo8ff8uxWiZbAQEBdOnShS1bttgdfC1iYmLo3LkzVatW5cSJE3bZIr6+vrRt25a2bdty/vx5RowYQffu3fnuu+/w8/O78zeygElNTbUpjGowGPD29rbu5/3+++9t2jcajUaqVKnCgAEDAJVxFB8fb5N5YAkuaDQaOnfujLe3t13mCajPMbvMA61WK0XMhBAii0GDBnHy5ElatWrF6tWrrQFaZ1JTUxkyZAiXLl1izZo1Lhpl/rB48WJmzZrFtm3bbLbOOhMVFcWWLVto3rw5JUqU4NFHH3VaeHHLli2sWrWKbt26UbVqVTQaDWfOnEGv11OsWDEqVKhAly5dCAoKYvv27bz44ovMnj3bWivsdsyfP593332XLVu22Lc8RdXgmDdvHj/99BPXrl3DaDTy0UcfsXnzZpYtW3bLxRghRN4oUsEGS2ZASkoK586dsztxbNiwIaVKleLUqVMsW7bMrur+wIEDiYqKIjEx0WHmgSUNuXr16tZVakeZB5UrV6Zy5coufe3uVqlSJbZs2UKXLl1YtmwZY8aMoXfv3jap2yaTiXXr1lkPht999x0tWrS46+d2VfAg69+9vLxuu+bBpUuX0Gg0zJo1i/r16zsJNKQDsYAesF3dnjhxIjt37mT+/PnW7TaZpaam0rNnT+69915mzJhxy3GVKlWKH3/8kUcffZSBAweyfPnyArUyYDabSU1NJSUlxXqS/s8//3Dp0iWbz9rHx4d+/foBqjDqmTNnbB6nRIkS1mCDRqMhMDCQEiVK2BVGBXj88cfx8fFxmglik+YrhBAiV7z++uuEhIRQr149HnnkEUaPHm23an/16lUWLFjAnDlzaNiwIb/++muRaneZmprKs88+y+rVqx0GGozG68TFxRIXh13NhD59+vDss89y/vx5mxoIlro/ly9f5tdff2Xu3Lk0atTImp3g7P2tVasWjRo1okOHDoSFhdG6detbjv/gwYP873//Y/PmzXaBhuPHj/P888/zxx9/8Mgjj/Dxxx8TeSNj9siRIwwYMIBq1arRsWNH3nrrLapUqXIH75wQ4m4V6GBDenq69UTx33//JTw8nLCwMJKSkvj999/tTiY7dOhAw4YNuX79OosXL7Y+jqVln6Vjgl6vp379+nYnj5YTi6pVq1pv60jQjR7OwlaFChXYv38/q1atYtasWTz11FM0btyY4OBgkpKSOHjwIP7+/owdO5Yvv/zSYdEms9l8x60a3RE8yAmj0cjcuXNZuXJllmuSgYnAAsAEeAAjgLcBFazRaDQ888wzDBkyhPHjxztMPdTpdEyfPv22X4OnpydffPEFzZs359tvv6Vv3745fGVm4A/gmxvj7wncj2pGdvvS0tJISEiw+XyTk5OtAamtW7dy8OBB6+duNBrx9fXl+eefB2D//v0cOnTIJrsg8+9Ys2bNaNCggV32icWDDz6Y7fiKYpExIYTIDyZMmEDv3r2ZN28e7dq1o2zZstYtalevXmXnzp306NGDZcuW0ahRoyJX/Hj58uVUrlyZgIAAdu/enSmgsJfY2EUkJl680S00FOiMRlORwMBA63y4QYMGADz66KPWbQ5eXl4YjUYqVqzIihUrss3Izapu3bosWrSIYcOGceLEiVsuZsycOZMnn3zSLlCwdetqevd+iOeeq8qiRWMICBgN3CxmXKFCBWbPns2nn35K06ZNue+++/juu+9k26EQLpSvgg2Wnuxms9muQKLBYKBcuXLUrVuX1NRUpk2bZpN5EB0dja+vr7VI4smTJ60nDJYq/hEREQCEhYXx2GOPWU8msmYeFCtWjE6dOrn2xRcRXl5e9OrVi169evHPP/9w+PBhrl+/bk0jj4qKwmAwsG/fPqftG50FD7y8vOyCQ86CB/mx28Ivv/xCZGQk9W32mZqBTsBOICXT5XOBv4GbaaBNmzZFp9Oxfv16m4O+2Wxm1qxZfPbZZ1kO6CZUAGMacAGoCLwI9AHU++Lj48PEiROZPXt2DoMNaUBXYBtgKf60BLO5Fqmpv5CUZEKv1+Ph4cGFCxc4efKk3Wc+ZMgQfH192bRpE1u2bLF7hsaNG+Pt7Y2npydBQUHWrKKsWxm6du3Kgw8+6DTzwNXbdYQQQuSeMmXKMHnyZF566SW2bNlCdHQ0GRkZhISE0KRJk0JVaDozs9lMSkqKww4OcXFxHD9+nG+++YYmTZqwZMkSQC2yBQXFotfPJCoqDb0e9HoIDo5Gr/+GoKBVeHi0sz5HUFAQEydOZOrUqTbzpp9//pmSJUs6CDT8C0wGfrzx757Ay0BZ6y06depEaGgoa9asoXPnzk5fX1xcHMuXL+fIkSM2lx86tIiePYewaJE3nTrtBg4CH6DmNf2tt+vVqxdPPfUUs2fPplatWvTo0YP169cX6ZanQrhSngYbMmceJCUl4e3tbd1Pt3r1aq5fv25zUlG9enV69uyJRqNh+fLl1pNKS+aBpQWPt7c3DRo0sDlxvHDhAo0aNQJUatfTTz/tdFxeXl6UKVMmL196kWUpnHe7WQdJSUnWivqnTp1i586d1sfy8vKyOWmMiIhwWO/A8veC3uJozZo11pT+m7YAe7ANNIDKdtgK7ALU771Go+Ghhx7i119/tTnwr1+/Hh8fH5o3b57lMYYA3wKWfuT7gKHAX8Cb1lv16NGDCRMmcOTIEapXr+50/BkZGcTFxWX5jGdSu/ZmgoNTOH4c1q+HpKREDIbdGI2dgU6MGTOGiIgIzp07x7p162wyD4KCgqy/HzVq1HBY98ISPGjSpAlNmjRxOr6ilDIrhBBFlbe3N23btnX3MHKN2WwmKSnJYTDB8vesBce9vLysGQhlypTh4sWLPP/884SHh1vbXGu17VELAllZsin3WC9p164dZ86c4cqVKzZ1w2bNmsWYMWOy3P8foDGQABhvXPY5ar6xC7i5DWLMmDHMmjUr22DD119/TYcOHShRokSm9ySZAQOG8v77Zjp1srx2y5/DgJZYMhx8fHwYOnQoCxYs4N133+W9995jwIAB7N+//64WnAwGA7GxscTExJCeni7FtIVwIk+DDVOnTrX5d4UKFRg8eDCg9qinpqbaZB5kLt4ydOhQfHx8HNY80Gg0dOzY0eaxzWazpDHnAUvwIC4uzmGdi+yCB1llzjwICAigePHit2zVWJTExMRw7733Zrn0Z25mBGSVDKzGEmwAiIiI4J9//rG51YoVKxg8eHCWg+pubAMNFknAdMzmUaSmRpCUlISfnx8DBgxg8eLFdOzY0SbLxLI9qWLFipw6dYqvvvoqy+N9TcmSKQQHg7c3BAer5iP+/hnodLvw979KQMBB4AXq1atHvXr1nB6wS5YseVtFrYQQQoiCwmQykZCQ4DCIYPkz67zK19cXvV5PSEgIFSpUsOvkoNPprMf8P/74g9DQUAdZk9l1+tqPmmOo4tCWFusxMTHWYENCQgJbtmzh+++/z3Lfp4G4G89hkXHjsueA76yXPvTQQ4wdO5akpCRrweSsTp48Sd26dW0u27z5HdLTYeBAR/dIR82LIoEBwAjq1avH8uXLAVVU9K233mLLli3WblG3y2Aw8PXXXzNr1iwOHjyIXq+31ofq3r07Y8eOpWXLlvkqa1YId8vTYEO7du1sTiIz1zEYOnRotveVk4q8kdPMg+joaNu2UthmHkjw4O5ZthDZ8kLVNjA5ugdZC0VatiJlFh0dTdu2bTGZTJmyiT4hKSmZpCQoV079xMXB0qWQlJSBwTAao1EFMbp27UqZMmU4cOCANUvC8tkGBwfj6am+RiIjI+nZs2emz9wPf//XuXE15curn0yjBU4Cp4Ff8fKagapFIYQQQhQOlqw/Z1kJ8fHxmEy2x3h/f3/0ej0lSpSgatWqNsEEvV5vU1z7VhzPLUDNIYwOLs98/U1Z5xdXr14lLCwsS9agEfgV20CD9RGAVTeuUyfjOp2OYsWKERMT4zTYkJSURKlSpWwu++ST7xkzBhyf02fw//buO8ypamvg8O9kes/M0MvQmyAKIqCCoggCSlFRuoV7ASmi2C8qdgWVa/0GFHu5qFjBLjZEvYiIKMgVxEKTMuj0Psn3x5pkkkwSpqRMMut9nnmUnOScnTOZnH3W3nst+LPyZzvwCImJ91BQIAM3hmEwe/ZsMjMzaxxssFqtPProo9x2220MHDiQ2267jbPOOouIiAi2b99Oq1ateO6555g1axaRkZE8/fTT9OvXr0b7Virc+TXYUNuIoaq9+ixbcOUteHDw4EF69eqlwQM/atKkCfv27XN59DxkDaLzDASrFYqLIyksPI2IiGzMZjNWq5WvvvqK/Px8XnvtNfvv/ODBg0RERFBaWsojjzxSuYet2DoDp58uwYaqmQdWEhJakZBwFvHx8bRt25YNGzYQHR3NTTfdZA8uuEpKSuK4445zebQNsPco79xS+f7mA+OApl6frZRSSjUUJSUlbnMl2P6bl5fn9HzDMEhOTiYlJYWMjAynIEJKSoo9+aKvpKSkkJ2dTVFRkUMZawMYSdXNv6tB2BJQgwRMDh486JT3ory83F6yvUoF7gdHbCxUJboWkZGRHvulIPkiHM9hYWEh77zzM48/Hg/kezkWyOyM/eTmLic5uWoZxsUXX8yNN97ock7cs1qtXHPNNXzwwQd89dVXbqtZpKSkcPnllzNv3jxeeeUVRo4cycqVK2uVNFOpcNWgEkSqquCBp0CBr4IHtZ15sH37di0X5Gdjx45l9uzZTJo0iaKiIvvvOTn5VI4/fh1QyNNPw19/QWFhFBUV3YEP6d37AOeddx5Wq5XVq1dz4YUXsnfvXvvMg7S0NLKysoiJiXGYedCB+Pi5JCQU2GcexMXBpEkgHYzLgBPsbTt8+DBpaWkeAw2eXV/547pcwx0T8Cowu5bHUEoppXzParVSVFTkcVaC7SbeUUREhD2A0Llz52qzEpKSktzcpPuPLYfFyy+/zCWXXOKw5T6kUlQ+VQECA0gAHnLax+rVq+nduzdNmjSxP5aWlsaRI0dcZjxEA72RHFDu9MUx0GCxWDhy5Aipqake23/MMcc4LdHMysrCbE4nJcWKLP10FyxxVMaXX66nR4+b7I+YzWaSk5M5cuSI0xJud+69914+/vhj1q9fby+l7Yktd1arVq04//zz+fDDD6stAVGqsdFgg58dLXjgLpgQiOCB8i9bdmhb9QxbYqPNmzdz4MABp99/amoqxx13HEOGDCE7O5tbbrmF9g7rDbp0mczxx48E7qdp0/2kp6cRHz+RhIQJJCQk2S/+H330ERkZGWRmZjqtFywvL+eFF15g5syZDjMPOgD3AztwThAVC5yMY6DBarXy2muv8eCDD9bhTMxFZlE8W/nvMjxP2ywBsutwDKWUUqr2rFYr+fn5XoMJjpXPQG7ebcGD1q1bO81KMJvNJCYmNrg1+3PmzOH22293CTZ0RfI33Qi8jdy0D0cSRPd0er27RJBpaWlkZGRUq4AlfYvRyKwCR3HAvU6PfPjhh3Tp0sWeAN6d888/nwULFvDrr7/SsWNHSkpKKpduvA2cgQRLipH+RXX5+fDiixZ++MF5+XZsbGy1xJqu9u/fz+LFi9m6detRAw2OBg8ezOLFi7niiiv4/PPPa/w6pcKRBhtqyTF4UJMlCzUJHjgmTDxaqUYVXLm5uWRnZzv9nsvLy+2Zr9977z22bdtGYWGhfQ2m2WzmyiuvBGDbtm3s2bPH/js1m832kqyGYTBz5kw+//xz7rrrLpKSkoiPj3eYTTCfc85x366KigqWLFnCnDlzqnVyLrjgAq6++mp27txJly5dKh+NAL4AZiGlqSKRIMA04EGn169fv56ysrI6Zvc2gOVIZuu3kUzUr+N+pkMc4LmaRO3lIqW2nqF793ygO1KK63wfHkMppVRDZbFY7NdtTwGFigrnAHhcXJx9VqAt+aJjMCEuLq7BBROOZuTIkcybN4+PP/6YoUOHOmzpArzi9bXffPMNW7du5bzzznN63DAMezUJ52DDUGSW4lzgYOVjLYBM4HT7sywWCw899BATJ060z25wN+MjLi6OSy65hOXLl3PvvfdiNpv5+++/sVq7YRi7kUTZPyMzNQ5Ve/2LL8KQIQm0adPe/pjVauXvv/8+agBhxYoVTJw4sVrOCJkRcgPwLd26RQEXAvcAVfnmpk2bxk033cS2bdvo2bMnSjVWjT7YUNvgQUFBQbULk010dLQ9OJCUlOQ2eOD4by2TEzy2mQe2322bNm0wmUz88ssv7Ny5s1rAaMGCBZhMJtatW8e3337rtK/4+HhOP/10DMMgPT2drl27Ov2ek5KS7M+dPHlytQSOgL1+9MKFC/nkk0944IEHuO+++2rUobFarVxxxRUA9movjmJjY7n00kt56KGHePTRRx22mIGXkZvyQ0jmZucETVarlQcffNBtEKN2OiI5GUqATyv/6/h3FAW0w7EjUj9FwEDgV6CkMonUT8BFwL7KtiillApltuSL7io4ZGdnk5ubWy05YmJiImazmZYtW9KjR49qORNqk3wxVERERPD4448zZcoUPvnkE4455pgave7XX3/l3HPPZdmyZW7Py5QpU1i4cCG7du2iU6dODltGIdffPZX/bostKeTevXtZsWIFy5YtIysrix9++IHFixdjGAYXX3wxs2fPdhgYEXPmzKF///6ce+65DBgwgJSUFDZu3Ej//v2RWRSjK48xHcfBjF274LbbDFatut5pfxs2bCA9Pd3r8o2ysjIef/xx3n//fZctbyPBBZm5YTJVAP9BEmP+CMgAUlRUFDNnzmTZsmUufS+lGpewCzZYrVZKSkpqlTCxtsEDT7MPNHgQfIWFhWRlZVX7HQ8ePJjExEQ2b97Mxx9/7DTzAOCqq64iOTmZ/fv38/3339t/t7ZpkhUVFZhMJvr160ePHj2cZpw45jGQC59n7gINjmJjY3nrrbcYNmwYM2fOZOnSpU5VXFzl5ORw+eWX8/PPP/PBBx94nP2yYMEC+vfvzymnnMIkSczgILnyp7oHHniAn3/+mWeeecZru2suBvgSOBcZiYhClnGciIyE+Gq06AXgD6rqbtsUAv8C/oFrYEUppVTDUlJS4nVWQn6+c4JAW/JFs9lMu3btqs1KcKyg1NgMHz6cpUuXcvrpp/P8888zbNgwr4MI69atY+LEidx6662ce+65bp+TlJTEokWLGDt2rJucBgaQYf9XYWEhc+bMYfXq1Zx33nkkJSVxyy23MHfuXAB+++03Hn/8cQYNGsSAAQN4+umn7QkpO3TowHPPPce4ceN488037dUknPtcE4DDyDXexM6dFYwYUcjNN1/IKafc7NTuzMxMZs+e7bVP9t1339GkSROOPfZYh0etyIxQ1yUi5cgy0PtxXCoydepUTj/9dA02qEatwX/j1jR48Msvv5CSknLU4IHtBjE5OZmWLVt6zXmgwYPgcZ15kJ6eTkJCAocPH+bbb7+t9jmYMGECbdu25ZdffuH111932ldMTAx9+vQhMTGRlJQUunbtWm22iS0b8eDBgzn11FM9tsuWe8Gf0tPTWbduHbNnz6Z9+/ZMnDiR2bNn06tXLwzDwGq18sMPP5CZmckrr7zCueeey6effkp8fLzXdr/zzjsMHz6cvLw8ZsyY4bWTYbVaueuuu3jiiSdYt26d0+yM+msHfIeUpPoDmcbZyesrau8FPCeljETqi4/08TGVUkrVlC354qFDMvXd3ewEb8kXu3bt6hRECEbyxVAzZcoUmjVrxpw5c0hMTGTOnDlMnjzZXnayqKiIl19+mczMTA4dOsSyZcsYO3as131eccUV7N69m9NOO4133nnHbcLFnJwczjrrLLp06cL69eu58MILGT9+vD3QABJQuOeee7j11ltZtGgRJ510Ep988ol9fyNHjuTpp59mzJgxTJw4kddff5377rvPpSz7PA4cOJsnn7yThx9+gzvvvJcZM5xzTRw6dIg1a9bwwAMPeH1fR44coVWrVi6P/gzkeHhFKfASjsGGli1bcuTIEa/HUeFtz549PPbYY7z11ltkZWVhsVhIS0tj+PDhzJkzh27dugW7iX4X8GCDr2ceOCZM7NSpkwYPGriysjL2799f7Xfds2dP2rVrx4EDB3jhhReqzTwYP348vXr1oqCggC1btth/p6mpqbRp08Y+va9Dhw5MmzbNvt115kHHjh3p2LGjx/Y1lHWYiYmJPP/88+zbt48VK1YwYsQIsrKySEpKIi8vj+bNmzNz5ky2b99e4wDIsccey7p16xg3bhzLly9nzpw5TJo0yam2dXZ2Ns8++yzLli0jNTWVr7/+mpYtW/rpXfao/PEHb7XDa7JdKaVUfVitVvLy8rwmXywrK+Pw4cP2G8bo6Gh78KBt27bVZiU0xOSLoWbYsGH8/PPPrF271p74MT4+HsMwKCgoYNiwYSxatIiRI0fWKHBjGAZLly7l/vvvp1evXpx33nnMnTuXE06QRNNlZWWMHz+eNm3aEBERwcknn8yiRYtYsGCB2/3FxMSwZMkSUlNTGTVqFOvXr7fP8Bw1ahTffPMNmZmZlJeX0717d+bPn09aWhr5+fl8//33fPjhh1x44YV89NFn9O7d22nfJSUlXHjhhcyaNcupjKc7FovFzWfNgvcZmM5lPw3DcOrLqsbjl19+4dprr+Xzzz9n6tSpPPXUU7Ru3RrDMDh06BCvvPIKp512Gr169WLJkiX2v5dQVFpa6vFeHcCwui5m85FNmzZx+PDhOgcPvOU6cBc82L59Oz16+OvGRbmeX9uIhO13Gh8fT9OmTSktLWXt2rXVfu8nn3wyJ598MtnZ2dWqGsTGxjJ8+HD69u1Lbm4un3/+ebXfdYsWLZxuisNNTT6/RUVF5OXlkZycXJmJuW4sFgsffvghmZmZrF+/ns6dO5OYmEhubi67du1ixIgRzJkzh0GDBjlcaG1fE6HSyXsESd7kbnZDLHAA8Jz9WtWOfv/6ly/P76ZNm0K6UwOBfw/6+XavoqLCY/LFnJwcj8kXXZc2HDlyhL59+2I2m4mNjdVggo8d7fNrsVjIycnBarWSkpJSr5khhw4d4qmnnmL58uVER0fTokULDh48yO7du2nRogWzZ8/m0ksvdZmNYMVd38JqtTJ58mR69uzJTTfdVG17fn4+Z5xxBllZWZx55pmkpqbSsWNHJk6c6La6RW5uLuPHjyclJYWXXnrpqO/zq6++4vLLL2fTpk0Oj1Yg+a0Ou3lFFLLE4hH7I7///jsnn3wy+/fv93os5Vkofv9+8803jBkzhquvvprZs2eTmJjo9nklJSW8+OKLXH/99TzzzDOcffbZAW7p0c+vbaZ5aWmp/e/q448/Jisry/5dX1hYSL9+/WjZsqXba7NfZzZ89913umwhBO3Zs4e8vLxqAYMePXpgtVr597//TUFBgVO0tn///owaNYqIiAh++OEH++80LS2Ntm3b2i8sSUlJ9pkHtuc4fuEnJyczevTogL/nUBAXF2df7lEfJpOJESNGMGLECP788092795tD2J07NjRqY42bAOuBT5COgRnIBmfj3Oz54bkEmApkgzSsRpMPFIZQwMNSinlTVlZmddZCXl5edWSLyYlJZGSkkKrVq3o0aNHtcCCu7xC27dv9+MMOnU0JpPJa6LE2mjWrBk33HAD1157LT/99BN//fUXc+bMYcmSJcydO9ehv2dBqlMsBvYDacDlSL4F+YwYhsH111/PmDFjuOGGG6rl2khMTLQHBN5++20uu+wyxo0bVy3QcODAAZ544gmWL1/OmDFjePjhh2sUUDnhhBPYu3cvO3bsoGvXrpWPRgAPADNxHswwAYmAcyLKlStXMmrUqKMeS4WPn3/+mTFjxvD4448zZswYr8+NiYlh+vTp9OzZk9GjR/PGG29wyimnBKilwlb+t6ioyH6vtm7dOvbu3Wv/vi8pKaF9+/b20rm7du2irKyMlJQUe/nfNm3aeFwy5Ndgw8KFC/25e+WF68wDwzDIyJBEPZ9//jmHDx92CiS0adOGiRMnArBq1Spyc3Pt+4qNjbUn/TEMg169ehEZGekUMEhLSwNkPeUNN9zgsV0REREuGYtVMLVs2dJLJ28rcBJQQNXMho+AU5CcB33938A6SwK+Aa4EXsNqtWAYTYFbkE6CUko1bsXFxV6DCQUFBU7PN5lM9uSLtpKQjkscGnPyReUsIiKCY489ls2bN5Ofn+8SaACYjXNupSPAEiSB9AfYZjocf/zxtGnThnfeecdt7ojIyEiWLVvGjBkzWLZsGd27d2fgwIH2fs2ff/7Jf//7Xy644ALWrFlDnz59avwebDeCy5Ytc8nvMAVJMH0t8DtWKxjGMOBhoCpfRUVFBcuXL+eNN96o8TFV6Lv00ku59dZbjxpocDRgwACeffZZJk2axK+//urT71Fb+d+CggJ7Cdevv/6anTt3kpOTwy+//EJqaippaWnMny+V2g4cOEBubq5T+V/H2UgzZ7rvRwcl2KB8x2q12qcWHjhwgL/++stpaUpkZCTDhw8HJJK6c+dOp5kHLVq04LLLLgNg9+7dZGdnk5CQYJ954HjDeeGFF9qDCbaZB7bSjABnnXVWIN6yCrprcQ402BQAVyF1phuyZkg5qjJ27Piebt36ETrLQFRwfI4k99qBJC29BjjT6yuUaoisViuFhYVey0IWFxc7vSYyMtIePOjevbvb5ItHq6iklKM1a9YwceJEl0DDLuA5oNjl2UXAV8hgxmn2R6dMmcLq1au9Jqrs27cvK1as4L777uOzzz6z3/Skp6ezcuVKlyoZNTdr1ixOOOEEbrzxRpdZn+Mqf/L4+edf6d69+mzP1157jZYtW9K3b90GZiwWC5999hnff/89OTk5xMfHk5GRwdixY70mBFfB891337Fv3z5mzJjhsiUXeAz53JcD5wFXYCuTCpIAtW3btrz99tuMGzeuxse0lf/NycmhQ4cOGIbBd999x5YtW8jJySE3NxeLxUJkZCQ33ngjhmGQl5dHSUkJLVq0IC4ujt69e9sHjUHuA31Jgw1BYpt54BgwsNU93rJlCzt27HBaxmC1WrnuuusAWL9+PVu3brXvKzY21ini1LlzZ5o3b+60TMWxmsC0adO8ts1dJmHV2FipWjrhznok87L7UpsNSxQWSyIaaFDe3Y/MfLGNtP0CfIHk/rjZ04uUCgpb8kVvZSHLysqcXhMTE2MPHmRkZNiDCLbHEhISNF+C8qmsrCw3Sbnf9vKKQmAVjsGG2lR0MJvNtbpRO5r27dszc+ZMxo4dy0cffeTmJj8Jq7V6P2jz5s3MmzevTrMasrOzeeaZZ1i2bBkxMTGcfvrpmM1msrKy+Pzzz7n88suZNm0as2fPdljeoRqCZcuWcdlll7kE1/5Cyrv/SVXJ1KXA48BGoL39mXPmzCEzM9PpM1xSUmL/bm/fvj3R0dFs27aNr7/+muzsbKfyv9deey0JCQn27/6MjAyngLFt4No2OA2ByYmhwQYfsa1bNAyD7Oxse8UFx2DC6NGjiY2NZd26dXz66afV1jouXLiQ6Oho/vrrLw4cOOA08yAhIcH+IRkyZAiDBg1ym/MA4MQTTwzY+1aNlRXPgQilQs1eJKDgOtJWCNyNTJv1XMVGKV9zl3zR8f9zc3OrJV+Mj4+3T3ft0qVLtWUOmnxRBYP7ig6e+g9W3FV08FMu+xq56667+Mc//sEZZ5zBm2++edQKYGvXrmXy5MksX7681uvvt2zZwjnnnMOgQYN48sknOeWUU6qdv99++43HH3+cU045hSVLljB9+vRavyfle1arlZUrV7Jr1y6XLbcifYxSh8dKgDKs1tkUFb1m/14fMWIE8+bN45tvvmHz5s1kZ2c7lf+dOXMmrVq1wjAMoqOjq5X/tVXmGzBgAAMGDPDvG64FDTZ44DjzwBYwaNeuHQkJCezevZsNGzZUS6A4e/ZsmjVrxo4dO3j33Xft+4qNjSUhIYGSkhJiY2Np06YNgwcPrpYg07ZG5/TTT+f000/32DbnqVxK+YMBnAp86mF7fyAmcM1Ryq9W4bnzWwGsBG4MXHNU2CsrK/M6K8E1+aJhGCQmJtoTcbkucUhJSXGbfFGpYEpLS+PAgQMuj44EPOV0SwTOdXrk4MGDTlO8A81kMvHUU09x22230aNHD0aPHs2cOXMYMGCAPRBQUlLCq6++SmZmJrt37+all17ijDPOqNVxtmzZwrBhw3j00Ue9TmPv0KED99xzD5dccgkjR46kuLiYOXPm1Os9qvrLy8vDMAyaN29uf0ySLz5HdnYp2dnQqhWkp8P+/fDGGxZycj6ktPQupJIJTJw4kVatWpGbm0tiYqLTd71j3oRjjjnGPhs+FNQp2FBWVsbChQvZt28fpaWlzJ49m6FDh/q6bT5ltVqxWCxERERQVFTEb7/95jTroKCggIEDB9K2bVt27drFCy+8UC2SOnXqVDp37kxJSYnbmQe2coTHHHMMGRkZHmcedOzY0c20MqUamvuBwVQvHxmPZGNWKlzkICMN7pQh0yBVIIRi/8Kd4uJij7kSsrOzKSx0/l41mUz2JIsdO3Z0m3yxPiUJlQqGUaNGMXHiRO666y6HfB/dkTXrb+Lcv4gFegPOf+8rV65k3rx5AWitZ4ZhcOutt3L55ZfzzDPPMHXqVPLz80lPT6egoIC///6bAQMGcO2113LOOefUOsFfdnY255xzzlEDDY66devG2rVrGTRoED169PA6SKn8o6Kiwr6czTGxY3Z2Ns8991xl+d88+/NHjpRgQ1yc/LdTpwhSUgZhNrfHbDaTnp5OTEwMqampnHlm+OSLqlOwYfXq1ZjNZu677z6ys7MZN25cwDsDFovFqdrCzp07SUtLo3nz5uTn5/Pee+85zTooLCzk7LPPpl+/fuTk5PDKK6/Y9xUXF0dCQoJ9qkpaWhqDBw+uVp7TVhqoS5cudOnSxWPbEhMTPdZUVSp09EUS5l0J/LfysX5IoGFgkNrUUOUA7yPT5IYAbYPaGlVbJyMjavlutiUhs3xq6mvgQeBX4FhgQeV/VU00hP7F0diSL3oLJpSUOAevoqKi7MGDli1bOgUTzGYziYmJmnxRhZ0TTzyRtLQ0PvjgA0aOHOmw5TngHuDfQB4SaJgB3IWUkRRbt25l586dPs3DUB/p6elcffXVLFiwgP379/P333+zZ88eBgwYQHp6ep33+8wzzzBo0CAPgYbvgC1AC2AYjrduHTt25N5772Xx4sUabPADW/JF2/e62WymU6dOlJWV8eijj5Kbm2sfmLYtfauoqCA+Pt6h/G93UlJ+wmwGW57S1FSQAoBNgRE45hT766+/PCQ0zQeeBl5C/kYmAxchlVEatjoFG0aMGGGvSGC1Wn0SbbdYLFRUVBAVFYXVauWnn36qtkyhY8eOnHDCCZSUlLB48WKnmQeHDx8mMTGR5s2bYzKZOHjwIPHx8TRp0sQeNLBVXGjSpAmzZ88mISGBuLi4au1PTU2t9fQnpcJTPyQZpG0te2wQ29JQPYQkEYxCpuKXA5OAFUhNbtXwnQlkADuRmQw2kUi26LNruJ97gDuRJFBWYDPwMpIIaoqvGhvW/NG/qC2LxUJeXp69k7llyxZ++eUXe0DBXfLF2NhYe/CgXbt2TokXzWYz8fHxmi9BNTqGYTBnzhyWLl3KWWed5RBQiwBuQpanFQBxuLteLl26lBkzZhAVFRWwNteEyWSiTZs2tGnThsjIyHoFGiwWC5mZmTz11FMuWw4Bo4D/Vf47AulnvAkMsj9r/PjxXHXVVezcudPrQKiqrqSkxClQHBcXx7HHyuDAI488Ui0xaa9evejUqRNRUVF07tyZhIQEp+/69957jw8++IBRo0Yxfvz4ylc9DIzB/SzhO3AMNPz444+UlpaSkZHh8tzDyPLlQw77+Q4Z/NsApNbzTPhXnYINCQkSRcnPz2f+/PlceeWVbp/nukwhJiaGzp07A/DWW2/x999/O808OPbYYznvvPMwDIM333zTfjG3zTywJWWJjo7m1FNPtc86iI+PZ9++ffTr1w+QJEneplxFRkY6ralRSh2NBhncextZe1qMc3LBl4GWyCiNavhMSCnXSUid9xhkWcWJwCvU7FK5A+k4FDk8VoF0DGYgAQuzrxoctmrav6iP8vLyoyZfdCwdffjwYdq3b09KSgrNmjWrlpTLbDbbl1Eq1ViVlpaSlZVFYWEhKSkppKenYzKZmDJlCk899RQ33HADS5YscQm6Gcissur+7//+j6+++oqlS5cGpP3B8tlnnxEbG+smmeQo4AecA+AgOS92IH0MCXROnz6dxx57jPvvv9/v7Q0Vttx7jt/vhmEwcKDMzH3yySfZs2eP02vat29vDzb06tWLiIgIp+/65ORk+3NHjx5d7Zhz584lMzOTUaNGOTw6FHgKmIP8Lg0kCeodgHNyz2XLljFz5kw3wbVrgH04fxYKgT+QgF1mjc5JsBjWOqZ4/fPPP5k7dy6TJ092iN5U2bRpU7UoXevWrTn3XEn8snr1asrKyoiPjycuLo7Y2FiaNWtmz2Xw119/ERMTQ2xsbI1GNoqLi/Vi70d6fv1Lz69/+ev8tm9/IXFxW91uq6iIZ8eOrwiN8qD1E06f38jI/URH76OsrBVlZa1r/LqmTR8kPf0pDKO82raKijgOHlxITs75dWqTL89vYWEhJ5xwgk/25S816V94qzNfWlpKfn4+ubm55OXl2X9sjxUWFlZLvmgrEW37SU5OJjExkeTkZCIjI53KRyvfCqfvj4bI3+f3p59+4qWXXuK9994jNjaWuLg48vPziY2N5YILLmD8+PFERUVx6aWX0qdPH66//np71nx3ysvLeeKJJ3j11Vd5+umnadu2YS9LrO/5ffbZZ9m3bx8LF1YlzYyN3Ua7dhdhMhVVe77FEs2RI/8kK6tqUPXLL7/kqaee4sknn6xzOxoqT+fXarVSUFBg/37Pzc2lrKyMk046CYB33nmH3377zek1aWlpTJ48GZBZBGVlZU7f+/WdgVZUVMTQoUNZuXIl7dq1c9laTmzsTxhGOcXFPbFanf8GcnNzGT58OG+99ZbLgHgF3bqdgMlUijsWSyw///xdndsciP5FnWY2ZGVlMX36dBYtWmT/pbpz0UUXOVVbSExMtI9a+LqmZyDqhDZmen79S8+vf/nv/P7ucUtEhEGPHmbA9YITfsLr81vX91GBLKGpLiKijFatomnVqm779uX53bRpk0/24y817V+YzWaPlRxcky/aRqfatm1bLVeCbbTK26BGeH2+Gx49v/7lr/O7e/dupkyZwu7du5k1axaPPvqo003Sli1bWLZsGWPHjmXChAmsX7+emTNnMnz4cKZPn86sWbPo0KGD/fkHDx7kiSee4LHHHqNjx458++239uXPDVl9z29sbCwdOnRw2cd/ccxd4chkKqVp019p2rTq+bm5uZSXl4fV35EtB8K3335L06ZN7d/vY8aMwTAM1qxZU+16lpiYSPfu3e3lUnNzc6vNQLMFE/x1rm6++WZuuOEG1q1b5zQTQrjP31RWVsbZZ5/NJZdcwpAhQ1y2FiH9C/dMpmJ69OiGp8/L0QSif1GnYMPy5cvJzc0lMzOTzEyZurFixYpqkZGGVONTKaV8Lw33SQVBprs17HV0ypdOQZbPuPs8xCL5T9TR1LR/8dhjj9n/Pyoqyt6hbN26dbVKDklJSZovQSkf2r59O8OGDWPBggVceeWVboN1xx13HMuXL2fJkiVceumlTJgwgbfffps9e/awfPlyTjzxROLi4khOTiY/P5+cnBwuvPBC3nrrLfr06ROEdxUc8fHxHD582OXRFnjO+RSBaxLqwsJCr7O9GqLy8nJ7kNgxaDxixAgSEhJYv349n376KYcPH6Zp06b28r9FRUXEx8fTq1cvWrZs6bH8b7BKQy5YsIBdu3Zx5plnsmbNmqMu28/Pz2fChAnExcV5WDIUC7QC9rjZBtCFugYaAqVOwYabbrqJm266yddtUUqpEDMPuAXndfogX60jANeotgpfE5FEoQVIckibSCT5pGYKr4ma9i8uvPBCewdTky8qFTgHDhxg1KhR3HXXXVx88cVHfX5KSgqrVq3ioosuYsqUKbz66qv8+9//5p577uHgwYPk5ubaE7zHxcUF4B00LBkZGXz22Wcujw5DkkG6EwNc5vTItm3b3CQVDK6ysjKOHDlSbQbakCFDaNasGT/++CNvvfWW/fm28r+FhYUkJCTQrVs3kpOTOXz4MP369bMvabPp0KGD08yYhsIwDB599FFuueUWevXqxaWXXspll11mTxNg8+eff9pn8owcOZLMzEwPJVMN4Dakv+kuyeSdfnkfvlSnYINSKtRVIJFQ7aDXzxXAB0g2YNuIdgLQBKlAoBqPOKRyy2hgLzL6VI7UjX8T/VvzrWCNWinV2N19992MGzfOQ6DBivQvnG8vIiIieOqpp+jfv7+9DGZMTEyDu0EOhjFjxjBv3jx+//132rdvX/loJHLdGIlcR4qRa0oMEtTua3+91Wrlscce4+GHHw5ksykvL+fgwYPVyv8OHDiQDh06sGfPHp577jn78yMjIzGbzRQVyeBMhw4dOO+885xmoDmW/23RogUtWrRg+/btpKWlBfS91ZdhGNx+++1cdNFFPPbYYwwYMIDu3bvTunVrTCYThw4dYtOmTUyYMIF33nmH44477ih7vBQ4CNxOVRCqHElC7q5casOiwQalGpX3gOuBrcgX1nnA/UDNE+EpR9FIsOE94FlkhsN5yCh3aE1pVL7QBdiOlLzcA3Sl7jkglFKqYcnPz+eFF15gy5YtrluQUpZPIKOvbYCbgX9iC7TGxMRw5ZVXkpmZyciRIwPY6oYtPj7eflN6zz33OGwZhJRjfgzJ4dAWmdHQ1+n169evp7y83M1a//qpqKhg37591fLiHHfccfTu3Zvs7GxWrFhhf35MTAxms5mSkhIAWrZsyQUXXGDPj+M6A832eDjr3Lkz9913H7fffjtffPEFR44cwWKxkJqayimnnEJKSkot9nYDMBf4AvmbOhUZ3Gr4NNigVKPxMlJmxzYNqxRYBXwC/Ag0C1K7Ql0EcE7lj1IG0hnse7QnKqVUSPnPf/7Daaed5lIhogwYjARaSyof2wMsAH4D7rY/c8KECVx77bUuo/hq9uzZnHzyyVxyySV069bNYUsLZKmme8XFxdxwww1cfvnltV5KZrVa+e2336otc+jcuTODBg2ioqLCqapgfHw8ZrPZXhbYbDYzadIkj+V/4+Li6NmzZ63aFK7i4uIYPny4D/aUhJREDS0abFCqUagALqf6eq8KIBv4N7A4wG1SSimlVKh4++23mTZtmsujbwC/UBVosCkAHgCuxDaYER8fz9ixY3n33XeZM2eOn1sbOrp06cKSJUsYOXIka9eurba+353i4mImTZpEmzZtuOyyy9w+57fffuPIkSNOwYSWLVsyatQoDMNg1apVFBUVYRgGSUlJpKSk2PMGREdHc9FFF9kfd0y+CLIswjkwopR7GmxQqlHYRvUkhjalwCtosEEppZRSnhw5csRNOcqVeK7KFAl8BEyxP9KiRQuOHDninwaGsOnTp1NcXMygQYO49957GT9+fLXZAiAzEtavX88NN9xAamoqCxYs4KOPPrIHFBITE5k8eTIAH3zwAQcOHCAiIoLk5GTMZjOJiYn2fU2dOpW4uDhSUlLcVhSpSdBDqaPRYINSjYIV7wnqrF62KaWUUirU/f3336xatYo9e/ZQWFhISkoKxx9/PKNGjfKQCd+Z1equr3C0/oPzdq0c49mcOXPo3r07S5Ys4aqrrmL8+PF06dIFkEDPzp07+eKLL0hOTmb+/PkkJyfzySefEBUVZV/O0KxZ1ZLY888/n5iYGBITE52SL9q0bq35upT/abBBqUahF5LFOM/NtmjggsA2RymllFIBsXnzZh599FFee+01RowYwTHHHEOLFi3Iyclh8eLFzJ07l1mzZjFz5kynm1VX6enp/Pnnny6PTgQ+xv3shnKkjGOVAwcO0KdPn3q+o9BlsVgwDAPDMNi7dy+//vqr0zKHvLw83nvvPXbt2sX111/P+++/T1lZGXFxcTRv3pwrrriCa6+9FpPJRE5ODpGRkR7L/zZt2jQI71ApZxpsUKpRiAAeAmbgnLchAkgBrg5Go1SNHAKygPZohYtw9wNwI9Jxj0Qqm9we1BYppUKX1Wrl2Wef5dlnn2X+/Pns2LHDbTBh8+bNZGZmcvzxx/Pmm2/Sv39/t/s7++yzWblyJRdc4DhAcR5wD/Azznkb4pHs+c3tjxQVFfHWW2+xcOHC+r+5Bqq8vByTyYTJZOLAgQNs377dKZiQm5vLlVdeSXJyMr/99huffPIJCQkJmM1mWrRoQbdu3SgvL6dLly688MILmEwmt8spgFpWM3BVgeTaiEH6Fyp8WYBMpPrcAaSyyfXAPwJydA02KNVoTAaSkS+Y/yGBhrFIcsjmXl6ngmMPcDHwFTL7pAKYCdxLVZ1lVT/ZwPPARqAdcuFtH6S2bAROR4KBtmnHLwBriIx8BS2hqZSqrcWLF/Pqq6+yYcMGMjIyPD6vT58+rFixgjVr1nD22Wfz3nvv0a9fv2rPmzJlCgsXLmTv3r20adOm8tFoYD3wL+BpoBiponATUqqxyssvv0z//v1DOhdAaWkphmEQFRVFVlYWX331Fdu2bbMHFPLy8vjHP/5B27ZtOXjwIOvWrSM5OZmUlBQyMjJISUmxL2kYMGAAAwcOJCrK/TU9Pt5fAwxPIL+vIuRGtG3lY4P9dLzGaCPSv8gDRgDnIn8rwTAFWE3VYOMvSOLW75Dk8f6lwQalGhVbicZS5M+/+ho+1RDkAf2Bw0iQwTZa9Dgyy+H5ILUrnGwEzkSm+RYinYClVM0ACrQ5SPZ2RxVADk2bPgIMDXyTlFIh65133mH58uU899xzXgMNjkaPHs0TTzzB2LFj2bp1K6mpqU7bk5KSmDx5Mg888ABLly513AI8CjyC9C9iqu27tLSUhx56iDvvvLOub8nvrFYrRUVF9tkEubm5fPXVV/ZZCdnZ2RQVFTF+/Hh69epFfn4+W7ZsoXPnzpjNZvt/bUkYe/bsSa9evdwmXwSqVXgIjKeBK3Ce5boDuSH+CjguCG0KJ1Zk4OJlJPBmAV4FFiLnN9CDe9/iHGiwKQCeJipqNP4ezNBgg1IN1vfIaEESMAZI9frs2glWdFXVzHNALnKz6agQWAXcBdSs86jcKUdqVec6PFZa+d8rgNOArgFsz9/AFg/bKkhK+iiAbVFKhYMlS5awdOlSN8smipCbj0PIjeVgHBNIjx07llWrVvHMM8+wYMGCavu98cYbGThwIH369GHq1KkuWw3cBRosFgszZswgIyODkSNH1ut91YfVaiU/Px/DMEhMTKS4uJiPP/7YaZlDaWkpw4cP5+STT6aiooJNmzZhNptJSUmhdevWmM1mmjeXG8aMjAwuu+wyjjnmGLfHq0nSzcCyIDMaXG88QT4XtwBvBrJBYeg/SIU3x3OcjwwaXQy8H+D2vIoEPdypICnpY8C/f5MN7a9AKUU+ElzYgFwYIpCpiA8Cs4LXLBVA7qLQNlHAFziWEhNbkJGlHUBvYD7QxV8NDHEfUL0mvE05MoPk/sA1hwq8VYsxDEvgmqKUCnk//vgju3btYuzYsfzyyy8OWz4AxiPfN6XI9aQtsBZoZX/W3Llzufjii7niiiuqVTFo2bIl7777LsOHD+fw4cPMnz/f48g9QG5uLtOnT+fw4cO89957bqsi+IrFYiE3Nxer1UpqaipWq5U1a9bYgwk5OTmUl5czcOBARowYQWRkJNu2bSM5OZm0tDQ6dOiA2WymQ4cOAJjNZhYuXOixgobJZAqx6hq7cZ8oHGRE/jM3jxcDLyElTg1kSe4E3AWVFMgMSddZigBlyPk9BHhOwup7Zci9hDsWDKPc7y3QYINSDc4/kKlWrjdDVwE9gUEBb1Fw/Q3choz2FwJ9kJH9M4LZKD9L9LLNAOJcHvs/4Fqk81gBfA08iZyz8f5oYIjbgwQV3CkDdgWwLQDpyEyVX9xsMygoGEhSUoCbpJQKWY899hgzZsxwyQXwB5LM0TGQXQLsRGZ6fW9/dODAgSQkJPDpp58ydGj1JVw9e/bkyy+/ZNKkSTz88MNcdtllTJ8+3an6wdatW8nMzGTlypWcf/75vPjii8TE1O8Gtby83B4wsM0ueO+99zhw4IC9koPFYqFHjx5MmDABwzDYt28fUVFRtGjRgu7du9tnKABERq7juuveADYjCS0vRkb3zUA4lumMo/qMSUeuv59s4CTkmmm7gV4PLEH6Gck+bl842OdlWwySoDGQwYZRyACKu2oxMRQU+P+eQoMNSjUoh5FRbXejrkXAYuDtgLYouPKAE5ELnW2a+3+B0cAzhG/JzkuRESh30fEKYLjDv38HrsF5mlxZ5c9FSNmx+mSsDkc9kBlD7sQCxweuKYAEkB5EPs9FLtviOXz4Cg02KKVqbNu2bdx8880uj2biPshajgQ6vwUkKaRhGAwePJitW7e6DTYAtG/fnq+//pqNGzeybNkyOnXqRFJSEvHx8faSjDNnzmTr1q32m/ujKSkpIScnh+LiYnueiY8//pjffvuN7Oxs8vPlhql169bMmCG5dbKzswFo166dfbmDLRABMHv2bA9HewW51tqCLyWV5+htJPjgLegfqpoDxyDvz1U0MM3lseuBX6nqf4H0S35BlmP8nx/aGOq6I7MX3Ckl8EtgzwB6IcFEx35iHDCY4uKefm+BBhuUalBsZYjcra+yAlsD25ygexzYj/OFDqRzMBsZpfE8fTN0jULW0a7DeRQqHklg6NgJehbPU+RMyHq9wJQ3Ch2nIp2uQqqfOxPBSRB5NvK7ugKZ6mpF1lNnUlISjp1epZS/5OTkkJzsOuq8ierXUhsD2I4t2ACQnJxMbm6uh+dXOfHEEznxxBPJzMwkKyuLgoICzGYzTZo0cVpeYUu+aAsadO0qeXG++OILtm3bRk5ODkVFEmxNTEzkmmuuASQAER0dTdeuXUlJScFsNpOenm7f76RJk47axurKkT6E63LFUmRkegVQPV9FeHgCyUtURNUsh2jkmvgvh+dZkWTU7j4zpciAz6N4WwLYOC1EAneun61YZEDBHOD2GMgyqauoSi5u6+csRoJJ/qXBBqUalFZ4XksOULPRgfDxLNVHem1KkS/0AYFrTsCYgDVIsOVhpAJFb+BmpDyiI3fBGJtiZLaMcma7+J4OHEH+5mzTR1/Hce1yYI1CEjVlIZdnW1LY7UFqj1IqFCUmJlJQ4DozrgMSnHc3jd7A9XuvoKCAtLS0Gh8zJiaGlJQUrFYrhw4dokmTJgBs3LiRb775hpycHEpL5VplGAY33ngjkZGRGIZBUlISbdu2tc9MMJvN9v2OGjWqxm2ouW+R2X/uFCE30uEabOiLBJ7uQGZQxgBTkaWYjr/vUrz3R23BCr2VdHYWsvT3ZqQvV4H83Q0ElgepTQnAY0h/8i9k6WbgEsXrJ0SpBqUdciHYQPUOQQLeL35W4CPkC+UwMAQpp9fC562schBJGnQQOAEYiySc8hVPnQGQzpG37aEuEvn9zTnK8wYAL+J+yUUcgV8SECraI7kZPgK2AS2Rz6+/6prXlAE0PeqzlFLKk/bt2/PDDz9w2mmnOTw6F7lWuAvgJ+IayP7hhx8YPHhw5b+OUFGxnNzcd8jJiSc7+xy6dZtBXFwCW7du5ZNPPiEnJ4eKiqp+S5s2bUhNTSUmJoYmTZrQqVMnp2CCLVHkoEGDGDTIdd24FfgUCQrHIyPCvqwQVIb3Eflw7luAnMujldCOQQJQez1sz0BvIz25BlnG+iYyw+E0JN9YsMUgfZ3A0k+JUg3OSiQhTw5yA2lCpl9NAs738BorsvbwVapuOjci68A/RQIBvvY0VTfCxUiJziSkUkJHHx3jfKQqgLvougXHKZ/+Y0VKJMYgv4eGZiKyrrIQaatNBHLTOtzdi2rpv8jv4SegExL0CocEnSZkFOKsYDdEKaV8Zvr06cydO5d58+Y5PHo8Mpp9M3IzXY7cyEcBaygrqyAn5y9ycnL4/vvv+fHHHzn77LPZtesjVq8eR25uKVarLefDOi699G3atfuA+Ph4WrVqxTHHHGMPJKSkpNiXcfTu3ZvevXvXovW5wJnIjK585FblLmAm8AC+mbbfD8/LD2MIXGLlMuQ9piDXo4ZmEXAl1ZcExAO3+mD/BcBTyEySMmAccDnhEXBvhnxmlQYblGpwbFnpXwI+RKa1XYT35QJv4xxoAAkAFCMXzV/x7bq6bcgoiWNuibzK45+N3JT64njzkWlnrqV74oHb8f/N/+vI1MI9yI38UOARGlZJyXgkt8MoZPq9gZyrDsC71L8DswyJ0hch52A78AlwHZK1WymlVENy2mmnYbVa+fzzz+3JEouLi8nJmUp29jFkZ79ARkY+LVsO4cCBkTz//BoKCtbYX//+++8zduxYYmJiSEq6ivbtCzGbISUFzGYwm8tISfkaeJ6OHS+hY0dfDTCAlPj+gapBhvLKnyeQgZgJPjhGHHKzvAjnG2kTMmgyz81rfCkHWUP/H+R6nYj0Na6jYQUd/onksFiCBKVsJVOvRyp31Ecu0q/dTdXvYAfS5/gG6cOocKDBBqUapDhkpsKlNXz+/+F+Gj3IDegmfDsL4GHc5wmwIDfmG4H+PjhOs8p9zUZucE3IOvY78X/SwxeQTo9jR+RD5H39gNQmbyh6IAGlr5DyZl2R2Sz1DfgcQjpErglLC5HEQpNpWIEXpZRqfKxWK4WFhWRnZ5OTk0N2djaTJ0/muuuuY+nSpSxevJjiYsfv8S6ceeaZtGw5iMTEfLp164bZbMZsNrN3714efvhhVq5cCfxKs2a7OPdcd0ctRILvl/jwneQgU8/dzWYsQK47vgg2AFyNzCi4GSmxbUFm7C3Hv6UJy4BTkJKjtn7UX8isk98J3rp+dwwkKDMf6YOBDLqkenpBLdwF/Ibz77oEOT+zkP6WCgcabFAqLPzpZVsEvk8SuB3PtZpNyAXEF8EGkOj2+8hUw0KgCf6P/FcgN9muUwetSIfnXqST1ZAYSAfmFB/u81U8BywqkDWft/vweEoppVxZLBby8/Odggmpqan06tULi8XC4sWL7ckXbQYMGMCOHTtYuHAhCxYsoHnz5k45ExISEgBJJjlmzBhASmbOnDmTxx9/nA4dOiAjzFF4TtTs677Fn5XHc1eRC2QU3Jf+CUxHBmUSKn/87Q1kUMBdla1ngJuANgFoR22k4fulJU/jeYns58jMB9eKKioUabBBqbBwMrJ0wV0N7WLg2FruLxe56L2JTNO/FEmeZ/vK6IGMorsLONim8PtaIoGre/0znjtXZUhnoaEFG/whG8/ZqMuQSg5KKaXqo6KigpycHHsgIScnh7i4OAYMkOWTDz30EDk5OU6vOfbYY+nVqxcmk4mTTz6ZuLg4p5wJsbGxDB06lAkTJnD33Xdzxx13cNJJJ9kTMzrKzc3l+eef5/bbb2fp0qWMH2+7seyG93KZJ9bh3X6HlEz8BUmIPQ/oXLmtJd6TM2bU4XhHY8K/MxlcvYoMnrgTgYzoTw9cc4LG0zkAOQ/5aLAhPGiwQamwcBXwHNWDDbHIWv7aRMn3IrMScqga2f8MWYbxIVIuZz4yqu16Q25ClhfUpQPSkETgOXkUNKw1lf40AFnS426JTiJwamCbo5RSIaisrMweSLAFEwCGDh0KwNNPP83evVVZ/w3DoGPHjvZgwymnnILJZHIKJkRHV5WuGzJkiNvjRkdHc9ddd7Fx40Zuuukm5s2bx4wZMzjmmGOIj48nOzubTz75hJdeeomhQ4eyZs0a+vd3nJWYgqzNf47q1/s4YGEtz8RSJE9CMXKN/S+wAkmMPabyeOOQgL5roDsByRUQ6rzdehlI/6Mx6IMMWrmTADQPYFuUP2mwQamw0AWZhTCRqlGBMiSj83O13NelyFp9x1kLBUjuhIeRZIE9gUwklwJUVaNIBt7Bt8kog6ErsibRdRkFSLBlUmCbEzRnILNUfsZ5tCkCOT9uF/IqpVSjUlJSwt9//+20zKGoqIhx48YB8MYbb/DTTz/Zn28ymWjRooU92HDSSSdRWlpqDySkpKQQEVF10+kcAKgdwzC4+OKLueiii9i4cSPPPPMMX375JYWFhaSkpHD88cezdetWWrVq5WEPDyMJoF9Drn+26/tT1K7S1Q5kiYDjEomyyp/JwAEkiP0YUpbYsRpFFDAD3+VrCKZJwBrcj+xXACMD25yguRNJKO4axIpHkk83lqBL+NNgg1I+ZUUuqGVAdwL7JzYMuVh/hiQ76kftS1D+hVQ2cLc8ohBJRHlN5b8vQS6KK4GDSKdjLNIpCHUG0uG5AOcLYSRyk32NuxeFIQNJCnUhMgIVg0yp7UVVx1MppcJbcXExR44ccQom5OTkcMEFFxAZGcmnn37Kf//7X/vzIyMjMZvNVFRUEBERQb9+/ejevbs9mJCUlOS0nKFnz541aEUukgi4ObLcoHYMw6B///51CFxEAy8iyRn/iwQEzkCuB7XxJJ5zPRnIgMlUZNBiA1K2+2Pk5vMCZBAgHIwCjkOWkzj2L+KRstKBXNIRTKcjeRtmU/W5KEdmy8wNVqOUH2iwQSmf+QiJvB9GptlHI4kE/V01wVEUEnSoq78r9+FpjebfLv9ujtRgDkdnI4kpb0BmdUQjoyp3Edwa0OXIiEgSgYn8N0U6fb8ha2zbET6dPqWUkmDCoUOHnJY5ZGdnM3r0aMxmM5s3b+aDDz6wPz82NpaUlBSKiopISkqid+/etG3b1l7RIT4+HsOomuFXv9KQJcDlyNLFaOT6PACpmBTIRIJtqV8Vpj/xnI+hFOdkkwYS0DijHsdrqCKAtcDdSJnHv5EZhDcD04LYLpBZrAYS+AiECcB5SCLSUmQJbyCSdKpA0mCDUj6xAVln6Drtfj4S/Z8a6AbVURu8L4GobaLJUHcqntcUBlohsl71KSTgEId8vhYRmK/yDmjda6VUKCopKWHfvn1OsxKys7M588wzadOmDbt27WLVqlX25yckJGA2mykpkbwB3bp1Iy0tzZ4zITY21mn/rVq18rIMob4mAh8gyw9sSxDWIwGHnQTuxrC+TgJex30OoGjg+IC2JrhikUpODaWa05fAFcCWyn/3QZbPDAzAsaPwbRUt1dBosEEpn7gJ9+v7C5GR8SmERh6DGOQG9kGqv594pN6yCjwLkn9jM1WdzVLgfiSfwstBapdSSgVfWVkZv//+e7VlDieffDI9evTg0KFDPPec5C8yDIOkpCT7MgeAdu3aMXXqVPsyh6go5+WAaWlppKWlBfx9ybLMD6i+rr0CSeL8H6R8YyiYCtxI9WBDBDLQMSTQDVIAfA0Mx7nPtxEYiizLDfWE3yrYNNiglE986WXbESSnQYsAtaW+bkfa/AxVyaAqgIeQi48KvLXAj1SvPV6EJJrajpQjVUqp8FNRUcHOnTudggnZ2dkcd9xxDBw4kKKiIl588UUAIiIi7EkWbXkRmjdvzsUXX4zZbCY5Odkp+SJAYmIinTt3rnbc4FuH54GKAiQhc6gEG5KQ93M2VUsyK5DymuGQWDpULcDzYNl1yDJKpepOgw1K+UQs1UcebCyV20NFBLAcyQb8BdL2MwmdqZrh6C0816SuAN6l5sGGn5CZOB8iuUXGAXcguRhq61vgNrp0WQ+YgVnIzBj9rCilauenn36qVtGhc+fODB8+HMMweOWVV7BYLERFRdlnIMTHy3dNYmIi06dPx2w2k5iY6JR8EaQEZIcOobgMLAHPuXkM5AY+lPQCfkcGaPYigYY+wWxQI1eKzGLwZB3Sh61JuW8Lklj7fmA/0BoJVsyg9oGkUiSfxaN06ZKFzK5YBAyq5X5UQ6DBBqV8YgryJeua/MhA1lWaa7CPA8iNYysaRoS/JVKFQAVfJPKZsLrZVpu63N8Dg5ERMdu+/gO8DWyidjkZ3gXGA8VERlqBbGRWzCtInotQCrAppfzBarXaEyVu376dw4cPOwUTmjdvzrHHSi6gDz74gJycHGJjYzGbzaSmptqXLphMJmbNmkVSUhJxcXFOyRdt2zMyMgL75gJiFJKjx514pFS1d4ZRgtzgp9MwghMGetPYUBh4728ebbujfyDXf9ssiV3IrImNwIpatKkcWdaxESgkMhIkAfuXwONIf1uFEg02KOUTtyCjz4eQzNEgf17xyCwBb9bRseN0JMpvIBUeHkLKSCoFclP/JO4TaxnAmBruZy7VZ0jY1v7eiAQeaqIC6eS6zuYpQnJIPANcVsN9KaVCla20I8DPP/9cLQljfHw8M2fOBOCrr75iz5499uSLLVq0oE2bqmoKl1xyCfHx8cTEuC+p2Lx5c/+/oQYnBcmh5DrVPQEJRAzx8tpS4Aa6dl2OjEyXA6ORPkm6H9qqQk8Ukgjb3VIJA7npr0mw4Uckd5Rrn6AQKZt6FTWfffk6MmvSdWlHIVIm83x0MCO0aLBBKZ9ogowa/xspR1UGnAP8C2jv5XUbgZHExDh+qf4BTAJWIWsblRoEnIZ0CFzrcl8E1KSsWj5SXsodC1LjvKY24XnZUCHwBBpsUCr0lZaWEh0dDcCuXbv49ddfnYIJFRUVXHfddQD8+OOPbNu2jeTkZFJSUsjIyKBp06oywRMmTCAmJqZa8sXt27cDkJqaGqB3FWpmIssN7kRu6pojwYeL8H4jOBl4F5PJ8bt6NfBD5X6i/dJaFWoeRKpBOM54NIBE4L4a7uM1PJdMLwPeoObBhqdwP7Bia9dnwIga7ks1BBpsUMpn0pDOwJ21eM0NuE/MUwRcjQYblDCQYMB9SDmqw0j27uuRSH9NVOC9Y1pRi/YU4X0Np7vPtFKqIbFarRQVFREbG4vJZOKPP/5g+/btTsscioqKWLhwIdHR0ezatYsNGzbYyz927twZs9lsXypxzjnncO6551ZLvmiTmJgY4HcYTk6r/Kmp/yFL3VyDwqXIevo30WWSSvRGKlLcgCxXADgLWELNAwSleO5DVFA147cmPAUabLR/EWo02KBUUK33su1XZB28OSAtUQ1dFLCw8qcuUoAuSIJId4bUYl998TyKEY0GyZQKPqvVSn5+PrGxsURFRbF//342b95sr+SQk5NDaWkp8+bNo0mTJhw4cIDvvvvOHkxo06YNKSkpWK0y2nn66aczbNiwavkSbGJjdWpzw/GJl235yLJPDTYom15I7qa6Ggk8gvtE1vGV22tqNJ5nT5YgszBUKNFgg1JBFYnnmzbbdqV85QGk+oTrRTweuKcW+0lC1mA+gPMogwHEAVfWuYVKqZqxWCzk5uYSGxtLbGwsWVlZfPnll9WWOUybNo1OnTqRk5PD1q1bMZvNpKen06lTJ1JSUoiLiwPgxBNPpH///h6DCa7LH1RDFoPn2We272mlfGUQUlVkI84lumOB/kii9Jr6J1LRogRZ4mkTD0xDlhGpUFKvO5ktW7Zw//338/zzz/uqPUo1MucCL+F++tlAZM2cUr4yHEm+NB/YjazP7An8HzJboTbuQDqsSyqTxFUAxyLJIVv7qsGqkdL+BZSVlZGTk0NMTAxJSUnk5uaydu1aezAhNzcXq9XK2LFj6dOnD6WlpezcuROz2UzLli3p0aMHZrOZJk2aANC9e3d69PA8Ldq1XKQKZecgCYHdiQemBrAtKvwZwPvIIMTzSN/CQBJJ30ftKqylAf8FLgY2UlERiazMmgfc5cM2q0Cpc7BhxYoVrF692h4RV0rVxd3A+1it2RiGLeBgQjJNPxq8ZqkwNgKpGLEdKSP1LjAdSSY2D6hpkjYDqWBxDb//vpZOnfogZVuVqp/G0r8oKSkhOzubyMhI0tPTKS0t5a233rLPSsjPlynJQ4YMYciQIURERLB7925SUlJo3769fblDu3btAGjVqhXXXHONx+N5mrGgwlFz5Pt5Mc6zz+KBM6hd/gelasJWfe0hJCHkE8A7SH/jOmBYLfbVEfgCOMjvv2+kU6eh6Gyc0FXnYENGRgaPPPKIPQuxUqouMoDvyc6+itTUj5EZDiOBW5Hs0978DjyLJHs6CVl/Ge+3lqpwshvpbOZStYznbqQW9iagqYfXuRNDaWlHNNCgfCUc+hdWq5XCwkJycnIwDIOWLVsCsGrVKo4cOUJ2djbFxTLduG/fvowZM4aoqCgOHz5MUlISzZs3x2w2k5KSQuvWMlMoISGBK6+8MlhvSYWcm4GeFBXdSFzcXqAZssRtNkdPFvwuMlIdjwSi+/i3qSqM3I/0J2xBrt+Br5AElDfXcl/NKS3thAYaQludgw1nnXUWe/fu9WVblGqk2nDgwG2kpr5Si9f8H3ANsp6tFPgPcC0SCe7u+yY6+QyZyvYj0BIpwTUV79UJHGUDHyPT7M5ApsypwLoc+Bvn5TvFwAFkNOzxYDRKKSA0+hdWq5W8vDx7XoT27dsD8NZbb7F3716ys7MpKysDoFOnTkybNg2QUpLJycm0bdvWHkxo3lzWIBuGwZw5c4LyflS4Oo/ff+/hdfmMs7+BwUgJ7nzkup4JTACepHbT4WsrD6m29DSSV2gYkhC5ay328R1SiSMDSSSos3kC6w+kIluxy+OFSABiGt7Lwatw5Nfsc7bayYFQXFwc0OM1Nnp+/as25zcm5n+0b38NJpPjl3k+VmsBZWVnsmvXh/jrAms2v0Tz5vc6HPsgFstl5OW9zP79R1+Xl56+nCZNHsNqla8ewyjnyJFLyMqa77c2g35+HRlGKd26veewbMdRGRbLC/z884Ja7VPPr3/p+a3O3+ejoqKC/Px88vLyyMvLsz/+xRdf8Pvvv5Ofn09FhfwNpaWlMXnyZAD27dtHSUkJzZo1IykpiaSkJMxms729fftWz42SlZVFVlaWX99PQ6afb/+qzflt3foKEhN3YDKVVT5iAQqxWF7iwIEu5OSM80sbTaYC2re/kKio/ZhMUibRYnkBWMUffzxFcXFvr6+PjDxI27aXER29G6vVBFipqDCzZ88ySku7+KXNNvr5rZKW9iRNm1bgLv2LxVJBVtbDHDkyo1b71PPrX4E4v34NNtQ8klp/27dvD+jxGhs9v/5Vu/O7FCir9qhhWImOzqFHj7/xT2mgbOBeXCPWJlMRKSmfk5KSBZzq5fXPI2v4SnCsudy06Qs0bdoHmOXj9lbRz6+jXLwFdkym0lqfKz2//uXL87tp0yaf7CfY6ns+bMkXs7OzycvLo08fmSb+2Wef8d1335GXl2cv+ZiTk8O5556LYRgcOnSItLQ0+6wEs9lMamqqPQmj/h3Unn5/+FfNz282sA53/QuTqYhWrf5Dq1b/8nHrbO4A/sSxb2AyVQCFdOhwG55LNoMERMYi5cKrgugREUV06jQdGW1P8H2TK+nn11EM7j4/ACZTGc2axdCsmfYvGpJA9C+0rp5SIWcn7qtXgNxE7sY/wYa38fyVUYjkj/AWbLgV50RVNgXA7cBMdMpjICQh+RX+8LBd1+aq0FdcXGwPJmRnZ9OnTx+io6PZsGED69ato6CgwOn5PXr0IDY2luTkZDp06OAUTDh48KD9eaedpon1VLg6CETheMPvbJ8fj/0U1afe2/xe+dPew/aPkSWArv0ia+U+X0T6F8r/BiNV1PLdbEvEex9Rhat6BRvatGnDK6/UZp25Uqr+eiPJdsrdbLNQu/WNtVGAc81jR1ZkxNwTCzLq4MlBZI2mJrj0PwO4B6ll7Rr8iUfWVSoVXEfrXxQUFNirNmRnZ3PssceSlJTEjz/+yDvvvGNPvmjTvn17mjdvTmpqKt27d7cHEmxBhZiYGECWObgudSgpKdFKDqoRaIX7foVNRz8eu8jLtgik/+HJZi+vLwA2oMGGQBmO5MvYifMMhyigDXBWMBqlgkxnNigVci5HEjW5dgoigA5A9TXBvjEYCSq4k4hU0fDEhOdoN8iFKKbuTVO1NAkJDl1PVQApGkk8WpvyVEoFx3333ef0b1uOhLS0NHr37l0tmJCQINOou3btSteu/grIKhXKkpBrw0qqzzKIR5I1+svpwCu4H9CIALzlXWgGxOK+fxGFVkoKJBPwOVLB5AukX1cCDEISmdc0kbgKJxpsUCrkdEWWLFyMfHEXIx2Bpki5Kn+NwB0DnIxcSBwDHZFACjDxKK+fDjxG9Sma0ciFKcI3zVQ1NAsYA6xC1rNOQ34XSjV8I0eOdAooxMbGAtC6dWt7qUilVG09iixZ+Aa5VttuE64CzvPjcW8C3sR9kGMR3q9N5wFzPWyLAC6tb+NUrTQBPgA+RKqWnQr0D2qLVHBpsEGpkHQBMgL9GnAIWWc/HP9Gje8D1uM8u8EABgIvcfQlEHcCnwC/UTUlMgEZdbjfpy0NLQXAC8ioTiRy038h/r3xLweuQNbJxiCjSf+qbMdwPx5XKd8YMGBAsJugVBiKR67TGyv/GwecC7T14zEPItc814GSWCSf09GqIyUjCainIVP3y5AgQwxSptufyz8auq3AI0iCzWOA+UBPPx/zR+QzcwDp05Qg/dUXkdkzqrHRYINSIcsM/CNAx1qLJHh0nZUQhUxhrMlIYhLwLXJT/SIStJhY+RPnq4aGmMPAACRgZAvArEcyc89BAjFn4/tcFtcBzyCjSLaRpDykg/Bf4FgfH08ppVToOLHyJxDOp/oaf5DBkzOo2WzN85B8Vo8AW4BOwDwad8LjJ5Flt6VI8syvkaDMxcBxyEDR8T4+5hFkyW2Oy+MfIr+jj3x8PBUKNNiglKqBxbivJFGKLN04jCzjOJoYZPRhmpttxcgSjVJkuUZ6nVoaWq4A9uLcySoEdgDXIEEYK7LWcbSPjpkHLMd9Qq1iJHnkf3x0LKWUUsqTHcB3uC+XWAz8G7lBronOwEMetv2CjLg3B04i/Ctf/YkEWxyXpVQg1/3lSN/CAE4A1iBLYX3hCaQP56oE+BLYDmgZy8ZGM3UopWrgf162xSBLI+rjGSRYcSEwFclafDWeq1+EgxLgdTzVpJalDnlI0quJSKfMF7YjM1LcsSAzK5RSSil/24nnJYMWJEBQH9nAmcish0uQagjtkABHOFuJ54TeIEGHQiQ3xxQfHvcTPFcGiag8nmpsNNiglKqBdl62lSDBgbr6GEnulI9USMhFovHLkRkV4aoA750BR6XAgz46birey5ul+eg4SimllDcZeA64G8hyiPoYg1RFKEL6FvnAHqT6RVY9992QHaD6sld3SpA+2B4fHbcZnmeNmND+ReOkwQalVA1cgyRzdBUJnEL9SkvdgvslGoXAvXjuiIQ6M5LYqibKgU0+Om4XoL2HbfHAbB8dRymllPLmWCSg4O52JI6jJ4f05gfkuuluWn8ZsKIe+27o+iHlxmsiBvjZR8edgfccU5qAujHSYINSqgbGAf9ELv62EpVJyKjEi/Xc9xYv28qB/fXcf0NlQsp91ST5o4Gca1/5DxLocJy+moAkq5zuw+MopZRS3ryJ5FKw3RxHIZUobgQG1WO/3+H5NqcIWFePfTd045BrfE1u88qAlj467mBkWYbj4FQE0s/5DxLYUI2NJohUStWAgUzj/ydSHjEbKWU0Bs/r/2sqBZna6E4Zvktc1BDNR4IpDyPnMR/3SyviKp/rK8chpbAeRuphJwMzkdwQellQSikVKB2BX4FXkSUPzYCLkFl49ZGO55ttE767wW6IopFzOQrYR1VySFcG0AHflcM0kCWwY5HKIPuRqiZXo4khGy/tVSqlaqEXvs+jMAupgOB6IYxA1lWafXy8hsQAliClKD9DKlPcikz7LKSqVviVyIiBL7WuPPYSH+9XKaWUqo1YJDn0VB/uczie8wfEIgH2cNYRSQi9EdgFvIVUnihGkm8mIAMZr/v4uAYS5Bjl4/2qUKXBBqVCSjlSHvII0Bcp9RTqrkEugD8hSRNBLoDJhPeaSkfpSK1xkBrYzyK/51bAP2jctcKVUkr5336kPGECcAZyQx7KYoBXgHORWZK2/E8JyCDHwCC1K5AMoH/lzySkGsRTwCGkSsc0ZEmsUv6jwQalgioXeIUmTb5DRq7Pw/Oats+B8Uj2YAMZ/R4CrKLmiYAaojik3OIryEWwBOkc/JPwntXgiRm4ovJHKaWUqovNwLukpx9Grqe9PDyvDBnlfwlZzmcgy/lWABMC0E5/Go4kinwQGeFvDcxDZk02RrbAg1KBo8EGpYLmI+SmGpo0KQCeBy5HyhAd5/Lc34GzqRr5t/kUWWf/th/bGQjR+H4KpVJKKdXYlAEXIH2MEpo2BXgcGcx4lqokzzbXIMH+4sofm+lI2etQnwHQCckfoJQKBq1GoVRQHEICDQVAAYYBkhzwCBKJdy33+BDuyzfZaiT/5reWKqWUUipU3A58iOT9qcAwbMkB30BG+B3lIzMY3JWfLgLu9F8zlVKNggYblAqKZ5DswO4UAe+4PPYl1QMQNtF4Lx+plFJKqfBnQUbx3VUeKATud3lsF54rSlmBb33XNKVUo6TBBqWCYhvO0xUdFQO/uDzmrUSTFWjqi0YppZRSKmQV4H6Wgs1BnMsrp+N+1qRNE180SinViGmwQamg6I7nTM+xSN1jR3ORDMruJAEn+ahdSimllApNCXivItEU53KQbYDjcX87EI/kkVJKqbrTYINSQXEpnus/RwGjXR4bBkzBOeAQgwQaXkf/lJVSSqnGzgTMRqo8uYoHrnTz+AtAmstrEpAKWf/wcfuUUo2N3qEoFRQtkOzP8VRd4BOAFOADJA+DIwNYDqxGyl8OQjJI/w8YEID2hpoc4D9I4qufg9yWYLICryLZxFsDQ4G1QW2RUkopf7od6SMkAAZWq1H5/8OBa908vxOwA7gNOBWpfPUCkjtKi9ZVtwmp7vEa7nNjNBYHkH5oe2Q27g3A4WA2SDVQ+i2iVNCcA+wGXuTIkU2kp5+G1LT2tFzCAM6o/PGXYiRj9S/IxeM8JCASSpYDVyHlvSyAhTZtBgLv4X16aTiah5Q6s5VM3Q/8F+mMXh2sRimllPKbGGTQ4mtgNUeO/EWTJjOAE728JhUJRLgLRvjKTuAtJNn1CKCPH4/lD38Bo4AfkUB+JGAlMfFeoEcwGxYEfwD9gFyqcn48gCQ/34QMbiglNNigVFClA/M5dGg76enBvlh9S1XZzQIk6DEPeBc4OYjtqo3PkJto59GGhISvkffyROCbFDTfIxd+12RhhcBNwFSgeWCbpJRSKgAM5Lp9MocPb6dJk2D2L6zAHCTwXYEMAtwJnI4sA3WdydlQnQ9sxjWhZuvWVyGzBrsGoU3BciXwN85V1UqBLOA64MUgtEk1VLqMQimF3JwPRy4e+UjnIB9ZjjASyPPTcXcADwOPAr/5YH934S4Tt8lUglz8cnxwjFDxAlDiYZsJ6eQppZRS/rQCeA7pZ5QC5ch1+hPgRj8dswx4E1iKXOu8VdyoiZ3ABrf7MYxy4MF67j+UlANv4758ewWydNPqZptqrDTYoJRCLsZlHraVAyt9fLwKYBpwHHA9MnXzGGT0oz4XqR+9bIvGNwGNUJGL+84AyO86P4BtUUop1TgtwX05ziJk2aOnvkddbUGm8V8E/Au4BGiFTO+vq21I8u7qJNiwsR77DjXlyOwUT8rQYINypMEGpRQStfd081kI/OTj4y1GAhzFLj/PAsvqsd8WXraV0riWDZwJJHrYFo0kAlNKKaX8aY+XbeXIjEpfKUaWNBxGZmSWVf73CHJNLPD8Uq9a4ukGWxJwZtRxv6EoFujiZftx6O2lcqSfBqUU0A7PiSkjgfeREli+iN5bkKmN7kY6CpFARF0twN37sFgikKodLeux71AzDqmp7pqaJwapq94/wO1RSinV+HgL8pch+YMexDfLHF/H8/LBcqQKWF30B5q43WK1xgDz67jfULUE98nD46lfH06FIw02KKWAC/D8dVCOlI98BhgC3FrPYxXgPQfEXuo+BW8aUrZLSn6JBCoq0pEcBo1JNPAV8juLBZKRQMMYpDKH4fGVSimllG9cheeqVhXAR0juhs7ArnoeazueZ2nmA1vruF8DqaRhpuq9mIB4/vprCnBaHfcbqsYCjyEBmMTKn2ZIP/Gs4DVLNUhajUIphVwo1iDlOC24n3Vge/y+yuf1q+Ox4pGb3nIP29Oo+42wCXgJ+BxZkpEDnM2uXX3o3r1NHfcZylogHbk/kbKX7fA0OqOUUkr53nwk8P0ekqfB3XKEQmQJxETqN4MyAxlscLdcIg5oX4999wZ+BZ4GvkCur//k8OF4mjTKy+pUYBKSz8IAeqJj2Mod/VQopSqdhqytXIIEE2I8PK+Y+uVViACmI6PtruKQEpX1YSCj+U8jUyr/gdUaV899hrqWwAlooEEppVRgRQCrgE+R0tSellVYkBvX+iRyvtDLNgOYXI99A6QiMzXeQPpBJ9Rzf6EuAgnCHIveUipP9JOhlHJgRm72x+Mp87J0CLwlfKqJxUgSIccEholIXfCF9dy3UkoppRqWE4F7kSV+nkQDh+pxjBTgNWR2g21AIxaZUfkykF6PfSul6kKXUSil3DgOz6WNYoGT6rn/eGRa5YfI7AMTMiJxOppLQCmllApXffGcm6kE6FrP/Z9F1XKHn4DuwKV4r1allPIXDTYopdw4HugFbKZ6DexI4DIfHMMEjKj8UUoppVT4W4jkEnLNDRUHTECWKtRXM+B6H+xHKVVfuoxCKeXBO0AfZBZCApCETEF8j8ZVQlIppZRSvtEfWEFVvyIRmTE5gvrlg1JKNUQ6s0Ep5UETYAPwPfADMlJwJvq1oZRSSqm6mwyMQ5ZS5iH5mjoFs0FKKT/Ruwal1FEcX/mjaq8C2Af8iCS+OgXP9cZDwefAA8h62GORzOJ9g9oipZRSoSgeCTiouikEtgIHkZKexwa1NfWTg8x2eRnJ2zUFqVqWFMxGKR/RYINSSvlcBXB35U9x5WNRlT+LgcuD1K76uA3JJG5bZ7sNKf+VCQwIVqOUUkqpRuR/wD+BLyv/bSCDGd2At5DAQyg5iFQqyQKKKh/bBjwEbAxWo5QP1Tlng8ViYdGiRUyYMIFp06bxxx9/+LJdSikVwmYCd1AVaABJtFkI3IDUHA8l/wOW4JzQy4J0DGZjMmUHo1EqDGnfQimlPPkDCe5/6fCYFani8SMwCCgNQrvq4yrgT6oCDSB9jb3Av4LSIuVbdQ42rF27ltLSUl5++WWuvvpqFi9e7Mt2KaWUz8TG/gTMAkYBd1G/Ot5H8zvwItWreNgUAjf58fj+8Cye308EyclrA9kYFca0b6GUCh2FpKS8BowBJiIJtD2VDfeFu4F8D9usyHKEN/x4fF+rAF4Fyt1sKwNewH2JVBVK6ryMYtOmTQwePBiA448/nq1bt/qsUUop5TuLaNfuPiTabwE+Q0bpP8I/0/8/4uhx3J3IRTbCD8f3h8O47wwAlGAy5QSyMSqMad9CKRUa/gQG0rx5FlWz/t5BZheswT8r1VfjPZiRj8x6mOCHY/tDKdIX8qQIDTaEvjrPbMjPzycxMdH+74iICMrLPXVGlVIqGDYASzGZiqm6QBch2a/H4P0iV1cRyBpKb2IJrcrDpyLlydyJpbi4dyAbo8KY9i2UUqHhH8B+IiIclxfmA+vwXwnPow1QRAFN/XRsf4gF2nrZ3p3Q6ispd+ocdktMTKSgoMD+b4vFQmSk8+62b99e95bVUnFxcUCP19jo+fUvPb/+0bLlPaSkFGO4ufevqChg795nKSw8yafHjIjoTOfO5Zg8XB+t1giys8/hwIH/+fS4/mQYvenUKZbIyEIMo2pUxWKJpLS0FX/91YvCQv38+ktj+n6oSd8CtH8RTvT8+peeX98zmbLp0mUtJpO7QGghpaX3sWvXmT4/brNmw0hNfcHDccFiMfHrrydRVhY6v+/k5Nm0bHlL5aBQFYsllv37Z+vn188CcX7rHGzo27cvn376KaNGjeL777+na9eu1Z7To0ePejWuNrZv3x7Q4zU2en79S8+vv+TgacphRIRBu3axgK/Pew/gGqREZJHLNhOG0Y7U1OWkpqb5+Lj+tgE4F/gFGT0pxWQ6kdjYV4mNzdLPrx/58vth06ZNPtmPv9SkbwHavwgnen79S8+vP+wEYvCUyyg6Os9P53wJkhfiENWXF8RgMt1J586+D3L4Vw+kT3EzVTM3LJhMS2jTZg55efr59adA9C/qHGwYNmwYX375JRMnTsRqtXL33XfXuXFKKeUfA4CvcJ+d2YL/6lLfiZShuh34FbmAtkCyLs/A85KEhqwjsAUpSbUH6AJ0qtyWFaxGqTCjfQulVMPXBu+5BI7x03GbAZuRJNP/QapQxAP9keTXvp2pGTgLkCTeXyPLUE8C4oLaIuU7dQ42mEwmbr/9dl+2RSmlfGwu8CjVgw1RQG/8F2wwgIsqf8JNz8ofpXxP+xZKqYYvDrk5Xo5zSWiQm/+b/XjslsCTlT/hJB4YGuxGKD/wR6pUpZSqozzgfaAAyejcuZ77awuspqLiXCIiQBJCWpGb5TX13LdSSimlQsMPwCYgDTgLSU5YH4uBg1gsr2IyRSGDDOWVj4+o576VCh8abFBKNRBPAZcjSw4sSGBgFDJVMKYe+z2DHTvW0aPHHmSd43HACfVsq1JKKaUavmyk+tS3SGUDExIYeAkYWY/9RgEv8Ouvl9C5814keDESSKlXa5UKNxpsUEo1AF8ggQbX6YjvAfOBx+q5/2jgnHruQymllFKh5QIkwbHrcsrxwPdI/qG6KytrDYRaUkalAkeLlyqlGoA7qR5oAKnm8BxSVUIppZRSqqZ2AetxnyS6FHgwoK1RqjHSYINSqgH4wcu2aKSig1JKKaVUTW1F+hDulCMzHpRS/qTBBqVUA9Dcy7ZSpNyTUkoppVRNtUByQHnSNlANUarR0mCDUmHJCnwFZAKvAcXBbc5RXQkkuHk8AugHtA5oa5RSSinlTi7wIlL28ccgt+Vo+gPpHrYlILmilFL+pAkilQo7B4HhyFpFC/JnbgCvAsOC2C5vLkJKUX6AlL0E6QgkAi8Eq1FKKaWUsnsamIv0K8qRvsXJwJu4HzAINgNp2xBklmQRMs4aC8wEzghWw5RqNDTYoFTYORv4CekIOBoHbAcyAt2gGjAhwZC1wDNIQshRwDQgKXjNahD+Bt5FZqcMAToFtTVKKaUao6+BecgNu6MvgH8gpSQbouORwZcngc+RZZszgYFBbFNDYEXKgW5GlqqOpH5lxpVyT4MNSoWVzUhAwTXQQOVj/wcsCWiLas5AZl401NkXwfAIcB3yVW2p/BmDzPaICmK7lFJKNS6LqR5oACgB3gIOA00D2qKaS0eupdcFuyENxGEkuPA/JOgQgQz6rEL7YMrXNGeDUmFlG57/rEuRKLYKDe8DNyAzGvKR0qDFyHKTa4LYLqWUUo3PD8iNqTsxwC8BbIuqn9HI77MA6VvkITNKzwX+CGK7VDjSYINSYaUVMkPAHRPQPnBNUfV0B9IJcFUErPCwTSmllPKHVl62lQItA9UQVS8/Vv6UudlWBjwa2OaosKfBBqXCyml4TtIUgyR2UqFhu5dtEcDeQDVEKaVUo7cA9/0LE3AsOpgRKrYifQh3SoGNAWyLagw02KBUWIlAptknA3EOj8UBNwN9g9QuVXve1r6WAU0C1RCllFKN3vnABCTgYJtBmYBcq14OVqNUrbXwss1Ew0wirkKZJohUKuz0A34DnkKyR7cBZgC9gtkoVWvzkWRWrsslIpFyXWkBb5FSSqnGygCeAP6JlMD8GymzPZmGWfZSuXcaUlY8z822WHQGrPI1DTYoFZbS0CSCoW4WkiTyUySJE0iHLh0JJCmllFKBZAAnVf6o0GQCVgNDkVmSRcgM2GhkgGNA8JqmwpIGG5RSqkGKRDoEHwPPIhUpxgATqVoio5RSSilVG/2AXcjAxZdAa2AmcHwQ26TClQYblFKqwTKAMyt/lFJKKaV8oQkyk0Ep/9IEkUoppZRSSimllPIpDTYopZRSSimllFLKpzTYoJRSSimllFJKKZ/SYINSSimllFJKKaV8SoMNSimllFJKKaWU8ikNNiillFJKKaWUUsqnNNiglFJKKaWUUkopn9Jgg1JKKaWUUkoppXxKgw1KKaWUUkoppZTyKQ02KKWUUkoppZRSyqc02KCUUkoppZRSSimf0mCDUkoppZRSSimlfCoy2A1QSoWqcmA18AWQCkwGOge1RUoppZQKdXuBF4ADQH/gfCAmqC1SStWNBhuUUnXwJ3AKcBjIB6KAe4AbgFuC2C6llFJKha7lwALAApQCicBVyMBGlyC2SylVF7qMQilVBxcAu5FAA0AZUAzcC6wNVqOUUkopFbK2IIGFYiTQANLPOAScDViD1C6lVF1psEEpVUu/AZuACjfbCoH7A9scpZRSSoWBh6kKMjiyAvuBbwLbHKVUvWmwQSlVS7vxvnZyV6AaopRSSqmwsQP3Axkgtyy/B64pSimf0GCDUqqWOgAlHrYZQPcAtkUppZRS4aEnEOFhmwVNQq1U6KlXsOGjjz7i6quv9lVblFIhIQNJDhnlZlsccG1gm6OUCivat1CqsZoPRLt5PAIZ6Ogb2OYopeqtzsGGO++8k6VLl2KxWHzZHqVUSHgJ6IpkiTaA2MqfO4BTg9gupVQo076FUo3ZMcDjSH8irvKxJKAN8DbS31BKhZI6l77s27cvZ555Ji+//LIv26OUCglNgB+AT4CvgWSkQkWrYDZKKRXitG+hVGM3FRgBrELKa/cBRuF5eYVSqiE7arBh1apVPPvss06P3X333YwaNYoNGzb4rWFKqYbOBJxZ+aOUUjWnfQullGdNgNnBboRSygcMq9Va56K1GzZs4KWXXuKBBx6otm3Tpk3Ex8fXq3G1UVxcTGxsbMCO19jo+fUvPb/+pefXv/T8+pcvz29hYSEnnHCCT/blL976FqD9i3Cj59e/9Pz6l55f/9Lz61+B6F/UeRlFTfTo0cOfu3eyffv2gB6vsdHz6196fv1Lz69/6fn1L1+e302bNvlkP8Gm/YvwoefXv/T8+peeX//S8+tfgehfaOlLpZRSSimllFJK+VS9ZjYMGDCAAQMG+KotSimllGrktG+hlFJKhQed2aCUUkoppZRSSimf0mCDUkoppZRSSimlfEqDDUoppZRSSimllPIpDTYopZRSSimllFLKpzTYoJRSSimllFJKKZ8yrFar1R87Dpda3koppVQ4OeGEE4LdhHrR/oVSSinV8LjrX/gt2KCUUkoppZRSSqnGSZdRKKWUUkoppZRSyqc02KCUUkoppZRSSimfCrtgw0cffcTVV18d7GaEDYvFwqJFi5gwYQLTpk3jjz/+CHaTwtKWLVuYNm1asJsRdsrKyrj22muZPHky48eP5+OPPw52k8JKRUUF//rXv5g4cSKTJk1ix44dwW5SWDpy5AinnXYau3btCnZTGi3tW/iW9i0CQ/sW/qF9C//SvkVgBKpvEenXvQfYnXfeyfr16+nRo0ewmxI21q5dS2lpKS+//DLff/89ixcvZtmyZcFuVlhZsWIFq1evJi4uLthNCTurV6/GbDZz3333kZ2dzbhx4xg6dGiwmxU2Pv30UwBeeuklNmzYwAMPPKDfDz5WVlbGokWLiI2NDXZTGi3tW/ie9i38T/sW/qN9C//SvoX/BbJvEVYzG/r27cutt94a7GaElU2bNjF48GAAjj/+eLZu3RrkFoWfjIwMHnnkkWA3IyyNGDGCK664AgCr1UpERESQWxRezjzzTO644w4A9u/fT3JycpBbFH6WLFnCxIkTadasWbCb0mhp38L3tG/hf9q38B/tW/iX9i38L5B9i5AMNqxatYpzzjnH6eeHH35g1KhRGIYR7OaFlfz8fBITE+3/joiIoLy8PIgtCj9nnXUWkZFhNcmowUhISCAxMZH8/Hzmz5/PlVdeGewmhZ3IyEiuv/567rjjDkaPHh3s5oSV119/nbS0NPtNmfIv7VsEjvYt/E/7Fv6jfQv/076F/wS6bxGS30IXXHABF1xwQbCb0SgkJiZSUFBg/7fFYtGLlwopf/75J3PnzmXy5Ml6wfKTJUuWcM0113DhhRfyzjvvEB8fH+wmhYXXXnsNwzD4+uuv2b59O9dffz3Lli2jadOmwW5aWNK+ReBo30KFOu1b+J/2Lfwj0H0L/WZXXvXt25dPP/2UUaNG8f3339O1a9dgN0mpGsvKymL69OksWrSIk046KdjNCTtvvvkmBw8eZNasWcTFxWEYBiZTSE6Ya5BefPFF+/9PmzaNW2+9VQMNKixo30KFMu1b+Jf2Lfwr0H0LDTYor4YNG8aXX37JxIkTsVqt3H333cFuklI1tnz5cnJzc8nMzCQzMxOQpFmabM83hg8fzr/+9S+mTJlCeXk5Cxcu1HOrlDoq7VuoUKZ9C//SvkV4MaxWqzXYjVBKKaWUUkoppVT40DkpSimllFJKKaWU8ikNNiillFJKKaWUUsqnNNiglFJKKaWUUkopn9Jgg1JKKaWUUkoppXxKgw1KKaWUUkoppZTyKQ02KKWUUkoppZRSyqc02KCUUkoppZRSSimf0mCDUkoppZRSSimlfOr/AdylfBTBvts7AAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X, y = make_blobs(n_samples=100, centers=2,\\n\",\n \" random_state=0, cluster_std=0.8)\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(1, 2, figsize=(16, 6))\\n\",\n \"fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\\n\",\n \"\\n\",\n \"for axi, C in zip(ax, [10.0, 0.1]):\\n\",\n \" model = SVC(kernel='linear', C=C).fit(X, y)\\n\",\n \" axi.