As noted on the first page, this review session requires the following software:
+
+
Python 3.11 or greater, Anaconda Distribution
+
+
For this class, please download the Anaconda distribution of Python. Be sure to download current version, with Python version 3.9. or greater. When you install Anaconda, be sure to install the full Anaconda distribution.
+The MiniConda version is nice, but I only recommend it for advanced users. Nice instructions for installing and using Anaconda can be found (here.)[https://datascience.quantecon.org/introduction/local_install.html]
+
+
+
The Visual Studio Code (VS Code) text editor
+
+
A good text editor is important for software development. Some of your classes will use a fully-fledged Integrated Development Environment (IDE) like PyCharm. For this review, I suggest Visual Studio Code. You can download it here: https://code.visualstudio.com/
+
There are several VS Code extensions that I recommend installing. To learn about extensions, see here. I recommend installing at least these two extensions: the Jupyter and Python VS Code extensions.
+
+
+
Git
+
+
Although there are many different Git clients and Git GUI’s that you could use,
+I prefer that you install GitHub Desktop. You will need to install both
+Git (link here: https://git-scm.com/downloads)
+and GitHub Desktop (link here: apps/desktop).
+
Some classes will use GitHub. GitHub is a website that allows you to store, interact with, and share your Git repositories online. Please register an account with GitHub if you don’t already have one.
+
+
+
+
+
Note
+
It’s also important that you have a quality laptop. I recommend a laptop with at least 16GB of RAM and at least 500 GB of storage (at a minimum).
+So much of your schooling and of your job will revolve around your laptop.
+It’s important to invest in a good one. If you have any questions about your laptop, please ask in the discussion section on Canvas.
Anaconda is a free and open-source distribution of Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda. Anaconda is widely used in the scientific community and data science, as it simplifies the installation of packages and their dependencies.
+
+
What is the different between Python and Anaconda?
+
+
Python is a general purpose programming language. It’s not just used for data science, etc. It is also used in web development, as example. For example, Python is used in the back end of the website Reddit was used to create parts of YouTube back in the day.
+
Anaconda, on the other hand, is a software distribution. It is a collection of Python packages that are targeted towards data science and scientific computing.
+There are other distributions of Python that are also targeted towards scientific computing, but Anaconda is one of the most popular ones.
+
Anaconda is a set of about a hundred packages that are useful for data science and scientific computing, including conda, numpy, scipy, ipython notebook, and so on.
+
+
+
What’s the difference between Anaconda and conda?
+
+
Anaconda is a software distribution, while conda is the package manager that comes with the Anaconda distribution.
+
conda is a package manager that installs packages from the Anaconda repository as well as from other repositories. A package manager is useful for installing new packages,
+keeping track of the packages you have installed, and updating packages.
+
conda also makes it easy to manage software environments. This is useful when you have different projects that require different versions of packages.
+
+
+
+
+
Note
+
Let’s pause here and make sure that everyone has Anaconda installed. Let’s test the following. Please raise your hand if any of these things is not working:
+
+
Open a terminal and type conda--version. You should see the version of conda that you have installed. Depending on how you installed Anaconda, you may
+have to open the Anaconda Prompt on Windows.
+
Type condaactivate to make sure that you can activate the base environment. Later on we’ll create our own environment for use in our projects.
+
Type jupyternotebook to make sure that you can open a Jupyter notebook. This will open a new tab in your web browser with the Jupyter notebook interface.
+
Type jupyterlab to try out Jupyter Lab. This is a newer interface that is similar to Jupyter notebook but has more features.
Visual Studio Code is a lightweight code editor that is great for Python development. It has a lot of features that make it a great editor for data science work.
+
+
IntelliSense: IntelliSense is a feature that helps you write code faster. It provides code completions based on variable types, function definitions, and imported modules.
+
Debugging: Visual Studio Code has a built-in debugger that makes it easy to debug your Python code.
+
Git integration: Visual Studio Code has built-in Git integration that makes it easy to work with Git repositories.
+
Extensions: Visual Studio Code has a rich ecosystem of extensions that add additional functionality to the editor. There are extensions for Jupyter notebooks, Python, and many other languages and tools.
+
What’s the difference between Visual Studio and Visual Studio Code?
+
+
Visual Studio is a full-fledged Integrated Development Environment (IDE) that is used primarily for developing in C# and .NET. It is a very powerful IDE that has a lot of features.
+
Visual Studio Code (VS Code), on the other hand, is a lightweight code editor. Visual Studio Code has become a very popular editor for Python development and will be better suited for data science work than Visual Studio (the IDE).
+
+
+
+
+
Note
+
Let’s pause here and make sure that everyone has VS Code installed. We’ll run a few test files and configure VS Code with some helpful defaults. Please raise your hand if any of these things is not working:
+
+
Open VS Code and create a new Python file. You can do this by clicking on the New File button in the top left corner of the window and then saving the file with a .py extension.
+
Make sure the proper extensions are installed: Python and Jupyter. Also, you might consider the following additional extensions: GitHub Copilot, Black Formatter, Data Wrangler, Excel Viewer, Markdown Preview Github styling, Rainbow CSV, Rewrap, Code Spell Checker, and GitLens.
+
Set the default terminal in Windows to Command Prompt. Use the “Select Default Profile” option in the VS Code terminal to do this. You may also want to memorize the keyboard shortcuts for VS Code. You can start with the command ctrl+`.
+
Open the terminal in VS Code and type python--version to make sure that Python is installed and that VS Code can find it.
+
Try running condaactivate in the terminal.
+
Create a Python file .py and try opening the Python Interactive window. You can do this by right-clicking in the Python file and selecting “Run Python File in Terminal”. Also, you should learn the keyboard shortcut for opening the command palette. You can do this by pressing ctrl+shift+p.
+
Adjust the VS Code setting so that ctrl+enter will run Python code in the Interactive Window instead of the terminal by default.
Git is a distributed version control system that is used to track changes in source code during software development. It is designed to handle everything from small to very large projects with speed and efficiency. GitHub is a website that allows you to store, interact with, and share your Git repositories online. GitHub is a great tool for collaborating with others on software projects and for sharing your code with the world.
+
Please watch the following video to learn more about version control with Git:
+
+
Now, let’s take a look at GitHub.
+
+
You can find another nice video about GitHub here.
+The code for this course is available on GitHub. You can find the repository here.
+
+
Note
+
Let’s pause here and make sure that everyone has Git installed and is able to download the
This course requires that you create a WRDS account. WRDS is a comprehensive data research platform that provides access to a wide range of financial, economic, and marketing data.
+Follow the instructions below to create an account.
+
+
Important
+
If you have not requested an account already, please do so ASAP. You will need this for the next session.
Follow the directions on the Registration form to enter your identifying information.
+
+
For the Subscriber, select your school’s name from the drop-down list.
+
Your User type, Class - Students with Code, has been selected by default.
+
You will need to enter the course code. This can be found on Canvas here: TODO
+
+
+
Click the Register for WRDS button.
+
+
WRDS accounts require two-factor authentication. We recommend you use a smartphone for the verification process. First, install the Duo Mobile app on your phone. This free app can be downloaded through your device’s app store. Follow the directions at How to Log into WRDS to register your smartphone and use Duo two-factor authentication to set up your WRDS account.
+
+
+
+
Already Have a WRDS Account?
+
After you have logged into WRDS, use the following steps to enroll in our class account. You will need the Class Code above to enroll.
+
+
In the top right corner of the screen, select Your Account > Your Account Info.
+
Scroll down until you see the Your Classes table and click the Enroll in a Class button.
+
Enter the Class Code and click Submit. You can find the code here: TODO
+
+
+
Important
+
If you have any difficulty setting up your account please contact WRDS Support at: https://wrds-www.wharton.upenn.edu/contact-support/. When opening your support ticket you must use the email associated with your existing WRDS account, or the email you intend to use to set up your new WRDS account.
Wharton Research Data Services (WRDS) is a data research platform and business intelligence tool widely used in academic, government, and corporate sectors. It provides access to a vast repository of financial, economic, and marketing data, which is pivotal for conducting rigorous research in various fields, especially in finance and economics. The platform is known for its comprehensive and high-quality datasets.
+
+
WRDS offers a variety of datasets from numerous sources, including leading data providers like Compustat, CRSP, IBES, and Bloomberg. It covers a wide range of data types, including:
+
+
Stock prices and trading volumes
+
Financial statement data
+
Analyst forecasts
+
Corporate governance data
+
Mutual fund and bond data
+
Macroeconomic data
+
+
+
One of the key strengths of WRDS is its user-friendly interface, which allows for easy data extraction and manipulation. It provides powerful tools for data analysis, including the ability to execute custom queries and perform complex statistical analyses. In academic settings, WRDS is particularly valued for its role in facilitating empirical research in finance and economics. It allows researchers, professors, and students to access a wealth of data necessary for testing financial theories, exploring economic trends, and developing new insights in the field of quantitative finance.
WRDS provides some usage statistics on their website in an introduction presentation here. This chart shows
+the percentage of usage across all WRDS data sets.
+
+
The two most popular data sets are CRSP and Compustat.
+
WRDS did an analysis finance papers published in the top 3 finance journals—the Journal of Finance, the Journal of Financial Economics, and the Review of Financial Studies—from the years 2004-2016. Out of all of these papers, the following chart shows how many times each data set was cited.
+
+
All of the listed data sets, except for those colored in red, are available in WRDS.
Compustat is a comprehensive database of financial, statistical, and market information, primarily focused on publicly traded companies. It is widely used in academic research, particularly in the fields of finance and economics, for conducting in-depth analysis of company performance and market trends. The dataset includes information from various countries and markets, making it a valuable resource for both domestic and international financial research.
+
Key features of the Compustat dataset include:
+
+
Financial Statements: Detailed income statements, balance sheets, and cash flow statements for a wide range of companies.
+
Historical Data: Longitudinal data that allows for historical trend analysis and time-series studies.
+
Global Coverage: Data on companies from various global markets, including North America, Europe, Asia, and more.
+
Segment Data: Information on business segments and geographical segments of companies.
+
Market Data: Includes stock prices, trading volume, and other market-related information.
+
Corporate Actions: Data on dividends, stock splits, mergers and acquisitions, and other corporate events.
+
Ratios and Metrics: Key financial ratios and metrics that are pre-calculated for ease of analysis, such as ROE, ROA, and EBITDA.
+
+
Compustat is highly regarded for its accuracy, depth, and consistency, making it a fundamental resource for both theoretical and empirical research in finance. It’s extensively used for tasks like asset pricing models, risk management, portfolio construction, and corporate finance studies. For students and researchers in quantitative finance, Compustat provides a rich dataset for modeling, back-testing theories, and conducting robust financial analyses.
+
The following two videos provide a short introduction to Compustat on WRDS.
The Center for Research in Security Prices (CRSP) is a renowned financial research database, primarily recognized for its comprehensive historical data on securities traded in the United States. Established at the University of Chicago’s Booth School of Business, CRSP is a crucial resource for academic, commercial, and governmental research in finance.
+
Key characteristics of the CRSP database include:
+
+
Extensive Historical Data: CRSP is particularly noted for its long historical time series, which in some cases go back as far as 1925. This historical depth is invaluable for long-term financial studies and analyses.
+
Stock Data: The database provides detailed information on stocks listed on NYSE, AMEX, and NASDAQ, including prices, returns, trading volumes, and other market indicators.
+
Indices: CRSP develops and maintains a series of stock indices that serve as benchmarks for the investment industry, including value- and equal-weighted indices.
+
Corporate Actions: Information on dividends, stock splits, and other corporate events that impact stock valuation is extensively covered.
+
Treasury and Mutual Fund Data: Beyond stocks, CRSP also includes data on US Treasury bills, bonds, and mutual funds, expanding its utility for various types of financial research.
+
Survivorship Bias-Free Data: CRSP’s dataset is known for being free of survivorship bias, as it includes data on companies that have ceased to exist, which is crucial for accurate historical analysis.
+
Research Quality: The accuracy, completeness, and cleanliness of the data make CRSP a gold standard for financial research, particularly in academic settings.
+
+
For students and researchers in quantitative finance, CRSP provides essential data for analyzing stock performance, conducting empirical tests of asset pricing models, and studying market anomalies and behaviors. Its extensive historical data and robustness make it a fundamental tool for both historical analysis and contemporary market studies.
+
The following video provides a nice introduction to the basics of CRSP.
+
+
+
+
How do these compare with Bloomberg or Datastream?#
+
Choosing between financial databases like CRSP, Bloomberg, or Datastream depends on the specific requirements of the research or analysis being conducted. Each of these platforms has unique strengths and features that make them suitable for different purposes. Here are some reasons why someone might opt for CRSP over Bloomberg or Datastream:
+
+
Historical Depth: CRSP is renowned for its extensive historical data, particularly for U.S. securities. It offers data going back as far as 1925, which is invaluable for long-term historical research and analysis. This level of historical depth might not be matched by Bloomberg or Datastream.
+
Survivorship Bias-Free Data: CRSP’s data includes companies that have ceased to exist, which is crucial for accurate historical analyses. This feature helps in avoiding survivorship bias, making it a robust choice for academic studies that require comprehensive historical perspectives.
+
Data Consistency and Quality: CRSP is known for its high standards in data accuracy, consistency, and cleanliness, which are critical for reliable academic research.
+
+
On the other hand, there are some drawbacks of CRSP relative to Bloomberg or Datastream.
+
+
Limited Scope: CRSP has a limited scope relative to Bloomberg or Datastream. It primarily focuses on US markets and lacks the global coverage found in Bloomberg.
+
Real-Time Data: Does not offer real-time data, which is essential for current market analysis.
+
Less Comprehensive: Fewer types of financial data compared to Bloomberg (e.g., lacks extensive international data, commodities, real-time news).
+
+
Broadly speaking, CRSP is more suited for academic research focused on historical analysis of the U.S. stock market, offering in-depth and high-quality data with a bias-free historical perspective. Bloomberg, on the other hand, excels in providing a wide range of real-time global financial data and tools, catering more to finance professionals and analysts who require real-time data and sophisticated analysis tools. The choice between them largely depends on the specific needs, goals, and resources of the user.