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\\n\",\n \" plot_svc_decision_function(model, axi)\\n\",\n \" axi.scatter(model.support_vectors_[:, 0],\\n\",\n \" model.support_vectors_[:, 1],\\n\",\n \" s=300, lw=1, facecolors='none');\\n\",\n \" axi.set_title('C = {0:.1f}'.format(C), size=14)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The optimal value of `C` will depend on your dataset, and you should tune this parameter using cross-validation or a similar procedure (refer back to [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb)).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Face Recognition\\n\",\n \"\\n\",\n \"As an example of support vector machines in action, let's take a look at the facial recognition problem.\\n\",\n \"We will use the Labeled Faces in the Wild dataset, which consists of several thousand collated photos of various public figures.\\n\",\n \"A fetcher for the dataset is built into Scikit-Learn:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'\\n\",\n \" 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair']\\n\",\n \"(1348, 62, 47)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.datasets import fetch_lfw_people\\n\",\n \"faces = fetch_lfw_people(min_faces_per_person=60)\\n\",\n \"print(faces.target_names)\\n\",\n \"print(faces.images.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's plot a few of these faces to see what we're working with (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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rGTvvr1RDo5ywFJb9GDGqpSiqBQNhXz8Or88VZ0LWjrbmg2i2E39+PDRy0guq+tYOoSoPa5X1/CeXQjKY2CFbvKcN/tyoheGa5bwKpbomdoMyrg97Z2dn66ZPQ7FN/dl/DzwKdK8wse2i2NURZ9cqG6NevHI0GON3z9+rn9eeo5uh94LO+wJhfhvJ/y9qcn1pRataAU4IIyvwiSs4oNcMbB2McMVaNhTEES+acYAzV9AQWtrbZzf5zfAr2L7nCWEaFcqZnB/aVre73DMArxEHORY88yecD2n2r/WfvbVOtrzLo6/VZBxk5gvbDoC/s2MhJO0FPzpCMQA8aZ5tk0IVg0x6MVAqQEnKx8JxJ9ks2UTJEeQ2YCtcZcf7sFAly1NwzJ/uQULFsAMaygygFOVt1+lWyAq6VLMuihlSc1K2WPP65JCAJOzLBWs+QJMNEQzO2chBSJIKOc7QHozGAB8EKrgtkOBtPsUW5vfO6NmB93UDvCTu1uESCWGNS6NaHrSOnemODmyrfTGMMDKMPcuBTTMoWNLBKjurP2VuvnCOsoRp7CCOcb85Hs8T+XPNnsQyn+uCVOCL1JllC3JEWGmlXqMHDWK7RssHoazN9kJLWiLhSC9KaV81oPbeNyKNMP5v03CbTImS5nzlllA5abOQpgutLIRb1rcoIfggYdoMyo3PMqEOAQXNucr2Fz7QxVC4wobGdXWi13oLy80ZZ2Ly8pwLZmi7L35aEfubCjbQnGAARZW2G11qL0gVHWmvU16R9FmTKgVALa/ew1iPG5WbOEaDaIiqhb4Uzpx6RkfcXGNZaS5ljGODDABiDkhNyFojew4cBYZiopuyEFS3EK4IxP7XHmVv00pqQw5ZTYX1UdIGy9lYW4FvwSaerjrB77rK09qVWetskc/h04NOvz7d58MVekxYk6qXobOvdNXIyXcGVe1kc//71qrUSE/UTh0R6IwHAG6tQ7C9FycYwg0rqW+gi5S4Vtx0UWUpF8omvhwyJRB/XN/kXM7g3WP3Nk8zG+6BsSACbjA1WoDEi51hP7RcEZ22DmJ7A4PynIyj5mZ5h2e9Bjg2y88ER1T0mgpBq1RYScMCUUmvpeAVfFSJOCIySY1YiheGsi/b7Ng5yvhyxrvMn76eeVYZ7+ge035OcM9KaEOeI7OQMVd0/gQv7Xr7giCzV93X1r2u5RQCG4KcQM9IYMZ9mCiTWtIFW5VpSTBTAFHo2Jdgx3JKASnVR2vtm5Huod5oOWNcLYnz7Ng8/NAhas5eY4OFRLEGYfQ+j8xZuCLCeYNUwDhqYyGevpWqN+tWzaag0JPaLPmtFzUkhUwn2+tXXvnq2K6FiLQuXjLC3gfI1hbi7IH2DTo0UNOUclbF9i1U5ayT+SNEWDmPoDAqbs9bKNXhHtUcOUnKeNduSPsn2cw3l0fcqCTkbuGzJHnXlGbk1+nxzwBIGIgI66W+VAK4jl/XokrTNqF3ZoCLCpZCab1KUiBKsbab7c+tXtHlss4jtJnD/SzaaMvc/2x9IWaXWV/p2VFPkpvI+pdbXew3DCDRbjVHc+5eCAdngkjsmn6XNqqXSQyiRpW0ZmdRm/tdaxhg46zXD2MIDlUB/u/15hSa5JnadedVS1WCIkekPLYCu7cCiZ11WrvMKFEqwuN/A0kL0EecoVHdxBg1R6CCtrrguBkUgZWf9zQwHAKQcqd7rmhBAv9pnrxp49T9SctFaXkoJrrpX+/ipqFcCN6Giq/EVxmDwXIfkoDLQtcU1MSEFG+PR6nRZsxfpGzPGKRsbmzMkH6JuDH4II0IYqc3ojVePTsheEIKTYbPRM2KubEEjBba6pHz+5lS3tqIPMnrkSN5XxEH0eereT2qLpasfa72rlO3+XS0j7/sJwg4AtTvWGThYDAPV2vHLXQe/adFzVzZBUa2F+9Rrc5iAZpHAVT8k+JnkWp5zHo7LEpvPeW1HOqJg//0+m3fBU+eABD6g+1crs2hrvQpKurMMRnW6x6xKYFKazam1bhi5nwu6f4VQgFcFA4lxm7IEHxaXAbTG8R4iEvKLNgWjInXRMkGqAikSwzXlDO8cvCXYVTYkl6LO1LCDhKXWEVSK7j8VCfZGu+SsBd5Wg5DPWugGKYxI0FZPkvlU9nCLVWuFF+WKcUQFwY0KiVkLUzJBmqa1qgSGr4iN6BskWlqTsxxYvTf9of2EQb8mG8h+yo94zooKMzglI9qokjBCkNbYPZzbjFyCF7kWHxxKYagvlk2W9ZaLaip9/cRqIGAcw8OdEo7smRhYcuhkRF3wwAAMY0ApRpGWDKBWCyBqIzuM0cxUlFtylkAI+l6qwMP1y+kwaeazXlZiEa9JxQGEpdkCILfJYmrtWidiQpH7zpmuCw53j/dArcTifuNlr4Lm5oxavVAIZz54YlGHVucSZ5cTZe2SvStxTM6rNUSIsg2eF2dMf0cTrri2GbkgcoYel1Xvl/O0l5K5C2FE4DwA2meN4AEk1FjVmDuLTeYr6/Bo4U8Oy7LgFovaGj5tr3rhAGqmD4raCCOVnCGRina7ewzDxP/vVGVHSyGmBZDSEkZIncG1/RRUwAWHw+MeYRyo5U5JZUBcVmRjkNbCQUdDO17Br87C1Iqc6Helh/ZVRlqp3e9z5/tXtXlIpCsfzAevkRs9aJxJFkNU3n4TrqJtAM2Do7FPC3/NmiYmUCo5U+mdrN3POn0PqLPMnBVqSlma8a3d72urgTyoXZRnnUUYg25kzhk1kcxbzhRpZs2QbwP5iXKM5UNpvYNZnUbRqphjjRpCcTBxjURvZ9hBo7G+HgtozxHJw1kYi44QsWX4tr3pascCsV6RbvqHXjLHhiZQlKiBVcqotTngPggxHDk654ABqHWEuVF4PY57fbgpMm7tS9TU3YImyagbQQ2MOmwz9T5QU5YwM1z7ZYxBsCSr1mcrZDzcJlO6NuTyfrK3xlSlyPfOsjcMwJaABXR1P1+RbDNwYRixw92b73dcU6vZCnGpsxPCBq4cOIjDhHALStEgvEGgHSt1GroAvS/9tCxQM0tAM89+Tyj4SSol1z87JWdEY6j2nrPKb/Z1Z31s+OyL8pV19MwREhAVifE7qqeGdXjz/abLIKKhMRkVBLH250FaTHpCX98DG8KIYZhwODxiv7uHZ8k5QhmCylO+upf87PStUv2SALAR3BqxTW+KaT3CRtrAsoHJBtVahI6oIyIadP1Fkan2jHRkU0anfmn9VZxiMQrFNCNQmBVpTOtilA/7CrL6GTaiEnRsExQACJYV3dVcSqO+G7NBMAxaQ6usn6tPSquKSJj1xtkPXiPU5UwRnaiK1FqAyun5K1Lz2yw1XHJwuxvbH0KYZsxKKUCihz26BMcyTk77r1o7AWqFsZYk+axVdqrUXMWA9PsFMVK8xHiIweIf1O9LI26Pe4hDl+shiPiqp4zJPNRvVdnAOVS/bXJ+yxXCwPvOJIqOyQzd85ZlyZ5oCYEzC9MFXH2rhg0N6t6wg2vVjI1ELKqyxgFie2rms7ZaVg9NyxIDUGuFAVCQKdJW6HprvHuHYAw5yVJanQgggtqA3Zvvd0kZVfVpu4BPDVfL+DZBH/c0CjPbhy36IYFLGIP2bMtnlYAeAGytmwDnGskA+HkZSBTFBUe1LM3yCSWJiFw22Aaf10sQGzkn0jpWa0WqESiMPBgD729IAOzONH1GybSbElZfJpOaLtlLzzqsE4Zxp7JtIZCIiJxVuQ99hth/TVZv44zeiy7DtAbSIiYJjbdGyWUiHVo06CmtHFhILKJ9bMN21CnBUnzQ54h/n3WQYpABOtjSFC7f29YmqVk0dP1i25SaLqyXklMmKoiE4fnfMSfkwrBczio3p1Jklq+tu9beOW4MSJeGW2tgJKsqBSkK2ynr93d3OwxjQBg81nlFXCKW5cyfo4var1PjN1qiZUiwhelusEWYgkIWsoRwINeVc0YYPAaMLEvnlVUKYBNJEVwov9cITGK4xID3Dw6AjahAr8KznGfkVLDEReHH3ploA76/qmcLtGgNrOt6Ay3Vt61r+qFvvaapMQmvgxF0hrOmimSITKLZCssVys9L+4wQxGCN3i9hXqY1Ia0Ry3mBHwKmw6SohYgswxiW9WvGQ2BEgnMb69haA8e/XyuVQHIqLJTdsm5iZxsAXfN1FcizwYAAFCHQRu43XCllBABhZPi8bLV4JQDOOaug/XJZmKATsL/fw08ewzRu2LD96ssFtVbY0s6vxGy1ovUrdo5RX4OznjDQ3oYxkKNcE8OPdG1haPsZl7gRJAAHIGA76jqRiLQmJqgVYInEGRjfHtKW/ejtFe31tuYuCYNh8hj9jmMokhyiZyhV4ElFOaxVApoK8iuBsAX2+j4dUtKe+8abQAIq23fL1wNjNICXYEVh1E5IwBg68xQo2fZM5AGeUTkJJj+3Pk/S4Q8qGo5pjdoaILBR1c2tQAcDaWQoEJOhmqLvMpGtH2PdPEu0dWdYdWfTKtJFuLzhoRSsdcss+9QS2EqIOOscYbTJ28M6egj8mgjacQR9LcMCaVTvC9m3Wt4Hfi+qbZgkihd2Y8T0M9kGxwmE4wePkZtupdZ4TUIAml6iMUQ9p6K423y9Zx3r7w2UIUrbQs8MzinDrRbJ542yBR3UvHkg6Rpsi+6tJdJRknpRRzaxnz/Qf81ywcMXYvIZ4/SBBfAq85Kv9Q+1yPnJKld9w63UYDTjiGvCcllQK5oYBVPf4xqZNFMwTONGGtA6Zu+VVu8y/B6Za71KdGJiVH9eULckKvlgdJ+bcLQ+Z7/wPP2W1RvGWrDZc7lmx3UrKQWIHvC4H4kJG5wSPNrHqwqrIVdlr/cZYmNE1qaX+okMUu5ZL79IQQwlCQ2+6xrer1AUAKhX+9rYli0gqtXBXRGM3nIJi1PKRMY0HVtjjCrlbJ0otyaxIy05I6YVdp35tTJcJLbrMAwI04DCfBPNIruz1P9ZbIHhks6n0owmIABtFZQ9k6A7p6zIGQBUfQaDoj3iOJczBeyO66vWus+KBAC/ksUqGUetVQvXABgaapvaDuf2RgvEIM4xdIynPjuEaexU65ySGTTaE8y8yz6NAXy2iKbrr8P2oOq/LUHDfpQaAG+QNaozaq3VnjMAmkk6ddJbHdpbLOlBo1aHBBMNZ3WfgovJSIYxaLHbM7lj3A0YeCzVRlJLoKArQo7pnOinPt91xNcfeL33pajQsHVZ+1HjSq0HOaZPEoHE+FtmEF+znzcG9I0XkYEKXGy1xk+tV2dKaiv959EHuZUYxAHJ7xcmlVCAZpDTyFJ93CqyrBpw1oqNgo8Y2iLBZIckEBGImcJi1K+ME9CIKvJtgXf7mvZ1EPOWq0q7TM+Qv/6+RZMk8xbjblQkJIzDRhC+D+CoFIPNv6/VtaT2rSWWKxawIk4yiaZi41RLykx4a4kAuqBJRzxdP18G+j19LVXiqai+wv91Va/PLpriIULj3TMuO98Hc52snApGVGoTSYkISyl5eB/153Leo5Sq8LYG7p84f7Ivavd5bRiqDpp1b4ObAlu6BCUB1RT9ORMMYKj9TctoLIPpQmxKOtYi58+f788r6aSCWtsMuQ2JoEut5XCVIiOmpJ5XYQqNJwnOYQwB+4FqPqVWrCkhdtME5F614NYgWKoJWrOtMVLLyNZobYSgRQ/TOXg+qCK8DQDLeVEnLzMNjbUkr9ez0iyNQQKgWdKtnCMAeO8R14zL5ciwDGkhlmFEWiees2aVHazEETl4pWdsVZhclFjU74sxwm7Nrw5K+UTm0Gd0Ul+8noagDEJAa0Iixp2i3RibJubA1HOOOFErTKHaQ+Y6ifQ03WpRTWtoRpKXEcJON87LMFS2cVqcvfdsSePIiA+cIYpIQsm11XtZ6eX5x2fEJSIuK+YTwdTOW+wfDhj3I8Zdg8tl/3tERaJ1W2VEXQBiesVA1mxRIF+GtSjKbs5C6vDpBnuuo85SbvWwWqlmWovqro6HSUUWZLKKZA7tvnVtXhu2uTyjVlnqhok++ROTVfS1SlUYW1q80ho7lIwDC8tCKdYgZ4s4r9rPKRNcXAe9t+03OpVE2lNSSvq54nIbItq6zkgpIqWV0amqZRvnAoIfYKyDNU61So0xTNwRXeGCGNdN21tKJKDiPZF2hjBit3+gkWa7CYeHg96/TaZ/lUhJoAbeZ6nXSwmIvi52IMPkK4dLf2MVKKvXWGvVmZzDGjBOBwwD1VB70fqfW591kGmNVHfUJnDoB/rUzZeNzMnAmLSh9Z9gSDkHwODkBhgM3m1f90opR7+BrXh5rRUFjcQjD1kFXmUbOpZLlUegB1jqC/KaKdEg4dPTCfNpRlwirKVD/aqJ8wbLWg8YEgzOmQSErfOMm0MjfaG+t2i/vYZkElqnqgboIRXTosTaGQt1ohJFcx2t/55kizkXwOSmdtH1oYmjFqOSUx9wUJSq9cxMNSih9vd1M5Gh+5TO4lut9bJSOwqLM2vW4ZrjERUcWRR4ZBRn216BhcbRSAOZkQqFk10T+++zmOW8YJ1XyiLZecSFfifyoPJxN+qYMdlv1SFloyBjt5Qgxevamap4gMB+pmXuQp75lODzWyztLywV1m7hVmusQnSbA42r89edQdO1HDWju50AUivLN9bGduxf95PoADcZW+dgTFHYv7d9xlmdi3uNYGkSIfvePUelMmTJiMGtyzYxij1J3b5cNVHTxWlLh7R8OBd0TBTNU+zsfE6oNSHGBTlHzLPDspwxjDsM5wlx+Ur1VcMojPnrElGzy2Q3AGO2/dJ0vVLrlawwayAjiESvywpA65KCpoQwUI+vH5hz8Mu8hs86yIVJKorX5zZKpqdQ96ukTpbLGLiYkZaIOEcsg0c6ZBzGEYP31NZhXo9vKgJROXIKCil1KXdhOKTI/6WJFVg0SJHSbYqUhSVWUYl6zTVxFxxKNx38/HLG84/PePl4xDov/Hk9UNqTeM2YfaslU7qX5QJUYTSOKrROh9gqddwYqEHf3IeuP686C0ItDKrpamidc+0hQclAq5A4zJbFWlJmuD1hRQQSkHmcUC0VsO1ApzVpxiTXa4zRvlqKZhuBIXJQ1TtyyTZvsS6Xk0aTtcqfdG+dd/Dch+e8089BSjQeMInqu6bqpAFZdN2N1FRrxbAbFaLrDf06r1jnVdnGpVSsl4XquX7Bcl6wu5swHaZNq0iP5sjrUVDaniW5wc1JsxHqg8jOGUmPLAAY+/YCDaKaRMSs1jctsJxMGpHzY2uXWWyMJZE1BGK9tiFAg/83dUQJLDSblt9rrF/67BbWVBrC3pN8TNGa+LUTlvcE6gbZ4W/oHtRSm3QgD6XuSzhvvVJatadRrlGccq+jKhnlEEaMw04HDd/dvaNMs2N8p5SAWrDGBfN84uy04GQtvAvwYcCyXDBNB+wvD9jfH5osI7N6Ayt9ee82SGTl4jH9vd3Dkuh9xfZIj7q1FoYzTIFiJRDLsdke+gwjhnGHZZ0/O17ssw5yPl4atjwEhHH42cPXH15rLUzKuBwvSGvCfJqxXMjRTIcJd+/uMO5H7O/3qt6+GweM3iN4jykEEjE3JBBQSsEKqll+Ki2uYAcuztO0aKKWCjMwFCmZqMBIfO2X5wvOL2ccPxzxl3/7Fxw/HPHTn3/A6fykxdwekoAxMnTkzRcNR45Y1wtKoYhv5FFXmkUJI5QXMcBYrmkc1Mj1tdXNftXa+oVK0Vl4DZqCHlSNmDs4l/bDwgxBBwP7wTPrUAgINGVC9rgAyGtSKFtgM18qOfxuyoM4CpWLWm8nFH8+vyDnyLT1ph5jnVFCF2rFfJqxzisSw2BhGhDGgIUh0XVeO91Qx2SSgHE3Iic6Q7v7vaIxpRRuPJcBzFbJKaUUzMcLlnlFjhnLZUFcVrx8PML7NsJM+8ycUaOxnFnX0xqMu5GhVCcz7ensioJeFv3Vquo7tVQs66LX+dZrnRdMh0ltRj+Bxnm76f90TkQwwkY/VQM8a3FtDnKuen4AsMbsldhFR8KR16AeuiamsXkOrnqqJUGoVSB4C199g4+7YNAFv3HQicUHckyIccaynLEy8eVWZ1xqh5IBWmNhuRYnAgDOeYVJwzAxHDliGHa4v/+KYNRpbF0N7HxSipjnI15ePmCeX3A8PmFJZ8zLCSlFTNOBHejXGMc9pgPXk6XUw60eEhDRclSaqxWJ0bC0RizzirTSGRcRl00Zocq9b8xikcUk7VuHYdhhmu4wzyfY39oHKfdL+lystSj50w9NH62KoVznhTOyCy7HCwBg2A1YLyvG/YjLy5mgo2nAtB8x7MhZ3u8nBOcxcpbZD0W+hgP7B0RmC+ITQrSmc7i1tiworQlPPzzh+OGIp++f8NOffsL55YTT+QnrOissQYN0P99c+luX5RlsIr+Vc0IIE32vg2zylZCvtRYlyCGzMIaMn60gwfKObLVZm4dfEsgtqw8ATKb6UClbh6tEFKV1V6C0/sGe0LN57y5DlPpEKVtpKHoAyZDcynjEuCClSHveZa6ycsrIAC7HCz+Y5OzCmjBMAYtz5JguC9PMaRRVzgVhpJ8VWG9gpyMOPweSOpOWDkBqYSw8cEVp521DrSJ23Wqkmo0uUZ0iSoUfAupABBByBGikDIuNnKI4HmHTymd9y5VzbLXqLmvsBx9QAEfOxrAgADWxbwlbPVTd3y984qz093TDZRD29ZJVB1h+/tpRSg1SSxic7Wst/epte2cuE2sImqQMR57vdb0w0/Q2GaQKYVjHzx3X+Cz1N1ILB+s98zgo0VsNYWTINbRh6QAAh+I9fPaMYJA6jTyviQP9lFbEdUaMK6z1CCnA2KI9jXofBCGqFRmCwBgNkuOy4vx8QVxWnJ7PpCK1RsQoZCGnzFxZ5BilHkn2UltU3kKLFSDmZxip38g4i7RuGaPA6/pAZOHm8/OJ6nnPZ8ynGaUUeO9xeb6wNJrXtoRhN2B3t8Pubod33z5iOkw43O3xsNttJoCIszRd1mqIAouaXuPWvYEG6OHIKWNdIk4fTzg/n/Cn/+nPePrhCR+/+wkfn77jYjQbt1KwrheM416VI6x1Nxu/NAzkDD+iKDQi7SX9lAmpWZVSUKZhg7/LUF4XM0EYvMfXDbm18pQDFJTymtkHQOGJkquqn4TBqwi3QoWasV/Be68MDTZqKNJMDLTxaqKOss4r1vWCy+V4k70GoMZpHCbNWnpR68wEtafvn7AuCyLPSRQYyRijBAhBT4bLDiUXjPtRyT0WjjO1rHKN1hqsLCqv5YVMbEOpUZEhSCrfl1LCslxwOT8jZaqPez9gHPcMT7axXTlnjLsR456yYufdZmBArRWVs9u+TzXOK+b5jHV9e+mzGFeFo40xrzSDr1mmhQOBWisCAiHbPNar/9m+NtacG72nDucVRMm0Hm6pZS3nhTJ17k+UDJWCF9cNwWa2dmIpunml7F3qYhzkGW+20Ts7/czkqRyp9aKUjGU5I67LzTSHSS1KZBTbbEjnA2WNwwTvAhF1bEOIhHxDPY9dK4sRBMMgIGCYBoRxwO58D2Ms5vm0gV1jWhHjAuc80kpKR9lsiXd9zbuquhZJzcU14XK84PjhiMvxgg8//gXn8wsL6i+kB+sDpulAI7h8gGekT17IWNuSHMeBwG+tQQKsCSlDeMXICUmDN4wyC3Y8a9J6kcBM091Oo+beOOeYsM4rLscL1nnVRtP7r+6xf9jj7t0dvvn7b8hZ7iaVnAOgggOZI7Lapds9kwkgvdBiDFaO9NfLio/ff8SHv3zAx798xD//j/+I0/kJ59MTVr6RwzBhHPeoteJ8foL2yGkEdpvG9XE3qUZgzkmLyVQL81gvXCd9OtGUjJKxvzuoIkuOTTRg/3jAuBtxeHfAdJgwjGHT+mEY0qvWwjDRqeaCxHBPD2cIRC7wqOW6QRO075RbJPuLCfN5VpiDmsOhxrDPHgCgHxEktccY6eG6VQa5LGcyegcyGiK/V7kOqMYvFx6cXbAssxo3nQZvHT+AnIm+UCZWc0GYSGPSeadtRAIxt8yE9iyuRMo5v1xweTljOS/qrChgWrEuF8zLiaNycpDTdFAixTBMGMKEOEfeVzLcgQOlML4mpoicly+V+ytvQxwxpknA1cqEC2shbHkJ/ABgmAZCnJaoz941k1wgbYFh0Z0pbVvhXu5+4HHfgpRTwnwi7sF6WbUdCYDqufbPTK1VJ8+QmMjKyEBUZv/hYa+wap/pZiVSVc3Saq1IOSLG22ix7vf3AHoN04IhTCR0wVM56H5Qx4E4QKo1LphnA8xXiFJXtwyBgsswjHj37lssC0GYx+NH/Xk6501gpEeXrLUbWczEmbzYn3VecfzwgqefPuB8fsHz8w9YlgtS4vM/UaY7jjs465lp3tqj1nVGyY3bIGSdz61f1QfpfKs9CfxTQEgabRS3DqSMYhuER1lMz3pqzk3IDst5wSIU6TWCBn8vFNFdyDAPu2HjXKWwnkuBsxaJm1bFSerD0EGQ1lo4ZrvNpxnzacbLTy94/vEZzz8+43x51sNJGoMDdrt7jONeD3jOjZErN/UWyxjDjC+KcGotNLTUSv8ORZ/ruqpGrEAMxhjEuOprlVKRlqiwiEBbfU1HVq0VMpZM9k8cY1yiQuQUObpmhLpaqE5aZ+OTcyHkoAumjDEIHIVeO70+67RX1/ZrGnv/miXkBe3/cp3D5zpoXCNEv7KUjJITYhJmYNZajmT6MVIjcliJTj5yhC0BSCmVyxadUhUHeIlh/7REghlTxrouWqsiSHjBui4b2KgUmgVKz4lVtZCSMpIx8CzSIAzRT4UbvdEz9nZB4AbRYbhYyEqX44Wa50vlvtAG9apaDYSNS4Z13JGqjtRnXfBN1cU2hyxC+sL0zYkcXI6ZeBLnRWH0XqtUapSNvGTo/uSi4iklZcQYNZD1g8dQtz2WSjbpXnvLRL9NEDgMu/YMVSIYUpblN45EbB34GRZbEuOKWssGBlZY0zpiuYaBnJNxHKRlDMOoI7YqB5M5J7jiNsmSBj8cYGcm4qyXVfkrl+OMeT6pnQ6e7vc47jFNd5imA6bpoHZQlmhqF5YLJZGEoLb9l9ZnHSSpqweFKwxDDiVTiiwX4oNDrLWJyToL7w3JQg1e2yj0olnb8Px0xuV4wXyeKWKfV6yXFaenM9Y5Yr2sMNZif7/HfJq1IT5MgYhDrkXkBFm0Rvi4RFXbp1YNIu68/PSM09MZ3//D9/j4/Uccn59Qa8VuusPj4zfY390jjCQB5rxDignDdyNenj9gWS8bJ3mLldaEw7sDxnGPGMkwhmFSBpk0vrZ6RsTLy096TZLhGmNhXyz1pTqLdYkIQ8B8WlSNRPUsr2BXMUrzacb56YRlXjGzg5RMsd8DKYKv8wKaBZeVISZRq7UWcXnE7m5H0GNwr/aQfq61IfjBIySKcHsW3pvuN0ehhuvLtlNuEjiMnFpCTpEgQnWWRR/6uC44HqNGypJNjuMe47hjsed3CGPAMBJ5x1jLEmb0etLSobAdG5d1nTHPR8zzidjNAKyxWh+apgOGMCq0NI47DNNIEoMMI8aZoOESClPjX9fvpG7svMXQDUl/yyUoRA+JUv0v4fx8xunpRAzeXOC90/JA4XmW5+OLwqi73QHjYcLDV/d8prwSvnpRATlPBIM2HeHCesvCKJW2rvk0U5DCdTQ1sGhMU3EaALrgNWkWX2tVeLufmlMSjeay3sKlwAOHw82CEQCYpoOWAuRsO9f0SWstTXu10ADvdb1gZXsn+xDjwp+7ZWLeD9jv71W8fLe7gzUEY07jAWtcEOOClR2b6+TqgMamFtWb1AUrl+OM08cjlnnG+fKsjN+Hh2+w25FTfPj6gXqF9yOkPg9An6G4RLgPFuuyYF5Oig4+PHzz2X37vBarbVCXyQbWkZN0pmrjM9BozmIIJRKXOuMwBujYIFbuKDlTI/VuwHSeYK3VdLpnA4qhpoGzBFMJ9OIZppJRTq1uVhXnj5yml1Qwn2ccP7zg/EJ4NkrFbneHd199jXE/Ync3beTZpO2DHrgBuWzlvG6xluWMXd61aJ4jHucCZdo8nBhgg2YccpXeugIpwFtrsa6z3pOSCuIQNYINDLdu7zdHcjLguFYa7jsElImEo/OacD6e1BFKf1XOCct80gdtGHZw1sHzQySwt4wPGnZDx0zs+tWkDmhI2KGkHcZx+Wy091uWUNh94PqIEAPWqEjEOO4QwoR9rZrBpbTicjliXS9YlgvBtWxISxFR54AYqfZDxuigLTrOOwy7geFoYjcePx6xnBe8fHjmbHHFfHnB5XLE5XJEyYkc4TDgcHhHNPr9PXa7A8I0YNxR8CP9kk1YnmBBkgczn9Sj1OCPe9Ng395BeheaXnOV55TgSWkJKqViXS+Y56b+IoEfqcIQarSuM/xpwHKaMd3tlMeg3IYlqkYoTNPy3JQFSnueJcNLa9K64LJeUErunGVSZRoA2ldn3VbxaLnMahPH3dBsH2+5iIB4rp3J57rFGgZmDUMUbKoqdgFc2tiQHTPXEKOSiCKjGAAnSj7g4eEbDsj2SCkirgsul6MyYWmPKWBc1wtqyaApRdRyp/Aqo0WJIevT0xmXlzNOz2ecz88apAzDpCUF78l2LZeF7MoaN0inoCWOFZlsSjAw+rsyxeeX1udZrEK4yAXVWj2oMDTaqoAbZAvDc71ckBBCmMbbT1MHwNJkwsA09PAGasiW3ksADDtFzCcL66LOZKv8cInOqBTWNw3qfNil0Hv6eMLp6ah1NeMsxiHg/ut7JghNcN5rVCPZl3XE+JLhvWQAbzWfcDtdHoBGe32Njx6w1tdJ8AffN8mAEtVu1jkwLb1S3VD7zuwm0Onl3uT9Zb9dcDrbMaUVMc5aH6RMKmnEaQ2Nz0EY4Rjys0YmqFS91wCNHasVTRLMGpgCWEti4C44hmpuN01WINKmRlRejcuR9hljDJaZmMbLYtRoFDacBHlbrU8q2aQU9FR+1Rn1NBjZWkuQKke9MS5Y1wtSXJF6hqOiBK2p31qPMJG04P5xrzqmEmgCUQXIS8pI1sI6ucetZUuWwJe3WD4MOvaM+kSrokDXWbmQ1PoyAipNmqD6/AzvAv9sxriMHOAFpJgRUlDFIC0n1KrP+EaXVeqFhUQjYlyxLGcs68xsTLonAq/3ZQ0AcLUozC7ITlqdGm3XIROyxzTqyfOA6ulmKIlcF3185nA4qYEX1OqA0ti7AJR4JihWXCkDMzAcDDSocrd7wOXyjDmtiDO1p5VayInVNiYsmcRB30QMeynhMTGOWkeIJzKfZpzPz1iWMwX+MICiUlmDx7guFISzJKiMlVPUstZ2doxRqTnJ+n9pfdZBXo5k8Mx+hHFlU7vomYuUZVb9gNUTcy/nDJcpexlGUn+X6Lm6ioGdmkAQyzwi3kUWl01Yud6QUkY+XvTBTWtEjgnD1EHA1mhGiUr1C8Gwzy9nLJcFz98/YZlX1Fww7EaFQB6/edABtX2dIEtTdyHMPtQRZWlY+i1WH1FVNgZEs+4mcjiL/e5AP1NaQ36MK+b5qIY4lwQkqkv66GFd5kjrdWYgtUUZtOw8dMqEMVANXokIU0paE6MeU4vD4Z1CGCFMZKhhEQYS3fbea3Yfl0hsVmOQTNLXRodKSGPxOO5uVp8RoyW1oz4LGHbcE2kNDu/ulAn88tMLai6YL0VVROjhC5Bp69RbRnUegaKm8YBxNxFRyrXpBTmz0HwgdCSnDO8C5nJCyhHeedwdHnF3916bs5fljB9//GdY6zBNB9w/vcfh7h2M+VvsHmgijXMO1RXUQPsubSCVBSDaNAZsmMvC2LwFxLp/oN5nKX0I0iP1PBIR6IUlrNZ9E4tu10KEKZFGW5YzYlwwzvtNzTdMIk5tCBlSycDKz7X02lYm3FCvHbEwj5q1E7y4QkZEGes0a6Q5i9sZm4BIs83IiTLjXjKv1tZbHMKA/f4BhiHzWywpz/TBd9/PaMyKUmxz+qxE5F3gcg1l72tcIGPhxnGHb7/9I77++m/x7R9+jx/+9AOenr7DTz/9GetKUKxkad4HZeNTYD0RHM19wru7HXQAeMy4PJ9xOr3gfH5WW2aMwfnyghRXzMupoVbLBeO441pkI6o9Pn6LYZgw7Xc8uzMptGsMBVqfI0V91kH+9KcfCXq831MzP8MX/SgZcGPmOhOWvy4RA0gDFUX6mRLWmYrcUnCXDDB2sIoUiUshckLi2qJskDAnRdYsxYxxXxjKZciAh/9KnXM+zzg/n7UADzYMMhGA9BQLak1a52jC0ZEHpkpUm7U38lYQ6zju6dDs7oixuM4YB4KPfPAsr2Rb5mwKTi9nzTiW5Uw1k5Ko18c6LPMJMT60A7MfEeaVRi2xxmebkUnUf3G6ymJl8kKOWWFcmRHnfUAIE4IfKJBgkXfTRc/GQBlshSciEBrRK+c0JFWJDFJTSrcZJiv1LGspQLI6norfW3uADS7PFGj98Oc/43yhB/hyeaHrZKMpIeRud4dpPGDa3WEIk+7LMA1sLIPuvY0ZznuM+1EJPNSu47SJXP73ftBzcTo9sXMP+Pj0HZ6ev8fp+BHv3v8eh4cD7r96gDF4NWewZ1X2S4y2PBef0uT9rUu0aaV3MEfK/mTkWRgDi5EPCmtaa6lWzFlcRkJNCbkmlErnqaJqhheGkQKS/Y73nFppLN9f2YN1jpiZQX9+Ji7EspyxzCcmRV26HsWskPkwTEwI8dpTB2M2LWA5RVKLMgYVBS4HRX50r42gCMJCvk2PtWRL3kMdnlxHzhnLYjTgD2GANQ5hGBtSl0nxZxx3WNcF3gfc3b3H3d07TPsDjKV65DQS3C9tcs55OK7vy573WbTz1EIoRExlsxfpVGhtJ7VW0pTNEZfLETHO+uxO0wGPj9/CGEPs2ZcPmOcTdrs7PDx8o0gblf+clns+Z8M/ezfOLxeexFARd7GN4OnkqKjOkRXGTCvJP7nAivc5AwsTMFyjSUvEKoQQKpbHjqgQlbQjH4SIJX5jbPXDeN9eW6+JCu5nNmzrumIYBhpiq+l6xXJeVOhcJpbEZSUFh5i4sF44tW/s0VsscS7DsGOCxxnDSO0yLji47GFMVumznCvm+Yh1uWBlJ1lyRspRM7h1vaDUwpFbRFr3BEHUinqYMDmnUnWU4SWuiUVtixECQ4wrZNq4gVCmJ2otmEad2yf9Y0IEqrXqlBSp46bY9vA6c5Z+NXFSAgm99aK6LUMvrokkA9AaOhEcMk7PZ7w8fcQPP/4TExYok3NMjJEZe6VkTNMdTWC/uyf0hM9b4DNMbR9EKpG+1TAOROBwFnFNsNZgXSiroGwrYjrsKdtZL+pAAODl5SfESDXRmFYsyzdqfPxA79dEGF7vA02l6GB1Y2BukLVriaUK+sHamZRWKQrkg0ecLdbVwLtVn2vpN6W2DT4/xqBUYvuSsMbAgcQdpmkPYI/9AzmnMPDggbUFgPN5wen5BfNyIkLUcsK6zIhcZ5aAOISR23kEIfHNKRqr7VkU4LfWpJTYQEcHuKo9xPRFo7/3Oemzv3bp+bYCLxL7FACzsQWKJ7WdPtDTz8MZIZ13j7u795imOwxjUNJRGEbspjtYOyOlVbkHpGWdUU3dOCVpIfQMubfaMHMReDyVXENcZ0QmQ1FGXzBOB+z3D3h8/BYprViWC86XFyzrhWrULmDa3SlbV65Hkp5fWp+1OF/9zXtiwTGz6HK8bEg40s8mfW7y4UQTr/f+4u2dt0q3vjyf8fLxiPnlguPTqQktA4rl18qU7JwwhAk+DDg80pSDYTfgbr0DiUNbDLtB5+09//iMj99/xHf/7i+Y5xNqKQjDiHUFnChfcCvC5XIkUpAM0ywFa5w12pKDS3JvrWh9iyUKP7vdHQA63BMzP8V4iOLHy4dnHI8f8Je//Fv92VKyXncIE0rJOJ+ftaHWh0FJKY8v3+Dx66/gfu+wu9tpw/b55Yz5eMHTD884fnzGsl6wzAT3URZ90fpQKbnJVHHkTnJOBwzDhLvHO+297OsvOVOmmtakgY+IcRtDZAIXKOKv3r3SOn2zVXkawTSwI6NzJCzaWivWy4p//p//AT/++E84vnxAGEYc9g/Y7R/w4cOfkBJlGEMYyEjs7vG7v/kj9vd7WEvyin4IWGciKFlriKTknQ4hF4Fzz/J91lncf3WvUON6WbEuEQ9fPyCtCafnE3788U84nZ7w8vITluXMcOQBP/30Jzw//4jz+Qnffvv3eP83X+Hu3WHTOA12hHJPSi6sM0pT76UJ/62X1LTlM0sQ1DfnA8ASqf/zdKIznuKKNc7a/xbjrJChdx6BIe1lPikRZLd7wMPD19QKsh+xf9jj4at7euaPF5yfzji/XPDy9BEfP36nbQS9UyRntyopyxiDDx/+rFMh9vt7HA7vKJgddvQZ7WtnZ0xXIulqyHQeeCh2ug0zHgCEuSoEFYFdpQ9Y1KomnnhBqI+l9icWGejti/c0BzKngstx5kyOho9TKa6oY12WM2AsDLesBT9gGFpvcF/ykefu4O5x//gO02HSAHu9rJjPM+7u3nNAuOD+/iv84Q//Mb7947c4fTzCWoecIi4ziYss60wlHmGpW6dcg88d8M86yPEwcSS3vtK9qwzBSV+RKO2IHN1yaQ5E6on6sHOGeH4+4fh0xHw54nR+UuhOIo9e+YEcLhVa05qUbLLOCwswVx71UxFn6ts7P5/x8vKTOuvRHtBoy0lrC8/PPyqLbBh2+t7OOljnsdvdkY4h7M0UdGTFSFmbCw4hjZhGYj06R4cSY0VyFvUiI2sc9vt71VSUrOZyfkGMMwyoXrDfP26UgCQS9qykL4dVDo5E9hVtMoSwvqRXtJSCl5eflKW2rhdlmUnrweX8gt3+XmtvSoa4IkcAjUUrfxf50NvxV0HQZ5iovthljpJJxnnVWul+/0jizYd3mHZ3GHfEDhQ4TtSW7u7owR73ozo9560SYizvs4q5SztJF0CEwatDIyZtpwxlScN0t7uj/txSsN8/IIQRX331twrH9udpd7drGXFH0sopqUBDASlUWUtjyuoNghKZQqN9rh6bwQeZ+28//vQjXl5+xPn8QpyCLL2oSZ2X9hBqDbAQctKdGOc8hmnAdJiwu9thutsRU36NmwkbsiiwHLUe6KzXJv55PmFdLzifXzDPJzgX+NwT/BjjjHHca8uDZk+1sbNlckXf52uyUWb6LZcInFjuq0atChNLIiJ1umEgJr01rhs03O2Ts1pGAeh5LZ2QgjCRKyqNrRMhdtaBlR5VSqhKGxPmaRCDGahf++HrBw2g0hqxLhF+8Hh4+QYxzhiGHQ4PezhnMUwj7h/fo9aK0+kjBwVSJ+Yxb52NKfU3ZpDjbmT83mtmI6zWuCaFI9MaufmcDliK9LOFa4txjbg8XwiuzBmnZ4IyjscPuFyOWBai85J6zQ7TeIB1HsEPKFWko6hYj44VKJJxWo/cDQS3sHj0fLzgdPqoECDJbyXUmpFSZrHgC56ff9A+s8PhHUIYteArf3fO03Rw+7p/7y1XSgQrW0s3vFbJpIjRaayBZWIDOc0B9/dfYZoO2B3ucDkd8fLyE06nJ6zrwiSAR7x79zv6HKERMAjWC5BRU9fMRdp3ChKcDzraaRha9CskBqGDA1TzWNcL12cc7h++wm6+x+P7r3+ROdaLT1vLPOnbjYEEAO0bDEMb9tpDomsl3c0QRjw+jjDG4t237ygDDA4lZZzPR5zPzxiHHcIw4u6BpBL7WizQ1fhEtoxbSehZ2c7Io5YmyujjEpn567UH1TkiRUlA6T31nf3u7/6OYPF5oeCwFCI/pITAtTjJkAFgnY06Z2MM1z63UNib7jczSAvXoG3damKmSCLtHz78CcfjRyzLBfv9fct0SwuY+0BaDD9lpVkNfQijtnDt7naYDhN/7pWISs5y3544iophGJWtKe+d4orvvv93SByAioKS1INDGLGuMx4evta6mPdOGbty/6UFx/OYv1qr9pTfiogm791nroTSQOHWbD1qjeo8BU7uAzzL7OjGrmf2KSOCInMpJMd1xStCozHE3Bb94ZILIzAchDM/RBDB+6/uCXXgnvaSCg6PB1yO75GWCBmmYQxpID+4e+zv93j56VFlKp0PhJ75FgTLWfql9SvaPApqoUMN79VgC4x6j3v+waostOcfnrFKXXFoCvdPP3xUYeiXl5+4+N0aV2ulKPjrr/4A5wNvbIQ1VLDf7++we9iz0WlkhxyzUrnv3t8rbPThuw84P58hI05CGDHtJ21SzzHj4/cftM4QI1GG7T31J0mmaS2zMsdWZLes/XeLFeOK+XzB4eEAP3iMTI6SqSeFZc9qrQjjgHeJIibp/Tq/nHH38ZFl8gq8H3F394DDuzsMu0HFFuRwhjHo/wCQExnicTfi/e/f4eGbB1bFb2QKgCW4HLHenp9/wMeP39HXw4jHx28xTZStH18+aCvEfv+g9ckwjgrVi9avC21iCbVYGKb0367v9N373+Ph4RttNvaDx+6OoLI4r6iHCcNuxLvfv9MapTyQtVaM06DSWLKMMTrk2A9Ba7rUAmM6B5k3Wqvyu4BMaKFsK7IAudRzAwsN/Kv/3b/aTKDwweHxm0e9nsvLpWvhaY35QjwyxqhqjYx8A3DT6SlkYMkQOjHatg0M/vj9Bzw9fY/L5Yi7u3f45pu/x7ff/h1SIs3Sf/zH/x6XyxHzDO7/vMPj4++UXJJL0sxnHPd4/OYd7t/fYdpPdP4Hj3XmWnPw2N1NctM6Z9ukKt//7h0JpqfC7R00yNtZhzBMePfu91ofP5+fMc8n7tnbwbm77nlr01ckHhBULvGzJQHmWy/KuomIY43V1iwKiJK2P5AaU1a73IiRlvkEbVQVwAGtNXAB8MV3nJKIvLRymfcBuRty7D2JOqRIPe7CkAeIg7G/37OUH9d+rYcfBk24/OAxMaGt195tZD+Dx28ecTle8Pzjs6KNYQhKDFvnz8tX/irWg76pbRqabXO6vhmGWa13qKpPmORFGLcm/FvYRh8//oUZT3Sg9vsH3N2/VwcWI2H+wzDg7v09Du8OCFwXksMmrFhjDYvmkijB4WGP/cNeVSREA1Lm6gEURR6PhMUT2eQrfPXVH5SZuSwXjiy9vq9nZftbDZQtJTER4QDbMXcdH9BiLUzO1OQ/oeHopsmzOT7Ulen8MpXeKYHG64gkgd1ai4tAQRQtDl2yLIgBic5TVvv4/httvk1pxTDs8Pj4DcZxh1KqNvdarm0Jc1MyV3GMfRbR17N71tst1jQdMI4js1ctOzUmfLEuKWrdKEr1MxkFKqXeyXa90pcl/XaZa3pSc5PPq03qnXCzvI887D44lMLiAuw4w+AxHUa9/QQnWUx3O52Hp4zR2iaKaEbA16Ayblr75/2+AYOVPhv1tw6uSe0RslR4viO1rfz+9/8b7Pc0R/DxmwdVWCEnRO0X+909pt09vvrqb7REcP1e+/s9hqmp7AgD39o2rNcPHrvDnseOOU0AUCsptPBevX//e2axjhx4T3j37ndqnM/npw5ObSRGafHwQzeYnUXROY3cwOJvvQg65TY8Fj0w6B0dtT0k7lUWARBrnc7jlMAGkoW6hop86vls2Sr1RFfbmKROSnGp089mYQtCzgYYA020nHMYvOMgBgA7vOuhGb2DFKQhqo4vPbdpTci/EgH8vJIOv5H0uW0kk2wbnkxvXpi2G3Bh0d/C7CbvxTDvcP/VPVJ8j/PTGc9PPxCrMq54fPc73N9/hXe/e4TlEULrEoFK8/IevnnA/VcPXHckskNhqFEYk+N+JEKEtXj4+gHnlwsO+0cs64WdvMX+8YD7r4hZeDnOePqJHPZud4+7u3f447/832qf2ofvfkKtGb6bhRmGEanTZX3rlXNGXBeUXJq4Ak+9EEFyX51CpJbn2AkRiiKwHR6/efzk/ZSJHOIAiFFJDNmaCzIbjVBbtiGzJdclcr/YompD3/7xWzycH/DV+VsiQo0D7t7fIQwepVQ8//CIuLb7KMaGlHScSs7VUpTlCjQma6t/3qZxfbe706kbwiQdxrCR3wPArRdVDV7vvGTJJIhaikI5cY4qeaVENWe5Jml0QrpoFQuEpYw7zrDF6Eq9OIwBI+8nQBm3CHVLa9T+fqe8gH5+3rSfFL7NXPtZWZyj1wq9xRknuUqvgawxRrOIuESMO0J63v/te0JOuFdZGKe1FNXllJaox28fdaZm74Tm06y1YB9cV3tlvgQP6w0j9RnvHw8IY2h14VzIgbIY+u//+Ld4PL3H5fh3cM4iTAQByr7Nx4sy+Wul4IrIgV77rkVLWc5DTjLcN92MGa8BHGeSSrS0BC97H5CNgWcHLdwMgp63z51kjVI/3LxHP3DaGBJWsQnWeVSQko8IRVhHQhbKH/EtuN/dTdxHX9TBeS4z0QUWrR9Ltt/bCnQOcnc39V9Gyb8ezv68g3RWH1KBZFQxP7Rp6z547R0UtqEYEz94PaTDNOD+/Z3WVe7e3+HphyfMpzOmwx6HB9JWBMggeY6APUdfUjMYxqCjaQxHzY/fPmJ3N2E/jBi8x9d/940+fM8/PrMjpWkYp48n1Psd9g97/OE/+iP2+zv4IWB3N+Hx20clAkntRuA3gDKOW8F9AEdkoF64MHjdA7nL2oLA7QAi4Sc/YySyKkUzBzksQv6Qr0tG09ce2wHXC2LJJpr0AAChtl6+/f1OCT05pc0w5FIqpv3YvTZ01JMwW+XsiFEXKFLqM6rUdKM1DDtqKmeY2Q++g8IMN61jkx3WUqi/jR0cbZPRlhD6GXAkTn16IhRvGGVp5IZWspCJG8a0TM4Yi2FHNSDH9X4ZBIu9UWejAQWg6AFACIKx5FTBrykO1xgoS3C5LPSaUou8UeYuwR4RmKxm7hLc7u53+neAGfDcSz1MA/7uP/67prHJqinTfkKYSBghjA3S7kkxpbAYALOBM4u3i0PNXG4YdwP293t9DblGHzy+/sM3kCkT8llk/+RahaMxnxdVvhKC1DAG7rNOqBUszJ719261Sk7I1sLmxj8gAqSD8VbRPRmGIMS5UjNkmLYxmdCrUmCqUYESEd9XJ4XGb3A+wFeZeENqZJMEpFybFX1v/kUY0BQowwpbxpCi2XJZWM2qm9LS/V9t1SEYOii51A3aU+UFIbrPv1FJp7+AT31d4Lkg6h/BY7ks2N/vsbJijTie3QNBGPv7PanhxIzf/YvfYXe3w3yeiWzAr5cTCeZaTxvuw5UskDFquIWdtrvbYfQBgdPxcTdi/3jAu9+907FaYONFuD/1Eu4f9sQKZF1McYTGED0/RYIhRbeU2Gq364PUjygSfW5LYtAa1ZUaDkVUppFb6mv5sB5+6MkOgLTi9AzTq+vpYKn+TLjgpYUSOTKMwk7cMuwnh7LkAhfdxkGK4DwAmCXC2u01bT/f2y9pJdlOmOc6mZMouajs4vXS8oPZBiqFSQukDNMUp3oWnTGmi0SgMOs13EysUgd/HfmWfg5nc97qtLs96+HHLa2eVXVsG0hwSxKa7LHOKRVGb84IY8DhYa+D00tn7CQQB6DMefkskuUJXGrWdjbbKLWiY5b6djJjDQX8EMmzrcG2/O8wDsrCLh2pKYxNwMKISAkjCHKvxt3IaI8FDJ31HmZvyMJten1Lbc97v/pzKGUQY3j+LGeXGvAxQmWSnBOzCRplLymztDpgPWePxpofeJyfU7tmbZMB7LW9AT6nRmrUBda1IFOv3QIopjn10oLNayf475vY/CoHKes6yvGBHQoLJBd1PEmhInGa02HE3bv7TaP/MAF/+6//Fl/94SvEOercwMTOrJexc8zgFCgDtXLrg8N0mPDw9QPu3t9hCgFj8PDW4W43Ib1L+PoPXyPOK84vNEZHagwy4WN3TxqsYAhLakbDzuDw7kC/Ezz2DzvAGNx/9cCkkdvUC4SwJFT+YTfSQ86QKFxjQ1Y2wPSL9DWJ6pQ4wsZT4KV+RI9CaplqIoUnnX/KsfI/mvp+yoi1akRHDz+3iDA8bK2FFWFqQ4ZJyEACYxtjsHLhP61JHXRGG5p6yxUGobC/dgzWWhRTuBG9bAMV231u9MaBBbizqDGtytITJ7qRcrN99i4vbtQ5CwnHeQffqYCg8rR0Di5Ux1SHEZODqJZkA+HkM7b3omwBjAh5JknRWTDOwtwgc2/BNRGNpsOEkVEFmZNpRVMTLbiSTGXcj0oUSyuxGFNMOrfROYdiy2afqSUt6v3LrOTUZ92mD0QZVRH5SmkTodpYqx2LfSpdoCJkNiG2CLxaC9X2zk9nbRvakkucCnC/9RJyTn/NKiGHpk9qjOPRaYVLDm1Qcq3QIQabeym1XNc0tyW7jMwyLSWRAME44O7xgIH5EEI8C4MnJix4bBjP1wSgwg7yPBFEOgCeek0lcKXz38TvBZES/WT6GfBnaZnuL63POkipP+Iqi5Qb65iosRsGtLNF9aVpP+FyvBDLyVkaUcWHTV4rJCKMxHGFD45qJNIzyU38IkElEWLTjqToYZhGpQVPIWA/Dhh9oDmRpSB+HWlyB0gZSKndDJs4R3JHQkKS6LKkjLgfURL1ownEOx1GuJ/sDQvqWR+0RvZoDy8VyA0GF6h+wdF0rRWmNnxdi+icHUhWJzB4jkn78QDqVRW6tjip9hATCzKYoAo+VYQWmMxgQtPUlUOJzgiIw+9rQNdkkP68Xhff+x7ct1yiYnO9KAgxGGwg52aTZmcbeGfTw5fVSZXUZSmm3YN+hqY4ulrb3LrcQ7HWbiJgebYExgOg0nAmGyABMUW+9y3yl0ha9refzaqDBur2/1uRdGp3tlr/LQm1l1z1z1qKBiFhDDDewZSi5zqxnShdACFtHmokUzcDk99bmMORYdZSConFd+1Pouwi2aLURxvKFbYoR0zIPK5PpBSt4wSCA9w2Hcfq59LfB1RN5xYrZwoOQm29ttKj2RMvUaUv0qoqlpDNru9fQ0SsZvfWk6yktGP44FArkduGibRxwzTgup/SeQ/rKupSkWvWiUUpZgwTtMWtD0hMqai2ZbbyPFhrgQDUGhSBENJaYVvVPv8vu8Bf0ebxy9h4w5wBz/2B+0kEnqVHpg11lShVo27XJssbQ/2TmSPInhqvRVq37UGUCE/IKt45eOvgnYW3DqOn+ud0mGh01WVpU0Y6oyW9SeqEksguOWSFhJzW4n5tkfevWrWi1sxGom4+r6YYZjuwuS+O07fZqbGwgjgmqRNItOVtgxY1CmPx5mt4ohZoLdozRfu6SVGCFx0sy47xer8l0yqlbCStqO5QN0hB++i37BFrf984FWNRLWD4DFZAA6ke5gFaBifBRf/i0njvnOtGfLVaSL9KKY052AUbxkBJOLVWFAmExNjWiqpZ7adLI5tr5f2Vs9BPivm1EfZfs/rX1usUrUxnkBP31LHTq6UoUaRWqMB4jllHkSkz0rvNZ2hBXssQtcQgCkZMkHLd9VFLjFFN58IZaJ+9a2bLbWyS+TTuRVDi1y/tg6ybQqybksovELD4GpyyuYMyugFmXHdxqvBTBOETBI7elPYAnEhJW96nSD/KHI558ywKUiaQvPBbhJAj0PTmI1gDC5lKJKWdNt2pcQY+PxD8s3cjJ+oxtKVBQf3hIzaWxTxETAMw+oC7acIUApbdBB8cqeYsUTdnGgc4fq2UM6XX+0nnQOaYMfsZiWXIpNAu0JxAMRK9+IFmSoZpwOCdvjYAeOdw2E04vCNizeU4bzMja/WmygPrg0c25Jhd8LCe2JcjR+6Cmd+yTlMqzU0UdY24JljJpBkSDFNAzY6Yws5soAYAeqhED1WgVVJO4WZzHiZrvdV2gxQbTO5GcWT0ui60qSJxpSZ5gbKctzpGqO8j6w2htKwYA6TYGeQCdYaN1Vk2MPut62J9BieohekICDmlBg8GT1GwNQr9yeQXgEgypXi2o22kzzANSlYbmFiTtI/XIVsazpyQVExfUA5tT3D04EtQ4zhqd87BD1XlCCXIqEyy6Ps04xybk+D2Ch3v1Y8du0FQohq7nEkBXFOq9JznRNyFwtkbrGnlgEKiBznSNBljiBi1u98rWWY+0VBdJaZxUDZyzdtaqzZkPs/KnKbB1UXhT9s5t5QS5tOC09NZg/WePSwZeWIioPOuq63yOS/dn5LZdMGCMbcJSABSGKpdHVJ0bBOwETAPIWgZTNrC5HmlMw6YIigV3UMRG5HeWtSKWsix7e6YwGSNQqW1VKTU+mypFs215ZRh1tbva33RLFa5KYXUp0ohxEwSFhs8jNiMUpAzlQgEXk8pI84klan3+LeSdCK3U9ChkGhP5ISI+WViwhpZoNxYjCHAc6aX7vbwQ8TiF32dXAqctXDWwjti22Wpn5TCjf8GHpTaD7tBFSmI+NBnHFaVfoJzyKUi5YwiWaez8JaIPDkm7O7IEUuU9yrav94ghl1kKnjJBdNhwt3jAWn9/ETqv3bJ/jZ2aKuZyOGRg+ucBbgFRYTG+/0RNlhaI9aZ/ket/LtOH+gcO6USpfk3Q0v1ZIdaiVxhnUOR5nbbRL7FcVhYhenkzPQtEQJFSr0u56IPQNEI8fZ9kPI+OTYHKLWR4puzkYBKIVLJvPllqN7aMsvINV2l/PewrDxHoIdQMyVutamZHIEYTOdE9LkRiCg4pmuS1++XQou2BbP8cRkCa8pYRduE6qYv8xbbrqQZHmsF8LQRQWgYopMhBbWyc1eGLv9cdjqNZne/V9uR1iZp2e+3/Lv/s78GcV45FSyXFeN+RLAeu4ednlOZUyuBCoCNPZr21J5gneNnxOjrRmbFr5dVz4Y4yXaNt+o9bVJ86qh5pm3ORuUzU2RReFbEGSBEtbY/kjyLc3RO0DWnmWAYLf9MN8WDS2bLeSE4lIMHye4k2/bBIwoClTuUwVqEscGsJCJQUde0KR2ois+F4O44r9y/TXwAsmdvVIOMa4LMa7zue6lyMVzPSt4he4dSSS3EOocptAKrPoS1IrMH98YC5nWGYIyBC2QMpgOxnoyzOnHcZa/ixi5QI79n6ClXoiE7a1GKhXeU3ifup8qpoJbYDAI7lmq2zE3JeGQCgER4novuQkO/1co5aa1PBBdqqYgT1Q59IuYerIGtDAnZLVOrh/FyKlofqRVai5VISptnO2iq/7fU36wjJ22tRXFbkWnr2+vVWmn0WG21iv56dNxRSlr81xpelz1eQ61vvs+pZc6yZyk0p9ZjPqLYBLs1uoZZfU6DxxZsSOamNUB2XLJHPfRnuS9RHvxe+k6fPXlftABIlkDa8neAIEJ0n683VuIc+r7HUqo6x1sEJpKtp5iQU/tcxll46W1LVtmrCl3znvuuQX13R+Osxv1I49hqUpWpV45xi93p55bzK0vq3eJkwxBQPcN5djtcXD6LvEd/NrR1ip+9wg5W+iT1rOfb9ZxeL3KMbQCE45JGqRkohubHZqOZb84Fjp1lX07riTmWg2KBWSV4F+QPQBdctCANuKofusZf8MGxPUqb+9iTrqhtqm5IQzlnPdtpTUgpqXNM3Lkg69egUp8fmPxyRlqJWUVKHHzYXAU8NV1mS8zInAtSLqieQgxvLfYMp+ZSMOcZMloqCrVaZxCSxxdoTRrXx2nA/rDT11is5aGqpKUKsLKIo/cDwFms1w1Yc8bDYQ9rm8DA5Wh4innUg+q9bwOXQRHSNExK6AEAi6JyZL3Y8JsuQ0zQdZ2xzhMXrOkak0larCYDG5QVLL8qLDKgtSmkSE3WBGNTTxKU1o+WTZe6iYj1RUHRWUqt/0sN+hVZ6TpqB/DKABHkV6iJflmxzCS3ts6rkisEtuwVam6xTs8v8AP1YhL5gj6DyMnpPEjnmpMvFdUyIcBZeN8yzypkjcgN49zAr3DfVU2lpKKj4iRCB6DRbmT2ZRgD9foxNGlcm7NphA7fZTa071Zh1GVeldCidejcjBQAzWr6SPytl8D3AoUKqqCiGNYCHqSrTIp/TIwSzkGAlkOYLJNzxjqvOmi9ljYhg0hY2745QTv84DcBnpx1YtJH3WcfaHKFXKuxFuNu0Fo70BxcLwVZ2CjLXNl1pv9lxJ/8L6IEt2PGC8RK0LS1K4xxgAeMff0ZZA+sNUhczqHaniOUxJhGzHG0F2EclPErZ1xqt/IsxGXlMk+Gsa3XXFAxY6D3d10i6oWZ2iZzb68o7ZBMZWLEQMiKEniQmAmV7GiPo96HFsSb307SWc4LRRGeMwV+9rJzCPxguVQ0ai6Fagej9xhDgLOUyY0hIA1ZM0nCj5lo0GHxljPFYQxUdwyUGeZSsMZ2kHIu2otEWSBQQfCqtRZeamzsqINzGIeA8TBhPM1UVGcDUHKm6NMnuNU1A8aqNcYYxDnqDf7+H77Hd//uO3z48N2/90H9NYuYeMRmLYkbw8WoVYqM5CEXOFSIT9KnWE3X23gViYsR7Rms4rR6aGoTcQNaoyq2RejWGoChXvqhioL2fVk1d4w/qdessYvwsj5IOWVlx6rYQcdSe+u1LGes86Epc3gp8EtNlGoyCAASUIUEU6D7vNmn0slnWYMwDWqEBYpuyTnT4XsJv+BADFSGd7lGaJ2BjRaYJFt1qoupxt8aFHALQqH+zX5uqpwHKVWkmBRilkECIjp9Xc54qyViBOu8KrMZnCXbfSP41Y4b0NfqhLGunzd1E1G4yV1645zAgF5mffI9dm2Qd/GfOFfdc5M5oPSDh0W7pp6oRed6i95I8CdyaQLxkbGmv8claguQkGduvWQ6Ethh1pIhruA6iKCfr935bQx0UXUSeFXqhCL8ESMFLOsSN8GLqKOhGFUUWi9O+QsAOBjNaidKLvq6NDCCYWy+Tgkme5sjNXZCKj4R7Nk3yCDXheSpJLOSh9yHlqEU25hECheMDLMGpvg6hxg8ojGIeWX4prEvpUdMDvG0nzB4qiuWWpEqZS+JFVeoJle6B74gF4JXU85IbIisIWbrwLDuuB9ZNYWjmkhZcOLJI8lZrclJ1FlrbYOXX874/p9+wHff/QM+fPjL57bvr1p6MJl11ZRlaNOkqb5n3Q7G0Ngg6xQK1roBR9h9D5M+AOIgRQlDYLX+4NTGMyV6NRtfCx782v2stAygovZZY35tqCUzpvuQmByUWwQuzrFjV95iLeuljU+zLEr9CbFuw3tsrJBksHkojTFdZsZwLbcoNIfJqlTckG5MTyrY1nMBUe+RJndqlN7UwHqD1kGP1VA9i9om+L53QgJa6+1hbZE80+9tiT1vteQ1JdBzzilC48egLMeejYsONg6Dp0kbxiAJsWyJep56P0NKTY4HblvV1ZXygvMWOdsWpFeB9VtPd05ZJ+AI5C3PWI+UVMjvg20GseaX86IZTtQWq6xkuH6W7q1WX4OUVRnilD9lf/vPp/aBA4AeOaJaq9UapMjxWWsQC9VxT89nGrp+XnTwvJS1pJyQIgXJA6twCURLz1PBOkeEgZ4rWygAtGhDxrNtiZKcKYHCK7OePyWS0O/Lz63POkjp31lYK1EaYgP3MtZSkI3RiEiGwi67EfN+xG4aEZwjoswQELzH7Nh4lyYmYEpRzHocAh73e3hL9cPjPGNNicQETrOmzDKVW9L4M9/M0VMP08NuB+sc9sZQZskPo0g+8Z1HrVUPsRgNSd+BFTkVnJ5PuLxccHo64eNP3+H48gGX8/NnD+Zfs8RolErjuM7Pp01rjAQt8rMAGGozqFc1MDW+PH6M2F8FgGP4kKba++AQl6toqlas80LwFhuUcT/CGIrOHL9GX5vuMyoxOiKtJb1NYkAEColrbNBqNzGkrxcA5JxvtV47CxJB0DmJheqggx0ANOKBwTZilUkGl+OsTlEyHgAIU8B0t8P9uztM9zusF4L8x/0EP9DP7+/p3ApikXPRjLbkzKxXB3Rs1X4J6SZnYu2llBpsnVsQojBqpmCT4CjKbqQ+fIslPYUpJa2tGkOZoDB9hbkr9dm+VcL3kCDXSxNLkS3nBfNpVkF8kp6jJv/pfsfvkzHsRjLK00iZNBKT2Oh+uEBKXLVWVdYCuvaprp3EOZH1sxrsUUBNI8c0g+Q63DqvSMzsFxSMzkdDFm61nPXcxuFhWHTCQDJGGnNlf2acn2TLjsfieZ6oJCQpgUnPLxdcjhdcXs44fjzROWbilAR7UqO1nnglKRDU2soGRgOK5bKgZLpnRATtHTo00NFpS96q4AxApQplq9otWe665eR6fdZBhsFrZA8AKxdjjTVIg4erDPNY0TpMSJ2RJtalxxQCvKOMbvShGXZrKPPjh3H0HrthwB33UpYKxJwxLyvm04z5PNNgTWdVB1Yenpwz5sgH3JJogGPjHXPGyiLBJWdlSRouNAOtV0YOrPT/WEc3Qw7+us40vPWGkIjMvoxxxXxaMExVdSdrLih8gIQBep1hbZ1kY6D1UKUx4DqC20zT6NlqMoMNgLLMhCb/c+/XImooPCokBcn6pT7T9w1eO/X/YKsSm0+vKxcUa2FRkEH33RiqxbhQYGzmbJ2VZlw76zLQdT7NG6IHTKtPSobqGLYX8odltnEvPC/wVjEVlWt3YsQ3Kj59Et+RP0REQmoxkk1ueh67zLL3tf09fdvtrurYJJuKS3NMAFrZoJDBs0XgzPY6uTT0KXON/XK8YDkvKGVAKG2AOozROZRZkKor4ygwM0HarX6pog+5DWjuzyp9Jg76MgVJy2XRurKWD/QZaImGZmOCttjbeUhjbAfxi5OwMOoYr0h0AvNtXkPgadfa6wa/EXk5v5wxHy+YT7PW4rV/sW7njEogJsx157A9l3xOSu7IOAoBd8zf7muOXkTtFWm+RtTamOeK0PxWB+mHQMzJlXpmYCJcWPV7GADAwZn2AQzPlxMDEcYB5gAYM8BZw1qpHt5RW0jpbsIYgjrJVArmdcVlJfz6/HymqRAAsddYmUcdXMqIaPd0NwzwXERfUsQc6cAmjaYrQiA1fz/4JrqbizISawUSWHJMIsDlgpzizQrqxlBURwzIiMvlCIDmylHkKiwuiv6vRQL69coIdoZR2MmiYynkDoGYtGdKnBrXoq0lcon7RO1mU3fkh6En3UjNlKBIIYK0umS75tef45Yr54hSxtajVgTIKU3z0xnk6BoEbiu1Jli6/sLPyXqh8xqXVWHlMHiEcUDJhckahHKQ8dxOMBEHqM66qx2b1MoZzrfg0FgDjuO6GmKrwfREhXIFm35qr6kv8XZ9edJ6JGzWZebJP64xKIdSCSKFR3WUrUvGnpnrIIFAXGmu4Hy86FSSmgsGNFm60NXc+/IEALp/ubTMozbnlVJCztwj6SsZWrYR+nzVqjZPyjELoyISNGm/ZO4gP2uoUtG1Q9xkvzVDdDDYqnI55/h72z2ha2zokDHctM9lKJEYDQMNIV4uC+bTguOHIw3s5uegZdr8Gfl+lKsyEIBWuqHN0GuRMpFes3+d5fZ1eMMcEpe5BzxlWNc0byUgLeU3O0gZHJy1Fw+16kMtU9OdH/SBTmtCdUWNoU73mAiOIFk6A2cIdpVDb/lAeucwx4jLuuLD6YQf/vlHnJ5OePnpBeNuJBk7fl/P2pFiSCzDN7OZcVlXrhOBoVnCwi8vZ4pulohxpXaNYQyU3mvLCTZalLVWrIylX+YjUo6fxa//2pVSZCORUHKisTPWwpj9Bu5RwfWQtcGZYMh2zTm2DJPk9Agyd0yM8L49GGTcCaKSLH0YA0o3qYVqc9BJDM47Iq/w6pEDiewlkpbaomQvjTHZhA2sdzDc19RHs70DfetlrMOynBHCRD14qcBYcuJZ9jE7fWClJu8ZyreFM8PcmtnjsuKHf/wBx+MzPn78Dvv9PQ77R/zxP/mXpD368dQo6wzxn1/O+PGffsRPP/wF6zpjHHZ4ePwK+8cDHr951IDw8tKYj9PdTsk6Pd1de2e7NiySQRPCT8siWsDUSF4ic3iLpQGbkewgqwavKFUBNKDAmVbrraWiWKAWJhglyhoXntazXBaUUrG/31N2YQ3Wy6K1qlJJOcpbq074/HLG5XhBSQVhonaRyUxYL9S3TQSSVXtQrTWo8sxUYrUL0lK4AV0Zwamoc+zZldLPLG0gxbQA95Z7rqtzjtd11JbddcOsr7Izmcx0eDioeMv55YyXn15wejrj+OGoWqq2c6gAVPoPIIGAYUfD4EXntl8ukApa7fdTyGpjaNk2x3ufqjNKHbUPCADmXPyKffusg3TOofrCsFqLlDQT6NJwubhNX4oxsE4yiKJZihUM+MrJ1EpM1NOy4DjPeDqdcX6mQxzXSDqork24oBpXMxjdzlCdkj3kwlPb59OM09OZcW1iRlFE/WkdRG2clnpZXNs4mBstmfCdS4YwzmJc4X0bUdQXz1sPIZE4XkWBXV+YaqV2RJD+deRj+YHaCcb9qAddWcNqfLNSwQHogZXovcF4kk1uGZTa39bVFjUCLAyddNd3K5bfNB1oz7ulDEmO+g3vb4qZ4FJrCIYtjTWak0xkYLICTyoJgaZSLOsFp48nOkdzbAayVDrjL2c8ffgJ5/MzcqJhteu6YuA2K89yXUIgImJD5mt1QAddiYRaP9WgVyXq93VjPPQe3mSr6bW7oFPRCoa1tQ7c9cMCrRXMsIKQQMdxiVjmFXHmmYE8Wk8CY0GciFWZdCZh7xCoruZ44kaDrGUP0poQfSSWZgxkNL0YW6u13aYhe4WG6GdqzpOyMQfR9VXHdKM6uzJXf/FnWpfCp5ZxVHYSJSgpQUmLjQxHENZoziLYbrU1SfZX5qXKvu8fKPgXQQY5lzJdhYQWIsIatkL/rz4D2v7XbVAtgVb/e59DSD7vIBnDlwbQntWmb86OyvDXavc948QgZj3s7NThOjy/VFF2oN/9eD7h6XjG0/dPePrhSVU1KHX2KvOUE7VoSEovcJix0Jl3UhuKa8L56YTj0wlxiQhjwDAFlEzFeNONM6oVqKkxrKJAVGlFzvHVRr/lkuw0pYhSElKKNLMwDA3msA1WTtaqo5KHXZZg+yWRxqEGF+663mCUBOMcDSz1A0XU1Nzc6mUi9aTTOvh6TK16FnLnCGVDyRhmfXhKV5cQJ3utbyrfLzfMIB/evcfx+WlTm7l+/5wLTM7KICZ5Qu4n7TJH5xwRcQ4T7t4dyGj7ActyQc4RH7//gPFI2sDE4qPs6fjxGfNywvH4kbOLgBBGznTo+ZsOE/aPB854uOcrFxhXYEsrVVRGeNp81hb4iNERYpWc4+t6Yw973XLVSpCwzQaZA5IcM5LnQejeaUtYfz0UZKyaQS6XRVmUh8cDpKZqzxQUllIwn2YMY9D5jwDBfT4Ql+H+q3utS1rnWMw8ab+mdSQYUkpB4Iy7ugoTochIH4y0z1e2zrGrg9VSUGNDSG615XRd19J2DFqbTgCmu4Ce5W667HF3tyNyzhi4vzNqC0cveq8qWsaocLvAqzkR8SwMAYfHPR6+fsD+Ya+w7OV44Wug85rYCUvfpayfe16vBzcbazTT1K/j9e9er887SJbAGsaAZDNM3DZ9izTQMI36xmklB5JT1jpeyV1RPlSkXGCQWh2BHaRIxX34+ILj0wnPPz7T0GJlJEWsYcWwsHhByvjpzx/w/OMzjh+OOH48QtT/H75+UHk4caCnp5OSLt7//r02Skv0bEpL1QsX3NcL0bXXJWrtsdbXIrlvtXIiI7ysM2JcMM8nPQil/A7OuG46ANRhbSBJ/lOmFpw+HrHMK1Arpm5KOzU0c9uIJedK0yKAdV5xfjlD9Dz7LDMuAl83Xc1t3xQLOKtRsLCWYTwdtdcyl1IK6SZyhoAug9PXvJH1uHt/d2W8sHkI5RqsNSjOvboW6bmT4MR6C/Oe/v3wNdXWjh9ecDnOOH58xuV4wuV4Uok4MUqHwzt8+zd/i+kwdQObCWp6+PoBh8eDOtZ1loCwy3yLMG67nkZlBKcNvN0/+ZrJcf9pn+XcYon98N4paYLKMByE8nQP0VsupZJog22MaYEujx+OuDAhZHe3g/MktUg9it2UHmNIYjJlOA52C/MjABkwXdXWyPDozHJ8Ip4uw6npPFBwKFNw5DmUPk8ZBK1zNjeQJd92DlhyJtuDG0nN1ZLVbsl5o0keDiTa/Vo8v6FOFtN+wv5hh/v3d5j2E/fFFxI9mCNxQd7dYTxMSCnj/HTCcllw+uFHuufGYH93T8RKb1mT2XfweZdZW4O4RFyOF5yfT3j+4VmFF4SYWUql2n9/rn7mvKpowSe+/7kZs79uYDLXhlyFFvh7xRXBp2FJezN30an+rGQypSCmhNVSxJvEGbGjTKUg5qwOLUk9ivtaRHnhEi6qQPLhLx8I//54wuV0hve+m5hOzajLZWFx4gvVOb3XbEgj6dIZC67pEQMrqfHoe4ZuteQ9cqaMdVnOWNlZqoi2REXorpn32PBhaz2mRIO/vFyUnCRw63pZFfaWPjIKCmi/Li9n1R8VwyAGLaUMwwbNw29Okw5S7SPKq9U7SGPMdqrHFQzYP9hvvYYxwHv/qufvtcHg6ylb50i9W825C8w67AYSgWeIDcYQ3V1gW9MMZ+CI/PDuwAaoDdt1rFTigtMgxpfQmIGfJDlVYi+XK8bqlfF7vc832OBPLAlIBeqX99aSRq66TzkRVGeqVVsiqijLZeHaY9HAXAcjyJQeDtziHFF8hlmpdikauSIHKOzjuES9Run3E7tDgSE9O0TYaRCpJAXyHEJhvu3RlS1XGJDPQrVgJ/n2q6JjanfVt1cZWPdvqd0KkjTuqIdcYFIlOwoRk/dhPtLc3WVesSxd6S0VHQLhAWRrkWLWrNEFT3D2vGoP6Xzi+5s7RrJsZqngoTedLdl+7s3nu0ZJfgWc/ascpGSJtVaYaBnGJMkiZa5yIVauRaJVkdjKrAijWWYp2sQPMIxVCiLXE09PRKTJ0v9yReOezzNOT9Sb+N2/+w7rekGMK4ahZbIf/rxSm8R8xLrOQK3wYcBud4cQgtY7EjNbxeEIQaHk2uBVbdYvrwznrVaKK9b1gnk+4XI5cq1MmFytJ0uvpcsEgNY3aAwFFwLjXY4HhUz711hnaqU5PZ/xl3/4R7wcP+DDhz/jcHjEbneP9+9+j+mw1yG3Iq5gnUMdCnwNGonLdfRTLjbsyqsspQ9MxPlba1Byx4Yt2zrhWy3LohApJW0u7vdFVu9gtFZqrsaOdY6I2