To familiarize yourselves to using WRDS, please watch the following video about WRDS Web Queries. While we will be automating the query process using the WRDS Python package wrds, using the web query system is a good way for initial exploration of the data.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Contents
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/_images/060bb9ef810c5d371cf7bb17a560d6aa5a13376f8fd9eff7a0c49570e75e3c64.png b/docs/_images/060bb9ef810c5d371cf7bb17a560d6aa5a13376f8fd9eff7a0c49570e75e3c64.png
new file mode 100644
index 0000000..28dbc93
Binary files /dev/null and b/docs/_images/060bb9ef810c5d371cf7bb17a560d6aa5a13376f8fd9eff7a0c49570e75e3c64.png differ
diff --git a/docs/_images/1a46eda6d79546038961851db2e02a10c9421482f5d222c0fe83d99e5681491d.png b/docs/_images/1a46eda6d79546038961851db2e02a10c9421482f5d222c0fe83d99e5681491d.png
new file mode 100644
index 0000000..01440d3
Binary files /dev/null and b/docs/_images/1a46eda6d79546038961851db2e02a10c9421482f5d222c0fe83d99e5681491d.png differ
diff --git a/docs/_images/1f8cfca31b7ab8f5062a2f21114d47d5e017997e2d01649558c1a4c3658fb694.png b/docs/_images/1f8cfca31b7ab8f5062a2f21114d47d5e017997e2d01649558c1a4c3658fb694.png
new file mode 100644
index 0000000..22a09f8
Binary files /dev/null and b/docs/_images/1f8cfca31b7ab8f5062a2f21114d47d5e017997e2d01649558c1a4c3658fb694.png differ
diff --git a/docs/_images/29e1d3f0f59fb2b03229eb7a05718a798d7c28ff72c51538912022cef57c405a.png b/docs/_images/29e1d3f0f59fb2b03229eb7a05718a798d7c28ff72c51538912022cef57c405a.png
new file mode 100644
index 0000000..fcb6861
Binary files /dev/null and b/docs/_images/29e1d3f0f59fb2b03229eb7a05718a798d7c28ff72c51538912022cef57c405a.png differ
diff --git a/docs/_images/34627a4b2d9345061bf5db407c8f3cdbff5223fd4729f48e7a512a64758a7550.png b/docs/_images/34627a4b2d9345061bf5db407c8f3cdbff5223fd4729f48e7a512a64758a7550.png
new file mode 100644
index 0000000..6da4fa4
Binary files /dev/null and b/docs/_images/34627a4b2d9345061bf5db407c8f3cdbff5223fd4729f48e7a512a64758a7550.png differ
diff --git a/docs/_images/4335910687ec33dda054893f759f2635bf5dd46b90bd08918f8f70c4958652d3.png b/docs/_images/4335910687ec33dda054893f759f2635bf5dd46b90bd08918f8f70c4958652d3.png
new file mode 100644
index 0000000..c9242d5
Binary files /dev/null and b/docs/_images/4335910687ec33dda054893f759f2635bf5dd46b90bd08918f8f70c4958652d3.png differ
diff --git a/docs/_images/464e32e7fcef14371dc4933bb2430b902f8cfc0f2b7889c381c575dbe1474ebb.png b/docs/_images/464e32e7fcef14371dc4933bb2430b902f8cfc0f2b7889c381c575dbe1474ebb.png
new file mode 100644
index 0000000..d15bd33
Binary files /dev/null and b/docs/_images/464e32e7fcef14371dc4933bb2430b902f8cfc0f2b7889c381c575dbe1474ebb.png differ
diff --git a/docs/_images/472f16a94f3717c946ca25269498cf65b84651258cdbee71469c155559a2c884.png b/docs/_images/472f16a94f3717c946ca25269498cf65b84651258cdbee71469c155559a2c884.png
new file mode 100644
index 0000000..66b2454
Binary files /dev/null and b/docs/_images/472f16a94f3717c946ca25269498cf65b84651258cdbee71469c155559a2c884.png differ
diff --git a/docs/_images/56e6a82a31e79016cf1353627e41cedb119b32b6d2dcee3c8c6797073c25f98c.png b/docs/_images/56e6a82a31e79016cf1353627e41cedb119b32b6d2dcee3c8c6797073c25f98c.png
new file mode 100644
index 0000000..281e6ed
Binary files /dev/null and b/docs/_images/56e6a82a31e79016cf1353627e41cedb119b32b6d2dcee3c8c6797073c25f98c.png differ
diff --git a/docs/_images/578d9d45d651b88291d52db0191cb30734a33da1241e1a0a793ed29b854823ba.png b/docs/_images/578d9d45d651b88291d52db0191cb30734a33da1241e1a0a793ed29b854823ba.png
new file mode 100644
index 0000000..6812bc2
Binary files /dev/null and b/docs/_images/578d9d45d651b88291d52db0191cb30734a33da1241e1a0a793ed29b854823ba.png differ
diff --git a/docs/_images/6394a97bd62de100c32e444d6ea5373259f06095b4b32f06cf568ebc7cfdc5db.png b/docs/_images/6394a97bd62de100c32e444d6ea5373259f06095b4b32f06cf568ebc7cfdc5db.png
new file mode 100644
index 0000000..e8f81b9
Binary files /dev/null and b/docs/_images/6394a97bd62de100c32e444d6ea5373259f06095b4b32f06cf568ebc7cfdc5db.png differ
diff --git a/docs/_images/657dd813fef1b4f77cfe786cd10a71eb6074d8b3b81fc52843757f7372890a7a.png b/docs/_images/657dd813fef1b4f77cfe786cd10a71eb6074d8b3b81fc52843757f7372890a7a.png
new file mode 100644
index 0000000..5168bcd
Binary files /dev/null and b/docs/_images/657dd813fef1b4f77cfe786cd10a71eb6074d8b3b81fc52843757f7372890a7a.png differ
diff --git a/docs/_images/67dc7853db9af534c5d022b8511eb3dda93306e763ea42d22ae547fd5952ff3b.png b/docs/_images/67dc7853db9af534c5d022b8511eb3dda93306e763ea42d22ae547fd5952ff3b.png
new file mode 100644
index 0000000..6f1d5cb
Binary files /dev/null and b/docs/_images/67dc7853db9af534c5d022b8511eb3dda93306e763ea42d22ae547fd5952ff3b.png differ
diff --git a/docs/_images/816efaf127e60f2e315b942a74da37523ff6308e5752e386d5db9dd8215066d5.png b/docs/_images/816efaf127e60f2e315b942a74da37523ff6308e5752e386d5db9dd8215066d5.png
new file mode 100644
index 0000000..0af7698
Binary files /dev/null and b/docs/_images/816efaf127e60f2e315b942a74da37523ff6308e5752e386d5db9dd8215066d5.png differ
diff --git a/docs/_images/8390f6811d48d06a84aa76a7767b5f57a0f7bddf17ead1aa933e4e99181ca932.png b/docs/_images/8390f6811d48d06a84aa76a7767b5f57a0f7bddf17ead1aa933e4e99181ca932.png
new file mode 100644
index 0000000..457c3af
Binary files /dev/null and b/docs/_images/8390f6811d48d06a84aa76a7767b5f57a0f7bddf17ead1aa933e4e99181ca932.png differ
diff --git a/docs/_images/868318a28d951193717abab9326b1c3b28fcf261b310b166405317bc0083c6dd.png b/docs/_images/868318a28d951193717abab9326b1c3b28fcf261b310b166405317bc0083c6dd.png
new file mode 100644
index 0000000..fa43993
Binary files /dev/null and b/docs/_images/868318a28d951193717abab9326b1c3b28fcf261b310b166405317bc0083c6dd.png differ
diff --git a/docs/_images/8a0f19cc29e490a81c4a44c7d0b688d0c6c82a8f5d21b7129c0fce2c3fb68abf.png b/docs/_images/8a0f19cc29e490a81c4a44c7d0b688d0c6c82a8f5d21b7129c0fce2c3fb68abf.png
new file mode 100644
index 0000000..7cc0ef8
Binary files /dev/null and b/docs/_images/8a0f19cc29e490a81c4a44c7d0b688d0c6c82a8f5d21b7129c0fce2c3fb68abf.png differ
diff --git a/docs/_images/8e7785a0ed1434831e933f4c214b24a92e41b1f1d64fd594d2482edbc997f67b.png b/docs/_images/8e7785a0ed1434831e933f4c214b24a92e41b1f1d64fd594d2482edbc997f67b.png
new file mode 100644
index 0000000..a98b6a1
Binary files /dev/null and b/docs/_images/8e7785a0ed1434831e933f4c214b24a92e41b1f1d64fd594d2482edbc997f67b.png differ
diff --git a/docs/_images/922b30b7feb198ab37f78af7885ce4cc4036272946ec5b20c8c9a8e5948bfec4.png b/docs/_images/922b30b7feb198ab37f78af7885ce4cc4036272946ec5b20c8c9a8e5948bfec4.png
new file mode 100644
index 0000000..dd87328
Binary files /dev/null and b/docs/_images/922b30b7feb198ab37f78af7885ce4cc4036272946ec5b20c8c9a8e5948bfec4.png differ
diff --git a/docs/_images/92c1151fac08c169f9ee201a841cd7f0bf32a7d9b9eca5500ed32b37f99dbe65.png b/docs/_images/92c1151fac08c169f9ee201a841cd7f0bf32a7d9b9eca5500ed32b37f99dbe65.png
new file mode 100644
index 0000000..721b76b
Binary files /dev/null and b/docs/_images/92c1151fac08c169f9ee201a841cd7f0bf32a7d9b9eca5500ed32b37f99dbe65.png differ
diff --git a/docs/_images/97fe5d7ffb10b3d82832bf2df717980d78bd501cc6fdd0f99e4bb17a0603a51f.png b/docs/_images/97fe5d7ffb10b3d82832bf2df717980d78bd501cc6fdd0f99e4bb17a0603a51f.png
new file mode 100644
index 0000000..a69ee07
Binary files /dev/null and b/docs/_images/97fe5d7ffb10b3d82832bf2df717980d78bd501cc6fdd0f99e4bb17a0603a51f.png differ
diff --git a/docs/_images/Compustat_Logo.png b/docs/_images/Compustat_Logo.png
new file mode 100644
index 0000000..c0122ef
Binary files /dev/null and b/docs/_images/Compustat_Logo.png differ
diff --git a/docs/_images/a192e7992db2079887b64c87f617a96de2d9e4d80d4d2f7e27b4bd649548a28a.png b/docs/_images/a192e7992db2079887b64c87f617a96de2d9e4d80d4d2f7e27b4bd649548a28a.png
new file mode 100644
index 0000000..8a37b0e
Binary files /dev/null and b/docs/_images/a192e7992db2079887b64c87f617a96de2d9e4d80d4d2f7e27b4bd649548a28a.png differ
diff --git a/docs/_images/a911cf3634dd822001207345fc93a99daf7b8049329821b1bfe6f4b61c21f59f.png b/docs/_images/a911cf3634dd822001207345fc93a99daf7b8049329821b1bfe6f4b61c21f59f.png
new file mode 100644
index 0000000..a1152dc
Binary files /dev/null and b/docs/_images/a911cf3634dd822001207345fc93a99daf7b8049329821b1bfe6f4b61c21f59f.png differ
diff --git a/docs/_images/ae6637bb83bef008a47a26dc9375c9d24026b0762d335edfa0db8132c2853c5e.png b/docs/_images/ae6637bb83bef008a47a26dc9375c9d24026b0762d335edfa0db8132c2853c5e.png
new file mode 100644
index 0000000..9940fac
Binary files /dev/null and b/docs/_images/ae6637bb83bef008a47a26dc9375c9d24026b0762d335edfa0db8132c2853c5e.png differ
diff --git a/docs/_images/b4919868dc0bd7f4e3ec8595941c5624da8ecf4fed3178b04ce75782f34e89ed.png b/docs/_images/b4919868dc0bd7f4e3ec8595941c5624da8ecf4fed3178b04ce75782f34e89ed.png
new file mode 100644
index 0000000..62a2a3e
Binary files /dev/null and b/docs/_images/b4919868dc0bd7f4e3ec8595941c5624da8ecf4fed3178b04ce75782f34e89ed.png differ
diff --git a/docs/_images/c33c7752977f75e33be2d3be6f99e04a3f99bd2d3f828761ff5c02443920e076.png b/docs/_images/c33c7752977f75e33be2d3be6f99e04a3f99bd2d3f828761ff5c02443920e076.png
new file mode 100644
index 0000000..f0ba9e0
Binary files /dev/null and b/docs/_images/c33c7752977f75e33be2d3be6f99e04a3f99bd2d3f828761ff5c02443920e076.png differ
diff --git a/docs/_images/c63aeadfe5de0d8a3896236c829990bb4d90aa73b774285075e8b6d7624effa0.png b/docs/_images/c63aeadfe5de0d8a3896236c829990bb4d90aa73b774285075e8b6d7624effa0.png
new file mode 100644
index 0000000..8bfb041
Binary files /dev/null and b/docs/_images/c63aeadfe5de0d8a3896236c829990bb4d90aa73b774285075e8b6d7624effa0.png differ
diff --git a/docs/_images/cac40559f2e431c3a03c1497e3ca18a831fc2455b0a330473e148a150e7eee61.png b/docs/_images/cac40559f2e431c3a03c1497e3ca18a831fc2455b0a330473e148a150e7eee61.png
new file mode 100644
index 0000000..e28a828
Binary files /dev/null and b/docs/_images/cac40559f2e431c3a03c1497e3ca18a831fc2455b0a330473e148a150e7eee61.png differ
diff --git a/docs/_images/cd8230c311657d821e1bb1faea3bbd0025828e85ad7e4e1bbbdaf25975646abf.png b/docs/_images/cd8230c311657d821e1bb1faea3bbd0025828e85ad7e4e1bbbdaf25975646abf.png
new file mode 100644
index 0000000..8fbdcd7
Binary files /dev/null and b/docs/_images/cd8230c311657d821e1bb1faea3bbd0025828e85ad7e4e1bbbdaf25975646abf.png differ
diff --git a/docs/_images/compustat_on_WRDS_p1.png b/docs/_images/compustat_on_WRDS_p1.png
new file mode 100644
index 0000000..6c6fea6
Binary files /dev/null and b/docs/_images/compustat_on_WRDS_p1.png differ
diff --git a/docs/_images/compustat_on_WRDS_p2.png b/docs/_images/compustat_on_WRDS_p2.png
new file mode 100644
index 0000000..8cfba86
Binary files /dev/null and b/docs/_images/compustat_on_WRDS_p2.png differ
diff --git a/docs/_images/crsp-llc-logo-web-01_3.png b/docs/_images/crsp-llc-logo-web-01_3.png
new file mode 100644
index 0000000..baab39c
Binary files /dev/null and b/docs/_images/crsp-llc-logo-web-01_3.png differ
diff --git a/docs/_images/crsp_in_wrds_thumbnail.png b/docs/_images/crsp_in_wrds_thumbnail.png
new file mode 100644
index 0000000..c7aa8dd
Binary files /dev/null and b/docs/_images/crsp_in_wrds_thumbnail.png differ
diff --git a/docs/_images/d984513af18f7920b859d63ba06dcdecad45c3a0ae6c464d9daf4ab606ce5c8c.png b/docs/_images/d984513af18f7920b859d63ba06dcdecad45c3a0ae6c464d9daf4ab606ce5c8c.png
new file mode 100644
index 0000000..d2de782
Binary files /dev/null and b/docs/_images/d984513af18f7920b859d63ba06dcdecad45c3a0ae6c464d9daf4ab606ce5c8c.png differ
diff --git a/docs/_images/de53193fe28521fb9e481f9e7bad44d0c4eece91b59597c2cb00673ab3b991b7.png b/docs/_images/de53193fe28521fb9e481f9e7bad44d0c4eece91b59597c2cb00673ab3b991b7.png
new file mode 100644
index 0000000..88fe3ac
Binary files /dev/null and b/docs/_images/de53193fe28521fb9e481f9e7bad44d0c4eece91b59597c2cb00673ab3b991b7.png differ
diff --git a/docs/_images/e5c36b7a44d3b765a52929938a5bf46ce1c0a4d4728006bcda581f700930bc97.png b/docs/_images/e5c36b7a44d3b765a52929938a5bf46ce1c0a4d4728006bcda581f700930bc97.png
new file mode 100644
index 0000000..1ef2613
Binary files /dev/null and b/docs/_images/e5c36b7a44d3b765a52929938a5bf46ce1c0a4d4728006bcda581f700930bc97.png differ
diff --git a/docs/_images/ecfc507e812947e3a43a10ea8605eaecae08efc4dc69502878c3ad25eb2f7069.png b/docs/_images/ecfc507e812947e3a43a10ea8605eaecae08efc4dc69502878c3ad25eb2f7069.png
new file mode 100644
index 0000000..ab1ca41
Binary files /dev/null and b/docs/_images/ecfc507e812947e3a43a10ea8605eaecae08efc4dc69502878c3ad25eb2f7069.png differ
diff --git a/docs/_images/f5d9cd5e7a92cc4be93475b1472979109c678c64d0d7e0411f1794b41cf84cbd.png b/docs/_images/f5d9cd5e7a92cc4be93475b1472979109c678c64d0d7e0411f1794b41cf84cbd.png
new file mode 100644
index 0000000..8ff48ec
Binary files /dev/null and b/docs/_images/f5d9cd5e7a92cc4be93475b1472979109c678c64d0d7e0411f1794b41cf84cbd.png differ
diff --git a/docs/_images/f81cf2b372abaf92be7d54be06bd1092f1001c5898726fe66d9194daa54283fc.png b/docs/_images/f81cf2b372abaf92be7d54be06bd1092f1001c5898726fe66d9194daa54283fc.png
new file mode 100644
index 0000000..3e0a6ad
Binary files /dev/null and b/docs/_images/f81cf2b372abaf92be7d54be06bd1092f1001c5898726fe66d9194daa54283fc.png differ
diff --git a/docs/_images/fc17938c4152220762dbe8a88d173212a091d566bfa78f1f2c1f468893b4fb22.png b/docs/_images/fc17938c4152220762dbe8a88d173212a091d566bfa78f1f2c1f468893b4fb22.png
new file mode 100644
index 0000000..009d53c
Binary files /dev/null and b/docs/_images/fc17938c4152220762dbe8a88d173212a091d566bfa78f1f2c1f468893b4fb22.png differ
diff --git a/docs/_images/logo.png b/docs/_images/logo.png
new file mode 100644
index 0000000..f33dc62
Binary files /dev/null and b/docs/_images/logo.png differ
diff --git a/docs/_images/wrds_database_usage.png b/docs/_images/wrds_database_usage.png
new file mode 100644
index 0000000..4fb6d95
Binary files /dev/null and b/docs/_images/wrds_database_usage.png differ
diff --git a/docs/_images/wrds_logo.png b/docs/_images/wrds_logo.png
new file mode 100644
index 0000000..0814739
Binary files /dev/null and b/docs/_images/wrds_logo.png differ
diff --git a/docs/_images/wrds_subscriptions.png b/docs/_images/wrds_subscriptions.png
new file mode 100644
index 0000000..4c1cb6b
Binary files /dev/null and b/docs/_images/wrds_subscriptions.png differ
diff --git a/docs/_images/wrds_top_10_databases.png b/docs/_images/wrds_top_10_databases.png
new file mode 100644
index 0000000..87596f4
Binary files /dev/null and b/docs/_images/wrds_top_10_databases.png differ
diff --git a/docs/_images/wrds_web_queries.png b/docs/_images/wrds_web_queries.png
new file mode 100644
index 0000000..b20e2a0
Binary files /dev/null and b/docs/_images/wrds_web_queries.png differ
diff --git a/docs/_sources/01_setting_up_environment.md b/docs/_sources/01_setting_up_environment.md
new file mode 100644
index 0000000..4cf6b2f
--- /dev/null
+++ b/docs/_sources/01_setting_up_environment.md
@@ -0,0 +1,150 @@
+# 1.1 Setting up your computing environment
+
+## Required Software
+
+As noted on the first page, this review session requires the following software:
+
+ - Python 3.11 or greater, Anaconda Distribution
+ - For this class, please download the [Anaconda distribution of Python](https://www.anaconda.com/products/distribution). Be sure to download current version, with Python version 3.9. or greater. When you install Anaconda, be sure to install the full Anaconda distribution.
+ The MiniConda version is nice, but I only recommend it for advanced users. Nice instructions for installing and using Anaconda can be found (here.)[https://datascience.quantecon.org/introduction/local_install.html]
+ - The Visual Studio Code (VS Code) text editor
+ - A good text editor is important for software development. Some of your classes will use a fully-fledged Integrated Development Environment (IDE) like PyCharm. For this review, I suggest Visual Studio Code. You can download it here: https://code.visualstudio.com/
+ - There are several VS Code extensions that I recommend installing. To learn about extensions, see [here.](https://code.visualstudio.com/docs/editor/extension-marketplace) I recommend installing at least these two extensions: the [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) and [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) VS Code extensions.
+ - Git
+ - Although there are many different Git clients and Git GUI's that you could use,
+ I prefer that you install GitHub Desktop. You will need to install both
+ Git (link here: https://git-scm.com/downloads)
+ and GitHub Desktop (link here: https://github.com/apps/desktop).
+ - Some classes will use GitHub. GitHub is a website that allows you to store, interact with, and share your Git repositories online. [Please register an account with GitHub](https://github.com/) if you don't already have one.
+
+```{note}
+It's also important that you have a quality laptop. I recommend a laptop with at least 16GB of RAM and at least 500 GB of storage (at a minimum).
+So much of your schooling and of your job will revolve around your laptop.
+It's important to invest in a good one. If you have any questions about your laptop, please ask in the discussion section on Canvas.
+```
+
+
+## What is Anaconda?
+
+Anaconda is a free and open-source distribution of Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system `conda`. Anaconda is widely used in the scientific community and data science, as it simplifies the installation of packages and their dependencies.
+
+
+- What is the different between Python and Anaconda?
+ - Python is a general purpose programming language. It's not just used for data science, etc. It is also used in web development, as example. For example, Python is used in the back end of the website Reddit was used to create parts of YouTube back in the day.
+ - Anaconda, on the other hand, is a software *distribution*. It is a collection of Python packages that are targeted towards data science and scientific computing.
+ There are other distributions of Python that are also targeted towards scientific computing, but Anaconda is one of the most popular ones.
+ - Anaconda is a set of about a hundred packages that are useful for data science and scientific computing, including conda, numpy, scipy, ipython notebook, and so on.
+- What's the difference between Anaconda and `conda`?
+ - Anaconda is a software distribution, while `conda` is the package manager that comes with the Anaconda distribution.
+ - `conda` is a package manager that installs packages from the Anaconda repository as well as from other repositories. A package manager is useful for installing new packages,
+ keeping track of the packages you have installed, and updating packages.
+ - `conda` also makes it easy to manage software environments. This is useful when you have different projects that require different versions of packages.
+
+
+```{note}
+Let's pause here and make sure that everyone has Anaconda installed. Let's test the following. Please raise your hand if any of these things is not working:
+
+- Open a terminal and type `conda --version`. You should see the version of `conda` that you have installed. Depending on how you installed Anaconda, you may
+ have to open the Anaconda Prompt on Windows.
+- Type `conda activate` to make sure that you can activate the base environment. Later on we'll create our own environment for use in our projects.
+- Type `jupyter notebook` to make sure that you can open a Jupyter notebook. This will open a new tab in your web browser with the Jupyter notebook interface.
+- Type `jupyter lab` to try out Jupyter Lab. This is a newer interface that is similar to Jupyter notebook but has more features.
+```
+
+
+## What is Visual Studio Code?
+
+Visual Studio Code is a lightweight code editor that is great for Python development. It has a lot of features that make it a great editor for data science work.
+
+- **IntelliSense**: IntelliSense is a feature that helps you write code faster. It provides code completions based on variable types, function definitions, and imported modules.
+- **Debugging**: Visual Studio Code has a built-in debugger that makes it easy to debug your Python code.
+- **Git integration**: Visual Studio Code has built-in Git integration that makes it easy to work with Git repositories.
+- **Extensions**: Visual Studio Code has a rich ecosystem of extensions that add additional functionality to the editor. There are extensions for Jupyter notebooks, Python, and many other languages and tools.
+
+
+- What's the difference between Visual Studio and Visual Studio Code?
+ - Visual Studio is a full-fledged Integrated Development Environment (IDE) that is used primarily for developing in C# and .NET. It is a very powerful IDE that has a lot of features.
+ - Visual Studio Code (VS Code), on the other hand, is a lightweight code editor. Visual Studio Code has become a very popular editor for Python development and will be better suited for data science work than Visual Studio (the IDE).
+
+
+```{note}
+Let's pause here and make sure that everyone has VS Code installed. We'll run a few test files and configure VS Code with some helpful defaults. Please raise your hand if any of these things is not working:
+
+- Open VS Code and create a new Python file. You can do this by clicking on the New File button in the top left corner of the window and then saving the file with a `.py` extension.
+- Make sure the proper extensions are installed: Python and Jupyter. Also, you might consider the following additional extensions: GitHub Copilot, Black Formatter, Data Wrangler, Excel Viewer, Markdown Preview Github styling, Rainbow CSV, Rewrap, Code Spell Checker, and GitLens.
+- Set the default terminal in Windows to Command Prompt. Use the "Select Default Profile" option in the VS Code terminal to do this. You may also want to memorize the keyboard shortcuts for VS Code. You can start with the command ``ctrl + ` ``.
+- Open the terminal in VS Code and type `python --version` to make sure that Python is installed and that VS Code can find it.
+- Try running `conda activate` in the terminal.
+- Create a Python file `.py` and try opening the Python Interactive window. You can do this by right-clicking in the Python file and selecting "Run Python File in Terminal". Also, you should learn the keyboard shortcut for opening the command palette. You can do this by pressing `ctrl + shift + p`.
+- Adjust the VS Code setting so that `ctrl + enter` will run Python code in the Interactive Window instead of the terminal by default.
+```
+
+
+## What is Git and GitHub?
+
+Git is a distributed version control system that is used to track changes in source code during software development. It is designed to handle everything from small to very large projects with speed and efficiency. GitHub is a website that allows you to store, interact with, and share your Git repositories online. GitHub is a great tool for collaborating with others on software projects and for sharing your code with the world.
+
+Please watch the following video to learn more about version control with Git:
+
+
+
+Now, let's take a look at GitHub.
+
+
+
+You can find another nice video about GitHub [here.](https://www.youtube.com/watch?v=pBy1zgt0XPc)
+The code for this course is available on GitHub. You can find the repository [here](https://github.com/jmbejara/finm-python-crash-course).
+
+
+```{note}
+Let's pause here and make sure that everyone has Git installed and is able to download the
+```
+
+
+
+
+
+
+## WRDS: How do I sign up?
+
+[](https://wrds-www.wharton.upenn.edu/)
+
+This course requires that you create a WRDS account. WRDS is a comprehensive data research platform that provides access to a wide range of financial, economic, and marketing data.
+Follow the instructions below to create an account.
+
+```{important}
+If you have not requested an account already, please do so ASAP. You will need this for the next session.
+```
+
+
+
+**New to WRDS?**
+
+Use the following link to access the WRDS Registration form at https://wrds-www.wharton.upenn.edu/register/?user_type=class-student
+
+- Follow the directions on the Registration form to enter your identifying information.
+ - For the Subscriber, select your school's name from the drop-down list.
+ - Your User type, Class - Students with Code, has been selected by default.
+ - You will need to enter the course code. This can be found on Canvas here: TODO
+
+- Click the Register for WRDS button.
+ - WRDS accounts require two-factor authentication. We recommend you use a smartphone for the verification process. First, install the Duo Mobile app on your phone. This free app can be downloaded through your device’s app store. Follow the directions at How to Log into WRDS to register your smartphone and use Duo two-factor authentication to set up your WRDS account.
+
+**Already Have a WRDS Account?**
+
+After you have logged into WRDS, use the following steps to enroll in our class account. You will need the Class Code above to enroll.
+
+ - In the top right corner of the screen, select Your Account > Your Account Info.
+ - Scroll down until you see the Your Classes table and click the Enroll in a Class button.
+ - Enter the Class Code and click Submit. You can find the code here: TODO
+
+
+```{important}
+If you have any difficulty setting up your account please contact WRDS Support at: https://wrds-www.wharton.upenn.edu/contact-support/. When opening your support ticket you must use the email associated with your existing WRDS account, or the email you intend to use to set up your new WRDS account.
+```
+
+
+
+
+
+
diff --git a/docs/_sources/WRDS_intro_and_web_queries.md b/docs/_sources/WRDS_intro_and_web_queries.md
new file mode 100644
index 0000000..c56b73c
--- /dev/null
+++ b/docs/_sources/WRDS_intro_and_web_queries.md
@@ -0,0 +1,123 @@
+# 2.1 Introduction to WRDS
+
+## A Platform For Financial Data
+
+Wharton Research Data Services (WRDS) is a data research platform and business intelligence tool widely used in academic, government, and corporate sectors. It provides access to a vast repository of financial, economic, and marketing data, which is pivotal for conducting rigorous research in various fields, especially in finance and economics. The platform is known for its comprehensive and high-quality datasets.
+
+[](https://wrds-www.wharton.upenn.edu/)
+
+WRDS offers a variety of datasets from numerous sources, including leading data providers like Compustat, CRSP, IBES, and Bloomberg. It covers a wide range of data types, including:
+
+- Stock prices and trading volumes
+- Financial statement data
+- Analyst forecasts
+- Corporate governance data
+- Mutual fund and bond data
+- Macroeconomic data
+
+
+
+One of the key strengths of WRDS is its user-friendly interface, which allows for easy data extraction and manipulation. It provides powerful tools for data analysis, including the ability to execute custom queries and perform complex statistical analyses. In academic settings, WRDS is particularly valued for its role in facilitating empirical research in finance and economics. It allows researchers, professors, and students to access a wealth of data necessary for testing financial theories, exploring economic trends, and developing new insights in the field of quantitative finance.
+
+## The Core Data Sets
+
+WRDS provides some usage statistics on their website in an introduction presentation [here](https://wrds-www.wharton.upenn.edu/documents/1400/wrds_research_data_overview.pdf). This chart shows
+the percentage of usage across all WRDS data sets.
+
+
+
+The two most popular data sets are CRSP and Compustat.
+
+WRDS did an analysis finance papers published in the top 3 finance journals---the Journal of Finance, the Journal of Financial Economics, and the Review of Financial Studies---from the years 2004-2016. Out of all of these papers, the following chart shows how many times each data set was cited.
+
+
+
+
+All of the listed data sets, except for those colored in red, are available in WRDS.
+
+## Compustat
+
+
+
+[Compustat Financials, S&P Global Market Intelligence](https://www.marketplace.spglobal.com/en/datasets/compustat-financials-(8))
+
+Compustat is a comprehensive database of financial, statistical, and market information, primarily focused on publicly traded companies. It is widely used in academic research, particularly in the fields of finance and economics, for conducting in-depth analysis of company performance and market trends. The dataset includes information from various countries and markets, making it a valuable resource for both domestic and international financial research.
+
+Key features of the Compustat dataset include:
+
+1. **Financial Statements:** Detailed income statements, balance sheets, and cash flow statements for a wide range of companies.
+
+2. **Historical Data:** Longitudinal data that allows for historical trend analysis and time-series studies.
+
+3. **Global Coverage:** Data on companies from various global markets, including North America, Europe, Asia, and more.
+
+4. **Segment Data:** Information on business segments and geographical segments of companies.
+
+5. **Market Data:** Includes stock prices, trading volume, and other market-related information.
+
+6. **Corporate Actions:** Data on dividends, stock splits, mergers and acquisitions, and other corporate events.
+
+7. **Ratios and Metrics:** Key financial ratios and metrics that are pre-calculated for ease of analysis, such as ROE, ROA, and EBITDA.
+
+Compustat is highly regarded for its accuracy, depth, and consistency, making it a fundamental resource for both theoretical and empirical research in finance. It's extensively used for tasks like asset pricing models, risk management, portfolio construction, and corporate finance studies. For students and researchers in quantitative finance, Compustat provides a rich dataset for modeling, back-testing theories, and conducting robust financial analyses.
+
+The following two videos provide a short introduction to Compustat on WRDS.
+
+[](https://wrds-www.wharton.upenn.edu/pages/grid-items/introduction-compustat-part-1/)
+[](https://wrds-www.wharton.upenn.edu/pages/grid-items/introduction-compustat-part-2/)
+
+
+## CRSP
+
+
+
+[Center for Research in Security Prices](https://www.crsp.org/)
+
+The Center for Research in Security Prices (CRSP) is a renowned financial research database, primarily recognized for its comprehensive historical data on securities traded in the United States. Established at the University of Chicago's Booth School of Business, CRSP is a crucial resource for academic, commercial, and governmental research in finance.
+
+Key characteristics of the CRSP database include:
+
+1. **Extensive Historical Data:** CRSP is particularly noted for its long historical time series, which in some cases go back as far as 1925. This historical depth is invaluable for long-term financial studies and analyses.
+
+2. **Stock Data:** The database provides detailed information on stocks listed on NYSE, AMEX, and NASDAQ, including prices, returns, trading volumes, and other market indicators.
+
+3. **Indices:** CRSP develops and maintains a series of stock indices that serve as benchmarks for the investment industry, including value- and equal-weighted indices.
+
+4. **Corporate Actions:** Information on dividends, stock splits, and other corporate events that impact stock valuation is extensively covered.
+
+5. **Treasury and Mutual Fund Data:** Beyond stocks, CRSP also includes data on US Treasury bills, bonds, and mutual funds, expanding its utility for various types of financial research.
+
+6. **Survivorship Bias-Free Data:** CRSP’s dataset is known for being free of survivorship bias, as it includes data on companies that have ceased to exist, which is crucial for accurate historical analysis.