NU0GUZFte92+rMCyUrd7Bo+AkT71WE8TCrLGAa/efC1dpg/xf7FxqgIlNob/2sZPwl4bwVr90GEkDjovkMFQES83nJpICcHY6hGlhIZ1fPzCaePR6wL1R9Pz2fEOeLj9x91asrXf/gaw0gzCE2XJc3HCy7HmTMgMsbf//lPeHr6HstyxuPjt7i//wp3D4/Y3+9UMGDYDUzi4jo9a5HKWVdFn9REAeT8trIM25muaV7vzY3UK1sy8GnRDU0S0MOiRKLZ3+9w/9W91h7JnhftSTeGBssLEYda5ILuNz0Ljm0DsF4WGh22UB3+KTgs84r9y0U5Ey8fjri8XHD88ILlvNA0lv2oIw/lmvuymIziM6Z0z3AnZFLrxqn+mrP9q4aP9ZEu9XIVddV9FkbQFEXO0ozcq8Ksc4TjIqzAE9f1sHVeFdIsDO2O7+6IyPBCNcnzyxmXf7joTSUo9R1kejfR7SNN7q5VcXZjDYZhh93hjnB01toUko8ok0hklFNWCEGU42V9bo7Yb1kGhuFmqj+mtPL/sRk8tANwHQVKj1UpRUk573//HgCxecXJCoNPXisx0/F8fMEaF4Qw4He/+xeYxgN8GOAD0bp39zvs7nYb5ybogMntXLRMRmqOuUF8uMoSM8sH9o5Tvl9eF9zfconUmSzrGCrlB1zgndZ31TExa+vrqrWiMKyskwY4IyIY2uFuP6owA7U1iHKT1aBsg2LUBt3BVwD0u33jvDyXSsjhfj24qlq8dP1NglDg26b8wzCyt3DVbQhZb71KLk2RhbMU2Vf5XAIL65g01wLYompaIiZOo5lePh4xn2b8+OM/wRoLH6itZndH5/X0dALfKKpdzivOT2ecjyfM8wlPT99jXclIr8sFeU/ktOkw6dACyXZlRFZxDq7UV1l6CxAbQ1szKd5zaoNrz8It5RRfIwWUUZackHPU71nn4BzByPuHPQ4Pexze3THsL2PukkrwyTi9YQzKbPKDx/5+R46xG65uOcDzg1em/PNPL0gxYzrOWLjWW2vFfFq0N1YIg441kVu9fEvk088q/bLX2XG3NCD4TBnh8wOTOyhGDkj5RL8MJPJmQVsP6h9sBABwXao9rMYA2UvzOcN8LOqbIzE5XfCY7nbqgAnbNqSOwCzB/eNei+9+8MpWFRJISgfI3LlhmLT+RlGO6Yyc1V4ZaldhOTrVSpRNJUjtVsNN+72nA5yUsNM/UEBtI5cEOiuvswGR2rp7f4cwBa1LiuJL3ySfc0SKK4Ywwvk7TNNBYSrnyVhM+0bJ3hiE/Dq7VrLCFdR6/cB2/7jNZv7CetU0rtkVZYC1RlBMuH2YlFCkgUALGHNXt5U9cDxVRkSdK5POJPIWmLBvryU6elGWp+mIbta25nkVzU5QlaXNNaJ/Fl+zFYU53MP3NbXa0C2WKrNckS7og1cVLtBzU1owJmdGCDfTgcbYLYOnIdeoSoCKS9R6tsC30no0nwmhiXGhZ8WP8B5EkvKDsjYdK/vIfEQABGGbgpy2CI5cs/b91sqZOn006gCoryG+cjuI+9NwOmk755whot3SPzjsBrWTNBeyTXPqW1WsJXTR2I70ZUlEftyNjM4l1rPtBTUSYiYodbZ2w1wFwDV07oNlFFMEauReyhiuNm+z+7xdbfEVjFzqhnH/S+tXW3ii6FbU2k+OMDq7LqUMx0bBB4/S0XMrU28TG9C+IGt5sKhlMtByWVimqyCMgYvDezhncXg8wHqH+6ezDke11mL/uKciMc/ui0vEMFF/TWMJHpXhenh30IG1feFfD3cW2bEmIi0RlxyKm2aQ1sGUSuOuStYMMq5LGzRqDazZEkk20mwa0BjN0O84Eswx4/iBSFAfP36H8/kJ83yC9wP2+wc8Pn6DP/yrP2L/sMee5+zJQQochAjUUlLBOi96UOUJvzZkOhDXb534K6dpTIOBSlPY0O/fYPlAIssX+YJANpaaq3POsCibh0qgT6qbxNa2wrD9cl70enPO/LMO090OgSfVFGnRcNuxSeIkNxNQeC9MAkpoj60GL8ExCtK343y6Buy83fQxqGMMBi4VHZosz8BbL2r3YgIS17gMw9t9873sde98aC+qaty+//07wBjeU5I8kwkmtQK7u0nFGz5+/wExLohxgQwPziliGHeYpgO++ur3HZPb4uGbB9x/dY+Hbx51WoUEgjJztdaKymxtgh4ZJeE9BDtCYy2sDBCI7Zm4JqHdCiURCTl5L8ke6XvU4ytTMg4PB+wf93j3u3fqJCWwyrHJxjnfhkpIm1MWtTJGTWgiTod6FWCcBpREkPo8n3E+RpiTwzqvGpiKU5a+b2OgCZFwUYR8JWddVLnkFxTttNvAwBhD98W8QQZ5veSBlkWMOYY8eJCplwnPjog4/U2nQyAz1RhysBZIVCQnYg5h0z4QZV7V463B/fs7rS+kmDR1d54Fj9kghEyGnNJaQ/MgudVDtB5lRFAPJUm7g7DWpJcMaEZKtAr72tNbrlpkgLBkjvQg55I2EMx15iUHRUg3ms0Vqp1KlAZQoz+swe/nP2Kdf48YV4QwYDqMOLy7w8PXDzolnNQ06G16BRIKcKgGoypEfY/bVQaF7u/XtUeJusHZvMrSiZJMpf24xZIGejk7rUezNc4XQLNo1QmVjEwyRxZukF6wTc8pT5XQz5xF1o0+s6iMAERwEkha9n0FEGoFhiZ8XmuFSzQaSnRx6SZ10bPdnoUeeqK3rypxt4UIb7LVdHnKdm5QPLFO6fuUKWxbbojU1D6T9x7uznXfL0oie//7903mzxi4kNjoTpS9cI+p9xZm3GMc9/BcH5a5sz4QTLi732t9WGXwOHuR56mJu2/r67L6AHCzx8o6bsOpb+UgP5U9ZorCAADWeYQw4fBwwOHdAfv7PSt3dfV1LoOgtuHhcv0r68rKcIVeY9YFj9Fq4Y++7h2mSKSf/l59at+stzzY3XGdkdoNJbDTPbzau59zfmKjLMpmoMKn1q+CWPs3/BQpoWj0QMM1xUuTYcmvXuc6g6APZ1jAmAyjZ7FhObDyeuOOoNFhDNR2wTUfgZtKLvCsaiI3UaZzCAmlV/XvPghEYk4yIZFD02sFFbmVYPCZWWJ/7SLYg6BVneNWsxIXrq1X/8BVK9j85gca3MMO1gWP0Ri8//07FQcXCvt04D49x8N4mUABvKZFy33JHPBcQ73/PsZWtRe72p6MARJhilus+TTrjMJrA1av6PmUmYvkVleHF8ZllokSBSV30w6cUyhxm10bbm+5ChwYbutrW3INZfDqzLNzm2xQXoPOPjavacxr6Ol18LoNVm6xhCfQtw/1zlB639A5FIHvSYoOGphJn2RcVgQWbz+8OzTSDAcqUkuUz0zoitOZs36gc+9ZV9QF30oxwW8HgVsLIGnGKNe3ZQx/uoXg2mjL/t+yz1fed/Pe3Xmz1iF4YlHv7iZVynF965YEwJ19l9IZckGp6AQTmqwkwBrObiul5wd6LT8EHWFI0z5EVrHtlRB8XPCwMpSheybkda/LS/L52kfuzjZIV8N8Zs8/a+ETD2uVF3XewtY2jUMbeznrs9YgM3T5c4ehv3hjjEYbFMlnVXAJAwkC1FxQeKMs907VGvR7/bDYHDNFGnxzfXA83oqaWp2zGuVTtgUVNjAAateeQNfYrl1YYM45qlXcqAaZuXAe44J1pRphjCtinLEsZ6T4gKGvMxnDrRusnVgqjGsN+0rESJ2iDhvucT9qMFBrY4KlmGDS9h4aa2BrRTVFGZCy74Z7SOVckNEgEoKwV/szcF0TaGSv7V5sHoQbQax/+p//WQ2FC610kE2GqLgAbaJHrX0WJAzt1DQ32TBI1iEwlPMUlKU1QgTkZS+G3cDZKjkBV1tGmZm1SVJlXqXY6IyuqDUgcC1OoV9ujrfczN6/nsK5tepcVDHyPbFEPt9br2//+C1+/NNPm9d3vo1pE4SqpILEjgiD7LlVYznuR3WQlvU3h1x0wIJI/8lnfve7d8QunddX58wYsAHumJcC6dXKdattSaBXBKI/t7VIY4jd37fsbIXhbwur9stapyUZSRxCGBHCiP3+HnfvHoh8d7/HMA2aLZdCQgZyzdTgbwFLX4srzXbs2bokTpFeTeNpgSaV7MD1XdnvlYemxzUqf8SYrTi6BDtViHv82rKH0nstZTLRvu1nQvZM3t8Mscr0eR2UaekhN5Wa+jXCKwJNcfZWu4keV3WQV56/Nu1IQIZlcgSZqQ5onUV1jQRiDI/AUS1RPgjycJXGqvWDx1CoT8cPgVo2eNNMZkFigXSsgUz76Gul6jBhoGNiblSHLDkre7Vwg28pGVm+lreORA6mKQZG2IiVCAQlC6VcsrCuJmgMbG0ZUDMKXAuiD6/vY4pB5RpBSrR3YmwVIuozxyvSQX//e6fzqYCk/50+OLnFenr6DuO4x253r45a5OWqbUalv0Ctych0DMl2hbXI507UXURqC6D83vjt9JOezT1UaXdILB9o1dgLw7rVNwsMj1hSIkr3uubqemVPUZvEmxr18vr3brFEx1dQAXEWn3zP7rr03FyhWIbrU6G2WZPGRZTkOFvkEk3wiHNUSFl+b9vXynaJYVSgjWcC19WEc9FLJn4q25aM0zmrTOj6qc+zQVtuc8YtFexgDAVQQxgxDDuMo7D6J4WSxd7RpRD02Y8CrK6SLShQ2U1pLQOwsTPXmZ2U3pC7DFFk6JxDcQXee6QS5cIZIKmq13sNv/cEuxagoAV97JOI+RwZCXwjB5ljQvUODpS5KW28VBi7vdHyPz3U+GRtg3awAkJMkHoPi6BLU3rv6Ar32tShRXgAtA4kSvjGGBjOVlx0zeB7R7PCCkvmcY9XyRnIpCov8A36a7pmmclhsxbWbLVM33IRnBqRUmSYlYrquWTKKD+hBSstFn3Gu/l+V38U4wCQIRf5N4DqrBgCbBXH0JycMUB1/WGHvt51INTDGRLtyXVdnwWNvK+WGIxaRZP2Nsbj5fkn5EPCOO6A0ogYKn/XBXPyuaX2WEvTDlVoTaPe5hRrrai5CwDddkqCPNiGIVwoYy+zcs7KEF7muYkUrIqzLFyTkffSbOx1xNHg29I5R9n/TpKr/8xvuUjdxjArFTwZpuqQZzACsrmGUnWs2PWSYFk+t6xsKZsQiK617bTh1TJDUMUJJCBDRwAphSj5aGi29GsqI/XquhSOdxal0NADgWGvP9etnaNcOCnmODgXEIYJI5OTdnc7jCwWLjVHJYXxYPCcs8KscsiLJfRQAsQGu7ZzrYIrPXolUphqEzycI+6EEGsERTCdkSHymkxaYsTBGgD+FWLZn2shMKaUkUvaZJG/2UGu80rap5NhaFVk2aDp7jXcpFGFaQoTUvvS59C2mphEV+Icncio9VkcZ4nGBjUiKgatU74NljWihILookbH0nc5TFS7XNix9rMJexhPxbhZhzXHpiVbQW0eMBa3muYhyh49a5a+nrEsF25AztvWhFJRDD3I8rnknjSIqEXHcjD8EPRnrLXo+4zkcMky1sDy+4jE2ZZpWT9dd7FMZJFouws8Ng41tyysn2zfZ5G3WC/HD4hpxeHwDiFMkMkc2RrY7sEHBJ5q+5Nj0iHQzlkkAxY1n/HhLx9I9enlzD2eGdY6TPsJu7uJB8AawJqNoRFodtpPGHZDi+o1e0k8h7WpWa1LhPfbAcrkYNHYfJ9atW4DQTbYKSYlW7z1IuYtTZ1HQut35OdS2l586EREMkkRiuNX4zq0c97PuxSYLqeM7LIKH2gLGQB0wgxERIsoiTkVMaOYrAgJjXhL1MvH5zMua5tU1AUbALbG/soIa/Ao570SY12N4w2Wcx7eD7i7e49pOuDu7hG7wx3G3YjDw57qsJuZsNtnU85nTmmLonBgoTwQI+fOwFnDkCt1J8SZBiKP06DzZwV9TAabZEnOgjFGxflRiNxDAZFVfwEAMudSAnJRW8o66i0hxlkZzG+XQaYCGJ4MYAyMyRoVy9pkLWLoZAO7ovXm50vlXqWteru8To4JxlmUwjDFVSYjk6edswje83QQIOUMFyhjLImai3txZsstJUqw4IIy0GkrckbQJq/LwM50MyZlv+gmZ46AGsxbSqEaJDvtDWzGpCOBO+TgKauuVipwa6RV9fcF9pCAp3eQEl0DBLEW0cLiKFIhE16Sxejn6DJLAOoc+2z2+rP3PaeoDV691d6XkrCuFxaiZ/q+RPy5AFcQzqcyZYDOZIokLnF+OuHppw84n1/w/PwjYpyVPTmOe0zTAcMwNRm0uELISN4P8N5jt3vA/bt77O73uP/qvg2rzoVRjxaJ11xQ7Gtj3Oo+pruvr/fg18huvdVSEezSejxzLrCVyDd635kjYIANc1f+JMciGTTXvnIbKVWUZ0Bi2ZTtoUHdprVZiEEVe7AJDLMESd3cRmbvy7PVZDc//ZkVus9bOFs/U60K/d1ieT9gmg44HB6w291hf3e/4XnIddRSKBDrnl3UJqHXdx/AbqHXYrbZcS0Fy3nBOq+4HGfEeQWsIfLTblASZo7088uZ5C1LznDcehXGQfsnZR+ttXRfQRre2VL7WAGa4Ehu/cgp0shAKlvFtue/Zt8+9wMiEp4DiQkDgPMt27pOa6UOaWpROGKToQg8xQ+qtGrUgq4wTI7BCMwVvGavgNRsyDl65+CthRMhAO+blmlsVGNhxgmjSnomRXUHAKpvN0Fxa8kmM6XnlB3fEAoBKMLh/66/vswnktVb96+1CQE9FOLk45ogcyL94Jno1E1LL0RqkMPaXqdqBn+dkQJ0EEWkQKTPAGwtRG1YrpyDT63rqLXV9DJK7XtQb7PvWcUYVpQi/W3QfRM6uGQu/Zmmn22svsQTY77/85/x/Xf/Ds8vP+LDhz/jcjkipVUVXrwfMAxNjYjemwIB7wOcC9jvH/DNN3+P9+9/j3/xH/9rgsL2I66nW0iGZf02It48o5/MYrYBjN6HGzMqXegg5iwID89Z7KBVgu0cYCvyKtAYXZfLBWm1cIEVbSSg5exPIEFA+uc6ViWfc6mDioPLkjVf1RT78y99p0QITK22yA7vU4N8dY+zIENNQEChVWM087nFGocd9vsHPDx8wxM5ekF8nuQjtW0j2W/VZ0AIdz3aYGrPXG3XLZN6cso4PZ1o1un5BSktMMYiHEdy0NNAIjBsY15+elHd6XHcYdofcPd4h2HXpjI5U2mOaK2QLhXlrtiWcInSj2SQ0v/aqwbJvfml9StqkFkzCOctXMqdsoojpp93OiEDAGxs6fGGEWerZjBqCNM2ahJYVDT+rDMI/MBSQ2p7sFMiXPn4cmq9mF3kp3JaFVjnJr5Nk955bBNvoDEGHvS5FJpJRTF0iVZLSXCOP/+tSDola/2xlMx/FqQUcTo/4XR6xjjuMe6GRqxQ9qJF5P1dzgtePjwzG/YCa71CLQRTu6aW0essxqwQldQWaD9bTVkiOWsN8m7UXiXHzhdXAVSPMvTOso9S5UxIFtCi7oQUVzj36Zmdb7PnBeu6YF0Xvd8Eo7MDLNjoZOrnEjEGUYJikebL5QXOBxwOj0CtmKYD4rpQndNILSzqWd7vH1vrgXWoqFiWM3766Z9xPj/DWod3X32Dx28fqT/NGhgO8pRU0SE3kvFcB7DXJKp2TwTG3mbuP1eH/y1L1FnOTydy9iljrZVECpjsZz1BcVLvzzlpZiiCHxt5N8nO0Gty8j1inWUJCocpND3PImIgCfNpIcdaqurDlkwMYjm3Yvusa0Qf5U3wWejPt9rBTLYqznFDBCP5yMznj/Skb7Hu7t/j4eFrUtMaqdQkqzG2gQSyhdnk1pakBMzGdpZ9m48X3b9a6Z6cXp5ZpzttavDW0Lle1wvSxwjnPIaniV83Y11mpEwTk5bljPP5BefjAfeP79poQq7fyyxKU0U3mBAVOR+klBSxyCSm9cIOsqFQfZD7c+uzDpIcnOU+sRZ5wYMYoB3ZQIgNhcV8aXO372aq0cPXT9+mn22SQZnh0VotnOdaZUxYjUFcjF5bSZnS925qPbhOJH1RJVcsLJC7zlEfqKT1xU9kSNebV4Qw0nqofhZP+Y2rl37bGi9islIGuSjZQiBrOxBTr+SKbPOmHmiNQynUPnK5HDUb8y8BIUwIYcQwDPrQxrigMMzrAxmqECZ9PSJHWa17+TEQFd81lEAZgR0RR+HT6zpkR38nYfPEhoOyyJQjdsN4k/2Wa8iZ5Px6J0HfYydZt/CvGNzrqSrGkMrRNB2USr9bHlBywt39e3g/wDnPxrGi1owQJj5XHrUWpLTi+flHQkusx7rONKlm8Ng/7AFAM4D+vfFz57e2Gq88c4L4XGfD2zP39hlNGII29et7Z+qYNolr4/Dt+rvSi7AR20ivokGVOCNBRFx3b+R1aEC6J4JhrViXiDhHtg2L7kdfapExcbVUrf0L65haybr6tLNAx+huuquthaaVQHIXiBTEdcGynN98vwGCWEOY4HnwhHF2U+4Qx2e5bmjLazheA9hIgyqW84LT05n0s9eL/sz5/AKZvjOMOxhQsEzInth86fFmu1poGEFlg1YLPQPnMwXow7BDyROGSYJxiyoiHqVSLzHfU+kXlyySyhurynX2n+e3O8iUlbnYKOyNBda9GwTqMam7iK6twMPD2Ia5y2HUxvzuIRXYBZ7rAykDl5Vl6DKPQpmxXlYdqCwDTuniCOsGyHHPJ+ondI77pwavEIJcz/VmbTLbzkFKm8et5OZor3tyBP271oKV50POywnAt5vMzFqjbF2RNwsjGeM67nE+P2OeT/jppz+pMo9CxpWG9ArUmBJFsgYG0+4eQxix29/D+wBrPYYwIgxEFQfeY8hNQIHYco2Kn0vueiNbTVSjyw4WpjFaETlHJSrlnBHjgvv7r2+y33wRSHFFSkT08EOD4MWBG2tUxF6EFYTYJMbPsjDFOB0UabBcK3Q+4Ju/+wb7hz12dxO/LT0zlBXSfsjZ/su//QuW8wVrnLGuM+bLEaUkDGNAfn9P77NrQUOtADLBda0Rv2Urm3mQ7OxTShtDqTJpVeo5b1/3FX1T+fyFp3SYmhFzQeHJQdIjZ7kFICFpsCvTaEhzmc6NjLiSgb7yb2WPGoMy0Dn1xaPkguU8a+1rPs0dMmA6kl5ShyfkkDB4DLsR1ZMztaDM1Qea3oKUkRihijPpS0uNU2BZUYiifU86MPsWaxgmBO7HFQH4avHaSXJ7U7FNPasPMmhG7op1iTh9POH54wdcLkecTh/pWYBBqYWYsmGA5XmTZDv79ykAyiYZoDGEAUDgmuGKeX7CfHnBOO7xEL/B3bsHUkXzlOEWcKLGWaQEIqLpvc4r5vmEdbkgpvVVIoT6Gx0kFc8rCjJKaRkkOQp2khw1kxFsGHwPM9hqqH+mU3kxFihrUYODtamZrJdVb47UCuIcMZ9nxDni/EJklZQi5vlEBIsUkRi2Eno3QbGrqvRTRD+RrBoTJUKYFHLRYjxHh7lQoT9nKfQu8M7/qujjr12UfTVmLmBgnde9I/jhGeu8KrxDDqZTynGORZbpdcIUkOO3SDHhbz7+S60TyCity/kZyzpzBkUH1xjKEI1p6hUpRRjTnHetFaeXgLSO1F7jLJwflIbdzqFkMI2dau2Wli/zN2NcNOKTdpcYqSf0FstaKhUs6wXLciZBib4/ToxmZVa2kBJc0ckatRSkhQbHHh4PCOPfb0oRWqJ1loz7h8QOlts1YqY+X2tJCpF/V+QRz88nVY46v/C0dWdgv3mkIMh27Se1d3hVWdk/e2at2TpJzpRSShooveU67CZ89Tdf4U//45+Ifh8bg9NYCy/Mzq41yQ+eM7GM+BQxv1xwej7j44/fY+Xa0jju4P2IaTpsmfWZ6vC0t6NOoskpYz5esC4LUo6Icebb3THUOZCTTIeeiYDd7oDDO5K9dM5poKQM/ArVgJ1P5IR7tRxyDK3f+XI54unpBzw///Dm+y2fqTmmZluqqSgxkxqOJDMGyNxTK3N4N6/FUnw7HiIxjnvqYeQEKvgBYSA7K0MNehUv6oLgB6Kra9q+FScmzPMZp9MzzucnSgyWE8J51OuhPmPP711RIw/cjlTnXZcG1S7LpWWocg3dnz+3fgXEmlG74ktmlRWAnYg05lqqRZrSGv5lieq6brBpOpCoVaGMPsJdLstGnzDFpGOw0pqwLGc9+DGtKDmh9LPOKmWdlH3MWJaLpvfkMANSitQHtKtI68TCyVuig7xezpGzuD7iutVomlbD3fzPWStd/4yVDbI0iAtBp1bKdqy3GMP4qt8rDEGZZ/Nph93xgMvuDsty3hAhjDHwPmjmTM6Rri34AdZ5NQ4AOiOMRrYoW2RAnaS2mvTwJAVkQpop3Z+lpBvuN12DDKhOKSInqvORQhEfXoallB2cC6prrEYRRvbBwdhRZRLpHlk1ynLeiXVqN8KANEuvPZYyX9BYA39eNHAUA5BihvW59ZZZw3MhW6Reuj3nzd5ugMLGaBlnol7cnLakhrdYUwg0gWc3EOpzSS1bKZmnAIE5BRm+emXwCos3pYzlMmPh2pIEv8J2zolYoa3eJy0bSe1KShHrOjO0njcDs3sD6hF04LA0icZIsKzU6STTlVVr1bFcFMg346zf5+tKXHOTYPUWa2vT2tfas8kJTSko2cJYtgOd4hNAPASRjQs1YFd38MEh50d+RiucCxiGAcNuVChd3k94JR30pUiiZOdSsx1OA5cc2IFap/tGEoLdkIlClEYVcOASGg14WBkt49Y0Dhb6EWQ/tz7PYk0rRHO0lI6kwb2ONL1jK2Mlh6jvi5PRUL3BL2AWKYuUL5dFjcc6Uxq/zk1uTWASIZiEMMDAYhz37QZ2os/LckGMM87nF97oDO9HpLSojJtkK95T8zIxpuzm8FB7xYXEfQHA2JtlM20Pt4safD28o3rU6fgRp5dnHO4fqO4ofZtL1LPnvX/VL1SrxXRor3v//q5/Y/AP0T85+xHj2k9ad95q4f744djqQRINakS5ZQZrW08pig5IQ37JpasXrJwxRKScONC5DYHBgNWUUlQ5v8B1Vz+0OozhoMuyyg1l7pmzrcw18NY8LgpQPniE4NXxxSXqfWrCGAbOex0zJL+3zquyrKf9RPvA9yGtCfN5Rq1XQ5SFEZlofJbU5697IUsu6vAlYxOyW4wR6zLfpCb2sNvBWYv/6at75Jh0Jqnak0pnbJ1XhGkguv/gefYg1ZzSmrCus7bNhDDi4eG9Mh5FTrJ/lkT8vZSqwSAATNMdw7geUhuUmakwRm1XrUW/XqvcX0bVnNFrEz3SdYlsw1YlZPXomiAjMS54fv4Rx+MHnE9Pb77fABjRaW13sgQxk+e2JGoXssVsCHoAkx6DUfWhOhWMuxE5Z0x3u00fqvNOYWjrLcJAOq+ObVK/ehEBIVOVXNoA949f09B6ZjIDRB4tQ1OCkt77xHreaU24XF4wL6dXgT9QFCH4zRlkShHOtYyCakMD0urVQOboGr5cStOKZC8NADlbgIk+Xh7kSi0W82nG5XjBellJ+YbHAg3TgLgbEZfdBqcm+LOxJEsf+THkS5qBDiGM8H7Aw8PXMIb0B6Xn7XI5wjkP5wK8Dzoiq5YKrJGMZqFax/H4ETHOyrgC7Gc397cuuYlyeKx1CANNJFjjgtPxI5FrpgHFWnU+qmvpjDbWktFIyj6T+ykTKsQZKiNTAxHbiAVXLR8ukMLS4d1BGYbDNDSqe5+VdLU7YRz6oem4xjnSqLPLkaN6pu9Xbve4YUAC3luAKOrLcsY47unBLtsIWPRKU8yAiZopANxHO7aodr3QtPr5NFPP2Riwv6fRbY7nkYriDik+WSUhSKYdVw4e51WvYZgGharTmuCcw7pEVehRbdiYCJZlNrawPHsSTp9lKsTNzmeNC9a4vPl2TyHAO4tv//5b1Fzw9P2zskdluLaJYKMYEVcaZSfXP0wDHr95wP5hr6UQF2jWZt+eUGunzKXSdkzQiw0N6udqSp1Q9lsyHCO9191AZwoUacoETGPmx5UGj68zz13liTxy/1r2mPS5prcqr1q73moZSJ28ORVt8XCWWpmY8KfQfGpDCvR1xHZbAGhlqRyyOl85Syll1POi6EaIQQmUjmsC1llY3whQEjyvc9RMcNjRPM4UEncgiGAE36LcOAzrQvZ6nVecTk8863Pt0+Z/r337VRBrrxgjcBtNy+CfCYUnbksEeyVSm81GP9J5C3CzZ0mF1UikZ9HozMFSCgZuudDeJE3Tm4OS15boQ6i+NKR0xJR3BAUy/XuddxzBC4xCEGQY28Bl+qzUDLyuF1wuz5rBSP3sVjVIuqbXN5IyHcdtHwmX+Yi79F6NgUBptliSBbTdFAr6QARnMNRWAZjQZM4AsApGB/GyJNjPXaMajNBgD81WixhfzlKKtMy03zeGfo7qXQtn9ER9N0aau9uInVss08kG1lq1X6qk1kYDQMWqpUbXsgH+tmvZMABW8ACWMyEjYQgQAX0XPKn0WAkkOkIYn/n1stL/LOLsnIUbaARZFiHv0iaI5JRQCrUpkThz60frxbEBZqcLu1gJVBRYEktx3ZC13nIF7xHg8fDNA87PxMwtc9ESSckF2RiGyBL8mlDGBoEOOxo+cHBWnaIYXABXZwwbBKWXPpPVs63TGpFzUYUpuo+R6m7eI0yB7xERg4xtUpfSFtKrtxDjdkt0Emi11grvSRlsHPeEYl1P4nnDRdlzucqmoKgDOucsWZk4Ufk5zfQMuMvAA6CzmZxVLehaqa+UnDKhG2lscH0tFBBWa1pglK5E/9WRG5hOPN10ZST6WlWbLwOyl+XMPZUX5NxKQ6Zi4yTfJIM0MJtCdSkZCYLrFoWOpKl0tKNmeCIZJw+xaP0Zy0LkCz2AfqD5a9PdDtOBGFcCwVHmmlWVQWjSTZrOKWMNoF40gaEkugQoLX/56QXrhVhr0idpvcPDNw+Y9kS1zwyXrfOKpw8/4vn5R/zwwz/TFA/ONkvJzLh6+/X6IWmQp9RZSil4evoe+/0jdruDEgREYcfBoRpoob0/CKZvzSkFZaVDLQN35d4B0IBDqPRC5sjGsKg8U+dd6WBcMjRJYd9Vi+c9nCLOJOeC5TIr2yyrxF5FSjQkGqDi/y2W9wE5y8NGerfreoGzHjm1ga2mdmpF3DvWGw9VI5I6oqFI+fh0xOXDGTlH/Pgnx/eKIFdRCxGjL8SHHBOef3imKLzQEHJ/R+OYpsOkZYhSKINdL4s6A9SqhB6CvoVo9joL12wmUbYal7WRwNYZ6Qaw9uAcvHP4N3/4WwDAh798wF/+lz+Tc2IdZ1Tq45VnV42yAakKXT3z7Zz2CliVM8V+uHHZ9Eo3g899rHwNPRxquTVimAYemUXOJq5R65klFeSaFRa8HC80Ri3FJqBuZMIOOc2UIr755ltMhwmPj9+i1nKzGqTMUy0pozinUKqSdfjn+gw3x4Ri25mWr4tNts4hCDFJoPpcEAFtuSulwjmrHQayl43AZjU4k3KCQLUABZ1Sw7eucSlQZYao4XNLSMt8vmCeTzifn3E+P2uphuJfS5SHWjVT/s01yJITSfmYwMahax5PPB+tbvUqpWlceuIqQKLgucBmu2GGwRiEkeaxCQzlQ9N4ldE2tVCdZZ2HDdSnxrbrTZL5e/33hL7ugoONDpYdu4iZO3YaQg1eLgsuxwuen3/E09P3OJ0+Mm3fIeWoTNhbLQlGPnUDKRqq1Bt3ecbpdKfahgJfFENMWCm096N6evk/edg3kWGng9iupW40XPVaLEVlkoEVkekDkywSCwXzSDSJSq0zbZRZLq2to26hKAOK3P10wN39+5vsdU+wICIHDz3OTc+XvldUgFx6xsT4CWkHtQJcYhi5Hvb4zSMuL2d6gOcz4io1qQJrHJwPOkKNGJZ0XUTCIlRj2A1cnxy4lcAo21ONe8wwptVoxNgIg7BBU00DU/rastb9W23+VrC2nK37acQ3jw/43b/4HZ5/fOZzMm9ER5YzwfXeOx4ZJkpYjfF4fSZ1NicHaJo5ddlj398pEKsE8jVvxbKlrCO1s1rRCE/ogwwKVJYz/Z/iqszJLSGmnanpbod3v3uHv/3XfyBewY1qkDlnWCPtMC2LbJCpgblCikpm5Rq9boZCS0YRIX8uyQzTAGPA6l6SjROZ0hiDZVkIYepnO7IdkFKdBtFCAmVnSn4AKoEHvg4YCiTXOWrmWTLrHU8HvHv3OyY0LhtRjmvy4y+tzztIdojOipGwqFW0UxNi5AGkq9OD5ofM8E1jONImF3VWvSST5ykd02HajP0hg29VAk1GB+lDzZM7xFhJ4y4svYbUhuhwGs1IZQkFX/QIJZuKc8RyXnC5vOB4/IDj8QPm+aS9j5kdpL9RRiN1iD7C6f9ODrJgXVq0tN/f0RiqaBHG2gaBVn610hyhGEd97dK/tum0Jz91eJpB/pQwuTjeys6R7lFWGS+SqOvYahWQ8V4lb5l+9B7UR3jYP+Dw7vDq/d5iScChAUEpyqDVrEsyZ2+RmYBUCjWFt9/hSIQRDuschp3B4fGgn2VZFpSyIMbWXE17TVnl5dL1T1pPRCtH9TUJJK01qGKwJcIB7aOUIASxSWvc3Gsy+K2/UwglCk2tF60B30r2zBoDa4ApDHh3OODrv/sa//w//DPm04z5NFMAByAZGqIumcMAtNLKdaBmrpma7Zz3uqziyCQzl4CyDxoAqa4B1RpudepQNBFb6J5NqUtL7XGdV205owTCddcmzsli3A24e3+Hv/2P/hZ/+p/+hO9/+Jub7LkIbrRAYSt4QVlkbe0X2LJXt06SRuslQEtXpKA1wjqnMx1xAVJcacADiEFsLe1lCEEdc88qrtwbao2Dy4EDVp5UZJtzrBWoqamuEWKVkAshg9NILXwxrljmExZmK+f8utXjl9ZnHaTU3QgrtzwP0UCUH7KTekVEznuipBvDWaDbDELVaJfrjEpQYH1QgUsokksoHRQo5J0gP1Or1lckSpYoRFZfiJc2EoCj64U2VKJCGdBZSsH55YyXp4/4/vt/wA8//CNOpyektNJBgkGMK6w9/qoN/msWkVK22aMMUSaxA4aMS8bx5QNyztjt7lEr0bBVZCFwZsifS2q5ffO7POhNdoze71quS5YEGY73NAIboYc+s2+ZScTCdTRjDMJEDM3KJJT5tCgppM9aDKvRjOMev/ubv8cf/s0fbrLfPcNaFJKWlXriLqeBCDvWYF1X1Z8taUQWxKSHlmOCK44MO0P4I0/tIA3MnZI3lvOy6fWUayHVk4C79/c69WNiUo/MFpRpG0IQ6VdhSTNBQ3QYtmZdTmHcVXqLl1VhqRiXxtb8uQL0b1g6Ls4Bj7sd/tXf/B7f/6vvYb3F+fmsGa8wdsVulMNEmR6LI/QKRk0vdPt1yUqkn1QIYhJcC6lQMkXZyiTScWyf9H0EfWGij/SYRmbdHz+eMJ+pn1ZY9xLwXEN6IuSxu9vhX/ybv8PT//5JJ5m89RLkr2iwkFGz00H0dGO2SErPJaimI9+w1J+JecOSd85pH7DjckPOlEVeLi84nj7yvWqOsg/CvA989gdM46E57lKR0RIi/UyMBiyXhfkL1Cu93z/g4etHPH7zqMHifLxguaw4fqSeyJQWFSL5pfWr74Y0yVrb9b3VVmw2JqoToennDbq8huxkM41Bm5RQm4K+ssvYcNtPzIETQor0bUmdjCTxpL+yGes4k0NcTrOOXtl+PoGbEi7HE47HD3h+/lFZlUJWMsaQrFF5HcW+1aLrv6Lkl6ZNqo3JxiCmFZYPoPceIQZ1rrVUrfkCXR+SpYy6gOa9teiwZzgaGFO1Z0kdZYGKBavB6EgRcv2N0dYylJwiwjBqwJS4qE4iDgJFtev1zmMYdtjv7/Hud+/w7tt3N9tv6rEUI5a1adzPJ3g/wFqWRss8RicXGCeEqNcRad/nabjOHdgIk2hDxu5+t2HgyT46hpZ2DzsMrH4iI7Y0IxJmqqXsV9+3b/KPWxFp1HbfRLUop8x1RxZJYJKU4zo76tufcWcMLO/V4D12w4Cv/uYrpDXh+3/4HvWp6sBcqUUt54V68LzDuKdAq0ehetuxgc+0TNAgQsn42tQO0gytrjE7TX++2SGWVNRiyrSXkgnKXuaVW9UaPC3JhCQHdP8aqmWt08lDtAfvMZ9uEwTmnGCMpTYlLnm4UOCNIYk2bFvwjJD8Ohaw7K8QFLVMI/tqKwDiJAy7Nld1Po/wPnDvelbBcLpXGUKSUxEXP2AYR2Xg03PxmswmowgjK01JL+m6XJhESspiYfDMGKfnKsV7fdY+N8Xmsw5yE/lUIVd4GFP0hteSkbPhRnKDdfawiZwgNU23TTYGKMWqwot1tLG1tMkhsmh6PWWC1naQYe1k6jTavMomc/d/obEraaWeq7hQJhZCJ9jLrxXXhOPpI15efsTz8w+q0iMQsdyfW82ClGt5RfeuUpMsMCaQ0TUWhSO0eT4iBJoQUVJB9gW+XSwMusnbHsxkJbibjEgHF4niBWvhyszCZmyaQzV8Lz61pLUgrlGl7QYzqlGTvk2qESSgthonGcCAaTpgf3ePd79/h8dvH99mg6+vk9tICHqk81mrFPctdrs71ZwthWdAJpK6qm772bWe1cNXVqbPOIShaZDKn73BptmIBJ96Zr1KkCIZqgi5y5mV/ZJ71zSG8+b76JyJoi8xKSlpWc6I64JSM4ZAGfItpOacpek7pVY4azB6j999/Q5xjbh7d0eEDpkp25HIpHYlZDKXW7De72kj9GxHsUHgfzHuHXvXgsc8maYKhh5252sRgn7l9qUUs5Zk1pkCDYHyvAsaVIuko8p16uBij2EM2A8D3v3+NjV2QBykoecwWiLg5EABVr1ykNzyAv07B3kcRFAW3gUcBmqXjSGBkmEiuNUFj/E0YhgDzi8TcoqIqdXg5Rmz1mMcdggDqX/RbMp2rsH3acs4piEGcaX2sMuFRsudxxft350Oe+zv9zrai0hWRhGyz63POkjnWipsTIatXtmbourSNDOjbpRAOaTd2YYbW2f+v+z9Z9Bt2VUeCj8zrbDDm07qnBRBCa6EJCxABne5JGzzfQYjgYpUUKYAG8vIUl0B4iJQgQFBGVuUMUZQyMIGhEvY+MOATSjAJgkBAmQLhIRa6laHE960wwozfD/GHHPNtc85fVrq923fe31mVfc55w177zXXXCM84xnPIKhIDw22dBNUbAMY6gB8Idzbp5xKDdFSAX1HdRZmpTLUwYo8LGrMMnXWDnMUjTE0jLYwELF3rF02OD44xqVLD+Lg4DEcH19JvY/AEJ3S30+xLw95JsctGeN/swNxEaI7OHiM9l0oVNNymKsnspYYy4NjJTHYvIdMxvxqw8Hvwytv7B9FlGpwaqm2FWIzd9OSCHQgck41q2EKcvBt1ElsmyWc7VPPI6EUGsZU2Nk7g7N3nMW9z70Xt99xnjzYCa/FYh80eYDEGJRU8NFZHx1dQggBs9kOqmoWHYYmB8QDZgUTOYZ6doLssp4vYBiHNNRx477FgI9JIptw9SY8BzASMzzkfRfrvak2T45XMrM2apwS7N3GOjuhJewg6dnV0KaEa1fYHA90EishGQC0VCiNwYXtbfgQcOneW7BerNGuqP+Sr9lGOFiqllS24rPIvbusKMXZNcOmAFKgzkOTvfdpcs3mnmYRcBpczUE+owIciLSrFm3TYXW4IpivJ8MvIOJMz9gSEts6kAW+9DMa9XyC6c4MlTE4v7ONqjwdXgMPCu50lNNTElIreOugvRmSlwzt04aIMZBpSxJ6xSxXvp8kbEEckVw9Z7M2m/p6V20Um0Fif+tM7CLVdSPpL4Sxc3SRt9D1DZp2iYODx3B4eBHNegFTlFitDtG0S1TlFNPZDiaTbZRliXprQpq0mjL3BC9fZz0BBzlIjbGBJvhNJMiVoqsYZUU6cQgKSgEhaIRAhVgHIAQJISycjaOldEhZJIQkgxGyCNuHVDHPiTsCQAzqIXsFFWsQm4sNFWHlZgwjpBtB0Ox60WC1OsR6vYhwwMkbhyeyrnpo09c9eARWctSgB75t11guD2mKRE3qOEWEF4Bxz6KIJCmOzJKDzAr3I4IGwxDJQcqRU8zXMMsxtvZYiprTqK1YY3Gxxcf2RGZwUXLLewtjKiilMJ/t4uwdZ3Hh7gs4f+sZ7E6nwPHxyWxytppmmbQ6KbJXoxowO0ljSiip4QVlXtrqUZYopUzOiOvrOTwlhEjIySbBScZM1MCk0kHerjAKXDQgo9XKER4hBeAQmcHjOraItHbuF+vWxFhteL5oTygJi2ZwLfR6Z/Gklo+v3/Q9jpsGhxcPcHTpCMvjI1TVJO3nAKW6JPHGjeXQCkrFoC2zaCn7iNePBNcNZz3tkQ9JkzaEMDAoM3EAIA8i42DeqFbknIUPQxmGS1Hp9aNzTAxWAdKNnVZETpQSlTG0H/3J2x0uzzjXw0oF1Rn0TY9Q0LnxDC27YYydlxIiwanj3kHKJqM9iQgJcxbynvMQAgrvYSqDoingLPW2tqs2JTAjgmX2eV3vILoeNgtKEKgqHiwTdnyCVptmSYLkHIQIia5rYR211JRFDRvFTEwUzJdP1kEaU2RjeXIMXSYnSRJNA65MU6mHvskQBAX+3iHICJfEZnxlFJSX0XHGaFyERJoZbghH5HIUhUtFcAg/DM56QLKj1MMNNCpCsWag7rPBABVxF4sDHB9foeijWWbafZuMuatroie5cs3RXE2Hrq+HUz2EVKPMlj9v265JbcefRzWtoAsDHQ90TugY9k+m/WAprtxB5gScFJxkAsZpHzLHGphEwpMQ4GE0TxNQiTHZNEu03Zpgl75J4thkpAucve0c7njWHbj1abfiGbdcgFEai1NwkOv18XB9URieI37roqKS7TCdbqEoamhBTey+HCBMNipcd5dRYSUXwQdAAYscgrOkVaviOY+BS4iKLM7a1JYwKMRE4htn8dGZJifiueGfpl8oQxkDQ6t9S+0mxIA+TlJcVAeisUhlUYPlHE97WedwZbnA5Ycu4YH3fxSPfPwBdF2DCxfuia0dAxEkRA1aISVMVaTZkDIJY8QhA3KQi5NaIRQaJgpnMzlts+Ujf85TaShzclzKySfT2CjLxyL/TMihsySRJlZko55CCFCSFIGmWxPMt4iQUnLWewoOMgQug1kI0dF5bWRiaeeBHBMmpVYkOefGwTARG4c+Zlbe4mxeapmybw7gqogocQdC17TUApaRKnMUy1kHpwnxkorHhBFxK/Q0gSaAGK+r1TGa9TGaZkEkSu7Vtz2aZoGmWSR1LP7TFCXqZvbkM8jpdAer5WEaFcKT3VnxngytglJj6EsIASkU1fliZJZG7vg+wko+NaHyw55knwqRjIBmlpka9+axSgsb56RSoiVQmlFtjBUyRG/TQe/bHn1H7Mrj48s4Pr6C/f1HsVodR0hiTHNmp3Ja9HdeMk7ykJKGMgtTptmCAIhhGUikPf98q9Uhjo+voGmWOHfuTprjqBQqkBA7Q4HMCNaaGnY5qGHWby5qzdklv8dgHDLafKTlIzpZnmLQNx2sddTLGJvibYweaYgpERq6vsVyeZSCr7Nnbsft996Nl/ztl+Lp992BW7a3sT2ZwHmP02ijZgZpur9hgNIAYHG8j75rURQ15vM9TCZzgmJjTYx7bnXs14VC7PfSqQbGQR6RBXLixpDNbJ5xXZgEudpMpi8/l+QIx6SJPHNURiUdTh4PtVoscXy8T8ScZgnuay6KmujxZY2qnuHW83uY5Vq9J7ic91i2LR47OsJfffwRvPe//iEe+B8fwfve9+soyxrz+RlcuPtCgvKaZQNn6Qx26y5CgDQNxYQiDdFV8dmhfryAMisX5M6N67iut6nWlvdVSj2IQvSdTbA3CzBwj2U+cSRHCgCktopNkX0pST/63J3nsHNhF2dmMygpAWioU+I2sGRo3zfg0XkAoKyJyMGAbDBJjG2dKZCdTznYkXKYUpMv3v+iNHQeI6qktRqEB1QcTVUPARgHIVyLp1qzgIh73ruBkW2tw3q9wGKxj/39R7FuFpHhP8DaJBLgaBB6TBxWy0NU9QyFKTFtdm7YqndDB1nPanTdegQP0ANFBfYhI7nOjeVoeWP22BDBUc1EOUkFbMkT2gd4Kld5z4vuDKkor1EAcNHwD9nhuIZDQzoVHIh4yKQekpJbpP9snA7CnzVfpw058RKCVHtYp9GYEkoZqtM5mmyR971x/YpqSSX6vh0HE1EoIWQnelSDEdTLFDxGUVWKnhnydkObCOk3Cn6xJArABsRad9U0CJ7QwG0r1pJYMwCURY351h7ueNo9uONZd+Kuu27BrTs72JlMoJVEd/J8EQAbQvoQoz0VgvrDrOuxWh5G4gVJg3Wthml7mIpUn4IOg46tHO8vE51yaJXuD0F7uXoSB4tKU3TuhIMG4GM2kzM2uSc5jeCKTiC+QUbVZ+HyPkpwNWnKAQl1G5TlBEVZR/1ig/neHOfuPHfi+91ai95a/NXFi3jo4Yv48Ps+jL9835/j4Yc/jIODx3Du3J2o6xluue+WNOLusQceo/qq97DRNvRFFxW3bAz4FImliKzfWWXkHTWIs7Pxp4AcI/uSRC/inioV4GNAz+fCRs1a1jcePSPx3ORtBHSuiHFsTIVqOsHeLbuYbk8xLYm4xuSl0zjmZLN5sLSEsBZSUtIjO0UC5SzdFluwCJY3gA8psCNnKpOd5uAi+EjizHvYs2eIfADl1CEgIoGAzKDb4IGgBtibgnaZ2TEkn2Ftizb2gTNPJJ/Py8hlTjLjYRM+ePQx2TDFMAT+WuuGDnLrzFakWPPkApq5SFmUhYKOLEkxNLhjTPJIhAQxpNE+whu2swkyNaWHkB4Kg7wRp+u5xiUvNmxlKrgPzsA7h3bdJRhkoN3HFoNA0kcsq7VY7GO5PMBqdUhanNkgT/6dzYzytBYZLIUyjt3gKR4A0EeHwhmzxACbElFqkGWr5zSKhthrLhlLFx92p4aJB2xUaCqBSoeeA5kQa8wk006GxbEDidknTWUnWblu3UbojiT5ghvgLIreab5m0yyxWh6irKY4d/YOPPMFz8eL/uYLcc/dt+F5d96JUhNk1VubWgNOehVFlcoFwFA+AOI1x4bvo+MrCAjobQdjqiHLj3JoRVUkmHWA/If9VWLcTsOXs3lZOZwt4SGMIqPp46R3NQjTMwkiBSZugBZzxMO5EGvsx2lwto8jgIpijqqaYmvrDJQyESYU2L2wgzuedceJ7/fBconLiwXe81vvw1/9yV/hj9/zm3joob9E01BL1WQyx4ULd+FFn/0CHK3WOLx0iINH97FeEAnPxgCMMhV6LrTRcMangHwgQWWBh4stDREBUlBAMQQvwBAcEgFlEBAQVgxGu+vRLNY0S7Ib1HK45W24jxG6jcGPgICSGpPJHLsXdnHnp9yF23d3sVXXqRYrhTgVBxmCg7MMBQ0ZLZ+BBA1DQnRUNrO9g45qTqZyqXQgY+seB2BCEPsXQKq/03vGMx71nIUQgCInQIOsBzROCAGhhz321sHJ8XPI4vsU5K2xXBxgsThIesFKGQxth3lyYCPk2sE5SoiUMui6NYx5kg5y58IOlodLyIWgMSKqRWipkd/anggDUlHPT+YkASCAjCEfQDYMzlmgG5rzk96qdVGbb2CwMpadR4UiZjucZbJDZFID3XifNEKFAFxPUnnWijik2WO9JuO8WB7g6OhyUn9nGJkO+2DUnsqllEZRlCma4iZ6m2VkUgxTBFhgGwC25ns4c/Y23P702wiO6GksEgB4reAc6ykSwSk1RBsFFWIU5kWiVls7QExpFmKsCaRIOkKnq+NV6qvre5rQbkwBnl7vepco2YvFPqSU2DtzG579vBfijmfegee87Dl47l13Ym9KkTVARA4XQjIiJ73KcjIybPlZo4hZJ2PH0wGklFEQeZ2goaKkiTBCDk5RSEBECJYfcn54uZyQa99yG8CQFQ4RNSMxwRLUTfqpNokOUMmgR7vu0u8ys9P1NsHvxFYltZKyNNjaOoO6nmG+vUMONhKDhBzPpjyp9Wu/8Qf4qz/7K7zvd34Xly49iIsXP5aeO4SA6XQHOxd2ccfeHsQZgcW5s3j4Qx/How88hosfuwieeSpZrFyS0o7qInNbD5mMj0PEyabkwQnfAzH8rB9qhKy0Q7V5D2stZbPLhgKNozXaphm1LIyTAomBgQ5IqRMB7Lan34Zbn3YrPvVpd2NnMoHROsHcp7UEOCt2yQFTTVRDKRch1nGAaG0NYwr0RY+iLxJTFSDYVBudDGMIIYm2M4SdS1puIh8+Q/euIufEMo3r3eAsI+TKms3HR5exWpNesBRU6yfC4kBa2xQB4K+RoAC9v46s3uutG57++e4M0+0pscWaDiGYmJp7ODe8qZJ6RGHO4diBc5c+afrw1NBMEAnVtfRAotlYA1tQxNqPTAysPlADq4e76nfTwROxFuqoObrvW7SxDsZN0i4jJI3X5r9P7zAng4ghM2TRXWpHILYlP/EBIcm0CSExm+9ie/ssdi7sxmnmDVZHK1hhY91XpgxFRRaZjIpHYUMiMDeYm+QGZhES6aYb/ovOmuvQQqgIqdKMzvXqCOuGlIim0x2cOXM77nvBfbj9GbfjOXfegVt3tlEXJZQQcGGoMbtTqv0SUztT8IGA4BFE3kEo6gfloNA56r9q2xVY1kobjboj+Fh5OUxCCAJCxB47OUYe8ns8Wny91o+dY2aMKCtkw9GnrN1aF/t8fWJm2s6mOZd918QBBNSWUhQ1qmqKqprAVAZ+NShKMTvxpNcH//CD+ND7/yc++sD7cbzYH5OkhKTPNK0wLUvURYGtusLZO85hdbzG/qP71DZkRbpuG4lfzMwOPiQFr7zumO9hynwChujXh9hqFFILAzlLns7RjyesuP5x5Muu5mRorVFNpjh7x1mcv+s8zsxmMFpDCQEwB+OUzIqQMoOCfWK/U9kpwHuVuA/5dYTg4D0FqpT1+aSjarseQeuENgkh4GOi40Um+hID7Zy7waQzAEk0nRznMJOUWfC2d2n0GfXsNlg3iyEpEEOGTr3MPLMzvn8WeLCAgw19RL+epJLO+bvOw7sQ+1b4AqfgESKDZqMdwVS8Cda21FcV+yHp+wZpanzjUiTdrU3KHJ1lYs4wuyyEgKI0aXIFRyzI2jvSBInOpsieawW2oxR9ebjCernKKO4D3d35AZ662inm6/RSSoa/uraBi6r/uQKHiv16TChxzqKPKkZFUeG2256BO551B25/5u04ePQAhxcPcfnjV9LkFD7gQiD2pJKx9NYlujUbGADJULNcXxotEw1Hs2zSwOv16jhpKVbVPApxS6zXRMO+cuXjVOftO9x516fivk95Np7zsufgJZ/xXFzY3sb5rS0YpSCFgPOeak7OobMW9pQcJJ2tTAoRmfg6CPby3sfIkxRJynKC4D0Wi316DrwlvdCqAALVbJiY472CNtEwZ8pGqTbsRWqbAeJMwghFp8+oZWK85qN9uoZ6ypZHKzTLJgovrFOQF4JPZKiUpQHQhiT05vM9zLa2U08YXSexMgEkHeSTXL/y//sZXL70EJp2IAhx/VcpjbqeYbo9QWUMtuoaWkq84HnPAAAsDxa49OAl+MhmXS8aahWIg72JXNKORlwBV5dImKU6ZPoiIVEsRces4QTrrVosDpekGRtrXwRpUw8tIw8syQkgcTekVJjM5rjtabfiOS/5FDz9woVEPHOeFG34TJzGUkrDYaiXAgSvBi9i/TH2H2f2m3oNOyjVwrlJmkDDeyWESDraXIJTSqXggq4nO+cbiUeeMXNWyQG5y/6+XqzRLhssFkexDHaEo6PLJOLSdyNmPoIY3evB4Q+IkItzOPtu7DyvtW4MsZ7ZxnxvjsX+MfYfO8DBYwcwsoCysSYW9e8SnEBiiClLpIMfJdKMgQBRzkUvEl5sozOjBzQKBcTa1jjqE2kuXheNPN3owXh36y7NwuOG0r6lQn+37rA+JufYtivU9RxSKhSmxGOPPXCjrcCQNZ4u3krSa8RSHVjDQwsCOUcePzMQXjgbOHfbLThz21nsbs2Sszu+cowrj1yhBuymT4e6iONopFZJngliqKPJCI0zRMLqK0kguHcJ3mPxbWryL6A09dAeHx3i6PgKmuYYq9UxJpM55ufP4NM+68W45zn34FOf+zTcsbeHeVWh1EODNyMSUgiYU8hkeFEv79UQGX0v/lDwaJoFZrNdbG2dwdOe+Wk42r+Mi5cexGp1FKHkMg7c9ijcuH5tSkMZWu4QsjFkeS9Yyv7i1wEAkQHurCNUYN2iWaxxePGIpq4fL9LsOx4Pxs8kB6NCiBRY1TXVHet6lhqnvfcJAaiqKepZjbPzOdA8Pgz1ia7Llx5CG8/4aAkB7qsOfoDUlZS47/x5XLlvieXhEs2yjaOkKEDgwMZObGRIjh0j7y3jrEKIJDjC75s3oSOWEli1iMoGcYDBYo2uadMQ5E2DnztHxGdWCImdM2dw4e7zePZLPgX3njsXmask+YgY/DvvT21g8qSeo2mXqa0jhEDOUGwEbMiuyTv4iIYghCg/WCbx9s50ySkqLSGdghV24zm6uiVuc8/YgeZzeElXm2Dt9TGRKLn9btRhwM8S2wxB7TU0fGB8DvL3DyGQAMINtvvGLNaiwC3b2/jo7WfhnMfqcEneXThoX9KNdTLh8NmnQOAPk8ENqbFZegBqyEB7oG87RPs8qCpEaICX7eMNMUMth+s1IdC4KoL/eJjsMJ2DFRz6vkXwHtPZDoqihDEFWDz3ia0bZZdPbuVwKhOepIg9oEoljUc+SINkE4152T67jdnuDNOyhIqOdPvcFhaHC1K0iDR5aimI7F9HQQZjPCwIMIz4GXrrbBTDZkfZt9zH2CflfqrpkLrSYnmA5fIgQpIKW1tncctt9+Bpn/Y03H3fbXj6hfPYnU4pc5QyZY8CpNkZhIBW8vG27EktesDUdaJbSdfhHVHFd2/B9vY53HovzTK8sv9InPSisFweYL3YGb12CEPfHTvH1E6QXZKQQ+Z+1ZJiJMzPznF5uMLicIF2tcZqfQTbdwlRyK+BDTmrW0k5iMCXkzIFQ3TmiHnJwurzqjpxB7lcHW5e4Ajao3NDA5R9dH5nZjPcubeHg3tvwcf/8uN09hd+mBsY7Uree8fXnJxmdJAyDkHYzDTy55+ChZ4MdJSnJDm5Nj6fdtTCkWfB8QvxXlPdcefcNs7deQ733nMbzs7nqIsifQYJwMVSibuBNugnu+ZbewhHgXRKw9VIDO0D6xEP2Z73AjL2rutAAbTtiji1KbaIKQ/AgMepsf4vB4G8ONjbdJgMv/IMXz6Lth3mk+YDkHlG6eY9I4foRxq4w/cyX5Ey6BvXfW/oIBdtgzvO3IeXfubz8cHbHoJ3HvuP7KNdt7GxvICMGLKL7QeEcQMSavSBgvN0EByTcwSEMMnQN8uGZIniRtmovs/yc866FHWzxNamIgkP6mRYkCHA5cECbdtSM2mEyO57wX3o1h0W+8f48z9/T9TvW4+yiWutVLs4pUWqJsNnkEJGqFKPqMz0kLqUqU+n2zh37k5cuOc89m7ZpXFCkyl2plNUJfVEXvzYRXz0fzwQNSMd6YwWOjW151lNeug5ifHj6QjW2pSxeGdhY90RcFgu21RrvHLlkdT79cxnfgae/RnPxtP/j6fjsz/tudibTTGvaugIq/J7KzkYMc0UfPH49YJPdjGs5OFHRo7qNC4RW9pujdl0B7fe+jTc9Sl3om87PPxQjdXyMNbRPLQuMZttYevsVpI7o+i4hzIa3joooxPZIYRBkDtJbpmhx9J7crAMpTarBsdXjrE6XOJo/wjHx1domk7fjtjjfP+UMpmxkFEIusZ8vgNTFTRpJIptdA2pwRhT4I5n3oELd53HnWfO4OMHBye639xesck2FCACX9MssDxYYNV2mJUWwRhMyhJPv+UW7EynOHjsAI98+GE89MGHCP7s+qiEtU79kPnKpwUxM56zldEg8Ozss4Rl13RYHa9HU1eYgZ2MgGCFqauDm7qeYfvsFp77Oc/DXXffiufecUdCQ6hcIuADlxMCOns6vUyf/rkvxEfe/wAe/ugDsY43bgPjxbV4SjocAiJxxllYq9N0p8LWVD4oi9RaJ9TA+s3bwfj8JbZwUjEaSgbB0VQORrCEEDR8ekUCLk2zwGJxQIMjIhoio0jNUAMeEEceYXetNdi1Gwu+3NBBHjx2iMW9De4+exYAcHzlGN45HF06Qt/1kH7o02Mc2tqoo5h6J2X6E27oQ6IPO9R7EG8GMyJ9Fk0xXJpfYL74QpP+atRi7TtL0fbyONUMdnbOY+fcNm5/xu04vHQIVsC4OsrIM8VrRRqn4yXZ6QFIZByiYGcN5olxx024Bba2zuCWW+7F9tltTKY1hBAoNE1vv213F8v7boUpNA4vHuDiQx2a5ghdt4bWBQpTQelcvP1qNR8A8TC65BR9cAlmp3luDjZSqVer4yT0fvbsHTh//m582l//NNzzvHvxzLtux5nZDJOygFYqNkojtXJ4xHPhKajKv3fSS3If5MhQRDKSc4mtCgBlNcV8dytpaG5tncXh4aV0H9ZrUvoRkWHJGaHrLdV+lYQpPbwbSFaswyoENajzuDKpVaqDrRc0pX69WGOxv0CzWmO9Pkqwat6uQ/dMpPPC91ApEoQuqylMVVD9KDbEE9LSoSwnqOc17n7O3bjr7Fls1zU+fsL7zQHxZiAGAGk80qJB0/ep7uxDQG0Mzs7nuOeZd0IIgeODBQ4fO0AfnR2TljYn9TB0rY1KmtCd6QYUJipI5cpULrYUMMFpEIe3CXKUaW6nuqp/FiGgKCfYOb+N255+O+66+1bceeZMco7JcWQ2prMWzSmo6ADAp/61T8UkKvYcXjrAwcFjKaFJ+5Qxb4UQJOlGEAhCAGwYAkaymTFrd3pAKGLCwk6PbX3ePrb5fS7fdG0/yFoC6LouSsgtEvPdJTLi1dkhzyrm54r3+cmsG2eQ+8dYti0+5TYaw/Lxey/gsY8+hmbZQh6t4IUfsZ8AwDl2iOwcr1Zk4SWlTNO7Q2xmdyGkLHKog9FDnK9NuTMu9PID2DfEcluvl1ivF3CuJxhyWmE7Qh50jYtUXL/ReuIw7Ce/uK7IESlFS2qQFotwTO5ItTaYTLaxe+4spjsz1AUpRGhJDrLUGsvzZwAAs90Hcfnhy0lvtihq2KKDknQc+LX5vVIEKNQwdNX7dI/7KNI8/NnG2YLHQAio6hn29m7F7ffdhae/8Bl45u234t5z5zCrquQcN50fQ6shvbc4hcmE8b2ye8+ZO7cS8PXQ+CuBsqgw2SJosp7VmE7nREwgoeHkSJVSCG5KZB0p4JSEcj7LHMcQX5qg0os00V5EBMVZj+XREusjcpDL46OoP7lKvV1koAYJRmbiDtR9arcpygplTcpKWtNIOhf7hK11qCYVtvbmuPOeW3HL9hYmsdXmZNdgC66CJkNA1zaktmQtnPepFlkaA6M1nnbhApbrBh//y49jsb9I8wmTOo7tU1lHYFB+MYYGE1CJZlCOySephBAAH9D3/YCOZExHbv73wUNHgQWtTXoekpEGyXROt2c4f9c53HPuLM7M5tBKwTqSnuP4mkEw6z1aa3Ea4n4vvOdeKCFx8cGL8M5juTyIZTC54URyIZYIz0MSaSs237Owh9EFBYKIELfMWaohjXATUkJ6Sc4mbuXQE21TGYImMbkUYPS2S2OsWG91qNlT10TOF8izwmuywz+JJcLjuNj3vve9J/Im/29fL3zhC0/stW7u+Y3Xzf1+6tdJ7fnN/X5i6+YZf2rX9fb7cR3kzXVz3Vw31811c/3vuk6PGnhz3Vw31811c91c/w9eNx3kzXVz3Vw31811c11jnYjQ4gc/+EG89a1vxXq9xmq1wstf/nJ84zd+43UJLW984xvx+Z//+QCAhx9+GK9+9atv+B5vfOMb8f73vx87OzsAqHj85je/Gc94xjNO4hIAAF/+5V+ON7/5zfjP//k/4+zZs/jSL/3SE3vtT2R97GMfw1vf+lY88sgjqKoKVVXhDW94w4le6xNd3/It34LP+qzPSvfrla98JV760pfi27/92wHQfbn//vtx//33p9/5vM/7PNx6660QQmC1WuGVr3wl/v7f//uf0PvyGfmcz/mck7uYx1k39/yp3fOb+/3Un/HN9Xu/93v46Z/+afyzf/bP0te+//u/H/fddx++8Au/8MTf71d+5Vfwjne8AwDQNA2+5mu+Bq94xSvwtre97X+pvX289aQd5NHREV73utfhbW97G+655x445/Da174WP/3TP33DC/5ED8Yb3vCG9Du/8Ru/gX/+z/85fuiHfuiT/uz/d1zr9Rpf//Vfj7e85S349E//dADAn/zJn+A7v/M78c53vvMp/zwve9nL8N73vhef//mfj49+9KO466678J73vCd9/w//8A/xpje96arf+/Ef/3GUZYmu6/D5n//5+MIv/EKcOXPmqfzoT3jd3POndt3c7//91h/+4R/iJ37iJ/AjP/IjmE6n2N/fx6tf/Wo8/elP/1/90R53PWkH+au/+qt4yUtegnvuuQcA0du/93u/lwYlA/ie7/mexKL623/7b+Mrv/Ir0++++93vxoc//GF8yZd8Cf7JP/knuOWWW/Cxj30Mz3ve8/Ad3/Edj/u+h4eHmEwmAOig/sIv/AK01njRi16E173udXjFK16BX/zFX8SVK1fw8pe/HL/927+N6XSKV7/61fi5n/s5/MAP/AD+4A/+AN57fNVXfRVe+cpXPtmtOJH167/+63jpS1+aDAcAPP/5z8e/+Tf/BgBl3N/2bd+Gtm1RliXe8pa34NZbb71qD97whjfgbW97G/7oj/4Iq9UK3/Vd34Vf+qVfwq/8yq9gb28P6/Uar33ta/Gpn/qp+NZv/Vbs7+8DAN70pjfhWc96Vnrvz/zMz8Tb3/52ABSUfN7nfR5+7dd+DX/5l3+Jsixx4cIFzGbXH6rbNA2JNFdVut+vf/3r0bYtXvnKV+LXfu3X8G//7b/Ff/gP/wFSSjzvec9LxuhnfuZn8Pa3vx2LxQJvfvOb8fznP//E9xu4uedP9Z7f3O+n/ox/omszu3zZy16G//7f/zseeOABvPGNb4TWGrfffjseeughvPOd78TP//zP4x3veAeKosA999yD7/zO70w+AAB+9md/Fl/5lV+J6ZRG+O3u7uJnf/ZnsbW1BYD8yC/90i/h4OAAr33ta/F5n/d5+Mmf/En8l//yX7Ber7G7u4sf+qEfwute9zp8xVd8BV784hfjT//0T/Ev/+W/xL/4F/8C3/7t344HHngA3nv843/8j/GCF7wgZfTWWrzvfe/DL//yL+POO+/8hPbhSTvIxx577Ko35U349V//dTz44IN417veBWstXvOa1+ClL33pNV/nIx/5CH7sx34MdV3j/vvvx8WLF3Hu3HhY61vf+lb86I/+KKSUOH/+PN7whjfgz//8z/GLv/iL+Omf/mlorfGN3/iN+M3f/E286EUvwh//8R/jgQcewDOe8Qz8zu/8DqbTKV72spfhN37jN/Dggw/ip37qp9C2LV71qlfhZS972ZPdihNZDz74IO66667076//+q/HYrHAY489hne84x343u/9Xnz5l385Xv7yl+N3fud38P3f//342q/92qv24Nd//dcBAPfddx/e9KY34QMf+AB+67d+C//+3/979H2Pv/N3/g4A4F/9q3+Fl770pXjNa16Dj3zkI/jmb/5m/NRP/VR6/729PWrKPj7Gb/7mb+I7v/M7Ya3Fb/7mb2J7exuf/dmffc3r+Oqv/moIIfDhD38YL3/5y1Mwc6317ne/G9/+7d+O5z//+fh3/+7fJS3R5zznOfiGb/gGvPvd78a73/3uUzMeN/f8qd3zm/v91J/x663f/d3fxZd/+Zenf3/sYx/DP/pH/+i6P/993/d9+Lqv+zq8/OUvx7ve9S489NBD2N/fx9ve9jb83M/9HGazGb77u78bP/MzP4Mv+7IvS793LT+xvb2d/n7hwgV813d9F37v934Pb3/72/HX//pfx8HBAX7iJ34CUkp8zdd8Df70T/8UX/zFX4yf+7mfw4tf/GK8+93vxqte9Sr87M/+LHZ3d/Hd3/3d2N/fx5d92ZfhF37hF/DOd74TIQS84Q1vwN/9u3/3E3aOwAk4yNtuuw3/43/8j9HXPvaxj+GRRx7Bhz70IbzoRS+KTcoGL3jBC/ChD33omq9z1113pSjt3LlzaNv2qp/JIVZe733ve/GCF7wgRSsvetGL8MEPfhB/82/+zeQIv+mbvgm/+qu/Cikl/t7f+3v4vd/7Pbz//e9PB8Nai4ceeujJbsWJrFtuuQV/9md/lv79wz/8wwCAV73qVbDW4i/+4i/wIz/yI3j729+OEAK01vjwhz98zT0AgHvvvRcA8KEPfQjPe97zqElaKTz3uc8FAPzFX/wFfvd3fxe/+Iu/CIAy8831mZ/5mfjt3/5t7O/v49Zbb8XnfM7n4Pu+7/swnU5HiEC+cvjpa7/2a/HzP//zo+/n3UX/9J/+U/z4j/84vu/7vg+f9mmflr73nOc8BwBw9uxZNCesB5qvm3v+1O75zf1+6s/49dZLX/rSq2qQ11r8eT/0oQ+lzP+FL3wh/tN/+k/42Mc+hqc//enJfn/GZ3wG/tt/+2+j37/tttvw8MMP49nPfnb62nvf+16cjQptm/vAwg6ve93rMJlM8Mgjj8Bai8/+7M/GW9/6VhwcHOAP/uAP8KY3vQlvectb8N73vhd/8id/AoDs+ZUrV7C3t4e3vOUtuPfee/GqV73qk9qfJ81i/dzP/Vz81m/9Fj760Y8CAPq+x/d8z/fgL/7iL/C0pz0twat93+OP/uiPcPfdd1/zdT5ZhZr77rsPf/Inf0JDfUPAe97zHtx777142ctehve85z3Y39/Hy1/+crz//e/HBz7wATz/+c/Hfffdh5e85CV45zvfiXe84x145Stf+UlFF6ex/sbf+Bv4nd/5HfzxH/9x+toDDzyARx55BEII3HfffXj961+Pd77znfiO7/gOvOIVr7juHgCDjN/Tn/50/Omf/im89+i6LgU19913H77qq74K73znO/GDP/iD+IIv+IKrPtPLXvYyvOMd78CLX/xiAMCdd96Jg4MDPPDAA6MDf61VFAXOnDmDvu9RliUuXrwIAHj/+9+ffuZd73oXvuM7vgM/+ZM/if/5P/8n/uiP/gjAU6NaBNzc86d6z2/u91N/xj/RlV/HQw89lIKKZz7zmemzv+997wMA3HHHHfjQhz6E1WoFAPj93//9dG94feEXfiF+7Md+LP3M5cuX8S3f8i1Yr0l5anMfPvCBD+BXfuVX8IM/+IP4tm/7tpHg/ite8Qq8+c1vxv333w+lFO677z78rb/1t/DOd74TP/qjP4pXvOIV2NnZwQ/+4A8ihIB/8A/+wSe9D086g5zNZvie7/kevOlNb0IIAcvlEp/7uZ+L17zmNRBC4Pd///fx6le/Gn3f4xWveEWKFE5qPetZz8IrX/lKfOmXfim893jhC1+I+++/H0II3HLLLbjtttsgpcS9996Lvb09AMRA+/3f/3285jWvwWq1wv333/+4NYanck2nU/zwD/8wfuAHfgDf//3fD2stlFL45m/+Ztx+++34P//P/xNvfvObo/B6g2/91m+97h584AMfSK/7rGc9Cy9/+cvxqle9Cru7uzDGQGuNr/u6r8O3fuu34l3vehcWiwX+4T/8h1d9phe+8IV4//vfj9e+9rXpa89+9rOxWCyu+4B/9Vd/ddLmvfXWW/EFX/AFaJoGP/VTP4Uv/dIvxXOe85wExT/rWc/Ca17zGkynU1y4cAEveMEL8O53v/uEd/b66+aeP7V7fnO/n/oz/omu5z73uZjP5/jiL/5iPO1pT8Mdd9wBAHj961+Pb/mWb8GP//iPYz6fQ2uNvb09fOM3fiO+4iu+AlJK3HXXXXj9618/er1P//RPx6te9Sp89Vd/NbTWaJoGr3vd6/DsZz8b//W//ter3v/uu+9GXdf4ki/5EgCEKj722GMAgC/6oi/C/fffj1/+5V8GAHzJl3wJ3vSmN+HLvuzLsFgs8JrXvAZ/9md/hn/9r/81XvziFyek8Bu+4RvwmZ/5mZ/YRoSb63+LdenSpfCTP/mTIYQQ2rYN999/f3jooYf+F3+q/3evm3v+1K6b+3366z/+x/8YPvKRj4QQQnjXu94V3vjGN/4v/kSnu06kD/Lm+r//2t3dxZ/92Z/hi77oiyCEwBd/8RfjtihAf3Odzrq550/turnfp79uvfVWfNM3fRPquoaUEt/93d/9v/ojneq6qcV6c91cN9fNdXPdXNdYN6Xmbq6b6+a6uW6um+sa66aDvLlurpvr5rq5bq5rrJsO8ua6uW6um+vmurmusW46yJvr5rq5bq6b6+a6xrrpIG+um+vmurlurpvrGutx2zxYBefmevz1whe+8MRe6+ae33jd3O+nfp3Unt/c7ye2bp7xp3Zdb79v2Af51/7aZ6Eoatx229Oxu3sB58/fg629bRSlQVGXcNahXbd4+KMPoChq1PUUz/ms52L3wi52zu8AAPquR7tscHT5CF3bo1t3AEheSBsFoSRp75UGSisoo6C1hlQCQkpIJSGEgJQSUsukbMF/SjUkwiEEBH9150oIgf5zHiHQv21v4Z2Hs26km+itQ978IgQglAR8gHMeVx65gsPHDnC8v8A3fePfvdEWfsLr//v/eQ3mW2ewvX0exhTQuqBrlwpKDQr53jtIqYa9kRpKaUgpweIfvLdSSggp6FoE7avSCkpLQAgIIVCUJv287XqEQHM30x76MPx7Y595v3ifnXXwztN/USYq/azz8D6gaZZwtkdvO3TdGl3XYLk4wPFiH227wnp9HK9Zo6qmmM128e/+3Q+f+H7/k3/y/dDaYO/sLZjMJ5jvzTGZ11BGwxQaPl5nCCE7e0jnKF2j87DWIfiA4D1E3HPaf0F/VwpS0b+V0dBGQen4p9GophXme3PUVYlJUWBnOsW8KrE3ncHHvdVKQUsJozWMkpBCQsn4jMR7ublk/Bq/Rtv3OGoaPHp4iMPVCkIInN/aSq/D91kIgcMHHzzR/f6//q8fRVHUmO/NMd2aYOfCLvZu2UM1LVFNK+jC0H7FZw6gc5zOuRJ0NrOzFZy/6n2EEMP5V/RMCAx7xPvFP6ulRKE1tJJQYgyuaaXIRkl51XvwngkhsNk1t+o6HK/X+OAHP4pHP/IIPvjev8Tli4+ga9eAENDaQOsC27tnMNudYTKf4Kte9ddPcLdpvexlnwOtDW65cC/uvue5+Iy/8dfw/Je/AOe2t6CkgHUere1xZf8IR5ePsDhYolk2yUYUVUl22ehkj5WOtkcJmLKAMqSBO6sraCmhlUKpyR6pjX3jfeK95p+hezL8nI82yHkP6x2s8/AhwDoHy193DgEBzge4eG5DCOidg7MOtrPo2w7OetiuT/ZOaQXvPV5y54Xr7tsNHaTWBYwpUVVTVOUUZVmiKA2qaYXJ9hS266GNQlHUMKZEUZeQSqJvexxdPkLf9fDWw/YW7aqF7R28c5BKQUgBJOPOD8C1H3A6gON/A6DX2Pw5NT6owZNRQAgjY0c/P7xWcqLZGaefExAAIAWEzz7fKbWQalNCSpX2JF0Xhr/z5w/BQwiVnKNScnQ9+T5J3m8loZSCVGSk04rGXGkJ71U0OlnwIYa9CwCEil5CxE8W9yo5zhCwieILIRAUOVrjSvo3AryzcMpBaQNjinQvkiGT+tR0K7U2UMokR5Z/VvqTL3MIMPJrFkJABomgAhn1EODs4GCkHow0n2823BwASq2gtEq6onzt/HH4wfeBjIAQAsI5MvDSAx5QUsIDUBv7JK+zb1IASgo47yHjmWLD1lqbDNNJL60LmNKgrMkhVtMK5aREUZUwZTHsl5TZc0qGWAn6fAAAQ/vhvYcLMfCNZ08qel6uFTjITVsSf44DD5XdAx8CAgLgyFj2cd/4iEuB8TndCN6VEDBKYbo9xdaZLZy5/QxWiyW8d2jbNULwcM5heXxERr3tT3y/81VWU0ynO9g6uw2lZHQwAa21aPseXduj7yxcTwEugFHAHXyAh4/PRIhOUiadVKPVyKb6ECDisyyvFVzEQIQDHSUlQhZspHsQz4GUEhJ0D6VzcJL23DqHIDz6eBa8o3PhrYPtLZwdgikBIOD6yVS+buggOWqbTncwne2gmtUwVQFdGNSzGqvjAKCFkhqmpIPfrTu43mKxL2B7R68jxVWZ2ug91BCRbBpCvikhCMgwZHRCCCDa8Gv+XsicYdyM5AR9/NpG5hOcTwbRe8ouhRDQ0FBGQXJGoNXYuZzgIkenR5mXlBpSqtGe0b1RowCDfjwzKplhVkaPsnCpJLRWV+2Vd/EwSwmZBxohwFsH7ylrYkeZnCQ/ACJABsBLCRHcyFHz51JKQWuT9t3aPn6NAjIA8N5mezJc+0mvspwMWUYWoA17wp87c45yCASEiILZWkAHFTNon4I3yh4HJCT9l51bU2i6P3owzkYpiq7ZiHB2njkt6RwABUgPGa4OjPj38jUY8+G1XaAIPWQmQQhxKiSFejrBZF5j+9w2JvMak/kERVVAF/SM5Z+dM78QkJyjkkM2yIsziNyhAsgc1jh73NwPJSW0lClQAJCcYwiAg4e34bq/z19nB8tfU1KiKgrM5hPYC7u4remwPFgCjwLr9QJNQ86y7xv0XYt2fTqa0GQnFKpqislsiunWFD4ENH0P5z3arkffdmiXDRzbbAEgBFhr4X1BjkmJlNxwkA1Btt31Fj3vj6GALSES0VGSUxTJXhmtoa5ju4MAnEd6ADmb5HsDDMENBY38u/RzQghwhBlCgOstmmVLz6tWKOvymv4oXze08EoZGFOhLCeUJRY6efe+7dA3PWxPGaEpDLShtNV3ZEBtZ69yXkIAQYbs32RAcse3mRk+0cVvQzDRYCwCGyQff0ACwQ9GL4QA+A2HyU6U/81OgaN7eTocp+QIQx7BXZ3ZJOcoVIJa6bPHfZBiyFokZzEZNCIFpFYpquLXFlKMIJHcUXgpKXDoHTnwDaOUZ+Y3vk4RAwE+I3KUlY9fT9L3T2EpqSPsEt83wqF5BvmEV8wsE6SXZYuPd144+wmOol5nFBxHw4EyOXYC+b4M2aKCgE/GIjfSmw5yfO3kdChbDPCBstU8ej/ppbSEjhkkQ3d87nJbwSUBYLgHPgRwqDTOjCmTDmHDzoTBOV5rT0IIo9dJX8c1yjSg/UH8nXxvctgWcf85jFFCoNAaRV1guj3DdGeKZtnAHBSwtoOzPVarYwCAdaeXQQohUZgSpjTQhUbwDFU62I4yR9sTLMkOJiEdKaiTEekQCfVIyIoagjsyp0Mwl+9xiK+nhICOjpDvT/qZ5ADpc/j4+z2GbNMFn2DVHPnjgDUlF0pCm2jnRDt2xk/WQRZFhbqeYTKZo5pUMFUBAHC9xfJwhdXxCt26g9YautAoqgIhGk5rHfqmjx9y2GCO1L2LxlpuGBI1hrqGGzy+ML40KeRVNwk+QEogBEGQQBAIoIeT0+/0BGU35ZoRBUfuWU0NQIr2T3opRbeFr1BJHR2EGME5wJBZKkUHwvsclqS9zGsFUtHP8l4rPWRmwYcExaY6kBTwbsi8vfMI3qMDIJ1IB3PIwDMHFyHp8VZmhkkrSB9G8KkPbpQ58/WqGAGfxpJKDfedzxGf06wOdq01+pzZ1/LXYNiazxsFXxT/cKDoI3JhrYC1DqKz6EqLzjoY5dBrB+f9qA7poqEOGDLGgAAtFXwIlP2JAfbLnQKAlOGUWsNFY2adG8G6pwGxSqVgSkKgykl5TXQgPc8Yriv/7Hw9eUanIOEzx7bp5PKsjmG//Oo4WwSQYOwcjg0Bca8BlwWN+c8CQFAq3QeA9rnQGnVVot+dYfvsNvq2x/7lGXrbwdoei8U++r6F0cUnuauPv6SUUEqjKGsUdQEd0S/rHfrOom06dOsOfdvDRXgd0YboDDlTWlGmr8mOqFiT1FImmNvHwMp7j9badI9kqmsj1R0BoDQGQm7cSyngA2AAWEdnX0oJ9D16AJ2lZyOH1/k+KEhAeXiXoWhlASElbGfjs0bJw43O9w0d5Gy2i729W1HXU+hCw1kXi5uAb3v08b8QANtRCmtKmvFFGSQ5yDyK1oWGiIQcbTSU1iNDnVaKWjJYSgx1x1G0KYb3oBMR02wfEAI5YjbwEj5lI64PAMZOcvQRsqiGYARHEaSU6ZCd9OKbSnWx8b4opSCFSnvDWSTVRwHF9de4Z1RvpIida2zKaCgtyVlu7LvSEipGu+yQvfOpfpuiSynhnYOzVEMh4s3V18ERZ8hqv5v/IcvImZBEAYmDtR1C8JBCwhTVqew3rxAwInGl68jg1PSzEU3wCWbNM1A6MzmkyvfL+6E+og3tvfceXdPFwCQGl22PEKg2NCkKrLsuOUchRCJANFqP6mdaKUyKAkbr9G82PCPokF9HSpTGwDqXIngfrn4OTnJpo4nsoQnulyo6lnwv5dgxcq2KyRcyGdIBbmWYlPfUXecafLYPNnstFyFWYMiigVj32ggyNrPQhFLF2pyTEl4lhI8CEWNQTkpsnd1Cu25RRGSOX8O5Hl1/OkOTldTQylCpbGuKoi5iKYBQvmaxRrtq0a7a9Dzzgebn1DmHvu3JhsazXU4ieacqoSOKNa8qGKVQ6LF93Nw7Du6sc7FcEyBwdS2XSTg+BSJEmvIhoAcAD9hAwWPX2xRge+vSe0spoI1OsCrXRP0NUMAbWnhjSpTlJEUKvNLDljHJXCyIdk03QCKc0fiQ+Brs8KQUFFnzazqfUmkhZCqm5rWGa61RXSfLMoUQCBFWvdEDL4S4BqgyXnn9MpFeTmGR02NGqkoHddPYMqyaLplhwXjzZQZXp0J7HlzIzf1CdGgUNQopEbyHjw5LRJzfe5nqFIHrAhy88OsxoYn3SbKzzYxv5nTofot0zTIZOnLIve3g3FCTPI11lRMMG0X8q9CGgQBGz4YY7pUcG5cQ9yj4/GuAEEM9NxHVIsLSNz0apZIB5mXi11TMhgj6I6PsYnYJUCYDIEGL+WfnRXUiAefHn5ezgNNYUl1Nxgsx40jQ3uM88wSv0Z746OCUlNgMsQUEIAajnP8+/xkQXwsCIoSRU0gEHeJAjYKMsPFzMjlIkUhTPn6N318KCtRNaVBUBZQ26TWMKVKQeBpLSEIwtDIpIMntNgVtGYK2kZikvRs9s2L0J18n13QpsLj25+GXye+zD4AUfP7GP5vfs/zPYd+R6sdDOSwLXvjntcz4KMNzer31hBxkUdQJpss3J/+wzlkoK2MW2STDTZkLwadKq0QMYVq7jA8xE3ik8xEu1Klm5kHR+KahGG/WpuEHYLlYO9R4ht+9Rra4EWnn78PU/QSvRgz+NFZR1NDaJOg4/2zEWKV6XM5YzWHV1M7Bda/0MANwHiE6PIYaRrVLzuZiO0LwEkLEe+NlqkHafnBWzjoESQZGKkkGP/s8spBDtgiLEETKvryPhAAIiBQYmPifSpF12wJNszyV/c5XCEgZM9egc6O5+XOJGScEoMYPHP+87S2Ey7Kk+HUnHEKQEGwUhE/Zf+DAUoohIo6GeFZVG3UzIFlxAE3XwWsN6RwKpSKxRaToPF8Jqo/nnmGqZGhuGDZ+4ksZTcFXCKnu72K073o3nMk8c1c+kWx4b0dkJbY3UiUmpAQb3cyZ8Z5FaM4x65VuX1o+hNRGoBL8l9U0g8hg38jWTFtLpCm44bnke6elRFEVKCdUC7S2Q9