+
+7. **Research Quality:** The accuracy, completeness, and cleanliness of the data make CRSP a gold standard for financial research, particularly in academic settings.
+
+For students and researchers in quantitative finance, CRSP provides essential data for analyzing stock performance, conducting empirical tests of asset pricing models, and studying market anomalies and behaviors. Its extensive historical data and robustness make it a fundamental tool for both historical analysis and contemporary market studies.
+
+The following [video](https://wrds-www.wharton.upenn.edu/pages/grid-items/crsp-basics/) provides a nice introduction to the basics of CRSP.
+
+[](https://wrds-www.wharton.upenn.edu/pages/grid-items/crsp-basics/)
+
+
+## How do these compare with Bloomberg or Datastream?
+
+Choosing between financial databases like CRSP, Bloomberg, or Datastream depends on the specific requirements of the research or analysis being conducted. Each of these platforms has unique strengths and features that make them suitable for different purposes. Here are some reasons why someone might opt for CRSP over Bloomberg or Datastream:
+
+- **Historical Depth:** CRSP is renowned for its extensive historical data, particularly for U.S. securities. It offers data going back as far as 1925, which is invaluable for long-term historical research and analysis. This level of historical depth might not be matched by Bloomberg or Datastream.
+- **Survivorship Bias-Free Data:** CRSP's data includes companies that have ceased to exist, which is crucial for accurate historical analyses. This feature helps in avoiding survivorship bias, making it a robust choice for academic studies that require comprehensive historical perspectives.
+- **Data Consistency and Quality:** CRSP is known for its high standards in data accuracy, consistency, and cleanliness, which are critical for reliable academic research.
+
+On the other hand, there are some drawbacks of CRSP relative to Bloomberg or Datastream.
+
+- **Limited Scope**: CRSP has a limited scope relative to Bloomberg or Datastream. It primarily focuses on US markets and lacks the global coverage found in Bloomberg.
+- **Real-Time Data**: Does not offer real-time data, which is essential for current market analysis.
+- **Less Comprehensive**: Fewer types of financial data compared to Bloomberg (e.g., lacks extensive international data, commodities, real-time news).
+
+Broadly speaking, CRSP is more suited for academic research focused on historical analysis of the U.S. stock market, offering in-depth and high-quality data with a bias-free historical perspective. Bloomberg, on the other hand, excels in providing a wide range of real-time global financial data and tools, catering more to finance professionals and analysts who require real-time data and sophisticated analysis tools. The choice between them largely depends on the specific needs, goals, and resources of the user.
+
+## WRDS Web Queries
+
+To familiarize yourselves to using WRDS, please [watch the following video](https://vimeo.com/436447434) about WRDS Web Queries. While we will be automating the query process using the WRDS Python package [`wrds`](https://pypi.org/project/wrds/), using the web query system is a good way for initial exploration of the data.
+
+[](https://wrds-www.wharton.upenn.edu/pages/grid-items/introduction-web-queries-wrds/)
+
+
diff --git a/docs/_sources/_notebook_build/_01_occupations.ipynb b/docs/_sources/_notebook_build/_01_occupations.ipynb
new file mode 100644
index 0000000..7a3bf4d
--- /dev/null
+++ b/docs/_sources/_notebook_build/_01_occupations.ipynb
@@ -0,0 +1,283 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1.4 Simple Pandas Exercises: Occupations\n",
+ "\n",
+ "For this part of the HW, we will practice Pandas using a practice dataset regarding the occupations, location, age, and gender of users of an app.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run the cell below to add section numbering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import the necessary libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import the dataset\n",
+ "\n",
+ "Import the dataset from here: https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user\n",
+ "\n",
+ "Assign it to a variable called `users` and use the `user_id` as index. (Hint: Use `pd.read_csv(url, sep='|')`)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Print the first 25 entries\n",
+ "\n",
+ "Use `head` to do this."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## View the last 10 entries\n",
+ "\n",
+ "Use `tail`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What is the number of observations in the dataset?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What is the number of columns in the dataset?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Print the name of all the columns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How is the dataset indexed?\n",
+ "\n",
+ "Print the DataFrame index and describe it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What is the data type of each column?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Print only the occupation column\n",
+ "\n",
+ "Show two ways to do this."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## How many unique occupations there are in this dataset?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What are the 5 most common occupations in this dataset?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Summarize the DataFrame.\n",
+ "\n",
+ "Use a single function (method) to describe the data (to show the mean, standard deviation, various quintiles, etc). This method should only work on one of the columns by default, but can be called on the whole dataset (a method of the DataFrame)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Summarize all the columns\n",
+ "\n",
+ "Run `users.describe(include = \"all\")` and see how it compares to what we did before."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Run `describe` only on the `occupation` column"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What is the mean age of users?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What is the age with fewest occurrences?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_sources/_notebook_build/_01_python_by_example.ipynb b/docs/_sources/_notebook_build/_01_python_by_example.ipynb
new file mode 100644
index 0000000..f16baae
--- /dev/null
+++ b/docs/_sources/_notebook_build/_01_python_by_example.ipynb
@@ -0,0 +1,1583 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "56ff7eda",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ϵ_values = np.random.randn(100)\n",
+ "plt.plot(ϵ_values)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ed227fb",
+ "metadata": {},
+ "source": [
+ "## Alternative Implementations\n",
+ "\n",
+ "Let’s try writing some alternative versions of [our first program](#ourfirstprog), which plotted IID draws from the standard normal distribution.\n",
+ "\n",
+ "The programs below are less efficient than the original one, and hence\n",
+ "somewhat artificial.\n",
+ "\n",
+ "But they do help us illustrate some important Python syntax and semantics in a familiar setting."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a864e098",
+ "metadata": {},
+ "source": [
+ "### A Version with a For Loop\n",
+ "\n",
+ "Here’s a version that illustrates `for` loops and Python lists.\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "1676245d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ts_length = 100\n",
+ "ϵ_values = [] # empty list\n",
+ "\n",
+ "for i in range(ts_length):\n",
+ " e = np.random.randn()\n",
+ " ϵ_values.append(e)\n",
+ "\n",
+ "plt.plot(ϵ_values)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "093877de",
+ "metadata": {},
+ "source": [
+ "In brief,\n",
+ "\n",
+ "- The first line sets the desired length of the time series. \n",
+ "- The next line creates an empty *list* called `ϵ_values` that will store the $ \\epsilon_t $ values as we generate them. \n",
+ "- The statement `# empty list` is a *comment*, and is ignored by Python’s interpreter. \n",
+ "- The next three lines are the `for` loop, which repeatedly draws a new random number $ \\epsilon_t $ and appends it to the end of the list `ϵ_values`. \n",
+ "- The last two lines generate the plot and display it to the user. \n",
+ "\n",
+ "\n",
+ "Let’s study some parts of this program in more detail.\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "85eba02a",
+ "metadata": {},
+ "source": [
+ "### Lists\n",
+ "\n",
+ "\n",
+ "\n",
+ "Consider the statement `ϵ_values = []`, which creates an empty list.\n",
+ "\n",
+ "Lists are a *native Python data structure* used to group a collection of objects.\n",
+ "\n",
+ "Items in lists are ordered, and duplicates are allowed in lists.\n",
+ "\n",
+ "For example, try"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ee1f60d5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "list"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x = [10, 'foo', False]\n",
+ "type(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "abb82775",
+ "metadata": {},
+ "source": [
+ "The first element of `x` is an [integer](https://en.wikipedia.org/wiki/Integer_%28computer_science%29), the next is a [string](https://en.wikipedia.org/wiki/String_%28computer_science%29), and the third is a [Boolean value](https://en.wikipedia.org/wiki/Boolean_data_type).\n",
+ "\n",
+ "When adding a value to a list, we can use the syntax `list_name.append(some_value)`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "dc1b46c9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[10, 'foo', False]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "2ce1d842",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[10, 'foo', False, 2.5]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x.append(2.5)\n",
+ "x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2621e155",
+ "metadata": {},
+ "source": [
+ "Here `append()` is what’s called a *method*, which is a function “attached to” an object—in this case, the list `x`.\n",
+ "\n",
+ "We’ll learn all about methods [later on](https://python-programming.quantecon.org/oop_intro.html), but just to give you some idea,\n",
+ "\n",
+ "- Python objects such as lists, strings, etc. all have methods that are used to manipulate the data contained in the object. \n",
+ "- String objects have [string methods](https://docs.python.org/3/library/stdtypes.html#string-methods), list objects have [list methods](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists), etc. \n",
+ "\n",
+ "\n",
+ "Another useful list method is `pop()`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "20b510ae",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[10, 'foo', False, 2.5]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "e0f4985c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.5"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x.pop()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "7840fbbc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[10, 'foo', False]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5bc40e53",
+ "metadata": {},
+ "source": [
+ "Lists in Python are zero-based (as in C, Java or Go), so the first element is referenced by `x[0]`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "537085ff",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x[0] # first element of x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "7137175e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'foo'"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x[1] # second element of x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e4c3bb9",
+ "metadata": {},
+ "source": [
+ "### The For Loop\n",
+ "\n",
+ "\n",
+ "\n",
+ "Now let’s consider the `for` loop from [the program above](#firstloopprog), which was"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "4c9df0ed",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i in range(ts_length):\n",
+ " e = np.random.randn()\n",
+ " ϵ_values.append(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9a328dd",
+ "metadata": {},
+ "source": [
+ "Python executes the two indented lines `ts_length` times before moving on.\n",
+ "\n",
+ "These two lines are called a `code block`, since they comprise the “block” of code that we are looping over.\n",
+ "\n",
+ "Unlike most other languages, Python knows the extent of the code block *only from indentation*.\n",
+ "\n",
+ "In our program, indentation decreases after line `ϵ_values.append(e)`, telling Python that this line marks the lower limit of the code block.\n",
+ "\n",
+ "More on indentation below—for now, let’s look at another example of a `for` loop"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "3989d19d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The plural of dog is dogs\n",
+ "The plural of cat is cats\n",
+ "The plural of bird is birds\n"
+ ]
+ }
+ ],
+ "source": [
+ "animals = ['dog', 'cat', 'bird']\n",
+ "for animal in animals:\n",
+ " print(\"The plural of \" + animal + \" is \" + animal + \"s\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6f6c25df",
+ "metadata": {},
+ "source": [
+ "This example helps to clarify how the `for` loop works: When we execute a\n",
+ "loop of the form"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dbbce3d9",
+ "metadata": {
+ "hide-output": false
+ },
+ "source": [
+ "```python3\n",
+ "for variable_name in sequence:\n",
+ " \n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "24e8c514",
+ "metadata": {},
+ "source": [
+ "The Python interpreter performs the following:\n",
+ "\n",
+ "- For each element of the `sequence`, it “binds” the name `variable_name` to that element and then executes the code block. \n",
+ "\n",
+ "\n",
+ "The `sequence` object can in fact be a very general object, as we’ll see\n",
+ "soon enough."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cd045b26",
+ "metadata": {},
+ "source": [
+ "### A Comment on Indentation\n",
+ "\n",
+ "\n",
+ "\n",
+ "In discussing the `for` loop, we explained that the code blocks being looped over are delimited by indentation.\n",
+ "\n",
+ "In fact, in Python, **all** code blocks (i.e., those occurring inside loops, if clauses, function definitions, etc.) are delimited by indentation.\n",
+ "\n",
+ "Thus, unlike most other languages, whitespace in Python code affects the output of the program.\n",
+ "\n",
+ "Once you get used to it, this is a good thing: It\n",
+ "\n",
+ "- forces clean, consistent indentation, improving readability \n",
+ "- removes clutter, such as the brackets or end statements used in other languages \n",
+ "\n",
+ "\n",
+ "On the other hand, it takes a bit of care to get right, so please remember:\n",
+ "\n",
+ "- The line before the start of a code block always ends in a colon \n",
+ " - `for i in range(10):` \n",
+ " - `if x > y:` \n",
+ " - `while x < 100:` \n",
+ " - etc., etc. \n",
+ "- All lines in a code block **must have the same amount of indentation**. \n",
+ "- The Python standard is 4 spaces, and that’s what you should use. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e210f170",
+ "metadata": {},
+ "source": [
+ "### While Loops\n",
+ "\n",
+ "\n",
+ "\n",
+ "The `for` loop is the most common technique for iteration in Python.\n",
+ "\n",
+ "But, for the purpose of illustration, let’s modify [the program above](#firstloopprog) to use a `while` loop instead.\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "2c9effd1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAESCAYAAAB6s0uLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAABqPElEQVR4nO29eZxcZZn3/Tu1V/XenU53OumsLCGsYYsBVNDIosPo6OOMDiqiD44MzIj4OAM64vjMi+F99XF0eBlcPgrMjIj6jqDiggjIoiEbBAghIQlZOmvvW3V3bed+/zh13ec+p845daq6llNV9/fzyYdQXak6ferUue7fdf2u61YYYwwSiUQikdQBvmofgEQikUgkpUIGNYlEIpHUDTKoSSQSiaRukEFNIpFIJHWDDGoSiUQiqRtkUJNIJBJJ3SCDmkQikUjqhkC1D8AJVVVx7NgxtLS0QFGUah+ORCKRSKoEYwxTU1Po6+uDz2evxzwd1I4dO4b+/v5qH4ZEIpFIPMLAwACWLFli+3NPB7WWlhYA2i/R2tpa5aORSCQSSbWYnJxEf38/jwt2eDqoUcqxtbVVBjWJRCKR5C1FSaOIRCKRSOoGGdQkEolEUjfIoCaRSCSSukEGNYlEIpHUDTKoSSQSiaRukEFNIpFIJHWDDGoCjDHsG5xGRpWbgUskEkktIoOawL/+fi82fOMZ/HDzoWofikQikUiKQAa1LIdG4vj2H/YDAPacmKry0UgkEomkGGRQy3LXr15HMqMCAKYT6SofjUQikUiKQQY1AH/cN4zf7TrJ/396TgY1iUQiqUUaPqilMyr+9y93AQD6O6MApFKTSCSSWqXhg9qPthzGnpNTaI8F8b+uPB2ADGoSiURSqzR0UEumVXzjiTcAAJ9712lY0iGVmkQikdQyDR3URuIJjM2kEPAp+PDFS9EcDgIA4jKoSSQSSU3S0EEtmdbcjuGADwG/D01hPwBgShpFJBKJpCZp6KCWoKAW1IJZS1apJdIqD3gSiUQiqR3KGtTuu+8+nHPOOXzn6vXr1+M3v/lNOd+yIChwhfzaaSClBsgUpEQikdQiZQ1qS5Yswd13343t27dj27ZteMc73oH3vve9eO2118r5tq4hpRYKaKch4PchEtT+Ls0iEolEUnsEyvni1157reH/77rrLtx333144YUXcOaZZ5bzrV2RSGcAaDU1ojkcxFwqIYOaRCKR1CBlDWoimUwGP/3pTxGPx7F+/XrL5yQSCSQSCf7/k5OTZT2mpEmpAUBLJIDhaRnUJBKJpBYpu1Hk1VdfRXNzM8LhMD796U/jkUcewZo1ayyfu3HjRrS1tfE//f39ZT02q6BGdTUZ1CQSiaT2KHtQO/3007Fjxw5s3rwZN910E66//nrs2rXL8rl33HEHJiYm+J+BgYGyHltCsPQTzWFNvMr5jxKJRFJ7lD39GAqFcMoppwAALrjgAmzduhXf+ta38J3vfCfnueFwGOFwuNyHxNGVmu56pAZsqdQkEomk9qh4n5qqqoa6WTWxVmpagJOWfolEIqk9yqrU7rjjDlxzzTVYunQppqam8NBDD+EPf/gDHn/88XK+rWuSWfejWFNrjminRE4VkUgkktqjrEFtcHAQH/vYx3D8+HG0tbXhnHPOweOPP453vetd5Xxb19CmoGG/0dIPyPSjRCKR1CJlDWrf//73y/ny8yaRojFZMv0okUgk9UBDz34kpRby57ofp2RQk0gkkpqjsYOaRZ9acySbfpQ1NYlEkoUxhnuf3odn3xiq9qFI8tDQQU13P4qWftl83Ug8+KeD+Obv36j2YUg8zmvHJvG1x/fgn3/pjbm1EnsqNibLi5gHGgOQG4U2ELPJDL7yy9egMuCjb1mGrubK9UhKagtyQ0/OyvuC12lopWadfpSW/kbhzeFpqEz7+5zcP0/iANXfE6lMlY9Eko+GDmrWU/qzY7KkUqt79g1O87+nMzKoSeyhBfBcWgY1r9PQQc1SqWWDWjyRBmOsKsclqQz7h+L876mM/Kwl9tC9IpVhyKjyWvEyjR3UrCz92fRjWmW85iapT/YPCUpNlZ+1xJ5kRldoczIF6WkaOqjpzde6+zEW9ENRtL/Lulp9s9+QfpSrb4k9dK8AZFDzOg0d1KyUms+noCmkpyAl9UlGZXhzWEw/SqUmsScpXB/SVORtGjuoWUzpB6RZpBE4OjbLP39ASzdLJHaI18psUio1L9PQQc3K/QhIW38jINbTAKnUJM6I9XWZfvQ2DR3UrNyPANAklVrdI9r5AVlTkzgjKrWEtPV7GhnUkBvUWsKyplbvmJWadD9KnDDU1FLyWvEyDR3UrGY/AnJSfyNgDmrJtFRqEnuSMv1YMzR0UMubfpQ1tbqFGq9JlUulJnFCTDlKpeZtGjqoJTI26ceITD/WM6PxJEbjSQDAqT3NAGRNTeKMVGq1Q8MGNcaYtPQ3KJR6XNweRUt2/zzpfpQ4YQhq0ijiaRo2qImFX7v0o7T01yc0SWTVwmYE/dr4GNmnJnFCGkVqh8YNasLKy65PTaYf6xNSaqd0NyPg0z57OaVf4oRMP9YODRvUxGZKcUwWoJsHZPqxPtnHlVoTAlmlJqf0S5wQ7xdyTzVv07BBjTsf/T4oNME4S5O09Nc15Hxc1d2MYHZBI92PEieMNTV5rXiZhg1qCRuTCGDcU01SX8ylMhgYmwGgBbWATyo1SX7kmKzaoWGDml2PGqBb+mWfWv1xcCQOxoC2aBALmkMIkFKTQU3igKyp1Q4yqDkoNVlTqz94Pa27CYqiCO5HmVKS2CPdj7VDwwY1uwn9gHGgsSqt3nXF/kG9ngaAux9l+lHihFRqtUPDBjU36UcAmJEXcF0xEk8AAHrbIgCgKzVp6Zc4II0itUNZg9rGjRtx0UUXoaWlBQsXLsT73vc+7Nmzp5xv6Rq7EVmApt7IQCDravVFPKEtUkiNB2TztcQFxvSjXOh6mbIGtWeeeQY333wzXnjhBTzxxBNIpVK48sorEY/Hy/m2rkikrCf0A4CiKEIKMlXR45KUl5mktkhpCmmfu55+lKtviT1ib5rsU/M2gfxPKZ7f/va3hv9/4IEHsHDhQmzfvh1ve9vbyvnWeaGVl7nxmmgOBzAxm8J0Ql7A9UQ8qX2esZB26evpR6nUJPZIo0jtUNagZmZiYgIA0NnZafnzRCKBRCLB/39ycrJsx+JUUwOkrb9emck6WpvCmlKj5uuUdD9KbFBVZjASzUql5mkqZhRRVRW33norLr30Upx11lmWz9m4cSPa2tr4n/7+/rIdj5P7EYBMP9YpZqUm+9Qk+UiaUtOypuZtKhbUbr75ZuzcuRMPP/yw7XPuuOMOTExM8D8DAwNlO558Sk3vVZMXcD3Ba2pcqck+NYkzMqjVFhVJP95yyy147LHH8Oyzz2LJkiW2zwuHwwiHw5U4pPxBjacfpVKrJ8j9yJWa7FOT5CFpsvBLS7+3KatSY4zhlltuwSOPPIKnnnoKK1asKOfbFYQ++zHX/QgAzSE5VaQe0d2PRku/dD9K7DAHtWRalUMZPExZldrNN9+Mhx56CD//+c/R0tKCEydOAADa2toQjUbL+dZ5sdv1miCl5jSpnzGWM+Ff4l1UlWGGamrm9KNUahIbaAHsUwCKZYm0imjIekEsqS5lVWr33XcfJiYmcPnll2PRokX8z49//ONyvq0rkg7N10D+Sf1fenQnLvu/n8bEjExP1gqia60pJ/0olZrEGloA0z0BkHU1L1NWpcaYd1e/1EBpq9TCzpb+X716HKPxJHYdn8T6VV3lOUhJSYlnU4+KAkSC2ucelBNFJHmgoBYLBTCTzCCtMsylZVDzKo07+zFf83XEvqY2l8pgNJ4EAMymZM2tVpihEVmhAE8bk1KTsx8ldiQz2QVw0IdIUEs5ygZs79KwQY0bRYL5LP25QevExBz/+2xSXty1Aim1mFAL0Y0iUqlJrKF7Rcjv4wpfph+9S8MHNacxWYB1UDsuBDVy00m8D5lEmoTaCE0UkX1qEjvE9h9dqcmg5lUaNqjpF6qNpd9hTNbxiVn+dzkyp3Yg049Bqfmk+1HijHVQk4sgr9LwQS2vUcRioohRqVUmqKkqw5Ovn8T4TLIi71ePcKUW0pVaQM5+lOTBMv0ojSKepWGDGs1+zD8mK9eyLyq1SgW1X7x8DJ98cBvu+tXrFXm/eoQrtbCu1GSfmiQfBqWWzezI7We8S0Wn9HsJt7Mf51Iq0hmVr+gB4Pi4aBSpTE3tiV0ntfcWVKKkMCyVmhyTJckDOaXDAR+oS0mmH71Lwyo18UK1QjQTxE0pyEqnHzMqw/P7hrVjkcaUorFyP8qBxpJ8JIWRetL96H0aNqjRztd2Si0U8HG1NjRtVEeVNoq8enQCE7NaGnRG7hpQNLxPLZxbU5PpR4kdYlYnLN2Pnqdhg1o+pQYAKxY0AQD2D8X5Y3OpDMaE0VizFVBqz74xxP/uRqntPDqB//O7PbLdwIRln5pPDjSWOCMOaqCampzU710aNqiRUrOb0g8Aq7opqE3zx8w1rUqkH5/bqwc1N+/3r0+8gXue2sfrcBINK6VGSl2OyZLYkTBY+mX60es0bFDLN9AYAFZ1NwMA9g/qSk1MPQLlV2pTcym8eHic/7/dgGWR4ekEAPBRXhINJ6WWUZmnZ5WWizdOTmHT/pFqH4anEZ3S1Kcm+1O9S+MGtTwTRQBg1UItqO0TlVrW+Zi9F2KmzLMf/7R/BBmVoaspBEBbNeabU0j1N7thzI2KU58a0JgOyE88sBUf+f5mDE0lqn0oniVpodQS0v3oWRo2qNHqy272IwCckg1qbw5O81X8iUktqC3piAEof/qRUo9XntnDH5vJs0qczAYzp73gGhGnPjWg8RyQjDEcG59FRmUyqDkgLoB5TU0qNc/SkEFNVRlflTsptWVdMfgULTjQl/7YuJZ+pHpbudOPz+3VrPzvXN3DU2VODkjGGFdqU1KpGXDqUwMaT6nNpjJ800vZKmJPUhh+Lmc/ep+GDGpJIX3nVFMLB/xY2qkpMkpB0oT+ldl6WzmV2qGROA6NzCDoV7B+VRevBTndgOLJDDLZO5XVMOZGxqlPDWi87WfE9LS8VuwxuB+5UaSxrpVaoiGDWkKw4zq5HwHBLJK19R/jQS2r1Mq4Yns2q9LOX9qBpnCAu/aclNrkrN5uMDUnd+UWsXI/KooCv68xt58RA5nsf7RHnBPL+9Tk7EfP0pBBLSkENXGlbgWZRfYPklLT0o8rFzTz18qUyQ7+XLY/7W2ndQOAK6U2IQQ1aRQxYqXUgMbtVRODmhtXbaMit56pLRozqAl2ftoB2Q6xV202qTder1rYxJ9TribnzQdGAQCXnbIAgK4wnN7PENTkjYrDGLPcTw0Q91RrMKUmLHpkTc0eQ59aQKYfvU5DBjWasO00TYTQe9WmufOxKeRHd3MYFA/LYRaZSaZ5gCK1yJWa6/SjvFERCUFR5yg1Pqm/sW5UUqm5Q3c/+qVSqwEaMqi5GZFFUFA7NjHHU5C9bREoioJY9gIvh1lkcFJzW8ZCfj6Dklx7bpWarKnpiJ9RLGRUaqWY1L9vcBoj07VlizcEtQptoVSLJIT7BQW1hByT5VkaM6il84/IIjqaQrzx+Y/7NePGorYoACDKg0wZglq2haC7Jcwfi2WDm6NSMznaGnFKhhWkRCJBHzeGEPOd1L9vcArv/tZz+Jv/3D6/g6wwUqm5w6r5Wio179KQQS2RZy81M6TWnt9LQS0CQE9jzZZhqsjglJbqXCgEtaYQKUN3Sk1l5Ws5YIzV1I3QqkeNoPRjsUrtFy8fRzKj4o2TU8UfYBUwBjV5k7YjaTEmSwY179KQQc3NiCwRMoXszaYfzUGtnOnHhS0R/ljMhTIUa2pA+cwin///XsEF/9cTGBidKcvrlxrufAznqvOgj7afKU6p/e61EwC0Jn21hswmBqNIDS1QKo1oLNMnilQ3/Tg+k8RvXj3OJyNJdBo6qDmNyBIhpUYsatfSj3y4aYXSj03h/EHUHNTKZRZ54c0RzKVUvH58siyvX2p4j5qDUivG/XhgOI7dJzSFxhgwXUMuQmNNrXaOu9IYxmRR+jGdqWpq/5u/34ubfvgifv7Ssaodg1dpyKDGp267VWqmoNabk34sR1DLph9bhZpaiGpq7tKPQHmUGmOMB91aaRuw61EDRKNI4avvx7MqjZiYqR1zjqypuSMhNF9HstcPY8bJRJXmyJiWITkyPpvnmY1Hgwa1wmpqNNiY6MsaRcqZfqRZk2L60Y1SMwe1cjggJ2fTfPVaK0GN6pDmHjVAMIoUUVP77U5jUJusIcepMf1YnTRWLWz5YzCKCOayaqYgx2fkThx2NGRQE8feuKGvPWp4Lim1crof9aBmodQcUkXmm2o5LvqhaX2j1FrphaObtqVS483Xhd2kTkzMYcfAOACgM+uQnZytjfMBeCP9+D8f3IpL7n7K00pRdEsH/QrfdipRRbOIPrS8dhZRlaKsQe3ZZ5/Ftddei76+PiiKgkcffbScb+eaQpWa36dgxQLNLNIU8qM1ogWXGK+plcP9mA1qrRbuR4dVNV3sVIsrx/YzZGIBaieocaVmUVMLFul+/N0uTaVdsKwDy7u0wde1pNTiVU4/HhiO4+k9Qzg+MWfYXd5LqCrjtVaaQKQ7IKuo1GjPRA8vBqpFWYNaPB7Hueeei3vvvbecb1Mwejohf58aQVM9qPEaAKJlSj8m0yrftdrgfgznV2oU1BZnzSzlCDqDwt5b04nauIlzpWblfixSqVHq8eoze9EaDQLINep4makqN1//ftdJ/Vg8ujiy2tEjUuWhxnJ7KWdyl60l5JprrsE111zj+vmJRAKJhH7DnJwsj7OukIkiBJlF+rLBAihfTW04O5ki6FfQEQu6fr9EOsNXj4s7otgxMJ6TftxzYgr3//EAPv32VVi+oMnqZfJCJhagdr5UTkotUMSU/tF4ks/mvOrMXrx6dAKAsfnd64jXRjKtIpVReYAvlP1D02iNBA1u3Xw8IQQ1ry4GxMkhZCzT5z9WJ6jNpVS+MJfpx1w8VVPbuHEj2tra+J/+/v6yvA9txe42/QgAV67pQVdTCFed2csfoyBT6oub2/mbw4aBy/rsR+sbJ9VzFAXoy9b9zErq/j8ewMNbB3DDA1tzTCVuEXdJrpVCNSkR84gsQKipFRDUfv/6SWRUhjMWtWJpVwytUe11vXpztsJ8HRW7/czIdALXfPM5/PX3XnD9b0bjSWw7NMr/36tpW6sdPcrZyuMGwyg8mX7MwVNB7Y477sDExAT/MzAwUJb3SWYKs/QDwFmL27DtnzbgI29Zxh+LlGn242B2cHJ3a8TweFMeYwpd7C3hAFojmsIzKykKmAeG4/jsj3cU1Swsph9r5Us1kyD3o1X6sfAxWaQyrs4ucuh8e/XmbCajspyUY7E9dgdH4khmVOwbmna9DdNTuwchPtWrBpuEME2EFpj6nmrVqamNzyb532slU1JJPBXUwuEwWltbDX/KQaHN14R5mxo3Ez6KQVRqhvcL6/upWdmg6YbaGg2iJWtmMQedkbj+hXhq9yD+9fdvFH58k3Wm1AocaMwYw7aDmsp4++naXnd6Ta1Wzod+nDytXeQCZWhKu6YY0yZduOGJrMmGnISlXAxsPzSKv/zOJuzMpoTnA79XCAvgas9/FHshZfoxF08FtUqRsLhQi6Fcsx+tnI+ArtQYs3ZekVJriwbRnFUO5qAzGtde+yNvWQoAuOepffjtzuMFHp9QU6sRo4jep2Zl6S9s65mDIzMYm0khFPBhzSJt4VVrSo1Sj0G/wtsRinXSDQm7E4zG8we1uVQGz76hzVGlDXBLmbZ99KVj2HJgFL9+tbDr2gpefxcWwPqorOoEtXHhXM2l1Ibb3DYfDRnUkgVa+u0ol/txyGKYMQBEg/oN2coBOSkGtaxT0rySG5nWbjr/87KVuOHS5QCAf/zvVw21g/zHV4NKLWGv1PjsR5eps5cOjwEAzl7cxq8hL9fUJmZStv2LTeEAv1aKvY6HhethxEVQ+9P+YcymMuhtjeCSVV0ASmuwod+jFJZ7qzmxpNQSVbL050wNqpHvYKUoa1Cbnp7Gjh07sGPHDgDAgQMHsGPHDhw+fLicb5uXRAFbzzjBlVrJa2q500QAwOdThFRR7nvSDbU1EuS9dOLqey6V4V/4zuYQvvDuMxAO+DAxm8KJibmc17NiLpWpye1tdPejvVJzG9hfzAa1tf