MsUVXTSBY7HZuSSjQgBIORvRBCCnYJXVIQNjo5LZMNSW02SqCoTEKfuLUjvZb3aPs+BS1G68E2jc7s1UEafT2iBNaOMk4+py54WEeEMv7dwYYNfJf81Oataqk2KQKC9Vc9D5vrhg4yBA9rO3TrNm1gSrntQAVWSo8YQ0pL6MIMBfgswxBKRlLOONqGzwko8qo+SHptIDhOkekh8o6NcTTCHqPPMmJdbmQF14L8RnAgR1neE1U467e60eZ+sqsoSqotKoJFjCkS3EownUMIHt4LSHl1sCIF7Zs2kXmrZNqvwYnKBL9yu42IES47frpXFKUHDziXQxYSQXqEGPiEfsOUZg8AowUpIpUEVVOUFzMkKaG1RlGUKIoq9njS9fN1dd36VPab4PYxPvx49O/hLEsIL9LZco4gfQ5OhtfyICaCGKEww5mO0KIcenr5+XLWQSkJlGNjwLCqif/JCC/yvzmCVzGKT7W8zfsiqNZjnUsG6XrQ5EktrmUF72nPYrAVQoBsBmNook3haJ+ywSxrybJcgkuznt0sA7Qb95Ih5hHkhwFWJQP7eHVnpKw93TshIDEEGYBEEEPNLYdry7pI/a6XL5ZACDg6uoTJZCtBrie9vHfUm4vheWfeQg+MroPtYCr16KEvXSoFXRDJpyhM6hs1Sg+1xvif89R2IWPNkbNpLjXwojNIpB0laMNUYUbOMiep5c7VB+rp7Z0juJxZtkYTZ8R7Ytd2NnFjeOX27Hrrhg7SewfnLKylXpIhPQ1Dr4wPqYibGqOZDHKN6PuJOpZrwaZjZ0b9Z0JmaTUbpghlwY+dHxvuq+AzDDZ9nCmEBIsRa/P0HSSTVtiRKWUoo5R8eCVCuHZjNP/JRKfN1gJkjorbVYask38vF2cQcDK2a9i4d1EwgLcpBT6CQHHeW3+NvZJSwgtPME/WVyolGUwVAwLnemqiNgU9GM6OXu8kF32260NbCS69xtvnZyBl5FLEOvfV5yN/HoasemAq59/3zsNbD6epvqIy45MbIl7sFIdWCDGi0OdGEBgic3YWTNQ57cXnJXjas+A8vKT3tdZBWfcJlS/4OebPLiBGhJtNhwhsoEe8P7Ggc61yjOAgI/UCX20nPIa+P+f9yCHkKycpGVNCKoVu2UCp0+v1TfZC5KSjyCtwLv7nE0rmrUMwHCgj9UwrpZJgQKE1KmNS/Ts/i5t2m0lj7BwTfA0kIp9UMqEcSsh032wKhjB6ZvgaeufQWQvnfOqV5SAnhOGMb9p7fl4fbz2BDDIgeJc+mHcBwVt4H9A11O/ofYCMdStTFqimZUrJOX0HkAz+5uvDRyJE5vjS72QRNxEcopOOEY5LF+8TXDsyMpw95oowG2UldqSbix9i59xG/9+Ndu3JL8r4FIwpUFQldCx4W2sjFCTTZ0TqSaI6oy4MkW3MoKojhBicoUCqDRalIbq2ytjEkjJLGUlUANB3Al3TxwcooF219HcXUusIAHrIAjGYveVzQ/eRnW7uFIIgwpRSKr13CAFKGVjb08OiDPq+wWm1eVx7/68f1A3Z+viBTVBrrD8pbprO2KxSiYRE2I4yJyEFEdf4LEelIdtZhGjAiqqALExyfCEEtLZP0B0A+LKMvZMiZY55/fF6JQU2JkyWYLiLs86TBv04A3fOQToB7xXQW3gfyU3xDHAbgVJj45vOD2d6McvsuWXBuavu17XgunwPpBjXPQnOBRDhP5WxyBlO9dlZziFEFl6w2XtKKYfPoCRMoTHbmWJrZw9Ns8T+/iNYLPaxXByc8G4PeyCkRFEUqX97vWhgux7rRYNm2VDZJAT0nSUCnvPUuVAMPe6mMphOakItBLFzmQzGKIRWCoVWKatkR8fZNZ01n1jSvD+TokjOVkiZWpjKmDn21qLbyB6tc1g1bQqeVYY4MfseANmnWCZM6EzvRhyYa60nJhQQG9eFEKn2FLxH3/UkrRU8pMw+mA9AdKo5JMkQn7cuQZfAQNrRRpNBUTI1oPJn2Iz4BP/JB56NBENcAqnonAxYBp1eJX+2EZnQaw0/k7+WiDDAaS2uMRKkqpJhBUB1QADwWVHdE8Ej+JBUhKhmGkaJUZIPi3UOEWvD+evn+xh6l6BDT02libWKeAAp8sSQwWfOzwsPgVirg0+tDHxwU9DhHUE/IgwtLjFAoOzZw7k+1StPY3F9JbUjZVE2HDUP5M3/wEDQSb8fM/H0/RAhwRgxe+GhQgxv1MbPOg8nHEQHuPjmtnfoux6mMbCdTUEGiwqY0sAUGloSY3BWlaiLEnvTKaZliVJrotBnmeT1VHWAjLThI+lFjkkUJ7V81m+cr5Q1swJKZxFCoFKBHKvq8J8iBtNOjNGKvOcRGDMo88w7Z6hu/jxDrkPDO1LNMoQAldWGAcpAOSuyPoyCF1Z/SZ9bElvcFBpFQWzWrl2jt90J7PDVi9XQygmpV62P11gv1rBdj67pU7lMCCqpkFgIBW7lpKTWkELDFAal1okdzc5NSgmNTNnISyjhSUaRWd1iqDFaGW1L8OlrXWznsFJCZ4xV3nMTkwQlBYQVUIKc67SuqAUqS4JG6juaHDbvve364Zx1T7LNg6J7neA9F7UmuVmcmYWcKfDP8NfyNWJJpdcfGK1cnxw1pm44yeQs+ffUYNzHTm6cVg/OEanB/fFSQYZjr+Vg8899Wou1VzehtyRFJigIyT8vZ9/5vUhGGgHCAx4e0l8dNaXsh2GNPtYgvKSHx/rozHzSvUyvn4KHYW/4fqYAI3OeCY2IzpGz+8095QdvUyjhNFYeaG0KBfC1eU/9tJswqcjO4uBY83sCAAPpZriXIv2cp0Ig+uyscQ3SdhZ916f30WaoA5V1kZwlGfWA2pjUspEyzlijpPL81dmYD4DlaD45isd3qJ/sys/BtZZ3Hi4G40KQOEUqncTFTpBfJz9r/D0miuROjJ2dSkb72tcnBCAFtRIwfCiEgIBP0B0wllLzYegBTO+Z2Ng+69tE7E8mngarR/V9d909ebKL34N7fttVi9XRCrazcHYg3A0oHCKCRKhgXi4b9igLNGK2LbLkw4dBLpFUm4Y94z5HH3tv2VG6QIjl5uvTPRUISlHdXyF1PFTGJNJVx2pteeCaIRABAa5nkqB/3B5n4AlqsRLE1aPve1jbDVBDSmsVhChhe5uciCkNyqpAOaXeNc4elNEwVZHEbbUhUkjuEHNyDtcikvGNbDx6TwzKO9d46Al+ROJfsIHP+/oG8s7GhccMKW0ynYAxSeeU2jwoexwyKdpnartQRkA4IobAXIPUxBGd2qhnedYMFRAF19QIxvPSXZ2lj7diRHISLCSgVYKvQ4ivZy289egDMVqZ8OGcGx1GgmEJsnWZpBx/fqU0pFCjbP80IVaGoLhXlLND74d6avAeIsps5XWNpJrDRjxcHQgmIo916cxLrVL/aN/1o4CNoFWfoO4c5ppsT+n5mpAucqkNKmMwKQoUhogTSg5EiQQrcSN1Bl/Rz9F7rrsuwWOVMjCKMreTbq6haxoY1vlzzHuFELLzS7/H+2ykHjEfE3EHQ0bDDpJhWFZckUIkgWzei1Tr8sOQguTIhEjZEi9WOOKVbE90CgxVp7/H/r7kZCOTn7RnDYqiwqSex705HRarMQWkVGgWa6wOl1gcLLA+XlPPow8whR4g7VgaYCEHqSXWiyZeYkA7q+kcViUqY1DGDL+IZLFS65Qd9tbS/inqXUzlgb6HFBRA1GrokPCRWcz7l86sGFjY/G8uWxAbWUJ6n2qXANDIPiJRMt3f9YKgZC5v3CggeQJSc/Th+76F9w5dNzSyhkD9O5tLCBEVF0xq0EfM+tJkg2tkiLmhuYq5Go3RVU4phCQFJ7JNvNHK4ZwcGkw1GRGzLv58iogq+fufVlJDAuRXO/z8ffO2F9rvjJlqhtpNriyESKCizKNMUF2CwrP3SQ4gsPMY761UTMYhdm+OHDjh0t7kfV3cgJwHJEy8ZyRCJMOlUJY12m5NLOpufWrRNV+vYFKTHPaNgywlFYLPpkKImP2xgc+M5OhsZdEsXS9gAUgfiJ2KYZ/4fnLdXcWGbClFrO1XqGcVds7tYFZXmJYlSZhphcoUCfqaFEVisurrGFwhBGRUezFKw8g+GXQAqIsMVTiNFc8L14CSgeTsQ8QgWRLPQEoB7yMzV7B82QDzPR6iIyCgoqoWAKp/iZBaWwCk7JuJPqXWWXZ0dT2TnSAvbhlp+z6VJDbRhYAALdRY0pKhQUEiGaeJlDjXY71eolnOKXN0LjX9p72K9pcVcwDEaRj9KEg2HZXUXFTL6axLdcc6wvpaqKuyfCkAn+mnSsFf5+sm0mWyP9dhE+dBj0sI1garOQY8IgYzPM2DSzxCCEA9yRqkFPSA9X0Day26bp31AvLhjg83/04keZC4d+yBk9durg8JdhAj55gb1jF0OnyN6jsBQEgkH2SGLYdY+WvxlzcgsA0HJKm2Qez/IUMLMqTNPu2VZ3+82CHlP7OZcavYdsEyUEye4fqC0jRWrKgLGm8loyKOEFFVCEPtF1ndkrPoaG+Fkgmmtj2x4NJ5kCKKCwBWDA+i8CIGM1ffU25d4fMmlYYpKhhToG3lKDA76ZUbsiFQG0OhfM2b+5MHUck5MlIRx6Zd7WhiX2kSbcigoNhfS7Ugn7JVzhirWY2zW3PM6xrzqkSpDbSSqEyRhLKv1fdIET3X4eJlgMSiC62SMklqjXAOJqv7nfiec1+xGz+fuWMhooiktiBHwvlBBTgtIYIY4M8MMmUjGQKFXjrZEQEnBqIIv19+W3JUbPj+5h4O5JLceLPsmY2i3VxOGgQRBqGVzbJNCBSU0Gi7YZzdSa4QAvq+Q9uu0DVteiY5U89FART3qSuFEEhWtG+6ocQSiD+gjYbrLaRWaE1PSkF8nsTApB7Zsugk09fDmDilot3WmfxjLrHI94hbk/he5MzYEKFuDtz5vZxzsJFXkfqMbxCQ3NBBWtej69bRCXp4Z+OHCLDWx4kMBLEppVDUBeoZzXQDkEg9rP5Ojc9E7hAS9AAImiUj1TDbcPPgchaz+bU88yOSxCCDlNe9hBRJJ5Q36HoYdF7zCsLH5DeLsmKNCqfEGeH3IhUZGj3Tgor3rneja04QXIyGaJ9lqksR8SkevphZFlWRZm+OHXA2W1KRBiu6PjUGs1C9UBJFaRJE3TUd+pa0HGVDo87aVYtm2aBrOiyPFuDJHGVZR8cgqM4JMm5OWgQv4MNQf9C6QFHUKIsWAgJNezpCAZvkn/xcpbmEGIx68DRL9CrnkQVkTICi0kw+yQPp+vLSgjI+ncsQZ9757Gx66+AsjYzTRmM6q7E9mWBaEsy1PQmYFEXSZRVCEYQVkAx5TsXnwFWFgFlZwXn6/eOmQds7OB8wryrURYHyhPe7a2msEk0vIbakEC6xOEJEhXpg6JcW2dADH+CMhigFsJEhU2YcSTkhkGZwzFAKpRI0mrezsESalgF9hFzzSRT88957NF2fskV2tm3Xw/XUZpALaEgpSQ80Bj3BB/Rwg/PUA1ErbGgQn/Sidr2eSiAu8iviHvBMUqVl6oF2lvoHQyD2p9SKiEZSwjmHtunQPnIl2YuijizXwqB3DtOyxKSkmaYA1RY7a7MAbghqbOxhBMYTQDjjpFmQHl3fJyKP9x5d/L1V16UapHcxEcuQmIRmeUognKMBAAyzPt66sYO0Hfq+hc5EdK3t48108H5KF2YURbjTCtWsTlngOCKj/4VoWH1kXo6cXJo+wfWIMHh8ebXHz2twm8GAoHweAEFbQmS1yWhISIpuDJfS33MoVSR4OKhwqgxWXpwtkvwW0eEBzrSigLfSAws1zxpj5shEAIZMyGFS7YOzFpdBLIaZf9yiY2NmqKjdg9tHhKRmZxfnfHYNDSNtmw6rwxW6pkOzbLDYP0bbrrFcHiUnZK1NerLWDcQTISQC/FVBj5IK2hQoyvrUGH6jmjVwFYFrIHr5VJPMDVn6PQAiqwPnkorpzORZc97Y7n369iiAwxAIcT+u7S263qK1PQmNKzVQ5nO25wYDlbOqzZmFJvazzeuaDE4gSn3b9xACJ+4gQ6zn+tg+5SyhD9cCu/gZEDbLtqNASO8sjFJXGbH8DPm4J7lO6ygj9AFKjLMJ/n2uf/HXfAgjA+0ClRfadZuIbANMPs7K+DVYDCV9XYjI4FYIYSA9nvTKp/0AMfCQ46AfGM6kkGQb8lIDZ5q+paCmW3dJGpDZrmVNtqWbTTIWMKEZRRyenEOhRilY7waYFGH0fsCm6g4Ff2kiCJNHY5LWR0fL9XfO9PkllNHwnur9m3X/a60bOsiuXaMtVsgnLgyRiIWr6YYWpaHZbttTTOY1pbnOjwaAjuqHnCpHAgrfNDb2AFIrSLppmYpG0mzcgBnp93iop6ChvohSVpmDzSdNBLnRJk5hCzlQ/uzxPUIYmJVenK6j9MFBeIJAvRzknFJ2o4cJ31or6DjGRhsVhRpUkoTixmSeuMIMSed8gsQBjATfpRTo2z5JSuUq/kVVoGv7kf7u6nCFg4sH6NYd1ssVFot9dF2D1eooaZFa20VGXQEeYcUOEkKMr0/K2GJkUJWT04NZrwnfZ8gF34+M1MUrd4ajr6mrSwXAuA5LBKcBCUmvm8Fv5Axkan0iAhFF950l9h8jOvniHsiBUYmUUcYRqmkVSmFaltidTEi1JHgcrxqsY11y+5Pf2WsuGpoeW4YsiSEEczVRjvaIqrZ5gCx6yiatlOikhVcKKifNZEIBPgSoQIpNeU2RSzAk0i4SO5mJITmjFxjYqb21sIwi2IjurNrI7vcpGBViIHxBju2EEDwUQGVEJZGyvNNYSipADCUQRjX4AwUfEIQY8TFsb8kumHE/OjvH5dEy1SaVVphuTVBOSurx7akndcjGBSpPZQC/IWJunadG/xBQBg2hOXskLkh6DgXVNekjC/iICJg4O9eHkBwk/d3COo+m75MP0UYRMTH28T/pgcnOE8OxLCoEBHTtGk2zQt+3ybgZU6KeT1BvTVBNK2pyjkwhJm4IQS0DDF861qY0KkEs3jl4JSF9zDIZyvLjIZlM8uDI3BQGEPnA5fE15A9eitpEGGW4ITYci3RYh/cChnwyr1Nsih6c1HLOQik9YPBKQ3oV+wI1+r6hhvxVgNYGSpEGruksiioWyOOh9t5DgWoKNLWddCD7pkMfZ3XyYnEBUxoghAiTtukepv3wAQcXD7E8XGJ1tMKlBy9hdbzC8vgITbOk+oa3SXx8Pt+LUzlCko6z1oLISAOcQntLtUgASULQmAJlNUV5Wg5SCGjWlYww9CZRbNijgQafgq2Nujf/ffPf/KfMpsBwy1GeTQIDiYw1i2fbU5TTCtW0wnR7isoYTMsS2/UEdVFgq66j7FeUmRNDGwO3K1yrvQMY2IXnt7agJDVsO78P58m4bMKYT3a1qxa266ELDdv1aFZUH1JKRcERaivL5SY3R7YBBDu3okdLMx0GiNIPz7KKil5KSvTOkRyfHvoqO0tGNMRaGMuWcR2MGa/5XvkQ4CXgI+kjTwLyPZZawkQ5NhN/Ly8VlXWZros1Wdt2daJ7zauq55hM5jh34XZsn92CqYZh5TxtxztPo65iKYrlAL0PiU+iNAXjIXYptKsW7ZqmgKwXa5R1iWbZYHW0wvp4iu5MB10YGE1BGO8pa+YyRNo7h9451MbARsGATWUekznWdH49UGjAR8nKvP+1sxZt1HRd25aCyraH7ckvBXdjSPsJCQUIoai53Lsk+8XGTSlN8wqlSELlfduTiEBnB+UWRVqbSkuEEG+OV5B6ECe2uVAxkJrT801JQbaPw2MlPVxCAF760fimwegO9R3+OiLMOtpsDIZsM+oDEKNel2aK5YSZk1zBO2qeFx7O8YikOPvM9eSEbB+zK4IrpdIwuoA2BSbrDmVdoKyJ1IEQoAuNPk5ZUVqSwMMGISFpLJaGhmBrFbPmoebmehqq2jVdcpBHl8kxknO0ycHx51PKwPsKOQmHz9G1Vk4CE3Hqvdbm1AgMXFdVUVFIqSH6F1nkLGMUuxmB5c5vM/t8Qiv7nfw1xj8SIXfr0DcENSspI1OPyTcbDjB7ltTG9/L3YAiyNBrTsoTzHlt1hVXbobUnD/n1bZ/OHwfBlBF4GBg44ajuFZ93kYIM+l+CA31ASKLbw8ADzuSkkghFgPcSnslqMRhI2TVCIoGwE2WFojIEAJpE6EGBN7ODnffw5Vj4gkg5g71L+xuDdroHAsYrOEPzc01pIo8jIAR/ahDr9vZZnNm7DefuPIf53hym0MlBSK0i1B0zes6QoziFLjyEJT6G60mBp+96tOsWtrOJ5QpQ0JLGEUpBfAfn4QodZ5h6WCehlYeOAaj1JBXHY8yEENAbtkkKpNYcYKhL5qQs/roUIvWf5ixnAFTfd2OVr8dbT8hBKqWhtUbf+5gd0IsbQ82n2hBkt9g/hncBx1eOIgTrUFYVVDwM3NRcVEVqMQAGKCUxlHxIBJJNY5FDUnRHri7vbPZPJr3HawgVcMTpfRwwKzODJyNsG9+vj9Fiu2qfuPH7JJbzDtJFUgJEgl28s1iuDuNw1S5Bk8kIRmc0m+1iMtnCfHsHW2e2UMVe1BACioag0bR8HFysJKpplUSI18uGDKfR6BuqObfLJg1WXR+vsDpeY3W8wtHRpdjk7EdUdWMqGF2gKEm9g0kYzIZr21UyDN4P54pIXzlFXqMoahSnNA+SMw1d0DlN8zJZgo8Dtg1WZx50AWMBims9fMM1jetgbNzznwOoxGBBZ7hdtwRttT26poMpC7QTIugI0IQPo8Z1plz/cnPlyjK8B0ZpmhQiJTrnsC8WQNMAJxwINs0S3XqOybymYCgeRy4XcMLGLR5ItoCKLMGHBCszaSoEJGamdz7yGCTNK2QHoBWM0RuBBNWthACsE1g1Lfq2R6d72JJJT0VSFZqUAxejMgbWOywnbVKY8nas6BJiK0IuXCAMneuGORvTMkGs1p4OxHrbbc/A057/NNz1qXdDaZWEJ0IIpAXbdAiB9I45AWAOA8n4kT3vmh5Hl4/QNV1CCF3vkqNsl00qRXgfCLGyJJWojYa05LCM1iiUghISnSU4dtW1w9Duq9pDBvEHAEmcfHNotvM+9fLmbTtKS3g/EJBsVPG60XpCWqwAoqQYt3k4SCFRVTMYQzf36OJhFBNosVjsE5HHWZiigpQqZgBEYy6KGpMIx852pii7EqYq4JyDsQbBeRT1takBXMOxURophEioiJuUYMSaJkgH70fUXsqCBsbYJluRWVrwAxU4IBqrjmpt68V6eN9TWN47mi5uSvjg0XUNvF+i7zus10e0B8GjaQY4xpgyybKt18dRYJ6K6PW6hust2nWHojSoZnUcVE31jyIOU+bMKWDIupWS6IH4+y26hpAB2w/SVLPZHg1lLgyqaTUy/nQ9EcaO6jB91yP0YUT88t6ScEAg6J4IYC6qCSkKxNTpZJBSxhprHNzNE00Y4kvqIg6jQG5z0Vnhax7qiPle8M8JObCscyOfMxmHNioRpxEgkUGYwr6/JGZvoXVq9OdhtXmrh5IShVbXHEibLxPrkefmc2gpURcFcHBwUlsd98bDWZcCE4YilYlMaW5ZYrJZKn0Mvx8cTQLhVhgd6+x5Bi6lHEQZ5CBg3fY92p4ckY2fBSBIsVk2aNctvPOophX6aYUwzSTqopHm7EcJarFpEQdcd22yL7oleNwWGk08VwzxAsBkPsHWmS0sDxYoiiqNeDuNtbW3jXo+Sfup4lSOENEldorNsiHyTdSzZYj/4LEDrI/XWB4uEyehrKg3V0pC/9p1SzNMlw3Btb2D0grVtMIkvrcpzShIyQUXyE4jaem62DZDJMChxpvbcLZBXO8XgkYsFlWB2aRO76OlAgrAT0o0qxYA8wcen0dy4z5IOcz74giH4Bsey6RiNtCgbVfouxar1TGc7eGDQ9e35CCVTpquZVlnNRbOIofskD88w5z5Q+z6QX6LI0aWvxNSpGiGhWhDIDLAiLkW+5TG13l9CAoATR6ID5O3Ljno01hsJI0pU12u75pUDx7EGzowSaSqpmnqhzElNbYHH+skFm3TJfKTTLUFYpVZKaEMGRzI2Ox/jevLoSQhRbp31bQiAxXbRxK0HaM0hmdZos5ZRzqteb1GKChFsx+J6TaOxDmTPI2lVKx7RRLMtR6aAfrfYLBunBv+c9Mx5kQDQi2Qgqx8X3PoPkiGdB1kLxGch7BylKWsFjQCjHshC6VgtE6iAaSUI2PNbRCD3oSJ015EB1AXBUXyQmB5wg6SEYK0h4L3fSCs8LnmTJCDitwBbqpaqeQIB/uRi/jzYvZp0nyNAxVMZSh7XHewXZ/YtkJEIQ4MGq8Ms4ZADeld26NveqyP1+i7Hq53kTAXNXNjy5XnYFRKFEYn1r8x1PN7WkFgWRdpz7iNjPeZR1kJSTrZPvYLWmvRNT2EbLA8XKJdUXZtYhmmmtXEeYiJiDYKbdGhXbWx5Eb7wWe6qAtyyIaUeqSUg8IQ4ggsS+ceIJ5C3/RYHi7Rdz28HWb79un+IAWx7LiL0sD1liQZ5UDqUYIHOFyDXX6ddUOLwzPKklyP7eC9g1IaxlRRXWeN4+MrBJ10a/RdO0AgYZlgQAEycqw72LZbAIC+q1FWHcpphaIzCdPmqE/qQUmfJoi4VON0vR1BngQTmhHjkqW6mFTDYrwAEgmgiMQUNuSbcKxj5YwQEosrL86f5OKeQc8jnoJPznE63UnGtK5nCXqcTndobqTJehsjfAogBQ5CCFhDxCfZu8ROk0pidbQiR6Fluj42Hsyi5GitqArUsZ2nntXJgHGkR/UMfth02lehuqF9JYpRc52bslgDKW2E2jsEn5GITslBcg8X9eWp9NCQo4u9sEKMHFnOqhwFUxsOMUcaCBoc/+im48xbSXiMm3Ry1LNF9WIyUu26RVmXON5ZoJpWMEajLgqSANMaVWFILSfKf1XGAFHpJB8kzK/Lf06KAkpKTMsSf/Xwwye/6SFc9b58/YiOcVMRitnCwUedTzk4UWU0dMz82TBygBDCID5u45SK1fGKnolIJhRCYLI9wXqxRrNssD5eQR+THZlurQfpxmyP8nvbrlq0TYfDxw6oL7izJO4QuQCz3TnKSYnp9hRlYaCkgJYGk60J5ntzzGe7aJol+q49+b0GYMoC6+MVmmUDU8Q5m2EQJFdap332LkRxAHL0q6MVlgcLKKOxfW4buxd2UE4qzHZm9IzHQGK+N0fwwOp4Rfu4WOPg4gGUkjBlAe8C5ntzKrFNSHFHKxWFKUREBocgum8pG7388cvo1m2a6eh9QN906bnRCX0wqOckjzfpHYSUSaKxiP3g5ER1QrxuNELvhhbn1lvuQ1FWOD7eR9+3cLaH0QXKcpIG2xKsegDvybAV5VAr6roWPFMyhADYDn3fEEuyWcLaDpPjLVSTGtPtCcqalOOLqkyMtqIqEtzk7XCoeRBm1/ZJlaNr+6tS7rwdwhRmNClEKUVQYwhUE9UhzkXLs4EslY+N9n3b33BzP9lFY54kfPAU/WsDGXRisdJsSJUU+qspPYA83ZudFYAUcXFNIdVrfYCHi31ikS1aGvjY95gy9AzyU1qjqEV0dDJF58qoUe+gdyJlAkErCEljdFwUPA8uFo49jcVytkeuJsJRu41f3zSkJ710QcpCmnUopUjBGRGUBvUQVg3iMW7MrOQ9BTCqh23WIzlq3TSw9HthIEmEkEQsQgzK8hYTUxCM1DYdyrpE33aY7sxgSoNuntAZQAABAABJREFUUqItC1SFgfU+jSOSAuhciWkgUQCKqgfIdXN/lSBx6JNeZTmBKYsIpQ4N3d5RzVVGBReuJ9pIhOGMjyBQ+jcHwpoNn5Y0Hi62Owkh0t4RvZ8Ct+XhMk2y4GdfSIH1Yo122eB4f5Gc82J/Qao+WeY+BDRUNmADvhmst6WBifepmlbo1i0F67MJdiYTzM/M0bc99m45i65vUnB80quoCvAINRYTYXKaZIKaoWkdtqLPMJajI7s3mdco6xJlXcTEBXBOYL3o0nO9dWYL9axG13Q4vnKEbt1hdbzC5Ycvo+96TLZq+Pkstct11mHZtlgerVK2yQz65dESx1eOE+rHCF6fjYor65I0Y1Xki3hqL1weLJPjLCclJIvURDEHqcQNy+s3dJBlRUIAfd+i7xv44GFMGWWRCnhv4Syp7fD0ibKcQAhWZVklsQEeemv7Do1cwkbyibU9um4av8fwJWHjIfbYQMfGdutTc7HtSdaJI0GGWkeMsgiZKUMPTDmtEpsWHFHEegcQ+2uuRf8N3JxP2ei1WgBOatF7503immpHkupkxlTQukA1qZOg8HR7mrK/lHlHh0gPRj+SXgoBCC5AiGxoajw4KgzkpTzST71QiqL0XCjewyelos0llYwPGaEBymiYaADR9SD2Hk07J1ECnxRhmNADXN34flJLKZnEmvl6RpB8hJsZOsuzjlw+kR0hz3Dc1J0VYugzzRv66Xux4dkNDees54oQ0DV9Qj6EFOg0qRf5aAw4subgrZ+UaKsCtvapJmkU1SB1bJpXUgJKQSIAkIktmn+mQRD85FZR0OgkqQZINW5gMpBkC2w6h31Elfq2w3rRJMYqzzPlAFhpamcaVKQGVZjEW/Aey8MVOci2p8C5pPp513Ro1zTpgmE4LhPw/Fte7MRtF/vC4znV2iR4NZ0LP8z33D63TQGZlJjUFdqdKWa7M8z2t9G26xPfbwDQhYZ3GRcjouzJVsgBHdJawSniLLgItTJaJKSkHsJ20DR1zqFZUguWlBKTrUlU6pLo1pHAtGjQLNYoSkPjtfLao3Poepugbdu7lO0uj+jPBKtHMhTXI/kZ5CCJn2EXa5eyEykw4iSr70j1iO/N4+7bjTb2ypWPo+9b7O8/ir5vYfsWdcXQXommsfDxYstygrqeYWfnAsqSiDfdusV6vcRisY/1eoG+b9A0K1jbJ23AYnkIY0pMj7cxqeeYTLcx25mhntfU8zWthg3KCre0oZbS+dUabbtG265SLx1j7VKq1AIxnROrkx8ghmABEMFnNGSThQdoI7lnabI9pX6aJyBV9MksOrAqOceiqCCljtljgXo6Sdqc2miobPK3ypwXS/+R+IJMNYTV4TLS7EnFf1MZJl8JVmRKOGehQiRqOMNeIYQUdbreXhVx072QMJVJ9QJiv1GEDcSMvtXJSXENFji9vlNdGpqUEWH4HB71lmS1uIWJCEoUlLFdJ8LNEGR4RwEJByMqwjubwVr+gAMZQzuiIal261m1JCQkRimNZiGxOlqnuks1r2EKqndNItS0dWaLepRnNdUhM5DXREIPQZHXaRU5hTXdmZFoRTbANoThjDjrAeQMyR7H+4vEnF4eLOActUQQmjLI9rGUIvftcXCWoxBSyaQtarueFGCmFYrSJGb2xY9dTAhG266p/7tdomsbWNenUhMTtoqiQlnU2N45j/lsF0UksPDnYNhxdbjEbGcGqRTk7h52p5SAnLntDEHgs/pU9ryoDJSuUdSEHNjeRvuhkpiBVNRTzhl6u2zQ9xSYlXUJ2/Y4vnIcIc8Oy6NjkqA0NGGGg4L18SqdudnunNpIYiBHjtWhsw59bGlpLQU/y8NFGt68PFigj8zYalpRYB31WbnEwMQuThIYabSWW0+6pPbFyKGQMjlMYEB9rrdu6CCPji5HmKKHcxa97ahxVxloXULKFkoZ1PUcdT1DWdajkVgAMSwnk21oXUTZujKRePiA9X2Lg4PHUrOsLu6k2lCEv6RSFAFFFZFm2aBdNujaHuvlAl3boO3W6Ps2GTelVHQ0AzPTxawzQbZOQjqSiwqBsi0mBgDDlPeBRUfMOB2N3GksrvFKqaGkgpQ6jqsheJXf3xSDco7SMaOTcRRVrJmQQgY5OGb1CSWBWB8MIUa5PkRpqfG8SCGBYEPKpNLXhUiwk+1tFEpXuPTgRayO1zg+PCDSVt9ivV5gNtul4OnMHo35qUyikXs/QMNKE4tY9jqKIvD9dDitgcmK5Q3F4KhcnFPZxbYeZjBzH2geAIxYlfH3+mhAASazxQCn0DDZ+aS9xMhJ5vVvgsGIcWitBaDR9x2s7WEt0HVDu485XKZ2lTpCYetFg60zW5jvzVHFFgUlJKqiSGzMELJesiyj417Bk17VtIKpTOoVpPKFSgEHMeYJSuuaDh0TNSKbURcGwjmIONev7y1C28JmQ7UTASpp+w5DyJVSo/LI9u4eIES6v1QL69E0C7TtCqvVMfq+QdcREZGC+zZzkI5Y1rpA01IyMD3ehhAC9YzEHYKn+lqzbLD/6D6UUWhuvwApBEqjUc9qzHdnJ77XvHhUWgo63VAiyCXxhEBsxWKWsENvOxzvH6EoChR1geXREuvVMS5eepCESqRCeXGKra2zmM5nsF0f500uoYuB+MQlrmpKLUkAolBDLPUoldTX+tiDOdudYef8TpLJ9I5ssakoECxKg3JaJWcZAgmcMOM2BKpl5snMwNBXI4Wga60bOsjV6ghFUY2iaiLoUM1GawPvLYqCYD8pNbpuDWd79L1JTEytNYA6QUvOWQTv4CLJp+87tM0yTY7faS+kBndTFiR7FqU4vQupH7FtmpiZtuj7LmH4RPrgFoEiOvSxEsMgCu1Tb2AIIdXygEE5Jb4opKRanIr059NY/JAPUms6zuWMEGqsm/I8zc3PwXqp9FoR5huNtGE93KxlITpAKSW8zPu4OLNBymz4HKQI38YIrZI4PljgcP8SHnvsAazXZGCOj6/gzJnbsbNzHkpqzHa2Un2Zs6g09kgOEKTkHk+GHk/JQXJgMboHkR3dx75D21nY2ODONRCC7YZ2GudsMsrcFwqQNCO3OAkxGbJjP6iq8Hnj+8HZZBJczlodWNiadXr5PcWSnIDWBuUh1fMpo6Wa7nRnCiUljFKYR1UZAMPQX4whX2AsMHBSiyHQHMbmP0VEJrwnQ0dIESm1sFMr6oLuj3boW9rn3vUJoWJbQJKY/RBAaAq0WXBCCLINdT1DOSlj5opUOuj7DqvVcWpbs7ZLDtJGXWDu3+37FkqR7WuaJdbrY9T1HM7tJOKLja1Sx1eOUc9qNH2HSVHCKGKz1vPJDTOaT3axKAHD9mmKSkY6QnY/UgsS6Iy17YpgTUWTdVbrYxwePhZbUzSK9XES85CK2pL0skFRl6nroIh1y6Iq0tnrI2kqH7vFdUaSL6VWmKIqIkzs0/WUNQXak7oinVdBUoHHiuasMmLGgXx+XUKIyMB9fNbwDS0833genFxVU2xvn8N8vofJ9pRGnazLSGbocNwssb//CPquQW87GF1AxKhN6wKFqTCZbmEymUNJneCKvu9wFA9y8B5lXWGyNcXWmS3MYmS1XqyhtE49ZNY6dH2TWk+o/kmvPdvaxva5bSriq/HUCs4emWzhrKNmeOvRdzY1tXrvUx2DgusMnioo6juNxcaWlIqIjFNUEU6NNRaS46KRUt46eLjksKpJBQlikpIBGeqAfFjpvz5OV+G6T0Bf9CgqomO72NvE9UuWCLPWwbY0985ZhyrCU/WM+p2WxwXW6wWcszCmwq23Pg3b2+cwmczJeTQd1XbKgiDzbmADp/l4AIqyRte3sLKHOyU4GwAFTtFA0NQXn+qMfUPiEDY6SnrIBuINZTBd7OO0KcAjhGQYVjwEl/RA8nllzVYdyUHeIcHefXx/ay3a1Rq9JaPfdU10ih5SDi1Y6/WCAlPbEeRXTrA8uh3LoxVWR0RYsOctlBQ4M5sBhhiu3NsnIEbiAuqUIO3J1gRFVaT6Ng3zpu8F77E6XqNdtVjsL2B7alDXhUkEPuq9o8/GfYtHl47QdWusVi0uX/442maJtmvgXJ/an5QkxSmSZ6RBxbPZToTpKmyf3YaQSGQba7soytFSsK4LhOBRlhOUZY26nkFAYt0sEoN/vV6g6xocHl7EanWEra0z2N29Fecu3E6MysJgvVjj+MoxjpZrTAqaeLFzfofO4imhUjy95/jKcXJGE1cnghK3yPgMwSjrEkUoMME8QcbT7Qna1R6Or5xNGbSUErPZLs7ffhu2zmyRNm0kUHJAaXtHpRWlUE1oSoxWCk3XJ/JV7ryqaRWlFct4BnycFNQl5KaaEBIx3ZpGYmeBuipRFgb67DYAkvtrls0Q0Ea1NkoiytFM1mutJ9QHSZkh18RUhFNpqjkXy3VTJEellEYwRcy4VCrkag0obTCd7qCqppBSxUG4k+gQRNrwelaT4Y3sQu9Dgo/4ZjGTkHQMO3QdRY/Bu/QaMrZw6MIkMg6/D0sl9U1HNblYc+LXlTKyMDeiaCFpgO0plWjSvqso1K2UTtkDG1QhiE6NaLC9J/hmvVxhtk3TyVnLs6gKbJ3dSgSSEKhdxsYRPYUr4nXR61opIxxNhXOurbXrlto/Yg+S7QeKd55dVtUUFy7ck66B21GYUcZBC8NoTDzh2iaQQ2JDFnlaNTGlhxYCIcYC9Tw/k79vuQ/XDvVBZD8fQki6mn3fkWE1NLbLmBLO9fR3W6KeTqhlJjId+ZzxoFqqrTdYLxdYLA5gLd0Lfi9ui+GaNe2phU/9shpK0zO6XjS4/NBlGpfmA7aqGi4EFLFnMo2aE48vJHASayCQsWRczJi9T86pWVItiocS1HMTHYxOjNS+6VJt8sqVR7G//zBWq2Msl4dxfi0FXmyDhtazYRpOVc0w255itjvH1tktUt1xDuujFdbHaywW+1gtD1Ow472NJaUZ5vMzkELCFCWKooLtO2xtnR2dexZSadcNgAr1rI4kvwjpek8tORNiYp5W6xi3w9jeJjm4vrMwsW5XRZZnHwcQ8FnOW2yKusBsm2qm870tqGwe5Hxvjr1b9jDZmmB1tEK3bhMqx3VknvakM9JYX5gk5sDPk4tZZdd0WBwssTpep+yP0BCayrE6XIFJiNz2NNudJ71pG3snpZIJBWOyzhM1JTd0kAOcWiQ1nMkkEl0KDW+pf7AwFaztYG1HLSCmgo9QEEe1pKJTYWtnD2VFDlQtBpKM1iZlTpN5dJBVQTcIPvbfGThLrE2CoASapooGqUHXNui7Fs47VNWEnHdMx4u6RD2jFhTvCL7q1qRPGlZDDZJmVQ6C5ZuLi8Q3UmH4ZBcbP4p0h8ZW/h5AivrLA1LTd44yl8uXH8blyw+hKCiz9d5Rtj/bhXN3JmhPGUW/11tYo5PByJVbmLmXCt12MFy26wk2spTpErPTxT6lgMm8xu310yjTLQg+sp1NDxxnDcvDZWKpqqyhd2DWcZCg4U9Rp5Kl9pIzSHtN3yuEQCg80drXjPNT7cklSF9CSpLJc46yuaZZwjsLqXRqiwrewVY9BZlTUhcp6gFylEokaUDvPfyjHuv1AsfHV2J26mO9ywBgBSQdodUi1vMVjKlQljWhCUrCdj0OLx3Gew1c3J1DSol5VWZtIGMG72mxtLkFi2F1QBF7vRvUqto1iWAzqYmlKYWU9P1Vi+XBEgdXLmO5PMDFix/D4vgKEfWi2hfXBvlauNdWRg1p4kZsYbozw3xvTiLeEdVoli2VC67s4vh4P7WwETGoQF1vYTbbhVYGpqB2K+csqmqKoqiTvbSR4W9tB92bKBpQQGnSRmYptLoosIyCAqexWB2KWdgAojA5tXlV8xpFXY7sXy7+0bc9TGnSxA5nPbbObKHviAXMSF9ZFTjeX6RgGqB2kXZN/Z3VtEKhiRxWGgMXAipToDFtCvbzroQ8geE6ptIKs51pPDMWBxcPEhFx75Y9zPfmSWKTdX6ByJpnTsamZOl11g0dZFVNUddbCaJQ2sSBuwVklBEyFfU0zd0WGdOuTyyzvLm7ntWoJiXmZ7YSpJJThc1jOmUTs905yig3x8ohNFZFo5pUUFqhntfo1h2xAh+j92nbh3F0fBn7B4/i4sWPoihqzGa72N4+h6qaYrYzw2xnimpWY+f8ToQTp6nGwWQLdiQcQQGD8kQI4dQOMoDkCLhtBhhrdj76wCNo1gtc2X8kk+8jibfpdCf+LjnY+WwXRVlhsb9IbL+ioqbhrm2hTYFuTYzgiZ2grIqUFdno+GzXR0ZZTwShQmO2MxtH41mjLrODBUduMTNwqYeQoI5yUiZRhmF/EVV61NCrJSQWi4NU0zvppdQwzYBrgtpoBK2g/dB/x3UM2/Vo25ZY3ZYefCkUiqJOjr1tV+gixMeTTKgWaSBiXZzREtaA1VGzeOf8TnQgGov9BdbrCYqiRghFvLc6Bk6cYUsYU2A+P5Ou6cz5c9g6t427nn1X6i8WUqTB19uTCcpYk9+c/A48vo7rSSyueQ11SCI9NcsmGbXJ1iQ50+XRCt26Q7Na4/DwUqo1dt0aXdtgvT5GQIDSBrXKdJxjBq91gfl8L+pHV1BKYTLZxs6ZM9g6s4XpzpQazIuSall3n0ffdqkXlevpVTWFMSXm8z1M51soqwJ7xdnIB4g92xHlKSclEAJs73B0+SjWvQjNme3MiOUqh5FkuSM5rcV9oNQG0aOaDOMKRUn3wkYSo4tM16IqkmCI7R1E20MphZ3z23Gu5BBcO+exd+sevVfMAptlg9XhkjLQ3Tl2JlNURQElqA+X+QzcY5xY8xjEz02soRalQT2f4M5n34nF/gKHlw7x4F99GAcHF7FcHqD6iynmW3vY2bmAM7ecQTmhSVPOuuhci3T+bGdvKO33hFkmfKAJtlFRlktCGg0dmZ+MXTPV3blhOgfhyiVMWaCalCNSRNf0CM3QWsHi5lKJlHInfFpSL52BiRtnYh9YgIiTItqWCuXkJIjcQoX0Bsf7dEDaVZv6n0xB+LwMNBBZOMaph76atAfZn6cF+Q17LtM1kfIM9eJZ28EHh8lkTk3XpkRR1JhOd+C9Bc/uVEqjnlUpAmcSgtIS7VoBnSBmcisibBwzkphVEtxMtQRv3ahv1JRm5CC1UaRYEfvSEulEBjgbJ9grD+cGBRkhBKBkajEYrptYxPw6qfgvT4cUlfuAYeILgDRXMCQnTg7NYDKbwnYFrK0TIxCCBKe1dijLCSEnkaTGQvKErpTQuhwCguwZyZveucxAQeosI29F1SGpoRMBbWh3qKYVztx2Bjvnd3D+7vOYzifYquvRKKDd6RTTskRd0AgiJU43a9xcbD+AsepQjmaoUqbmcA7o2m4NIUSUZqtQ13N47zDv9tIgd4QA523Ujm5S0EKQZ5VYxXU9S0QPUxgoMQiKl3WJ+d4cu7fs4coj++mzcv2S9pt7qXm2IzPeaR+ZWSklEVMYeZrGmbmmKpK03yjLOYXlvYd0IvEQHGdocSYsQKO8qmmJyXwC1zssDhZ0rzSJf/NK48gyjVtOLrwcRFUAKlPxjN9qVqOeVSi1TgIVAAmMc4+m1tS2UZzdHkHwoyB8VmHn/E4SgiCZzStwzqJpl1ArShjURUKvnPOprKPNEDzlAfH11o3FyqPUGd14oklThKRifaYgqr6rkqFJqh/eZ4aFoqe8qXeYYh3rgb3LdAupTywNTM2MKheUTWUQonMuJyVmO1PMtufUb7Q6irqeKmqTagR4LBb7aJolClOh7yym21PUs4qUaJSEKCRkPyjwKKUgJBK9OD9wp6Wkw9J8nCWEAPie1IhYcEHrEjs751BvUXZAmZ1Jk70TOSSq3mijE8mmbTq0sYfU9h36vkcISNEvHSKkB4iHonLPJdcR+XyEgDRHkh+a3ODlgD8X43ncDDvDfF+5RsU1NiaIndq4Kxl7EUNIRB1AAvEeM5HLR71JpUmggaNxIiVQps3XOp1sJSENIUQSephM5kkeUGk5iC3IwVlKHVs1tqgPuFnGehqzPUFtEVqbxLjmWnM5KbF9fgfn7jyHnXM7uPf2W7A3m2GrrlDG/fMhoNQaRpPc12b2mK/TCAL5nvM5o5mvwwB1hrtZPLxvexweXkr1V97DVIKJBpplDr2jGnvXrXF0dDkxWut6DhPrwVR3IxWqIg4aZ7KSkhLzqkJ7dhshAFcevpx0nZOxZs3UMKjpOOsgOpv0V0MgIYPJ1hR7t+5R8BNZ56Y0mFVlmsDCggynFXS7uMcJtux6Itx1fey3RZwFOkG/Q+WQ4ytH6fMwMSpPDvh7zFcANISk1pxRfTk+55P5BNv1BJOSCDr8+85Hbdw4pKKoS1y4+3xKTghajeP44jk/s7sd+9kLbJ/Zw3J5hOXyKLUjdl2D/f1HUa5q9E2HrXPbqe+TGbxC+BuWyW7sIBGSEADBODupeZ/rejk9l6fUk9GhyJTx4TzyAIAQx/csDhdYHdLoI4oQ6tS6YK0FuPTkw8A6ynQwvSMolvq/JqmOAYBU5yO8xGoW/HdmeK4XDSDEMENxWkJpyrroJocklssPCMOEp7Hyegk5S1DDbqx3TafbpFa/Oxt6RSsKVNIw2WjYhRBAjOxYUGB5tIoN/ePeIK518uieECX32IBTVunRo4dqVOwjiq0HUcLJaQUviKYfckQh6+1juJgIETF6ZG3TjYdQa43CUBbV96cDP/E1Ds5xuA8sDsGBB3+dM2wbGdAsrLxerNG3Papqmto/8qyoqibpQTdlEVtd8vccAsHZ9gx7t+wlB8a9l+26TRF1WZcwhYYu6bmpZxXO3XkOF+6+gPM729ibzTAtS5TaQCsJHwARwmh00KZz5K+fRg9kfo0peI51JW1orF6SkUw9qD3qeispR9WzOukzs00BhnPVdz3aZYNmSdl711EPIyERhLbUsxr1vMZ8b4vEwkszgppLY7CzPYcyGrfcdyuKukQIpLnq4uinvu1TSwrDqqTIJKMOKwkQ1LMak+0JTKFRapP2vjTZe4LEvdvl6QwFb9cdlFaDdqpjveRhvBUAlEZjZz6FUgqXH7qM4IFu3WG2PYWpisRw95YE2jkIt10gCHzJKNVQ1w8hQGmJSUWMXRkzdQ+apmKj3zCFiedBJPGG6aRO+rm26yG1QlmY5GS1lHj6//F0bJ/bxi2P3DXiEiwOiNXdNCvIfeJd+PmEgqKqxGR7csNOhBs6SBqb1KeISfLIEmaGZvqfvFiGCxi0Kqlpf+j/AsjxcMG9jSNIKFLQA/zifMoeAWrqRhIpjn1cZZF9n9iRnEVJLVFGog+AZIiDD2hXTYIPeYo8Dc7VKdLz3kOE2GuWZY3EZjy91gOZPaw0bNqAm/qZ7s6tHpx5cAq/mbXly3tPIsRuGJhMEbGMEVsGf2b3ML+/wQ3OV8YmdqUkMeIiMiBGcxEDBLNctYKKzlLYWAvoo/P0IWpzCng/XLtUKmXTp7F4q5jFCgCQ1ANKZ1bFeuFYgs5rBRHrZaqXkSY/9HOpTiflmyTDOCmjWLJKRh4Yek1ZqzaEQD1e8xq269GsGnLEkew0ZN+DQlFCdrLnkQW6uygywE4vd37pfj9FS4iBlczvS9kkwWdCCvSFhusdpKSm8NpPEgvSFDqJDI/UlUIYyeVxpqe1J4k9QxrS9axKSi/1jF5PGxoPNkCeNEJsWlfYOrNF9cFVi3W5JkZ3M0zSkXGyjdIy6pSWiaDGQaQ2GkZFxnB8D4ZWmWHbR7Wm01jsAPMMNdUjewpIXEn7VGqDUAPVtETX9nCW2jVUhE6FEFSKEiIFicR2H/oZQwgopEnvJ2L/rdE6BQUhnk0OIlkmk7M8b32aKANFQ6+5L73pBrm6+d6c+iYrqi8yw55JmOvlKg4a79GXffQNiD7pSbZ59H2LpllCKXrYpZSpYJoOgVajBlcessn9TVzIzXF26pu0WB+tsDxYYnl8hGoyjVqcOglAO0fpezIIUTSAamVqMMjRSWibNblHEgTPmeNa3KZeX7fuhqhcy+QcpZLx5g16jixkTpMuTs9BEjMx1vsKgzLC0yyHlkMYzhJ9W/qs6T5mGCFm80wCYOHgrmnhIvynZUFMymxvlFbwkghLdC+YqIQ0tUNFGHwyr5NSBzCowvDDzxkWR53crsOybQDQuy6SSGKtB4DrJbyP6ify6nabk1phw6iyowsiIGxAMPlnEJnByCULdWwlakUL2YtEQzeFRslQVTZyJ2XaziV9UO7r2jq7BRNJEs2qibVzkXokWRtUp15WUiJZHa9wmRve6wqdK8k4x8+rlYLy42eF+x7ZeZ4W3DdW0KF7XtZlat8aWotcamznnxNCJGY1Q6qEIrGOrUOzbNE1JB7OJRZjSsy2iRgz2ZrG+i7VGVMGKYYarRQCpSaHefaOs3HGbInF4YLY3It1Eszm6Tam0JhkPXkM6fHieZJ59s4sVuuIoHQc634nvTb1pXm0FU/MqGY1TGUT/K6VxPb5HRxdPsLxlWOsDpfwzicbwa/VRhGHo8tHKdAk3gPZBhIfIXtSxgkzmlGuEEgoIN5nbsfThab715Ktmm5NhtKND+icxbpp0/D1+c4M1aTC3q3Uq9y3ZNO3zmxhfbzC5YevYHmwRNd0KOqCWl18kchBj7du6CCbZpnqYcaUWC9JPFZFxhbVH2O0LxkuocxLAskYcAE71baiVt7RlWP0bQ8hFCbzSaorKK0z6NYnZ8eZhvceCmQ0y5oiAqFkijq5+ZUdNNct2fECSONo/BbDrfT5knpJZgyVUalW4hwpeLB+6Ekv3mtjiigbJ5Nx8C5AxX3WRaZZ6j1cIKjHKJNqkqkWGJVJuL2Do+vpnGBazmz49bjXUikJrxQg/JDNuwDApZ5RgkuH6e05hZp73MjhUA+SMJTZ8yISS5VqpX3TDapJrBYjxKmRdKgPcmgvERKQGNdReV842nVxCDfXU13szWOnT4xrCamKVH/nQa4MSwGEyOTBI9ViqDe0rkpUZYHpNr3e6miJ5eEK2igK6souZfJcC/WOXrtrqA3CPE2hUApVZM2C76+4GloFrnaOp+EkvXWwUsKUMp1j6g2kDIGeM2I+J0ZjiJNQ4l5x83m7btPf2Thyfy6dcZV6DLfObKV+uWpWx76+WXJk7ByBGCgBUFKgrkr4HWorm2xPUqBprU1TbHjMVjWpErJWxBFqphgGBHvvE7yY1x07ayPbc3WdXXtyiyd4KKUQNJ29vrOQa5qa0SzWUFphWZaYllQbvXDhDPENtKZ+3ON1prXs4ki8LkHldWzNm25N08Sk9fGassfSoIi6v1JQQNA7h1XXJieli6wfNA5ePrpyjGbZoKzLWNoroaVGCHo87NpoFJVJ3ADvPJpVg+UBqcDtXtiFLjS2z8ZaZJTmZEH5660bWhwWFeeJHF3XkCeODaW2l1CeFAuYhc8GfQRLCZEaPb2jeY59R9JcJhoOFljWZoxf5+ta5A+eEKF5xmPMctngSykSGYJfA2AZORbAHdiVo/eLjaUjYxLrJ2zkTnpxn5aM5BHei6tqRXLsZHJYm0kQ/D3bu5FKDfXNqeQcKSiJP5/V5BA/gwj0dyTZuUFxYywGHc9CjOoBJHJC+oG4f956eBX3N449UlrBG03og6QH2mVjvk5tMZwtrs4YN7/GLSEcAHg/NDHzPgzIxsDwZUfgfYATQ8P/6LUDE9FkmsnoSo/V7ixGvFSL4d+z2o3QG+dcarbXhUHXW7gwZLrJOWKoM3I7x7VqjqeRtds4q4+JXgmqNxqmogwihIAiG/3l80g/2hKhSEbMpuBCwlmV4FqdEI4JJls1ZjvzBOPxwIKiLgiSRqwrhzBykiIIFFqjr0tMYpbD9bA0givaCG6m5/fl6Toqy5is99l9yBiV8Tm6UUbzya6hXCFSicr1FrZT6NsuIhId1l1HAvaSZoGup1Ui9HBJjJW1WAFLKhlVdqaoIgxOiIhPiJPS5BzpnA3Q/6rtEoGI9JzJLqECJS3eo1ms4a1LbVCqGDJ9pCHwEsHFMWl+GG9WVAUmW9Nk47bObA1TXjK27fXWjUk6wcO5HqvVEYSQmNRzLA520vfLukwQJt+IpKQvJeJADWKWOYJO2nWHdtnAWof53hzT2BPEauxSDfBg3iPHWYf3AaGnKBSCe+ZoUxEhfO8D5EadBoisy/iaQYeB1q9iXSCpLoQI3wyN9Igw2MCCOh3IT3FLQ85svAZWzlEyfZbBiY6ChyjM7F2fDLlQEiYydOsILzHrDiHAYxwopB6nEAAM9WMiZEXWWpxoMZnXaVqF0gIyDHA4tzswjMgqTAASgzTPjF32UPT9POlfnvTiLICzR2AIxPIznEatxcCI+iN9yngApN5ZU5hkjDdHaXGdMtWFPIlHSyiYSqOa1ZjuzHBuPk+DjvvYesQC3sQFKGItmX6fAyPSOi0o+4+fSwqkdo4EpXqK4nXW/sHrNGuS6+MVvK9GBCh6T1CLSyT+JWZrhObzQek0y7GLfZM8mJucSzWrMYnZ0mx3RqIjM5p0wkENB9D83j4EtNYmwQQlZYI/Weez0Bo2MmlZ99MHIvAlsX3mOiBAQKR6m48OwTqHXinqf/Q+DXJmKJLt6EkvHiZA+0x2hUg2SKpFSisUsQbqvEdpDOZVBX02ts8tm6Rqo7REPZujjqjfzt5Wgo5XXRvvRRyfF2dJaiVTO9Gq63C0XuP4gCZ4dE1HjORI4tya0rQXAHj0gcewOl4n9a9yUib1KaMUZlUJ52kfuyhoQj2zNCZr75ZdnLn9LOpZhWk5/Gzvbky0fEIOkusjzvVouwbNah1ZSsRC5NoTwZhEF6YsUKW+JDasCBSFeE89LzsXdjHbmaKoisQ8HdH+3UAkGf+XPiCNXBKk/CBqkeb3pd91Ir3/kOmIFK3lDojZmHlPJxNynPPRkMYDd0pi5dT7l8Gd0cEw9LlJFgHIYYk4HYVrtlwD9j4QXb7pUkbMEGwaDaSGBxmxqTbvZ8oDCQGk7JkdhGwGCEkpRdM6MgIRj99iKEsomYhZ+T2RUWeXo052VEVRoywnp7LfQlGgRfD9cB0AZWQyDP16o9+L55B1e4X0UNkkFBmnBeR1KO7XlVLCghxrSD8vUc1qbJ/dxrkzO9idzdJnORP/zs62WdJ8PQjapxTg0AdNz1C7arGYtqiLlogqbJxDgFEK0jlUkU35eMOTT3K1q5YceSzRhKwlLDF+Y6AdQoA3OsmaGWsiyYzq5mVdJhYkr81eUp54A1D2SvwBCnSDD4lzYKNT9Pl5FCJNnqCaGfMOyGEKIVAZM/p5XptEKMSsykcbYp2Cjv1/fdsDQsBEoslJryR2z+RGDYg49qldE8TKz1/fdliXxUB6CYGgz6rA1rntZJeM0SiNgZYShdborKWaYppyQzqvRV1ga1rDRMlMH51TawfZO2UIzWLUTyuFyZSC7XbV0oSg/QW6tkdZE1w+3Z4ixM/I941Zut77FGzNdmfY3d1KwhiOVZGsTzX8661PyMKT0aVevG5t0tBTas9gg64SPJcmhWeZDs3Ks5HWS9R5atYtRkVvKSURYm5QA+GIUKmIKaexQT71KAU5bjFID3/MWvg9uVbH7+sjFTqpqLCTYILFDYRuP9klhILkNo/oZLgRmZ3L5h4EHyAUswOpztPH+o1zPtZMXOwbHcQYqE42DE0N4WqIMYe0U/CS3VsSc/AQXR9rbyr1DHL9Nw3eFZHVHAhS4wkiDMVADD2BUg4BAWVmp9QHKQanmGfhOXSZ74MQIg10ZhnEdO4xROk8VSXvCwU8BRgekXYPwA3TW6hvboLtyQTTktoUnPfYnpBKT6EJol0eLlMGpgzV7/nMcp0ZUsTZexZd36M3JgofDHMYtVKExNAHgD5Fx8ir7yxMrBsqEyFqrlOHccA01AIleiUhosqL9x6+8ujjhArSD41syVgDZGUbPk+k1DII9ksVG/ulgBAOzgyarfl9FwKJ2Wpj7TkY6iPVGeFmc/nA+q9jcREfiYRehmTYeXDAaSp05Xs6cBeIPdu1PZTp0CwVnPXQRU+ZMfeclwZFpVEXRWxPEdByOEfWO7Q2UEtGJDN65xMhbxIFKaQQEV4dyj1SSRhBxE8d20OUFChUgcoYrM5uAQCWR0uqs8dJH6kkFH0AI448wYZrmvOdGWXCUqK149Y2RoOut27oIBlfr+sZqmoKrYsoxrxKG8xDeU0VL5DbOXRIjgdAZI22aRimNiqx1vg1qmmFekaFbho6iwRfMeuIsWq+4aQnGIcjKwWNwREyZJs7N6TDcf3NYdiNhXOTKIAQKXs6rdE0xhQwBdViTaFR1GVqfckdPD3giPi9GJxRfNBdJC4wlds7buqnCFsbPZKREVIALmajeV0rF3AX2RSKSHEPkXgFACrCfc66UT0ZPsALn3rHbIRnGWLbhHR9dOIuQxDycWUnvUKWOTBZK2XnXEvJ9mpweENWLcNwtnKmKr/GJouQlZsCszTjKKB5VWGrrlEZA6NIO3RvOoWLzvLShWNcWSzw6NEhVm0XG7yL5DgOljSc1/UW1ayGVgou+NTqIUWEXqVMzwo7YsXB7Sk6yq7pUtZL80uHXsZ21UZoXSWYWMVzr6WELgvoydBcb52PcBmjQUhsVBGhUr62VbPG8eVjXHnkCrGwtcJ0e0oaorF1ygUP50Mag+cCtT4477FqO1x68BK896imFfzZbVRlEZv9h+vj908yavDD5xADG55tGRPoTKEx35ufyp5762CxgdZElIjINj200egKjT5r0zOVGTLHwqA0RLbJa/MhBKzaDkfLNQnsL9bJFlXTCvPpBLvTKSYFZXtt3w8atFsTVPEZrydVek1GO0qtMbnndixvPYszt5/FlUeuoFk2OLx0iLbpkvRc4IzQuYSQ7d22h+3ZFOe2yMF677HqhjKN66n14/HWE7I4AiKOeCFZM5Jt6yCFStkMN3mKWlBrgHOQdmAJsdqCd5HxFbORdtVGiIEaN4VAZKBK2H6owVHdzwNCwHEUn9eJOCqSAiLIWBPIsxK+mMF4hew1bG9HqhisyOEjfJPqktHJABgxYk9yUSOtSnJrOUTEGR5HywLgpCU5Ld4bZpACGGWdKfOJ6jHeOrhg4ayK1x17+Tb2mTIsIAgRZzUiFvxFClqSOpLPCCtymAgQLzCq5AwDg9O8Nj+m7/M6DTYlLx5jVWzMKAwZ5Mf7O9pDcTWJLD3gnO3zfRKA8ALe5fVwyoZChGJpOHhJUXqse0khU81QeA8pgMoYzKqKCB9YoOl77C+Ww1nuqJFdcH+eFImUk0OoiqHMeGbyRvnTEgkAKFigcWI9eiXRxVYx0VsKlloKsgiiG5SZeBWFiVCxorqWlKl1gDM9YOhv7ZxD11scPHZAY6YuHyXolT8Pzwx0JiQCCWd/iGPulKRh3u2ywfKA2h4mWxNqc1J6GA8mPUQQqU5Jn2UgF9pMwjGdISFQzWqoUwoC6flCapvJEbXgfdRb1qh8CaFU6hVnWFZFaB6gzJjPjvMe1nss2zbNTWX0kCfhlLH3kVEkvl9Ga1RlARttjgseCrGfOIT0n1EaW5WEOUeIY7NssDxYjJ89RqY0icFX0xJntuaoo+ar5Rqwd2j7Hl0c6nwj7dsndjeETL1ExhQxNe3R9QKqJaPCChLKqkR990oRESYMbD+A6ijswKx1CKsWznpqEhZEuuFIPF/BU/FbqiGt9k5CjmYaixSN518bwTYc2cvYshBC0inlA8PRCE/VZgZoCEN96tSWkIMIcFTnSE99BlfnqhFDoBCNdNhkl+YGe4BYEgPTeyg19OPpMBbx5domv18+SJofIJlBHsCQYTOTjx86KSUgQ6qrOiEAkEoM4v4zQ5BeP0JhOB1Imya6iBjkDZ89V8Hhz5H2kv+OaztK+rksUgeg4hSBIYggeBaaZBt5qK1RA+NPxb45H2Kfokeqi3Ht0HqHxf4iEXaGHssonSbGqjlsrIQYO81N1a3TCkqC95FwY4nt2fQEmSliVnohYHsRoT6dFJcAJGFsKQRCYEJNdl/EEKB5hMgcpbaso8tHWOwv0CzWmMwniVjDz7v3PjlHo1QSEef7S/sf0LU9jq8cQxmajqGUBCYAZecCziPtJTvZZPBjosB9rPz6pjKoJhWqSXUqe+6cS8Fnbr/YBlA9bkxY4YCbFI4GB5n/LhPI2raL/YV+QJcUqd6UhqHoKKAhJdUt40QP9D0sInytYw+0ELDOoYjvW2qNWVVhXlVYzTtcmlZpshAPvACQxBrm8ym2JxMYpUiEwVp0zlF7SVRp6poW3ZN1kJtRPBlQwnEJflUIwaeZXkrRPDXnJNAOUlvBkdTZZHuC+d48Kd20axqh5KyL/UPkDLp1R038caaYkGOIK4ntaota1mmYsIqZkfcywX7pGtiZeKoN0ER4l3RjOcJiCMTGjRwmyLshu/Th1CBWGmuUjWnxAfmwZiGHumHu9NLv20gs4npJ3H9miBnu/5GCpLwiC1MZnWDTPLrlrLTvhqi3tI5g2tJAzAgmK+qBYBB8QFnT1Jd6VqeAiOvMAGIDPPUOrhdreD8c9hzedDaOqcHpBCZ920MXOs3BA64+97SvbmSE08rOJn+L6/Ds3HNdYv6+lBJBCAjvU61SxuhaR9IMw4OpztNb/OWjj+Li8TEOrhylgcGL/UUqSbA2cRkJCkIIFMaQ5FzWrC2iU2SHyQZsVKc/heWsB0Sf+mhZUi6xw5MgQJ9KJHzmWf2E4c9cuk3Gz57Ypc6haTss9hc4unyExz76GACgnk9w4Z4LRPCJA5ltnH1KGbeMcHQGs0cHuXVuGyEEHD52gEsPXsJRTXql051pUvnhbD2X7OPWkDbOOOzbngIzCShNQwV0rMGdxkqyeNEmSq0gCjFqVaNzCkAhlrvoDFXzmqDVWHNlpMF5j+Omoexx3aGclJhuTdNEDiFIFH+rot/n/dAqjleLpKjekkDG4nCZNJonW1NMtiY0EismBQogDeEYHPodn+q8OTNVS5o36bxHa3ssmhZt38N6GvbQHK/Rxf1/0hkkzVWz8N4ieJdYqQHxptuOGHl9FSnwxA7jOWNXZVshECPU6hFTklihKsFYOcRGGYqMtPWQapEAG1CXel/yB3tUM+I/E1sOo+nUA3waRrRxJhRxAzg70s1I7CQXqcaMcX4eA7a5UkaYRXdJQk4KyJh1eSGgAXgpozhxSM2+vHQR4VI9HrM1yjitgw8B3VrAG5WU8hmO0YUeFHEyuEsIRBk/NXKA9L2BFJPvaUjQl3tChK1PdhG93qTrveb7BKJu5NlkHiANmUb6SszikYbURiRtJNgffIALQ+bedz3avkcfIT6Gs4QQKVttrcXR4QKPPvAotUvFWjPvHSMOUilSOqkrWOeS49WK5vFtzn/cXKflJIcMekCXnPMQ1qJrM1EPKeClh/Q0a5M3N2esekf6zIUeIE6m+1vrsDpa4vjKEdWslk1SuCFhfQnpZRRmoPp433TIL5sdLp+LclJiujMjlZlLR2hXLQ4uHkQVJIt6Vg8oTt5e5nyyJ6ze1TVd0ke21o16tU96MRufme0AseXB5MYIZTvnIqxNYivktHXM2GOfaPaMrDtqp+hbQgGkErB24H/oGPRxlk/BC1LbS9NHwXQOTiP6tV6sIZXEuiwT+zR/LlM26z06N8Cnq7ZDkbVG9c5isW7Se+SD39eL5sk7yBA8rO1hbQ8Xh5AGMJbeQ0qKmDgb45l/0kuEQL2MyC6c5wsGT7VIG6nGnMqPhYd5lBJPkdAJ+gSQsjnWEvSFHlHycxgs9VRlklRt0yUBcgCZuPYwt5BFylkdhX8m74c76SXjkOCUzaT659CEDmRQnxj3SbrMyfBQWgVJDdXOAf2wd13TDk3N0TmyAlFIZV/G99XAUGs6uF5C6thSEid3E3NzkIULIWsREbGNw49rfLlzZCIRGc6QJMVY0/RU9jvWeQdH7VPAcT34FBicZNL3HWWdSKpL7bJJiIMuNAwoUNFGwQuuFRPE1a5brLsO676H8wFCDnV2jtytc1geLvHYA49iGZVXkmqUkvB1mRrv18drrCYVtuoq1RvZSVLmhewzh+y+De0WJ77fcoClGfZjyUGGPFlVR3iBIDyCHoQmeDSS9x5OUv+gr0Lq57Se+ib7psfx/gKHl46w/+g+mmUT61NVRJpivSvC+X3boSsIWclveT5ppCwLiF0SFl8drrA6XmH/kf2k9cnkn01mNpdxujVpT7OT5EHZSb/6lM54HuxLJSHDUOIw0iRtYM7gEHsydaFhMo1mF3kIAOB8wHrVUB/jukuDvoPzQBSL4XPGWTUQXycQsYpUsyiLz530+mgFqSSWU4JVNTtncfXrtH2P1tqhB9Io2JJIa50ldIoVgGzUu2XJzSftIHlafd+36PsGzk1G2QU3b3dtC9MU0Fqhb/oUQQijUuvB0aUjHO8v8NjHP44QPISQmE630zTqdtUmSbpuTcQJpUkqSkZY0Du6oHxumovRmWz71LohxDCJQQjAwQMeCdKzliAVrnfx9dBhskNdgidbbGaLkprgT2PRRPIxM47rsgBGDyBfa150B3huoR7VpPoohbY6WqXX7qPahDLD7EtuGRGCshGuJ9P8zALKD9J/QogIjw2amVw3sl2P3tCMvk2lnhBoxJnLYLYk1WZtCkqIPDCGnE96HV0+StrCImZeOSFns5bLMnwhgCVqUxDAU1C8D0mhZH20gqmiko4cMlX+k8+akBbNYo2jZo1F06Dte3itYRAo+w8BhVK4fW8PF88eoqxLXPzYRTjrkyKMjH2FPKw21VGlwJnZHFpJACY5RwERBSCIoZwzQIHTmejBwQfXtJkVHTIESGWqXEIICOfhhQB6oFc2ZZiceZi2T+fL9hbrY2JUPvJXj2B5uMTqcAmpVBqlR8GtTWfMWodmSdOFtCaSYF6f53u/dWYr9atOtifoux4Hjx5gvVijqArsXthNMyZVnhFKgjNZkJwdpBBRqN2HOGf1dPogmWzI1+JjgKwKk6YdJW4Dk3LSMISsfBPZ3r2zWDQtDi4eYnm4jCPZLCbzCWzXo5pWKZizziURhhACObSeHBonGVIpTLaoz5llE/umx+HFQ2zXNQV3hUEfbUdvbXKKy7ZF1/URAeghtaRWp2hbaAJLTIqidmwfM/kbzd98QjVIgkCiBp+zkLIYPUQheDhPho1bIvhgeT+MwmrXLVZHKxwdXkTXkzh437cEnxqF9WI9QJzep2hqkWkzMlTF7DYyWCFlfMMDJxDC1ZqqiWzDzcluIGNkF50CuQSXbKwEBzwFi1luaU5exhbNP8/14MHgAyBpykrfRBgpe3hZ4m3QIhVJEJ3rlsrTKDKO8EMIyZH0sU5so4A765p6AIi1HQAjg8EwOWfrCeaO2aizg6OBjwQdcToZ+9HlwzQL0xQaIlMc2fyTe+pGe4usVsXkrnjdXUuSiiwqwWQlAIMB5rqr91GUgsgijNT4MHZUk6LApK6ipBc5ml4IeOOhvEqv3XdUw5vMJ0S/7ztUxiCUIZ2pay3OHE9t3FXMvL0b1JhsZ0cEpmAChMyh6wAVAoJWJAAfnz/Xu/Q7POmkb3ssDhZYHa1wHLWeAVArmaIJD4cXD9PPsgEFEMeHEfzqespsc0ERbsdQRiO4YXIFG3SlFQ0FnlRJtJ5rqHy2GdJl0h8QW6+iotVprNRnGoUKnBycYS4DyeWR68lbAkDvLDobs7+GoOLl4RKI+8C/Z0pqm7GeoE6GaK33aKODY+SAA37eD0q+LEQjsOq6xHoNISSH28RShHNsz8NARuJ717s01tDF3tt8+PmTnubBpBznLJzlmYQF9anoYWiotX0aGsuQUZow7QnfXx6usDjex/7Bo1ivFykD1dpAKJLIqpbVoI8Yb+LyYIHV8RpXHrlCky3qEmduP0OMv4LgV289ekGDPznbFJIjUpn6GnNG7fB3f1WxWohMT9PzQQlRjzQazVMSCgjeXeXo2PEzLJJnjdciltDrhOTM4IBuTbTmtm3T+ChTmtTMmx5mMQyllkqkwn4IIWUoISBlilwb7uNcOT6AZPyBELoEo6uMXWx5EHE84Jyp0ddcUkhhZ+z96QQkly49CAAJIiqqMVlFxHvOmeNmMJICrBCSQe2iCDQTvcpJObp/APeXkpFIToMdhnPwmbHI4c9pWWKrrjHbnRM64jxC28F7GhHleos2BlTBETu8mpZYNC0qU6Q+PxEElBiT2DZh1dOAtYmclAUFnSVno1WSgtTOpJ/l8+60hPZZEBgFBngiCiMPPF1icbjA4aXDZDNIRFticbjElUf2aYxYvEcAoSX11iShHSzGTZkGvSeT0ea7swypklgergjtaDtMd2ZJV5rJV1ybB3AVEQ1imEU7rU+JxWpdCt6klIBw6XnXxeCUQ6BMltu2cqY8f7+zDm3Xo11TJta3PQ4eO8DqcAldGEy2SNSinJTorINRDkr0kNIlmLbpOzRx6obSCtDDRBYbCZHs6FbLNZSUqIuCpPqcw7Jt0fR9vK7B6fHffSRF8TQoxMDeZQ65eAKqRTd2kJ4k5mzfwhb/f/b+PFq3rCoPh5/V7L3f7rT33K56CgwlnRpsQE1QRCNiN3BIKKRQHCPEOBQhYgCRoaDEiBg1JqKiJIgRBaxhjF0yVIaoUWPQaFSQpqSo9tZtTvc2u1nN98dcc62133NuU9Q5mny/u8a44957ztvsvfZaa875zGc+c0AwGgSk0pF0n9iWNirlUIjuoMtEOS+H1I+Nm992XYvpdAdSatT1DM2ixnBMfdqKQVDI6Cz2L+2haRaYzfYwGIxJsCAU1fJB7r2PG00VOhrDSC4JUa3p7AGRcRbepnvJI0YfFnhQkPHUb85mNZHHMRb1FG3TEAGGjeASLZ9H7uXlhrL/h2FNek81HBCMFNRGqlHwmoNjoUsdO4jTd0uowILzXsGFxc9EEykFmiBIQFELK9+omJujHAtgWhGjV87jRhKOy2shXcxPew9IeTzsPgCxW/3qiTWs+BXquOH6pKi8pVrP1eX5DopFXdPFFkJdQwcr6dKqnnOQVKbC38bCChE2tIXJYD0lCQbtQq5l1jSwzsWerEI06Do6DCL0jsQGne7OMJgMMW8bdGYYWbE8mEAhRSqq5/s6jiiSFZv4Or0jhq7g8jDnocKzzxWkpCVHWBkVjSsPsWhi3nHv4h62H76E2d4cSkkMJwOsnVzH6tYq5SUv7ePeD34c0+k29vYu0BwojeFggsFwElIcBbquRte1gVXuYE0HqTQm43WcvvUsNs8QnLp2ch3WOMw6g71Lu2jD8x+tDGP7JnaQcv5AXqtN52aHuRDAoDryOeezwHYGLsDTVMxP+507yMRndECtCzCekI35oqauSUHLmQ3a3nQB5zyGe8OYV2RSFHUIIXNjrMWFi7uY782xe2E3zsf2uZ2YApvvz+lsGlaY7c0BKTCuqlQq4/optry+kx3PZtFEhDA/I6UEIEWsvb7SuDqL1bvg0VIUySQdIPfuqGefENzFIcGs6aJIRqkaDDAarZHQgKzhnUXbLjCbCUip0HWr1EF8ZRQfrNQKFYaQUqMoqBCUtTq5U4T3HO06QDIjigWcRVbKkVRSfCCL5MY0H2x8CHIUkSTDijPHOayjSCR2AA+SeTwn/PfyNR8gvCyVokgpSOg3dJeQSsaaUb5fFfpOymDguqYlzUQlIUMdI8NMUkqYQkMxqzh0KadEfZKi4nkHkoh8hBjDfcb38eszpwUQx2Ykm2YOpUjflDsR5KPngBwy3/A+3gtHhNzxAEBgBScWMIs2RIk/JUmiK8yDaTvUXYvOGOjgpMybBtuzGfbqGm3X4dL+lETiw94CyEHl3L73DtKFPHBNwt4mFGP38p9sTHFQg5XZm0c9eC9FAgtfi00cAICUTpgwJaWEWnJKGBZlpKStWzSLNkaPdT3D+uZWPKTZ8eEonp0x720QJnDoAqIlpabAwHQ9J7TI1iC3uGJWrCo0zKxDsyDBbyklinAWxhKV0OmGz5QID3cGzTyUQR2DgeThrIcEOZ3xGpZh1EN+xmvB2BRgFCV1XymHJYpBQTn3ug36tlQ7ubK5gkE9QDtsY6RqjMH+pX3MdmfYfWQnonmxUXJAHLlHqAkC9bH0BpmKVY6e5We7R6pQcD4ZfEF0oTyAuNK4JpKOC0asLAdxISmFaBSFIBkwjjhMa4LhCnmXQP4Yr44pcWtuQ1kOsFjsY3f3PLXQams0zRzD4QpGoxVs+ZsIGpoMcfq206QFWBVomy5CBaPVEUarpCbP7FjuIcf4FHv+Mde4nCO1qaZRBHgxubb+0EORH8xyM92jGkVRwXQk/6S0gqtsVEYRRkQFH2c9oHzMF/L9JaWOtLkZTmEN1tHKiJqHBliU2W1CkBFlz5f6M5K4s5QSIiACukzthHSrYNrw7EMOsm1UEB5OxcjRcAejwDlI9hpZmCEq2Ejysh0b2WOK2JtmDmcN9nd3UA0r4ExegsJ/i74GrifInRxCH0uB2hBBcq9QrUkPlEUU2qaLRpEhHl0oqlML5Rqz3Tl2V+aYNZTPkcbgbx56CPc+9Agu3H8eQgjU8xr7l/YBBOUpSzwBZy3Ye3POoV1I1HPq1mCsjfAq15YJL6KeaK4ZywbEuKOfc1VoONvFec0Ho0axX2bIjwtBqIR2Hk4FqI5FsQ2xVptFg2ZW4/yDD6NtFwCA8foYozWqUdSFBkbAql/B6VvPYLIzwWSyhvl8H21bYzbbxf7+JThnQ303rQOW2RyNVrG+uYXx2ghrJ9ciFFspifHaCM5a7G5fQtvWMCaoM9WBBCNEFEUHEMqiEmFpsT/HNERCOLlx5HMOJD4JIKPh4J/zs5CK9pxSkohFYc9KQYSbpu2oG1AhMajK6Miun1ynqK1usbezjbZuqVQjyM2xlJ/31EB955FtTHdmOH/feTSLGs5ajFYmgURFc7R2chVKrxKa1HRYtG20MSz43uc0pDTNcuosT5dQ5UKqRrjSuGahACr1aNF2NQbWUK2eZrJOEq7mIlGeOO4ZlrwEjdUTqyiqAs2ixmSyEbw4h6JgCHaEU7ecwijoJA5Gg363iTDKQZkmNDMEAGINEokCIF4XH9QxNxcmOqr3ZO8DEHOQRAdPIua5ET7qUZZDOG9DI+l0T7okWE3Y/FBRkCwikN0fQziR+JJtCJ5HZrTyM5ZSovUkhUUtrOhwYlo6a+j6wErlwmZmx/qQezaGGMW6oKQ6K6Hkz44jenJSbGR/AuSZF4HokzOK7TEKM3jvMZ/vop6uRoUXxeUqWQQZI+LsufTnONXjFqVOZQXMsOQmwN5nTolA11IOf7Y3Q1FqNIsGJ1ZXQnseh7/4i4/g/P3nsX1umwxrOBBUkVjeXd0Gklzag8ZYmGC0286E3GYiRC2P/GdtqFM76jFaGWKaMYPz1l9CclNqAb9oIrOa89cmoBZMhOKODM2iwXRnhsVsjul0Ozjaq5isT2IbPZetr8n6JDRMrzDfX0U9pwCgaeawhu6ZkbLhcBXD4QST1TWsbK5gOBnS+g/lZ/Aeg8kQ1jiMRitomjm6rkUzX8AFibciNMq+XHokb+d0nCOlMGwsr2F41XtPgQ6TANmIeGrozFHkcEDC46VSaKoSwxWHM7efwXBliOnOFOc+fi7WWF966FJsp1dWBbin5Pa5bcx3Z9jefgRaFdAF2RIfbMjG6Q2sba1hZXMV3ECibloMB1VUl6KHGVBGKdAJAd2qeM6wEAeAKGvJdkmEPZALlxw2rln4zzmbINYMZgVy0oKLk8oXY7KCftbyZDy+HJQoioLCfYEI+5XDEqtbqxiujDDZmFDrp6XCbBY5ZhyfD6BlncE4KSECiZ4FHxAh5BZZbqD3vqVDOYbwx1i4rnUB5yyM6aA6SRBDV0RCgHDp0JYhN+qRRcmZR5jfe4KWk5fOOqRCCLRFG2XhWH5NqlQewspGeZ6IC//ZaeBmwpF8IlIOmNV82MAksgJ6zoZUYWGH67cGEML3Cq+PctCGMmibBdo2NHBdYhMu54BzmD3BrImRSsSMIhLJchQiRvqtiTlcEk4mQsjepX1IpXD+0g6mowGc9xGSqmeU/2HvmVmIca0aQ3WuUe3KRqLCcu4mXvvSv7mJcmcM6vboe3CqkLvO2aHLrGyCUBMJxwkB51QohxE98Q4TGkTP9vfIwFmDoihjr9O4RoMDAwDloP98lSbBAK0KdKF0jc+0qqJWa+WAoMScKMcOFOnolhiuDGOgYJ2BtAqmU1QfGchsy4AUEYy6SAo6jhGdOevgpYiOHLE/k3zmYaxVIMCawfFmRR0ZBFx0qYm0BNLkXkwXWOwtokoakZcKNKHHb1u3mO/OUNdzQiOrgmRMB0XgrhA0O9lYwWh1hHpGfR1Na+CrVEHBZDKlJaxl1n3Qona+94ykDgGWtRBWQDiai6s1qL5mA8kRpLUGlj1U+Fj+GT2TAJGxQeR6RufIeBIcq6DGJGW0urWKakC9x0Zr45jXLIdl9DxiE+MQSQCIeRcerLQDUO6CSk7Szdsuo1a7VPtIB18S+aZ7NSmv5FKOjBcVk0uOy0AKIWFMHepPJVRHvfCEFFCh9yVDx3nTYRZdzlly1BsS0TFwxqLuDNqgYt8smmjsm3kT6wH50LehHtF2FvW8hnPhUFMSHXcU580ScsHOWDRh4ZnSxPpCAD3HIhcr5yFljkgEWrxwsUXWcQzTtZBKYVFPsVjso541qejZ+xjl5RHAQcepb+DLYYnRyhDlsIq1bfxZscdoZ2BachDaRUsqOnWLvfO76OoO47URRqvj+Ew2Tm9gOBlivjeL8KLi9kAhslKdQoMmGJhgJC33vrMBXvVBnisjAgmR9F+FQGeJDLQznx/5fMfcODuoAofCu5c7vFKNrEHXtDDGoK5nmM/3YK3pRXt0KAZGo7G0pjP4tig13KiiSFVK2G4lOdAh/SIl9WmMakvOkZxjyIkJidhy7OTNJ1ENS+xvF6hn87SWjYVR1K4rjxz5PDKhGTRLax71YCfWWkei+Tp1KuLfc+NtLj0CmCQmY7cUmcHxbFSVUiS0XmiMV8dQhcLexT3sX9rH7vldNEGqMz0L+vdwOMb61gmM18YkaTcexLV+9vFnMV4dY3U0xPmLO+gCZDsYD1AAKENAZF3fwc5zi2wYy5AfBhAZybazQW7uiLRYWWYuqs4E7+pytYYcYuesLSlF7OoQD8kgBFAOSowyYk5Oh3eBuMGsoxz2OhS2yJQ6DsCglzFql/WcfEr8coSWs6aOY5RlFcpp+o6HCLlFqRV1C1CS5prvKbvWREBIm0NKCYTGs6RGglSvGBiYJBVoY+NSnnvuVec91X/lRd0OqTF1DhEJJWNdLDP4REYAddIGWDs1n86jd+G5x5+A8BK5Hu1Rjs60gAHm833M53uxkFhICS+DcLzKovHgrMWoOYuCSXBBBtZ2Fcpn0j2zlyulp1ZXJu0rH5zIpm4BIbDzyC4GowGqjQlWt1Yx3Z5iuj2Fs6wNnFSMGGbqpRnQj84YLssH5yIhJYRLbZkWbYe9usb2bIbVI57viw9ejNqduji4JX22nuN1cr4xHLAEsVqYroWx5LwLIVAUFVZXtzBeH2M4GVBDZk+11UK4nkFiKFyXRU//NZeU9M5DaVqfLAtHOeck7q2UIlTFOlTDEuO1SSQWMerD8o05tC2ljEb4ahHcYx3e91nZMSeXO3yeyqlEcFqQkXhywqVxXKNLerNKS2gQa72oCuIhBEY8OempoTV/HqfH1k6uRcia02WqUFjfXMWgCO21BiWcdajnDbq6hRQCw7KMkK/L5zBDz7gMrahKKlezKe3HJW+mO4IIkjYWHwb9v3n0vSKfCkCViPlBEvLts0OJiaojKYTe71JElx2YOV23f318Df2/D/Mqcvhx+bNEzsHoGZ3EdE0PxB0baaQoBtE77X2nSQ0+E2GEDEe83Ky+M09QA9yeS1L3jHDQx0bRkhiWvvNoAVSZE4IAtZaScghMo45zieTx81u8Ryy7oUNGRfJPfBahzlIVOsGEPrsH2NSZIstTHfXgHGRdz9A0BA3ZzsBphdAwMUYUPJ/5hmS5OZpj2ducvdxHdihJJeBcdkDljljn0ArK+RqzBV1obK2t0oHqPGY707inWMjdmn450GHre3kPpHsJEZWgnpbOeyzaFrOmwbSpj9xA7jxyCR4OutyMqA+wlN7IHNuevKPJ6tusIbZ3KEInlnsZiXsxcncOPn5O6GyRrSUV4DeUKUXBbcm4ewg3FufBeTyeYpbAFKGcDUj55gMymvzdUhLiYtNePS4DmY9Dz8D8Z4fA3Xlej8he6TVKhHZjmhwuuzaOn0V1pF1kXFPXmgLjtTGGkyGVyowq6LJAySiIlKEXKukFl4VGF3gMxlgoY+GKgJBlKTNkazs1WtfRrnhvo7PEaSNnH6OBzPMu+UQuT7TWVS8nmHftZrIHayxyiQDr8x0IizNPKz804zUFtQcAqZzE58aQHqQ9LILke5GedB7z+3H8N9OE+3Blgl1cLGQ9jrF6YhXlrMR0bxfe274HJgCX1R/SvfS1WFlPkYlRkZQU8h9FMFI8fzLUBPFhDiA+M4a4AUTv3XQGDR9Y1kH6VD9qs01HJAoBGTp/0GItYiTprI/rAVVB+YmmgxckMCCUjNHWcc01QOkDazvMZjuYTrcx3dnD+sk1iho80Im03nJBdYbqo3ceomylFIpBkUkyLtWuhoiYo0jpPEUhQV/NdC2M6bB3aR/zvTmRpKzFeDhAcQPVrM33F5jvzgAAzRyp640gtiRfo7UmNKg2ME1H8l7OwXlAgITSRfgDIHYMuX97G+d2d7G3N8UNq5Mjne8Pf+RPMBquYjT5h6EHJ+I8+czxiPkkSfvRSQnAkeg9ACFojouiCoZI9Wp48/RDL92QGaNeratKebWY78+MG/+exTScc+haDx86Q/BhKyQ1j189sRpTIbosIlu8C3n/XLIzrwM+juFtFnQgGUchEoFR5Ovc03u6ukUbz1RCGtouOe9ap9ZsWlJbKrFK+2C4MkQ1rKj8Zt5Aaup5OpwMMAws+rXJmM4O9I0y5xc9PCaDAaynpgiGVZG4z/BSJBwJXYVCNSgjD4Cl50xLhC5mdl9tXLuBRLpg+nkOnXK3DRXZoC6UXSBQ9RmS0oUPOZNQThDaVAkhosgtT/5hxjFi6VlvwghxIXmbfS+5f0+5Yc+/K39DzrJkQxkVeDI263EM9kDn01nIhZKBJKFvDxEWNLSCDApsEeaM95TyCulv+vyYY1maq1wpJhrgjJzCZAeAvGMAvUbIyxE6t+ly3pPhVJRL5PpKwMRaOCFE8PITLJx/Jl/vcQxiRVK5x2y2i53dR3BiepLar41VLG1ahi6JyZpFPM5DaJlUceDi/uB5dVe5h5xoAO8jXb7uWoxKUtCZb6zE8oC26WI0z9B/jFIldVYwQY+yrVs0wUACtJedc5Hp5zx1e5/WNc7v7WF/Nie494hDyO3tczBdi2aRumvw2iTngdaEXoLdGW5XlvWdfcyVcekTS7p1TddHotDf93kLLR7a6ci05O/Lnwv/IB7I4Rry7j9c58pkk9iCLwQIbQ3YsI9oLwRnMkTHx+UI0rym/+fna0/X2iSuBoBYMsaOgsreb60N+9ZDiiLYBhG7bzBZhpWkhCCCVjWsCFYNfSIPE6PI8+Ol1hgUJaphmUqqQulJr3Z3yUjKIM7BKBnXGLeLFl3dITaGv8K4Jog1XxzLD1AICQEZWhnJUOMU2kYFIWEyKBrSUiTDWL4Ilj4WUTsqYo0lGtkhziMewuYgHJEf0I/WE8vfF2tkbN9QshxafP0xeXss+6Z1AdO18YC2xkFqB2EFJFyAgSQpQ7AKhhCHzlc0mGz8M0jCp7fGQ0Mp2dNr5QOe9SVVkwwB6d4uiXrLvqGW4fv485l238td+yUZLpddozu+KFJJDYMWbVtjsdjH/v7FIBpQoRpVFM0yizYavGTUc2in53TZPpOOCuQzggr4sM0uRorYyYUP+npWo206jCtSJFkJ9cTwHvPdGTGRQ3TiXTLKPmjXsi5sU7cUQfI6QL/jvXUOi7bFXl3j0vYe2kUTWehHOfb3LwEAmmaBYTvqOURRvUlkTc+z8qRl+M/ZIAsZDFC4GSqbMTZCpEIkZ44MpIrrMJ6v2e9ZXITRIp6jXkRq0vxSYwPqEKS0glMW5XBCAvVlQShNIEsRmkboCX95LHkyx4eULKO3MRXD8yaSzjKfC9ZYtHUSgmcNbN6fFH1KWEn5awgQ3ApqScUs+KrlKJ/kLYdVCR3PARsIn+kCcwOptUapFMpBiXrWxHOYa4sP7KHwM1LyYvZqqpftGqoxh79639OrGkhWL2F1DoKjTFZr5aiRbeYZ60IH/J1eLyChbOr0UFRlfDBd04GFyL33sV5GZ/352HvvJZUDZMK0aT7Ml8svepsv8xa898HIpP8fFlXxcCGxz1p+udNw1IMeLtGnnbPouiZQ+8l71lrFDU4Hae4QZIbeJumuXmFwj2i1BF+HhVUOqwCpiNB4GdClQlHQAs8Fgemw9zFPSF51kBvUoZwndAzJn1+Ebk16jzGpUTMfKHlbs+MYShco/ADeOyilQ40oMQqZcCAE18MGo9Iz7IlIBRmg9y4RIXLVIZvBU5wT9lnnFt4jPE/z/Tn0eWJIKqUwriqcXlvDZDDApUGJ3Qu7kPsLeI8YNWn24DNCnLOpHRNAcl9MyAEIqmq7DvdduoQHt7dx8YELxzLXAOV8p9Nt7Ow8Aq01RqvD6HjogtY3q7nk2ppCCmhVxCL2FJ2HDkChjpDPAOsTdMiHaIJJk/PH5wszKIuqQKGK6JxQLthGuUQhKLfFJSQqlC9YSzqw/BzLYQVdqNjSCp2htn2eUhx5fpqQgJRKOerB0omMQplQd8uM6ijpJ/tnZNd0WOzP4T0Zx3JWUt/HwJ4WlqF8C1MVKJRGFXpzllpjczymve2oLZX1VM8sw7nlAuGH2Kih00g4f7nWsg7NjouqRDNvyHBz9KoEfXfWIL5337w3WUxiTpKEpu3AfYavNK65zCN9mQFJMxn647gbAx227PmRFqdG29bw3sDYDs5ZqEbHvncM2TGkN14doRoPMJyQ0C9/J28EblUTtTyzg19mhJzDvALeRIeRPXoQ3hWgPIZYl0tMjnp4T9GKLmiuaA4PRtIH3hPGwXwXs1X5voPc1SGfQ+9HzLnkn8dkBTr0iwD/Ug85PoDcIftbhIiU61njNTsPyBzyDXW0EiREnTk7JGR8PN71YDBGWQ5QFANMJutYWdmM3mksLo85b+pw1Sd0ZdFFKKFgA58Pn0UM2SxER4XXOZDmnPvXzXanGIwHuFQWODEJNWdawYcWbm3dBANpe+Q0IWQ4zMuoWwyE7iAudQ2xzmNnNsNDOzu49Mg26nl9bE7goBrDOoO63kddz2A6G0laVxtCiJ6RUzo1NSB2tkTXpbKsjvV8bRLR57XKz41Ll4qCahlVoUmsYU6tkRaLWTjjbFAPS/tBSo2yLKOggTOWhC4CMURkaAPLzXmViHbRCcgcweMY3oc1FwQATOikwfAwQ/L5M+BUFreGys9iFjnne2O92aLUoX2eRlUW0CsTaKlQamJzW0etqoxzsbMHt9Ay1kZ2LF1zOvOSPnAgWRlqhAHI4ERx2muJkOl9FERgByRnfV9tPCoDCU+1VcYY2MIGnVYDIVJNDEAkEGccurKDm9lQP2mj/JPWBZSiCLHrGuS9IVdW1jHZWMEwKMJ7T+oOxaDAcDLEYDQIsIVO0FsoTYgjP2iWCrz5b3rvIcYxg1AO3H6AsWT4blzDhn4sQ2mVYKZ4Ef6qX5vn64SUkMKHsoxwCLFRcv6ARRPBMMaIPEKBgRkWIleKbl2kTucQY2+Bgp0mdSCCZBiSawMZsuIuDXFNxQjteCLI0WgVRVFhZWUTo+EKVlY36YALUW0hNHzItdBhLPv5nJB/dNYFIknq1OEz6Lkok6BFD87KDCnngXneurrDQgjsXdyL0n2DInW6MIa6KSymJNW4LBQhJUUwXGOmFPWBJIgVsJ6MY9N1OL+/jwsPX8LFhy6inlFv1uMwkKPxGhaL/VBWsw8T2nIpyYSmMK/ZfSR2bsqRK61QDsu4ptpFGzvA8KFYz+Zo2kUm/9YvVWMGM8+nlEQ01LpE1zVo2zoq6yCkPSh91MQopqpGGA4nKIoKg8EYVTUKzX8TYxkAhBOh7ETHZ80NwmNpyTHl2VlijsiL4UzmxvDGAYJUnZBF3JxXJRWtGdq6CfW6Jp4BSiliuA8qDCfUiakaUmqiGlYYlAVGZYVhWUKKIgrlm/B3xzwPHxpJ5EZRShjteqge93VkSUuvQg44NlZPqEEymEQu7FpChLj87TDIfnlcXazchVDW2dDzsaU/XQsTpOZ4wXg3gJRU48KLfLq3Gzpx7GA23SF1CUkLkBivoa2SVOi6GvP5Hra3BxgNVyEVRaEAbZqojD8ssbq1FgtMudbJcc5H0ubPmZ1pwjKxgJyskpFDrLUH8koMqwC0iVkA+jiGt8TUKwcFrCF1D3oYyXBIJwCkCDyPGvMIOeUal3KQLvOo5UGmMucLcop6UZXRYBdd2ORSws98bOtENWoGXdfEA8cE0WnnBlGPFwCM6+i6Qn71AKxtXISAuqaLzbmPepw9+3hU1RBlOYyQ5GxvHjqUOIxWhgQRZ2pNOTyWz3W7aCKERX0GCb7jWt/hZBCfhelM1BXmfohAJgzdGiymxLSbbu/jwgMXMV4b48EzD8VC7b/9i7/F7oUdXLz4YHwexjSQQkHpAuOVVaxsTHDixi1sntnA5oQ8evoeh84YbHcdduZz/PWHP46H/vYhzHaIHUsEh6MXZzhz5jbUNSneNM0c+zv7WD+pqQejsYBWxKyNMKiPzFKhZIQDWe9ZKQmEOXbOYbG/ILZo02Fv/yL29i5if/8SjGkDobDAeLwKpQoopVGWg+DkpwiR/11VQ0xO3BBTTcZQrno+30Pb7sZUU1FUEYWoRlUsMyGZxXTAS62gwpKRSoYozhwKDx7l6BrqsiMzWLFrWsi5jOicMy6mTwDEWk4pJeb7c8x3Z5jtzaODkveuZMg4r1pQhcK5x53FaG0U9z2t644IcFqiGlaBTKOiJKUN6YDoyDDrWIbm7MbGyDz2suTrYNRJCHK6QW315vtzLPYXtCeNJdRRXhleBR5FHWSCjUgCzToT8V0pffw3P2OhCGYtigpl0aIrBzCDMeADtV+XkEqTxFBRQkpuKEteSTUaxhwWH57WWtgZHcSMkVhjIxwLBC8QB4vK86iGPLWUp+sZkCvArKx+n4S1j1c3kQktnBeIgt8hUuG8zLWM+HxixB02bMZc5Q3BETgzWbnIWSoR8471rI452a5pg1h0jaaewXmCPAaDMbFs2fBm7EAOwFkrtyf/h0z5x6Xo0R2G3x7B0LqElCr0PQWRPLoWui0CPT5AaqMKngUaDjvMPBHTIhmg62J+Sxsd2oElbdmuadMcdl3yfJ0jLd6OIh4AaJoBmnmDerpAu2gI/nYkQbeY78OYVOYhhKQeq44PPzLMw9GAaPhCRGirDb31Luzv4+JDF7HYm6OtWeDj0QFM1zqGw1UUxQCLxRRSKhjTxn3onIP0/R6EfF/Io8ilw42JZUUQ2Cdim46t9YxpsVhM42cpVWRn0CA6HJzTpLVH640dJ4CjTAUpFcpyEI3jeLxG0WNwhFShg7wZXV/OAeD1JCiUD6Idx4tGkdMsY66cSX82MG9t2O/cqBwgtIM4I0VAs+gc4CYG5bBMpWZIEoIAgvNnsXN+B7O9GfbHJKzvLUWK/BmD8YB0bUtN8n9ZaoEhZ0YVhCAhB3Ye3cDC66Q3vYwAst3oQhu0rqGomWwQk6OuPO/XrqTjuXFyFyNILhxk2IKbvVpLeRCpJIbjEZTSKEpSuBBCUL+1UkNrnXlb5I3wQVkNq4jZc2+92d4c0+0pmqaJffeGkyHWttYSpVoreOUBKAjpAYekghIVUJKEXH5/zJTkkohluJVzMrGh51WKTB/LYMYo0cSJrOMc4e/SClgpIC0rzKT6peXP4PviJsTclJTzZUJQ93QZitqlypslp/6QLBC9mC6o88DOLOZ22rZF08wjA1RARv3KoiDigyo0BIsS+MSM9WF+6Vn0o9xIOTc29OQ7JgKDd2iaeTQyQMplz2bkfDnnqS5Sy5jJzSFoRjDaBWm5tm0LVkOSsoiHSBcIAkQYWKDt6tAbtYnRCO0Bi7bNCB/lEFU5QLFbYefiKH435fBoLzpPxpgiJeq0w0pVw5UR1kYjjMoSSkrUHbW/ao3Bud1dnD93EY/ce44MuHW9/NJRj+FwQmdCUUEpTYX+YV8K4YGizwamvD/1q5QyKBsFI8NMaCVIDg4AKegUCuXAYjgZYmW6gXp+BhcuPBgQsAbj8Rq0pteX5TAiAlKSA8Gtrpyz5GzE9IxEVY2wceIkigGJ9bNikpQSXdOmSDdr98ZMWDbuDBlbk+Qz+Vkfx+BorK3p/q0hgyGVgBmW5Hy0IvRgpTWtCoKwR6sjDCeDiDyNVsekOxu0bnWhUA6q+G/vPfYu7mH3/B4uPnghOIU+olYdQ+paYTCqMAzCDqsnVlEOiAQEUJ9HFpzhM66ZU4cbrRWsqaCKrOlBzL3TPZu2A4mjh0bxdRvlMUVgL1+N13BNdZApr+TQdS3adgGlNIw1EAGS6LoaTVNCzlU0btwkmdQTdO/zYlLVJfm4vG5oMB7EQ3vt5Cq4oehiWqOtqVs7iwTsXdyLecnBZEiswYzlmefFonG0+YQekm9kODZg9KzH6owLbaSAojyevm3TnRmkkmgXLbU12r+Iohig0GVYGA5CBK9P+kSm4UODqf5KotBFjOzrNuSPu75jYEyINEod6PIe06aLBovni0sIAOqkwnM0m+1jsdjH3t4FdF2LqhphsrKBlbV1jFdHGK9PIlynVFYnGGtLXVTVz/ORHKnz4zmuiH17++Fe9AoALnyX0kU4xAl+KofUxSEnU/QjXRsOdDo0iBFcxv6aAAKzkbQ9TTei9VzPYs6LD7OiKKF1iaKosLFxBqOVIQbjQYw+m3kDrUtiqJoWdT3r5dScI31PZiwKQQzcpuuwXzfU2cM5PHT/Izh/33lsn9uJ5T2QlC87jtIaLu4/ceJGKEV9AwGga6mcQIdnIZL+R9zHy0pBzlHZk+1MZGqqQqMECWqoQlHBvhA4tX+Wyl0WTXwvRSpdRD2sJU6Edy4QCx3adoHheIKV9QlWQiciHXLUtFZ8REUAZPPvYK0AQPkveCpzKqsiIFgpb5Yi4+MxkJcuPIy6nmF39wJF7M7i5pvvwIo9gXJQxUiKGKEkYwjnY9Pi0eo43pMu6IxYTAnKLgdl1AwuhyXGq2OcvPkkvAcuPXQJ9XSB+f4iGizqfdmE3PkC0+0pds/v4uF7HooEKU5nAaFrUyD/iJDCU0FnN1ex4pHQQhFRGyZcNYsGVZYuuVrkfo11kHkheCr1sKaLkIQxIT/ZloGcoKIAAERicUV4pMuiNuuI3p9Rt/MDS2kNpak+UGqFsikhlUJbk7duQ5RhIvFhKeoDeoneXvPkJREC+puvzSUNyOABcVsmoWRUezjqwQzCZkHEi7qexQOhcBWkz0kskhqgChHr7hiTYGYaydEhlmYwmYPzy1KSI1KWQ/IaQ7E0R9RcTgOgF+0zlk8SbXN0bUMFxbpAWQ5jeQer8fAB50MuleeSRMmpZg1KpoiXWa383I6J4VfXswPrhY2xdiSErXWJZrHSc7ryKGd5kMNB0BT3DSSWYDgEhUXhiwi3sWGzpgM04BztubIcoiwqDEakPjKcDGM+yVlyjpwhhSpnDdrwWeHCer3vACLl1F2HtuvQGIPGdNh5ZAc7j+ygntUEUQaSHXXOOKbCdSFQDQfQmoxYLrdGohgSwJUPL4bVuClBZLQrCefIeWTHRIWi8XbQohwUPUeZkYp4lnAJm6MSttFkBePVEVa3VrF6YjUyVnlfcBTrvYdqw72ENQ2kHpcAIKUnJSyJeKbkvWtxTGfK9va52H+X1/bm5g0oigG6ltRsZFhLTM7JAxcd1kWsLZR94XHTGjKclsRARmWFQVFA3iDQLJpoIFkqcL5Pko66UBT0LBrU8yagcyRMgACTlsPQzGLVYzihwKmoCPkSMqvBZo5Gxn63ofzDhjyvy7gOnOK50rhmJR0eFEU2aNsFmnYBj0R2YWM5rAdRczOp7neh9INq6fgKvQ/1S61BLRahS7dG19BEMISRN04tqgKT9TFMN4DtTC9xfLmRbyYO9fOao2X2JUM7vGm6mthb/J4i04496rG/uw0hZDQ8i8VedFLKcgBpUo2Ylx4+iyB98KogfIR6GBS0HR32e3sXQpPYBbz30LqA1mVk4I1GK5CCNkJRFGibBh4Og8EI49B8dro9RdM2mE9nuHjxgQgRro63MBqtYjhM+ZginycR6paCogXLinXcZLsMdZ0RAk/wPkOWRz3m8/2YW+KcH8A1wAXm813KqV4YwzuPyfo4HpLLQ0oJaETiA3vVdOtBLQoSXvpAlqBox3ZDCKFiDtLDx2soCqohjXB3cDSNsRCtgNcezmkyjEKAO1QRqa5LpSpCoA1KOXXXYlo32Luwh/v+5j5sP7SNxXQGW1UojKOI/5iiGXZ2yqpAOaBGunsX9qLyFu9jmUGR/B5hQy0fAtoQi/UTA1KIICDOLGtvo7MLAFIp6JKez2RjErt0tAsSqc8l43ShMVodxVQQQDD5fHdGhBHnQ46Ozj5bFak9XNMBoadrLKXQCspY+MBgdeFQFwLRqT2O8bd/+xdomjnato5zu7Z2EkIITFbXwv2KiExQrTqt0ZJFxFWQ2QvPRBepFMZ7j5WNCcwKzV27MsRoOMDWygrE6gr8KSrVaA11iZkGgQsbosmmbrF/aT8qPi2mC4o8pzPMpzMMRkPoQmHjzAZWT6xisj4+gHARo7tN8o9CRGFyrn2kNcKdjxyuVlZzjTnIVD9H+LyhhqDNPCbWvU8J7no2Ruk8Ct+X6s897TzE5UXhnIdnuSYROo+Hw5Vrizg5L6REUcnwMxHzhiw1RfRlMiD0exeh0tg0mRO7zvc+g6MmKly30UgyfZyZdMUxGUgeWms4V6JtC+oX19YoywYk2hCKabP6MS5kz5l+QpDaiwhkG60KFMUAWhPtfT7bxSIYhdFoFSuTDVTVCKosIKUiJyEctPP5PhaLGZQqKPcVGM3MSHbOYTicYDhciTVRRGhJz1/CRdgjJwulCMvH+ixj6DXWESuWc0ZHPaiuN0H9ROLQkFJDSoXFYoq2bVAUJdHZV4ax2wnQ70GqtIKCCgxLdSDidAAQc40+1Zm1i4jC8LO1toNS1BuUvG0dO4UAoPkN5BJnHYpmED7XRJi9LKlh82hlCACYNQ22pzNieF7cw4MfexDn7zuP2f4erTlXRgKHCWv9qAfXFOb9S3k+TNtGoYOcFemVhPSh1MrayK6MZAvfD76UllCQvR9aS+SpelbDOyo8995jsjZGOawwWksMa3K4RUTAnKFUTj1doK071POaoiWtMBYEP0ol496jx0wOHmvCKqVigb0uWdaOeuRy26WrSZ99soPhd46OrQVmsx1UFUH8zDlomy6qzZi2gwmIFBPK2DGXARlhfdPdR3aw2J+jqEpMNiZRQvDi6jiuWY46uQ6eS6HawH6XUsZzowh1ldWQ+AuD8QBrJ9dw4uxmyIFWIX1gQr9cE3SibUb4onwn96VsQtUBBznW2NhB6nLjmkk6uWdDpBw6tJTSkfVoTQfTtei6Li4El0Fkl/s8/nnOtJRapfyDlNCeC9X7xcIAUAExtObPjklhAMKLSE5hLzI3jt4D3mZkC9ZeZeNoEgzDkXLOLj2uQQe0DiQdA2MkNVGWGkBBeUghoDRgDW8uCa2DwgjT5INCBmnlligKymsZ04V6PB9LMzrTho2Uaj2ZnMXd1oVUvc1WllV8fkR1J3V+mRm9PC/AcFpOf+fcV8z7htpWNs7U52/lWOc7GUgZ5j7onbY1gBrT6Q7WF6dDF4dDusHENAJ511Kp6DQeFhkwdG+DgpANOS9OZeQ5V5LHKtAu6PBwjiyC0hLSCXilQm1qGebOAIG4UoY/XWCszvdmWExrbJ/bxiMfP4f93e3gfA3CM9UZWe3o2ZXUOo+fcabw5H1wuirIjtal8gpSht8HR1Y4Dy8dvFZwLsHc0bnRgYmdK2c5qjd14UDnfc160AAiKY2Ncpp7KtlhchrX1HFEXw2rkIMMQgyac5OJZAYAjtEdkPEfroxCA3LXq9c+juGsWTqDPZpmEXPfRVfAdopEvTsbO14A1PyABReElDHqYlKkDRJ7hAKR4WOofrE/Dyky1UMk2qCgxl1QvHXRqRFBqUcVROTk2sqVzRWMVqkLSKEUjDAhHeOiCADD3j6kcDiS5/sCAmHJ0N+M7lxuXHOZR55vIfk4gVrNIvwhpUKnWsignpO/nmtTvHVwAbsXZWKG8WbknBY/SKMNYfrOpWalnpmddG1SSqhhmco2spDZO8RcY2yRY0w0iofdJ0c3tiP1iDbIc5kgCC0E13mKY1O9EELGKEZrYvstFlMis3SBxeUtnCugnYe1MpKSuAOHEIiHswodM0arwwgLU43XCIPBOBjfLh7Qu7vnY+mDkirWZQghIQLkNxqt0OdDYr7YiznN0WiN4BDu7JIVzvN8dW0HY/pFwSr0juuCB2taE/v8WUu1s5snt45nviEgZKrHVSp14mibRWzEa63B2tpJrM7XMFoZxU4NBPtlusJSxLxXToOPe4AZ3x0dtG3bwtnUtsn7xE6h9dzFPGnXmh4JTpcaokiElbIqULWD4JELrG2tYrIxIRWe/Smm2/u49PA2zt17DhcfvIh7P/o3aJsFhFSxCD4am2M6tJtmDg8fI5e82N90LZr5AjaUAjBhQ/kgSB5ahPmATERCTBCellJAqio29Y2pE0FONzvube3QdQvU8wX2d6YoSuo7mCMwXBK1vz1FPV9gPt+F6VqIUB+5srKOajzAYEyRu3Ye1Sg8G5WaHHB0DJmabpfDEqduOQUd0k4XH7qIZU7EUQ6pdBQFobI8Ym4v5nuYz3ahFfXE1HOKtnSpMduZBZWmNkroDSdDgsLDYFnELtT9NvsN5vvz6Kzw74Uk5qkMyIrJuiENJ9T1Y7I+jo0M5KwOPYKHGK9PMBgNMF4fU7SvNanuZM5013YBmk0kN2cdmkVD6aBFDWctlC7QNS2U0phsTLBxZuOK8/ZJYYRuSTSA8iRVZCSSBF3W3FhJaCEiTZ4KaGWELliPT0oBLwDv+5JHzPAi9ft+bsR7DykkhAIgJIRJEQt7qAyNcnsm/uwkXBwizPC6tm6pNVDwOricganbh3mZRzkYYqLIivKD5Jg0sZbLOwdRCVgrIISGRTosuFZIsLwb5z4KjcJ5akDqxrH42doOWpvwfRo6CEBwUXVOc2fYkYuNnfOo3CiLIImmXYRC4FzzkpPingkRgZijgjHp2uTp2UCxZyLReLyOtVPrxzLfCA6JlIrWUkgjOEfqT21bRyPtnIkHHKMUUkp47aGcoiJ3IWIHh5xtyTVoNsD81gRiSoaq0FxJeLj4fABSnOLnTnljzkcKSJDiUjWq4F2JcShAl1Ji48wmJutjqEJj98Iuts9t48L9F/DARx7A7vZF7O9vAyDGLJU1GHhbJMTkGOT9moYUtdq2htb6QJF829WhznoI7z0KDzjlUvmRJiKXF4mQ4R1HYak8iZsicKSiCw0/TNFoW7eUNpjvR7Fsl+m3ArSnmmYRHUgpJcpyCGDY04v14bt4X7CQgbO2xwPg61VKYm08Qr1ao1200SAf1/BLa8x7WuNt16BuZhi7dQCIgUE9XWC2R9Fyu2jJmQmMbFXo6IjrUkdlG2dIbL0JcDGAaEy991FAQBccHSqM1sYkMRrgVH5tNUyoFM8pnekOLQy6zqAJhruZN2hmNZVyhHwo5x+bRYPFdBFsTCJgKQWsba1hZePKrdwepRYrEQzgU/G2MR2k1NHr9d6GRWZj+x8pJYROZRxMNAgfCu9Z8idIHWULNNG7ZUpiL0NWWY4HysNZJC89EGzYMOa9I9nz5smnTuMmGkfTJkMPJC3SXDLtOAaLJtDh66B1qhFt6lms2VK6gBCUJxTeAQGKMm0gBhQuPmFW0PABFipsGT6TIlGSDBRQUkMXiVSiFBEpOOeTVHdSk9jSD8ENtIuKerCpIinp9+HsJEJOh1rIMwkEsg7Vn5ms7lFJjdWNVaysH21fQh68dmSIkHleKM++iAQkG+ZMF0Q5Z0EAdia4XAHR6Kf7dw5JX9YmYlueRpDI9UiDRCCYfm8iaiGVCnXGAs5qCEkkD2bLVsMK1loopbB+aj2SS3Ye2cGlhy7hwgMXcP7cA5jNdlDX0+gQsWPL7MpcbekoR2daSEUCAV2X5L/Cw4jwPe0xunalQosrrUL3mIzZ7KlkSAaJGskiJQEtobwUsVuLgFx1TRdqtjssFlOYroGxBsY0sVl5QrhsRHWqahQdyR53gp9jJtFIIicKUto4jzENpCRWBgPsTYZYTBeUonLHJxjA5zN3zGAn0BjiklhLZR2mo36VjZKop4tYImE7g6IqUQ6KGNWzCLuzqQGx6QywSFUDfBZ762Lf2qLUqMahZ+RkiEHQ3taFjuz1Ighh5LAw558j0sQdauo2GkdWNxMCMfJt6wbcLNk5RmmIoMXlK5cb18xi7ZFpvAMsR5BUJxY9FAhiQAoKpwfjQcwvsWRZ3qnD5nnKLF/J+RydkR3iRDkHn9VDwftYcB7LMjKolDuFwGeZ/Dyn6hK0yrUyrHAS852FCp2v9VVx66MY3ns4TwZC6xJlWaGuDfb2L8I6g6oa9fJbDI85Y8EAiFSJtIP4s3RIFIGIwwxRgso1tC4iNChDoXlshG25r6eOEToXRAspMJwEiS2VZNmczctlsojeeaiALJASD3mqtGHJUFbVCOPxGm550q2YrF95MX+yI6qjhHXsLLF923ZBWqEBJRmN1nDqzC245Um3YHVzBfs7U7TndykKDpqnOXYfBaytg/LsRaeyIzKsgrqJgMknmZH0rGDlIumG57RnXMNzWtlcwXh1jK2btmLd6Wh1hOn2Ph6+5yH81e//JS4+ch7b2w9hb+9ihHQF6OAyYc6V6mBNibbujiWqMaZFocuU254lMf6iKNF1gAvlNV3bQNZJcYsjFma45zCrDFE718xVwxJCSjpMVRuNsHK0pq2xMX1hA1pAqjsdmFSjVIGyrKB1haKoMBxOSLShGlHrN1ZZUjKy9JOB1NAFnVcdwvkUNETLqsDGZALjiH0ppSBd0uNsdyXTtdGaokYI+3uXsLFxllSgnEetZCh3SVUIo7VQzL+1Rueg1lHljMtsdKEwWhuRJm6ouWYJRXZkWB2Ma3pXNlcwnFDVQ4RugygHFM0Zq3bxmpdKYDGtqZk4Q6jBOJo2rdn5/qInUemcQx0CjKIY4PStp7F2cu2Kc/aYaJg9Yg23vQJiBJkr1XDkyI1EwwdkDyxLIHNdkOLcWupCALAIM0c08WIOXFfvM9nw0gf0iBPeJ3FpG7xN/n42QlQHRMyzSCE/ttY0eakBfX9ZDuMBysXkpCGZWJdMyGFHgUSFVZThip+pBJQPReulhrISzhXBQPZVQBJLMKkLESOWGITe+R70xa1w0r34CAumH/p4j+wYmeCBUvSQ4N7JZB3rp9dx8uaTx6Z9y9Aqe5hts6DDsm2SYABfy8l1bJ7ZxHAygLUOi7BJ41hCPmi+idiglIKVNstxufjM6BkSMckjiWjwHLE2KG3uMsp6MYFhvD7G1g0nMNlYwekbtmCshXGUP3vonofx8D0P4YFPfBzT6TZmsx20zSLCilZKCCNDvpfm31oLZSWsPT4iGgsrGGNDXVva512HJJoQCtvpukoAA+jMYef5jvs+kHkgRJxnaygtkOsUK62C0HgTv1dJDaOTgZSC8o1FOUBVDTEcrlIaYVhFiTSWTWPWLRACBbUkNReEyVWhUQ6ot+diUGE8GkIoSd3u59laOsIhM+PYfwYWbVRxIoTQBNGFvJ4wbwAd63tDoOCti2IBHJBYE5jooTSOHTwRnkkUdhkPUA0r6KDCw0ENkM5lZy28S2pAQgiCgecET3ehaXje2D6mykyCeAkt2MP6+mlM1idYP7WGcnhlsZdrb5ic/Tv3ZJmcEb1a+Cg3x2wiblrK0mk6dCUAAKltj8WGzLAxtBpzftl3x8NbiIPG8RASTt4lnKNTLtyOUU0WwvOi4NfpQofaR8qtudYd+I6jGnxAxvuUGlU1BMtedV0boqthLEcgUk1qtMsqIUQtp3q7HK4OGvChK0oqmGXiCSfL0/z15bDynnqcJ9BlAW6ezGiAEKInpQUgFvSyCoZzLkoHdl3y9LUusXpiFVs3nMDJm0/iwY8+eCzzzbC9z2BVzjs57yAFKb+srp7A+ql1bJzdQFkVaJsO0+0qtgLKPy+fO55zqXzMTQHsbLi4P5xTSTjBJRIDRZIuMpr5cClK6tIxWh1h4/QGtm7awsb6Km7c2MC0rrE9n+MTD2/j/g/fj49/6CN46KGPhq4WXT+FIQkGNKaLf5hlvNwa7qgGrXEXyUnDYkgoUcWsaQvvyMhztBMNd3AGpeprEScEKq1nGQhTRVmgUU0oLE8pE4KjV6BUAQGBriDClIcHN1IgyUTq1DFZm8SmCZznLIMYhi4SKY2cf8Tvy/OsXBs7KkusDIZYHdahtZ3BfDo7lvkmR1smZnQWRJBIxcHi+q5pI49BFyo5wooaKRA6SOcEG0HuixkbFAfB/1iaE89dOhvyUg5XFVBNB9N0vXpUG5oW+NpHp5LRvrZu0dXUxYUdcRaqb9tFIuw4Wt91PcNgMMbayTWc3FyHcQ5XOsg/KRYrAFAzQQdjSHuwaeax8wZT5LUq4D3VX0lunBsWB0coeR0UgKW+bamkIx7uwTByfgsAwavORS/DBQ0/9iwiRVkEQ1fISKLgKNdlCu8584oi1UAwClAvR1LHpezScxCQFFWkoIN1Z+cRNM0Ce3sXYw4B3qOqRqjEADHPF+Ac7YvUZolHgPfKQRU3MnpzzHNOsGqk2If3MdGKpemYbCN1UsCI+q8hepKKHAvWWOQ5tMZif2cnGH7quFAUA2yeOonHf/rjcfq2UxhUJdrF8USQAuRktZkARtc1gKeylcFgjNXVLdx86x04efNJbK6uoCoKmFN0kFAOhAS3uT8hibD3HTcR4GSpCYHgbupdK2KUmWoDdSLIcBeGwMDkonWGUycbE6yfXMeZjTUMihLGWXz44/fjgY88gD/69d/HhQv3R4kx75PqCw8+PGndcBTPBdhHj5JU1RBakw4rC+SfuOFEzJWeu/cc9i9JuNDto+0MmlB3WxQGRVEFp5Vg8UQ8IeUg05lwQDu4ImNKKwWvHVyhYFoJX2oMQO3znJ2gbdZilEfzQmdPWRUkDRha7jFbnL6fIimtqUY1sl+dCxEQITld3UaZtI3TGzi9tYFCKWyMx3De4YYn3ICdR3awt3c8jaq5Wwkbfz67WPavLAexGJ/TS02ziG0JF9Oa0i1DamsllUJZFqiKAkoKCCQBfOeofZWzPtY7RqUsTgmFmt7RcIBSa2qgDI+66lA3Lez2lFip+/PQgYPKctq6jeTOtiZhhyZDltgZ4ebhTMrhdmdlOcRtT74NT/iMJ2BcVdhdLK44b9dUB5lHkLTBVJx0rtdiAymECKUDAR5y3Hew72ErTXWEiokJDNdmKggAUrEu0GON9q8pUeJ9ppBw8BDgyFHGvBzLnCWvUoQIN2kjMvsqGuRgSPN2L0c52BDmeVK6dh0gIaoHbNs6wlBFQYcLwdEpQiG5MJvJd6UhpIiQcx4RHnRMss7sYf7YWVBKkRiDEKF7QRAmiJBX/95i37bgUVtwKysymJxvHY4nOHnTFrZu2sL6iTXsbu/3ocwjHD50qmFWtrUdmA05GIyxtnYSGxtncPLGLYzXxhiWJbSUGA4I2iwqytd1dQuEUoH82eWDewDma4k7yPAzkcEpciL1NZUqOGlKYbQywnhthNUTq1jbWsNwMsDKaAAlJJquw4OXtvHx//NxfOKDn8DDD9+D2WwPTTPHsoTj5YZzLkaZx+EEFsUAOraxI+NMFP4JvPfYeWQHi3IB3ZawzsB5hzY4ufyMlNJQnY05LZrL5JSxxmkOE3KOUpcFCuMgNTVqdo5Y4oPO9Gpz88bAqlDQWmMwohrfYlCkMqqQ72WH2lgTNZtNy2Q/D60VBpMhOTRjKldQUmJSDbB14xZWNldQlcMjn28AMULktZ47SVoVVPIVylX4vONAwHY2qOsUUU+V1wU1MpbQUpD0ppRQUkI6ByccbEkKPC7TVhUitC7UCqOyjGucncZe7bAIylssF2cymbsQ6eaHDLcz5EiZ0yach1xdPYH10xtYPbkG5z01Dr/CuOYc5PKm4sQ+3ZhN5QfeYzRaJcsNB+4EHz1S11+wicKb6iF5onjB8YENKaJXzQ+PcpbI6iA5KkkFyGTcgvoOMyyFiBuJanJSPR7HWjEBH6JKvl/v/QFB3aMcShfIJc9i9KEUBoMxrDXQSmN75xzalvRa+TmIAAnx+5iRp7SCD3BeDkvzAcORMmKUmDa/lf38JcPeHM17h/iscmfHh2g+Nwbc+45LClhVhdiZ1JtvNCEI5KY7bsYtt5zBynCIj/7FPZjtHg/8RPPUxbwjw0HURPkETp28BSdP3YobnnAjVk6soAjox7iqYNZJNWQxXaAJUKsqCJI6jODGdpOjZ+e43VG6HnYKWQmG0QytFYoBKZWsba3hxA2bWN1cxagqMa4qWO+wPZvjI3/6Efyf3/8L3HvvX+KhBz8G7qsqRHmogcwRCyYEUSNzC9EdfT/IqhrGnBgQZM+21rB2cg3OOpy/7zzmezPYbhSvretagly9R9fVPZif5izJzjGjUhcKrtS986YQOjq7jDblnUFYuMB7n+oqs3KdwZj6PY5Wxwmi7GzcOy6ws43JBLJDD0LuWHFmcwOnVldRaU0H9GiET7n1Rnzihk/g4XuuXJf3WMZyGoDWpYQuSgzHE4xXR+SEhnstqoIk32ZzLKYLqEKhqcdZH1MXolEJhChQSQElJKwUMFagLItglMM1hPN5UJWotMawLCktlxldPndUiMhd0G9tm66HPKaWWKlhQ+TEZOkJYwy6rkZRDHDy9I3YunELpzbW0VlLBv4K5XrXrKTDF374a0iWqq5nkTwCAFx0Hb0xKWAGJU3uiNqjlIOyL8cVtP6Y+Zp7E+w5JKyb81ihi0JgR/LkSCUBJaGACKkmGagAfbDKAqujhO/mkdf8UIRLm2i0OsZ47coyRZ/sSJAXJ5cNRBAcF0piPF5DVQ6hi6rHtrSGKOurK5vQRUl5FSGAAVD6EtZ6wCJ1bECWsxUe1noIRxGkV0RSgBCxkJsPej404vs9IQReJpIEQ0xA2ozWWtSzGvWcjHp+MI8n61GeavXEKk7ceAJPeuoTUBUaF6dTXHjgwrGRdNp2gbap0TQLYnY6i2o4werqCdx221PxuH/wD3DqttO46Yk34eT6GqpCY1o3uLQ/xaWHL+HSw5cw3Z5iEVhzRVVgdXMltoxq6yayeL33MU9jcpZ1Bqf2CD6S9UhDTjzAdEVJClZ8uLTG4sMf+jge/OgD+B+/+T489NA92N+/CGO7uMkPOLkhv51/H5W0OGitMRxOokTdUQ6WDDSmg9ZEdNk4vYETpzfQtB1GayPK7TYdKj8ilMKTkHtnWnRdG++HrlXFGmtrVIRtI2s1OCzVqIr32g6CDigz1jOFHe99bM0GreCDeLbSisrGWoPFdJGhJD4aXWbDtwuqJVxMa9SzOjI2b77jZtywvo7NMcnaaSkxAHDLiRO47Sm3HVvOlyJ1F/7mnyXlqNHKCKO1MZpFEzsqcdsp0xrM9vco/8tlRKE+XJc65Sal6J3ZQAoyhAhBlZYQgpSa6q6Dh4cKJTSNMWhakrlbTGsqzws1lm3Tod5fxHOHVXEARPET5jKY1sA6aqZhbBclG1dXT+BTn/mpOH3TSawOh7iwv09r5LEYyHzkobkXfTINRwzWUiK0LIfouobo+q1G1xALyofkNbO9WEs0wSQhFylBklIiy38eIn3FsKyLyeFM4zHLm+V5TDayziRyERvp/PAHkB58eB9f82A8wGTjeKTP2BDCOgAHnRIlNVAAAz8KuV5Nc+0M5vNdcJf0qhpRyUareg6A96BaU5lEzvPNDpCcFxTXQqYWVfx6nnsEwyelhHBZL82l8gCWfSI4tQlF10Q6UUrF3PRwMiQizKkNnFxZwYXpFHt70yRsfgzDxI0UyoGEwHA4wcrKCWydugEnbz6JkzduYWWVVTwcLk2n2L2wi51zO8E4zlHPaqg29BXUKnYeAEBG0vooiZX0fsmx40gfQIj26N9OZFG9UjCdJrZj3aKZEbmjVS32AHzig5/AfR+6Dw8++FFMp9tRmJphtbyeL0a1GVJBeXpqgF4UpN+6snn0a5zREd7LrJiyNhphX9YB5aF1Ae0hxABladAFh4DyaY4OQNXAOQ3tPJxTUIqMFTtTulAoEVAglRxtKSVsmGSG/FltK6nyyMhUl1qFkiYZO3/kfSm1JiIht2ljiTNqNWZQVAVGK0NsnFjDZDBAoROhTgY0YuvEGma3njry+ebBxjBXauKfx8bIBRsbD12qQL7TqGuLtm4w251hvjcP70Mg5iWeBosA8FnLaB1F+yHTtgRtKskGMvVKZYHxNhhJQp0slGcAjEsBKRdtrQMiPB7WFxJcPhiMsbq+gVO3nMLKcAglBbpryK9fs4HM2ZH0pXJJokvChU4f+/uXoHWB4XCCrmshkKIhE1TihaADlxPeXFQNJM9QaQUZdBf55zy8R8w1mkDxNW1gP5mkuSqYNRhznBRtst4gRbcCQuUPMhlrVvzh2j0AGK2OsH5qDSduOCbps/D9cQNJBZtBlxBUwD8cFhgMxiTq7R329y9hf+8ijOkwHK5gMmEGF9GwdaGoGF2SEQQSzM1tk5gFJtiZCdJpJNSw5JxwrVRrIASoKFqlPFdPNjB41YvZHG2AMYtiEMkBXK4wWh3h7OPP4vSNJ7G1soKPnDuHCw9eJOq3PBzBeKyDO86zfqlSGutrp3D69K247cm34eY7bsbGmU2cXFmBcRa78znu/9iDuHD/BZy//zzO3/8I2rZG19Uhf3wC1bDCiRu2UAwKXHrwEnYv7IYmyU3MjUVJva5FFwg0AKnaCKH67DohomTjdGdKB0ndUjPx1mDv4h7+1/v+AA888BE89NDHSDos1Hcujx4SJLL8XWiTpXWJtY1NIiSdPXEM882C7JYE8jcm2FxfxYnJBM770CaNlZhKKOsATMgIhY4r3pOIftMsIISEUhqFqWI9MJ8H5NgyxBrIfT7BqyaQrEwg0rCBXO7LqILWbdcmBrAOpTactoklKkHsu5nTId91HSbrY6ydWsfjTp7EymCAUiUExnmPUVXi1q2TqHSB2I7lCEeOEgCIBDIWDmBG9HAypPrCusFQDIlYFNCztq2xd4nk9LjbBiNsUklUwxJFVWI4GUTngTqdqCgzF+85dOoxRYKvTZCrq+fU7aOru1js34XOH9FmKJmheyXcglAaho1TuoBSOJtbZ3D28Wfx+NtvonSE8zDWotRXNoHXzGLN/gc+XFm/kSeePfBBNcZwuILV1RMQQqILTMuuHaEoK5hQgO8CdswMx3zwgutRp5E2N1GLCZ5qFk1UdW+bLkJYOeMSQIJBbKrRVEpBBO+Ph7OBlRmMo3cOTdNF4dwbHn8WN9x2FjdvbgI7O9cyhY9qREdBAhIEjwA6Ut0ToUhDgGjYk8lGaFk1iQnp+XwX3pNaSKFLVKMhijJEjFaQ/FVeHykFnOtHiC7Q/SVcijhFIuCwc0JQYF/6i3POANDVtPjblqCp4XCC0WQltg1jCOrs487gU55wC06urGBnPsf2uW3snNuOJSTHMUxHeq8QIjh2K9g6eRNO33ATTt58EqsnVrEyGsI6h4v7U+xc2sP9H76fWIcX93rNjoUQqOaj2N5ttDKCuJE86aKiPouL6SLUcS3AOrhNs4jPtiiqyAh3mbpK21ZommGMjhjaXixmuHTpYTzwwIcxnW7T7+BjnTGJr2fC6SAyBakrEdxlgrzEZLKB9fVTOHP7GWye2TyWCJLbrFFtqMbmDSewMSbykwz5J611MGZ04OWEOHagk8AFNU8gKr+GMSVsR50mWOPXORe7UQCIznQ9r9GEQnROz0Tn26e8GElMKiz2w0UIgWpIfWmLQRFEN+hM62pSeFnsE0OyHFTYumkLWzecwOZ4jKogPgOTWoT3kF5gazKBlhKLhx8+8jnnQXs3zSOV5RHbvRpWOH3baTxy7zm0dYvZ7iw6u1qTMEjbLrBzbgfzXWKXFqWOsCqlzAoSbw8/I1EByp0zFJrXRlNET8aU60AX0wXmu3N0TRujSWorViaDm3VSIkGC0M7KOnC3KUZlqmqE25/6ONz8qbfg1OoqZk2Dumtj5H4lh+RIWCbshfBklhVR40ejNVjboeuoYzrlS5KShAwehg/1TLHAF4APzWQ5f8kjFuIKkfquLSk3cGQppAQCpZ4YbnT4c8sr/pyoq+oTyUdCHohYikGB8eooRhNroxGmx2AgeyNQqIXrs4njEALCkz4kE0vqeoqua0NU00IIhaaZh7xkljt0YbEynL0EX/OIsGuWCwYo6lBaUbd6Kfq1VcZFJ8R7REafdw5FWZEayaCMsMxgNMBkfYLNG07g7PoahmWF+y9exHyXOk8cp/atjSpCipyMaoTJZAOTjRWMVii6LZSibhjTBfbO72L3/C5Bq7NpjEBN1wBCou1qdE0LeA+pZWxy7D3R3r33IQKUcJFcZmL9HwBqnCxE9ICJSETPdD7fJYHmjljMi8UUOzuPYDrdjmVXIn9eS9EDkNSDCMmxJPGhyXGZrJJGJakiHT1TmzrauwDfF9RjtCgiq5Px5ZzxW5TslFVwzkKKvEbU9+YJSE5mu2jj/edkKTaQTKJh8Wx2Oljdi+UOrVSwnYznhpTkOKtCh3QNdQYxxqKrKYLsuhZVVWE4GWJtaw0b6ysYVSW0VLHZuhREmnPeY1iSI3/lwoPHNuL8oI/WARRAjMcjWtfTGvuX9rN6ZgXAhmCBGq6rQsEGgQchqAkFt5zi4KRrOhJYabrYNSUnivHrACona4JYOpdwkCQgOymh1Ckrp2GSlA2NwYkQGhwnZ6GLEqPRGk7efBJbN55ApTWmdQ1jHbRSGBRXjtivwUCmiJFvKkJoocTDZ5GF1gXW1k7hxIkbcfLGkzh//yOYtbvY27uAtl2gLIek36pJQ5QVQUQgVorAWDWdDeUE5sDBzIudVBRa6lYd2JF8bYSrc22UhRECUlH+k6ncQMq/AYS7dy2JZVPerQRA7xmMBjhz2xlsnt3EU2+9BZPBAMOiwPTqE/ioB0NpzNyVUgIFIG3K9VKhtaU6SUGlNVqvQhU6dB7Yw87OI5Gdubd3AUonp8TBwXsBoaj1Czdd7i/cJLxMDGAdc7lFqWENoQFcO0V92WxkmLGj0tYdFvukTlOUFcYrqxitjKjeNMz/5g2buOH2s/jMOz4FN25sYtY0+MT5C9i7uIf5/py80mOCWNkolUWF8XgNmyduwNbZ09g4s4HhyohgLwAXp1Ocu/ccHr7nITxy3znU9TTIodVw3kXVjnoxxd6FPWqoaxyqUYV1vY7JxgQrGxPsPLKLnfM7uHD/Bcz35/DTfUipozYmIwBSUmG3s5aak2fyYG1LrYqozx8V1dPzVVBSQ+ki5DIT05wiyX77La4VK8K9b526AaduOYXVE6sYjAfxUDvKQb1EO0wm6xiMhti6aQtroxF1oA8HQV63TNeLWPvMLY2kUnCqoMYJXRsUvFJJiLUd5FRFokdlkmoKGbEOs70ZFrNplJfL0zw8OAqntULPWKkCZTuA1kUUCYAQkSTCLO3h6gZBe59yS4BXh9BKkSOQDeE9xlWFQVHgoSOf8YzdyTnn6DS4IBRAztza1hqElJhsTPChP/5QNFaMRnj4SJJqZkETOjgxtjMQiwbz/awfbRAaYBWe/pwSaYfFBrzzEZ5uF20ykJ2JnVjywUhgM28o1xuIosY06FoqCZtMNnD70x6PO572BNwWugEZa2GsxWQwwNpoBDO7PDv+GlZ/prghRFy4YdajQWI9ViEkiqJEVQ5Rjamg1HsXN3LbcFQj0NYrFL5nMCobwihLF+ol2SjGB+5SDocV3BM+TZJPzK7KvSSZ5ffyxcN6rHB9UpAqFMqqwPrpDdzwhBtw89YJnJiQaPa1JHkf05AHPf98CKS5KbjrNzdk1RpKFZjNdtA0c+ztXwIANM0aiuqmWORvOwsvHZyTve9yUgLWQjkZPbhOJAEBAFEMnpUuWFoqijSEjhBdKH/QusJohVrbFIOSmHLDEoPJELc+6VbcesMp3LSxAescLs1muPjAxSi9RZDM8RhIgNZDWQ0xGq1hc/MGbJxex+qJ1cjgbYzBxQcv4uIDF3HxoUto6llIG9RoO1p/FM04tF2D/Uv7xMRrWgzHA+rKHjpwcPPXta01LKYLzHZm2D63jb1Lu3jooY9hb+9CZJPmZRccJbXNgkhFQekHAGSEUamkg3OQfG8AInoAIBphhnSrcoj19VOk57pODWlZMeaoR9c16LoWw+EqVk+s4vTpExiVJbSSFGGVweAgFevD+16TcuEcpBWwQgLBcYRB1C921sAABGMHhmq7aHtauM7aEC06sNKKczZ56ki13vxvnk92VqRUUA1J0nGvRJbcXN3YwNnHncETPuMJuHVrCycmk0hIOWwIIQ4YzqMaedlY+EEvNeZCrboqNFY3VzAYD+CMw8UHL+D8/Rewv70HuDAHDFkbC9l0sWNSvIdQF8piAJ3seoEIELqeSBGg9PRzlvxkLolzLj4LrnONgRoTM50LqTbiAZiuhbEdVle3cOqW03jiZz0RZ9bWUOkC87aF9VRGuDYckljAFebtUZF0AKRaN3CeI0QiEIBI1HFdlAFGpYVOeR6LTlErG6VUKMgWyQMDG0iKVmSQpVtubwUgkEO6GLkIkVQaqC0W51zS+3LjHu/BUbo6r9HMBb6LssBgMsTG6Q3cdGITN25uYlxVqLsO5rgNZD4CO5S8uDDC/TITlAlPPQ3IIOXVdQ3mIYFizBnwo/fSx27teY4vdt1WDkIYuKDJ6WyAhGJ3FPLOuZCXI/AYTRqLruuiRNpwZRQlunShMVodY/3UOm45ewo3bmxibTTCI3t72F8sMN2eUlf7zCE7rkGSYkOMx6tYW9vCZIOEv/lAbroOexf3sH9pj8SR2wXlOTKhd/5jTIvZjIQNutakQ08CVVmgrGhNFVWBelZjthqcx5pyofP5PurFNLZe4s/kJsNdV2drNRBxsvIQygf3nUohZILV4HsREwlvlxgOV1CNqpj+4N6WRz04dzgYjDFZH2MjFM1LITAsq6C4FTrKZPW1UvoAaXoIK6gBsQe8l5GVSoTAUJ8XInIfDnPVpAbiXDqSQ6ncD5U7ibBBzGXYlNJRt1cI7q8Y4Go6AKODsra1iq2bTuLWm85ga2WCcVVd0eGl7zqeNZ7DmvlIjODQPEBLlAXpo/rbTlFaYN5gsU85cilV6PpCzhobsVxKj8tuvHNQLoNVsxSa1or0Z7XJriU7V4LzzZUD5NB4QKRaycSRIAe96xqyMwEWX13fwIkbTuBxt92AtdEIWkl0XBcuZYzYj8RA5jeR/33YhLPqvo5NSDWcd1hM9+A9Jean022U5RCj0SoGg3FU6496k7oMf/dD60jvXc5LCooYOZRfFiCQGT04qvpIEaWlKAqWQUi4jEypjTObWD+1jqfecTtuP3UK66MRFm0bE9zHMjjvmMFgiZjDOcQgrqxY4iprTBzmqKhKrK5RTWQdpLqatkazqGGU7jHBGEYFEOAO8vrkTPbalPXgudjXMHh6YTOl3IGJ18FNUYeTYTj0BDbPnsDZx5/F0z7lcbj91CmMQg7moZ0d3HfpIvYv7YXOIcE7P6bDg1IDJba2bsLZs0/A457yOJx53Bmsbq1iUg3QGIPFrMYj957DxYcuYXf3PBaL/V6TaTpg02bf3j6H6fY+6tkGNB/G4foLpaEqCbG5EkkH93/4fsz29/DIuY9je+dc7JnIIyqh+ERjBwS4qTXDqfwnTzXw4HZK1MqrifdN6kwTDEbDUD/oo+iBMRbYXDvS+eac1snTN+LkzSdxdn2dJMuEwA3r6zj7uDOwncEjn3gECKgFq7HwXohF/ZbQD4D2Rl4J5OGjiAajXGzEiqIkxnGIBjlvmxtDhEiQI/V8TnPng5m1zKZdWzuJ9ZMbeMo/eioed/uNuOPsWUyqAbSi3KO8zNnZGYMmK/c5ypH2bTgX4SA8IsTaNR26tkOhdLy+02e2UAZhirbpsHdRYbq3G+fT2g5ty4bNRofOGAltKlhLakf8etMmgiGTMKOISwiMGKFKhCk+q2U4xzNR+vCnrVssFjPM5/touwZKaQwGY3z6sz8dj3vKbXjKTTfBeY+667DoOighUZUKa6MRkXSuMB6Vkk6+KPjiYtdupM3LWqyR2VQOQtNfF/Ms1lLXhK6tsaiG0LpMLW10ibIchsJoncSJpYQQKkYjec+9eE3WwXoPawJpJCTc2RtehnJ7C0jJIAKdoteN0xvYOrOJGzc2MBkMoKQkvUGfeqsd9ciNPxt0NuL0+xRtcySGbA5YDcRaagtUVSOsr58CN/3VukDbLrBYdCjLYXRoeLEioExJiShIdDVqqZ401ZxyvrGZN2Cx7bIsY7ssLjNhRlw1qnDTE2/CbTecxs0nTmBcVdBKwXmPnfkMs905vA+dMMIheVwGUmuS2traugmnb7oBJ285RZJyA7qm7f0p9i7uYeeR3ZCvaoNRzJSAwtoWQpAjUs8w3ycqvHWudyiysXTOoZ4ucPGBC3jwY/fj4Yf/FvvT7Qin8jNmo4coYn9lJ5UH7Rl2qAiOQnDsmCBTFlVgzRKVv2uodGS2M0MTBLlx8+kjnW9WylrbWsPK5ipGZQklBLQi6bGz6+uYnp5Fh4zyYwpwlC/XWsUaRqkVNADvSwjRwTmZ6lkj2pQaKvCgOlyK+iw/R5+ik97cChL/c8iYl4J5Ey57ViAG9I0ncdOn3IjH3X4jbtzcxLAs++kL7+N6cNl52hiDxTGUeMTbyM4IcqLYoHWxk1E+rHMohxXWT63jxNlNAEA9C70jvQ/C8QklhAUcbLQDPfEQ69C1TYSfhRgCGpCOyZAecHR2A4gRKSOC+fUDSFFja1DP5pTu6Kht2mi0ilM33IBbPpXyvlpJLFpC/Lz3KIsCw6LAqCyJpHOF8aiFAg6b9EMZcgHmKwdFMHpFfBgusPW6rkHbNSibOZQuwmFdoSwrWNNBKh0hDSFDmx9VwPtA4Am1iwmjJhaTcFnkFXINDDnlqj2sHUrGkX+monEsqgIrmys4s7aGk6srGIRGwiaTRDqOkXuouUOy/J1sHHslMuxdByEEpkavrGwm5mCh0M3oEPch8pNLOYKY1LcO1irIzsKFSI7nkL+DjLIPNPA2Ok7cVJWVX6ROohDloMQtZ0/hlq0tnFpdhZISzlNt0t6C1Ed4LeViEccz38QCXls7iY3TqZ0VCTFLNHMqkJ7tBFJO14Jzg85SDSozKOk5GbQd3QPnxxEiB8eeL0hYu5k32Dm/i/PnP4FLlx5CvZiGXJjr7S32/kVWi3rY3uvfV4InCUlJUDCXeBTlAFqFnq5BJaaZN71uFUc9vLcQQmG8NsJwZYhBmGfOz21OxljboBIgFh6PqJGk0jDpAa8Cw10DmqM7brnHhBSHeGCzIaO1LWM0yU7DYXsMQIiqgVxUIclOimSQQTJ6G6c3cPpxZ3Dr1laEj688H+QwddZg0XVXfO0nO9gg0r/7gY5zIUWylDIy1qJQCsUqST/W84aatAdDlDtx3MdUOvRSA2wcnfOxCTpF+g7ShetwSEYyrFVjTIYU9p8HX7sJXTvqZhbFyAWIib110xYef+YUzq6vQwqq0Tdhj5ZKYRiM49WejfBXcMs/8IEPPJpn8P/Z8fSnP/3IPuv6nF99XJ/vv/txVHN+fb6vbVxf43+343LzfUUDeX1cH9fH9XF9XB//Xx3H1y78+rg+ro/r4/q4Pv4fHtcN5PVxfVwf18f1cX0cMq7JQN533314+ctfjhe84AV4yUtegpe97GX4yEc+ctzXduj4zu/8Tvz6r/96/P9zn/tcvOENb4j/f81rXoPf+q3f6r3n2c9+Nr7u674OL37xi/H85z8fb3vb2x71977mNa/B+9///k/+wq9x3HXXXfjYxz72qN7zwQ9+EP/+3//7y/7+8z7v8w787O6778Zv//ZvP+rru/vuu/GWt7wl/v8d73gHXvjCF2J39/Bqoqtd2yc7vuVbvuVIP+/6Gv+7W+Of7Pg3/+bf4K677sKXfumX4gu+4Atw11134eUvf/mRff4f//Ef45nPfCbuuusuvPjFL8YLXvAC/PVf/zWAq+/LV77ylWiPkYH6dzXe9ra34fM///PRNAebk7///e/HL/7iL172vYetn7qu8ZrXvAbf+I3fiDvvvBMvf/nLsb1NesGHnUv/1w1/lTGfz/3znvc8/6d/+qfxZ3/+53/uX/ziF1/trccyfvVXf9W/8Y1v9N57f++99/qXvexl/nnPe178/Rd/8Rf7/f393nu+8Au/0Nd17b33vmka/0Vf9EX+woULj+p7X/3qV/vf/d3ffYxXf/Xx4he/2H/0ox890s/83M/93CP7rF/6pV/yP/iDP+i99/5tb3ub//qv/3o/m82O7PP/Psb1NU7j72qNP9aRr8GjHH/0R3/kX/GKV8T//97v/Z5/2cte5r0/nn35f+P48i//cv+mN73J/9Iv/dKjfu9h6+fnfu7nes/qP/7H/+i/93u/13t/tOfScY2rlnm8733vwzOe8Qx8xmd8RvzZ0572NPzsz/4sAOChhx7C61//ejRNg6qq8L3f+704e/Ys3v72t+PXfu3XoLXGZ37mZ+I7vuM78GM/9mP4sz/7M8znc7zpTW/Cb/7mb+K3fuu3sLm5icVigW/7tm/Dk570JLzuda+LXsZ3fdd34YlPfGL87mc+85n46Z/+aQDA7/7u7+LZz342fud3fgcf/ehHUVUVTp8+jUmQgjts1HUNrTUGgwHuvvtu3HPPPXjVq16Fpmnw3Oc+F7/zO7+D//yf/zN++Zd/GVJKPPWpT8V3fdd3AQB+8Rd/ET/90z+N6XSK7/me78HTnva0T8IlubbxYz/2Y9ja2sKdd96Jj33sY/ie7/kevPOd78RXfMVX4LM/+7PxN3/zNxBC4Md//Mfx13/91/iFX/gF/PAP/zDe85734F3vehecc3j2s5+Nl7/85WjbFt/+7d+OBx98EOvr6/h3/+7f4Sd+4iewtbWF22+/HW95y1tQFAVe8IIX4OTJk/iRH/kRVFWF9fV1/Ot//a+xurp64Pre+ta34gMf+AB+6qd+CmUo8P+DP/iDA+/94Ac/iF/4hV/Av/yX/xLf+Z3fCQCYzWa455578Id/+Id4znOegz/4gz8AQF74C1/4QjzwwAN43/veh7qucf78ebzkJS/Bb//2b+MjH/kI/tW/+ld4znOeg8/7vM+L73us4/oa//tZ40cxuq7Da1/7Wtx///2w1uKlL30pvuzLvgx33XUX7rjjDnzkIx/BdDrFj/7oj+L3f//38fGPfxyvfvWrYa3FV3/1V+O9730vqssUi+/t7WFzc7P3s4cffhjf8z3fg6ZpcP78ebziFa/Ac57zHDz72c/Gb/zGb+C7v/u7sbOzg52dHfzkT/4k1taOVmThOMcf//Ef45ZbbsELX/hCfMd3fAee//zn46677sLm5iZ2d3fxvOc9D/feey9e9apX4Z3vfCd+9Vd/FUIIfNmXfRle8pKXHPqZW1tbeO9734t/+A//IT77sz8bd911VyzTOOxcunjx4qHz++Vf/uW47bbbUBQF3vCGN+A7vuM7MJ1OYa3Ft33bt+GZz3zmoWfjyspj60ZzVQN5//3345Zbbon//xf/4l9gOp3ikUcewTve8Q78wA/8AO666y4861nPwh/+4R/iLW95C172spfhN37jN/ALv/AL0FrjW7/1W/G+970PAHD77bfju77ru/ChD30Iv/d7v4f3vve96LoOX/EVXwEA+Imf+Ak84xnPwIte9CJ8/OMfx2tf+1q8613vit+/ubkJIQT29/fx/ve/H2984xthjMH73/9+rK2t4R/9o3906H184zd+I4QQuOeee/CsZz0Lo9Hosvd8991347u/+7vxtKc9DT//8z8f26Y8+clPxjd/8zfj7rvvxt133/33cnjMZjM873nPw+tf/3p8+7d/O97//vdja4tEeC9evIi3ve1t+JVf+RVUVYUf+qEfwmw2w3w+xytf+UrcdNNNuOuuu/DBD36w95lN0+A973kPvPf4oi/6IrzrXe/C6dOn8Y53vANvfetb8epXv7r3+v/6X/8rbr31Vuzt7fXqqV7/+tcfeO8XfMEXAABuvvlmvPOd70Tbtvimb/om/OiP/iiGw8t3q5/NZtEA/af/9J/w7ne/G3/8x3+Mn/3Zn8VznvOcI5zR62v8/7Y1/mjGL/7iL2JzcxNvectbMJ1O8fznPx/PeMYzAJCT87rXvQ4//MM/jF/7tV/Di170Ijz/+c/Hq171Kvze7/0ePudzPueAcfyjP/oj3HXXXWjbFh/60IfwH/7Df+j9/p577sFLX/pSfM7nfA7+9E//FD/2Yz92YD0+4xnPwDd8wzcc630fx3jPe96Dr/3ar8Xtt9+Osizx53/+5wCAL//yL8cXf/EX4+677wYAfPSjH8Wv//qv4+d//ucBAC996Uvx+Z//+Yd+5j/5J/8EQgi8973vxWtf+1r8g3/wD6JDeNi5NJ1OD53f+XyOb/7mb8aTnvQk/MAP/AA+93M/F1//9V+Pc+fO4c4778Rv//ZvH3o2Pu95z3tMc3JVA3nmzBn85V/+Zfz/W9/6VgDAC17wAhhj8OEPfxg/+ZM/iZ/+6Z+OKin33HMPPu3TPg1FUCn4zM/8zJjPedzjHgcA+NjHPoanPvWppIajFJ7ylKcAAD784Q/jj/7oj/Abv/EbAHBobuuZz3wm/sf/+B/Y3t7G2bNn8Y//8T/Gm9/8ZozHY3z913/9offx9re/HVVVoW1bvOxlL8Ov/Mqv9H7vs2qX7//+78fb3/52vPnNb8anf/qnx989+clPBkBeUV3XV5u6ax6z2QxlWcb5ulpR/JOe9CQAwNmzZ3u5gvvuuw+f8imfgsFgAAB41ateBQBYW1vDTTfdFK99sejLmPEz2d7exmQywenTpJzyWZ/1Wfi3//bfHvj+T/3UT8WP//iP4wd/8Afxxje+EW9605su+142kAAV/77yla/EV37lV+JZz3rWgc/Nn8GnfuqnAgBWVlbw+Mc/HkIIrK2tHZobeazj+ho//jV+XONjH/sYPvdzPxcAMJlM8PjHPx733XcfgLRPzpw5gwsXLmAymeCzPuuz8Pu///u4++678c3f/M0HPu8Zz3gGfviHfxgAGcMXvvCFvbzayZMn8da3vhXvfe97g2rSQWk4fv7/L43d3V28//3vx6VLl/DOd74T0+kUP/dzPwfg4P18+MMfxoMPPhidgN3dXdx7772Hfu6f/dmf4ZnPfCa+5Eu+BNZa/Jf/8l/w2te+Fnffffeh59KV5jffV+xsMppy8eJFAJc/Gz/ZcVWSzhd90RfhD//wD/G///f/jj+799578fDDD0MIgdtvvz2G3G94wxvwpV/6pbj99tvxF3/xFzCGFCb+5E/+JN4cK1A84QlPwP/5P/8Hzjm0bRuT4bfffju+4Ru+Ae985zvxIz/yI/jKr/zKA9f0eZ/3eXjHO96Bz/7szwZA0cnOzg7uvfde3HHHHVe8n7IsceLECXRdh6qqcP78eQDAX/3VX8XXvPvd78Yb3vAG/NzP/Rw++MEP4s/+7M8AHJ9yzmte8xp84AMfgHMOFy9exObm5mWv7UrXccstt+Cee+6JZIGXv/zlOHfu3FWvm5/JxsZGjJwA4H/+z/+J22677cDrn/CEJ0BKiVe+8pX44Ac/iF/+5V++6nu993jd616Hz/iMz8BXf/VXx58bYzCbzdC2LT760Y9e9R6PY1xf48e/xo9rPP7xj8f/+l//CwAwnU7x4Q9/OB66h40XvOAFeM973oOLFy9edR4ZmcnHj/7oj+Krvuqr8IM/+IP4nM/5nMuqi/2/Nn7lV34FX/M1X4O3v/3t+Jmf+Rm8+93vxh/8wR/g0qVLB+7n9ttvxxOe8AT87M/+LN75znfi+c9/fi9FkI9f+7Vfwzve8Q4A1E3miU98YkzJHDZPV5pf3lf5Mz937hz29vawvr5+2c98LOOqEeR4PMZb3/pW/NAP/RDe8pa3wBjqxPHa174WN954I1796ldHzLiua7zuda/DE5/4RDz3uc/FnXfeCeccnv70p+M5z3kOPvShD8XPfeITn4hnPetZeMELXoCNjQ0UBbUD+qZv+ia87nWvw7vf/W5Mp9ND2YpPf/rT8Vd/9Vf4tm/7tvizO+64A9Pp9LIT9I3f+I2hv57F2bNn8ZVf+ZWo6xrvete7cOedd+LJT34yxuNxvLYXvehFGI/HOH36ND7t0z4twgvHMV760pfi+77v+wAQJLG+vo7nPve5eMUrXoE/+ZM/iV791cbm5ib+2T/7Z3jxi18MIQS+8Au/MEZ01zKEEPi+7/s+fOu3fmuM2L7/+7//sq8vyxJvectb8OIXvxhPecpTDn0vR1W/+Zu/if/+3/87zp07h9/93d8FAHz3d383XvKSl+Cf/tN/iptuugk33HDDNV/rUY7ra/z41/hxjRe84AV4/etfjzvvvBNN0+BbvuVbcOLEicu+/tM+7dNw77334uu+7usO/T1DrFJKzGYzvOY1r4mIDAB86Zd+Kd785jfjp37qp3DmzJmYR/5/fbznPe/Bm9/85vj/4XCIL/mSL8F73/veA6+944478MxnPhN33nkn2rbF0572tMueM694xSvwvd/7vfiqr/oqDIdDjEYjvOlNb7rsdVzL/P7zf/7P8Z3f+Z34b//tv6Gua7zxjW+Evopk3Cc7/t6UdC5evIjf/M3fxNd93dehbVs873nPwzve8Y6/t0Py+rg+jnpcX+P/9w3nHO688078zM/8zBWJTtfH9QF8Eu2ujmpsbGzgL//yL/E1X/M1EELga7/2a68fHNfH/1+N62v8/65x33334Vu+5Vvw/Oc//7pxvD6uaVzXYr0+ro/r4/q4Pq6PQ8Z1qbnr4/q4Pq6P6+P6OGRcN5DXx/VxfVwf18f1cci4biCvj+vj+rg+ro/r45Bx3UBeH9fH9XF9XB/XxyHjuoG8Pq6P6+P6uD6uj0PGFcs8PvCBD/xdXcf/0+PpT3/6kX3W9Tm/+rg+33/346jm/Pp8X9u4vsb/bsfl5vuqdZD/8l++GcZ0KHSJtfVTuPFTbsLNT7wZ47UxykGJoiqgCwVdFtBaQUkJ6xyMsbCdgS4LKCWhlYJzDsY5mM7AdhbeeyglIZSEEKInfA3nYW16rRACUksoreCdh+0M2rpF1xqYlvT6hACc87DGwnYWXdvBhc9wxsJah67p0MwbdG2HdtHSd3mPZt6gbVt0XQNjWjhn4ZzDfL6Lup5hd/cCTNfAeQcpKPD28Pgv/+Vdl527T3a86EX/AidP3ownPvXTsLq1ho3TG7DGwjsH5zx0qaGUojlRAkJKVKMKRamhy4Key6BAVZVYHQ4xKAqsDAYoiwJKCCjZBw6UpPkfVxUKpVBqDWMtnPfw3qPQGkrK+Hwc/+0cnPdQUkJJCR3+dt6jsxZ7iwXqrsO0rmGshfUOStB3CSEghYjf0VqLzhjM2zZ+jxACnTUw1mFeN7DW4smT8ZHP9xve8J+glMZ8vgdjWrRtjVOnbsF4fYyVzRV47+Gtg7UOUkpIKWCMBQB4l9as9x6mNbDGwlmHXPBGCAEIAakkZJhv+nn4vUrzIqRI78nen88Lf5/tTLwGqRWkktCa9qPSEros4mcoLYHwHS5cPwCogo4B2xlMd2do5g3qWR2v9Vu/6SuOcrrx/d//8xiMBxhMhpDhvp11cM7BWxfmk+8b8f98z7rUKKsC1ajCeG2CYlCgKDWkUvHz8rmTmu6jqApIJaGUir9zzsFZB+8cvPdw1tM51Rq6Hkf/54vg3/Mz5mciwzMznYWzFs56WGvhnYd3Dm3TwRkL01nUsxpd02G2v4fFYoq2rdE0cxjTwdoOP/MzP3ik8w3QGm/qGZp2gclkHWduvRE333EzhpMhVBHOkjBH5bBEWRUYjAbQSqFQClrSHPI+l0KEc0FACYlCawghoLK9nf+dr1/Lc+09nHcw1sE6+mOcQ2cMTDhbrHNojYm/t+HnXd2ibTqY1sC0XXx21tq4V4UQsMairVvsXthFu2jRNR2kpjViO4v5/hzf9s1fddl5u6qB7DoyGloXUEpjOBliOBmiGlZQhUJRaqhCQ2s6WAulojF03qMMEyeFiDdnrEUXDuD8sLZhIfLByQbVhs0spYRUAt4D7aKBkIIOhfAZQgqYztDDcA5CCgjHG4v+jvvG+7gpvCfDSgYoHRzee0ipoVQBrUt0XQ1junAt6XuPehRFhcFgjMFkiGpUQWkyTg6AEr7/vdkCXD5g+R48wiYWiMYsvV3Eha1kf0HzKeXDQnXO9Q4ffo0Pz9Q6Fw2k9x6SP1NKwNK8ds5FI+2QDr3DhhQCAn0jcRyjKEpILaHbgu65BDl+OhykLhkmPmx1uHYnfDCgHs442M7AGgdrbe858bV75+CCgRQyzbWEowckJQTEgXtdnqNoJJ2PxsQ7BwfABbk5Gl0yzE5ASMDHz+x/Hn+Pd2QwoOSxzLkuC+iy6K1ZpRWkl/DaR4Mfr8d7CO8BQXNfjSoUVYFqSI6g1ocbR/JJRDz8lVZQSkJqFedPOgEDwAGAcxASEF7E9/LewSHrlI063ZRaeia9yeWPoO9UEkpLlOWQggSpIYVE2zWwtjvy+abvtrDOQOsCVTXCeG1MZ3dwgqSUvXMDoACADRkPGfbuspOdzpD0u+QIIwYVAKBlCjCs87AqGUdjLZQUaI2FCWtYhHMrDhf2CvJ1m12MFJA+/D57XTTSzgNKkP2qClxpXNVAemehlcbKyibWttawcWoD47URqmEFwZMbIkS+CWOTkSnCz/l3fHh2bCSdg/UO3gMy8+xoEsJBJH28WWd9NGoQAkopuIK8QGsseRSdIU/OWFjj4sEmBKJnT56hj4vcmA7GkgdnrQ0RJN2HUhrD4QTeOwixwHx+sPvCUY7JZB3r6ycxWRvTPAsBXSigUBBZ9EEGnw5aPkj4Z1LSM5FSxqhNCnoO+cHNUR95hvR6GQ4HKQSMd+hsUtTnw1tmB6d1DjYYSYA2kQxIAp915A2SofVSwsNDQKRNErzD/GDhjbnsgR710KUOhycd2tqX0IWKm8t7WiscIQJI0UMeVXQGXfg974tD7Yv3ZLTYUIp+BMnf2UNVlg7d+N0uPXfnBIRwwdHTcMrCe4okkSE1QojgYSOuHWfJ024XLZoF/T1QCrI6eiewGlWoRhV0qXtGkoeLjmu6X6UUlJZQhUY5KKELDV1qVMMqRLoiHpKSTuS4D/j3vehcCrKKUkIpmmvTeQgR9o9WECGazefcCwfvM2ciez5Che9QkhweSHhBZ4yUZPyVV+iaLl6n1iWkoH2tdAFrjsdAKlVAqQLD4QQrmysYr417561UAlIp6ELH84ENOl2rjOhSFRAlIQSdMQKodBHOmsMjSCVF3O8uQwNc9jdHj61VUKJDKyXQdVBCwsFDqoRQWutgNaFqvecTDKAQAs5l114VZHeaFtY6eGOhlFyyrAfHVQ1kUQ7CxI4xnAxQDkuoQkfjCCDCEFaQByyzibncyCfmsJ+x4bQmgzEQIqkwKQwxynCjbimSymGZ+MADTHnYvNADVUBYN9YaOGfgHEUDWhdwzkJJDevMsR3Yw+EqhisjlMMKRVlASEkOA9A7TPL5zaPkdC/JkOXzIEUMinqGlSN8/jc9C/TeIzli8tl3I0T70eMTKIAYdS7PE32ugBTJODrn4OHjxuu/1h/wbo9ykKEiR4i/W2o6tLx10ZGKc+gPj3JyYxXXoepfNx3SMose0YskL3efvfRDdijH3ztCCoQUgAEAA+8UISxe0mt9jIfS4QFygimdQakJF+45P3yOciTH4OB903yoeKaIEP0qrcIf+rcMyISQIsKb6QuS88HGEVc5jwCEKDH/bx8+985FZ+iwAyR/7pACYun8IYO9BP9KBWqhVhzbeQIAWmtoXaAcVBFq5nEg6pZk5LXkwEdEaFUHhJCcanK62SHmCDLuofz5MioSzhAX/i8yhMB7DycltPdwSlFKhp8jzzFSUMBpC/JCs7kLhxVPs5AIDjDtB99lKKF7jAZyNFpFVY2wsrmCUYho8gXuskOQIVWtdcSrAcTogA/rZZw5XmyIQjhPyXlDPsBQ6P5BryS8ENDawwBQ3sNaCeUVnLXRmHpLEapzjnKWnckgqLRIZPDihKPr7jqCmBn20LqEEBJ1PQM6oDPt1abvkxqrqyewdnINg3EVoShnl3K0QH+huHRoR9gqixSd97DB8OURpKWJCfkA33Nscu+R15GNC8rH1znn0IXnRovZwzpB8HgwnAzX+BA98WexcbSeDmOtUsSbG24hjg9i5cOZoSYpRczx5iiDM32Dz69lWI3nzFuXOSAEp/UMr1gyjiHKAfq5xuWRG2fv0896B7nNrsV5+FLDqywa4zk1NsJ93qUIsms7mM4ko2CP3kjGgytfZw49p8EHQykVzU05KPsG8ZDIMx+cjmHE5QB8uHQwJiOYHHDvEfOTDJtHRyMzhtEpQua4SgnhOE/tsmfez8nRuaOu+NyPYqhCozAVBuMBikFxYN7o+ukZKCWhtcagLKAlGcMyGMZCSpRaQQqKKPlzcuOY8xWWR24kww8AiBhVWu+hvIdWEtYpGOl6cyMgAsKVIPT8HuKQArA5AiGhCjKS+bq2V1nfVzWQGydOQpc6bmy+kGh8sn9LkUJvNnb5iAndkPTl4Tx5v01nYJWigzW0L1mORPnA7KxNB1ehoDoLqxWkUrAcPgPoWoOu7mIiN/+sCMmaDk0zp9xL8ESMoZ8b08JaA6V0iDAkiqKCtQcbpR7VWNlYxWR9jHJQQerMmBkiAADBCGYQSXwOWaRjs40pwzOxzkH7vnFzYEJMIEOJBKWqEL2K7P3s1MhsozufDIeDyJLuNvx7CTpdik74cFHBS2XnywTHynscy2ENAF3bAc6j61pykoqKDmnPhC8To0Mh6AARvEsByCwHSM+C0QeaN96YKnuWtGcA4QEJgqiBzEAuRYmZXxFeh/h6ugiERBoNOuRlJBMBQCu6jBQTDu/wb2soTQGkCM9ZHyHjoxw2pENUoXqEpfzGpJQ9w5LgM8RcvAuONBvOXiSUOR4RauX8cUH7hp1m7xyEC68P0GjXtD105XKjdza5/uuFFIDl9ZDII9HBWjpThZBQ8nj6RxSlhpSjHn9EaYqolJZprvL8eGbweQ6JtKNjBBkdZ+/hnSUYNZuvy0WUy+kC5z28Ur2AiX/XaAVjRXS0XYj0RYYswHt4KWndh4vidWaNi/sXCGtLkBOs5lduqnx1iHVQZJj0FbwCIEYhNjv8CKMmr5AjAyZyxPdnh3kkdggVJ5UHR0FAgnUjBOZchBg9YYH00OODF70H3TP2gZzjfW7wGV510QHgqEIpHb2+4xhlVUAqBXJEQxQOGw5shp3S3B/mSef4vs+eixAikmny58YHshQCPjwvCR/zNOw1G2ejtycEJebpM9L30vUi5iDZ6ViG0/NnyznJfBx2+BzHsAFycc5geUvk38lIhlAH83J5/oPXC+ezhAnPyAoIYXtwn5ACzolENBMJvYtOqMsOXPQhsRxNYBwqj2addfCKSDvKSdjw3Jzl97nI/iNGbCKesPE46nEAhpbEYiXLlz33fI9aF5aijIdw3Lug9AvD2QkuzCM2RKJOj6TD8DMAa0JkKtN19q7ZZY7LEnwa5zvsMb5+/u74++CECEls2pjndh5WWkpTHcOQWgFCQJc65hlTXjY5EvTicE3oI1aS5zAYR9qv+Z4O8GluIK/h2vJzSgom9dDna6WgpSInwzkIT9/J0SOf51aI3v4DEBnEEfXxlIIQIT0klYgM7suNqxpIKSWssZC8iJdGvhid92i6Dl24aCUlRlJAS4VCa553OkwZ1mOmariRRMzoT6DzHp0x6IyFtRZd00XGoOlMJOkwLMvQSPwsviZNHqSz5HmY1sB5JuWQobSWo8euR9Zhw6p1Ca00rLoyA+qTHfzQeBOmXC+VqcjA3lXMEA6esdIJqotwtbVQUsZIzACRps1zywYUIMMGkOcnhYBWgA+HKbPMmJ4tcJDRxsaWmMgusFsPIZgAkcXK380wbA7Hk1OU2MbHMUxHULkxHYriEOhJSWgtenkba2x0zjiyN52l8qaQu6bcS274PJzrM/wAhhZTKUj87gDtxfKHJSePIUQux5AB2vKiH40myruLB7JpKW1grYzlB22gznMJFl3v0U86Ozt8D5d/IRtBYuZKJLSDIl9iBCuRrpENoZQp/SBDTk1pFZn3OniYQggspAzlOQ7OCcD0mex+6djjZx4P9QxS5OfE5w20h3MaJjphAVUIJTk8TNsdyE8e5dBaAVqhrApiaAdUUIdI8jBH2zoPgQweRs52p/PBhlQWzyXP2eUG72+f7XkXInnbO//pbLHexxISAGiNgYeHlgpWOjhh+w6IS/bDMTQe8ureeUqJODaQCsXgMbJYm3kDYwyKkvqnOefQ1QQ/MA0+hwx0oVEVBUpFRrFQOsJ7TVhUbBDzuhbOVQHhUMygwl4+qrNZHVKCFblesms7OOMIIg35xnbRoq1bOGMJcm1CniUkcpUqUJbDHiknvy8R3HqKcBVBrNUwUcCPeEgliRHZtPC+gCpUvF9nHaTSibYe4DtmoS1DVtYTHF2EhawVs4ldXOQHvl8cJFkR2zTRsdmIGefihmGjyg7NYWdrvolSbjLkE5D9LqyLzlAdJOe6j2WEa6JSpgI6Oig+Ypu09kw0WLYzaf1lG55qEEN64DJEkZ63ns2LEOjlyqxxUMzQ7mycA4qmwiEeCCm6oPRCnk8F6MD24QAXUvRgQO8B4dmhTNA9HdQyrKujZ7G6ADfyyKMx5WUkAHovE1KhJbEYA0FHhHpGhmnpkE9s4OUonR1JFSDCSuu4xnldqUYCBvCSnCJYIuPEtZdFjBy9pnugtWKtg/QyIjw+cAMoJZJBgvyMw5qKn3lMXmA1qshIZwENjwN5vIzPIIUI7FVisC47w5zqiw5V9nmctpHOwQjRd3oPMZJsPMkmUFpmOU3H6/aweepH60gOFtsRyRFjIlgW5WM0kIydi+CRsZW2xh6YWIae2DhyntGFxdRZ28tN0bPoRzCcj8wPnv7DTIQaaEBYwGbJ/Xg9zDxkHDpQ8Dny7GPSCjpQ/I2hmp2eR+WzRSUYYqU/xzWc8zCdDTCx6G2e3DNejiTiQRsNUVp4PL88tErRJBs2vs8ejIdk7HKyDUFbgPOCCNzBmHDNK4/lnAMPNo7O+wyqDSSIUP7BxjhCXMc02PmhZ3uIF+/9AbSCoT8AqaygyAQcsoO6x1QNv1+6APqYLJ0hpQeQiQpYgmktbIKSbNiPgvK0MqQWHEJpgU+HSroV3/t5Pq98kPO6Og4DyX4HfX+eg0vElTxXxfPIjgbXPjOjlfdCvAfZ/0y+r/x3eT4s1QFKCNHfA3G+MgJcvNYlJ6fnfDgytDS/KQpjbkSM7s3xRIzLQ5fFAQTmMIOc/yzNEXr5SOcBgYNze+gez84J2zvncyJbQK+WjGPPiLLx9C7+zWk1vgYZSpnogw85yySV9HivqNxPXn19XxPECg0UVQmtFXyI1vjL+QaUVsR8KgoMyzJObt11ET5tjImwKkOwpeLauyQOYL2DLdKDUpz7QYpOuDDdeR+VcWxnYdou0NUN6nmNtu7gfSBiAGQgjY3XwBvEKxUPs65roJyFUhpFUfZghPgwJJV9HMdIEbGJOVRnltiToSaME+x5EbQqNKkbBRJPLMLlqM9aVIVH6RxUWVLqR0pIvjc+OMKc55Eyk3eYSMNFwFxALIJxNI7gXSJvERmEjGl/EwkRGHAh8WOzzdFZg0XbwkQ483giSH6OTlAqYbmMiZVRTNtFVmu+6fjgzvMZMd8U1rRwfEAzDB7gxeVDlg8ojvD4YM3qETnn7mxSfLGdgWlTpEJ5JmZxAo4P5lD/x05UNFTOEwLkPZwNbFF1fGIY7ADHe7YOXgo4myBpinhTNCCEIIWgIkGCDKfmheOx1lmIUIfoAUEs5A5dNGR8fpGz7sM+y0hwguYvzhuXKnAe0WYGHIjOhhdEwIKxsSY7R9niunIOaJIKzDEFjwCAYVAtiikY5whaFwJS2fhzZxx8Se+JezrWKxPKl3gK/Sh0+Tzgn+eDHWHpDzoGTL7sLAAZ6kg9p+EsGtNhHuoYjTEROgVCRYP30FoHxrklxMF7OKdSkFFoyFAhARB0f6VxVQNZjSrYzqCoinRwqOS9xQ8qNSnZWIud+TxODEcSSkqCNZaSvFFuDJ6gNNBCM5FRmS0+UI7TeirXsJ2NUkIs/cTGL2LRkrx1rTVc4agGSIoIazjpojfufcpDMmFHCAWtJJznnIMJucmD9X1HNVxg6DKD0HsfI5cc7lke7BHpgmCkUiuooD7CkSJj+6nGKUUskcQDQHKuZGkB0c9EzD/ya3hzsFcYYVHvg1Hte5giM4qcZ2RIhZivAV7NWaTHxGJVYXP5DN5jZqR3PjhOZECE8xS1yEMOB5mtVfaUrSMqg4/ucmQaO7cEbfG/Y5lGdr9ZJJrKURIj1QQIdnl4F2S3OhMd28tFlHztLpAhjiN6BBCQD0HoDkEIAZoU/ZIUQYQZEf4N0LUpLzM1oOBIBMUsCEGMSu8hrYcqFLwKOXRhIJ0MbFKbGbZ+JNVDaA6DwyUrtWTkRZt+FyMi6zO1Ln/g+7x1UX3p2NIHYbBMp85yn/ysvXMQGXTN65gDGk6nlAV6ez4/x/lnPSWu/Ptz/kk2Bz1Cj5SxzpGgVQvvc+Y3Ys21DzyTg1GkgPci2invFUWNsl96wqQecZV5vzqLNUjx6IISurwxkR0I7LECIefVJNxYSBE9kaooUAT4lT0JD482GDWbJdvzTRJvyiXpOc4vctSYkxnYw04XEQ4WnSjeEDZGAvx9ufce8xJKw3sHWMAGVqtzFj4Y0eMYnGukg4+ijKj9eJUHyoanyOjYPP951FdqTTWHMknD8TwAiGzH5VwkOSxJfYe+L72Gyz040s/ZqQRXp2ccyQ3eRz3GXGHJOBdUjRL771iGFBAuh2MOvkSE2kim5UklDrBLIejQz29TCAEP9Pi5lOdBLzJPJSL8HHAAUuYaQFEwExvw4bBTHpQ/ixAlmBlEkSYQNI3R+x7+svi9S4Sq5YPuSIZI6RrhgCSwkDsMYa2J5SjtoGPosqJwzqFBeniVBCaEEBAmRE5WQjl/2Xs7YBAP+24hArnbxwiXX5tg1iQesZyO4lQVO/PLRvSohwr529wALj9nzpGyNXPOgXV9jHPQzvXOCgCXdZQFDr8PJYKRQ3xgvd9LIQHpYF0yvIfNSXRCl64n3gs7raH+Me4vBzgVgqMQkF1pXNVAKq1gO4uiKsOfose2Yo+bDwUmLsigxDCuKlRFgUFRxAgyT9Yyy9JnC4yVGXjwQzE2hdQU0RLkK6WgiNJYdLalwzWIkXctkXSYdagK3fPQD8PhnU+5TyUVRbdAlKJr2wW6rg1lAUc/4gayHkISjGZtKKgSoX6n0AeFmYPTwgLDLAmlZJKJ4miyDDlirVTE+tnzzqNJjhjp4xMpK/4s0w1linYe+fOQ4cDOD0H2FD08GtOhNRatMZlnSNJuzhJkchyMSgA9+BpIUYC32aElBHSWN+ToMpI3wiEXi8nDa0QgMOXGNzeo+TUw+SycuPBIsmsAIJyAY2Qmf78ndp4sE7lIBSo/Q73W2KhfDJAh5FpBIMizIZRDOZI2LMriqiy/T2ZIRQ52XqSdOybeeSLKZFEzX2N+LlAOr0/i4M+CVsR6leSwWIOYdwIQSrbQQwK43IXPmAg9KwFnAywc2avR/4jXIEQypn0Wfbpu/ozYiCETPb+cMTjKwblWgs9F/L8qgsycTuiJ8z6kXRA0tkkkYFCWUILOmeUcoRTiAFEnziWjKln06LIgwwXkkBob+MhsFYIqIQZFQTZDWjS8DzKyJn+fCDXBQoR0VJkgbdN2UXnNexEZ+pcbVzWQ6eEhkEHSDVvroOhaYA0xzYpC00Eco0YdNT65QNwHL8sGJXfu9MA3yMwpgEgkMRLJFlA/f0ALvWvDRjI2lKeE7heFhg85R8kK+55YiBzzK5UOFxlgSXgFGX7OeUguA3HOROHyox6MuxMMBEAjwousRB81JlUq8aAFrno1jik3kHIEERo5xIi5LNqLcAl7chzxLL0vLnxwLZTI8sr0mginyrRZetGSRzQQXOwbSyjMMTJYQWpJNKfBAXAeEGm95VDjYUShpA985c1Gv6dNSVOYDGlvg4fvkww3Ln/u8iGa/Z+hQR2iBSFEdC68RzwQvPdRpCAnoEhJxqWoSlSjCuWgvOI9fTKD6wmJkZ0c7Dz6iveTGUe+T+99jE8YVuXrzw1qdFi8hxCu9xydtD0FI3Ykeqo5GVQez55gFS8X1fI90DMlaH45Vxe/L4gzMBHyONM2+bzyubE8X3x9vHdZd7XQOtYjxs8S/bkB0At84P0BJzkfQnABVF5j73sseeajMN8EILEZ71Pe32UlUlJxne9BYmd+nTEICF2frjSubiDZC8om1HsAgT7uhCdPTQiUQqMqNMZV1RMFiBcGilKEF9FDaJm4s7QxmFmpg7qCcw5C+siMZdFa53U/CS5l7OjRtZQrVIUCUEJqC2UoEe9sP2dD7DKdRWk0NayRGD1DfnDWHpuaTjQKJhUOO5cf2Ck/QvqCZBzZCxQiLdaeHqJIeV/++3IbctmgikNeG+sXo2eYCDmKvebMi6bPJVr48nt7zLRM/5TzzMc5rO16BjIffIADuKyRXjb4sZyCjWk4l/M5zKcy1+wUWZS0rOOakIU+HMufzbkzktVKCIMVAsJRvovflwra+4LqQkpIEVpKDUoMxoNrmMFHNzgyQ2SMhvVxCHuYI126No7OQqE4zyt/SBi52Day1kdcg0ovT3XRfE2ss5rWYFq77KB/MiM6q9n9eedTBGmYW2CvmkL5ZEdeERAdoQN5dMTrZM5IVRQRbeL7IGNF64TPmRwFzB1mJv7lIxpOIahLC/rOTC5PaVkEhp1JZLlOJSEZCclSGugFUjj0394Tgsh5+cuNqxrIdhH0RrPN2cN3mTmpU90j48+HHb757zjnxBEkkzS6UPuiJfUZY++BH7AUAo0x6CzVEDXzhuqJbBI31qWmGpewGV2RcpckgA4UVQsjBIK6c2DsFijLAaw1pLQvZYwUWTTARxLPMUmfNR26pqUaR+NiyYAI7FXvPRByYtTuR6EcltCS1faLmF+MOYH876z8Jj4XIXp92RKhR8U57y+8DJpy6X00/+jBt0zb9p76PuYGhWnbvDaklFHoYTFdRJamECLlj496vjuSm5JSw/sCQgoUZXHgEIEBXIi46EAnBRcn0jrIi/XZEJCyU/qYHFI7bHBmZDlazQUBhOgbjGXjzfPLjhQAeJE7K1yjmTrk8N4RQqAoNcphicG4evQTepXhgtHSrAMrKHoQjvKrzjoYT2xzvseiKqCMozXAzlYuI6cSQ1Rl68RxOVrYLyzKkCDSQ84oIQAlIUMeOEKCjsvdiKxFX+DiM4fwgE3/Z4KXVP2csrVEzGkWDerZHG1Xw9rumlCIT3YwAtXTpA1QdgwYQlqM0zHDsoQONZAAn9keAonpq6WM4gEupE+41jpPv/D7mZPguXQsrFuSDxSxPWIrROgDadGGCJLfz908qFuT7e0TpRW8l4CwsFZl/UV9RBu4BDCXV7zcuAaI1fbaoqSHGA5K3rR5hBUeckrepoiFnku6oRQ9Zp545jX2v5OGDdAq5xgX+/PI4tMhx+i9D8LLGbvV2ihUftiBAoSHHkTJcyPoMqNonY1i6McxuE0X5zuEZW83JKUZy+QhiZjDuUclZfx/bDPDEXYwlMv3fWAulhb2ARgvP9szg8vQOG8MhksAA3MFvD+vf/We1l0XGqKSzN/x9CYESDfVWoOqGtK1h0OWog7XU8zJvWSGP2PeylM94vJ15pBf/P9l9iXnPpF9DxCimADFskoMOyxSeAiLxNzO5ioXW+fvFlJG9h792wIQS9eBGIke9cgPJY7YosONDMazlspj2KLlFyd4LiSEJFUUdtbzM4AZrAeeiTj4MyxFiVIKkukz/LVL6/cw4yqX8lpL185BhnMketKZ1KT9uM4THv38LWJ7rjAZCZWSqTb6MCQq/T59tnU+ngu5YczvKSel5cIAkWOS2Q7+Hk4Hec/ND1J9JNuMnJsgpYBzyZmMKy2Q58KbIsT6mLt5WOOigfSOiS0yrNFwcKtkIKNyi6Klzv0GexPm+/3/+hOXHeRhcjqT6MBcS9e15IE18wZ7l/bpuhwiqcA7T+UfubpOZ9DWLUWRme4ks8li/kYVIZKxMKahfIlz8SA1pouM1uMYfD3WuuipUh2P7uW7opcKRPKNDkLw/HeeB0i5xMMj/GVHZFkHd/mz0us8waohumFWq/ceyrnoAQIZ4UT0IVte8HT/5N3R86NcsSvUoRqoRzGs6dCZNotUdYw2XG1jl3iT5Sv4+pPzlUoHGLqLcA9EbyNyOU6MMH0/z8gbPmeUSiVJAk0DQijqvCBSdOiEiBJpuXg3HQKkt8qCEkorONuH/Wj/JKPM0VJRHr2B7D33EE3mBfT0GoZXASEOrtNw9VFBSmlFNZKhPpjziZczOstQnPeJjBO/J5Rz0GsOdyT5HOQInP5egtDD+ylgcvFwNqZD19Vo2xpdVz/KWXz0I+8qw85FzmPgFlfLqTEg02AVIjre3AQ5pgcOQZf8YfOWSUq2IVjJ1XUYzo7laEGLlSJKF+tWY8ohg8JJcJ72iNIy1qpyOREbVGevDc6+JiUd7a/8Ms4twhjYEL3Y4H101qIxJkKnxlrM2zZ241huO8VDKSIHWOfRhqiPPXnTGSz2F5jvzdEsGsx2ZuTFG0q888HVzBsq1A1RCEvN1fMFuq5B08zjxBbFAEVRUXd5pQALOMElCxQdJK/cBKLO8eTGrA2tvjoT1fVZHF1mi9waC6UlbCfRWUML2FqUKsGiuQiD9R4Ihb75ImZnpcuiYm5dpQw3Rc2o0wJxc+SfFWtW4XvniQufzQucX8vz7CWCXqxNh0eo+2Rk4EqQ5GMdHh7Omnh/DL3ZzgYYrI7iEznSwWStrmvhPeWQtC6hlCa9Xl3E2rI8R84qNWwEYm1rICYZ05HylKHPFUKiLAcoigK6LKKUXZ7bis8grH8lKTddDkoSEQhwFKdF8k7q0dFzHk1wKps53XdRHQNJh6Mokw42VWgUpSYodbklWCgjU0EpJaYOVHLEWC/aexUQiLB3TZKNdBlELdXB3GU++Ps5UiUxhrxMJsulZvcFQSxK4Q+JJrPX5fnAeH3u+NI2UjOyE+Y7k70rSh1L+LiygK+v1ArOM4GRngsZMiIg8euUFLE1FgequTOc37t1JBrTGYNpXUf70BgTX5t3e+J0Wt12aEPNqGkNuppERLq6SyVQuRMvSb/XexEEZAzamtJXbd2hntWHgQC9cVUDGQsyMyptbB2THdamszABmzbWxjo70YjY/3Exr2GMjXnNuAEy2nvKbYbyCks3x61LvHU0KW0XC+rjhAU1Hc41so5s17T0Wm/RdS3atoYxLbquhhBJv5T/kBfTJ0KQ5JvqsV2PDWINEZTpLHU7DwWvPEQWSHGU4SzRn1WgZ+eQKv/tAoMYxvRUb1zGJs6HEAJOEjNsGTrJBQTyguFeu7NQ8N+GDWAyOI1PGl4DDAsLdr+XDhBrLeRxynIJCSlShxbOaTeLlpSaui7CYEymsM4ElIEMmfceZTlMah7OQEod1wwAeOngFHm2AEOJjGJQpNqZFs6aXr9R7x28GxA0lxW589pVmkonOB+qQj5uOBlGdrBpTYywWEfZWhdawdH3uMAwbesuSDMefc/TvHMJQcWp/o4Rqihknu1Leg+XJvSjwBzG7sFmUsR+sMJ5uCC+7SxILzWD3HLnhz+LGkcvQd38+0NqHHkIcTCHzJ/Jn6OUJnlD2W97dRwjv27OyeZnL5XckGPOaTPnPVqjUCgTo0YtFapCJwESpWIvSKcB5VxU8FpmseZR46Jt0HQG+3WNuuvQGIO6a+M6zM8tblAR9YgDea9r2ui4RuWrfD30nhOjJKHptaVg6WpR5DWwWD26XiNVBEX8grrdCyIJtHUL05AUV95fjOWwmnmD/Uv76BrKG1Yj6mxdjareZucNzP8HSDCdI0Q2BkzPZY+4sQ7tosVsZ4a2btE0czTNHF3XoqlnMCHiSwebiX0eydNXMVKJ9WMuS0brIvaB5FZcxxVBek/qQECaa19knmp4pr1WOdaiCx5Up21gAEtopNY+rISvhIxauRzZdaFLB5AYwkCCuoFMJipzJPg5MRzSa5LNnrdzqLsukrEUZIRshQj5UynRsXFl2DjLpdnOHpuSDt+DCNAkkygov73AYjENta9NZBta20XY3QRDJqWEswZKF9C6jeLnZZmILkKwdmie1ydRCBtKh7quCfW2TTQOzpEhVo2G0kU0jGyAdanTa62FLjWUVlg7uRprHbumCxAywZHWpAjZGRf2Mu2BZtGiazrM9xZHPtfc0Z6kElWv52nXJtRECDKEHnRdEUU5RKJPSomohxvUVGIeEmzsAOE9XFy/KQrK/3B+3+XRPZCaWiNBpb33HWbcevCjT+VakUhYwToD0dYQ4vjWN+fTAUTnIhpl59HVHRrnImollUQ7HkQEhD+D10+hNEZliclgEBW7+EzhOkkhqFyPUDgfneR522Ja11i0LXbmcyzqBl1DEV0SRMlTDq73/YTeMOGSznSG2VlCNEnqZSUdJpXw5CmcK41rMJAWOf04CmRL0VO0mW5P0SwadHUXYVMhBDEyQ9H+fG9O0JmxWDmxisGowmhtHLtRsGerbb9bd5Qay3IkSsnAQE1ecFu3mE9naJo56sWUDKRp0TQLNM2cDJr34YBRvcmu61k0mloXcYPSfUgoFTo96BJFWQX463i8PYLs6MFqS49IFSoWpfOgRe+BzqDxHkYbdAUtyEFRUNmET4atMSlvqgOc0QTvrTWGPEqkAuAeYhAWe060ct5HWJY3Qf776AVKGfMMrOijvY+eJt9LhM6Cnmw5KOmzF54IWZeB449iaF2gHFQkjGEs6lmNtumwWEyxWEzRdTWaZgEb8s/54HIf5yQ61cLDB4dLQQgDa3U0aHxgWp/amIUJ6B1aPCfeWTj4aISBBG9DSBRFSUa4qFKXF6nRNh2aRYNyUGK0OsJodYTVzRUMJkOM18YQghzP7XM72JN76JoOzjoUFa2Rsir613eEg51i7sDB0LIQgCx0P6KzHkyeYdmx3BjFdl9aQmvdc7CddUAbDtalFlb8d24QWSsxjyYBktFkjkOCQj28y+o2l84CzjM7gGq/s/2U+jHK4JDr4BgdJxGNSG/VoAzRa3DyjcUiwI6ms7AMc8bnww5aaiFYVCXKQYnVrVVsbKxiXFWJ9xAa3uctsWK/xyAn2oZosek6qkQI5S7slPZU0gw1vOfRq2W1CZEqSk1nhi1jl6OUEkspGl0oFFURbYl5rHWQPGLYGr/Uw7UG7aJB25Bxqmc1mnmDNrTDctZFJmLX0O+5UWsxKCEE/e20I4iFD4nQvDVOhPOHwxkhx2DaAAfVXYBOG2KHmRY2qN84S5RhIQQU6JAR2QFgrQklHTKy5EhmLi1+IiRxJ4/jW8wM5THk58vMW80IPF2TFo4QxOSjVlkWi1Kj7ipMBkSY0lL22oxxpNeYDk1n0HaGnBTRV9dho6akxKAo4iY3zsXIs2u7IGKfaPmUVyKYrwotZSinQXPqmfgF/jwbcx8817pQMG2qVWP93KMe5HAV0FqHvBHdS9e0MLyGTEfRYvg309XzexBwoaYyIRBCiGBAdYCAgjfsk/NFf9PzlpIac/N7mdiRWrH1vemuK6CURldURC5TRDCyjg6Y0cooHAwao5UhdKExGVSodIF9pVHPmpCLIUeURO9VKJm4fJ3sYxlFqXt7J8/JRaUXztNeRpiBzqMsDxsiR34v/8w7R1Vc+nD4UghB+mfh3w4uPCI2ygLQgLMilESEMjcpIaSnMh/naAVcYa74V3SPIAddClQdoQtNM3rU8/hoBqfKwkUAQCh3IxF+7gPKgvz8spiHdYnJTa2zTKyVBYBKa5TBeUhEnuQU5E2Rjcv0U5cYr5wOILvRxQ5MjGwwqkT6yehfJ0BkPikgTMq7ghm2SkSWs+1sdBKuNK6hmwdtlKIqyUprii5MRxc+35/HfouL6QLNrMbepX3yAvKJ71pS4BMKRVGimTeQUmAwHoDzjnGSjEUTPOzl/AMPzg/Vsxqz3TkW+3PU8zrAqok27T0dZLoooTx59kU4TISU8dDxIT/pnINRbS93RPkfC+6RV5UDNM380MLyoxhNs4BSyfgVFdVzsqhzW9N1cv1nrvcoBCKRoxpVWFmfoBxWGI4HMaLrTCK+cM6WcXzuCpIK9U3s7DAYD2LZQ1NTbq5ZNJjvzuOzpjVD8NFwMkQ5LDFaGVGT1kKhG1ZRZanUwdP3HnVHHqwJAu1SUecG3ZEKDLM+j2NwiQcbdHY+uq4hhmFbB8SA0BSGQjnvCLDBSk6XMYHckq1jAHFT0l5gqDQc5tKn7hFChpwkpQa6roE1XZA7TM6jiLmgAkVZQUodod2iKGFMh9nuDLO9GR1+1qMclNg8OYFWEs0mRZpCCiymixCBEXHjuFjDHEG2TRdl13if55Jn+QHGRKQewcVTjWGfmJEVvIvQmUX2Ga3B/w2vl/2ohFWHMgax7Qzl4o2Flw4SMp2c3sM52WugjODI5gzi+DuZaouLcNCXQ1or82mJup4dy5xzWQlfM0OUxMJOfU6buo3SnADAHWEOI9ws9ufQhYZpO4wnI1jnYh2jCDyHfPTRETonqiAfBwDNXAGg1AUjjxxgsdEkhIly68k2hAjXyeBEG3inoLPv5AYDEe2yDsWggFpc+Qy/BgNJxlGXOpZ7mJYaTrYLmkwqbO9iEX4vusoWGv0dhKg5nO4stVuRAlZS9OZsED+XPixw8iQ53DatwXx/jvneDItpjen2FPPpDHWAw6ztYEwX84vDwSQ8Fdp0JAAQIIYsKmANVq6TtLZLVOYodaegixJlWfVgr6Mci8V+IHsUcL7szZ9pOyzCocswRIR8MqF2rmGbrI9RDSsMQrsbumfXazzLz0iXITcUnCBmAvPm50bOhlmO85qg8/15LJ1RBTERh5MhVjZXMJxQbWFlKhSl7h2GUfdRiEgUiSQeKSGlhy4UnCNP9bgUdTifiMzxq+sZ2nYBY1q4wCTVuooOHjlTLDsY2IeemK1CyOCgWVip4byD1uyxDgK8prMDP11LJAEtsRkpmg+EtFCSskz40F1D4hHFAFprdF0BKSSsJSPYBdjVtB1WRgPq+FKkuTVtBxtYq5wHOg6nhFMD9awO6BI5VkordK3BYDxAURUoh2WWa02EnLzFG0vqqcDc9sGJjHOWycctH/JCCGI5LiFBQoB1FwMJK+ml5jBr/vo8Cooi5SFSElyLzLKWvPdCzltqRXrAx5pjT0QuhHXH5wfrB3MaTQcVpvx+cq/CB1JkW7fkXAlEtjOnXpi4x2SenO3Oa965ICNnaI59cL6pf2YiZKnQgYRLjoSkf0eGf5CdY+eSUEoLg+RYxTNHUXcPXShUwwrNvLnivF3VQIqghs4XCSB6RiYwSTmxinBBsRbRU9Nf5T28T6oGzhEjiUP6njC5U6k+RzIGHhL1xsVDebo9xWx3isW0xmx/irqeRmIOwapBjUcz7KQhhURRVlCqiAbSWoPCGTTNguoblzz0SAzIYBsldcxHHsfougZK6QOCBt4jqD+Q6ga3+eKFzQ19uyySW+zPUVSkiMJwCQCUoX1ZOSzj4ikqE/F7OA8TcnHNvAH3RGSSFUeP7aJFPV9E1mZVjWLukMsRlA4thgYl9P+vvT+PtSy7y4PhZw177zPdqaqrevLYdjBgMOGFj8+8KBABiQwCJEgwQyAkIJGAAiQRKGZwwpAoBBxBQiRAIBSCE8AQC2UAQhAEkALBLzgyEMDGxtjddndVd9Udzjl7WsP3x2/Y69yq7mrsus7Hm7usVrlu3XvuOWvvvX7TM7BObhkgrbOF4LOZDpRMCZJP9Fr+AkjrAFTBRzaZEKxTO5OC2RScUzKwdtp3FZCIASY4bn12PHsckVLgQ9XzvVfKNkp1IRZYgQMvVasSFFWoIhUJUZaKwCBIEpWcVp/eJ2zbM0K3CnjBEvXjbNthNZ+RuERTIYwBznv4mpKhekb30kVJn0VGs0vQBkiYQAQ4YqR2veATcnbwFSBzSOVfy5ysQLFKMEMWgMadOqdT9WFhTCpKyqlxnvTno84gzyej5dhj+p3yffqStKyBhdVgr6AfA9TzGlVbYxguJukGoAEZACXFaZLVkz2p6grQ5sfkKhSltWyggSiGhJHFv2WsJsR/mUnWHkDCjtiMYBdCSuilSuxZ1IVRw5IA5ZQ0sOtzY82O5ZwqGWFqo8fAnQY7nTFloDTc5fL34PneO0DyplVNpdDwMASEEIgzFSZY+Ww5QzNv4Cunc8n17bX2krcnGwwDIUylGqJ2Tq+INkJ2efj6EHZGgsnWUjtmPBlwcuMYp7fOcOv9t7DZHKPvtwqiGIaeA2RAGHtGE9ZomgWaeo6qbpCRUdfUmiJuGc0AnKswDB26bqNOHTFGeE8zR2cb3Q/HiNaLEgpo2zXte9Wg5t8jD2FpCD30ox4C1hG4ZBxGbNdnGuzNDUOzTGvpsDOEyJ3NlmgWDVZHK8rWmwqL/SV8RQAZaWm0Z1usjzfo2x7t6RZdt8Uw9Lo/IpFljWXwExn1ShLTtz3asy3qWY1qVtMcrK7gnOWK1ejDo9ffU3tP5AKrplJo90WsxfIA8/k+qqbWxKqqam3HI2ekHPmzn6DvNgoAk2RMkK1VNdMqjh5oi6aeU+LQzNDUc1jjKHGTxCESCCeEEX3fMmBsVOSq7HNkfcq6nmm7X2aYMYZpPhxGWMsBlueXfd9qVWq9xe2nbiMcrbC3t8R8NYfzDmEIKg7QzBsMjF6/30sQhNvTLbpug7ZdI8UAGIOqqjG/vYe6IQBfxZ2ralahmRE4ZL5P8zrxo5WDOxVVEAANtKW2r6wSIS2tdUEC84vpCEJafKK8IhXk0A6aOAoPz3q3K/BQVIopJpL7K6opKTx8HbQouIgV4yTWnlPiqnh6n1VTo5pVWB4sCaDlHZnLc6dPeLriQiLnztAO2gWQs6OZNzRamc/g7BzGTu5AOROa9axt0W47nD5ziqHAqUgSUs8qABWMmauL1GJvrt3EVLSwZU4pyTt9xgwMWcFGvvZTkWNp35tFg6Z9binFewsFjJEQYrWHr6sp69VSmN9A5RXp1Lc9+k1H1d3JhrPEDpvNMcaRQDR1PUffkxCyuIRXdcX6jzPsX00adOUCD/2I02dOcfupY9y+9X5stqfo+y2God3NsvkgGAZSqWjbM674Kszne5jNlqjrOZbLfUan+p0Zxd3QhOWf1KatLozmUVbAsqh6nDiecnNI20DaItYaxHEBazsMAwF+xjjsCBt4X6Hvt6i2Mwxtj/neAsuDJWaruWboJZdUZstnZ8fo2jWGsd8BjDhXoaln8K5CM5+hmdXaDoljxHZs0XcDtf26AdWMWvazxUwfKjnsrDVIlYdluyPrLTyobXuvbO8DXcvlIZZ7K9TzemcemwIFl67bIPQ9Tk5uYr2+rbQPCUo6fzSG7z/LQYpAXWMYKOs2BomrPpmxRQA5BOXntu0p/b4gAZIAZn2/ReSWrudEpKpqlkU0GoyNoerb8N8pwRPZPKIPdWctTp4+offOrStpaQJAZE5i1VQ6E76fK4RAFUMY9fMJKpzwAwF132AcR00k6oZQxs2iQQgRzbxBDAnNoiHke13tAJh01iRC4HG3gpTvzSnBjhSkwjCqTB8l8EHR8eL8oDw8tg8TSpC0+6rZ1CGg92JVc0qCr3ZJinPmIkF/AFF8CI0rNDm6rs45uFlNeIFZjb2jldJlZNSiwEAGrhHtbgL9lGBMY/pCHtLBLRfqCgIAQ4zoxhHrsy02x2ucPnM6iYEUicx5ugYA9N4qEJFoTKTlKxq8Oi/luW8YRm6vk59xCfKRz143z23n9jyUdAj26yuvUkWUQXEpbAgd5LzXoW+/6bA53WJ7QjPCvt9isznFdnuilQ1lzAOsdQTPdvQAyPwMoCxPFD+GljZ+c7LFen0bp2e30LZnelDJ4SBk/pwz+oFEgMM4KCp15KDZNHMgJ9TNXA+ZXPAeDaQk5yzdWBiF7ydts17EOq/SUyYlKWUFW4jfoAATrLUAu3qkVMHaoDOrYei0deycxzD08L6lzB2ELJwO+wkeHbiS7LuOZrydBIfp/c1mS8RUTW0Qa1U9hmg9CXYwGJju0YSIelYzWjOhymUVQF8zEYCbWrG+CKL3e82XC8xXNPdyziIMNQG4YsLYGXQdJS3bzQk2mxO+7zrKopF31HOmQBmRs9kB2pRJhTz88vcYWXasJzoJcS45QBYjgJSi/i4BDQmy2ntL7WI30QgIbDYFyDDS3Gh9e60JaF1Td0jg785TdR+a8Z4w+A9kpZB05iR7KEA5BY/FESlHBcv1bY2mGTB0NPMShRxrLXLNYhpSIRZtRLFPkxapLKkCU4gUtOydEoEyAhragZWd4sSjG6N+XVR8DN/fpeqPiQbG5kl791xwpEfN7FhQXcQah1GfH2qdTtKDDQfIZtFgvpiRoEckn11jrNJfRkuftRwP8AsiDFMCkngkEkKAAdHDZnVFFA8OkO26xfasxeZkq9dc6WDcupUgTXx6upaSUC/sgtu9ZO2G2TRvDJzkKkTkLgAjgEY/9h5I1ucRIMUKaBruAjz0LeyVwhixPj7Dyc0T3HjPTQxDi2HosdkcY7s9w+nJTWzbM5pNOY+9/auYNUsA0FanZwSUFT+6eY3FrCF5Op5TddsW2+0p+m6DlOgBqus5B9oK8zkBckIYcePGe4gPmbakxGJFISKh77c4O7uF2WyJ2WyJ5fJwmtHUc+ScUaWgMyPnvLarAGB0HjZejB+ktOyc8wQIEpNqa1ROzxX6lCkmdMwdEoBJCD3GcdBEJIyDVi+AHBASHBvMOmoNNosG8z0KGO28QRhGnD59ipgCBpbnG4ZuJ4CHMGC7PcV6fQtdt0bTLLBY7KOeNfCVVH/UwmkWDWaLGXkN8vxzmsOxLF03Ihat15TShbT6ZAmQyVcOY6YAEQPxfLtui+32FNvtKWAM5vMVobA5iE1JHrXy5/MVrLEM7GF1IV9pK/RuSw/rRPJygpql9qokOUJVCjyimDhgBNKp+L01mM2WtP/1DHXV0KjBUYU1jB22ZxVuP3mLQFWMoqy5zV431CVq5g1VRxfAPdUq3Vr4qsbMGMyaJQfKQhouJcRISGLvPYahQtXVCGPEbDnDsiP0fK2jmCmhkiWAQnlNofAIclPUtiT5BKCvITqxJQ+vDKzSetQZnqPqJXCAFJcjF6gIUFceIw4kUyC3XLHPV/ffXgwA+k1HDjXnRhe+cmiW1PUxzmK7adGuO+IBFxiHnpGtYzdArPcEaU5ydQUFbBgxdqPuGxm2e4Q4YAwBZ5st2tMt2nWLsR9Vkc15pwpT49moVI/17TPkDDX8dpXH3pU9zBYz1PMae1f2UDeVdpnk2tC9xl2uamqxiqEA3X8f5AySBp2EdpQWnrWWrFEYjp1jwvZ0g5ObJ7j91DEjSQNyTgyVb9EPHULgrKCZo2nmmM1XWMz3UDczVNUMy8MVFntzLPYI6FE1NSrOfkU6izJ0mneJoshstuS2ksNqdaiHUbs9o8w50+DfWY/V3hHqegZnvWqxkvZlhGEwj/x8TpGsbfiGlvdPvDl3YfMCqQyUG+YdqllNe+9oVhQjaRCO/YBxHLWqEWqCtJmFqmKdY8ktRtcxQEGXMVo5zJYzyui8Q7fpUM9vw60rrUisFSEB5lGFEcCoSjNV1WC7PUVdz/XAXu6vMI8zrA5WWB2tsH91H1U9EdonMERGsCRa4OsJ7HVR80cAjNKukLPwxcSomX5n01ACVvkakas66V4MQw/kBGPdTsuTOiEEDLPOa5fEuQl9V3J76X4SQIGHF8FmVyGnqNk1clYlKBHxoMs3eVaOY6/CF9Y41Nz5qJzjatMSz3MITLVIQAM6cLxH5RwWTY1+JJ6rok3u05pmVTPUsVZkMzCheKXzIUmCfH0cB/QtCXs7RmATTy7qbNvGwp5plP2hyka0mQWNPfajojlHbsnlLK4QWVWq6DUKYQCex8lzYK2DLeQpc2b1J6nGFpbOElsER2N2ArOvPOrZ/bcXA9gSLU7dC8eE+ar22maMY8T6eI3NyUat5qjFzM8ov1cJRM2iodGbn8B+8vkJWJUne6qUiHMdaK+lYmwWDYFAnZtQtplM7tt1i/Xttc4ou03HRP+aVHfGgL6lhG6xRxVwM2/U8BuYaDulO49L0/1xr4L9+QVIfoDF4cA6A5edRuQQEjanW5w+c4rjW08rClPakdJeQs6wjiq++Xwfq9UhVntHKju3d2UPi70FFgcL/VrtPWmHOpahc1ZbTHIg7e1d0exlb++Q+HOVw+npMztiAJWvcXB4HXVNHJqTk5sQeD6pnxBwx7lp1jrtg7TCCCFojZsUTe7zEjNmOfwcIw0JYcpk/A5oQ4vtdo3t5gQnpzepPcctZ2stqqrBYnEA7ys1fp4UkRxE8EAECSwPs+tZrQr/3WFHQ/e6hhfZtJzR5xZ0Xkc9qCPPyhxfY+kMrJaHhORMB7j2QoPF3gJXHr4Cby3OTjca8HNMiJnkDI0RuPa0Jxe15IGn2QmDodRf1GE+38d8DpiDawpO2m5O0PUblTMEpiAl94rMCp22PAXF6nbuL8M6sM55OOYxGkO2a0IhcaEqgmBkelLQRJQ6DvRzch2E6gQOtKT7SUlt5EAhAQJosDebYTWboak8ZlWNTd+jH0egvb9ycyJMbsxcZ1y2mP0plaidjidV9MqJAUf0vMaYUDGRfLac6WFL14E0N2XuJ4AOApaMdwTMdt2pUDxAXFU5/wDAwKpGszHujsTB2kJzlxM+myxszAQ2sha23tWwjtwGBmguLQyA+700sAMKxKpnIn5f8Z4HnN06w9mtM7RnWwXhdJsO2zO6B5y3WB6uqBKtPFduNZ8Z3OIcJ4vEmDLbEyYMgQ0LuE3rKofGUCfJVU6VkIy1mO8taD45BJzcPMHQUmJkzBzWRQVPWdurFZ71DvO9hRZu1hnuFkz8zzBGwNM1Tc9DeOSeAXKxoKquntdqzgt+8cCmn2NPfEhfVzg4uorZcobF3hzzvQVuvOchPPPUDbzvfe/AMHSoqgZXjh7C9YdfhD2uJOZ7C2qZ7C8UsLPYm6OuuOVqqKyeLanK7PsjpBSphVTPcfTA1WlIXnvU8wazZYPZ6uPRrVuc3johoIQxWCxWqrBwcHJNaR3ivuB9jbqulSQtWdMwtEWP3aNuLqYVApD9kqAXQ5gIs1LRRBbv7bctNptjnJ3dxnZ7ptl2DCN81cAYi8Vij1tuB8r3SzGgmS0Jqemttl70wOT2xNiTs8OCUYOz5Ywl2Hqcnj6NgVuMovISAgV1OtwrNM2COKfOY+g7nB4nvOf3DTYnW9x6/zNYHe3x8H8COQhHTII1gY+mmdBFrml8QFSIuqkAsyi/QTH7+8MRhm5gjdaBZ4g9hr5lzqxD08w12AGANQ6z2RKeNSMluwVAyOjU0GycZ5jGTB2KGEetQF3RepaWv+y5/IzeC3FEzhFtu6aENVPAqJuKhSEsZssZyZA9WqFhIfQQOwwh7Hi33q8l4iAiCC9BKgwj2nWneypJl2AM5HMaMyIEi76lOZdv/YSkZMBRGXAnEBP0Nax3MJHEGnJMiMFoAiliDJp4WK9mBd5R50pQkdRqnOaHZctuUpWipICCwBS8UQQtmd8L/uJ+L0koBKE845kjdTYIoCTuNSnSGKeZs7ORNTh95pQqMWdx8IDDbNHg4IEDLPYXqGcUJCXwC7fQeYcxBHRMXdn0PbqRUO+uIuUds7AKEJotZ6gqD+8c2q5XlR4AGLsBe2EPywNC3+8drUjQgGelorYluJVZU2O5oARsjBHb0y2hckOaFHYwWcs927pngKy41ekKQ9K6qRA9Od3HQK2N5cECvvZ8oM7R8JzJOjspZwwtvK+xf3gFVx++gsXBEsv9BRb79KHnqzmEvC7IJFnGUL+7WTSYzRZoW2rL1vWMM8dpMN7MaroB5g36tkeznGFoCSXnK7/z4JSHlPzuuqmUhzORaQOMGfRmk9nSRSyRLpPKT94zwBJ/gYKKaIIKbSOludINnPXauq6qGZqmQQgeoqsraFPpw1ezioLCrEY9ryEQeWAS/IWRVrtD3cy1rSi0GO8n4ITM5ETknQ4Th5GpPwAw9KMGxNlyBscKQFXt6T7jSrbkL13EUk88GSG4CcEHQM2QZcmh6CqHKs/0a9MsdepGUKUuCF2WQSsUPfT1rOVrNgeMRT43LzYMMhPxc1LQcTvAKlGHIqECmkFTtTXRPcahh7Vb+HWlFfrqaA/eO/QPHiKmRoXujeG5Und/vQqrhl53NAYIgbl26Q7t24neElBVNeqaRjNNM+dKnBP1NM0I/UgzKFeRhRMBxs4JAVgDNh5CyVe0BZdyHHsCV+WEup7B21q7VmKrNAVfw/qqotzDKMnotBND8z7PNBARYZgqO6laL+oel7an12eM5s3eTt0jKkwWqBu6N6pZxZXb1F611uDwwSMsDxZYHi6x2KNzv6r85Ak8TuOhmBMCCwKIcQHknnWEsvZsuTVrajSVhxeO+uESKZLABrk3JcxXM9SzGsvDFQV1FoComGWh1aRhoXSQ5nPve51HU1dzV+zh2dY9A2Qzb7RPrHDapobPGdE7Rbk98OgDqmghrQ5feewdrXDloSPsX93H0A0QC57FwUKH63MOZnIw55g0OGpW7yirX+zN0W0W2G6pOqmamobM84YuLPfIBZUVxoDV0Z4izsp2XrNo9PXVmDclrVx87Ylfw7M+Vd/h2eTFkA4ml5CqatA0C91PAIgDfw4O2tZ6BYfIfums13k0Dc0BfV0xstTTZ583miEKx2x5uMTqcIm9/aW6eVDLjqklY1TYPIGqahAhvdeMm9CUng+zhba8Jth9xOaUeJXbsy1my5leK+eomtH7h3Vby/vgIhYdcI4O6srD9COszQrpL90FBDFcznPoM2OHKiSKTYQgnUSfy0OY9hcAyJXDVxkzEG1G2okyogBIP1gCr/cNqqrhmfnEwRMkpnBlNxuZlXYIY08UHR4r9H2Nvu2x2FvAOotr/Yi0oOA+ZweSnDPC2dl93W8BAhljgBYYMSrvdrM5xnp9rCbC5GwSsFwcYP/gGlarI+wdHgKA/sw4BrYjC0QlmtO5klKlohWyBDlK2ISs3RhwVYOBrMaEi0rJjqP7eT5D3dCsWvSHM6uyyPxeOJXWWr1XLIPf5Gtjx4T4nDX50nGCvaAAmbKS42t2UVo2DXn35oSh8sjzjKMHD9UZw1pDLeluwMEDB7qPi/0FmnlN9LD5DI33aKpK/X5b36vFWIgJwUYEa8k4nZ8lEYFwlYPnNu2yadRWCwDqI4flaoGD64cqiSmBcG8xV5PlwPzgnNn1hRMi8bEFgFNvebyZtfv5fJS57t1iPVgwwo+CRu29KrQH5h/lnFFXtEniSJ0zKVGs9ugDXnn4KvsyTgeeBD0Z9grsX+J64sFuN4xqeWUdG3wWcnEQKSRQdiozJeFQzldztTaRga8gPqWCTFE4hUb5nsYYah83o86kCDnbPa/s4wNdIbD0nfLsdgWeI0POm2YB5zyDZKAtobJyqapaP0fRzUG3btGtW/i6wpWHjrA62sO1Rx/A/v4KV5ZLrLsO4xgwW9KDTYE3wFqDuq6Rc62Ht/eTGLnM2LQS5IdN1Ji8d4rumy0aascfLHHwwAF1BwrNWAFBCEk53MOa5gNd5WxGqkPJggVkIQHepIkCA3agoPkuUS0aLHUvJiWo6VAuKQiqAmOE4sKOMXHaW1HoGcddk2SZ60uANMZohiwzUAGTNc0CVdWwLZBH5Ws0C/KtrGaVtiTHbsSm71E5h3lV8du8/4mJ7LevHICaoPqVw3xvjvlqjm5DwXG7PVPOadMscHT0IK4+dB37V/d1ZtiaXcpRCAFooa1WIb07JvBjGBEATnwHVfExxqBZNGozR0hwalM3DQm+N/NGRxFD12Mc6WeHrkEYScR7ub+Aq53+fgGfOO4CSdKFMSDGzFquFyPpV656VqGZEz/ZM7aj8ZVa27m5RZrN4A4P9Iy3hr18Y8TwwrBjdCD2dk1VcceBDA5CmgQ9rDWqxxpSQjcObABQ0OnkP0uvUbMjiFSRmAN5b08/h1jrke/tVIGXpu9SQYrpcsoZywVJXsr5H8vZ5HOsewZImQEZFpgQyyNrDKJ3eujO61o/nDhBxEzitU1FTgkCGxZzY4C5KLaQ+ioWZQiZDDOZqFu2Is633cRipvQCs87og5h4fiqk0hwTojFAiOquLZWvKteMpd7fpHRhjMFF3dI5J0JL6iwERaAR+ysm3soF5zlgSWeZACJGD2N57zHGSeWe25n1vMaipjZHH6ht4lm8QfQ65WYWMWa6LpV2D4RbJHsprhwiBtDMG+U1zhYzzJZEK1kcUGtnwZVLTAldHsnuqNC2vIg1DgGuCuo3Wga0HYrA+UOMLX0MoxqlvXP+e3buUWnFFp584o1oSqJ7SshIjIbdBY2py0suaRGGBdTjDv0DmEjotrJMi6pZpIFb6/NaIf+1J7SrPI8WwEURbKxz8MUekCFChfneHHGMmJ+t0G22GMYOVVVj7/AQi705A9bsbgIF7PjFypJWK+mdRsS46+Ij3yOVt689qrHBfL7Smf18vlLlKQEspphQDVRNSjCU32OdJfGCgu7huQOEMMkp5lCCAKdE8CIW0TFklmpROUY1W6stdQConJ9s54j+qK1RkZGT7pIESQn8gSvIiSvpVK4vxEhmBOOuLKkpng/RazUccOU9iJG6BD4Fc6UpERxTguPgfbfvq5yAgJzudU53N7ve2bd7bazYUAFgaxJg0dTwljZXMkxxllb/ryJ8zEONWVXhrO7QD0QSVXKoc6peIVywHNPOOwuBqr1+26PvSE6IWh9G23f6flWQm3Ve+eb0lUOyk6mqMYZfB8gua7YpD0EYo9oA7SDZzG6L7CKWmOYKbUNaC4TsrbE8WKpge7/pSM5t3ZGp9RCmzLVyOsPb4V1xtUGvS7SOZtFgVtXULvEVGh8wr2lwvndlD845LPYXBXGX5MLEKFtm06V8nFrTFEGTCPm1un0INHtvMePf6zHEiJE9KmWvxfD0Ila3bnlbSlPZaU4l/3Z+ZigHnVaHfNCL8PNUmVoFZGRWRCpnldLOohmPhQtWv1deo65nmJxnpqAtdm45Z+W6SltQRDPk/XrPyPAZ3UNVQ9d+/8oeAeb2V7iyXGFvPsferNEM/eQ+7zc5aGSe5XlgbpReIYdnzmzCPkxiBURLYJP2RAcoAX5o/yVZ3J5u9frQ561IinGM1PYbAlzlkDONZGbLmdLVCAdhFeluDIgXvDfHcn+hwfUwHRS8yemzCYLeFpgNusZs88a3cE6FZiwmjqYIrd/vNVvO2HCCvTO5+vPWEv+Uj7OKz3Sxc6usAYpKLKSEkcFbEowAoBtHbIcBbdcTV9tZOFCxNPDnEwAgKe6UggkSHEXrmvjDEuQq7+HM5BcrlWIZf5yh91lJBeqcBmzRhq3YqQiYlJSs+yAD5IxJpJQk00NbOa99Z6kWBxafPR84xG+wG0d0PbU5cybLHecd5suZ9oq3HbUsyoMBmA4nUbUY2mGnNI8iHCxO4DFhzBkptqryA0y8KNHsk1aA58qG2hANV8XDrgVXEieF8a6i5vdzpRh4ZkQtHmMngNJ8b6GcuNnxTAXbRT81x4RaqjR5KPPuQNoYg9TQZ5/vLXDloSMcPHCAZdOgribuoTFkTXTloStYHSwxdINm29ZbSlgKCLi1hufVE9JSPPskYFIFQIjo2WLGrXmiFTjhiElFoO+d4PoX1dbuW2r9lwmFdBokwNN1sSgTJtHfpM9ptcuQDGXbJTJXWmsmZ4AJ0dLSF2Ndi4RsrXoXEgGd2rKelY6kmpYWbJkBO1dNYCLJuAuu7pQ80gzGWsOzGOJ+OmuxbBoczOd4YG+lXn0njz9+X/db9o3eaoYBNDjGOCm10NjFomqydkHUCcMYAHNVpgEo0em3JKAv1kYAKCi53TOl5iTSsyanahVjspcToJ5y7LjqFjoDMKnSSJu37KKUijkTr1ISAKjLiCCaqbq6GKS2uPSUnTdnyZ/VWqterNJyLfdK3Tm4shPZOGMMQow6exxZYShnqi6tNdO4hO9/MWCw1k5+qhk6Q7RmcgCRlXNGBOjZKSpSBdFlopLISEFatfJ9Qwgan+R9O+eQqzvj1fn1PFqsU1tPApD4+XnuBceUyfA20RsqZ5BDiHeY4Yp4rK88lg2DAbBb7kqZ7OzuzSiZuTEs5cSAiVSo6xvQQxFSgIlGZZVyEpUaAf4Y5oiREgQN2b3KT2kloK+/66hwUQd2Yj3ZlCeStlX0aLVLoFermslRQOcuPNMVMMBO9cuvuzxYEshp0aDmLoA+QIZ9Hbmt1QyjBgLnHJpZjX7R6BwagIJtbIECnSpYq6LlVVNhzkIQtffw3OoB6GGKmfc8JHaLmRKg+74yzTcND/EnEWwKfJOW5pSIaXCUzkJxn0qA3c2SS+SjiGWzybWVwMvfn/k6Fa9ZcvtMSrDRIKWJusAfQ9+3vo9zM08ZL+x+fO4CcQbeVBUnLBdDOSiR4+CulKI5YyKUb/HBBF1e7q+09DVYGYOxG2HsuPv5y99bzJMJvW1YWabSAFaF6TmR7tNsNWctWKYzFChW6Qg4Tp50vHCOrqFJdiEQnrl9WdrUle3h+7nOt9sp4LGXqLWwhjiLAHYqs5yzVm+Wg2T5mm2enDlk5AJAxWXudQ/t7EVxXjzfe+/8jFzasnrWsStL1GuPnY7PB91iFSX18g2RQStF6pwzrMkYg8WQI2ImdwJBF8mbs5ad5WtSZFk2DWpPw9g+BCIkywf0lnzqvEdVgBCAaRbnCxpIEu29cSKZy5wsp4yQAlyeFHFK4WuZPdBsjF47BQpMkfvmUm1K1Zjy5PB+EYsCH7XTRB5O509+QgkLstU5txOkSpFhAUPJjASgmZvIYy0PV1gyzcbzTMLxwLypKizmlDVL60sMmgECcPmKBMgjCziXQUFaiXJRjDETiZeH7bX3mNd0GEvGR+0astOKUuHwA3gRy1We9zzB5imgZ2Pg89SGUdukgtheXhtZMlO724w852mWonNtb4u54fRwlz8r19FZi5Qskk0wKd3xvVrxFgFZltCFrDNKpdGfs4aud0Nz6Itc0nGQViQKB4xQOGaUbWnROnWsGesrqYYchC4aQ1Cxh9JHUPQ8EiNWU4j62b0x06xckhFPxruSxE+jgynw5ZzpOUrprnq10j6X5FQckFTwnO8jMYyWZPCikm7petD8302tSG61hhjh2XWjHI8ZcDHE6FIDOhsAaHWWckY/CuCRvByds6hrAgNJsST3OzDZhonVXYppBznviiQuZcrzpDBLOcPzTNTkIuEwEtwxzUPFkiyLdKEEcOzyUJ9l3TNAUlZzXsNzGtpK1u+dQ5MzIZ9iRCgOD5GLk56ys0ZRSjJEzcio2OMrp6zDY2t2I7xk7kJydpVXYWxR2TeVg8FE1AWmh7Gq/c4cUl9XqpcQWf6MWrlDR551IQRtD1z0Enh/CAO5PLQixODRb3sCACRqAzUzMpVd7M13smwR/TWGAkCzoEo9xYTt6VYRoc2c5aKshXcWFScljfeIKaER1LLPqL3DllFoYRhR1xVBs5dLfe+S+UU+BGKmrC2m3UF/ThlDCPDcLrbGKAquH8kejRwfonYIxu5ivPJ87XeCuHIg09RlAMgWyWWrFaSsFLMmZfK91LHIOxlrWVGXFSeKlq3Jk85uzpZbcVlnnfTauy4H8hqyynbvVLlMDgkwVDVVPHMHiLZwvN2iHUYEft7bYdC21P1c7bqFMQb1rIavihkcB3zRUi3b0hIcnedWYQEOiexO0a47lS2kfeagaAzbKOVCbjEDduKm+soxkMUijJW61wMEhBNd2pJLJ0hngNvXfI1tnIKRtFGJpM4SeiJ1N4waHMUk+G5gxfu1whiUfiHnatmuPI8dsUYQo9RZkCVndh8CtsNAikv8WQAwp5IKnJqBnDmzgPkY9Lrxi03XX95DztreF0wLJdOOfGKR9R4VGy2kpIERIGBZN47UXg0kmZhYPpLUddLzamnfO0DGyY1bbgblnnivlAypCMpFrRNWkjBWkUlTeyIrEAOA/ns0SQPk+SBGB8md5HFpdVBmMiFN5U+yhGJNUwGucGDNKSs/MjG3SgLj2I2qMpFiQE7TQ3FRi/YmqYpIGFhztav0vakCC5OP1YnATnYvMs8R92xRLRn7UWdAgtolkBW1WhTGzVmmrNp7ysiQEUej8O5l0+i1qp3TYX4ZJEOke2Y7kIdejBGjNRhiRMU3aeL7QaDigW9olQS7qAqSK5hJ+UQiEWBy0drj5A2uvE4Z1uGOdk0Gz1/57yZj536V31Pmf3JP6mtIi9/sxD8NpiVXWA99QP+NXrt4RlJGNlPLXmzqpO0mh8lQzJTK5/N+LQk+ipT1DGIqBTus0TntBGYisIv8OyCJINl4jd2giZ/sa4wJMBHZZW2xyZ7JDIyqTKvqOL5OeojLSEf8SCXAiqsHvV+rbVdt4crP58yHctDnUsZBGhzL5Oa5O34f1Mopaaen7OwJepoCFH2vVGwy5nLFvSYzvzFS528IgalnUwVoOeF2xiIxWd8pv9LufGa9f4sYo21g/l6SxLQaMD2LAAD8WpbQ5FJspYwpMMr1K1rZ1A4O92xp3zNA9tsO27MtlgcLOGcxeIftwKo0fIBKX1pQRs5aCp5cUeqgmr8vxIiB33goZnlU9lvNcKxUkaxwQnOHgsaR824bD1CElLUGVgAl7s6MPWOX/z/Hy7UAAHVTSURBVBaGEX03qFVXv+2xOV5jc7rB0PVo2zW6foOhJ63TnRPrAlbOYlPVouP3JC2JsR9UEUO0EBd7c6ZS2J23JpVDzhmhY+5nSqSIY8gM23HLxRijh4b08q2h1huAqQUCuhb9MKIfRtz2U6tmzu25EGn2HHkuPYyUgAztoHOXiqkjIUZt1w+BPCjHflC+amaVlYvwJgQo+4+YHm79/NaoNNh0Xc4roOw+3ClMB2TpUhDGAARoGxo4V52KAwVnlZMpLNS8V36/Kd5fWcnqe+TKVZKoElQEkN1Uu26RYoKrHK48fAWroxVq73HaUnXnLTmShAtAVQrIjkBRpIYjz6iDGGlbRgRP4uEyd5TPpM4vgYSsA4PUhG6TM0mURT6UNaC5YgRhCQDkiy4LUkazaLS9X4qVy++PsTAT4OANw8o+3OrdOey9Q4oGOQWuKs8l2sbggqSd+TNbNa13zuJ4Thxx8WqUs3b6gULaD7sBMoaAECP6MaDnpGrHCcXZnWTTGaMdwlATtUiQ2jCEYI4jxQSpCAWXUHMcASYaCAXppMETAEZO6PoQCAHPSHjtSPUjdaRU1jBg6Cbd3Wdb9wyQKjrLHm52DGiLACmbe7c5jAA+zvNqbCagQWLIcM8bLhWkY/STQH93XpdbR8aYnVlaKlqIOq/j+Y4MzU0RWA2gpqE5Q4Pj9qxFu26prdmPrNTRs/lyry7yZbZzv9duz5xUQsTZe2BisxsCKhbY9ty+qWqvgCpZiUE8AM0ewxAmGxpGV5btbwA7KDZBtVH7M6IbaV8USWwNnLMYncUwBmyHfqrmeb5Sms3KnoN/VjJs8fOLMRFRnGk90maTCv8iVllhicGJIiXPgcRMAsCIOwVGSUXAHF+Agl8zb2CriWYBlwtXGijSsVxZEbtZhTX0GqkU3qTUUj4gU6UrnECo+zt9fQou5ZxP3h8h0+nAJJi8npP3dZWBXR4hdTixUzvZOgu4UoOVZ9sxalty7FmrmPe9HDPkIkGQ5CWlRK+P6ZqGMcJYmg/KdbfWwtQVXJV37tlSa7U8Z6RbNuYBCFPyfkfXQHiROSMMVpV0yur/IpZnibu+G1C3PbYdCUJIC31WsbymnQJi5b1y2ytOKiLfLyXFQyrsVFSi8nmkA6V7eq7rh5T02rXDgBnPQyVI1gUDQfcdGUA1jW1yBjhhkXjShxEDdxRHruADn0OinpTT5KTyrPt2r40VFXrxcIvOovOjfngpdwEqy8tsYyqTTeF8QfMmKdWHGNEH0tST17LmzoPj/Mo5K0E+J0K+ic7hdCBw69dZfXCmIjJPoItIvnDdhnzQqILhrCOQYsY4dqysMV6os0T5+URsOkY6AKpx8jmzI/G63EDyWolBBVVT7dy4KqxgLbctmYdkp4MDwE4GqZkvIwylHTOwz1oYqMpTWgPTI6KJ01yI5fBkLiztb6SslUDwhE6Njj6TtIXJyiay+k9W9HG+gGrmjn1PGcbtBkf5L50DMEiAEVf5kWdLAFh8gZI2w/NJky18BaWQ2DvuSSCbzEAonpGIeIEGil2LsvLwvQM4lApS9g7aNe/8CUCVTCrneQ49mTnf72XPVeXGTAbSWu0WB+r5JcEximTYGAv1Fk4c7oLULTmkOquPiY2QLcIYuOUsuqz0OuVcUAA9O0HPGg0Q41C2tBO32ncRuM5RS996C4zQbsF5dPH9XK7ydI9yctu3PdrKk4uRMQraKeeOtXOovOATeC7P/MNQ4E9kbni+q5YzNOCeT8IliFJ1yOYI3YBx1vBrs2mAn0JUKQxgDb0Hl5KOAWJKGk+6YVTjZzG4FiBnilNSey9k/D0D5PJwpXqGYYyAGSkzrUh6qKsI1CHKDBMSUtqtU7YGQDdLhrzrbTuR1mcVRktt3HlNpHUZDieeE7anW6xP1mg3a36QuMIdI3Gg+pGV5XeVY8obWrJyCYRDN+Ds1im6TY92TRVkHCP5WPakYxnDyAGr9OC7mBtaZpAC0hmHHv221983W07UihgigiOE3GxBLgkl1aKs8rpNj6HrsY5r2GwhFDlJZMolX2s8OYFbY1B7h8AUk+3ploKeSZMvXOWmzBxZD/hS3k8ANzFGtOtWD0b5WmA7IgmIrqzALgjAQM7vFPx2WvG8NDOWGYYeqhT8VcZwGPi9uh2ai+ynVERaCRWQeOG2aqAN4lVI5H9jDLxv4BmgAg/YPCkknZ9nCuetTILoII6K8qxqUlYJLOV41rbYn8+xjFHpWeMFzCBny9kd7XKhvehnKWkdFgyyEfRn0ja32u9VDg7cOmUtZ5WN5NmxBFYAO52WGBJgKHjklCZAkKO5lyDetX3I71MVeCqnCbVIE6rgAXcE5NoINU2oUjsBI5uLU9KpHPq2V0/FftsTaIb3UhTSRF2nRLkKrzByoiwUP/EOlVanVuyFgIIo8wh4JjHAT8ReSOgFek50w4huHNCPAd46BOfQVJ5EC/h3AVCxgIGBQu0wYN11OG1bmj0yTkMKu6Hr9RySWbR1FrAfZIAEoIgtFNkdAIyVIImEPD3NIc8Hx5wZ3JGyqqSMkeS9jGG+Jd8wO62RImPXQStzlWaLJTlZp4kIP7QDuY2MDR8A5zPJabAfWIi33/ZMMN6tHIe+U9sdeR9GeinAhUnN6ecVn8U0od6G1qr5p0DdXaFpu9ibszKIQ8WHQMoZ40CVr1SgRGbmwDTSsL0Poz4AApbpw0S/MdnsXF9pe3qmSTjvUIlUmTEIFQFzrHcY/QgfvCZDiSk0EuTV+mgYJzUbNwGPdO8vYMk8rzSQpmyZrrrOPdLUSpIkK6fCtipnMvKuBPwibeQ7f2dZxaWQkEzaOWTOf690FIwxO9USTNQkA5iCpBzmQtGS3xPGAKRJyUdoCmEY0XY9Nn2PxnvszWbawrrfy9qybYop2BmzIwOnexR3ux3nA7+8pv5dgmwBXDPWsNRl8bu0kjc7P192DZ4tyTbcmRJpOQAsvzghhkvHjvLa6Gtw8iqqTfJ9F7UE9CWJXRwp8WqNwawiXdYZJ8BltXeeSSB7L+1ZZ8kyTz4z0WuIqaDI0zwhVIFJW1ooVvK6NDMMCt5rUoRPDt5OoB3Zu8SyRIJpaccRYwyqm7075kkTgvlPMR57XobJE3rL77R17GCRXEJgmyPKPLIin6yZbjoJjoEHqe0woBtYLZ9J7ZV3TBDfJdPuvBdu1TlXYbm/xGJvrnMr+a+qPfudFZCs4iZJgRCSJGFH7QaRTRvZ4y+Mg5oPxxhI+sha5GzLN/S8NvkDXTKDTIw+GxmA5LmNSpJtdJPNVySFtTxcYTmfoWYeo3CUbo80e5xmh/SAx5Eyt3YYsO0H7QD046gDbskQFYLND3YYRhV7zjmzUHxD3MaqUiSrdw5dRc4voSIdXkIF91oVEPQ6TLMkI+AHz9XQxQVIay2SszA57dx304FZAML4EJDZKLXpKXhlJBVpr5paq9/zS/bwPLCnXNR2nGaMGhyjQYyU+IUxwMMDrqSdnAMZ1ZO9m8yek4p5MLaAq/Z+22O97FA7h03fX9h+35m0Cu1iF3SkidK5apv25Bxy10wAjhinma1QN4wx6Ar6SCk+sNvuNpp03q1NW3bDhDc9JVDTOKEcB5wPvJJ0Secg7lSRF3OmiIpPilmDo1AuRmPQhxHeWcQ02bcJSK9cgiKNPO9LfC9XTaX6zrAGlZt47ABU6SZnQCz2XEXCJ0q3yIRW70PAMI5UpXLbXwRjpFWLInErpSnDOH02mTnLyEKEZHSfn8dePw+hAPIES3yz+EoQYIQgC5bc62OTlMgpQ1Zp4UlwpCol4GS7xclmizgGMkJmH7DKeUY/Tv3tsQCZWEveksgkVbc62kOzaMjYl1tSoqsJa9iPsgAmyE1cZBckBMC8u5GzjUheixSc5MEkXUtFXuU7D7X7tXQ+Iu+BTZCttegjtYBiiJr9kkYlq/RXXgWExxix6Xts1lvcfOJpPP340+i3PepZxYGHDgxJELp+0IddgDLCWZODxHNVLi7jm9MNNidbbFeE/M2PZKwWcwJ68FxzUdewxmB0EQOLmAuHLAwjhn5qWeaY1DaomVGQEX3Ti1rWGbjsAM34UVQAxcxRg2NWjVAS3hdHCMeKK7Vq+gLQdpIc2iUiO6WMYMKOh15Ve+RM7U+zMRgGoxKHAthJ7MMKgN67iaoAVNxJnN0TeKt6oKLnlufIYjRrmU9cN5UCdMYY7zgc79eqZzXGTu41SQTvHFtk+gIA1q/l6yHdB2nFA1DwDI1wqsnNgyUtjSGd1JwGxDAFrLqpdlqlco0kGJbJDCB2SgXlIU4JhjiByHUQAFr5uSRIlNxU7UhdYMIt1BJZMsKQADKEiMrFiWDvHAymDtTAHPAhRqV3dOOoLfhZUyulCwBq7zQeyGdfNg0Cdy9E7GFRN9gOPcaBkchDwGne4IZzWM1mqrka03R9AQq43TAoanVkBKyc8SkksiRLuwVXmQQ9n/1+XiAdbYtZq1GY+CMRSIQ8c5VTqG3MCTYlVUOIKSsJfAikyxoZVeQBDJbmmbUv2jmGrFZMprYbmWNOiLxxCKxD2uoccWh7DMMAWIJV99ueScBeKxC5qSdNxEwHo3yuJOimXUeEcnM/FEsvNLt6pCwiBZaF2Kn6DWOtQA4AVIHnzHxNYLNtKZAdb9BtOuJQRvo3aa92G2oxR84oc85o11SNn906U25Z3VSY75OPZwoJfUsmpMnSoeHXHu3pFgCh4mo/7XspTwhxJHEWyTlYG9llICOJAATbYkkb7rzp7f1ccijGaGDSLspTDjgAxb2fNDiGMCILRQTiFED/SeUpZORsdnmOsIZI8GVQKP5uzAQ2ySy4kFNCFGSgs/DJISUDGw3puFrsBHZrDXJF87Tl/lKpBNvTdqfVqG9J+Gt24pzd7yWJmQI1BDRh7pTPk+dd5r4pZu1ASECy1iC7Cf1qXKFkZM89v+eBMMUvk8patUJjKg5U/nkFSxFdJiExJWnUKtdaA1M5SvhchimTuzxVvpqEpTIJuzhk/DQeSDvzdGOMBkaiZiVSC6NIrq8hM2nV1uaCpw+jBiG5p2IlI7UJlGmMUWeOUID9SqS77M+mZ7S+tVjU9R3i6ClNRswhMdexCILTXu92InaeLdy7iLxngKxnDdMdBgQAPnBG7zKykKgdty15k4cQyYgzUSYms4x+HNGPNOsYh6AUhJ5vdF9R1lcxcZ160nFCl7aDKiAMXYtbN7YI44AQR7Z1chRQwoAUEg6uHVBLkiWjpC0jD5pujjUaFEXeTTP+Quw5xan9d8HdVQDUVhNnD0LgUYAUaat+a1UKUFsnIWGwVLGMIWJ9e4317TVOnzlFv+kwDgRG2J6SE8h2u8Zq/wD7V/exPXtA50PtusX2dIub772J9dltpByxXB7iyoNXsDxcEU+MW44yN8qZzGMDU05WzYyDJHNjkQHvYSTrZ/PZyHZjOWe47NQZXBGJuLj2qizDqLmExILgBdKtONRkhkPzrBGR5QYF2UjXhsE6zHOMQQ7ajOwJjGEYzQtj9GCXtu7O2E9+Z5yqUAwGVUV6xM6RcXeyRPg22ejcRd63qE3tXdlTa6vTZ04xtAO6bbeTTVfOqauKEK7v9xJnC3BQtMmoCksqrrksEfIQk9uh7TVBsUUyJTQxcIvUWqviCKXnovD0BE9Q6ipLgCqrcbmW0u7PNnEgp/cpDkNxjPzeBbEpwJRCoPyO2fUkXiCUj4tYNCoLCAMpBUWdkyakSPtPAUfALxHeBiRrtT0qMpCbvsd2GHDWddi03cRXlnuorjBWJCrShwBnDGKeOPFhDGjPtqRYxkWMY8s9Vzn4kLCxlugZKamq13lqyaCV42R9dT7ZLGl/muT8Kc6SewbIsR8IFcY3XxiC3jwqsswyTjFOyKKcM3yMOx9ILFH6ba8PcXe8we0nb2F9vEEYApaHS+xd2cP+1X3K5mLE8Y0TnN06wzPvewa3nrqJ9foYt269H2dnt9D3W3hfY291hOXqEN7XqKsGMQasjw8hDhPIJFdFs6SkXKpyU+lAnA4i50pR8JEV51koHeki5+m092OPrttgvT7G3t4VeCdiySKzlRXCTE4bNBOR6rzf9rj53pvYnmywOV5TG81ZbE+2OL11grOzWzg9fRr1rTnmT61w/NRDqjQUx4hu2+H27Sex2ZwgpYjt9gx932J1ax+HDx5hvppjvpphtpprZdSuOz3shqMVmqbGvK41Ud/hWDqiiNS5Rk4kRSUt1tKGxhhzBzXgfi5texWqPaqZeY7QLchccXIhGhGZRFtrCGzExtwVi2BDWtTOIneFmLY1GmzXx6cYw4Bx7OCsh3Ue3nsFidEMUgjwCeM46H2akt8BMsm8FDljBBCakak9hq2uFrDOoj0j2zmptnKGqukIGOIiKkiZi8ai7ZdiVpELYzAhTzmwDe2gWqZjN2gyITSukophmdblK4d6XkPmvLxBO7JwMSYkkY8rugWu8tr+VClBx7MzViAa5/V0BkorlsFa4nxEXy+TH7qnSJ3s3Mbw2XQRS5I9uX9pK6azOfIcr+dRjuANBMkqM8duHLHuOkWOCi1uc7LRTtNib0HWe1zhyT359MkptidbPPO+p/HM+28RY+C0xeJggflqjquPXEU9r6mNzUnS1hmcdZ3OOsu2v4ikj2GygStb13QvlTQtotaUSOR7tVmfF4pVXkPnePwBynaQbjTDb2WVF2FkRZUUKNMyidoZQz+iPaMZFkDZW8U0hqEdcPup21gfr3F26wzrs9vYbE+x2Ryjbc8wjj1XSwPCOGhQK28KzSL4v7KFtquEco4btjM3OLeRctdf0JJ2b4xUQYZxQGwmHzWcu7lFIWJoBwRLIgrdukV7tkW37ZFSRsMzlr4SuaeEGEf0fUbOEZWv4TYVvKuQkfRwlr2gWdGIgYUinLeo5w0W+wu92YZ2YF3MVhGUJfpM9VnTNJMTo1qXJkeESWy+MBG+yP3WFvuuvNh5IrHMsOXaG5CtmAhYSEDN3DbKPCeGIWpTLJ4XZBK87tsO682x8m2dq+CcR1U1RedgAi7tvpcAG3ZBLJMfJZCZNkLQ+lHbvb72qGfkgTq9NtvKpaQqWBcxh/Rc0UaVnDPckcowNlCyFacZnsx8RSqxvCZSPZYJldxbEtDIsGCSt0s4x4PkbkGMuyAt+jMX8nWEIHbeIjU1J26CYJ3OGDlLZBlrFIiVisq+rGzkd13UPV52QnZWeY7kpCwDa4AhsuGxcyoUIhiRkGJxhiZuMbNiF9sSdlWHY/n9MWF7ssXmdIPt2WRLVs4Gc5oMow3P6p2hzlPMVI0K1x6gOWTgkV4UTEjxOY0B8VhVjq5ol8vo4oPlQTqe3+WUdvr1O0PPQhdx5DI88UMmhpwhJmz6nsnfYBQTEIZRraYEwZhiwuZ0ozOwm++9ic3ZKY6Pb+D09Bn0/RbD0KOu52iahf5pnYP3Faq6ob/zcL68W/N053OrVVBPUbOQ8iCizRarqwksJIHjItZUGUSEcUDbrtEPHRqtGjABBeKkRQlA/TaHbsD2rMXZ7TU91M6SC4gnJDL1/kd03RohjBiGHienN+nmMxbek+uK9zX29q5oFlbXM1QViVwv9qdqXzK+2zeOMXYjNscb8qOMST3yDAzJgcmDyii+7Bzq+Z0HceJK/zxY5qKW8qYYqo98bj7ElYggVo1hFwhBSlqLyHxG5ezlDMDp/aIBNAPjOKDvt9huT3F29owmJM55OOdR13OmPjk0Df1/5yqIWIAkOFqRM9iEPkuhKMWiDtuTLbwXhC21smfLGcIY9e8hJXTjwOCIUuDj/q1m0cBXHttxq18T8JL8fwC79J9+1ARWOzwCMHJkRCCuEM4zT9GRBjFVyYZNks9dz3MgmciOQMlNlbVUdqXno69HpJjIBOEc5xWY8mdp+crrS0VazsXAxYJ+zwWs8ryWJULtAACmqAjoRlqWlROuJAWjsaD+iNUYieO7nfggWIctS2SmmLA+XtNYhw2tlwdLzFczNIsZAQdZV9qxY9F81pAZgp3cQ8qVUsLIbWGyXOQkR/bWslWdzZpUKbbBTl2E51rPYwZZIywa/sXTUF+CiOhNarXF/eBoDSxvrGYffPNVswrLvQVq77G3v0Q9q3Hl4SsY+1Fh8kM7IKeMtVlj6Hr0/Rbj2NNN3yzQNAt47+FcBe8r1PUcVdVgsdhDXXPrbzkjorCzoCS84ABF+f+7aFS6abw+NDJj0uriArO8ckkFmXIC+L+UI2WitbxXo22TftsrkGFoqfWxPtkQIbjyjPidqbzc/gP7LHvmMY49YiAgUIxUtVrrUVU1Zs0SdUMmyL4idZhm0eDw2iFWRyvMVzPU81rVipbdgNZ22JxuSO8zke6rtBvvtncinC1LVXcwzWXyecXu+7rXd2bvO/eE2l1NGbOBhXMe3k8GuZN+6DSrTJxdB27LphS08huGTvV2q2qGqppBRJkFNV0GS+89rBUT7MJZJmduawekcxWfcQTqGrsRm5M1HXQhYP/KHlzlsXdlD+NACjKLWUOavDDoxgGV87iIzrbjFtc4jPzeaWYtYxwxG1BxCX5e5bqUZ02OSQE6KjYRCLHqK4eh65Fi1vZsSdUhkQE3ubd0Q0HspwAr9lBEhxmJbB4m2UPBTeh+SwVfBF3ksvW3e46ogHm6S8v1Pi7p+kllTQbUdvrsvARgSa5MCQZRwVo5Z1gDNIwriCljmI/aCQKgiYuAdkQUYOzpWjeLRv013bm98xXptFZNhcO9JWYVWa+JQED5PqiaTWq5FQq2QznS0M/PbfTzicIHHSDJ3LiCcz2SKQ4RHlzLGznfiZFDR9QXQpTqjNo7i6ZG44nPF3NCPW8wDqNykraOhrjNoiEgSj9DXc/YNT3BWIeqqnn+Q5VOVdWYzRao5wSzF91LfU+KUCz71HdyqYyZuErQM4gqSeQPRYDcbfMa6wAzVbT6b4ISjnHHnqfbdGjXHdrTrdqDNYsG9Yz0FqvaY7acafto7EdVgpGWrjEWVVWjWcwxX7Fhcml2vJqjntcKqJHqpWpqhJEoKKKWUvFsUSgbsqwV8vTUqkIi93DdZ6kYMIlvX+gqWz757so6ABh1azUble+z1iLbjJQoeUECJ2ME6CGqBh2w49hNB62v2Q290kzcWseVvEPlazZXFqSsgUh0AdhphU9IPewg+YZuhLFEg6rqirL3+VJ5yKK7KYejtxdzrwemV4nijOOg5wHEOMnNlURvwQsAmNqamfbWlF0GoeGw1OHQj3RPScubg6NskHhGImeEu0i9qaycYeAe6z/HlDDaSexa2ug23+lUIQ4ecu5oK7BEz8c7aUX3c50fHZXoXGBCS+t7zpNrh6rgFB00z91B7z1Qi4E4dwG4paycdeae2xkpnDXzBvO9OalCOattdAFUVpWHt04t+OSelGqe3t9krJ7Pn5fyHD5H0vt8wTr3DJDCt3OVB8aJyC0O93cjccsGi/WIoIy89zC1wbyuFeEIAHm5Qt806IZRL4SvPep5Taow1mJzvMbprevTbOXcYVmW+1XtqXIUmH2IyG4i9MbAWqGD2LREnYUYM/EdCUgw8odizlAK0JnQhaJ0WLLMOtT1DHXdwPtGb2rLbQ1p+5EFVkIVKxVc77Y95iuqohf7lDgADE9PWVssJTilFAl3zmGxP6fqk29q8eRr5g2qelf3FYA6IgzdgM3xGt2WqCXLwyWaeUPX000qJAB2buiIqIeKIOPopfPOIXk/l86FinvKOotsDaCSYUkdHACwnFxhyqtZq3gPQqH8wqsNgXR1idNIHD5rLapqQcmf9XC+mKFnkuIibeIpsRB0tdzPmvQV/y7LZY/sp4CzPd1ic7xBHAP2r+6j4VZWU1VoqkqlxcS3090laHyw69b7nsHJzRMV3k/J6XVIMaM0qBabqHKVxruhH5WqQP9oAEa9dpsOm+PN1HZm9DoA5eFVjchohoKqMN3PNBO3cHBADVShwghg3PYII81LZ4tEDhXOAf4cdqGgVijNgp83Ia9La1iq3ItYxLOFvi9goq0YYGcMUoJhUkoY8i7WRP7NWTK2t87Ax0yJsp10W1MmSyw5b60ny8FVM1P97jESBzPEiJFnveMYsHWEBG+qCo2/M5iVVaRRcXWHwBQ/ADu8ZfkZuVdUwege63mgWEdFKimqq6jAHFxRtheWKMVNLS1Wx9nAoq4Vkp1zVr1VSqyybm7d0Ixk/+o+ZRazWg8dcg6fzEalnbUzE2XqgyjQwEx0DgmyZcL2XMlbRjmA5/9/4WJzACEX79RJLT9nzplJsXTID92AnBL18hlp6iuvVj2+9nA9gWOq2gM1u37HGqEZSRVjjLDeKlK1YTCOZIllNZgzlMdG/+YwX81IYKLtVXNVkIRVXSFVDhWw47ByfsheXhCqFi7m8JD2ufDSJsAK/XsKdLDK+zfWwhfdCXEBoftq4trKtXLO08PqqAthbYIX5KuxJE/Hfz8/I5L7lq757lxcAmPWNsfd2kYBOVeaAIRAQC6Aqq9m0cA9dEWNB85n1hdRtP/RW9+J4+MbWCz24FyFyllFb0ZrIJe57OzoUA9TAJJAmhMpaZW0IOH0hmHUGTHN38XyihKygSlnhATvNUgS0lbAI1Ml4tgQIbCFWQykKRxCQMUm1OoclKfzRV5rt3Ljz8nXhoLpBTnWKJUoK3hRlnTtXCFUfmdHcAoyIkMpYEwDg8qLf6xV/dWSBy9VqOOqUL6eMyUV1hA+oUSjCm2wD6PyJ2kv2aPSWGSLiUIG6Dw4URUBIN1xE8t+n/fivNt6XhWkyLDJZgrST9tgdrKgMpb6xKYIdIFVUpq6UlHzEh1XksgJjUR+dME7eG4H7nzAlNC3A8QpgiDskxqJLHmfcYwYpXUq2fbOHGA3St6zzcHB8UMxi6RfJ+91gqqfr9pTSAigiisMRNz1lUczb1DPG50/ACxk7cQkdrcK9Fx9hxBo3rto0CxmaOY16lmth5DYLk1VDBNyOYMTIIjtSQNTlE4m4WcgOgsHwPBNfT4w7lxLJjdfzP7eCbAwzvI9bJGkqmVEqbXunK0S3bOlAXeJvhWqBz0jk6ehBdNErIf31c71NMbA4E6AzPmWUUbB80IuIZEAJoFv+X6hBGUGrqwPV1gd7SEyalwOn+c7o/lA1vueeAfabg3vXoJmxgpGlukUtrjGOUtkmRJcDZJQ2hYyVYzOnbMAYxqN9QkOk4+sHsKRvA2HbpyUnIokQdrmcl9Kp8MI9iIYRnAyTkHpCR53aBpZCwu6T6TKvbMqAu7lcP/BLi0OinNOKkY5w60xbJ6++/5UqUbOdGkv8886S0G29o6l5siEXXx/y98XYoRBcQbbKYGQ35FBVaIJBs5SN0MAO85YTepULJ3dm3bm8HepPEurtw86QC72Fzi7dYbt6QbWOSz2FirJReg3TxfbT4Rn2VihfAgabdbUO6bJuvFZ/Abp+5UKoDcrZYskwUWHZd1krgzporvspsNVNjlEDJlK7LEfdmZFu3DrafNIsUYUMZ4doVCiWi9qpUSOIpvNCdZnt6nl0CyQU6M3ucLVU0JoKUD27QDnKbjRnJAqxBij7qf3HrlJKhoMPhCEx0XzN8sBstHsuFxyM4eRuLGeLr5WkbVUorZligHtebNoEOcNZhlTJVkGB1bVcQzRl98x9OOF7LNk1VNgZoqrBDdMoKlyxgFwGxWAMfSnjABiidYFC3RnmikCVVFJUEt/uo5xp2MwJUcZMhB/trnKHWk/f5jMikni+p4TSQnCGNSzW1jsLwCQ+tG8rrWSSJnEPe73evef/B6cczg4uAZf1TBuDpFNLMFaO5+Tk1hJQoDdBC3FjKGfZoJVQ92mbtvdsV+SJMTinjKGOKISGIWrKV0pteNKiZW5DJoZzdu7TcczXmqdivRj1RD/V0wFKFGhzleZYMq1F1TzRayB5SR9XfF8lsBPOSYY79XBo/YO3jqt9GSMJAFOpN1iQT06f60MJus07xzPISffWSmyAkthUkGVuCMwJZ4hknC5Ae1dMAaoKDhWDBTyzmLLEpkIlCRFTpRyPHf/SKs1FW4892hp3zNAvujh69zerDB2E49qp/1RcEoyADhS9LDG7H4fr1BkIrQRxK3Zdr1qBNKhSOATQbC5ynMPGQhU0NKsiqXwSj3LHCdl+ZyneZurRCCWOVbxXDVZVDGRg4pWbrLZosiTLqaiQeGAEmNA122wbU/hqxqr1ZHOUXa8CzFxiWTGtyNUPQSY4mYwFlo9CsBAhJcliBpnCZjT1BPireSNMneMQD4RwU6E/hwJvUr0gSlAj/1A7S5nEWeVVpI7YKrziYdcqwsiUasGaxKZuMQVysQ5PT9j33GZh1PZOAA7e6S8LmMBk5imMc0Qp3bubru//F1le+uOKrOoqu72AjK7FCEPSnwM0sD2cWvmpC164j+m3YPvIlbbnqGqGvT9lhK+nJmG4bXDAGBHyUicG5Do+ZMkV0VLWF4upkmQ21qj7WTR54wx7ggCaNXESOqyDSpL9iSWVaalw9x5Uo8ahwm1KUuAa/JK50c6+pxZQ8YJY4+u39zHnZ7W6TOnCGPEcn+BtJpBzIJzhhYNxkCTI88tV1kyNhPnjJhEdWwaNZm42zqWJc82IbQZu2EtLJ/LpN9tOBjTPo5j2JGP04AdDGoPTaqlkszIyAwyiy4hOXaaCnH3Xs7Ts16OQp5t3TtAXr2K1ayBtRZnt85wfON4p+UAnBtK5wzAkofbXdqQ4i4hH1572iGib3tVAKEPx/ZH/CF85RCNAYuYaKtUzHkFpQbQwVBx289nj2QSX7x6V6Wj6HlPe8jk7CyWU7u96imQXqyBr1Qtw9Cibdfwvi5k586BlKyh9k2Eois9Z1klT9KYwibLT552xtAD7ZiHpEG2nKnowc8VKx+44hIBQMXMJ9AKHTzibB/Y9Nk6hyZERGNgQiyc5OVe2g00ErgucilwgnWHLbDzWaZglKZ5JAOmkGgEWHYjzs8+KEkjxZ0dv0O+RvRMPQvvUIIk7h68cs7IhugnGfQnzCSVmBkMIs+t0E8EXzB0g7ozXIR6Trm6bkPVSL/FMHTUgbIsm1c4oMQQYTgoyR7kokO00ya0UzWWEtupBWrvSzepFA0RcCEh3Y2625TShpqYxKT3fNkmhAUsCOVPYgaJPXP5fcwSJ63TmaEHMnMfFS8REoahRdddTIA8uXUbANP2xrjznujtTDqpllul0sIkcOLU4ZMKshQBBwBnEsmL3uXcp+oRCqjJKepoTQQprDGImJKZFCJynXX8lvLkN9t4ErIAB1ihBlXOIbiI4CzJORZJCHCXEcU97vV7BshHj47w0OEhVs0M71rewOZ0w5VD1Auusz+Jym6aSYoFSs4Z27ajN6btTezQDHasjpxlpfxRH2riSRXC3GzV07cDcZyY9C/IwbquGTlFH1PoDv4unLwyyIvFlbS3JEhK+1WdPC74IJGHfRg6HB/fQIwjFot9NA21pET0WVB9QrqXr6VE1R3xyTb6fVVT6WeXA8KyFmLdVLCLBtnS0FycKORwko8s12ZkT01ytRg5AHs12RYglWN1HMeBIUXibrqKvAglUAu3U3UilbwfLy5A8kwIKDLLRLq3ZbgqZ3nGCHJyasPlvAuwyWaq7GwGchaktCle59nJyjuVzLkKFlnGAXdWfCJgsBOMrdmRGJMOTbfp0G079NuZal9G/vzZ3L0i+GBX4PbP2dltVNUMw3CN5Ao9dRxKzqyY3Mr1F11boUzovc+ejqi87lVi1K4gr60jFKXj+1P2Bhx0z3cJNIFxFohJkdZy6OYMMmpgdGYYa/Rtr/+u80Q/jX/kXpElZ9g49NhsjnF8fOP+bziA97//XajrOWazBeZ7c3JA4pa7MROSnOaI0zwyJyLjd+wX248jevFslQTQGvXyFfAMQJ1B+bykpGWQQc4gAsBRJ46y2GBEPcUP7h46MmgOMaL2XueczpIlljEGJhrUnoTLI2MWMrfF9bpqYjBhVJ5r3dvNw3tUAFazGVEzmgrdpgcYIJMiC/QWBuEGU6QuJZbAwUskhsSstRRXlpaTcVZbLDIjs84iss5rFFIwX2QJjqRGEjCOxOlz1tOcw5SyUFkdxc+3gCVAillxuc5nrRdyesge7lTlJMA+9B0LJpAuZWZwAwC2TyKOWAqUHaeUyIIqZ3I7l/31UzVIP0cHyGw5Q9XUaOYNfQ8folLVq1xVUWkFBklJomMMzW8Wewu1DwKglku+clOGnTIwBuRMruKSyeu9IxB4vs8uCqTjvUc0kbVAd1uagkotrwkwtY0AaODJBUZmpwLJZXClNpJJIJH8IrHU1yvuO03EzrWJRFD/juBqzE5wtIy2PX+rys+GgdzXx2HSs5TxhwXufP37sCTp7LsNhqFFjDS6SWx3ZgwKJxc6fKlLZGFMwf2Mcv5YrTyttTBVSX+Z3r8Ex6ohOzUNAueqCoBnyfw1aavmPCnt6LkhtAJjVDIRKBDI8nmlXRwTwHPUCSyUMIz0bLft+r7vNwAMA81iR3ZSKqXnUszUs+QllZ2zBiFKt4iJ+dJh4WfTOgubjc4xJTgKoEeCpjEkLABMBsfyp1A9VC6uuBZytgPAAAqalt+Lt9N83+WMbCcXmlJKruSb3q0oeq51b6EAPgjmdYVFU6OeNeg2vUr6xJAAQy0y6+y5g30yOM4xUUXSjdiebZWTJFJiADTTM8XNBUzSUwAYbs8SVGyOqQag7HxBeqEdxpGNmCMrlIxeD9kqUzCZYp28b7aYCiO1AZy/45CYDsz7L8N1t98DADGMGMYOXbfBMHQ7vCY5RGgO47TiCkNA27UERigOQa30U1bKhq+cKlyIl2GZwac4PRhlu0hNWNmCCyCOGanssMAAV+0CGCp/f0qkcqEUHWPuCD6B5d/iBQVIV9HejdXu46CHZ94dKQgHUknpKpYci9bmOfqKXlNCslprdWZJXxfEadyhbui9hgnVerfgWAZxa50a/pZmwOWStmFIZBUnXZxQzpdwMbNISULbbo2u21DCN0bqJowRdabfWzXk61gzyMi6gLFov4Z+8scEiD5kHQlryHUj0QrmRze1atBWzJOWKkXOoDKwli13CSQyBkohqixaPatJcs45VHWRJDEoUIqByM+PtVmFAiQA9D2NUbbbk/u+3wAwjh3/ybQWpi6phiwvofqIzKAxRv0gyZA48PNIZ64YJNSW26XWcgUXNK/z1qm3Zs5ZLbJC5MqUzdllL8rOSs6MdeD3F2xkIYuIzDx6RbEiczCW+wx6zkjVWCZN5TnzbOueAVJQp5XzmFU1ZssG69sGKQmKNcHGQorIWdg0gQ7EWWLs2LOx69W5o7RLMtbAe6cHRRyDVo1VU+nNvr69Vo3WcehJpDz07M4RMHCAHMcBVVXD+xpNE1HXM3gPhGC1lVsbIs5PF4MOfFE7oeyeZd6kzVpUlhcZIMsDj7IvqiLblg6Vvt9i7OcAaj0gPCNNpWJMKSkIY3N2iq7bIMbI0nwkbVbPZ2rw28wbAtVUXqv45cFqquh4HhQ44IpJbLfp9BAJgYTV+7bD6mCF2Wqu2b1UljlTkjMOYcpGCwCRVJ7GGK5QR2oVj+GOfbofq5nXDOxICM4CGBQIBTCq1mU457V9r2T/MGIYe+QUEZO04Mn9RWghOWdUVQ1rPeqqgXX+zgpDqrcU9YQt7y/nnLp8yOw756wIWPo3x8F3N6gZpUSgSFqzOpJsz1o0yw3ZD4WAWQyoGfySElDf5/2Wg6nrNthsTrFe38ZyeUBgF2tZUctQJ0Nl6QJcP7JhO5Sk364pAezbDvWsUZWo8nfJmIUSIZIClHGA7CNyVi610HQEJLg+Jj3jMIwqgD5jPVkaCSTEYGBs1r2mGb/T/dbXzlmTevWx7XpOFPoL4/p6T1dxGFriJ3P7k0COI8bKw1mDlKbxi/g4Sjs0g0A1nu+NODJwLmf02173VIzo40gOMgK+KvEQQtMLY9CiRYQELM+iJeEH+ByUeW0m2siYxHeY99vQPR6T8N8ZPDgyZ16TlAl3IrHgWfftXhtboogq51DPG2p/FkT9ZCmbtixNVQomJ5YZCiHQjbDpyWFi06tKRlkxlm7hhqHQDSvADB3drF23wXZ7osokmnnnzAolgavAGlVF1ZD3FVKa+HayJIvU95sjcgHMsSbp0HkC72T+2sWBRnZbdPS7SJ6sp5t8aDVByXmSehJzaOSMwd3ZRqK9qEnPdtGQluqsRj1vUDWVznOMtSwzOAlgy0xZb/KmR7+lW0hmyLlNDJAYdqx1rDMMwnAafKwYymauTDWzM4Bw1uKESrwolREYA+upRRbHZ5lDGdG+BbeqpkSstEiTvU4pIYS+CHxkGUcB0O58Lx3Q6a7gBvm7aA47oR6ZCQnrfcXP0J2VYvk6Goh3UNtkH6VIZPbXC3xopZzve4A0xur9PAwtttsTjGMHP1CQj2NE9LuVjfOOD3V6hj2m+67PGamPOkYgjMHUVpbPXSIWBcBxfo8EfBMGMgTvtz3aM5Lns5aoIFVdoZ43OyjxOz5jwaGU11aQmwTgYVRzgaaZY7k81KTlIlaKgVyBGNQoQSOlrF6Q4uRCrIjpfpQqDR7I2cEUqkA5k8E6/Y6kAvOCILWMZjeG2AU9329lR0q+j4wrPFPLPO83t8+LOXFpmyf/lgBVcBPUu4jb6Cqq+ThOXa9nW/cWCkgkXFs5h3ldKaIVIDFhz2Wu1NOlDJTemIlUSLpNj+3ZFqdPn5Jha98r4i6zvVLftztoLuc8moZ4WmHssdmecoDoOBBShWfOSW8BQF03qKqGM/jZDrQeZhrQm1xkkTEicIA1xhTBMWoVCWD63Rewzh+egjok940N2u0Z6nqOg4MHiILTVBDxYQloOQNVQ+LvfvSaQRpjsNzbx/JwidmSBN1FlLlsOTjvlMvoKs+0kWlQEYZRD5Dm1hmGnixu1rctVZWMVASm2aLIEwIROVMgztkim7voUCZOQpJ4d8YLqyAnqL9jYftdxKxYsxlngRCRUkDfb5FiQEwsxegqVFWtBz8gCGhy6AgAbI7qvlEmdTmn3W4EJ2Byj+WcKDhykKSWeoW6apRX6dy5R7k4oFPMcGYKEjRTo5bwOA56HcMwYgwEnBisoBGBxX3ebzrkLEIY0LVrHB/fxNHRw7CWjM3HbqBkJE4HrIwCpHqU+55fEO1mjTgEhED6oNWs4o5U1tnmwPKGMo6Q91LOO0VBqR22aM9arI/X2GyOMZutcHjtCEcPHmlgpEuVd2gj8poC1JHrqcEg5akIYOFzADg6eghNPcfR0YP3ebdpSdHQDx36nq616KWGYcTICemsqpSGN80QrQqGp5zR5kFlOrdnVJG265bBeqMausv+6CyZRcxFUm8nYZEEn6/r6nCF+d4cq8MVJSPGqCYrgB2lNmcMcvH1EKk7Repixe8wk9RoGALGflCLxWdb964gGc3mncO8bnAwX9AcacNVQz8gRkcqEkIRYDSiyM8ZBowMbY/2dIuTp0+x3Z4hpYjl8kC/txpnqOs5QhjQNAs9gOSDGeswn6/o33hWExNlRYmrOzoEAv+dy2iuuqy1mM2WqFjCrm4qkjeKlD0PY0eVGWf+znokFYee2qtJfk+6mAP7fJU7XQsWDtieoKob9H2Let7oRRd0sFj7qJYtI7nCSHupeqntoJU6gJ2ZoswWqqZS2kdVVxpIy/mArytCwTYVqllFqj7jEQukU/tWWlulRKC4GIjs1fmMnmD5PIN4HqTeD3SlGKk9XXukGNE5q+r/guQ7P9z3vkJicID3tQYwSTJWqyOocwc7pUxydpFb5qNWjtZ6PUjke8bArbC8a3ArbhIhDLBOTJVZvs45uKpWVLOoHpWDGUm2aHY6tcNK+bExRoX43/f9LkYUw9jj+PgGjm8/hZwz6rpWS6Ju06krh/UOlaIlp9eq5zRiSPEIQ9trseA9dbuieM+aIiA6oyL7wvUV0eyhGzBsexzfPKFA7B0eePBhLPYXOLx+qOjsUCDu5RQ97+IhVZTMz2WkFLhdKwVGPW/wgg97gfKRL2JRwprQtqfYbk+wPiY7QWk7AyQ9uHEOTeVRO4eqaVB5j1kiesfAhsljN9BZvu7If5aZBr6iDpF0n6TVb7gY8d7jblOpoRuVkyv7J+BCZFL3iiHC7BvVCi7bwBr4RC87shAAV8nWEuBTxEZo5j5g6KgD9FzrebVYPSODvLVYyA3srJL4cxY1G0Ypif6hK9qXDNIRoAU5c3gcPHCgh66qGwRysydUaje1AmLQw8IahxBHxBjQdxtEdl2PcVTLJmqRWuUDUubtqUXClZGgZ6VdG8Oog2JrJ4m3qRV2b+7MRS0BNwxDx5V2r/zCsp0NznCdczqTbJYzVIzGc5WjmS6jT/X10zTb8qlSIQU5YERgvGzDWmfZ8ok0XSnLp/3x3qnHmxz8ktUpQOucHmLJVyo7EM+WNNyPFcYI5z0b77opAUhJOXfC+aL7yHE3gp72um6o+vET8IM+Q00VT+DPnCcKElWakwWPwOSBSR7Oh2HqWhQtWINeJebofvcIYeA5zdQOPy/GXAJPypHB+f0XYIYQwS9iqa7suaRvGK6iCjUlrIVyUtVYlWujVvUkuO8rhxknfWIcLWcSgdJ4PICp20ZBlTm6LsIytWDo2NJqJKlFN6ux2CfH+3pWc6tXAGXSOBM6iNGqKCcSN8h6dk3ydkKPimFE1SzQzBtcefiKthUvYoVAezkOPfq+RbduMfYDwtAgzQRsGdAPoyJKAWJAna/cRGBAxEhE5EGWr5lPLeAwHf9M97jgTgAoD9d5q7NagEcakZDD0TuacTYVbGbRF1uYHBQtYhF0KGeNwnnXqlnAnfG5laKeF4q1YsKodw6r2QzNosH2bEtDa7NLnygJ3c5bvYmkJDfGYHW0wv7VfSz25rj2wmtKmpY+fUqTiLBsnoA69PDlVszQkzkvoTR79EOLEAYm1JNsl1hhVRXJps335woWkbZA167JmLhvEflmisbAWgFdBHZliDonsPZibmYowHyXLwdQq6Rt13DWoW3PsFjsK5hAsja5Wcm/kSrAqvYQaLY4HfTbHt22099KXpyVVn3GGEULimKOeB6KlFbVVKgOFgoQUk6aK7hfPEME6D4o2zeyysNcDjFB4140D7LbdHDOouID0HmnoI2dtj3Prc7rpjo3gZpKcQBxSqgMtQJ3Z1J3p1DQAy2t16hfkwNO5+xhxBiGKWnqWwg6FqlmbVN+/2aiXsViNkPXoHQisQrxH8LFzcIkOFIgTshhwO3bTyGlhL29Kww8orm1oG3lvpIxTsxZKV/GWcz3ySA9MC+3b3sMXT+JDICTeOcm5w1QwSbPi8zOcs6oZzWWB0ss9uaYrebT88f3sZovxwl9rdePOzE65xPPWb6P+m2PoSeA4X5zgL0re3jwJQ9ib3+J1WwGXIC8n8zJ226NzfoYJ7duEzhr0QBYqIhE3/ZoZw3mVYVVQ2AnEQMPKWLghNrX5CUqyS+AOxJpXxF6VYJruU+1n0j/p9uWfu+VPd27oR8VvJMiK4FtiWPqGjtJ4pmJW7kdBmyHHmM/Ks8TIPCPBEWqHPm/ljqLz7XuecKXQ1FrDGYVHaDee0KWVR4204EoGZtjno8axjKKaLacoZ41OLxON8VsMcPycDVVC0VPulk0BCneE93AtKPnaa3hG23UtqIxBIGnWYKFNQ7WeUawVko/WOwtMFvOlI85DgFdv6GqbOyQclKIMwCu0KKiF4FdntP9X+dRiLvyV+PYoe9rRr51iONC5wjWGkLxyXvjuYpnMWpB1vnaTxk2f57FAQF3lvtLbcOSIAD187t1q9Vc1VAVLrzJ8xxHOXAJeBO15SHVucgACoG7fMjkXhC0X2DD1XsN1D/QdfrMKXLO2LNC+ofun77fWFaySdGjpSqOtXbn+xGnYEiJ4iS08GyrBH4JZcQYwPupqrbWIfkadZ5ri4h+v6Ofsbuz+JwtczwnXMD0O4xScRb7ZGIu8mEiOI22w/1cdwSUlNC2Z7DW4vj4BuMFpEPh4KrIc10H66hykRU1CEUFg1S5mlx+lJZAfpzGGNhTB+cJDDJbzPTeFYQsQNXLnCtHqjbT1K4ruknyvSqJF8XEoZC3k6qWq1QARAkxDRYHS6yOVljtLbBsGjTeX0iABKi13fdbbLYnOD19Bu26xfJgyTNvNqZoB2znPRrvKViDzv5uGNENIxdFULSwoIxDYRThnEPlnVpanW/VW2NUUQcAKu+QmmqqvnPGbDlRb2RlJv+nnFUSzxijxsn9OOrYaOhHCIc+yxnWjwQQ6ggV3/ctuu65tW+fdwlUopl8RQebzlTyLsBAZkeKcuIsq5k3AM/A9o5WqGckpl325gHOtD37sDlCUqZAnJtSb1VKfYIEO555ejhH71UMlZv5TL3f6lnNQJap9RtHFhYIo7a/zjfLdf6YAqyxIFHdi6N5nEfH0aEiswKaT41jx++b4dohwg6BW4aFELIVBt3EM9SqslAjWe4vyf/xcG/yjowJMRBCtaorbZF6RpuRiPmk1br7fg0yg0NyFC3WcUcHMSXijpVBUg6jwLwrEoEIFwbS2Z5s4NnItXThkIdLD780VXQSHKXlDExgnvOBUP5+/prK/y+DRfknikBSBhUBtVG71UxjB2s1sOtrpIxsCHiSiwBf/g5fTd2AqWKW7tFFdUmmlTPhBPqehPn39jaoqgZ+qAo6AotdiyITpGtFDhnTNbHa2lNJypx2uaMWsJnut4p9ZwX9LQhr563yMMvZt7EGKBoZ6h4i5gV6/iX1gpR2vXwdAHNVyW1nvppTcKy8BpWLWHReDxiGDtvtGVEzuMCQG1YoVdt6wHYYCB2aRaScOMlEnSEvyPL96t45aYFaBdOI8ARtmtlBbJfnka6ioyQdj1g8/2VxknNWPe+Rx0als1NKwMjBPQx8ngQCqAk/9NnWPe/+nCd7E3HgaHylQtcCoHBiFMqzphAC0E36hVVTYblPrbjFwQJ1U2lLSlQmBPIsGXcYJ5WWxMikMkAOLbVSuk2nFcYEubfwvtHsmBT2mScYEw9rqX07DNSSJU7lSFkiJrJqyrFAFGZkMx00F7HuVplOXoAEDhJOZN9vMXSDJhokvddr8KIgReotUkEYY1hJhJKWakZV4NFDR/SwHi53NDEBuo7SlhU1EXpf2LV+KhSRRMItZ0c3eRRt3azZuKscUogM5CF/xBiDttfHfsTYjeg21DK7iPX0zSfQ9y0BQWppT5OH49jvIk7l+pSfu1TtKP+d2nV36ubK0q9PJebk7QgOhPxvBjyDs055aCllWON0tqldjYL0HmOCtyTDlXNmQQ3iDgNEH5mvZpjvzTGfEeCr59altFsvak2tdqsAtNu3n8RstkBOEU3TYOhG9t9kSoVj83YHWC/7OiIMUE6dcZbskbzdsSATLqSIYTSLBqvDPW2Xbk9pbCSdrxRYiYpn0QI+zHzGqf6r5Ws3Zm1jl9KZ0k0b+0GBMXVNFev+1X0cXDvAwWKx455xv1eMrIXMSPjj4xs4fuo2mkWDowePlIYh/HNJRuX6r7etmqnLc+srj8p71XHNlmetoGdmyFmNt6WKnrAgVp09AovFTKBOg6auVBWnHQbESDGlbirUfC/ElDCw6EAfArqR9nfoBnUYSgzY6dYttmctuk1HcSIIZe6DRLHKkg8mMFqRFqPKbre9JpFeKkcAfHP6yU2DXzNjN6OOrLiPlJUkLny6keeYMufsNoSikoM0hnGnZegF0m0tz5RYEJ0zwm7b0aHfbehnYyg+K1WjyFT5pBQJKJEFVOHvhNbfp1VWDdPeTFQAqWa6boOuo9npYn+hGWu/JbUPOSyMMTDiepDp71WiuZh4Ps6WDZYHS1YF4ZmaVJ/SCmkqLPYWegCo0kjRGj+vjk/qGIXYNP9MDBHZTQHFjREuUUUwDoESoyFwO2Tg1uUFbDaA9eYYGRnzZ1ZKfbFugv+LgIReC/5T53bS0ixeUyvQLIFAqB+lKP5uh6CsIOnnOehly2LaXuc7OWdY0CFtSTaWfjbtKg7lPAmrk4YyZ9AxUiusqjHfW2Cxv6CkMwRkZDS+0me9uX9bfcdnLe9xma+fnj4DANjff0DRxWEYmRbkNShN7vBWjbdTykAS9RwS5pclyFXL0nCy14JSlteylhWeUkIesnZjBO1pzDmpwTgJdwuFQDjessZ+0A6Z8IHrWY3l4RJ7V/aotQpcGCgqpQnkRcn1KW7degr1vMEDjz6Axf4C3jvS51235LFr2NjAWW0V5yy2eTyPrQFvLUxdIcSE6NLO9ZXgOInx05mSkqCsp1aq6Et769Bw4AWApqoQbFQ91orHeR23ojd9j5PtllC1rAtdnjVhpHO/b/tC15e4zPJcPtt63ie8ophyxsDBsdt0TCaPWOwtYL0MshNyHpG9V9V8a41mdLLOZ9bStxeOTGT388CzKwH6SBAtnQjCOCCeI2wbRxtuxb0DEWMHBEs6piTU3BKwJ45qqUM6lpxRpUmXNRUVo7MOTT1/vtv3p1qlD+X5w8SYqRVJaNYt+n6rlXdiMI0cxM28Vth1CXCSJXOY2XKG2aJh0fBprmYMKYJYY3Q+lVJCzyTnyC1qsfKRKhugikquvSwJGMgTiCTF6QGyyWo7lWStgiZI52cS92sJ53Z9ekLdjtqru4TO88Qhgz/ElMTsvpZW0txule+XWduOj+hdWqnlM+FY5tAYmTHuuoBkS3PHBMDIvhsKgtquTgnGJJgIpnUEfQ/WONT1HIv9BWaLGbkpcJKlAfKCHO7LZY1wLqnVul4fwxiLrt+g7htUM6JjOa7szjusSGtVVgwRpvIwwE6VJ/qrKmQSJ9uycmZ73ijZwU77z18vK1NFS8Y03btD2Lnv5R7WpIppKMv9JVZ7C51tXli9rq11UgrLOeHk5CZmsyW2p49St2lOz+IQxXGHUKqeDdZznJLhZK2eMcYYVNbDICpXMkv1GAt935QBZHoueKZu0nTvWze5iJTI2cTdjJiSVpUxC58XOOs65fKK4laKGdbR5xbfz7Gjf6MgH5Qe9VzreQdI4Z5s+h633v8MnvqTp/DEO55A1VTUlttfoJ6RyHUYA0wkN2/h0bmKB+5c0aVEJpnSWw5MKUiMWOzbXuXpxp7mbII4TZGG3X1LG9K2ZztBhAjVPFdQ0QIe6Hejasi2my02m2O07RmGoeXK0KGqGjjmpY0j9exD2N3M+WIf+/tXn+/2/anWanXE7/lOg1zoQ0wPlLy/sZ9QjilE+FRxBkjz2aqplKOkJsl2ooJUtYdvquLgn3Q8J/HiST9U+K4+ZyRxB+GqXyHtISFaA1dFuCogJc+tHEIIy/fFQOoa0l4Jw4ggnLR2UHBO2X24n4sk+AJu33o/xrHjeewCxpCQefIJMZ6LhHZyPKAWKI+tzz1vVPVPMoWZM3m6VlJZE23pfLSVgyWbBGACkJyfSxpuwcY08s8MEESrs56rRiiyG5mk7+aLPVx5+AqOHjrC3tW9ySHeWszrGiklbPoeB/dvq+l98yy8rLmlO5JiwGZzjBAGPPH4HOmRl7F1G9EGrLOYr+YcrCxitHCZWuOxqTDKWCQl0MeZZmY5TZZqPnrV/pTrWNIxpO0tQLNsM4M+UPxneL6bMQRSh2nXnQZMV9E4ZxwmIf+qqVQ+8+CBfVx55AoevXJlZ153EWvi4UbkHDCOCTeeejdiHHH1j69jviJ2Qs4ZQ0u6zd26JdzGvMZ8RYCdnKeE1xjSbPXOkYtHQbkQHm1ZrFjszvfLwGiNvA5ZW1UcICVIDiFgzIRcHiVAJvpdZ6cbbM+2JFYgRhh5EmTo2x79hoKoaoKPPdr29J7i8M9rBikfuhtHPHN2hqcffxpP/fFTePL970RVz7BY7OPowUPsX93HfG+hfosxTWARQbae/0/mj3qoKl8lIoo4LhOZhT+UOJPJPOMxxcnknCf0alGFTW2uadPiGJhL2GEcJg3Eko8mrcx8bv5U13OsVke4cuWh53d3/inXI4+8nOdRu6CNyX5LPqvDfL4H7+udVlHOphAXL1xSuLVTNTUfFFAuoyhZSJUiaiOuaOuOjNDUg9QZAAY2212StLPU5ssZKe1m+mpgncvPlRWdBoBQhzJo18poeqDu95L5zLY9g7EOTbOghIGBSHJvUGU2IVZN4T4i1YRWIueqXWnbZ0PAGqpCi06B8henGqJESksliHEXgCOvS/YgMqOOMDgHngMQQq/vr6oa1LMGq6MVoZCdxRipjSUH3XYY0A8jHpnPcBFLP4eRoEngIxH3OD65gdl8RSpF7DNqmQ9HwuRW5+wxRO4Yucn7UQKe42ffTEbYOo8dQ6GkZMqifkpE7N3vO/r3rM9eOd7hD0iHcT8ipahdKcviBIcPHuFwj6gdZTV2EX0SGRMJdiLnzKCoU5zeOkG77jDfWxCfGSRQsTkdUDWjaqzK2VCzcpevPJyxikr11ih2Q/ZQK8ni+QB2DSg87784iAAimg6UiP7M3UtKSOjM2ekktoM+q9ZanvuOrBNLcYPau5H0olkg/rnW86ogBaSz6XvcOD3FjffcwJPvfQLvf/JdqKoZVqsDPHTjBdSmY1h0xsSBM3aXBySHCQC9sfTPLLy5ST1FtANV8SPuEsel9QhARZsFCj6V95PMFqFWR9U0HcYOKQU+nJxWBZkzrpim/rmo8RwcPICrD11/Ptv3p14veMErtFKU1pN8VrrRAwOhRjT1jCTORJlGUHLJ7CQeALgdXqmpsezdpLWKyf7KTTBq2cMxTlY/xu5CtbMlJQtjKn7YA2LY5TnutMMcBc+y/SIAH5nhBG6nT/OhiwmQIYxINqJt15QAVQ3myxWMtahnlc5eQiB6kS2TCWuQudVztwRQNlaDqu7nbrVQ8hGnrxWfN4v0nIFNbud7y/8vSZ0xBqYA64C7DXJfzecUGPev7ClgLoaIekZUg5wzun5Ae7YFLihAynujzo24zjvtjEgL0DmP5fKA2t6V5/alkNRZzLxncJ1NJFbBXQdVhzonoJAit/9Hp24npUi+8466AndU67vXhp6HycpKArCxVl2HxLRZlvc097/68BVcXe1h2TQ43mzoHjEGF9HUnlD406v3Q4uuPcPJyU206xdh7EfMV3MCWsaMdt2yXmxUXeaqrgC+X7yoNZlJ0QYAnCFXDRcto6yLIJnv5ECr2DifKVafEYNUBNyYGORXJKIxRLTrVsE5iRMlgBC5A4M44zg5BqUUMIaBxlP3MKh+XlqsAHC83eK9t57Be/7gvfiTP/xjPPHE23H71pMw1mKz2cd73/UI6+hROV729wGZL2bY84ddlJuWDuZkE6u0TP5qRtp03mEcvHIijRGY/aT4LnMG4dftWHDxBg89CQqoK8ZImbX46NH7jSrnRT17EhSez/fwwhd+OF78ipfh4Zc9fK/t+4DWy175YZCZYrnkhiDE7oj1yVrF2mOclIzKqkN4jBRsIqy3qFDtWPg4PwEXXExILiJVHqFoMUkrXG5Omk1MdjMAGIHHNy7zGAU2PrTUKheFfSH/yzJmmgmV3LayMrs4hN+IcSQ3mMTqNlXdYDVcweG1Q52jn5e6SykDTEAuK/V87jrQIVIpaIMoB7vV3d2sd2TmmUH2VzsZuLWq3kMtL8/3QgE+yXFHYGCSeDNY7R/g6MEjXH30AdTzRj1Bl02D2js8c7ZGe7bF+ngDPHgxowRrdk2lCcFLbvMZpBL09NNPkDCGq3DUPQjkB1DPiIKk3ZC6Qj2j1n6wFsaMVG2GXaF+SVA0yFlKgmPRChSh/JwywOYLqiNsNTqqAXWKiSTLBhLTMEVi3rHDRc4Zs8VcRSj2H9jH1UcewMte+gIsm4a0b2OE4zbjRSzhcZeJPnJGiAHr9W2cPnOKxR7xPj27bwRmDQw9AaSaeQPkjPkefZZ5XaPxXueFxD3n38d4BBGZkSC5kzhimi9KBSldJAmoMnsUE2RpV9NZQrPezclGJSx95WFB9ljdpkO77rA53SpAylpLxhlDq/iN51r3brGCYN9Pnpzg8ZvP4H3vfB9u33o/1uvbiCnAwiGMA9br29gcb7A52dBGYlK/kJtS2hbEbxQek4PjTDfFRHy65CdOnTHKgwnVyIbAFESDp0NW2oFinEo/tguiEBBJjInpHAM7f4isnOPKqVKeWSnz5Z3HfL6Hvb0ruP7wIzi8doDl/vJe2/cBLSHp78wewYoS/cD+jFk1ZuUQNIYspTSopMnUdOypLWWsQayjQuJTyrTnQuoNCdFb2DFq2xVg01RW1NldbDwN2V9WyRfNw25QIJXc1Aq0El6T0kEy6yieU+rn9vzz8W/7QFaMhDgcMQDGwrENkzUOi705ac06C18HrXJjjNxaNjvtMc2Muf1Gf2eAlHXIOcFkq56PCvy5S/Wo2Xa2O/6QujdIWvV7bxAjIZNVMhAs5ycONDnDWQdf1UROPyJBaMvguVlVo3I06+z4Wp0X4r7fy7DLfAabBsicir8eAgnfr9e3MZstMfQHGNn2Kow1qobOmar2iKPXPSpnxOVMUe6ncp/lT5G83HG0caIvbfQwKbVdFZEdGY2ZBbhDllpIVB1rBdZUOHzwCFceuYIH9vbgz80ezz/z93NNnQtJvDJiIMJ8e7rF9mxLYiwp7Xy+PGbmVkdUseCb8n8UIAFnpv02yAD/WyqCXToXIHXv+T85R0YR/M/TTFOAmmEYVSRGBADonBbeIyGZRTFHwFGyUorKeU/3oOo9jwqSZhF/fPMmnnj7E3jX296Jmzffi9PTp6cyNwWcnd3Cye1bWDy9wP4D+/De31HFGb75FGoNwAk3z+6SqJt5rSAfAErzqEQnsXKTp2RmjhNzpKT6KA9bnYvGgHHsMAwkTC7UDu8J2FP5mix34uT7l5FR13Ps71/F1auP4gWveAGOHrqCxf799jmgJRzTyG4GcnGJWgP2d1zj9PRpBpjIjMOiWTQ855t8NY0xrHXotJ0lnNIYIws/eOXSKTRe2kq8d0O3C1TKKQMzIGfh5aXpBm57tOtWDbKJXjPNkwX9Kr9HZaXOcQkpa6fZkEga3u8l2bXN06y5rm8h54zl6hB7VyafTXK9oMCRTcFZdHKwsih7zjIWhHNl5et0PiZt1tKmqqxwSoGCUsBAW7hIO+4UKVQ7BxvNnCYuZs4J1jWYzZa4+vAVHD14hMUe3cPeWuzNZjAGGEJUvthFmVSXqwySkE6BMbCwiGFElzc4PX0ai8Uexv46xm4gHl43KCWnamoC0bAwv3UWySXiOJ7j5p5XwZrAIkbPKcMANkoerVaN5XWS9mrg+1lM4KVbE4agLjhChJ8tZ3joJQ/hBS98ENf393HatmiHoeTFX8iaAGHUWUhGvB4J5Lhen2J5e4V+Q8R51eSNCSnTLM85ixipercMyqk8CZtLkAOYqsJgNEGdPpu2b1k9emfZai3utGTlZyNLxfVbEkoXCqCA+MShRfjWRAPsVTJSVoyjFknpHijtewbIddfhxukp3v4//wh/+Jt/iHe84//BM7fer9QCQX/1/RZnZ7cwf2qFgwcOMF/Nyc1BQC9MppV2oB4o3qK2LINWecQmYhZJXUJBD8xlGVgmKDD9Qw7rktQ+slrC0A4ww4hoDEvVRS2tJTgOQ4+YhBBLvEZjLcTnbxw6GGMwm+3j0Uf/HB57xYfj+ouv49E/9yjTIS7mwD55+qSYodJhJ0Pos5NjnJw8je32FJvNMYahK2D7Vi2RyoG6qzzqTY2xH9HMmykTYyCM8xZOLYKovUK/O6uweM704OuDw8mLIGBlBjf2JPPUrVusWSO3Pd1iYL9BafuKMP3uHJna6qKNuZPp4/k5gH8gS3R2c578Qtfr20gp0gyscpjFGYlV9CM7w3jWYJ14kAB22qCyh3r/y2fhargELU5dlul7LQiJKTN2g0yAFhP1a0LansAqCWPv2fFjLFw7Aryvsb9/FddecB3XX/Ig9g5XAHgmVlWYVRVOtlucdi22J1tFjV/U2mWO3mUZOTNo1OEciY3sXd2nABipetNzZFbrPaN8t5gAdsWSRL0cuwC7NCSZH6rYttu175MlwvtDNyotTYKjcPEkOIpQyepwiesvfhAve+wFePToqPj9pFikHYMPdmPvsvT+hHTr6NzrsaUW6+nTmD29xPHNE2IeuMmFKY4R29MtUuT5bkHlArhzVTps0IcikXg7/d4QzU5L1RgDJ9UjV6OBiwJtqzLVSPiUZGMVJlClt8h56hpSy5vstySIKobBGIQQuPNG4MwQPkg3j5gS+jBic7LB5niN9fqYHDYiKS7kbPkGHtXHsdsyP7JySAU0P4YEw2oLSBkkNWkAxzB5doZIKcPJ/CkTXcBVtHHOWYRxNzh5VsIQkEqyaVf8Ok6AHxIyH5gDM82O5CYiMAYfmMhYrQ6xWh3h4Udfgusvvo4rD1/FbDlTNZiLWOvba33QJTloN1tNQtbr2ywSsIEoB00IOseVL38WflDloIuFZF+MEWM3EtLYjoghwPlJ9AFFy1NaGNIFsNYy32vEyD6ROWdNYto1OQb02x7bs1apOvJeSwk3ua7WO5UQm+bGEzftIttPQGnDFBD4IeraM2zXZzSL4eQqhBHL5b4idIFd2Dq9mAAJOEjmXWBCuZ7t69O/WxgzhROGMuwc9iJsAAAxOtjCNIDem6URwdE+rjxElaNYpckMaQgBZ12H7VnLpOpxR+z7f+caR8n4M5YHS8Ij9IMm3RbUFvUgayScO8AB8CxZ0NtlUJwCpuyn+Ko6PwVHSRQlaMgsfepYRe0upJSUWuWYmrJ/dR/XXngNDx4c4HBBlbvlIJE5OMScnj/37gNY5wOlSQmBAStdRxZYOZHjjwCPtDNUjSzIwg5O0sko2qbnA6ec0ilDAyIABeQIQMcWzwCNgS2QElJRlRKyPZBIfUx3nAd0jSbcQzhn4SaxJMbAFnTPrnIl63kFyJiotdS2G3TtmlB/OcEaybgJlt11G2w2J9iebDWzlcqwqj2sp9ZFHAkEgpS4PWS1By1CyZNX4CT465xDGCtWW2noAEiZ1Nq5fde3PV8cvjCs5DL2Aw9mp+HsOHQTlQK1Zqpdt0FOEdZ6PPTQY7j+0Avwkf/3R6oUGwDqYV+QNuiN9z/OaL6kQKH1+piEhjfH6jgi7905MkRumgUWe3NsTjd6PWReUG1mGBilJm06ufFlTSr8fidAi4uKMaRd6SqPGAKhhdlYGcAO56vbdNgcrzF0A7ZnrQo5ALtzNIAy6Lqew/OcRw4xmRvThcy4g2R4nxdRJOhBHsYexjqsNycMXXc4OyOFF+8rzOcrEM5hF5BTfr5S3Jy+Z/LSlN83OVvwx0xZuaY5l0AfB/E7Fdk5cRGx3AK01gIecCEhmhIARQ4kVx+8jocfexgv/IgXYXW0goirC9DieLvF8c0TrG+fYX37DPc4Oz7wJW1KprsA04GdM1fJGTDWKcq/bc+w3Z5ie7bBtRdeg68czm6v0TNCkZI8C2eBijnAxlJyaHbuqwnEJHsjiUZJcRLLJjFQllkjUY+yukKM3UDiHAwYaTdbvV51U6lX6pWHr+Dhxx7Gx77iZXjJAw+gcg7PrNfwzgLwQAjoxxH9GC4kQJ7vakz3pMEwdGjbNbbbE2xONkgxqXpRFG66SM9Vnlub7HXKFV65r2UbWpYFBcWU2ULLup1/j2lXgaeUOIxpt3ocWW9afWw5ec8pY2BaR7/tlQ4o7ytG0V/t2QnnPgTI1WyGK8slrj5yFVcfuo79gwfQdqQBKqTjAILKi0KDZYknIeqnENHyh6pnNfWvK4faVjCeZ03ygDOqTLwChROn+p3OAnx4U6kddC4UQuCLGvmmZhWFdkDbbrDdnmGzOUHfbTDwJsmFHMdBUazWWsyX+7hy5RF8+P/1Klx7wTU89NhDmoFq1nhByi43bryH2gEcVEjIeYvAA3UxhKbKi9zkj44exIMvfAEeednDuP3UMY5vHGO7OVGUlvMV2SJ1S63YjDWEwOSWJx3I0MAngUosgARa7wu1nd0sG4pSHbsB7bqjmUG/1c8yPTwEAbfO6/vRZElv/F3UZo4Xs9+ejY4nhB+hQwFoZp1TxHpzjKtXHsHe3lW86MNeQsH/tEUQ2yU7iR+Uz90UEHeDVkntOP+c0j5NNeMuaGlq7crrC6dP5i/jOEA8UZtmgfl8hUdf/ggeeME1rA5XOkO13iEIx/kpQjOKOzwwVfAXsvIu17esQowxsNloAjGOPW7ffhLvec/vY+j+b8xXB7j2wmt48l1PKvhLgGbSXgXoQJc5erlKhRfxt/WV10Bb3uNUrTDVgKvEoRvVbq/bdGjPWj1DqqZWEf/5/hx7hys89qqX4iUPP4hXPPIIGu8xRjIfXncdNn2Px2/dwjNP3sLp06f4Sx/75y5gq/O5BGH6eggj+m6Dtl3zSCUiRrsDqKP7e9T5X7Ps0I4jmqpiHmTBWEBWGrA1gm6nPys7zRwBKcCS/kwZsAScs+0Iz9CebdGebdnObFAgp1J52C0lBEGsTvdPKs4O6rAJQPODBOnU3mPZNDi4doDD64c4PLyO27efxDj21IY0ki2HKcO1REqvZ+QQELmyE+X4el6jampE75iaYHSwG2RGxSCb0JOqiur1yVxKq6CsdkxjPxYXVQwxmdohnEeRlUuB28Sc1Sdqb+ScMZstsbd3FdcfegGuv+g6rj58BfPVXN9TTndasdzPtdkcAwC3gYNmPXJhdw5LlsWbzZZYHSyx/8ABgUSGEc/crNH1G0peYmDpsoT6rFEQlXhEymcD6MYSHdKy4rHMlRvdqAFWIPCy5IEKop7P7WpR8pge0gThrhrDwbaY99xt3aMT+QEv6xwhRTVAOrU7C4Fm0SN3FnxVY2/vCq48fBXbsy1SzNicEpfqbvPH81mqVI3nv+/cJwU0cNz5OqWgA4zIKDKlR1SpEu27tQ51PcNif4G9K/uYLRtYS5Jy1tJrjYGMctcn64lPxjzBi+Ke4lk+f1lVn//+rtvg9OQm1rfXWB6QhmnVVJN7Az+T6h2aMwy7f5yXO9ypbuTec3fez4KIlfM/RzHkjawVzVZsYUTOEVXVqHQjWccRaPHgYA8HiwUa75VTfvPsDE8eH+PkZI33/dETuPnem7j91PGFBEhNTHGn/nVKkQqGsdNAcr4jIm3lyWtzQIgRY4zKh5Qlrf2UM7dQp0Apz/8u2CwjCiCtSJICj/dEUUt+b9+SalGKSef/O+5RLKABw6HZGJjM5giFfvbzMZu4Z4Cs2CT5FX/uRQjDiJvv/b9wfHwDKSWs17f14Q1h5DkB2RnNljMcPXiIo4euoD3b4uZ7b+LWk7f1dVV70BrEyFJPrIAhrQzRNAwhcJXhtFoR9NLYD+i2nfLs+m5QWkjH8kLb7RqbzSna9hRdu0ZkwmzGNBCPjAIFHK5ffxEeevSF+LD/zyvw8Mse3m1L8jCePsPFHB4nJzcBFEjElBC4GhAT6LL15ixpaq6O9vDAI1dx8MABmkWDs9trlhdbYxhanJ2O2PozjOOAppkTmMc4VS8RI+gp22QXBfUlNPp1rWCsoe9hZZ7yRg2FybE1jhwYzqnHeO9RVTNUjPSTlla5ZMZmniN4fjCrrqfrK79P/j4MLbbbM4RA8m1HRw/hxa94KV76qpfi9pO3YZ3F9myrTjQ7D76iR2MR3OoiSKadTF7+FK9Jugemw57f3E4rUH4mDhFD2ysIIYwDUo6oqhn2jvbxwKNXcXBtH1VTo+8Gep6aCk1dYdt22Bxv8MwTz6ihtWPQi+f58n1fxtxxQNFnvgsflNoaGIYOt4+fwjt+6x2w1uAljz2K20fkJ3v81G1KGHLe8Sas6ikBK3510do2rMoz0Tgk4cjZsFYoX5888XtHRUkOaDdbOOdRz8gZ4/AaqYotD5cqfu+tRTuOeOrkBEOMuHF6irf89v/CH77lD/HEOx7HH/7hb+LWrffj7OwWvuZvf/bF7PldligvdVxBxhip8t2bkz+kIeBXConl9Aasb5+hmTcYhhFDHXQ0BkB1UuV2leDpuP0tz4hwImNOWm1KNZlyZpeOgE3bYX28xuZkg7Pba6yPNzwHjVTlM0WtxGGQ4pKFGUmVqARnOU9npbVkil4qrt1t3TNAOkvuzQ8dHOLmC6/jRR/xQvzxO16AYeiw2RwrgjKEgUE6Z9icbrVF8+DRIdbzGYZuxOmtM7qhzrbEW4pxp1VXEtrFMWLoSTuVBueVzrvE9DIM1OqQ4NidtdqWbdck5r1ZH6Nr1+j7VtuqdKEmekLla8wX+9jbu4IXvexluPbCazh68JCUIyDBqmj5OXthE7GxsGDJ2FWeEKWRsmViHSFP63mN2d4ce3WFMAQ88IJncHp8SwFUYxgQIimOjGMH72s9jAWNXLa6jCFuncDuy8hFwAa6bt7XzCO1+i0S3JHJzog8+IzO0jLfnMawxqao0uTpV00VnaHWvL8YEnVdz2CM5baLoD8j0z9GFpJIWCxWODi4RvfG1QNttz/zBJnPjv2o92c5kyn/SzFyspH10C05jrTnE51DQFgAFKFc8ZhCdYZlPhYTo1ZHhDjqZ1vsL7A8XE3BOybUiwbGWfTDiOOnbuPs1hlOnznVlnmzIGcT4TRfxFLhB+EqP8s8KOeMnCICj8sff887cPTgIdphwN4RIXFPbh4zPzgpLSlzsIQxKnk5JSJ36Ujkc8CmopokVRZCR/bbHu1pq3O5+XKB1eESy4MVHvuYx3B4/RAHByucbbY4vXmC9/z+e/DWJ94KX3scXj9Ezhnr22v84f/zB3jf+96B49tP4ebTj6toyUUs+dwpp53zQ/6t77fYbs/QbtbI+YjM7ZuK7QR7bE4oMPVtj/XxBlVT4+TGMQBgnM8wr6ZEKsp8EwZNVcHlBKKCOUREIBZejUViSJ6TASEltANVjNuzFqdPn2B71qI9o9a/8E6TszARyIVKUsrcyYmTA09OWbtT1azC3uoI2+0JrPPAB0vzAEjxYn8+x7X9PVx95AHs7z+A28dPFdkyIyLZ9LTdbEkXLyTsz2eoncPp4RJ1U2n/vt/2gDFKQ7DWqiVM6QE5sLi4aF7GwAGyl5bqSN6OrNbSt/0EFGGlnK7fYBg7VcTRz1W0D6t6htXyAA9cfRTXX3wdRw9dwXxvody782itu8017teKKnsH/t3TQyvvRf5UuDTPDau6wuFigfbqHg6vHWI+36Nrsj1DBB3649Az6GCE85W2FCUYS/UjPn2SZUnA4zej4gqR27fOTTy88n1KZWCM7LnhSolMY613O0hCfnkNjobh8kI/ud9rR7eXEdkk60aajSGQalNTz7A6WGHvyj4OFwukRJDy+WrG3M8OLp1rz0lgZN5jsuz5lyfT4/P3lWihJn6uaK7vdD9kZiYBWl5fqs1UuNqISbioz8hYwrD4Qt/2OLu9xtltaq+S5mmjsoRinH0/l1TP+nfsanjqM8rJg5F7MwWEADzzzPtw68nb2KxbzJczxEgG3kM3Mu2jAH8E0pdNKUMaEJJ8SSubguIulcMYotlEvobjENBzq08QksZa+JrmlQfXDnF4/RAPvfRBXFmuMKsq3L59ipOnT/D4Hz6Od/6v30fOGXt7V2CMQdue4Y//+HdwfPwU4SL6Lb+PixndlGIR5yt36gAOGiRjINWy5f4SddujaogbKdJzBLzb4vTWGaqGdKCxwA7f0drJ5OC8apRUjrtfA4GUQsAQAoaWZp2b4zUFx3XLnEa28ZMk6FzDQYCd0zWeKGK+9piv5pgv9jCfrdQt57nWPU8c4aU03uNgvsDVR6/iyrUHcXxyA85VhGjlh5IUL45x69b7sP/kAY4eIq7PajbDw9eu4pkHb8F6h+3JVgOZuNOTuGzUeaI6P4/T7KrfOs2a1eaK26x9S6iyzdkpwjhgYBTnOHboug3xBfkgl+Us0Rm8r/DIIy/Hoy95KV7yyhfjBR/2AjScYWtlxZyrVMDe7wXP/0CXKRQpdK57/vAA7jhYpc15bW8PzlqMHzni1vtvMVpx5AyVwBtpjIjBwXLr8M73INJbficR2v0eCpDeVfBVTQ4q3La1zmm16IFCIWVq19Z1TQjnplJHd42/1qBin1HryXFkeXAxykXD0CLGyH+OGIeeTV85QMaRhCIOruHggQMcPLCPRdNgVlU4WCxw+8lbePKPn8LmdIu+75UOo5UoU4bEV1Kq77KiUf1UY5EzVX8lkXk2W8J74tTVM1HMIUGCFDOPFXjey+hw7xsyEFjN4bxFv+1RzWpKNBIFx2fedws3/uQpbE626DYdrCP6QT2rdjoCF7KMlo47QVLvN0iCkfTfYxzx5JN/jMM/voY/fMtj+P/+pY/H/OohTh65ymCZLQF2uF0vnFpgAuwAIKoAK95UTdxBY8vsPYyUwIgYdrem7pS1FgfXaIxR1R7NvMEjL38UV64f4ureCn/83ifxvj96H37z538dTz31J3jyyT/GM888gZwz6ppdMVLCtj3jj88dMl9d2Jmya/htd6pnSrRGdO0aN2++F1evPYyrjLoVANjmhNqbt95/C+vjNc5uneGJdzyBsR+xOlphvHao3pGAeDuSzFztHWJyCDaprndIU9dE6CIdI4OHdsDmeI123WF9ssbmeKNGFc47WNGTLkBHkS3zJuu9vHPt56sZmuUMh9cPcfYMuXiQycMHq6STiSwuTg7OWcyWM8xnKywWe8g5Y+TqbBhatK3HycnTOH76GZzcvIp+DKhmHqvZTDPZfttr1rs9beF8T4GPWyIxUJVZOm8AUNWQnPOk0s7E9LEfMY4EVxb9VGqZjZNagjEKVAGgTiR7qyO89MNfgWsvvIarjzxA6hx+mu/QDZb19JbqRntD93lRm21qrT3Xkn0MnFSEkQ7a1WyG6w9fxYMvvk4tnfVxUXlGDVT02aJWOxK8NJHICRkWYhYtv1OpGMXPGGM1OAoFAXBIRaYmnnrGkTyYdY4pQLvtU2OMChfQPKzCfO9i/Dfbdq1c3hTDNO+NgeHg9N4Xi33MWADDGYOGtSivv/hBhDHi+OYxjm+SBCPEcDcnBSjlTI4SNtvJ3kpbTcLBtJqwlJ2Dab+hqEI5vEgIIBWyiRbeN2iaOaqaQCzb0xbdpmdj7BW2lmant5+8pYIOKK6rzPEuYs7+bIeSKuqcu892f5bagccnN/H4Hz6Oj/mLr8K1vX08+OIHsb69JvHqJ28jjhHWGdRgIJKZ2vcAJns9TnwdJ2Gq+mXomU8xoWUwlqs8dZZWcxxc20c9Ix4p0Zl6PPXeG/iDp96O9/z+e/DEu96Dd77zrdhuTrDZnuq9MAydfo6pgzUFlbsClO7T2u3Q3HldY2Jd1uNbOLt1leZ185pQ/SsyohA1LgpiGxw3t0mUxdAYpKorlexz3iFUAb1zqDx5PKZMpgflPSyc6H7bq6/j2a1TRcyKlJyg3S1b7cnZp8pcIe7wdqva69k0W82xPFji4IEDrI72sLixT7TB8Nz7/bzsrgYue2MmBZxqVqFpFmiaBSu5BFImGHtY67Be38b6jOYaIUVYYzCrKtRNhaqmakEct/u23zkA5IOKht55KSHLATIMAd2mU2UTQXoKeZ6QZuMdczVrHYTfX1UN9lZHuHL1ETz68kdw+OARDq4dkNSbStNFIE0uGcZgZ+B/Ueu8FNmzLZnPRK1wKUAu6hrX9/dx9NAVdJsOs3cvleRurd/9+TwBSWgudG7WVxxoO0GbdR3lUNe5jXX6kBhjYApBBfWY5Oxeb3h3p7C0qPqQyLNXjdr7vYah00Qq58zJSZrmqKADpWnmqOcEXHHWKn/w+vUr2J5usdhb4OTp4zuSG50/MsAq54xsd1uKcp+WSLspEbGQebno60pCI3QodWrINPOtPCHFXeWQYkS3ZUeDuEI9o33cnmxx+swpunWLGNKOm4VosF4UMGpnnZtvl/tmjLlDpF54wTff/36EMWJWVXj0yhGeOlphc7rR/RHcQghhcq8RChEfpmEYCU0vqG1fGirT+dNtelhv0cwbHD14iINrB3j0keuY1RW6YcS70/uwPdlgfbLB29/ydrz7nb+Pxx//Q60ayxV5Nmz4f9ip5CazhItcd9tTgM4c4kOeYnO61dn/cj7DsJyhmlE79ezWKcIYsDne4PTWGV2D5QxpFZHmSUXZ4xhgR8eB0sGzS4x0BunsyhrgtFLfksC4jMziOaNp0cqNgYuDMaoyVwyThrQtaGPNosF8NcPyYEHBfjGjZP4eCYnJz1Gm/NZv/dYHfzX+D1gf93Efd99e63LP770u9/tDv+7Xnl/u9/Nbl/f4h3Y9234/Z4C8XJfrcl2uy3W5/k9dH4L+yeW6XJfrcl2uy/Vnb10GyMt1uS7X5bpcl+su63kFyPe+97342q/9Wrz2ta/FX//rfx1f+ZVfiXe84x0X/d7uur7pm74JP/uzP6t//4zP+Ax827d9m/79da97HX7xF39x52c+9VM/FX/tr/01fMmXfAk+7/M+Dz/0Qz/0p/69r3vd6/Crv/qrH/gbf471P/7H/8AnfuIn4ku/9EvxJV/yJfjCL/zCnc/4wa43v/nNeMMb3nDH11/72tfi8ccf3/na6173Onz2Z3+2vpfP+qzPwr//9//+vr0XWScnJ/jcz/1c/M2/+Tfv+u+PP/44Xvva197x9Te84Q1485vffN/fz+U9frH3+Pl1ud+X+y3r/5/3+54o1rZt8VVf9VX4ju/4Dnzsx34sAOBtb3sbvv3bvx0/9mM/9qd+kx/s+qRP+iT81m/9Fj7zMz8T73nPe/CiF70Ib3nLW/Tff/u3fxvf8i3fcsfP/ciP/AiapsEwDPjMz/xMfN7nfR6uXr36oXzrz7le/epX43u+53sAAJvNBl/6pV+Kl770pfiIj/iID/l7+YZv+AZ88id/MgDg+PgYn/VZn4XP+7zPu68crbe//e14wQtegO/7vu+7b6/5ga7Le/xDuy73+0O7Lvf7A1/3DJC//Mu/jFe/+tW6sQDwqle9Cv/m3/wbAMD73/9+vP71r0ff92iaBt/xHd+Bhx9+GD/yIz+C//yf/zO89/j4j/94fMM3fAO+7/u+D29961ux3W7xT/7JP8HP//zP4xd/8Rdx5coVtG2Lr/u6r8NHfuRH4pu/+Ztx+zbptn7Lt3wLXvGKV+jv/sRP/ET88A//MADgV37lV/Cpn/qp+KVf+iX80R/9EZqmwYMPPojVavWsn6frOnjvMZvN8OY3vxnvete78PVf//Xo+x6f8RmfgV/6pV/Cv/23/xY/8zM/A2stPvqjP1ov1k/+5E/ih3/4h7Fer/Gt3/qteNWrXvUBbPm913K5xBd8wRfg53/+5/ERH/ER+M7v/E5Fon3WZ30WvuzLvgyve93rUNc1nnjiCdy4cQPf+Z3fiVe+8pV44xvfiF/4hV9A27Y4OjrCv/pX/2rntb/ne74Hv/Zrv4aHHnpI9/i51tNPP426rmGMwete9zp85md+Jj75kz8Zv/qrv4qf/dmfxXd+53fiL/2lv4SP/diPxbvf/W584id+Is7OzvC2t70NL33pS/Hd3/3d+IVf+AX80A/9ELz3uH79Or77u78b//gf/2PcuHED//Jf/kt8/ud//h33ULn+y3/5L/j+7/9+XLlyBeM44rHHHrt/m43Le/xDfY9f7vflfv9Z2e97BsjHH38cL3rRi/TvX/VVX4X1eo0bN27gR3/0R/HP/tk/w5d+6ZfiUz7lU/Drv/7reMMb3oCv/MqvxM/93M/hJ37iJ+C9x9d8zdfgl3/5lwEAjz32GL7lW74Ff/AHf4Bf+7Vfw0//9E9jHEd89meTQO8P/MAP4NWvfjW++Iu/GO9+97vxjd/4jfjxH/9x/f1XrpBU09nZGX71V38V3/7t344QAn71V38VBwcH+At/4S/c9XN8+Zd/OYwxeNe73oVP+ZRPwYINS++23vzmN+Mf/aN/hFe96lX4d//u3yGwCOQrX/lKfPVXfzXe/OY3481vfvOFBUgAuHr1Kn7v934Pv/zLv4zHH38cb3rTmxBCwBd/8Rfj1a9+NQDgkUcewbd/+7fjTW96E37yJ38S3/qt34rj42P863/9r2GtxVd8xVfgd37nd/Q1f+d3fgdvectb8NM//dPYbrf4y3/5L9/1d3/3d383fuAHfgDve9/78LKXvQz/4l/8i+d8r0888QR+9Ed/FNeuXcMnfMIn4Kd+6qfw+te/Hp/2aZ+G09NT/Kf/9J/wFV/xFXjNa16Dn/mZn0HXdfimb/om/MRP/AS+9mu/Fn/37/7dO+6hv/f3/h4AYBxHfOd3fife/OY34/DwEF/5lV95n3Z4Wpf3+If2Hr/c78v9/rOy3/cMkA899BB+93d/V//+/d///QBofhVCwNvf/nb84A/+IH74h38YOWd47/Gud70LH/MxH4OKBWw//uM/XvvdL33pSwEA73znO/HRH/3RrMfp8FEf9VEAqPX2G7/xG/i5n/s5ADSrOr8+8RM/Ef/9v/933L59Gw8//DA++ZM/Gd/1Xd+F5XKJL/uyL7vr5yjL86/8yq/Ef/gP/2Hn30u2yz/9p/8UP/IjP4Lv+q7vwp//839e/+2Vr3wlAOCBBx5A13X32roPar3vfe/DQw89hHe+8534+I//eFKWqSp8zMd8DN75zncCgLZfH3roIfz2b/82ifFWFf7+3//7WCwWePLJJ/XGAIB3v/vd+KiP+ihYa7FarfBhH/Zhd/3d0mL9lV/5FbzhDW/Yebhklft1eHiIRx55BACwWCzw8pe/HACwt7eHvu/xjd/4jfjBH/xBvPGNb8Rjjz2GT//0T995rbvdQ7Ju3bqFg4MDHB2RbGGZBd+vdXmPf2jv8cv9vtzvPyv7fU+Qzqd92qfh13/91/E//+f/1K/9yZ/8CZ588kkYY/DYY4/h67/+6/FjP/Zj+LZv+za85jWvwWOPPYa3ve1taiT7lre8RTdVVCpe/vKX43d+53dI8HkY8L/+1/8CQNnJ3/gbfwM/9mM/hu/93u/F53zO59zxnj7pkz4JP/qjP4pP+IRPAAC88IUvxPHxMf7kT/4EH/7hH/6cn6eua1y9ehXjOKJpGty8SdZSv/d7v6ff86Y3vQnf9m3fhje+8Y34/d//fbz1rW8FcHHaq+fXer3GT/3UT+E1r3kNXvayl2l7dRxHvPWtb8WLX/ziu76fP/iDP8Av/uIv4nu/93vx+te/XtVZZL385S/H2972NpL4227xR3/0R8/5Pj7lUz4Fn/Zpn4bXv/71AGjvZL/ket3tfZxfP/mTP4mv+ZqvwRvf+EYAwH/9r/9159/vdg/Junr1Kk5PT3Hr1i0A2KmI79e6vMc/tPf45X5f7veflf2+ZwW5XC7x/d///fjn//yf4w1veANCIOfub/zGb8Sjjz6Kf/AP/gG+9Vu/FX3fo+s6fPM3fzNe8YpX4DM+4zPwRV/0RUgp4eM+7uPw6Z/+6fiDP/gDfd1XvOIV+JRP+RS89rWvxdHREaqKdDf/9t/+2/jmb/5mvOlNb8J6vcbf+Tt/54739HEf93H4vd/7PXzd132dfu3DP/zDsV6vn3UDvvzLv5xFuyMefvhhfM7nfA66rsOP//iP44u+6Ivwyle+EsvlUt/bF3/xF2O5XOLBBx/Ex3zMx1wIcrJcv/Ebv4Ev/dIv1ff4NV/zNXjsscfw2GOP4Td/8zfxBV/wBRjHEa95zWs0Czq/XvziF2M+n+MLv/ALAQDXrl3DjRs39N8/4iM+Ap/8yZ+Mv/pX/yquX7/+vAbcX/3VX43P/dzPxX/7b/8Nn//5n49v+qZvwn/8j/8RL3nJS573Z3vVq16Fv/W3/haWyyUWiwX+4l/8i3j729+u/363e0iW9x7/8B/+Q3zFV3wFDg4OdqrL+7Uu7/EPzT0u63K/L/f7z8x+5/9N6+mnn85vfOMbc845932fP/3TPz0/8cQT/7vezuW6XPd9Xd7jH9p1ud8f2vV/wn5fjMHe81hHR0f43d/9XfyVv/JXYIzB53/+5+sc63Jdrv83rMt7/EO7Lvf7Q7v+T9jvSy3Wy3W5LtflulyX6y7rUmrucl2uy3W5Ltflusu6DJCX63Jdrst1uS7XXdZlgLxcl+tyXa7Ldbnusi4D5OW6XJfrcl2uy3WXdRkgL9flulyX63JdrrusywB5uS7X5bpcl+ty3WX9/wDweccDgND0hwAAAABJRU5ErkJggg==\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(3, 5, figsize=(8, 6))\\n\",\n \"for i, axi in enumerate(ax.flat):\\n\",\n \" axi.imshow(faces.images[i], cmap='bone')\\n\",\n \" axi.set(xticks=[], yticks=[],\\n\",\n \" xlabel=faces.target_names[faces.target[i]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each image contains 62 × 47, or around 3,000, pixels.\\n\",\n \"We could proceed by simply using each pixel value as a feature, but often it is more effective to use some sort of preprocessor to extract more meaningful features; here we will use principal component analysis (see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)) to extract 150 fundamental components to feed into our support vector machine classifier.\\n\",\n \"We can do this most straightforwardly by packaging the preprocessor and the classifier into a single pipeline:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.svm import SVC\\n\",\n \"from sklearn.decomposition import PCA\\n\",\n \"from sklearn.pipeline import make_pipeline\\n\",\n \"\\n\",\n \"pca = PCA(n_components=150, whiten=True,\\n\",\n \" svd_solver='randomized', random_state=42)\\n\",\n \"svc = SVC(kernel='rbf', class_weight='balanced')\\n\",\n \"model = make_pipeline(pca, svc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the sake of testing our classifier output, we will split the data into a training set and a testing set:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"Xtrain, Xtest, ytrain, ytest = train_test_split(faces.data, faces.target,\\n\",\n \" random_state=42)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we can use grid search cross-validation to explore combinations of parameters.\\n\",\n \"Here we will adjust ``C`` (which controls the margin hardness) and ``gamma`` (which controls the size of the radial basis function kernel), and determine the best model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 1min 19s, sys: 8.56 s, total: 1min 27s\\n\",\n \"Wall time: 36.2 s\\n\",\n \"{'svc__C': 10, 'svc__gamma': 0.001}\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.model_selection import GridSearchCV\\n\",\n \"param_grid = {'svc__C': [1, 5, 10, 50],\\n\",\n \" 'svc__gamma': [0.0001, 0.0005, 0.001, 0.005]}\\n\",\n \"grid = GridSearchCV(model, param_grid)\\n\",\n \"\\n\",\n \"%time grid.fit(Xtrain, ytrain)\\n\",\n \"print(grid.best_params_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The optimal values fall toward the middle of our grid; if they fell at the edges, we would want to expand the grid to make sure we have found the true optimum.\\n\",\n \"\\n\",\n \"Now with this cross-validated model we can predict the labels for the test data, which the model has not yet seen:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"model = grid.best_estimator_\\n\",\n \"yfit = model.predict(Xtest)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's take a look at a few of the test images along with their predicted values (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(4, 6)\\n\",\n \"for i, axi in enumerate(ax.flat):\\n\",\n \" axi.imshow(Xtest[i].reshape(62, 47), cmap='bone')\\n\",\n \" axi.set(xticks=[], yticks=[])\\n\",\n \" axi.set_ylabel(faces.target_names[yfit[i]].split()[-1],\\n\",\n \" color='black' if yfit[i] == ytest[i] else 'red')\\n\",\n \"fig.suptitle('Predicted Names; Incorrect Labels in Red', size=14);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Out of this small sample, our optimal estimator mislabeled only a single face (Bush’s\\n\",\n \"face in the bottom row was mislabeled as Blair).\\n\",\n \"We can get a better sense of our estimator's performance using the classification report, which lists recovery statistics label by label:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" Ariel Sharon 0.65 0.73 0.69 15\\n\",\n \" Colin Powell 0.80 0.87 0.83 68\\n\",\n \" Donald Rumsfeld 0.74 0.84 0.79 31\\n\",\n \" George W Bush 0.92 0.83 0.88 126\\n\",\n \"Gerhard Schroeder 0.86 0.83 0.84 23\\n\",\n \" Hugo Chavez 0.93 0.70 0.80 20\\n\",\n \"Junichiro Koizumi 0.92 1.00 0.96 12\\n\",\n \" Tony Blair 0.85 0.95 0.90 42\\n\",\n \"\\n\",\n \" accuracy 0.85 337\\n\",\n \" macro avg 0.83 0.84 0.84 337\\n\",\n \" weighted avg 0.86 0.85 0.85 337\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import classification_report\\n\",\n \"print(classification_report(ytest, yfit,\\n\",\n \" target_names=faces.target_names))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We might also display the confusion matrix between these classes (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"import seaborn as sns\\n\",\n \"mat = confusion_matrix(ytest, yfit)\\n\",\n \"sns.heatmap(mat.T, square=True, annot=True, fmt='d',\\n\",\n \" cbar=False, cmap='Blues',\\n\",\n \" xticklabels=faces.target_names,\\n\",\n \" yticklabels=faces.target_names)\\n\",\n \"plt.xlabel('true label')\\n\",\n \"plt.ylabel('predicted label');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This helps us get a sense of which labels are likely to be confused by the estimator.\\n\",\n \"\\n\",\n \"For a real-world facial recognition task, in which the photos do not come pre-cropped into nice grids, the only difference in the facial classification scheme is the feature selection: you would need to use a more sophisticated algorithm to find the faces, and extract features that are independent of the pixellation.\\n\",\n \"For this kind of application, one good option is to make use of [OpenCV](http://opencv.org), which, among other things, includes pretrained implementations of state-of-the-art feature extraction tools for images in general and faces in particular.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Summary\\n\",\n \"\\n\",\n \"This has been a brief intuitive introduction to the principles behind support vector machines.\\n\",\n \"These models are a powerful classification method, for a number of reasons:\\n\",\n \"\\n\",\n \"- Their dependence on relatively few support vectors means that they are compact and take up very little memory.\\n\",\n \"- Once the model is trained, the prediction phase is very fast.\\n\",\n \"- Because they are affected only by points near the margin, they work well with high-dimensional data—even data with more dimensions than samples, which is challenging for other algorithms.\\n\",\n \"- Their integration with kernel methods makes them very versatile, able to adapt to many types of data.\\n\",\n \"\\n\",\n \"However, SVMs have several disadvantages as well:\\n\",\n \"\\n\",\n \"- The scaling with the number of samples $N$ is $\\\\mathcal{O}[N^3]$ at worst, or $\\\\mathcal{O}[N^2]$ for efficient implementations. For large numbers of training samples, this computational cost can be prohibitive.\\n\",\n \"- The results are strongly dependent on a suitable choice for the softening parameter `C`. This must be carefully chosen via cross-validation, which can be expensive as datasets grow in size.\\n\",\n \"- The results do not have a direct probabilistic interpretation. This can be estimated via an internal cross-validation (see the `probability` parameter of `SVC`), but this extra estimation is costly.\\n\",\n \"\\n\",\n \"With those traits in mind, I generally only turn to SVMs once other simpler, faster, and less tuning-intensive methods have been shown to be insufficient for my needs.\\n\",\n \"Nevertheless, if you have the CPU cycles to commit to training and cross-validating an SVM on your data, the method can lead to excellent results.\"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"jupytext\": {\n \"formats\": \"ipynb,md\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
},
{