3t/DFdqXnrBpNMq9jwr8/g6n991lDvorR0czjAr6lKKTWqR25YsxBtUar9lm4xQJmTUgz7tVoAV9vSbx7FJnvVjJQ1qG3btg1r167F2rVrAQC33XYb1q5dizvvvLOcb5uXUim1cln6By2miejvad+rZkw/ZoOacJOlVXTQr6AlHEDQ7+MtCscm3M2QI5VG5cVUhtXEhol6n5pV83VhfWovHR4HAJy/rIM/5tU+teHpBIamEjg2MYcxod4VF4JaUzj/TFHH9xCVWp6NUlWV4fevDwIA3rWmFy2R0tci6ftYiuvSMahVq6ZmusZqJeVdKcrap3b55Zd7chVf6EQRO6LBchlFcocZE01hP4anrfdUo4u9NRoQ0o9CUMvecLqa9FaBvvYIDgzHcczlYFQ6tr62KI5NzIIx7T0iwfmp3nLjqNR87mc/ziTTfCr/2qXt/HFSxl4LaqJyGplOYkHWfESLneZwgNdqi23AHp52n358+cg4hqYSaA4H8JaVndh6QFO9pbwx6+nH+X8vExmroEZGkeq7HwFvOCAHJ+cAJTe7VA0asqZGaYlCmq+tKEefWkZlGJ7OnSaiv6f97te02m2LBvkKeDqp7/FFNxwyBgD6Lt5ugxoptZ7WMJpDuSlOr8LdjxZKjfrU3LgfXzkygYzKsKgtws8dAD2Nlki7trVXgnEhVTUSz23FaI7MX6kVkn7c9OYIAOCtpy5AOOAvSy1ytoRKjfe0CjW1cJWNIhMmVVvtmloqo+Kabz2Hd3/r+aL3JCwlDRnUSm8UKV1daTSeREZlUBRgQXMo5+dOu1+L6Uey9DMGzGS/fKPZYNklvK6efnRXUxP3ebNKcXqRVEafwGCl1ArpU6PUo6jSAPBFBOCt8yGmHMWAY0w/Fm/pZ4xheEpQg3mCGtVuV3Zr02zKUYukraBKkn7M5Nbfqz370dw2YXYgj8aT+O6z+w0u5XIyNZfGSDyJ4ekEX5BXk8YMapnc1VcxUFBTWWm+QICe3utqCnEFIcLnP1ooNT39GEQ44ONpNSrC00q9S1Bqi9s1Neg6/SiYWHgvnMdz+mJ6eL59amQSOX9ph+HxUMDH3aleqnGIQW1EuOEY0o9hUtyFK494MmPYT3A0z03NvKUS1SKnS6hwZ0uYfrSuqekbhVYDUrV0TOZF1H+9cAhf/fVufO/ZNytyPKIhZzhPTbUSNGRQo5RCeJ51oJjw70vlgNSVkHVu2kmpic3XiqLkKCk9/ajX6gpNPw4K7Qa8bufx9COdq6BfsVTnbvvUGGO2Sg3Qbf3Fjh8rB2NxMf0oBDVRqTlcU/kQTSJA/vSj2QRFCyOgdAqXfo96NYpQn9qSbJbFrHKPZ9XwwGhlNhAVFevQlAxqVaFUSi3g9/HXmCnRBT40ae98BET3o71So/qOeaqIU/rx+Li7VMWQ0Bhu1+DtNZx61AAx/eisFI6MzWJ4OoGgX8GZfW05P/diA7ZRqYm7K+TW1IqpjQ6ZVub50o+0KKLBx0F/6RUudz+WRKnpY7IIGmhc7T61xR3WO3GMZT+DE5OVST+KSs18PVSDxgxqRY7JsiLKe9VKc2M3f+nNONU/JoQ+NQBoDhvnP9INR0w/9mXTj1OJtKubilhTa7HohfMiTs5HQEw/Ot+kKPW4pq/N0u3pxVFZdjU1o1Ir3sVLSm1J9gY7NpO0nSfKGLPc0b2UCldV9RaTQgYK2MFran5vKDVVZTz9uKRD28PPPLR8NPuZD1YoqIlKTaYfqwQfUjpPpQaUvlfNqUdNez9rpaaqjN+ouFIL26Uf9aAWCwX49jZuUpCDwk2pxWZqiddw6lEDBKWWp6bGU49C07UIt/V76HyMzVinH6361IpZnNBN7PSeFgCae9cuOE0l0vwGKC7aSqlwxfpe+Wpqzs3XqYyK7YdGyzK+aiqR5oOgl9goNVq8DE4lihpYXiiiIpbpxyqhb89eOqXmNqjNpTLYdnDUtig+mCf92GTzflNzaZABk1a+upLSbhY097HL5Kqkulq+FGRGZTyFVYs1NTul5rb5msZjiU3XIl5swB63ST/SjbA5EuCDsoupqdFNbFG7bhyyS0HStd0SDvDvDVBahSt+L0pi6XcyitikHx/800F84L5NePBPB+f9/mbo2ooG/dwdbU7/U/oxrTJXY8vmi3iepfuxSiRLNCYLEIcauwtq3/z9XvyPb2/Cf794xPLnlJNe2GptFInxOX3GC5lWuZGgj/9ezdydmFVqVFNrMgZMqqsdzaPURuIJqEybrN7VXDuWfqcJ/YC7PrW5VAavHdM2rbVXat4blSWmHK3Sj+LsRytHbT6GstfUguYwT2vbmUUoAHabhgqUUuHOljioWS2Aw3nSjweG4wC0LXlKDfUdavNdc7eXUlVmSDmfrEAKUjwPZuNQNSjrRBGvUqqJIoA+ZNit+/FQ9kLf/OYo/vLC3E1QB22GGRNNfKNQ4/uZTSIADFNFzHMfRdza+mml3dkUht+n8N4sL0w0cMJpLzXAvk/tnif34sFNBzGbzGAurSKjMixoDvO0jxkvDjUWm6/HZlJIZ1QE/D6efhRVU1FGkexNbEFzGJ1NIRwcmeEZATN217Y+Kst43n71ynE8sesENr7/HIOyc2JG2DGjJBNFLO4V0TxBjc5jORZ7Vr2o4mJgci5l2Kfu5OQczlqca2oqJUalVv2g1nBKTVUZd7mVJKhZFNmn5lJ45ci4ZUM2XfC7jk/m/IwxZjvMmLBTalZBjU8VSaR5GiLk9/FaGLGIHJB5GrCHzHbsedRiKklepWbTp/bQlsMYnk4inszwdPH7z1+cs68e0Wpzc64WybSa89lQjU10P/Ip/fOoqXW3hHmriF3Ka8imXYUWA+bF0T1P7cWjO47hhQMjro9HXFymVTbvCRe6U9p98zUFs3J8L2hEVltMmO8qvI9ZJVfCASkGNS+4HxtOqSWFi7wUNbVYMNf9+MVHduIXLx/Dw596C96yssvwfPri7j05hUQ6Y0iBTs6l+QViNfdRfD+zUWTS5HwEhJraXJrXUzqbQjk3ZbfpR/NMylpJP+ZTanZ9apRS/o9PXIzTeloQDfkNiwYz9LNC0mi/3XkCQ9MJfFTYUb1UUD3Np2gLnInZFEbjSXS3hPl12BQK8Jt0PJmBqjL4fNZB2woKaguaw7zGY9eAbV4UEXZGEbohF9IDan5uMqtMi8Wp+dpuFwCqMZcjgyEuXlsjuYuBMdO0kZOT5Q8yomIdn0khlVF5nboaNJxSE3tLSqHUrNyPpMIoty5CTsG0yrD35LThZ7SPWkskYDsgOGZj6XdMPyZSls5Hwm360XxTqhWjSD6lZtenRl/Wld1N6G2LOAY0oHDDQyKdwWcefglfenRnWUYajQn1F3IbjkwnkMqofPHUIig1wH1tGDBb9MP82rI1itgFNYvzNpfK8NRpIWlEs4FqvqOsLINawLnkQIu8cgQ1Oift4nxXYfun0bhxYXDS5fi7+WCuXY5U2SzSeEEto12IiqJPZ58PVu5Hmm9nZXUXUwXmFGQ+5yMA254i6/SjvpKzarwmyP14cnLOcVSR2KNmfH1vpNvscN+npv/ujDF+Q3S7A0Gh1vQ3TkzzG0I5etto1d4RC3ETx0g8aRhc3BQOIBL0gb4KhQw1nk7omQWqqQH2RhG7Hkyr8yZawwsJTOYhCPPdU83Z0m99XDSLsRzpR6uNgDMq44uRMdO5P1mB+Y/mc1xtW3/DBTXRzWRXGykEs/txci7FL2arlZqYqtt1zBTULBpTzVAKzbyfmjgii2gWal5Wcx+JhS2a8SOVYY6FXnO9r5rN14dHZvCLl4+5GiSdr0/NKv0orj6jboNagUaRnccm+N/L0chLN7iOphBfzIxMJ/h1GQ74EPRr34Nitp+hm1dTyI9oyM/fI5/70Xx9W523QUNQc39Mc6bjn+/UD76jh8XO1xmVWfailbWmRkotFkQs5Iefz3fNTg2a0d2oAFxv/jsfzIuOaptFGi6ocTdTiXK+ZBShVIR4EZmDWkZlhptGblCz30eNoBSaeedrcZgxIVr6reY+EgG/D73ZFgKnuprZvUaW4um5yu9+/cVHX8Xf/+glbD4wmve57vvU9N9BTC0VrtTc3cx2HtWDWiFpP7dQ+rEjFjSoKFoQiXMXi9l+hnqSSHnRtWV3UxsURqyJtFictyFBYRQyONhsoJqvrZ9PFAnmKjUgN+AyxirmflQUJWffRFrInLFIa4YfrIBqylFqMqhVFj2dUJpNLc01NbEuZU5DmVduu45PGjr+aWLFsq4m2/ejFXVS2E4F0PdYMqQfhaDjlH4EgEVt+etqeg+dMf2YVpnrFNH+oWls/PXr8x7hc3RMO043fTj53Y/aalc8n3PC1Bm/yzS1OHHejevOENRKvNEsoKcf22Mh3ps4Ek8aJvQTVKstJKiJdn4Ajn1qibReI+tutulTs1Vqxacf56uAkxaLYNFgZj62RFrlaexkRi25Atfdj9q5bjZN9RnlQa2V//98U7D5MKthqdQqTCmniQBCn1q2P8ZJqVFQo2nx04k0BsZmAGhfvmfeGAIAbDhjof37CWpDvBHq7kf9RiWmB63mPorkG2wstht0N2sBMBbygzK45j2drBgYncGHvvsCvvPsm/j+8wfyPt8JmlTuJsUTz+N+tJooQue2kPmghonzeY4rlVHxenYHbWB+N99EOoObf/gi7n16n+HxMcEcpKcfk9zY0yQENd6ALSid5/YO4WuP77YN0KKdn94H0IKpWbmTqgv6FbTHjIYbWgyItdlBwbVXyE3ZvDiYt1KzqKkpisLvH+bPzfy557sO/uuFQ7jloRddj9QyL15bTA5IWsisWNDEj3mwzA5I+nzoXiBrahUmUeqgZlJqxw1BzXijp/9vjQT5rDxKQf5p/zBmkhn0tkZwtkOzZCig7wwg3oAs3Y9CUBsWLP1W5LP1W7UbiOmPfKmWkekErv/BFn7BvyqolEJRVcbt6m5SPBTU7CeK5M5+LNQkAmjBkZR7PuPH3pPTBmU4n/Tjc28M41evHse/PbnXEEzGhPoLKbVRwShiUGoWTf1f+eUu3Pv0fmyxSfGKdn5Av7ZSGZaTgiVl3t0czqllU9p2KqHv0i4q8ELqYmYD1XxVit2gBrouzK+fs5DNc33e94f9eOyV43jliLvvwwSpb1NQo+A5KixkerLf03JPFaHPh8oS1R6V1XBBrVS7XhPm9OPxCT0o2F3gLZEA1mTTAzR66XevnQQAXHlmT14Di9Wsvkmrmppw0zo8qinCrmbrel0+W79du4GbBux4Io1PPLAVbw7H+ZfwtWOTRdfhppP6UFc36TL6bGz71Cym9FP60a1JhHDrgBRNIsD8lNof9w8D0G7A4ip5XHA/UsAZjicM1yHRbKqpqSrD4RHtmrFryjenHyNBP38dcwqSO2ctxr+Ju7RPZ6/pfEaRVEbFvsHpnGvIvDiYt6XfZpsqu/mP5iCWT6nRQtdt+jl3eymjyjUEtawhp9y9avRdWZxdGFd7VFbjBbWspb9USo27Hy2VmvGCprRPcySANX1aUNt1fBIZleH3r2tB7V1revK+J3eqCatquomKSi0S9PMvI9Uz7NKPfKixzQ3Mrsco36gsxhhueehFvHxkAh2xIH7yN+sR8CmYmE3lbfa2Y0IY/eSmR46CfyF9auSiixS4PZHbbVReO2oOasXffP+4b5j/ndLZgMnSLzgTpy3Sj+bdHwanEvyGblf4N6cfAQiGFOO/sWu8BrTrlL6PtDjLF9S++uvXseEbz/CUPZGbfiy9pZ+O2erYzGl4p8UNY7pxzI1ST2VU/nxK4ZrTjxTUOmIh9GTr5OWeKkJKjbbCkUaRCkMfQKmUWjRoHFt1wjH9qKd9zqSgdmwSOwbGMDydREskgHUrjBNIrOCpoux7MsYs04+AnoIkzHMfCUo/2iu13BuY+Pp2QW1gdBZP7xlCwKfgBx+/CGcsasWp2dTrayb3p1vEeYZulBoF/6Y8A40N6cf5KrU8QY3SrxQ0i00/Dk7N4Q2hiZ8UOWB0P9JiZnwmxa8VUcmb3Y9icLSrkehKTb+meAP2tI1Ss+nB1Oc/prOvLbgfLQI+DTbYN2gcYJDjfpy3pd96+Dk1YOdVag7px0R2nijgbocEcaHUwvdM1L9/qYzK076iUiv3vmp0jmgmqjSKVBieTqiQUhPTI3raJ4jVi1qhKNoq6kdbBgAA71i90NVx8fmPCX2VR46rVnNQE25cVnMfCUodjMSTlitj+kJ1xIxBsTlP+pH643paI1i7VNuyhQK6Wa24hRxgTu9LiI2pMTujSNbdmDIYRcjKXWBQczEqK6My3ni/tr8j+37FBbVN+41zEQ+P6IsSrtSaQmiPhXghnwKfuODRB2Vng9po/qBmtvQD9g7IoTyDunmv2pw2dFmcSmJl6afzZa7d5dbU3Ac1xhiefWPIcFO2V2rzN4qICzI36WdazLVEAtyRK2ZK6OeKoi1ue9uyvWolCmrPvDGEN05O5TxOx047cY/PpEqyQWuxNFxQo5VbKbadAYzN11NC4zWQa3Wnfc1astt9LM9a9x956SgA4Mo1va7es8mk1Gh16/cpOb1YYt3Eau4j0RoN8N/FKgU5ZVGHEf/fbqqIlYI8q89YTywUccU6nWe7FFEB5VNqjIGvnOmLWqhSa3MxKmv/0DTmUiqaQn6ehi62pvb8Xi31SDdZCljiZp0dsRD8PgWd2QXJoWytrDlkodSy19SRMT04Wo3wYozxNNOC5tz0o3lUVr5B3aLCHZ5OQiyVWZ0besysiOlxSikXcl437R/Bx36wBV/42av8MTu3dNhmo1A7x7MVYvnAalGz69gk7n16H6/10ucpukfFPRPHBBOJ36egp5VqavMPavc+vQ/X/2AL/uY/t+f8jBYOva0R3h4zYrNTQyVovKBmU/gtFtH9SMGgJRLgY4fEm724MSMAbhbJqAwhvw9vP73b1XvGTKOyzA2ZIqJSs3M+ApqT0SkFScqjJWJUguLQZCusgtqZWXdnKdKP03kMGTQjU1Hs62PkfgR0swgFw4Jrai72BqP+tDV9rXwRUkxQY4zhT1ml9mfn9AHQFdbEbIoHBroJ0udvqdTCRvdjPqU2OZfmN3xDTa3ZOv04ZFF/E9Ft/emcm7BV+pE+H3MQmeE1J+04ClFqh7K/8x5Bjdhlduwm9ZuDmNP8R/G5sxa/48bfvI6vPb4HP99xDIBxRBZhGIUnTJABIAS1+QWY7z9/AF97fA8A6wklCSFVT7Xb4anqOSAbLqjxlVeBNys7KMAk0ipvCO5ri/JgIqZHzGqHVukAcMkpXYYA5ESTqVF2wqJHjRCVlV3jNeFk67dTavnSj1ZfxDOE1OtIEfl3Uanl29iSCutNoYCtSg369GshbVJqhVj6AXe7X+88qgXzsxa38RV/MTW1QyMzODo+i6BfwfvPXwxAD1i0am8JB3gfnnk243xqapSiawkb3bBdNkaRfHNNxcWAeQqGVWASx9IZHs9+3h3ZQF6IUYSuq2Pjs1BVhkz2D2DhfrTpU8sxhzkENbElZ9aipkaf4XN7NTMMpd3bo/r3WLT0877EbEDvFZRasU7jH205jH95bJd+nKlMzmtRYA8HfVy1V7Ou1nBBLSFMiigFYnrqzWzxWtvaPrehlO9hlZ30IQY1t6lHIFep0cajVrtlizcuO+cj0Zd1S1k1YE8J9UDj6+s9RlbY7R5Aqddi1Nr4jPuaWr7Ga0BPVQH6/Mdi049uRmWRUjurr01o3i+8BvF81vW4dmkHVvdq19KJybnshHvjqh0wpgkBU1ALGdOPA6PiZJx0zs2bm0RMQcpqTzVV1WeK2o2AEyf1U7qTrpmERcCnmqc57W1WaoW4SulaTWWY5v4UgqlZqdkZpKjEQJmaaYehBEallvs70u/yx33DUFXGXb/G75J+vY3OWCu1mWSmqJ00fv3qcXzhES0Ve926pfzx3Ckq5Cj382usmg7IigS1e++9F8uXL0ckEsG6deuwZcuWSrytJaXuU4sEfbwA/+aQ5sRa1BbJsdpqf8+6zrI/O7OvFT5F+wI4TRExY66pvXxkHABwXn97znPFIGTXo0aQrf/EpEX6cVYvUhtf39n9yLefN02RWDOPupqYfsy3Q4CVfd2MOAaLDDfFNF8D+YcaqyrDa9ketbOXtPH0dTFGkT9l+9MuO2UBOmL61PYjY7N8C5IO4byb08/G9KPeJpLOqDnmAvPKm9v5TdeU1VDjsZkkV8BdFrNHAf06mpxLcVW3tFOziDvX1MzpR+3/56PUAODo+IxjUCPD1Lhp/zJKw1NAcXI/xvMENbomhqeT2HNyik/RabOoqU3NpXKUWjTk5wq4UAdkRmX46q9fB2NaQPvf7z2L/8xuvmYk6OPp5WpOFSl7UPvxj3+M2267DV/+8pfx4osv4txzz8VVV12FwcHBcr+1JUkbi26xKIrCV9v7eVCL6lMShIuabrDkQFzYEsG3PrQW/37d+ZYqyw6z+/HlAe0mee6S9pznNpuMIk50NGnHLAYNQpyGYvX6drUtu1YD7oA8VrgDclxMPyZz0yEi9AV0Su0qiiL0qhmVWsFBLU/z9YGROOLJDCJBH1YuaOLXTqH9VKrKuPPx0lO6oCgK+rNBYGB0xjD3kTCnn41KTU9pH5/QtiAKBXxcvZtTgrpSM76mlfuR/m1nU8h2MSkaRej5PKg5pB/NfWG0GOlwqKnZjaQSg9qRsVn+mfgstqmy22aHvu+92fPmlH6cMRhFco9JdHI+v3fYcc/E6bm0vpARvucUXE9MFBZknt07hCNjs2iLBvGlP1sDv08fDZbb4J6r1Oo6/fiNb3wDN954I2644QasWbMG3/72txGLxfCDH/yg3G9tid3Ym/lArsE3h7Q0YK9BqYmmhty61LXn9uHqsxYV9H6iUptLZfB61h5+bn/ueK1C0o9ON2T6cprrdvkmiljtHgAAZ/Zpx2reqcANYvN1Js8wZXJHxmwm9BM0VYR61Yo2iuRxP3KTyKJWBPw+vU+tQKW26/gkxmZSaAr5cU52MbO0U1Pah0dnuIIQFzLmz9+qpjaTzHCTyJL2KF9smVfedkpNdD/SYsOucV9EbIUgVdFvo9RSGb2/Swwa6YzKjR16+tH4bzftH8GZX34c/7HpYM4xTBqU2qzhXmGux1LQNO80TWk+GhDulPYzph9znydeE8/tG+bXfbvwXRIXz2P8M9d/TsG1UAfkQ5sPAwA+cP4SvrCzyiowxvRevqCP9yxWc1RWWYNaMpnE9u3bsWHDBv0NfT5s2LABmzZtynl+IpHA5OSk4U/Jj6kMQY0+bPry2qYfLWbuFQMptdlkBq8dm0RaZVjQHOK9ZiKtBqOIc/qRVoBW0zBsa2p50o/chmyj1A6MxAved0rsUwPc9QLlO+fkgEzxmpr231KPyaI5ijTfM1KkUYSmiLxlZRc3gpCyOTw6w1ft7Yb0o3XjPKDXHKcTaW7nX9IZs00nkbvNXKej9GIyrU+/sGvcF9En9adzlZrJnCCeK7EXVJzQr6cfjQue7YdGkUyrhikshCH9ODZrOyIL0AOHWanRwpVS+a7Tj6ZFjRigAWDLgRFep7JyP86mMrwWKfaSUgtFIb1qxydm8dRuLZP21+v6+eMxi2s1mVG5yzYc8AvXS/n3cbOjrEFteHgYmUwGPT3G0U89PT04ceJEzvM3btyItrY2/qe/vz/nOfOl1FP6ASAWNN4wF7VFLY0iZkt/sehKLYOXB8YBaPU0K3dfIenHVsegZldTcx6TZZd+XNAcRk9rGIyBK023mI/PTVBzqqkBuXuqFe9+tK+ppTIqfv3qcQDAhuw4tKjNuKV8bD2oBcf1q/QJNGJQE+c+Eo7pR67U9J0j+juitkGNbqBmo0g05Oe/Ezlb7Xa8FuGW/kSKP59+H5UZdyUXNwLNqIyn6Sgw+BT99cwTRSjQjlmk2M3pR6dtqnSlZn0tklJzujanRfej6fMXA3R7LIi5lMr3DhQXKuL3m8w94udMDdiF1NR+vHUAGZXh4hWdOGVhC3/cPLwdMC4awgEfV+51q9QK5Y477sDExAT/MzAwUPL3mONOndIrNUJUaqILbpqn8Iw3+ELhQ5QTaW4SsaqnAbo7CsiffuRKzfRFTQsz50ph6ScoBVnoZBFxcgLgPCqL73pt03hNUM0klTHX1ArtU9N+z3gyk1O7eW7vEMZmUljQHMYlqxZkX9+63ykf1BO5qruZP2ZVU+twSD82WTRfpzKMp9GXdMR4ytBcU6MGbgo8IuYG7HyN14B+3sbiKX5DFF9bbHI2B4BJ01DgWCjAPzdzrZL6Fs0GD8BsFJl1XACL2+yI0OKPlJqTkcloFDF+/mKAvvw0rX+VjkdM5QeFFDa14ogLGV5TcxnU0hkVP96q3XdFxyMgpB+F8y8uGsIBH1/k1G1NbcGCBfD7/Th58qTh8ZMnT6K3N9fCHg6H0draavhTaqiPpxBjRj7Eek1rJICmcCBHwaQzKr8Y5p1+FIbP7sgqtXMtnI9AYX1qpDLELUAAY8Cybb5OWO9+bafUANEs4l6pzaUy+hY42S+Qm16gZgdLPyAotZyaWmFKTTzf5uOiJtprz13EHZdWNwo3iNPYCVGpjVm4H8X0c1PID59gfogJv+frJ7TPo7/TWqmlMyr/Hq1YkLuhLXdAZoNTvsZrQE8/Hp+YRUZlUBStNYYWLuLN03yu6DyTgtAGJPtz/h1gr9QYY8aamph+tAhqtFiYmE3xNhBx1+teQanZGZmME0Wsm8hjoQAuO9U4lEHsUwP07yTVGTstjCLUgP3c3iF89Pub8fRua6PeH/YM4fjEHDqbQrj6LOM9mjJSYqpUN4lodUdKR1dzVFZZg1ooFMIFF1yAJ598kj+mqiqefPJJrF+/vpxvbUlGZdh9XJsWQNM8SoFYd6EVmtkoIgaGeacfszfoY+OzfMVsr9S09wr5fXmDKQUexowFbrpphAO+nC84/Z7ijEVCnCruqNQKCGqk0vw+hTeXOik1N5Z+QNhTbZ7ux4Dfx9PDk4Ym8TTfXui95y3mj/M+tQKMIowxroLEG9jijigURbsh7ss6ccVVe3s0yPunzOdDNK0czPZb9nfEeDpJ7Ds6MjaLtMoQDvj4ZyBidgYO5Wm8BnT1QWuprqYwgn6f5Wac5nNF55nMFrGQPvU/R6kldaVmmMua0Lcz0l4rw6dnWNXU2oXvCi3cxF2v+7JbOaUyzHaqiVOfGh1nNOTHZacsMPzM3B5jnudq5X48PjGLrz++Bx/7wRY8t3cY//zL1yyD7Q83HwIA/I8LluQOcXZIP9L5bo8Gqz4qq+zpx9tuuw3f+9738OCDD+L111/HTTfdhHg8jhtuuKHcb53DwZE4ZlOandpqhVksYvqRVmhmowj9NxL0cVVQLKTU6Mu0ckFTzoVOrOxuQl9bBG87rTvvPm3hgJ/f2MQbMqV3zA5GQLsp80ZTkzIR0zlW/5aU2t7BKdeWdn2qQtCwCaodlG6ym/tI6OlHY59aoUYRQJj/KKSefv/6ScymMljeFcO5S3SXakSYIeh26sNMMsNXwWJQCwf8WJS9iYlbkBA+n8L/32phReeIbu79glFE3CPrQLbZf3lXk0HtEWQW+c6z+/GzF4/geLbv0TGomTIA9FyrzTjzKTUtqNG/Mym1rDpKZZjhuqFrNRTQe60oDWs1fSjg9/HPmVKQ4uuJrlC763PGMFHEOv0YC/nR2xbBKQv1NLPZdCVmBwI+xRDkegWl9v8+vQ+MaQvCQyMz2HZozPA6R8Zm8IfsVj4fvtiYegSsjSL0udDn5PMpVR+VVfag9ld/9Vf4+te/jjvvvBPnnXceduzYgd/+9rc55pFKQPbx1b2thobb+SKmH2mFpu/ma1RqYo2rWMw3aLvUo3ZsATz7D1fgex+7wNVrWzkg7UZkAcbdr832ZXqNlnDA