"path": "notebooks/05.08-Random-Forests.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# In Depth: Decision Trees and Random Forests\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\\n\",\n \"Here we'll take a look at another powerful algorithm: a nonparametric algorithm called *random forests*.\\n\",\n \"Random forests are an example of an *ensemble* method, meaning one that relies on aggregating the results of a set of simpler estimators.\\n\",\n \"The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, the predictive accuracy of a majority vote among a number of estimators can end up being better than that of any of the individual estimators doing the voting!\\n\",\n \"We will see examples of this in the following sections.\\n\",\n \"\\n\",\n \"We begin with the standard imports:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('seaborn-whitegrid')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Motivating Random Forests: Decision Trees\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Random forests are an example of an ensemble learner built on decision trees.\\n\",\n \"For this reason, we'll start by discussing decision trees themselves.\\n\",\n \"\\n\",\n \"Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero in on the classification.\\n\",\n \"For example, if you wanted to build a decision tree to classify animals you come across while on a hike, you might construct the one shown in the following figure.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"[figure source in Appendix](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb#Decision-Tree-Example)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\\n\",\n \"The trick, of course, comes in deciding which questions to ask at each step.\\n\",\n \"In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features.\\n\",\n \"Let's now look at an example of this.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Creating a Decision Tree\\n\",\n \"\\n\",\n \"Consider the following two-dimensional data, which has one of four class labels (see the following figure):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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NPf5JS06yN8eabq3NIZVS9XikfesjaIHs8UlZUSHnQQZ0/I4QyrF2NYMf9HnhAyl12UfslP+/1Svnb3yrja3WD3H777tfK55Pyb3/r+/WvqpLyd79T8xJCynHj1EsIKf1+WXvsscqwW1FXJ2VDQ9+PP4qxDbY1I8lg9ykkMnXqVBKJBIZhEAgEcFgpuw0nhqHU1K64InV2g8+nBJMGgmuuUbFyt1tV52VmqrS1xx7rey51Y6N1TDoeV8UqPUnHxmLwf/9nHsMHFbp4+WWlS33zzbDlliqd8KCDVKqe1WJjIqFS5hYubE+Ni8dVLPypp1TYxiwXfo89VAz+sstUPrPHo2LJ996r0i37SmGhEvgKheCqq5Rka2Vl2wJzzhNPtGfxmJGbmzo0lA6NjSr7JVl4Y2MzgAgpey8UXVFRwR//+EdCoRD19fXccccdbL311p0+s2jRInxdf6wDQCQSwePx9Pi5gltuIf+++9CsFtIAw+lk9V13Ee6vwH0X9Pp6PN98g/T5CG21lbVUaQ9oTU0UX3EFWa+9hmZitA2PB6nr6Gn0LGyZMgX3ypWm2wy3m6rzzqM+KYgVj+MsK0N6vWS98AKFt92GZrJImPD5ENEomkXudKywkKqzz2b8FVcgIhEQAulwIF0uym66ieBOO/U4776gBQJsOGeOaY/LhNfLuksuofHXvx7Yg0ajFF9zDTnPP490OhHxONHJkym74Qai6VRnpoGIRsl65RWyn3sOEY3SvNdeNBx+OEZ/bzKk/7taHxnqaxMKhZg9e7b5xr647FdffbW84YYbpJRSlpeXy7322ktGIpG03fr+kNbjSTgspd/fc5hgv/1UbHck8vPPUhYUWMeB3W4pd91VytNOsw5ZJF+ZmVKefbb1NfF6pfz+e3Utbr1Vyrw8FeJwu1W4yOMx3y8vL3WcGqTccEMpXS7zsMjy5YNz7Z57TsqsLOs57bnnwB/zuOO6r5UIIWVurgrV9JdgUMqttuocsvJ61RrC6tX9Ht4OiVgz6kMiWVlZZLY2Rs3OziYej5MYSbrAy5alTvMSAv7yF5UhMdKEoJLMm6dU+qzCGNnZqkPL3/8OBQXWmRW6rqoIr7xSKeB1zdzwelUl5cqVKjxx+untXcpbWtq7yyR1NkClw+Xnq3LunlIIf/mle741qNDJzTen3rcvVFVBdXXqMNFA51eXlakQUNenOSnVe3fe2f9jXHutKszq+DQVDqtioVNO6f/4NqOCPj2rn3DCCVx00UXMnz+fWCzGOeecMyjhjz6TnZ26HHzatN63zRpKli1TP85URqe6WsWCv/hCiRNdcw3xBx/E0dysYss+nzKK22wDTzyhYsbr1nW+QQmhbgw77KD0VazCR7GYEsyaM0dJqc6YAYceqmLP8+apCtLeEo32vZt5U5Mq+V+0SOl8nHCCilWfcoq6bslKVjMyMgZe/Ouzz9QN0+yYkYhqbHzxxf07xt13m4+fSKgUyqYmtW5iM7YZDre+P6T9eDJ7tnn2hNcr5fXXD8rcBowFC6TMzu45pU7TpDz88Lbd2q5NWZmUH3wg5cqV6u+lS81DF06nlDvsoEImPR2rtNR8rs3N5iGPnl5CSHnUUe3jGIaUiUTP1+bLL6XMyWkPDbhcKnRjlZHS4ZVwOqXcYAMVXhhI/ve/1CGY/ffv/zFShZ58PinXrOnX8HZIxJpRHxIZFTz4oPI4Omon+3xKn+OMM4ZvXumw8cbWHmJHDEMV0XSlpES18Zo8Wf19883mTxyxmCo5TydEYKVB7ferxgOpQjJmeL0q/PLjj6oPpculxthlF/MyclDe5H77qfZnydBANKpCN1aVk5qm5uDx0HTggcobHuinwd12sw6tZWTAH/7Q/2PMmmW9ze0eOgkDm2Fl7BrsTTZRj8dnn62M9A47KMP1wQfqUX4k05tKv56SfKRUVYpWIaKkJnUqPB743e+st19wgTIYHSs9hVCG8ZZb1I0zWU2oaer9M89U+2y/varGjMfVjWPBAvjVr8ybIbz5pnVM3wq3W8XhAwEqrrpKVWUONC4XPPCAOq+O5fgZGUpTZSC0av7xD/MbTUaGErcaiam1NgPO2P5XHj9eLdb0tsS4qkrFBCdP7luZdH958MH0y7p1XcWekwYx2QJt8WJlSJ5/XsWdrejJu9Z1lZP9pz9ZfyYvT6nvXXWV6ibT0qI8/CuuULrShx2mYrALFqh/k1NOUTKuRxyh5tv1phMOK4O+eHHn91ev7n3TW8NQZfh9KZPvDYccom4y11yjvPiCAnXNjj9+YNQW99oLbr1VXZekNx+NqmOcf37/x7cZHQxHHKY/DGo8ackSKXfcUcVDMzJUHPnaawcm9a+hQZV/v/yylE1NUsbjUr7xhpR33aXKyzvGbw8+OP1YsNcr5T33SCmlXP70053ju/19aZqU55wzeNV/qeKyTqeqPOzIe+/1nK7Z9bXFFm27j4k4bTisvjcvvyxlff2ADTsmrs0gMZJi2GPbw+4NFRVKIKixUf3UkzHRyy5TqVN97d8npfI8r7663VuPRpX3K6XyGDVNpcm98Ybqf5iXp7yonsIdoLzRRx6B3/6WiSedpOK7/UHX1Wv//VU4o7S0f+OlIp3z68icOSo1MRQyfzJwONr1sIVQ55Gbq8IVRx7Z//mOBDyezo0ibNYrxm4Mu7fceKO5HnQkAjfcoBa7+tK9/L//VY/J4bAKszQ1qTGbmlRXk1BIhQVWr1bxzlgMfv/73i+MvfYawizfubdIqZrtPvfc4BprUNfUarFuxgxlbDsihLqpTZigyv4dDrXomZGhGhmfcoq68Tkc6iYYj6vON6efDjNnoldXD+752NgMMrbBTvLyy+YFHknefFPFZXsTQ5VSeejpLJRJqTIfXnxRZUoccUR7oUoqMjKUfOyqVYi+ttfqiGGoHor779+5SGMwuOoq816WXq/qPmPGtGlK8/uxx1Qx0K23qhzsY45RTwRXXqmeXjr+OwWDUFZGSX9zoW1shhnbYCfpKXMkkVCLd6+8kv6Y4bAKtaRLc7PqDC6E8szvvx923lllU5jNz+VSXvD8+bDhhsiByhRIJODdd1NnhgwEM2aoDjt77qnCF5qmFiP/97/UzQN0HQ44AC68UC3qdTT6N91k2ajC99lnfXtKsrEZIdgGO0k6YYhAoHs3lOpqlQFx440qU6Ijbnfv0q18vvZ8WiFU9eEFF3RvO5bkyCNVRkJrebnh96cutdd1dYyzzuq5JD8SUd5+ZWX68wf1pPDOO0od8MEH1U0oFZttpq5psnXb55+rJ5m+sm6d9dSEUEb+vPNUqmNvY+g2NsPNcKx09odBW7ENhaScNctaWB+UrvZf/9q+z7//rYSRfD5VcefzSbnbblIGAu2f+e1vexZn6pjx0XHl3zCkLCmxzqI466xOp7D05ZeVGFBmpqok9PlUxsusWVJusomUJ56oMmEMQ8opU3rW0dY01ZAgXcrLpZwxQ2VyOJ3qvz6flM8804d/kD6y006W52MkKyxb9bHlnDkq68LGzhJJwUjKErENdkcCASnPPTe1Qf32W/XZt94yT0tzu6U8+uj2MaurpZw6NT1j/dxznefz00+pU/S6lIv/8MMPUkajUj79tJRXXCHlvfcqwf7775dy+nRlgPPzVZOAxYuVGqBVc4Tky+PpPi8rttrKvFmEz6fUB4eC117rWUGw47mdc87QzGuEYxtsa2yD3Q+G5OJddln3H31GhpSnn97+mT32SG0IamvbPxsIqPzoVDnHJm3W5I8/pjbYJSWdPm56bf72t+5jeDzKE21sVOeU6qkCpJw4sedc9MWLrefqdEpp0pVo0LjxRnWOSQ8/1bllZEgZiw3d3EYotsG2ZiQZbDuGbcbf/64U7nbaCYqKlOLdf//bWQ40VZd1l0vJlSbJyFBjmsXIvV4VUzYTud9wQ2sFNqdT5Y3Pn6+ySs47D2dZWefPVFUpCdau2R6RCHzzjUp5u/lmSDYusKKmBlasSP2ZH3+0ruiLxVI3QR5ozjpLSZ7efrtaW0hVaRiL9Rxnt7EZKQzHXaI/jBhPYNttU3vYXRvJJhJSzp+vPD5dV3HUjAwp995byi7NHzrx1FPdhfE1TR0j2bsQpHS5ZMLrlfLNN9v3feCB1JWBSbU8w0it2Kfr7cp/Vnz0kfWxNE3KE07o23UeCCZMsD63rCxVdbqeM2J+VyMQ28MeC5x3nnmetMOhUvHGj+/8vqapisRPPlFa3BdfrHSMX3/dWgkPVKbI00/DppuqMRwO5VGD8pSlVP8fjap2aIcf3p5Pnki0bzejY1XgxInWn0skzPOlO7LVVtbH8niGVyHxggusn27OOGPwdUZsbAYI22D3lSOPVOGIjgptfr+qwnv4Yev9Zs1SBvsf/1BKdel0vNl/f5WfHQopI73hhtZFPoah8phBlTBbFdP4/Z3Lta3kSUGdY08hjWOOsT7WhRcqEajh4owzYN48DLdbhZIcDnVOe++tCptsbEYJtpZIXxEC7roLTj5ZKdQ1NsI++6hOLKk85v6QHLeiwlplzzBUbjiom8fvfqfyoTsWk7hcSomwYyPaoiLrTt+a1r1MvCPLlqknBTOj7/G0KwkOF5oG997LikMPZfr337fram+55fDOy8aml9gGu79ss416DSU776wqEc1aekkJHTvY33orTJmixKuam5XxOuYYtRjZUb/6tNPUQqRZOXpeXmrj9umn1gVCkYjS/7jggnTObFCJTpumKiRtbEYpdkhkNHLSSaYG0nA6Vcilo3HVNBWSqKpSr+ZmuOce1feyI0cfrW4EHePyTqcKnTz+eOrQTVZW6kyMwWgaYGOzHmIb7IFi7VoVGnniif5LnPZEYaEq/y4pUap1mZng9RKeNcta60TTlJG28oQdDrXvnXeqdMZNNlHhnq+/VumDqdhrL+sFxxQtsiKNUP0DRBpSD58O0oBoQP3XxmasYodE+ksioWQ9H35YeaRCqNzeq6+Gc84ZvONus40So/roI6X3sfnmrAZm9sebdThUuOSYY3q3n8cD992nhJgikfb4ekaG6te4556dPt7SDC+fCkueBd0JiShsfDAcdBd4cnp36EQU3rscvrgFYiFw+GDbP8Lul4PuMv/8jy9A9feQWQKbHtn7Y9q0IyVUfgXhWijaHPzFwz2jsY1tsPvL5Zcrqc+ujWAvvhg22AAOOmjwjq1pStQ/yZIlAzt+Q0O7iFNLi1pUTZ5XVw47TEmfXnstLFyoGg2ceabKROkQTpESHtpL/cgTLZBo1bT66QWo+wVOXgSiF899TxwKK96BeGs4P9oEn92kxj/m1c6RnKblLv69uzLs0WZw+uD1c+Dwx9QNoyekAcvfgtULwJ2ljH12imzIsU7Fl/Dk4RCsAs0B8QhschgcfC84Rnjb1NGKbbD7QzTa3vigK6GQMuaDabAHk/p6lYpXXt5+I3r4YXjmGdXIeKutuu+z1VYqJJSC1R9C1XfKWHckEYW6pcogTt87vSmWfQEr32031kniYVj9AZR/AaXbqfekAe//YRLhKqA1ehNr/Wd75mg4/cfUxjdUA/fvBo2rVOhFd8E7F8Nul8Iuf01vvkYClv1P3UwyimGTw8GT3eNuI5LmcnU9ol2KRJc8B4kYHPHksExrzGPHsPtDZWXqhgYD7fEOJf/8Z2djDepcAwE48cTun1+zBk49VaUHFhWpMNHq1d0+tuId0IKN+KkAOgecowFlsNPll1cgZtHwPRaGn15q/3v52xBr0tqMdUeMBCy6K/WxnjkGan9WcwR1g0lE4IMr1dipCNfB1w/CvyfA0/Pg3Uvh9bPgXyXwwzOp9x2pfH6LugZdiYfhpxehMUXfZ5u+Y3vY/SE3N7XBHs3ZEckwiBk//KBywZPVnMuXq5h6c3N78cx//wtPPQVffNGuk7J0KVs+fDK7sACJRgtZvMvlfMkpgEA4VJgiXYTW2vrSbJvoHA6pXwYyYZ7pkmhRXr8VTWXKYzdi3bfFQrDgOpi2R/dt0oA3LoAvblX7dlwQjbVmTz53PIzbAvJMokwjmRXvdH9KSuJwQ/nC9TtcNFj02cO+8847Oeqoozj00EN56qmnBnJOo4fMTFWFmGyu2xGvF/70p6Gf00BhluOdxOHovP3881XhUMdKx3hcvXfuuervigrYfnuyV7yPgyhOIvipYh/OY0f+BagFyE2PSn+KGx+sjIPpFD0w49ftf+dMAeEwz2TRXVAww/o4DStBT1ELVfeL+fvv/wMW3aEMm1X2ihGDz2+1Hnuk4s233ialvZA7WPTJYH/22WcsXryYxx57jIceeojK3nYlGUvccYdq09Uxf9nvhx12gLPP7v75Dz5Qi3elpaod1qOPptb7GC7mzrXOvfZ6VaUkqIyQl14yr7w0DHj1VfXfG2+EQADR5XMuQszlcry+MFv/AQpnpj/FcVvCRgeqzJCOOH2wwX4wvkP90LS9wOE1v85Ch21OsT5O9iRrbxIg10RoMd4Cn/yrPU5uhRGDdUMoZDhQbHMqOC1ajuoumNyPpkE21vTJYH/00UdstNFGnH766Zx66qnslqr/3linqEiFCG6+WVXRHXaYMsJvvtm5khBUjvN++6nKv/JylU1x8snw29+OPKN9+eXmJeU+n2qemxRMMozUYaFEQr2ef95a/0TTOOzCxex7U++neeijsNvf1SIeAjKKYM6lcHiXtU9Nhzl3r8abB85WHSuHBxxeOOS/ygO3InsiTNgRNJMHKacPdjYp4qxfrubTE0KHvOkqzm30or/zcLPRAbDhAV2MtqaeRH7zoMoa6Qkp1ZrF+1fApzephUyb1Agpe28pLr74YsrLy7njjjtYu3Ytp512Gq+//jqig0e2aNEifD31SOwDkUgET08Nc0cgWlMTG86di2YSFza8XlbfdRfhfgokDfS18S5cSMnFF+OoqkK2KgVWnXUWDUcf3elzUw4/HK+FPnhk441Z8dxzTD34YDxLl5p+JpGRwer//pfI5pv3a75Spi7IjEQiOKSXNa9n0fCjG9/4OJMPbMRT0LOljNTqvHPcZCJVDuIhHeEwEDrMOLGWzc6o6fb5cLWDV/aajhHtwSfSJZpDggG6R7Lh8XVscmoN2hALCPbluyMNWPtGJov/WUSkyonQJZpLIgRsfUklUw5usty3pUHnvRMmEVzrJB7W0FzKDG12ZhUzflffr3MZaIba5oRCIWZb2II+LTrm5OQwbdo0XC4X06ZNw+12U1dXR35+58DWzJm9eL5NkyVLlgzKuIPOQw+pWLeJwdYiEaZ88EHPjQR6YMCvzcyZak7LlqmCmBkzGO9wML7r526+WRXIdI17+3x4brlFzemUU5RKoUlsXPf5mHrYYYMuc6quzww237rju+lXemyxFJa+BqveB3eOxmbzIG96IVDY/cMzYfHWsPYzzFdFNfW+JgRGi7rLGDH45b5C9IZCDk0h+DgY9PW7U/00JJoBCTIuSLQuYyz+Rykzty9lym7m+z20DzSvaF/ITV6DJbeOY6sDxjFlbu/PYbAYapuzaNEiy219ConMnj2bDz/8ECkl69atIxwOk5OT09f5rR8EAtahAykHv5y9rwihCmU228y6rP1Xv1IZIZMnqzBKMsb9xBPtVY6nnKLi9l3DRD6fChWNAk1qTVcx872uhzl/U6GMVPz6AfDmgt7BOdNdKmyz6RHg9ILRRZE2FoIfnoJ7doArnHClB546Cup7aPgzHCSi8LFFnD4WgvcuM9+vcU3qrJuPrxvQaY4p+uRh77777nzxxRccfvjhSCm59NJL0UfBD25Y6ViR2BW/H/bdd+jmMhgccIDKmEm2RpsypXN8IjNTpfhdeSU88IAqLNpmG7jiitTXZgTS0gxrP1Ex7Yk7WWeq5G8Epy9RWSA/vwwuH2x9Emw2D14+zXpBMhGFss9a/4jDkqdh+ZtwypepY+1DTeMazJ8eWqlcbP5+wwoV645b5NDX/NjvqY1Z+pyH/ec//3kg5zH22XRTZZjef1+FF5LoulK7mzdv+OY2UAgBU6dab8/JgRtuUK9RiJTwwRXw0bUqBTHJfjfDFseb75NRpHRNdr+88/vpLMq1HdeAliblsf76/t7Oum80rVWVnD88rZ4CJu8Ke1wDJR2UhD05qqrRCqvUvh6zbkZZTvpQYlc6DiXPPgtHHKEaEWRlKdGknXZSetKDsEBr004iCqtfy+Tl0+Dtv/XNi/viNljwT1XN19LU/nrlNFj2Ru/G2vRIcPXQda0jMgE/Pt+7Y/SV5nK4cyv45mFV4JNoUdkc98+FVR+0f86Xr54wzLRfHF7Y5jTz8XOmQMm2IExuWk4f7HT+gJzGmMQ22EOJz6cqCMvLlTzq0qUqLztVP0WbXlP1PbxzCfzvXPjlVWhYDTdvBAsvHc+iO1SM9M6tleFOF2nA+5dbx2vfvaR3c5z6K5iwgzJsvZnDUPD+FUryVnZZcomF1M2pI4f8VxXRdDwPZwYUz4IdzrY+xhFPQs4kcGWqvzWnGmOnC2D6XgNxFmMTuzR9OMjLG91l6yMAKVUsVBqQO015ebKsnNfP0fjypWISMYFMwJd3q8/EIoCh1lmMuHp9dhNMngMb7NPz8YLV3YWOOlLZy+IXIWD+K6qs/fObIVQLmaXKUEZNsuGEBhsM0TLHD091XwxNUrcMmisgszVVKGcKnPEjLLobfnpeGd2tTlSLqmbytkn84+CMn5Tey6r3wZMLs44ZfSX6Q41tsG1GHUv/p/S0Q1Xqb3dmgv0KryT24yoWx28m3qFiJSnWZEYsqKoR0zHYLn9qD7c34Y0kugvmXKxCAM//Dn58zjq27cyA3f9hvq2lST1JRIMwZW7/jV5Xz7ojQuue3eHNg10uVK90MRJKo2XyrjDzN32bZyIG3z2ubsrRZthgf9jujPabyVjENtjrGYlEhIbAN7TEanA78snOnIVDH+YmuenQ3AwPP0zosTdp/jiPzMTvaWRHQBAL6Ty17hJUaWEa5YUdqF+e3udcGaq8fenr3Q2a7lZeZV957nj4+aVWffAui3FCh0m7wn7/Mdc7WXQXvH62MvTSUHPbYD847DHr7JWemL4v/PCk+Q0qoxCy+hHBk1Lpq7x7qQqxGAkYvxUcdA8UbZr+OIkoPLin0uROCmlVL1FCW79f0LuxRhN2DHs9Ihheyc+r/826ureob/qCdfVv8/PqfxMIWXRLHyksW6ZywS+4AN+Hz7FF4r8cx94cwCm055Vp9NZYI6CwFz/sA+8AX0GXeK1PhWSm7wWf3ghfPaBan6VL4xplrM1S3HS3igOf8C4UmxSBrnwP/neOWgSNNivDFY+om8prZ6Y/h67sfrm5TojDC/v8O3U1aU98dhO8cb7SF4+F1A1q7Wfw352gsbsaryWL7oaKRe3GGtRYLU3w3HF9n99IxzbY6wkJI8rqyscwZBQp1TOtlDGkjLF63RMkEhZJsSOBI4+Empq2ju4aEhdBZvEoG/NCn4d1emHnXmSnZk1QedVz/w7jtobSHWC3K5Qhe/zX8NZf4LUz4F/jVYZFOlQsso71Jlpg1YfW+35wlfkiaDwM3zyojFdfyN8Ifv8RTNxZLQbqLhVmOfwJmHlo38YEJYj13t9N5iyVfvnH16c/1he3WeSxS5UB1LCy7/McydghkfWEpsD3yBRVDvXN31KQs+0QzihNfvlFNYIwUQN0EWQH/s1P/DrlELpbGed4NEGiRVcGUsKe/4RJO3f+rJTKiFZ+rRbGpu/dOefam9s5XvvgnlD9XbuYfzKk8fIpynsfb9KYpyOe3NS6X4FKdQNAqPZbu/xV9aIEqPomxTm7lNEqnpX6+FYUz1JGu6VZnZs3r3+eNUDVt9bnasTgp5dVTns6RFLIjehOCNePrCKjgcI22OsJ0Xh9m2fdFSljfHh9HSsfhrkXw1Z/SP3jlFLSHPqZ+uYvMRJh/L4NyM2ajUO30NvsDxUVqpzdQp87i7IUO0ucPknR5hrHvgGfPb8WvXwy7izVniujqPOnA+vgkf1UZxlQC2y6E4563lwutGElrFlg0XklohY0e9IEmbhz6mvdvLY9lrzwTvj2MVXxmD1JKRQGq8z3i7eAz0TipLe4M/s/RhLdnXrh1pEiq6QrE3ZUi7SmHYTi6ilhLGKHRNYT3M58NGH+i4gFnTQvK6BptVrAeidFfrKUBmvWPc7aqmcIhH4m1LKG6oYP+WXNLUSi1QM/8Y037lwZ2gEDQQXmLqxGjCnTV3HM6xonfgKeLCjaNsQuf1Fd1ZPG2kioVLWmMmWsq75VcdFYUMWFw3Xw6P4qla0rNT9ZNzaQRs8619KAZ+ebG/zkL7OjgTNiyrN8s1XOdYezzWPNQlc53iMtW6JoU+vqR4cHtvxd+mPNuVg9NXXF6YNtz1CLxGMR22CvJ2RlbNK5JC2WwPfFKjI+Xo4WiLLm5c3U2yH45P9U3rEZjYFvCYRXdPLWpYxjGBHWrnt64CdeXKwaGZvIW8bxsoDuuWSaiJORn+DIz6cweVdrD3bxvSrccMcs+M801RzXLP/YiMOiO7u/nzXBXMAoSfZk620A3z+l0vFMNTUsPFFpqIpHKWGL38L0fTobbWeGuhn95qHUxx4OhKYKbbo2nNDd6lpu+8f0xxq/FRz+pCracWWCO1uJbG31e9jj6oGd90jCDomsJ2iakynjjmVl5cNkvrCY8Ze+AAmJFCCDGtXSyUdcBAh0pxIb2nx+93FqGz+3DK1E43W0xGpxO1P0j+q6TwA+vgG+vEd5taXbq3xjb74q4IgGYMpx9zG1/jeIjxcgEUTDOoIEL3EnFWzTaTzNCZsf42DPax14U9QmLb5XZVL01BEGlEFd+1n394s2VRki1T90f9R3ZsAO5yjDmuyyvvpD5bGP31ot4n3+n85ZDumSiKOkWXU48mnVX/Hr+1W8eYP9YPNjwN2HvPChYPrecMJ7qjJ0zcfKI97it7DrX8Gd1buxNjoAzq+ENZ+o61iyrSqXH8vYBns9wuspZeOlWyP+ehki3Nmt25WriZDDQk4HrBeHEoa1hRHoJBIhSNNgx0JKRrR+WbuXuex/sOJt2jL0jBh85veTO+1NTnjzOzzff8yyN7J45dUDCYXbrZLuVkbw5EXW+ccNP7l5+T+qQnLVByqbIh2ErmLGZhz1HNy7c3sYRehqLtueodTqnpmnFsBkojXjwg0yDlN2T53GJjTreG/J1u0PS0KoBsDFs+Dtv6o0v1dOUwuee1wNGx+U3jkOJaXbwrGvD8xYmmP9akdmG+z1DO3Sv0O4+zO4ixC7cRkLOY1ETLPUc/C6JxCLN2G22iNJ4HYWpD2XL+9RxrNrSKBrWCIagOof4fnrNmPeC5sx8ySIPgDvXQ6NK5U3u+XvYI+rOhtrKQ0aA99R1/Q5392xMd/csCNGTFp2T7dCd6sehmbkbQBnr1CLgSvfVeGILX+n0s6+uLmzB2/E2kMoy95UhtuKpOHvdlMRanH0zQtVDDtzvMr7vntbJdqUHL/6O3Wz2O8W2KoXsWGbkY1tsEcBLdEaqhs+IhRZha65yc3chpysrdBEHzTIv/rKcpOLILmeSmaeUdItgyJJQc4uNId+7hYWEcJBtn8Wei+qJhf/N72QBIARVSXpwWpVbbflCeplJDBtpyWlwerKxwlGVtL4Szbf3LADiUjvvu5Jo7nrRanT85w+2PpE9QJVDPP1/dZ6z5DaWIOqbtz2jyps09Kg8pSR6tW0RhWgfHk3/OEzlS0RrOoeT4+FlMc965jUuh42owfbYI9wguFVrKp8BClV4DIGVNa9QWPgWyaXHN97o52RYZkip5Fg56sy2foc69297nFMKDqMsurnWp1siSRBpm8m4wv279VUUhk0MxxuZawyOqSrWfU+bAouIRhZiZQxVjy+NUYsvfV13QUl26mskqyJyrMet2Xv5rnsDWXs+4MRU3nXM3+jmh18db+6aSVJtKjskhf/oDJHLMM7Esq+6J5vbjM6sQ32CEZKydqqZ7t5s1LGCEcraGz+htysHiozuvL738NNN3XrLSk1DX2v3Zl9bs+Jt1kZG+P3XUAovBLDaMHrmYDT0csVI1S7rc9XWKS1mZCIpq9jUde0sO26hcqzkYmeLajuAv94OPoFUi5YgtKteO8ytTirOdQC7a4XqZBIfwtMQIWKQMWqf3qhs7FuQ0LZp5CbolVZvAXqltoGe6xgp/WNYCLRSgzD3A2VMkZd08LeD/q3v8G0aZ0bJng8iLw8uOOOtIfRhI7fN50s/yZ9MtYAO56rwgnpSIBoLiW+lJFmMUjH65a35Vo0j/ldQehKac8/HrY/WxWl9GSsyxeqmPGSp5V3G6qGz2+B22ep0MQG+1nLk6ZLR8W9VJkkwgEb7q/ymM1ItMALJ8Bd21gX2diMHmyDPYIxjBZSWTNDpuizZEVWFixcCNddB7Nnw6xZ8Je/qPLvKVP6PNe+kFkCv/8YJmzfWj6eoQorpv5K/a21loS7/FA4QzW1TZcM71RAedVTjvgKTe++SCp0pYD3lyY4awX4i+Ge7eH6Qnj0QPNUPoCXTlZGtGMWh0xAcB08uJfysrc/s/Vm1AccXtipg8ZJSQrFAKcXdr5QlbinCsNUfKk6lacqg7cZ+dghkRGMxzUOiZU4sUaGd1rfBvb54PTT1WuYKZwJJ36ivL+WZpU+pztV9eH3T6j3pu4O0/Y0b0VlRX72DtQ3LcKQCdy5YeY89CAL/jCfRIsDI6ZjRB1IQ1XMGTF4YHdVOJOMBf/yqsr6OPQRmPHr9nGDVVD9vfVxq76Bty5UndXzN4IPrlTpe64M8Jeo8IRVUQyosMxOF8CG+7W/t/sV8NBe3ePUTp8SosoohJMXqhTJpjUWA0uo+xnWfgoTd7Q+vs3IxjbYIxhd95CbOZv65i+7xbE14aAge+z88jKKOmt75E1XMeG+4nRkMaXkt6xZ9zTh5gD+qbXs++5NfP9/v2Lpw9upD0mlEJeIwrpvuhhEqbIsXvg9bHhAuwBUvKXnBcUvboPdLoOt/6BeUrbHtX95FZ46AuLR9kyRZA755sfAZkepYpyOTNpZ6Vu/dJJaqBVCZcfM+ZsS7Af1tOLOBqwMNipMU7nYNtijGdtgj3DG5e+NEBp1TV8g0JEYOPVMSosOxeXMHe7pjWi87hIm5/2JO/arwuEP0/hTMbHGzmmHlV/Bwrus48RGXFXkTZmr/s4qVYp9zSmKbnSnqn4sbb0vdFyE3HB/+O17SsC/9idlqLc/S72farFyxiFqkbbiSxWXHj+7u5ZG7lSVf21FsnDnnUtUpxZpqEyUHc/r/LloQLXuCtepIpeSbXteSLVKr7QZWGyDPcIRQmNc/t4U5s6lJVqNrrlxOQsQA5GKMEJY9UUdH1yuU/6JH1eGxlYnCnY8d2CU4hwuQdMvxZaaH7oL6pda7y9EZ89baCrc8eyxmCrFgWpdZSZyJKUqyf7kX+150WtrYLOj08ss0XRlQJOs/gjevgjKPlcpj5PnKoPctWtNR969RBni5Gc+uwm+ug92f8wBM+GHp+H536qnCCOuzrdwE1WZ2HUx1oirnpSf/ls1JPAVqIXbXS60bnVm0z/sRcdRgq658Xkm4HYV9ttYSykJRlbTGPielmjNAM2w9yQSET5+8nUemutn+auZROp0mtYIPrrG4J7tUvdjTBfdpZrXmsW/hUNldIRqrfePRdo95SSbHqkWRq3ImWIu7/npv9UrHlHNBVqalCLga2eo3O3e8Mur8NDeSp8k2Wll6evqfM1CNg6vkiQNVXc26ImoKp1ffPU4qr6H536rQkHRZnWjigWV6uCTR3Qf8+l5qolCqPUrFKqBD69SIR+bwaFfBru2tpa5c+eybNkIbzFl00a4pYKfV9/I6opHKK9+iWVld7Ki/D7iiTSFNQaQVZVP8NHZO5IIu0C2fxUTLRoNqyQL088yTMl+/1FZFJqzfbVPdyuhoHFbpX6UL5zZ2bOs/Br+r7Q1g6TLfVN3KeW4Qx/pPo6RUMbMrLIzFlKeb7pIqTJVui5CGjFlgLc+CWYdp2LheRvCNqfDad8q7W6zdEOZgIoP/Xx8g7l3nojC2o/VQnCSii9h6WsQ73I+8bC6+ZT3IePUpmf6bLBjsRiXXnopHhPZS5uBRUpJLN5ENN6I7EdeVjwRYmX5A8QTTRgyiiFbkDJOKFLG6koTKzOItERrWPdtC9EuMeW2uYYFi+8dmGPlTFEGa4Nj6smerP7e8Vz1nqMHUf2q7+CHZ6Dqe1i9QGWTBKsgFqBTSEToMPtU+OP35mXswarW8nILyhfCf6bD/bu1y6daUbMEIg3m2xItsOx1+M2DcOYy+NPPcMAtyninCpUgofJL647puku13kry88vWlaqxCPz0Yopj2fSZPkea/vnPfzJv3jzuuuuugZyPTRcC4eVUVL9CLKEa9Dn0DMbn70dmxsa9HquucRGGqYhFgki0inBLBV730KjeR6LrMCIuhGZtLWNdDIKUsORZpdfdtEbFVnf5a/uCYCoyx8OWf65i5n2dlQSn7akqFq1i3DIBTx2uQgpSglXrS6dX5ZNnW1RiujOtjSGom0b9cvUqXwizjlVNf81IxpatMPOihYDiLVSWiBkZJTFyprpYZ9F2zEi0tyYDdS0s7ynSzvceLPrkYT/77LPk5eWx667rka7hAGIYMaKxehJG6sKXUGQ1qysfIxqvQ8o4UsaJxRtZU/U0zaEUK2VdkFJS2/g51Q3vgWVeN0RaTNqqDBIOPYPsGVUgzePxwmGwwb6d33vlNLUgtvZjZbCX/U91g/n81u77R1oqKa9+mVUVj1BV/x6xuHlAfPzW6VVPxsPWxhpUvL0qRX62yw9T90xPYyQWhG8eUjnTZhRuYr2opzlh41+bb9vzWvNiHqcPZp1XxfZnWRT7CMgs7aypstGB4LR4uHZ6YeODzbfZ9A8h+/CMfcwxxyCEQAjBkiVLmDJlCrfffjuFhe3f/EWLFuHzmf3r949IJDIiwjBS1CL1H0FrAOlFJDYCoxSRojJRkkDqX4O+ou0djFJEfDaC7nJqhvNt0CxWxIwstFhni2Z1bQz9B9CXgEjl4jkQ8e0RRqn1ZwYQiUS6XuLHu7Zmyc1zVRy7w1ZHRoK9n1uJf4Jyfeu+9/DucZNJRLr7GJrL4KD3luLOUedn6EtA/wEwQMjW+LhGpG4HVjywOcufyiUW0MjZJMJmZ9Tw/a0FVH/Rv55Susdgiz+vY4N5DZafCVU4ePPIqcSDWofzkJhWs2qSaYc1sM3llaZjLX8mm8VXjet8PYTE6TfY5/nl+Mab18aveT2TL68cR6JFHVPosMWf11Gy/zo8Hg/f/F8hvzycRyIqwBDoXgPdbfCrh1aRNb1zef9Hp09g3ScZneagewyKtguy6x1rLa/DaGOobU4oFGL27Nmm2/pksDty3HHHcdlllzF9emcFmkWLFlketD8sWbKEmTNnDvi4vaG++Wsqal5uVdBTCOEk2z+L0sIDLfdbVfEowfAKJB1/TBpuZz7TJ5yK6PCcK6XkhxVXYP3gqTFzyoVoWruhM7s2hhHlx1U3WHaJaRtNuNl4yvloYujysUKRtawsf4if792GH27ZBSOuIRMauRuFOfyhLMZt0f7Z185s9aRNIijODLWwuNXvIdKyjuXl93T6t0kSa/bw6i7nEwu0u7lOn8qeWPlu6lh2Tzh8cO5alaOdxEjAL6+oTA53Dmx+tFr8/PIelT4XrlVVkFbhmJmHqY4yoDSvG1aoxrvJXo3fPqqaFgQqVQhi8q6w/21Q0EO0zEi0djA3VOMDzdH5u1P5FXx5ryq1n7KbWsA0S7FMxFQl5+f/URWpLr8qyZ9zSedO86OdobY5qWynnS3ZSxJGSzdjDUqMqTHwDbmZW+LzTOi2X6RlnZL7pKshMYjGG2kO/UxWxoy2d4UQCLQUpemkVasdbilvHSfFMMLJxOLDh9RYA/g8E9hw0p8oOG8hs05+mlDZOAqLN6V4ekm3z0YasSzpNuLKYEBSpc/imgmDgm2XUfGuyrnL2bSCTc5+F3deiNULfksi0nsro7tVlslhj3c21oF1cN8uyphGAypU8eGVsMtfVDn5zheo6sp7djA32M4MJXYVj8CrpyvjrLtUpeWE7VUmyubzVQ53uFaJP7lStAUz4io2nohByTapJWPHbQn735zGuTth98tVVWc8ouYwhsoDRiT9/oU+9NDAdftsidZQ17SIWLwOj2s8uVmzcToGoHpiAGkO/WxpAKWM09D8lanBDoSXIy1cOCmj3Qw2QGbGDJqCP2DmZWd4p6AJB/F4gOqGBTQFv8dwxSivXkpBzq64nDkACKGb7p9E13xMK/3DsFVNOh1+ivJ2oygP5CRJJLqOYHglHldxp2YI0/eCH581z83WdJi0i/r/WLwRq/MVmsRdoAYo2G4lu9z7KLonhtBgi0te46vL98eI6qQlHyiUccqZDEc8A8Wbdd789FHQsLJ9ATDZbWbBdTBhJ3U+xbNUd/M1H3fO4BCaMr6zjoWnjlISrvFIe1bG6gXK0P/pZ/WE4Ouhyc8PT8PLpyhjLQQYBsy9RGmWDISBFcK8g7nNwDNiPOy6poVU1v6v1agZBELLqWn8mEnj5uHvq8jRIGAYLUhL9R5J3DBvoaIJB0IIy9VzTXT37orz9iQYXk7CiNBuhASacDE+f19i8WaWrb2ThBFGxWuhvnkxjcHvmVbyB9yuArzuUmW0TY4rhIPCnDkjosQ9FFnD2qpniCdCbU8WOf4tGV+wH0JobHK4quqLRzpnQehuGL8NlLQ+QXo9pQTDyy2fTBp/KgYks695EYcvRt0341ly81xqF0/AlR8gc0otuszFIfIYv5UqRmmuMGkQ0JoJUb9CZZH88bv2hcDG1VD2mXm2Riykmg4nW7Ad/SI8/zv45eXWKsWo6sd46MOqacHS17p74DKh0vq+fay9y40VK99TC7Vd87/fv1w1vbVqfWYzMhkRBjsaq2811u3fcEkcJKypfIKNJ5+Ppg1NUMyQCRKJILrmNT2mzz0RKw9MCCd+j/nNJTNjYyrrzMvZVPx7827vu5w5TJ9wCtX1H9AUXIJEkunbmKLcObiceaytep6EEaKzNZYYRgsVta8xZfxxCKExvuAAyqqf7xxzR8fpyOnUACEWb6Km4RMCoZ8RQicnc0tys7ZB17oviA4k0Vg9KyseaouzJ8+mIfA1ACWFB+DwKFW/Z46G8i9a+x22qCazh/y3fay8zNnUNnzcLSwiDY3A6lzqvykhY1I93uIAFe9uwKdnHEmiRW8r3InVZ5A1vY4TF6pH/GgAvrgdvri1tWlulxufEVO9FH96SXWHAWhY1To/i6ySjqXwLj8c+ZQKodT+rOLToVr4704QDVrHt2NBZeR7MtjvXGxdrPPe32H2yb1TQbQZXkaEwa5v+tIyXADQHPqJbP9mltsHAikTrKt7h/qmhSqDQSbI9G1EScEBOBztwUGPuxivewLhyOouXpxA01zkZM4yHd/pyCI/eydqGz/ptAAohJNM30Z43ebZGU5HNiWFB1FS2L39tVW4BCAYXolhxNA0J9n+TXHoGVTVv0u4pQJNOMnJ3IrC3F3bFi1bojUsL78Xw4iRTP2rqn+X+uYvmVZ6Erpm0Yp8AKhp7G5gQa0LNAS+oijvVzh0L1ml8LsPVM/E5nJVDNI1Jc/h8DNp/DGsqXwcKRPI1uvjcuTx2VnzEJpAcyYwYhoLLzykW9w6EXHStDyXr+5X3qfLr+LNQigP38yARptVsUrSYOdOTV2kYla27i9Wr0gj3Dkbok2prhgg1EJmT1Qsst7W0qQKevzjeh7HZmQwIgx2NN6A1YqSIRPEEwMgKtEDa6uepSn4Ex3zlJtDP/LT6h/JztiMcQX742iNqU4aN4/y6udpDv6M0BxImcDtLGRi8RGdsja6Upy3Ox5XMdUNHxCL1ePQ/eRn70hu1uw+6YNYLq4lt5MAlEHK8E5hqte6fXZZ9YvduttIGScWa6Cm/iOK8/fo5dwkochqwi1l6JqHrIyZbTHpeDxAfeBrYrF63K4iguHlWP37C3RaopU4vFPb3sueaF2gApDhmcTGk88jEF5OPB7A4yrC4y5hzu3L+OGaHNZ8mk/916WWi4yJsIsv7+kcLnB4VcjDzGALDZwdFvyyJsDEnWHVB90/7/R1bk7QlW8e6rlBb3KcLX+bxucyrD19aajtNqOHEWGwve5SmkM/mqZiaULH7bRo4T1ARFqqaQouwcpbbQx+T7ilnOkTTkPTHOiai4nFRxKPB2iJ1+HU/bicPfSVaiXbvwnZ/k0A1Si2uv5D1tW/jVPPoiBnJ7L9s9Iy3lIaeFzFRKLmxS5ORzaaSM8rjidCRFrKzY9DgobAV2kZ7Gisjki0CiGcVNa+QSxej5QJhNCpqH2N8fkHoGluyqqfbT2HOEI4U6YcSgw0rfc5sELoZPo27PReRmmM49+GcL3Gd69tD5r1YmzXmPWMX8Mb55l+FIdHdSbvyOGPw/1zVRglGlAhEiFUhsjU3a3nXbG4507yTh9sdIBS5+uJLX8Hn9/c3eMXmtq/Y7reqg/g0xvVYmnxFqp8v7h7pM5mGBkRBjs3cwuq698zSXkT6HpGa7unwaOmcQGpMilAEks00xT8npzM9uRgh8PfKVzSG9bVvUNt46dtxqrFiFBe8wrByCpKC1OXidU1fkFV/bskTDuzqsXE4ry90vbaDSOqfsEWHrthcZwkiUSYNVVPEYqsQaBjyCgdr2cy3FVR+0qrFkqiw7bU+eFIA49rYJ/Zvbkw66CNeOMP5l697lL61B1xepVu9Re3dDaozgzY9ChVMdkRX4HSKln+lsrq8GTDJkekfjIApXPSk0SqYcAuF6WX4THnYvj5RRVGSt6EdLcK9XQsff/uPwX88mCr3olUKYc/PAkH/1c1VbAZGYwIg63rXqaMP45VlY+qEmwkAoHDkcnkcccOuvZzMLyix89IGaOxi8HuK9F4Y2ss2yyX+zvysrbD6zY3UnWNX1BZ96a5oZM6uu6hOG8vdM1Nc/AnvJ4JOPTuz72xeBPV9e/TGPwBaSRMbpbteE3SFJMYMs7KioeIRNcBRspxzJ6gekIC0Xg97jSfYNIh0lJJ0FjF7re7+PiijQiWd7g+QoU/tj9b/Vm+UJXEr/tGbfNkQ+YElfucVapS4zY/xuwo6h44fW/1SpetfgcfXZ36M4kIPLQnnFfZc9MATzacvAgW3Q1f369S+2YeBtv/qb3DT9V38NP9+Z1K72WiQ8ed/VRGic3wMyIMNqh0LBV3XEos3ozbWYDPM6lfxjoUWUNNwydEY9U4nXkUZO9IhndKt88pj7BnxADJhzcHl1iq40gZpzHwranBltKgqv5da6/UKKK4cEcqal9CmToNMMjN3Ipx+fu2VVLG4k0sXXt7a5Pf1IWuQjgoyu3+DB9PhKiseZ3G4PekbFLYTzShE2kpHxCDLVE3l1BkNUhJzhyNfd97he/+tScrHtkBI65S6n79gDLGVd8r9byO3WiCVSrE8ZuHYOah/Z5SN7ImwEF3w4t/SO1lRxqVjGnH3o9WuPyw4znqZcZX96FK0c0wlHrgFsf3fBybwWfEGGxQ3VUyfSZL6H2gtvFz1tW91R5yiNUQDK+gMGdXCnM7i1a5nHmWMdz2uTnJ8ZtngPQWQ8ZT5nIbFgY5GqtLvdCoVVNe82y3t+ubF6Npborz9mg11nd0W2A0HU7zMaHwkG6FQIYRY0XZvSkXi60R9HST6Iqu9a8qI2gYvBEM4nZ+ycTwGrRkSEYmEDrMuvAddj27iKysaZ0U6d69xDol7n/nwozfDE5l36xjoSUAr/4Ry0tlxJWy30AQWAcY5icSj6gGBgONEYdlb6pK0OJZ7Xn0NqkZkxmYsXiAdSZhAyljVDd8QDRW3+n9wpxdECaFK0kEDtyuQjK7VCL2lQzPVIRFGbgmXGR6NzCfh3CkMPSARThCyji1jZ+RSERZUX4fhtFzswJVVDOXzIzuN9DG4HfEEs303lhr9NZYC6H3aQ3DiKswxmsLw8xdsYobq8sZLzoY6w5IGSOa9WEnYw0q/mw13eA6CAyiuOH4LVPrcQhd9YMcCCbuDKn+XdZ+NjDHSbJ6AdwwDp6ZB6/9SS3O3rWNenqxSc2YNNhNQWudSyklDYHOor9ZGTPJz94eIRwIOgcFNc1DfvYOTB1/Qidxpv7gdZfgdZe0lo13RBWz+H3mv0SXMwenI8di1J7n1hD4ikSihxSEVqSME4mWmW5rCnzf82JhJ5LeW2+NtZMJxYf3+rovvhduKIZ7d5Z8PNfNnjtNpPQdjXiKcVpi1d3eS9WXUBpKH2SwKN2+cxf5rnhzlJZ32DD4LBzm83CYFqNvoSllsK2p/qFPw5oSqIRH9lVrAC1NKtyUbEP28L6p95VS3USfOx6ePExVgqZqCjEWGVEhkYEiYURSLHAlSJi0wyrO24PczNmqolDGyPBOxeueMCgLnkIIJo87hoqaV2kMfqdKsWWCzIyNKCk4CENGqa77gIbmrzCMKG53McW5v8Lvm05p4cGtVYFxkgZQoIPQUnrfUiaIRmvSjteDhtORbXECPRtQIZxkeCbh0DMJtawlGqslfYMtyPJvTnHu3LTTJZN895hS9lOhDIEDgSOkscGfptN4Xym+7Vaa7ufUu2vWbHIEfPVf8xLzgk3S09HuK0LAMa/BvTt210/RXbDHP+GhukZuDtS13aolcH5eHkdlW/y7WeAvAs0pMWLm33XXAOZqL7rb/HoacVXpWb5QiVN1255QRnr5W+1rCkvfUM0n/vDp+lP8MyY9bJ+7FE2YF7BowkWGZ5LpNpczh4KcHSnMnYPPM3FQs1M0zUlp0SFsPPl8ppWexMaTz2di8ZEIobO87B7qGj8nYYSRJIi0lLN63RPUN3+NzzORaaV/UIUomheHnkl+9o7kZG6Z0h66HDk4nTndniCsEEIjN9Ok1xWQ45+FsLi+AH7vhkwedzQTig+nIGcOsVg9vfWu/Z5pvTbWUsJbfzGPO+sRjUU37E3cRFZACCf52Tt2e3/upUoOtVP0Sqg86ANv79XU+kTRZnBuGex5XWujhWLAodLyXjlb8su0TApe8hGUkqCUhKTkuro63goGexy7I/5xkL2R+Qqnw6NyuQeKtZ9YF/IAlh1vvrxbiWB1XACOBaC5TGWyrC+MSQ87wzsdhyOLaKyOznFWga57+9Rea7DQNTe6q73Apb75K2Lxpm7iRVLGqKx5jWz/ZnhcRUws7tyaOhKtor5xIVaGUdO8uF0FynXr0XYKxuXtZSkKlZUxk5rGT2hpqeqUxieEk8LcueT4Z1Fe/VKrCJMgVZcbcyQVta+Q7d+kVxoy4Tr1yG1F6Jti6vCRSQte4kgEmnCQlbGJqZZLVimcslgJJX3/pPICp+4Ou19JJ63uwcSdpUrjYyH4+DogrkrhQeBEsO2FhYSLE9Rsq6xgREpuqqtjz4zeucXbXlnBe8dPU+JardEuhxeyJ6nUxYEie5KKv5utnWt6603JhE9vNL8RG3FY8Y7SX/Hld98+1hiTHrYQgqklJ+DzTEAIB5pwI4QDr7uEqSW/N4kdDx6GEaO+eTGrKh9jzbonaQr+lFI3paH565Tx4XBkTbf34vEAlbX/SzmPSLSMNeueJte/FT3fpzXysrez3CqEztTxJ5CfvUNrBoeGy1lAaeEh5GVty/KyewiEl7bedHqfew0qhTIQ7l0ahMOTupdgwg1ncwh3sz2fMQ29fhfcZSeS7zrE8mkqqxQOugv+0gAXBeDol4bOWLfNOwqf/MvqyUGwyX9yOr23PBbD6GVfkpyNWzjtWyUGlTMVCmbAbpfDyQvNmxf0lW1OVSEdMzSHdc56qgVJ3QWhmv7PbTQwJj1sUD0Dp5b8jmisnmisDqcjB7draG/B8USQ5WX3EE8E24xwc2gpXncJk8cfh2Zy40jZsAC6xakNmWB5+b3E4k2qHVaqfWWMptDPFOTsRE3DByk+mcCQ8ZQNDTTNSXH+Ht1K1msbv2iVe+1vF1bZy4VNFWudMheWv9398AmHZPWBAZzCwS8rZ7Dp+b/i2R80dJcyiFucAPvdZG1MeiJkGDzW1MTTTU2EpWQ7r5eTc3LYwNV/pcOGVVheToEg77vOEgQeIdD6EM7LmQz739KHCfaCcVuq6ssPrlTXXSbUjVZzwFHPW2fGFM5UuuFmGHGVv74+MCY97I64nLn4fdOH3FgDlFe/rMIbHQyPlDHCLWXUNnxiuk92xiaWKX8SA5+78zezObiEeCJEuil28UQjdY0W3V3bEK3aKr1HacL0ztCaIWUCn2cyhhGjKfgjDc3fdEvHNGP/21R1X8dIiu6WOAslBWdWc56Wxz5HlFK/WCMeUZkK8Qh8/QA8f0Lf5hoyDOaXlXFrXR2r43GqEwleCwSYV1bGokjP+e494clRFYpWxDLb/+0dwAH+vsklDBW7XgS/XwBbnQjT94GdL4QzflYtzqyYc4l5g2CHV0nMDuTC6EhmzHrY6SClQV3j59Q0fkI8EcDpyCQ/e2fysrbp94JjwogSCP2CmSGVMk5d0xcU5u7SbVtu1jbUNn5OQibo6FYJ4aQgeyfCLRUIobemBWoEQsuQaWd+KHrOFJE0BX4gxySu2xNmjRj6gt+3EcHwSsprXmprbCwx8Hs3ZELxoZbef/6GSsNjwfXw0/NqwXDz+YIdzhasqopS92Q2y7tKiKN0Nn58Tmlu9KT30ZUnmppYE4/TcdnOAMJSclFVFa9P7P0CdothcFdDA482NtIkJZ4PNDAE49/xMfP2HPxr1XWOewyWHqO0WD1CkK/rnJs3cGX8g8X4reCgO9P//Ab7wh5Xq0VlzaHSKqUBGx4Ae/9r8OY50livDXZZ1XM0hX5q8whj8UbW1b1JpKWc0qJD+jW2YYRTCiolLIpXHLqP6RNOorz6VYLhZaoVFW58ngnUNH5MbeOngEQInfEFB6FpbvpSPdgTfY3z52Ru0doOrT9etoqhr658FCljnc4sEP6FippXUwpkZU1Q4Y39buqyoQp+edWke0zyqE5YswCy5/Vuts80NxOxiBnXJBKsiMWY1ovQSEJK/lBRwXctLSRvrZECdeNfeVgzaw4IsMcRpRSUO3HNNNBPCrOJy8WBfj+HZ2WRoY3NB+ftz4JZx6vGxrGwahCcv2GPu40p1luDHW6p6GSskyiRp+/Ij+6Ex9X3RFuH7kcgLM2o22kdonE6spk8/mgMI4ohYzQ0f9OmIdI2noSy6mdbNUIcAxKGSKIJl2nWRDpk+jbG6x5PuKW8S1d5By5HPvFEE0I48HkmteqPd12UdFCQs2O3Rg9Jklor4/L2RtfTl12VEn55OJe15pGo1kmmbmRrRThFwYqO8rR7wwehED9Go5g9B0knxHXJ9zfWcF1tCZsd5UB3rScBXFqVFo8d7lkMH+utwVYFMlal3AbNwR/7ZbCF0MnP3oEaE8Oj0t9263EMTXMhpIOahg9TGK/vyM7YlMZg5+pDIRzoWgbxXpaQC+HA7SrupiWd/v4ak8cfR23jZ9Q1foFhhHG7CinMndttzLrGhVTW/a9NVEvKBNmZW1CUuxs/r+7qHnc4BjotsVp8enuXHikNAqFfCLWUoWtesv2bdWrg/NZf4Jubi1IKKiFV9WBv2dHr5cVAwHS5WAIbOHsXJnolECCUyshrUDkjwvQpBvoo8qYTUhKVEo8Qg67AOdiEDYPXg0E+CYXI0XV+k5nJTPfgdWVKst4abKSBdRjBSJl6ly6FuXOJJZppbP6mtVWVGtPrKjHtrG5GLN6EkUKWNNJSzpTxx5Phm0ZZ5TvozhhuZwGFObvg921AuKWSxsC31DV+ZpqBIoQTh55JLN6ArnnIy9qWgpxd+lWGrwkHhTk7U5iTuuY5L3sbsjNnqXxtGcfnmdxmZB16BvGEeZ8sSaKTZGws3syK8vtIJIIYMopAp6r+HYrz9iI/eztCNfD5fyARsT4nhw8OvkdlLPSWk3Jz+V8w2M3IeoXgDzk5uHtpVNPJWteAaC899+GiLpHgutpaXg8EMIACXef03FwOzcwclYa7LBZjfnk5IcMgJCUaKix2ZGYmf87PH9RzWm8Ntt+3IbVNn5t6rqrPYv+VdYTQKMqdS1NgCVK2u3ahlrUsXXMb0yecbF3+3YaWUkda01wIIcjxb05FzMGMDWYCqunAqsrHVPstKdp6G3aYHULoTCqeh983fF3pdc1FlomoVn729pTXvGLy7yNwO4twOXPa3lmz7gli8UaSN0RJQonw172F113KindKVdaIRcKGrxDmvwyl1qnnKZnsdHLv+PFcVF1NRTyOjnIFTsrJ4aScnE6fbUgkeLG5WcW1nU4OyswkR++8XrB3RgYfhUIpvex8h4PcUeBdBw2Do8rKqIrH24Jf6xIJrqmtpTaR4ORc8+KskczZ69ZRl0i0PbcaqIKlp5qb2cHnY67PJJ1lgFhvDbbPMwmvu4RwpKxLtZ6Kr3o95k1xe0tl7f8wZFfd6QQJI0xl7RvdKhY7EoqsYVXFI5bbhdDJ8ZuXj6+teq7NczVHIqUkYaSKEXTHkHEiLZUIoeNxjRs0byLbP4um4I8EwsvajLYQTjThYELxYW2fa4nWtDVP6IqUMWobP0Hoh6c8Vsk2fTfWSWZ5PLw8cSKrYzFChsE0lwtXl2uzIBTirHXrMICW1tDATfX13DJuHDt42yVk98jI4I76elbEYqbetkcIzs3N7fO1r4rHWRiJ4BaCHb1efINo+J9vbqY+kei2UhGWkjsbGjgmO3tULZKuisVYFouZBhnDUvJgQ4NtsAeDpADTurq3qG9eDBgIdHKytqY4r3cNZ62QUrYurJl5SpKm4I9IKU1/eIYRZVXFI63G3gwHLkcuBbndww6xeBOB8C89NumFBGXVz5LhOSutVmc1DZ9QXf8eyawUoTkpLTi4TYJVSokkgUDvtyEXQjCx+EgC4WU0NC8mYUTwezcgN3OrTouN0Xg9At2y001LrJppe5g3zwXVPHcgF7EmWcSrmxIJzlq3rtMCZDKz5E+Vlbw3eXKb4XIJwcOlpVxXU8NLgQDJqTsBv6Zxfl4e+2f2vvwwISX/qKnhxUCA5CwTwF/z89m016Olx2uBgOWiqwNYFIkwJ00Dt6Slha8iETI1jd0yMvAPg6FfF4/jBKx+lRWJ3sow9I4+GexYLMZFF11EWVkZ0WiU0047jT32GBgj11eUtxhGE46Uncs7omlOxhfsx7j8vUkYEXTNM8Bl6wapF/yScfTuxq0x8H1K9b2sjBmUFh5sqrURiVYhcPRYNZmkIfANBTk7pfxMXdOi7t1uElHWVD3FpOL5BMK/UN+0CEPG0DUvBTk7k5+9Y78MtxCCTN8GKcNTLkdOyvN0Owvw5Cghp/euMEiE23/kugfypsMmqR3wAeHVYNByxUSiDNvhWe19uDI1jSuKiri0sJBgIkG9YZAApjqd6H28pv+pq+PlQIColJ0yUK6preXPQjCzT6OmpqeZpnMmAcPgtIoKfohGkajMm7/X1HBZQQEH9eHG1R8mO52m2TugzmWjXi4w95Y+GewXX3yRnJwcrr/+ehoaGvj1r389rAa7vulrqurfJpEIIZFkeKdSUnCApXhRV4TQTfse9hchdNzOQlOtZQC3s8hycS8aq02Zqud0ZFsKIzl0n0nM2hwp40TjqSsIpZRU1b1jmamyZt3jSNnezzFhhKiqf4+WWE2PDYX7i9tViNtZ1No9vvM5d1Th2+WvEHRW8PNdpdQvA1cmbP0H2O2yvpej94Y1sZhlrnZYStbGzP+tnUKQ43CQ08/jtxgGjzY1mc4hIiV3ORwMRq/dA/1+lkSjpl52HJjt6XmV98J16/i2pYWuV+iymhqmuVxsOgTZGUmKHQ529Hj4OBzuNh+3EPy+y5rFQNOnZ4p9992Xs846C1A/Zl0fOjGlrtQ1fk5F7SvEE82tnpZBMLyc5WV3E4sHetx/sCnO39O01FwIJ8X51jlkTmduii44OuGWMtbVvU0k2v1m4HGNx6Gn11ZLCCduZwqlfCCeaCaRoqWYIaPdQhKqofC3aZWT95dJ447C6chqk9RVIRkHRXm/6pSNM+mAJv70M1yaUGJOe9/Qt7zrvjDN6cRr4Rn7hGDqAGiOpKKyh0f1SiFYPABl9F05ODOTYoeDrt9kjxCcmZvbY/x8XTzOx5FIN+MIKkvmvw0NAzXVtPlncTGbut14hcCF+vdzC8Hf8vOZlcYNqD8IKfueGxQIBDjttNM48sgjOeiggzptW7RoEb5BCL5HIhE8rRdFkkC6XgBhEr+UGiQ2REsMsbSaCYa2GhyLaU/Y0iG+FZphrssNIIkhXS9ZnButYWQBaJCYjkhsQUtLEy7fKtBXtx6rNdLWsRCyq82QDkT0IES3n1THw0WRrufTe37ttKOGiG+BMDrnXxuiEbSVIFrAKEAYE1MeP61DYYBWgRQ1gBuRmISg8/ev43dnMJHAT0KwQNOIA9tIyQzD4DSXi5CJ0c6QkruiUQbTT2wGTnK5iFuFU6RkG8PgL/G+qSumIgg8quu8p+u0AOOkZK9Ego2kZLKUpLIS3wrB9U6n6XUDKDEM/mPxdDJQWH1vlgrBT5pGhpRsaxgM1DN6KBRi9mzzJpd9XnSsqKjg9NNPZ/78+d2MdZKZMwc+KrZkyZK2cUORMlZV6OZ5ysLA6alio0mDEZnrLTORci9aWr1ht6swrTznYDiL1ZWPIWWic5w2+d0VEkggnCsoGTeTsur3ESJmHtMVXf90IjSdyePm4/P0LJ7x/fIX6HWLL02jeNw48rPVv0HCiLKq8lHCkVXtH9JXAospLT7cNL2vd6ReOuv43Rks4lJyzrp1fBIOE5EqMPU+kKFpHJyRwcutuchhKfEKgUMI7h4/ns2G4LF+xtq1fBe1iMAKQaXbzcwNB6fWO9lEZmE4zF+qq3k6kUAHYsC8rCzOy8szjc27o1GMsjJL3dxJPh8zS0pMtw0UVt+bmYC55esfixYtstzWJ4NdU1PD73//ey699FJ23LF7p46hQhk9ayMylLrXPSGEhsdtoc5uQYZ3CrnZ21PbsCDl56SMUVn3FtCSZuxaIytjE0qKDkwpodoRlzOfaKz3osMdqxvLqp7tbKxbkSRYW/UM0yechruXXWZGGo80NvJxq7FOEgWihsGTzc04gfnZ2WRqGhOdTvbw+XpdWNNX/pKfz7EV1p2DxzsGN2nsm0iEkysraelifJ9oakIAF+R3l2uY5nIx2enkl2i02xK8VwiOH+SY8UijT9+UO+64g6amJm677TaOO+44jjvuOCKDEP/qCY+r2FKKVAgHOf5ZQzyjgSWeCFHX+AnplJYnjGCPetjtqAXCdI01QFHubpjf3zV0PQPRZZsQTrL9s9oWfmPxJppDSy3HlzJOXePnac9npPJgY6Pl4qKBClI90NjI3Q0NXFlTw3V1dVQPQhjCjK28XjZ1uUwjW24pOaGXvSAB1sZi/L26mt1XrWLv1au5ua6Oxi7x8qp4nFMqKphfXt7NWINa9Hy8qYmgiSaLlJKTcnJwi/ZvmIaKgR+amckcb3prNWOFPt1SL774Yi6++OKBnkuvEUJjfMEBlFU/16VARMOhZ5CXve2wzW0gaAr8MNAifK1oOB1ZPX+sA1kZm5CfXUFd02cgJRKjtYtPKROLjqSmcQH1TQsxZAsOPYP87F3Iz96+bf+WaFVKMSyASDRFf69hQkrJe6EQjzQ2Up1IMMvt5oScHKZbLBLWppGHmwyJhKXk6aYm3gwGeba0lIJB9nABbhw3jmPLymjuUFbtFII94/G2go+YlCyKRAgaBpu73RS1zqsxkeCppibeCoVwAjt4vTzQeoNKnvV/Gxp4MRDgqdJScnSdkGEwr6yMmkQi5b+9A/g5GmWrDrHisGFwSms6X7h1rhpKm+XywsJBX+AbiYz6wpls/yY4dC/r6t4h0lKB0Bxk+2dRlLsbujZy/0ETRpSG5sU0NH8DSLL9m5KbNRtd8yClZF3d29Sm6V33llQNdq33EYzL35O8rG1oDv2IIeNkeKbia60IHZe/J+Py90RKwzQ+r+v+HsM1LsfIKlOWUnJpdTWvBYNtaWkrYjFeDQb5d3GxacFHidPJql4sgsVRRTV3NzTw14KCgZq6JSUOB69NnMgbwSCfhsNkaxoHZ2Yily9HCMF7wSB/ra4m0Xq+MSnZx+/nj7m5HFteTtAw2p4gFre0dPsXjQLV8Th31tdzYUEBLzc309SaQ56KBCr3vCNX19byXTTa5pUnfwlr4nFWx+OM7ufnvjHqDTZAhncq00pPHO5ppE08EWZF2d3EEs1tTwYt9dXUNn7GtNKTaQh8TV3T5/RsrNPVwU5+TumHFOXujruPSoQuZw752TtYH8nEWEsp0TUPDi2DuGEu6CTQU/aRHA4+j0Q6GWtQhiUhJResW8eHU6Z0Kz8/OTubK2treyWpGgNeDQT6ZLCllHwWifBMUxPNhsHOXi+/zsrqZvw64tY0DsrM7FR0sgRVSXheVVW3kM6bwSBfhMPUd9DPAOtvXgx4KRDgwoIC3gmF0roW+brO9A5FJyHD4JVAwDSEEpaSe+rrOXCEd9YZDMaEwR5tVNW/QzTeREddNinjxBNBKmtf76SfYY6GrrnxeSb3UIKukePfAl3zEolW4HTmkpe1LV73uIE8nZQEwsupqH6FmIXynkJQlLcHXvf4IZtXOiT7M5ohgU/C4W66EYdkZvJTLMbjTU29UtPrbRR7bSzGM01NvBwIUNVBq+OLSIQ7Ghp4tLSUyb2oupPAvfX1pnOOSNljHndXkobW08OCajIefV1RUaeq2NrWLBIryoco7l+bSLBKCCYZxojQPLEN9jDQ0Pw15iKaRmsvxdQJz9kZmzK+YD9qGhbQHPoxxSclTcHvEZqTyeOO7dFQh1vKqWn4hGisBpczn/zsHdtCHn0hFFnTmpbY9ccl0HU/mtDxuSdSmDt3WHpu9kRdCiMlUaGMrgghuDA/n+OysnisqYlHm5pASiuhQEAZrZ16sXj2VFMT19TWEpfdEzgjUhKRksPWruX6oiJ2z0idHbw2FuNfdXW843IRD5m0Ze8jyVj0r/1+FlgoDwrgEL+fk3Nz2zRYElKiC0Gerqe8iRUNcry/Kh7nr1VVfBmJoDudGKtWcZDfz0X5+UOW1WOGbbCHGCnT6QZu7Zlpmo8JxYcC4HGPQxOuFD0apdqWiLKy4gE2nnSOpc5KXeMXVNa92WpcJZHoOppDP1Octyf5fQxVrKt7y0ItUKJrLjaceEafxh0qtvd6WdzSYvpYngA2N8mdDhsGa+NxcjSN8/Lz+VNeHm8Fg6yMxaiJx3ne5DHfIwR/TFNmdGU0yjW1taZz6jQPKTm/qorjs7M5y6LHY0U8zpGtC5DGAKoueoTgT63nM8fnY0uPhy8jkU6hFq8QnJeXx9HZ2ZTFYhy6Zg0/tcb+deBgv5+9fD7eDAZNtTtWxWKcXFHBuXl5zBjgHPaIYXB0WRnViYS6IQoBUvJSczO1iQS3jBu6J9SuDL+Pv54hhMDttI4fO3Q/GZ6pmHnZAgd5Wdu0/Z2ZMSNF+XpnpEzQGPjOdFss3kxl3RutN5Lkj0rdWNbVvUksniqcYU0ostZyWzRW32tp16HmiKysbjFqUGp623k8TOmQKRKXkutqa9ll1SqOKStj79WrOb68nOp4nP1bF+0uLSzkqsJCxuk6bqFqOzdzu7m/pMQy66QrTzY3ty0I9kRESh5obGSNxSLo7fX1BAwjrWXtDCEwW8J3oEqzfULgEYISh4NbiovZvNXD1oTgtnHjODs3l4kOB5maxlZuNzcVF3N0djbV8TgHdDDWoG6GzwUCrIjFmO504jNTswQWhMMcW17O9y0D+z16LRg0XShtAT4Oh1lhVXw0BNge9jBQlPcr1lY9a9o6rCh3d/y+aSwvu5uEEWnzUJXmR36nLi6acDC15ARWVjyEYUQwjJhlLraUMcIt5eSydbdtjYHvLCvJkJKGwLc9do8xQwgtZeceMQz+wjeRCLfW1/NNSwsZQnBEVhbHZ2fjNXnMzdV1Hiwp4azKSqpbY6pRYI7XyzVFnfVX/l5dzevBYCcvcnEkwryyMl6dNKltEXA/v599MzKoSiRwCUFuL3V41sZivYp3G1Lyv2CQP5gUmLwdDKap56huSAiBU0rcQqVnSpQ060GZmayIxXACU5zObgqNTiE4LieH40zmcF1tralOCMCSaJSHxo8nIiWX19SwxiRuHZaSa2pqeLh0YPTrAd5P0TxCoNYJBlv7xQrbYA8DWRkzGJe/D+tq3yDpSUsMinJ3IzdrSwA2mHA69c1f0hRcghA6uZlbkuXfrFuxi9tVwEaTziYYWcmqNe+DYy1m8XGBbtndJmGELSVKJQkSib7FNrMyZrZ69d2//BmeSZZqg4PFF5rGTRUVbUa1Cbizvp43gkEeLSkxjU1u5HLx6sSJ/BiNUpdIsIHLRXGX+Om6eJxXg8FuC3YGEJKS55qaOlXkCSG6jZEuM9xuPgqFLPWYuxIDvo9EiErZ7WmhNwmjLYCQklKHg7/l5+MUgtleb9uYG3UwYMuiUaoTCaY5nT3Gmt9LETeXqGyTSwsLqUyxyPhNSwsRw+hxgTNdMoSwzL/SwFLEayiwDfYwkZc1mxz/FoRa1oA08Hkmdoov67qHgpydetSpBmUA/N6piEQzOMvNs0aEICfTXAjL6y61jIVrwpV2/8muFOftQSC0rFXpL2keBJpwMa5g/z6N2VcSUnKbw9EtZa0FFQ99obmZIy0q/YQQ+DSNh1vLzpNVdvNbu6V8GYngBNNYa6S18GY7r5fHm5ooi8fZ3O3mqKysPhntIzIzlUJdLzJQPgiFmLNqFTcVF7N9h8XN3X0+XmrVNkkHiVqIzdX1tpBHR1bFYpxVWcmaDiL/u3i9XFtUZJlh0dNZJKsfe5rjQNaXHZKZyf+6pHMmScCgdpTpCTuGPYxomgO/dyp+3/S0my6kQuBkYvHhCOFEtCVFaQjhYHzBAZbVjZm+DdF1H93j5gJd85Lp27hP83E6spk+4dTWgiBfazfzWUyfcHK/OtL3hW9bWixDCWEpeaa52XLfbyIRDl+7lhdbU+hWx+PcXl/PvLIygoaBuwePqzoe55jycp5pbubjcJj7Gho4YM0aPguHe30ehQ4Ht4wbR4YQZLSWa/f0I44AzYbB6ZWVrOvgqZ6Wm4uv1ZvsSKqz0VA9GbsSMAzml5WxtFX3u7m1Q/pHoRBnVFpXsPaUHfNpJEJMyk43mq5s5HKZhrT6yrYeD3N8vm6etEcIzs/LI8sijFWfSFAei6W9xtAXbIM9xsj0bcSGE08nP3sn/L6NyM/enukTTiM3c0vLfYTQmFryu9YejU404UYIJx5XMVNLf9evDupORyYlBfszY8oFzJjyZyYU/RrXMAg89ZRVYaX/AfC36mpCsnP/nxZU9+yHGxvZ0eu19AA9QrA6HifSYf8o6iZxZmVlr3K1E1LycGMjf2stbkm2DEs3Cp6Qkiea2heQJzqdPFZayk5eL1pr6feGTie7eL2WY8akJCYlq7ssZL7Q3EyL7F7LGkWFLH6yWBj8S35+SiMUTCR4Jxjk3Lw801CERwj+YiIa1R+EENxQVMTfCgrY0OkkS0q29Xi4ddw45ps8hS2NRplfVsbuq1Zx0Nq1zF21iicaGwd0TknskMgYxOnIpjj/V73cJ4vpE04mEq0iFqvH6czF40rd2GA0sZnbbbnA5sL6MXdtLEaZRfy0BXi2uZlTcnO5pKCAf9TUdDL8HiHI0TSqLPK5JSpcsWcPudJJ/lJVxTuhUNsxGqTsVWgkinrS6Mg0l4u7xo/nmyVL2GjjjfFoGkujUY4qK+vmKYrWMS6rriaGWmC8vqiI6S4XC3qoaPympYWNTdLvSpxO5mdm8rDFE04Ytci3j9/P/SUlXFNT03YOG7lc/CU/n20GQQBKE4LfZGbym8xMJa86fbrp58rjcY4pKyPYerOKtebBX1dXRwtwfB8EtVJhG+xRjpSSxuB31DZ8guGqY0X5pxTk7JqyD2IqPK6iMWWok2RoGgcmErzqdHYyqgJVqn2cxQ8rLGVKDzZppA7JzKTU4eCO+np+jEbJ0XWOycpiQShEpUXoI0F6YlEAP7S0dDLWfUFDaYmY4aS9KnEDl4trCgv5a3U1GrQV6CQ7kAZa5/BzNMqx5eW8MGEC36RIrRNSpiyVn+Ry4RbC9CnIAWS37ruZ280jpaWEDQMJg9rtPV3ua2ho0z3vSERKbq2rY55FamhfsQ32KKe85iUaA9+pFEEBochq1qx7ksLcuX1KxRvLHJ1IMKmwkHsaGjBQhmgTt5srCwsptDBkk51Oy0d2Ddi+w+LbNl4v93Tx9oKGwSddikaSCDpnV6TizWCwx7BOT7iEYF5WeiqNe/v97Ozz8XYwyOpYjHsaGro9oUhUqOna2lpCJtKoSVogZWf0vTMyuKGuznSbLgQHddEMGch4dX95LxSyXBuRwC/R6ID2nLQN9igmHClrN9YdkDJGdf175Pq3wOFY/wRyrBDASbm5/DYnh/JYDL+m9Shp6hKCU3NzuaW+vpvRdQvBKT1UKB6alcVdJn0HdWCCw8FGTic/R6PkaprlTQMg1upV9hW3EPyxVVf6zMpKPgqHEcCuXi9nm1RCRgxDKfqFQjQaBg4hiJncMFqk5ONwuMc0w0iKVmCFDgfn5OVxU11dt2rI47KzOxUojTRSec8GKgd9ILEN9iimIfBtijJ3QVPox06VkTYKlxC9MgInZGdjSMmdrYY3ARTqOlcXFbFBD+Pk6Tp3jx/P6a0LjEk/dKLDwSyPhzmrVyOkamG8mcvFP4uLKTURbRrXD+0MHbisoICZbjdHlZUR7vAI/3YoxMfhMFcLQbIJVnksxvxWKdWQlD1qQvbk7zqBj0MhDuygDtiV47Oz2dTl4t6GBpbHYkxwODghJ4ddhjGFLh0O8fu5o6HB9OknS9PYsBcCXOlgG+xRjJGitFtKQ1U+2vQbIQQn5uZyXE4Oy6JRvEIw2aSiz4ygYfBdSwsz3W7ChsFmbjf7ZWTweHMzLzY3d8rdXhyN8ps1a3h7ypROMd8fWlr4p0XIIIlXCMtFPwO12Phic3O3Cr5kcc+Dus6ere+ds24dtR2kVFMZa58Q7O7z8WKKfG4NKIvHeScYZIrTyTSXi6BhsDwaJVvX24SfZnu9zB5lHWTmZ2fzbHMzlfF4p4pNjxBcXliY1nekN9gGexTj921AY/AHpEnBixAaGd7JwzCrsYtLCGa2xiObDYMnGxt5NRAAYH+/nyO76FB/F4nw+9bKymT896dolJ+iURZFIqZZK0Hg2upqripW/T9DhsGxZWUpC0eS3V8+CoVMy7wlEEok+MyijZ8EFmsaUkpWx+MsjcXSKqbxCsE2Xi9/zs/n7VCIZos4dgS4q76+LaySqWk0t6YlxlFPG9cVF6cdzx9J+DWNJ0tLubuhgRcCASKGwZYeD2fk5g5KRxzbYI9iMjNm4Kh7m1g8TsdaMCF0vO4SvO7B7Sa9vlKbSHDU2rXUGUbbo/CK+noea2zkiQkTyNd1Xmpu5q/V1d2805CULI5EUhrEV4NBrpQSIQSvBwKmFZQd0VDqdp+Ew6ZxZp8Q7JqRwYvBoOUYSW2QqtYqRSs5WK8QZGsaubrOMdnZHOz3owvBU6WlHLp2rakGh0yO17qtpdWwJ58Pf4nFOK68nJcnTEgZxx+pZOk65+Xnc94A54ObMXKWW216jSZ0ppWeqMrS0UE6EEIny7cJk8fNH+7pjVluqK2lKpHoFLdskZKqRIJ/1dayOhbjUhNjnaSjJqLV9mS6388mbbi6sqXHw95+P9t5vd2qLp2o+PeeGRlskiJbYYaUaK2hHqtAmwB29np5e/Jknp4wgd9kZqK3Hm+i08klPRTBpCJiGDxgsjhr05nRdzuz6YRDz2Dy+GOJJ4L8/Ms3bLzhVuj6yO1l2RNSShJGCE24hlwcKh2klLweCJiGMxLAa4EAuZqWtgqeGQIlfYoQaY1zSGva203FxVxXW8tzzc2I1vn8yufj0oICHELw5/x8Tu4gfpXELQTHtRYHFTkc7OT1ssAkvOIWghNNFPdAVftdXlvb5w6kceD+piZ0ITgrLw9tGAWWRjK2wR4jOPQMhMwZtcZaSkld0xdU13+AISMgVYxeaaBYZxcMNXGwlAOlddvyWKxHQ5spBM0p2o890dyMpGdlOB3avFyXEFxcUMD5eXnUtIo0dRRdmu3xcHRWFvc3Nnby2mNScp3TyaNr13JoZib/KCjgrHXr+DEaJSEljtYbx9/y8y3jsg80NpqGY3qDBB5paiIOXDAE4YXRiG2wbUYE1fXvU9P4cac0xebQz4TLythgwukj5kbkFIKJDgerLcrVJzkcbOxy8Wk4bBl7dgH/KCzk7zU1NJks1HU0ez01sNWgmzCSR9OYYFJcUhOP82hTU7cQiwE0CEFDNMqyujqebm7moZISlsZifBWJ4Nc0fuXzWYoegWrg25+niiRhKXmsqYlTc3NTVkeur9hXxGbYSRgRahoXmOSUSxJGhPrmL4dlXlacnZeHx0KI6Ky8PI7MzrZ8pBcoT3Vvv58XJ0xgXlYWOZpGpqaZdlbpum9H3MCRWVlpL9S9lmLRMUlYSpbHYjzS1MRmbjfHZmfz68zMlMYaoNTh6KETqSKdzzhRqYw23bENts2wE4qs6SAH2xkp4zQGvx/iGaVmH7+f8/Py8AmBv/Xla5Xe3Mfvp8Th4PqiIjxCtIU0XKhCiqdLSji8Vbek0OHgkoICFkyZwqdTpqQ8pgcVj3YJgQvI0TTOyMvjr70IHdR3WSi1IiIlT3VQ9UuHY7OzTW9iAhin6+zj8zHT5eJgv59/FRWR1UOFYE83r/WVPodEDMPgsssu46effsLlcnHllVcyebKd92vTe3pqFWZlzIeTo7OzOTQzk69bPcEt3O5OHWt+lZHBe5Mn83ogQE1rp5rdfL6UpcqlDge/WPRfRAiuKypCE4KQYZClab1emNvM7SZDCIJpGG2rnGortvV6OS47mwcbG9sqOr1C4BaCu8aP79azMmgYXF1ba6qx4tO0AdXfGEv02WC/9dZbRKNRnnjiCb766iuuvfZabr/99oGcm816gs8zGatENyGclp1yhhu3prFdisq8TE3jiDTFlgBOzs3l0urqbnFrNyoTJKmm5+plH8gkc3w+snWdcDyeMptDoFIFe8tZeXnsm5HBs83N1CQSbOPxcHBmpmm3mYMyM3mmuZmfotE2o62hFk6vKSwclVkiCSn5IBTiy0iELE1jP7+fCSOlNH3RokXsuuuuAGy55ZZ89515R24bm57QNAfF+ftQWft6pzi2QMflyCHHP2sYZzd07JeRwZKWFh5uakK0Vkc6hGC2x8OFA5A14RCCB0pKOKOyktWxGHEpTTNe3EJwWg+iVlZs7Hbz1zS8Y5cQ3F9SwpNNTTzZ1ESzYTDb4+Hk3NxRWfFYm0hwXFkZ1YkEodYqztsbGjg5J4dT+3gtzeizwQ4EAvg7yB7quk48HsfRYQFkyZIl/ZudCZFIZFDGHQuM7mvjBW0H0L8F0QQ4kImpRFs24aeflg7IEUbD9dkf2Bb4XNeJAbMMg2mRCMsHsKjkKmClEJQLwRuaxvetHrCGipWfFYuhL1/OUFyp2a0vAEIhEnV1Q3Lc3pDO9+bvTidrhSDR+mQQA5CSu+rqyF23jlkD1Daszwbb7/cT7LDqbBhGJ2MNMHPmzK679ZslS5YMyrhjgdF/bWYCveuU0xtGy/WZCcwZ5GPMkJKLqqtZFgwiW42JBBJCMHniRGaOcJW8oaSn7015PM7SNWtMezm2CMHb2dkcNX582sdbtGiR5bY+Z4lsvfXWfPDBBwB89dVXbLTRRn0dysbGZoj5OBzmzS6dwQ1Uhsj5VVX9LoJZnyiPxUgVxOna/7I/9NnD3muvvViwYAHz5s1DSsnVV189YJOysRlNhA2DR5uaeLqpibCUbOPxcGpubo9a2ekQNAx+aGnBLQSbut1tVY2paEwk+DAUIiol23m9pgtfT7bO1QxDSj4Ph9nZ9rLTotTpTCnQNXkAFx77bLA1TeMf//jHgE3ExmY0EjYM5peXsyoWa8tx/l8wyLuhEHeMG8e2fdR3llJya3099zU24kCFK1xCcEVhIbunaNp7f0MDN9XXt+2TAPb0+bi6qKhTSmFDD2l7ZhWYNuaMdzjYyu1mYSTSrV2YRwh+b6G/0hfswhkbm37wZFMTqzsYa2gPLVxUXd0WH06XxZEIfygvZ+sVK7i9tcFrQEqCUlJvGJxfVcXXFrrW74dC3FxfT1RKQlISlpKolLwTCnFTlwYI23k8WOVyxFA52zbpc31xMZOczraCHyet2TY5Od2kA/qDrSViY9MPnmlutuxkXp9IsCwW6xYakVLyaTjMy4EALVLyq4wM9szI4P1gkL9UV6fsjB5p9bzvMlnEut2k72Ryn8ebmvhTbm5bcc9RWVk82NjYrfLRLQQ7er1MHOD84bFOnq7zwoQJLAiHWRyJkKlp7Ov3M36A9b1tg21j0w9SiTPp0K2beFxKzqisZGEk0rbv+6EQt9TVUW8YKY11km8tdDaWR60jqQKoSiSY2GqwCxwO7i8p4Zx166hJJBCGQVzT2M3n46rCwh7nkA4thsFLgQCPNTXRIiU7ejyclZ+Pf4yKOmlCsKvPx66DGPu3DbaNTT/YyevlueZmS33srkUgDzc28kUk0skwh6RkrYX6nxlWOhu5uk7QYpyYlOR0qZCc6Xbz2sSJ/BiN8tWKFey5wQYD1vGlKZHg8LIyyjrMZ0UsxuPNzdwzbhzb2wuafWJs3upsbIaIE3NyunV5AaWj8fvs7LZy8iSPNDWZetGJ1ldPuIHDLMrdj83KMhVgSo7/Vmv/yY6I1j6VW0o5oO25rqut7WSskxjAKZWV3Z48bNLDNtg2Nv1gktPJfSUlTHM6cQtBRqty30k5Oabl3fWJvqtGe4RgssvFCa1qf105OjubbTweUwlTA7iytpYfh0C21JCSl0xuDkliwKvNzYM+j7GIHRKxseknm7ndvDRxImtjMYKGwVSXC5eFpzvJ6eQni1izA6X30dUD14ANnE6OzMriN5mZ3bz2tv2F4Ly8PD4rKzPVCIlKyYONjVxdVNSLs+s9sQ5d4q34OUW83cYa22Db2AwQ6SiznZqTw0UminweIZiXmUmew8HdDQ3EpSQuJZu73VxRWMiUNItw1sbjuIUwrVQ0gF+GwFC6hCBT01LmcttZKH3DNtg2NkPI3n4/y6NR7mxoQBcCiSpw2c3n4+z8fJxCcHx2NhXxOH5NI6+XUqolDoeld6sBU4bAUAohOD0nh2u65H4ncQAHZo6cPp2jCdtg29gMMafm5XFEdjYfhELEpGR7r7dT+bJTCCb10bDOcLspdThYHot107x2CcFxFvHvgeaY7Gy+aWnhlS5tyXTg/woLye2jpndvCRoGGuAdI6mEtsG2sRkG8nWd3wySl3nruHEcW15O0DDatJk1IfhTbq5l1/OBRgjBdcXFnBaNcnt9PesSCbZ2u5mfnT2g2ShWfB4Oc21tLUtbQ0Cbu91cVFAw6jvZ2AbbxmaMMcHp5I1Jk3gjGOSrSIR8Xedgv5/SYYgbT3W5uK64eEiP+Vk4zB8rKzst3n7V0sJvy8t5pKSEjUex0bYNto3NKCAuJe+GQnwWDpOpaRzk9zMtxUKkSwgO9Ps5sEOTkfWFay16RUak5N91ddzRC23qkYZtsG1sRjjV8TjHlZdT29p+ygE80NjIsVlZnDsArcPGEkHDsCzRl8Cn4fDQTmiAGRuReBubMcwFVVWUx+OEWr3GONAiJY82NfF+KDS8kxuBpFJjGY3NfTtiG2wbmxFMRTzONy0tpql6YSm5bwB7PY4FMjSNTSxi1BowZwClTocD22Db2IxgKuNxUi0Vmul1rO/8LT8fbxdPWkOJZp0zykNItsG2sRnBTHA4UrafmmpXDHZjc4+Hh0pK2MnrxYFqJvArn48nJ0wY0HZdw4G96GhjM4IpdDjY0ePh43C4mz6IRwhOHMD2U2OJmW43d4/ibBArbA/bxmaEc01RERu6XPiEQEN1hXG1FsIMZPspm5GP7WHb2IxwsnWdJ0tLWRiJsDgSIUPT2DsjY0gqBm1GFva/uI3NKEAIwbZeb5+7sA8GcSl5PxTi7WAQpxDs5/ezvceDGOWpcyMZ22Db2Nj0moBhcFx5OWtjsbb88FcCAbb2eLh13DicttEeFOwYto2NTa+5pqaGFdFom7EGlRe+KBLhwcbGYZzZ2KZPBru5uZlTTz2VY489lqOOOorFixcP9LxsbGxGKFEpeS0YNO1qE5GSh22DPWj0KSRy3333scMOO3DCCSewfPlyzjvvPJ577rmBnpuNjc0IpKmHvpT96Vtpk5o+GewTTjgBV6tSWCKRwD2K5QptbGx6R46u4wCs2vmWjPLilJGMkNJEh7ADTz31FA888ECn966++mpmzZpFdXU1J510EhdddBHbbbddp88sWrQIn8834BOORCJ4hkiEfbRhX5vU2NfHmt5em0d1nZd1nWiXxUW3lJwcjzM3RT/H0cZQf29CoRCzZ8823dajwbbip59+4txzz+XPf/4zc+fO7bZ90aJFlgftD0uWLGHmzJkDPu5YwL42qbGvjzW9vTZxKflLVRXvtKoFCsCQkmOzszk3L29MpfYN9fcmle3sU0hk6dKlnHXWWdx4443MmDGjX5OzsbEZfTiE4IbiYlZGoywIh3EKwVyfj2K7mGdQ6dPV/de//kU0GuWqq64CwO/3c/vttw/oxGxsbEY+U1wupqTofGMzsPTJYNvG2cbGxmbosQtnbGxsbEYJtsG2sbGxGSXYBtvGxsZmlNDntL6eWLRo0WAMa2NjYzPmGfA8bBsbGxubocUOidjY2NiMEmyDbWNjYzNKGLUGe9myZcyePZuWFisJmvUPW/a2O4ZhcOmll3LUUUdx3HHHsWrVquGe0oghFotxwQUXMH/+fA4//HDefvvt4Z7SiKO2tpa5c+eybNmy4Z4KMEo7zgQCAf75z3+2KQbaKGzZ2+689dZbRKNRnnjiCb766iuuvfZau/CrlRdffJGcnByuv/56Ghoa+PWvf80ee+wx3NMaMcRiMS699NIRJRg26jxsKSWXXHIJ5557Lt4R1N9uJHDCCScwb948wJa9TbJo0SJ23XVXALbccku+++67YZ7RyGHfffflrLPOAtTvStf1YZ7RyOKf//wn8+bNo6ioaLin0saI9rDNpF1LSkrYf//913vRqZ5kby+44AIuuuiiYZrdyCEQCOD3+9v+1nWdeDyOwxYpIiMjA1DX6Mwzz+Tss88e3gmNIJ599lny8vLYddddueuuu4Z7Om2MurS+vfbai3HjxgHw1VdfMWvWLB555JFhntXIoSfZ2/WNa665hi222IL9998fgDlz5vDBBx8M86xGDhUVFZx++ultcWwbxTHHHIMQAiEES5YsYcqUKdx+++0UFhYO78TkKGb33XeXkUhkuKcxYvjll1/kPvvsI5csWTLcUxkxvP766/LCCy+UUkq5ePFieeKJJw7zjEYO1dXVct9995Uff/zxcE9lRHPsscfKpUuXDvc0pJRS2s+FYwhb9rY7e+21FwsWLGDevHlIKbn66quHe0ojhjvuuIOmpiZuu+02brvtNgDuvvvuEbXIZtOZURcSsbGxsVlfGXVZIjY2NjbrK7bBtrGxsRkl2AbbxsbGZpRgG2wbGxubUYJtsG1sbGxGCbbBtrGxsRkl2AbbxsbGZpRgG2wbGxubUcL/A7YX3DHsR770AAAAAElFTkSuQmCC\",\n 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