8nwv6YiisymEVIbx0VH5EJu589XztJ9lx2S5NIrk1tQKD2pWtn5KPf75eYsNiwtaRDDmfk4hBaxwwJfTqtBvqnF1NBmvN7rhWO3WYNxfzY+OWNCQfqSgeyB7s7dbGL7nnF5Eg37sH4rjtp+8zA0MTulHbbGnnxeaPGK1xYvZVEOLBwpq0ZC+ODM/VwwkYj+mmCYnF/GBYU3t2k0f0hVp9jsubC0VEDIjdg7IaRfpR1pUkVoL+JScz1xcoHSYhpYvaA7xRWdzOIB7PrwW71+rZQr+e/sRw+t8//kDYEx7L6vPVrf0i7sL5I4drHavWkWMIrfccgsOHTqERCKBzZs3Y926dZV42xzIZSeOpyoFogmht9WcfjQqNavAUPD7mepDVpNERAL+3D4bO5yDmnVApsftlJqVSgO0gHjhMm3rFXLz5WNc6NVpchHU3IzJAoT0Y8bofixGqZlt/aPxJJ7NroDfe16f4bli0HTrgKTUY5fFrgtm44Z5qyC6EVsFefFm2d8Rg6IoPBAlMyr/PA+SUrMJau9Y3YNNd7wDn7/qdK64gn6FZzGsUBTFoNZ0pWaVfjQGfzJjiZ+ZnVITA4lo8jAEtQ4Katrvadf+Q/VKWmSYv+PiXmdWmCeKiEpdVJ2AHtTaY7lDy1uEhXKn6fMO+H34xKUr8PbTuvHY312Ga8/twwcuWAIA+NUrx/k5G4sn8XB2C6xPvW2l5fHqQU0/p1ypCalKPiqrSlNF5n+HrSFoD6tS1tMAY/pxEU8/Go0ifNuZEgS1QpRaoVhtcqlPE7E+drtRWVZbZZi5aHknfrfrJLYdHAXevirv8U1Q+jEW4mrDqaZGK/P86UejUiu2+RrQDTeb3xzBpacswK9ePY60ynD24jaDWxHQFGLQryCV0WqS7S5en4YFW234Kga1aNCfozQpNWhVXxUf6882cocDfrRFg5iYTWFoKoH2WIjf7FcsyHU+Eu2xEG6+4hTc+NaV+N2uE2gOB/I6UFujQR6wySkZtlBqOe7HWaNSi4X8XDmYg5qo1ESziOjSpc0uKf1oH9SMDkjKytB5bIkEcGIyd+oJIV63KtMWDvT76vVB7bXedlo33nden+V3vcWg1HK/a//0Z2sM/3/x8k4s6YjiyNgsHn/tBN573mL8x6ZDmE1lcGZfK9566oKc1wD09OOM0CguNl4TXN1XSak1VlDLph/PKHFQE51ji9rNNbUUGGOGXa/nC82bZExLjZyxqCX/P3KJlVKzm/tI6GlA45fXyc5PXLhcU2rbDo1BVZlljUZEXFE35VkJa8fktk+NjCLzm/0I6GahBzcdwo+2DHBlbVZpRCTgRyqTdm3r1+c6WgS1Lj3QdFgsJij9aFVTiwnnaEmH/jrdLWEe1E7taRGCWnPOa5gJBXx8W5x8iNdXT6uDUstTU4uGAlw5mIchi47DcQul1hoJYEk2/UjpdLv0Y4fJEDNt6kPVR8jZpR+Nj88l9aAmplIB7Tx+80NrLV+nkF5UQKt7vf/8Jfi3J/fiv188iivX9OLB7ISVv3n7KtusjtVEkQR3P3pHqXmqT62cDE0lMDiVgKIAq3tLFwQAO6Wm9/0k0mpJg5qiKFx5nNHXWrI5lkCe9KNNPdBuqoiTnZ84a3EbIkEfxmdSfHamE+PCpHJ6Xzd9anknighKjTFWtKUfAD5/9en4X1eehtN6mpHMqBifScGnaCPRrIhY3CycIKVm1Xco1tTaLYIe1Uqsps+IbQ+kVgAYHJCJdIbvt7fcQakVg5h+7CalZrEZ51zSHNSoTy2rboK6UjPPjTQotbhz+pGwU2q8Vy1uNIrQteZU82WM5Vy3ogKaNaUfnRDLAlYLHSs+kN2q6Pm9Q7jnqb0YjSfR3xnFu8/KbbUiLIOaMMyYkEqtQlA9bUVXU95Ve6EYamrZVbq2f5empiaFHbHt6lKFv6cf04k0zhOcdKXAaqoIrVgprWbGrnZgtVWGmaDfh7X9Hdj05gi2HhzDqT3OC45xIaXpxihCfWrmOqQZsaYmpqyKSj9GgrjlHafilnecit0nJvH4zpNY2d3E7dVmogWOytLt/LnGCzH9aLVq//DFS7GkI2aYREKI17EYHEWzyMDoDFSm9bmZ5z7OF/H64kYRcj86GkW0z5jOX1Sw9GdUhnRGRcDvgypMHwGM6UejUcQYrO0WjRRARk3uR1rQOu1gkUir3GXq9ylaS4xwbGKfWj5E048bpQYAy7qacNHyDmw9OIZ//8N+AMCn3rqS7wJvRZSnH6361PRzRPXQoXluTlosDaPUKKidUWKTCABEQ9ppbIkE+I3W51MMN3urYcbzgQJzKetpgHGwLKGPyMpjFLFxPzoFNQC4aLl7s4g41DVfUBNXw/mUmj4mSzXcNItRaiKre1vxmQ2n2qo0QL9ZWO0bZgU1NVs103c1hfjq3qqWGQn68a41PflrakL6UZwqQnWmFd1Nrs1HbrE0ilCfmoWl39wLKqbsxJssLVLMiwar9GNhSo2moJiMItmMBv3X6voUH6PgKB6f2SjihKGm5lKpAdqwYqKrKYQPXug8lpCOZc6hTw0Qr5fqzH9smKBWLpMIoJsQFpncXaLpgu+lViKV+Ofn9uH0nhZccbr7fdjcoKcf9S/dZJ6A3GLTL5bP/UhcuLwTgLugNi4YRfK5H+dS+mo4b/O10KdGta2AT5l3T6Eb+KR+l0FtzGKuI6EoCldrhdzgAOMNdEmnkH4UlNpBoUet1IjXSbepT83KKELKlzdfUyAI+g03WVqkxE22+VGDUYSyEdpiSVwQ2I3U05Wa9jrm2a5Ow765Kzfk585co8NTbyTPh7jYdKvUAODd5yziv9sNly7Pu4CLmjYnBnQFLf5bmtZkHq1WKRonqB0rj50fANat7MJbT12AG99qtMKK6YepRGmV2mffdRoe/+zbDNMDSkExln679OO4S6V2/rIO+BRtUsXxidwNSg2vKfSp5XM/ijexWJ4vbDBAY7LUedn5i6HQSf1W00REKHVY6LVBgb8tGjTVt/SgdmDYfjzWfCF3bXssyJWWlVGE/m4ekyYqNZ9P4QYPUhPi/mWAvVIDjDXHfDW1cZ5+NC5c7QxU2mO6gYmn9SzSj+a5slaY+9Tc0hoJ4ovvOQPXnNWL6y9Znvf5zunHXKU2k8wUvANHKWiIoDaXynATQjmUWls0iP/85Loc+S4GNbMzyqsU43602jtOfI18Qa05HOCLjW0Hxxyfa9mnlqcPKGaac2hF0Ke7Hym4hCsU1Ard/ZrcdnazPN99di+6mkK21mw7aLxXf6cx/WYMatr3qBxBjRZNPcLg44hFapbOE1dqpvQj1aH0UVlaUDMrNbs+NcAU1Ap0P7qpqcWFoQBWnz8FjnyLMfF9gNw+tXx8bP1y3PeRC1zV+t2mH5vCAX4tVcMB2RBBbc+JKahMyxs7jeopNeL2M6V0P5YTfV8rq5paPku/8cvrxtJPXLjMXQpSfM18Y7Lc2vkB8AK5ln4svketGPj2MwVOFLFTan+xdgm2/dMGXJRN67plZbaHzjxHVHSzHcwqNbvG6/lAzdliW0LE4tzM2ig1rrCzNW7unKT0o0mp0dBnIPdaFetqtkotG0Cm5tJIZdQc92OLg6VfHApgZRSaLcAo0mpQaqUxolkRsexTs3YJ8xRkgZuTlgJv32FLxC5hkkipi9tOGJRaidOP5YJmSE5apB/ttsyx+/K6ab4mLlreiQf+dBBbHZRaKqPyNG57LMQbpWkSuvmzpZV7k4sUDu9TE3ZTqHT60WxVtyIptIc4rcqLuc7feuoC/O6zb8uZSkKN0OKGmCvKUFN7x+qF+H8+cI7BmWk50Dhbx+kW0lypjKoPAQ46K7XmcADTibR1+jHmPv3Ymh0QrTJN9U2a0vQ0Es9q92u+4AoFLPfUEwca50McvVdITa1QYhYTRfiYLNM56m4J48BwvCp1NW/fYUvE62U0iTghpuVKbekvF2L6kQJFvqBGXyrzbs9u04+A7oDcfWISk3Mpy/cSA21rJMBvVirTvlzmG0BBSo361FRmWfwuJ4XU1Chl5vcprs5rISiKgtMsWipo8jo1prfHgiWv5QKaA/UvLzKm8CMWN3wK/uIsyem5dI5jkE8Vyf5bqqktbo9iz8kpxLODoYN+JedaFZvP7Ywifp+C9lgIo/EkxuKpgpqvZ4QAa7X5ZiF9agtbwnjrqQvQEgmUdSGmB7VcpWZO1XcLjtlK0xDpx3KaRJxo4TMA0yV3P5YL+lKnsz09KUG52KlMShsdHZvl8+u0lbP9tjNmFrZGsKwrBsaAFw9Zq7VxobYX8GvDfEmQWI0icrvrNSD2qVVeqRXSpzYyTc7HYN46Yanw+fR9soDyOB/tMKstQD9PrZEgv9FOzaX54zyomeY/klLraYvwIb/jM0nMJDM8YNNiaomL9COgZyFG40nb5mura1MctG31+RdiFPH5FPznJ9fh36+7oKyZKPE46XtuVVMDqmvrr/ugpqpM71GrsFJrFeY/1kpNLRr081TcxGzKsMq0M7n0d2r7eMWTGe7ME40mbtUp1dUe2nzYsk7GTSLZG4miKGimDVMTuQGBVubu0o/Up6bX1Ky2HCkHdhPlrSClVs40kxWiKiqHScQOK6UmTnvRd5hPcXVDgcB8XmkbopZwgE9bGZ1J8mtVnIDvxigC6CngsZlkTomh1VVNTVdqc0U2X1cKOk5V2FFizsZURSnrajRg131QOzw6g3gyg1DAh5UV/DIC+sU9NpPkF4FdCs8riNPSJ2Z1g4sW7Kwvl3DAj77sJBXatHRCUFVut/m59txFAIDf7TqJK7/xTM7uvHyYsbDzr5MDsrD0I/WpVU+pmYPavsEp/PrV44bH8tn5y0X1g5qg1ITgJe6IwB8PklIz19RIHfn5wmgsnjKkHknptMd0Feik1EQH5LRp4Soamcx75elDAWyMIin36cdKIX4f6FzzMVm2Sk0GtZJzbHwWsZAfq3tbHEfAlAMKajQrD8i/BYoX4JtczqZ4nSyfwYXMBYeyzbnjLkZkmbn89IX4z09ejP7OKI5NzOGGB7bi8z99GWo2NWRWagAcHZBup4kAuvsxLTRfV6qmZmfp/+yPX8bf/vBFvHJknD82mp2nV+mgJrqGy+F8tMNKxVIdJyoqtdm0boPnln5j+nGGNzQHuMIaF5SaeK0qisJTkHY1NUBXaicm5pDMGpd4TS1snP8qMp3Qj8WqpqabXrxzvwj4fVy1UtC1VWqtMv1YNi45ZQF2/vNVuP/jF1X8vWkVSUEtGvRXPLAWgzj/0W1QW9ZFQU1TaoXY+UXeemo3Hr/1bbjxrSvgU4Cfbj+CF94cAaAHNXHyhNNUkXgBKRy9T02tuKXfSo0AwNHsdUM1YSC/nb9cGJRaBWtqfJPQtIVSC/r5tTASTyCTXfzYpR/13jA/Tz+OzaRsJ99ct24ZzlnSxifeWEFK7fDoDH+MUuI0/xXI7VUTF1xmpaaq+sLKS0oNQE4Atq+pVW+qiPfvsCVA22K8cv1phJ7vrw07PyE6ILnzMU9wWpa90dGXuxDno5lYKIAvvmcN3neeNkn8uX3DhtcUt7N3mioipnjyYdWnVs2JIowx/vu+md3qBdCH51oNMy4n3QalVtrp/E6Ym68NOyiEfLxee1Ko3dgZRUSlRtvyjM0kbRdg11+yHL+45TKDScYMzX+k6745HOAGHp9Pr/maF116KlSoqWV/L/E68FJNDchNlds5hUnZj8+kuLKuFA0R1KqF2SDh9WkiRKtFUMtn9iClRrMBC+lRs+Oy7ESM5/eaglpMVGpZ95tDUHNTUxP71OYEI0IlsKqpxJMZrjz2D+pb8owKu15XEprIv6A5XNG2FLPaSmb0eZ7RoJ+bMajJV5zXqdfUTEot5OcKyy796Baa/3hkTA9qIna2frH5mjc1ZwMd/VdRKpctcEvMpNRo0LRZqbXHgjxVWempIt46Y3WGWZm1eNz5SLRFdYWZb5oIQTW1wyPzV2oEbWG/89gExuJJ3iwrGkWoR85SqfFtZwoxisxvL7VioAkYYt1IdI+KSo1b+isc1NYu7UBbNIirzuyp6Puad76eExp/NfcjKTUtqIkWeL1PzWjpjwkDi8dmUkWnygE9DTyc/VzMC1c7W7+YfoyZaqpierWSwyLcoAdg7fjtlJqiKFXrVauNu2yNkhPUPO58JESjCN3sW13W1Eay/TpuJ/Q7sbA1gtN7WrDn5BT+uH9YH5AsGkWySs3aNp0xPMcJPf2oVtwoYmVbnxAmyB8enUEinUE44K+aUutti2D7P22oeE2YlAqpLVpwkCKj7xilH8UalF6PMzZfN4UCyMQ0uTcWT/KWj6KUmulzsFNq5pqalTOXtx6k3E/orzQxU6rUrqYGaCnro+OzGKywrV8qtTIiFooB7/eoEWJNTR9m7PyFb4kE+Y320Ei8JEoNAC7NqrU/7hs2DDMmnNyP4iiifPD0ozDQOFpho8isjVLLqIwr4Gr1qQGoisnJbKIxt1vQoomUmliDsldqfkNNTV+AFf79NI8qs1vI5qYf9QBrTj8X0nhdaXKMIhZT+gm+WWiFHZAyqJURcaNQoIZqahZ9am5Sp0sFB2SpghpNmn9u77CefoxZ9KnN19Lv05Vaolo1NSG1JgY1ANg/FIeqMr5bc6WVWrWgwDSX1qZYUGoukr25UgaB+vdEc0+uUYSUe4DXwsZnUvOrqTXlCWo216ehpmYKFPq+cN67X5gDMO9Ts/iukK1f1tTqDLHZuibdjwn3acRlnaUPautWdiLoV3BkbBYDY7M5r+nkfuRTGQoak1WFPjWL9ONkTlCbxsRsiptH2gvcYqRWoc+AMc0kkqPUTBkEUd1ETLMfxa2IKBiNzdMo0moaLpCTfrQIaowxw3Blc0rPy0pNrP+pKuO9edZKrTq2fhnUyowYyGrHKCLu2O2+HWEpt/XHeU1ovkEtFgrg/KXasOOMMFCXcFJq0wVY+vUxWWrFjSKWNTVTUHtzKM7t/C2RgOOUi3qC6mKAloI0t1uYr0uxDmWn1JpCulFkYjZl2f/oFkVReCoTME7MF49PHPZt3pFdV+oU1LxbUxPTj2JDudXeg9UyijTGN6OKiF+6mkk/Gpqv3e8usJxs/cOlU2oAcja7FF/Tbi83xliBlv7q76eWVhnfTofOH/VI7R+arppJpJoE/QofPpxIZXLTj6bry5h+1E0mojqKhf3cQasy4Mh4bgagEDoE1ZzjfrSw9NO1qiha4DIPCi5kQn+loW19ZlMZQ/+ZeUwWUL2hxjKolZkWQ/qxttyPWk3NnaUf0B2Qh0dnhEbp+d+ALzu1m/89EvQZFBQPaqZCfDKj8snrbhpYefpRrcJ+aiH9a0jvTefv/KXtALSgRnb+aphEqoWiKAaziNnE46jUeI+b5mil8YtNIU3p0rWTzCqOooOa8HmYXcJWi664YGBSFIUHaBoUrKcfvbcIpvaT2WSGp+n9PsXSRMTTj9L9WF8YlFqNpB9p9ZtIq7zI6yr92KmlH49NzPKbTymU2tmL2/jNwhwkaSVsrqmJU/tdTen36bMfK72fWsjv42pkzhTUzu1vh6JolvA3Tk4BaKygBoi7X2dy0o+5NTX9Oo3w9GPGEFTo35oHAxR7rYoOSPN33GoD3WnBJCIeD6B9/nyYsYfmPhK0QJxN6krNSqUBulFkeFofYVYJZFArM7WYfmwJ586sc7O7wILmEJpCfr4iVpTSmGP8PgWXrNJSkOYbURNvbrV2l0WCPldWdFJqhin9FUr/GNRI0ph+7G4Joz+7YeXWg6MAGi+o8TRiKnfaSyzkNxg1DOlH3uOmGupUNMZKTBv6TU7lQhCVmvk73iJsP0XETa0mQb+Pt5TMpjIF7Xpdaej8zqT0mppVPQ3Q0uRKdmfwkXjl1FrZgtpdd92FSy65BLFYDO3t7eV6G89jSD/WiFLz+ZScIOYmqCmKws0igPb7lmojy7eepgW1rmaThVpwP4rbe1D9xE2PGmDsU+M3zkDlbipmq7RYk1zZrZ1T2jy10nMfq42o1MypYUVRDAsnS6NIShWGGevPNacNi53eQfMfAXv3o7jo4tem8NyIYBYx7+DtJcQdJeYcetQAra+xK3utVjIFWbaglkwm8cEPfhA33XRTud6iJjC4H2ukpgbkpmLcqkyy9QPGyR/z5QPnL8Gn374K/+vK0w2P041BZabZiQWYRAC9Ty1tGJNVuUSGuQF7clZvNF/V3QxAH4LbSEYRQL9pzqUyvJcvItzwxe+YlaV/Lq2rHzEVLboW55MmFxWfOTOhDwfQ3Y/ibgH8uIX5j142inBLfyrt2KNG6A3YlQtqZZMOX/nKVwAADzzwgOt/k0gkkEjov/zk5KTDs2sDMZDVSvoRME5XaDKleJwgswhQmnoaEQn6cfs1q3Mej4X8UBStj2k6keY5/2mLlbkTlH6cSaZ5+jRSwZuKeVL7hDASjJQaUem5j9XG2iiifzZaFkFzMNoqNYttiMRgNJ9xbmI62LxwbbEwMlkNBRA/fy8bRcThy1R7dtpvbmFrGLuOV9YB6ama2saNG9HW1sb/9Pf3V/uQ5k1rDRpFAFODcwEKc5mQfixlULNDUYTtPYQbx0wid2XuBFn6xdpHJdOPpCrI1i2mH0mpEY2m1MRJ/VbbAhmUmo2lf8ZkzgCM9dl5KbUmJ6OIXlOj9LjV3Ecx/ezl9GOsgPQjINj66yH9WAx33HEHJiYm+J+BgYFqH9K8qUX3I2AOau6Pu1xKzQndAamnH61uHE4EfHpNDdCMA1RnqwR8qki2pkLHYRXUGs0oIjanz1pM2xDrvVELpTZXbqXmok8treq7X1vV98Ra1WwNDDSedWEUAaozVaSgoHb77bdDURTHP7t37y76YMLhMFpbWw1/ah1aqRWSwvMCxQa1pZ2VD2q6A1KsW7if+wjoSo2IBHwV3fZDrKmRSgv6FUSDfixoDhk+g4YLasLu11bTXsRMghi0xAn/8TIqNfo8FCXXmBQL+nm7Bg2jFkdkEVZKrVJ9koUgGloSNnupiZCtv5Lpx4Kkw+c+9zl8/OMfd3zOypUr53M8dUd/Rww+BQZXYC3QWmTTeF97FEG/glSGoa0EjdduaA7nKrV4gSmcgEmVVdpOLdaNxNQjBdZV3c3YMTAOoAGDmjDD0aqmls/9qDLdeGOn1OYT1JZ0RPG+8/qwsDWSs3D1+RSs7G7GvsFp7D4+hUVtUZ5FEI9VHJU1a6EqvYLYpzbHa2r5jSKVVGoFnbXu7m50d3fnf6KE09sWwS//znlLeC8ipmMKSc34fQr6O2J4czheufQjn9qQq9QKdT8STl/UchC1UGrieV/Z3YQdA+MIB3yeTEuVEwr4ibQw+1GYwiKeJ6tNQgF9ir9YYxUXB/O5VhVFwTc/tNb25+csbsO+wWm8cmQCV6xeyOt7VkYRg1Lz4Ocs9qm5GSfXnU0/VtL9WLaa2uHDh7Fjxw4cPnwYmUwGO3bswI4dOzA9PV2ut/QsZ/a1oac1Uu3DKIhi048AsDJbA6KBpuVGD2q5ln736UfjCruSdn7AOKnfam4m1dW0htbaSWOXAqOlP7eHsDWPUQQAn5spLnJKlX7Mx9lL2gAArx4dB2DtzBUt/V42ilCgzaiMK063Sk3sIy0nZdO3d955Jx588EH+/2vXaiuZp59+Gpdffnm53lZSIuYT1D5/1ek4a3Errj6rt9SHZUmTlW2abzvjNv1oDGKVXiWLlm6roHZaTwsAbTfwRsNgFCF1YGMUEQOBoigIBXxIplVezzI0X5co/ZiPc7JB7ZUjEwCsswji5z/r4Sn94jHROXVWalpQS6ZVTM6mS9q7akfZgtoDDzxQUI+axFsY0o8FNo2f3tuC03tbSn1ItrRYzH8sVKkFTLWQStr5AT1VNpvM8PqPeKO94vRu/P07T83ZsaARCAv1xryWflMgCGeDGik18aYcC/kR8vuQzKhlDWprFrXBp2hq5eTknGAUsW6+nqnwmLZCCPp9CPgUpFXGt5dyUmqRoB+tkQAm59IYnJqr7aAmqW3mo9QqDTnaxKG106b5evkwux8rrtQsamriZxDw+3Dbu06r6DF5BWOfmmZOMDRfR63dj4B2w51CWk8/Cj9XFAW9bREcHp3habJyEA35cVpPC3afmMLLA+OW1yZdb+MzKd7870WjCKAd69Rcmiu1cJ5U/eeuPB0Bv1Ixg5M3z5qk6tRSUKONGcWgxjeEdJl+9PsUPpkE8IZRpFJGG69jZekXFx12zdeAHhDJKGJOR3/9g+di3+A0Tu0pb2bhnCVt2H1iCq8enbBOP2aPe1QY/OtFSz+gHZcW1LTrNF9W4/pLllfgqHQ81Xwt8Q5t80g/VhpK41iNInLrfgT07WeA6ln6EyZLv8Sm+Vq44dN2RNFgbi8omUVozzSzcr94RSf+et3S8hy4wNlL2gFodTWn5msKvuGAz7N9rTGuKt0ptUrj7SW4pGq01tAgZj5RJGm1Z1UBQc2vIHvPtN0jqlyISk1NanJxPlMu6gkx/WjVfN3fGcUNly7Hko5Yzr81K+5qmS/OWUxmkXFhSr84fDkb1KZza39eg2ZSjvGamgxqkhog4PehJRzAVCJtGG7sRWj1Lc5t5OnHAuoSmgMy96ZZCcjNN5vMYC5dug1W6wH6LMhAAxiVtKIo+PK1Z9r8W+MNt5BFTilZvagFQb/CAwFgNDFRENMNLd79ztGu45RRqPR3JR/eCrEST3HzO07Be8/rw6kLK+dkLAZSalNz+g3DvLuwG8RetWoZRebSsqZmhgKTGBDcKmmzUqtWUAsH/AZHsE8xplDN++l50flI5JpxvBVGvLsckFSdT799VbUPwRW0M/ShkRnEE2nemwQUNkRanCpS6fRjRLD0k1VaBjUNCkzktgv6FVe7mQO59R63uzaUg7MXt2Pn0cnscRg3JTWbQrycfjQrM6nUJJIS098ZQ39nFGmVYcuBUcwIk0UKSeOI8x8ruZcaIN2PTlDAp/RyITdRs4qIVXGnDGrCBnIVo/l686rzEcgNuF5Tat46GomkSC5ZqTUl/2n/MKazhfiQ34dQAV84sVet0s3XkaBeUxG3nZHkphALueGbA2A1g8XZi/WgZm4tMAcKLys18zmsdPtLPmRQk9QFl5zSBQD4474Ry21G3CBOFanWmCxSIwGf4ukbWyXJCUwFnBdRRVhZ/ivJ6b0tfJFlTovnph+9Wxkyn/9Kz0nNh7eORiIpkktWaUpt1/FJHBmbAVD4jUGs01T6i2q+cYvbzjQ65s+iELUlqohCFzmlJuj3Yc0ibY9IsyvX/Dt52ygilZpEUna6W8I4rUebZP/71wcBFL7TuOh+rHT60XxTk6lHnfkYE8SA6AX1Q3W1fDU1L6v0nPSjVGoSSXkgtfb7XScBzC/9WC2jCCEbr3XmUxczKrXqB7UrTl8IAFizyNgmU9PpR6nUJJLycOkpWlCjXXYLvYkFqmgUMTvIpFLTMbdXFFtTq6adn7hi9UJs/sI78VnTcOqg32fIFHhaqZnTj1KpSSTl4eIVnRB9AIVMEwGq23zt8ymGG7AMajqB7HYnREFKTUw/ekCpAUBPa8SyXioqUi8HNfOxSaUmkZSJtmiQD44FilBqvuoZRQBjIJVBzYgY8AurqQnpRw8HCsA0YcTDxxoNmiaKSKUmkZSPS1d18b83F1hTMyi1KvQzie8pg5oRMTgVsuAQg6GX61RA7gamXsUccEMup7tUCm8djUQyT8gsAhSebjIqtcrfVCIyqNkinptatfTnw/g7ejcAiwE3FPDB57EtcmRQk9QVFy7vsG1wzUegipZ+QAY1J8QUVyGpOa9Z+p2I1opSE65Tr43IAmRQk9QZkaAfFyztAFC4Ld4wJitUhZqacAOWln4j4iKjsNmP+nMLTUdXmmiNGEXE4Ou1xmtATumX1CH/eM1q/GTbAN59Vm9B/44cdopSnTqBNIrYIyquwtKPtaPUxEDmZaOIeJxeG5EFyKAmqUPO62/Hef3tBf+7YPYGGA36qzKiSlQjMqgZiRTpDBTTlrVUU/NyAJbpR4mkRghmlVq19ocSp5i0xWRQEymFUcTLgQKQ6cdSIYOaRJKFJopUa3sSaem3R0xzFTv70etKLVoj6ceQ38eHHHgx/ei9I5JIqgS5H6vVTEo3CL9P8XyjcKURFUFhY7JqSKmJ7kcPbxKqKAo/l1KpSSQeJpjtU6vW2B9SanLbmVwMSq2AOo6hpub1oJb9/EN+n2EOqRehACyVmkTiYUipVSv1IwY1iZGSKDWvpx+zn7+XU48EHWtDKbWDBw/ik5/8JFasWIFoNIpVq1bhy1/+MpLJZLneUiKZF9SnVq3VJxlFZI9aLsUbRfTPstBm/EpDwczLJhGCjtFrcx+BMlr6d+/eDVVV8Z3vfAennHIKdu7ciRtvvBHxeBxf//rXy/W2EknRUJ9atYwilPaUSi2XYo0i4YAPq3tbEE+m0dkUKsehlYxaUmr0GXhtQj9QxqB29dVX4+qrr+b/v3LlSuzZswf33XefDGoST0I3k2ptJnnOkjYEfArWreisyvt7mWL71BRFwS//7jKojBkmxngRqdRKQ0W/vRMTE+jstP/CJhIJJBIJ/v+Tk5OVOCyJBABw9Zm9eHlgAh9dv6wq73/h8k68+s9X1cRKvdKI5pBClbTXgxlxxqJWhPw+nCtsn+RV9Jqa985txYLavn37cM899ziqtI0bN+IrX/lKpQ5JIjGwsDWC//OX51b1GGRAs8a49Ux9nqNV3c148c531UQ7h+5+9N6xFhxmb7/9diiK4vhn9+7dhn9z9OhRXH311fjgBz+IG2+80fa177jjDkxMTPA/AwMDhf9GEomk7qCbZyjgg99jW52UkuZwoCbaOdqzE29aIt4z3xR8RJ/73Ofw8Y9/3PE5K1eu5H8/duwYrrjiClxyySX47ne/6/jvwuEwwuFwoYckkUjqnHCgutNeJEY+cekKxEIB/MXaJdU+lBwKDmrd3d3o7u529dyjR4/iiiuuwAUXXID7778fPp/38q8SicT7cLedB40JjcjK7mZ84d1nVPswLCmbdjx69Cguv/xyLFu2DF//+tcxNDTEf9bbW9iWIBKJpLHRnYHeS3dJvEXZrpAnnngC+/btw759+7BkiVGiMsbK9bYSiaQOOa+/HVed2YPLT19Y7UOReByFeTjCTE5Ooq2tDRMTE2htba324UgkEomkSriNBzJBLZFIJJK6QQY1iUQikdQNMqhJJBKJpG6QQU0ikUgkdYMMahKJRCKpG2RQk0gkEkndIIOaRCKRSOoGT7fnUwud3IJGIpFIGhuKA/laqz0d1KampgAA/f39VT4SiUQikXiBqakptLW12f7c0xNFVFXFsWPH0NLSMq/tGCYnJ9Hf34+BgQE5mcSEPDf2yHNjjzw3zsjzY0+x54YxhqmpKfT19TkOx/e0UvP5fDlzI+dDa2urvMBskOfGHnlu7JHnxhl5fuwp5tw4KTRCGkUkEolEUjfIoCaRSCSSuqEhglo4HMaXv/xluau2BfLc2CPPjT3y3Dgjz4895T43njaKSCQSiURSCA2h1CQSiUTSGMigJpFIJJK6QQY1iUQikdQNMqhJJBKJpG6QQU0ikUgkdUPdB7V7770Xy5cvRyQSwbp167Bly5ZqH1LF2bhxIy666CK0tLRg4cKFeN/73oc9e/YYnjM3N4ebb74ZXV1daG5uxgc+8AGcPHmySkdcPe6++24oioJbb72VP9bo5+bo0aP4yEc+gq6uLkSjUZx99tnYtm0b/zljDHfeeScWLVqEaDSKDRs2YO/evVU84sqQyWTwpS99CStWrEA0GsWqVavwL//yL4aBu41ybp599llce+216Ovrg6IoePTRRw0/d3MeRkdHcd1116G1tRXt7e345Cc/ienp6cIPhtUxDz/8MAuFQuwHP/gBe+2119iNN97I2tvb2cmTJ6t9aBXlqquuYvfffz/buXMn27FjB3v3u9/Nli5dyqanp/lzPv3pT7P+/n725JNPsm3btrG3vOUt7JJLLqniUVeeLVu2sOXLl7NzzjmHfeYzn+GPN/K5GR0dZcuWLWMf//jH2ebNm9mbb77JHn/8cbZv3z7+nLvvvpu1tbWxRx99lL388svsz//8z9mKFSvY7OxsFY+8/Nx1112sq6uLPfbYY+zAgQPspz/9KWtubmbf+ta3+HMa5dz8+te/Zl/84hfZz372MwaAPfLII4afuzkPV199NTv33HPZCy+8wJ577jl2yimnsA9/+MMFH0tdB7WLL76Y3Xzzzfz/M5kM6+vrYxs3bqziUVWfwcFBBoA988wzjDHGxsfHWTAYZD/96U/5c15//XUGgG3atKlah1lRpqam2KmnnsqeeOIJ9va3v50HtUY/N//4j//ILrvsMtufq6rKent72de+9jX+2Pj4OAuHw+xHP/pRJQ6xarznPe9hn/jEJwyPvf/972fXXXcdY6xxz405qLk5D7t27WIA2NatW/lzfvOb3zBFUdjRo0cLev+6TT8mk0ls374dGzZs4I/5fD5s2LABmzZtquKRVZ+JiQkAQGdnJwBg+/btSKVShnO1evVqLF26tGHO1c0334z3vOc9hnMAyHPzi1/8AhdeeCE++MEPYuHChVi7di2+973v8Z8fOHAAJ06cMJyftrY2rFu3ru7PzyWXXIInn3wSb7zxBgDg5ZdfxvPPP49rrrkGQGOfGxE352HTpk1ob2/HhRdeyJ+zYcMG+Hw+bN68uaD38/SU/vkwPDyMTCaDnp4ew+M9PT3YvXt3lY6q+qiqiltvvRWXXnopzjrrLADAiRMnEAqF0N7ebnhuT08PTpw4UYWjrCwPP/wwXnzxRWzdujXnZ41+bt58803cd999uO222/CFL3wBW7duxd///d8jFArh+uuv5+fA6ntW7+fn9ttvx+TkJFavXg2/349MJoO77roL1113HQA09LkRcXMeTpw4gYULFxp+HggE0NnZWfC5qtugJrHm5ptvxs6dO/H8889X+1A8wcDAAD7zmc/giSeeQCQSqfbheA5VVXHhhRfiq1/9KgBg7dq12LlzJ7797W/j+uuvr/LRVZef/OQn+OEPf4iHHnoIZ555Jnbs2IFbb70VfX19DX9uqkndph8XLFgAv9+f41I7efIkent7q3RU1eWWW27BY489hqefftqwT11vby+SySTGx8cNz2+Ec7V9+3YMDg7i/PPPRyAQQCAQwDPPPIN/+7d/QyAQQE9PT8OeGwBYtGgR1qxZY3jsjDPOwOHDhwGAn4NG/J59/vOfx+23344PfehDOPvss/HRj34Un/3sZ7Fx40YAjX1uRNych97eXgwODhp+nk6nMTo6WvC5qtugFgqFcMEFF+DJJ5/kj6mqiieffBLr16+v4pFVHsYYbrnlFjzyyCN46qmnsGLFCsPPL7jgAgSDQcO52rNnDw4fPlz35+qd73wnXn31VezYsYP/ufDCC3HdddfxvzfquQGASy+9NKf944033sCyZcsAACtWrEBvb6/h/ExOTmLz5s11f35mZmZydmD2+/1QVRVAY58bETfnYf369RgfH8f27dv5c5566imoqop169YV9obzsrl4nIcffpiFw2H2wAMPsF27drFPfepTrL29nZ04caLah1ZRbrrpJtbW1sb+8Ic/sOPHj/M/MzMz/Dmf/vSn2dKlS9lTTz3Ftm3bxtavX8/Wr19fxaOuHqL7kbHGPjdbtmxhgUCA3XXXXWzv3r3shz/8IYvFYuy//uu/+HPuvvtu1t7ezn7+85+zV155hb33ve+tS9u6meuvv54tXryYW/p/9rOfsQULFrB/+Id/4M9plHMzNTXFXnrpJfbSSy8xAOwb3/gGe+mll9ihQ4cYY+7Ow9VXX83Wrl3LNm/ezJ5//nl26qmnSku/Fffccw9bunQpC4VC7OKLL2YvvPBCtQ+p4gCw/HP//ffz58zOzrK//du/ZR0dHSwWi7G/+Iu/YMePH6/eQVcRc1Br9HPzy1/+kp111lksHA6z1atXs+9+97uGn6uqyr70pS+xnp4eFg6H2Tvf+U62Z8+eKh1t5ZicnGSf+cxn2NKlS1kkEmErV65kX/ziF1kikeDPaZRz8/TTT1veY66//nrGmLvzMDIywj784Q+z5uZm1traym644QY2NTVV8LHI/dQkEolEUjfUbU1NIpFIJI2HDGoSiUQiqRtkUJNIJBJJ3SCDmkQikUjqBhnUJBKJRFI3yKAmkUgkkrpBBjWJRCKR1A0yqEkkEomkbpBBTSKRSCR1gwxqEolEIqkbZFCTSCQSSd3w/wMnDwWE/UmPTwAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ts_length = 100\n",
+ "ϵ_values = []\n",
+ "i = 0\n",
+ "while i < ts_length:\n",
+ " e = np.random.randn()\n",
+ " ϵ_values.append(e)\n",
+ " i = i + 1\n",
+ "plt.plot(ϵ_values)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bae1c9a2",
+ "metadata": {},
+ "source": [
+ "A while loop will keep executing the code block delimited by indentation until the condition (`i < ts_length`) is satisfied.\n",
+ "\n",
+ "In this case, the program will keep adding values to the list `ϵ_values` until `i` equals `ts_length`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "7f9acae6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "i == ts_length #the ending condition for the while loop"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6a4977e",
+ "metadata": {},
+ "source": [
+ "Note that\n",
+ "\n",
+ "- the code block for the `while` loop is again delimited only by indentation. \n",
+ "- the statement `i = i + 1` can be replaced by `i += 1`. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9fe1230f",
+ "metadata": {},
+ "source": [
+ "## Another Application\n",
+ "\n",
+ "Let’s do one more application before we turn to exercises.\n",
+ "\n",
+ "In this application, we plot the balance of a bank account over time.\n",
+ "\n",
+ "There are no withdraws over the time period, the last date of which is denoted\n",
+ "by $ T $.\n",
+ "\n",
+ "The initial balance is $ b_0 $ and the interest rate is $ r $.\n",
+ "\n",
+ "The balance updates from period $ t $ to $ t+1 $ according to $ b_{t+1} = (1 + r) b_t $.\n",
+ "\n",
+ "In the code below, we generate and plot the sequence $ b_0, b_1, \\ldots, b_T $.\n",
+ "\n",
+ "Instead of using a Python list to store this sequence, we will use a NumPy\n",
+ "array."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "f3463484",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "r = 0.025 # interest rate\n",
+ "T = 50 # end date\n",
+ "b = np.empty(T+1) # an empty NumPy array, to store all b_t\n",
+ "b[0] = 10 # initial balance\n",
+ "\n",
+ "for t in range(T):\n",
+ " b[t+1] = (1 + r) * b[t]\n",
+ "\n",
+ "plt.plot(b, label='bank balance')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9278f1b9",
+ "metadata": {},
+ "source": [
+ "The statement `b = np.empty(T+1)` allocates storage in memory for `T+1`\n",
+ "(floating point) numbers.\n",
+ "\n",
+ "These numbers are filled in by the `for` loop.\n",
+ "\n",
+ "Allocating memory at the start is more efficient than using a Python list and\n",
+ "`append`, since the latter must repeatedly ask for storage space from the\n",
+ "operating system.\n",
+ "\n",
+ "Notice that we added a legend to the plot — a feature you will be asked to\n",
+ "use in the exercises."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e323dec",
+ "metadata": {},
+ "source": [
+ "## Exercises\n",
+ "\n",
+ "Now we turn to exercises. It is important that you complete them before\n",
+ "continuing, since they present new concepts we will need."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf406a06",
+ "metadata": {},
+ "source": [
+ "## Exercise 3.1\n",
+ "\n",
+ "Your first task is to simulate and plot the correlated time series\n",
+ "\n",
+ "$$\n",
+ "x_{t+1} = \\alpha \\, x_t + \\epsilon_{t+1}\n",
+ "\\quad \\text{where} \\quad\n",
+ "x_0 = 0\n",
+ "\\quad \\text{and} \\quad t = 0,\\ldots,T\n",
+ "$$\n",
+ "\n",
+ "The sequence of shocks $ \\{\\epsilon_t\\} $ is assumed to be IID and standard normal.\n",
+ "\n",
+ "In your solution, restrict your import statements to"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "4da50a38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53e448a3",
+ "metadata": {},
+ "source": [
+ "Set $ T=200 $ and $ \\alpha = 0.9 $."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8eeda2bc",
+ "metadata": {},
+ "source": [
+ "## Solution to[ Exercise 3.1](https://python-programming.quantecon.org/#pbe_ex1)\n",
+ "\n",
+ "Here’s one solution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "4578e185",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "α = 0.9\n",
+ "T = 200\n",
+ "x = np.empty(T+1)\n",
+ "x[0] = 0\n",
+ "\n",
+ "for t in range(T):\n",
+ " x[t+1] = α * x[t] + np.random.randn()\n",
+ "\n",
+ "plt.plot(x)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1ee91d3",
+ "metadata": {},
+ "source": [
+ "## Exercise 3.2\n",
+ "\n",
+ "Starting with your solution to exercise 1, plot three simulated time series,\n",
+ "one for each of the cases $ \\alpha=0 $, $ \\alpha=0.8 $ and $ \\alpha=0.98 $.\n",
+ "\n",
+ "Use a `for` loop to step through the $ \\alpha $ values.\n",
+ "\n",
+ "If you can, add a legend, to help distinguish between the three time series.\n",
+ "\n",
+ "Hints:\n",
+ "\n",
+ "- If you call the `plot()` function multiple times before calling `show()`, all of the lines you produce will end up on the same figure. \n",
+ "- For the legend, noted that the expression `'foo' + str(42)` evaluates to `'foo42'`. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "682f5011",
+ "metadata": {},
+ "source": [
+ "## Solution to[ Exercise 3.2](https://python-programming.quantecon.org/#pbe_ex2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "c74097b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "α_values = [0.0, 0.8, 0.98]\n",
+ "T = 200\n",
+ "x = np.empty(T+1)\n",
+ "\n",
+ "for α in α_values:\n",
+ " x[0] = 0\n",
+ " for t in range(T):\n",
+ " x[t+1] = α * x[t] + np.random.randn()\n",
+ " plt.plot(x, label=f'$\\\\alpha = {α}$')\n",
+ "\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6cd8434a",
+ "metadata": {},
+ "source": [
+ "Note: `f'\\$\\\\alpha = {α}\\$'` in the solution is an application of [f-String](https://docs.python.org/3/tutorial/inputoutput.html#tut-f-strings), which allows you to use `{}` to contain an expression.\n",
+ "\n",
+ "The contained expression will be evaluated, and the result will be placed into the string."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "710b5d7c",
+ "metadata": {},
+ "source": [
+ "## Exercise 3.3\n",
+ "\n",
+ "Similar to the previous exercises, plot the time series\n",
+ "\n",
+ "$$\n",
+ "x_{t+1} = \\alpha \\, |x_t| + \\epsilon_{t+1}\n",
+ "\\quad \\text{where} \\quad\n",
+ "x_0 = 0\n",
+ "\\quad \\text{and} \\quad t = 0,\\ldots,T\n",
+ "$$\n",
+ "\n",
+ "Use $ T=200 $, $ \\alpha = 0.9 $ and $ \\{\\epsilon_t\\} $ as before.\n",
+ "\n",
+ "Search online for a function that can be used to compute the absolute value $ |x_t| $."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1cd2da19",
+ "metadata": {},
+ "source": [
+ "## Solution to[ Exercise 3.3](https://python-programming.quantecon.org/#pbe_ex3)\n",
+ "\n",
+ "Here’s one solution:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "8996551f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "α = 0.9\n",
+ "T = 200\n",
+ "x = np.empty(T+1)\n",
+ "x[0] = 0\n",
+ "\n",
+ "for t in range(T):\n",
+ " x[t+1] = α * np.abs(x[t]) + np.random.randn()\n",
+ "\n",
+ "plt.plot(x)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "97ff965f",
+ "metadata": {},
+ "source": [
+ "## Exercise 3.4\n",
+ "\n",
+ "One important aspect of essentially all programming languages is branching and\n",
+ "conditions.\n",
+ "\n",
+ "In Python, conditions are usually implemented with if–else syntax.\n",
+ "\n",
+ "Here’s an example, that prints -1 for each negative number in an array and 1\n",
+ "for each nonnegative number"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "6080e122",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "numbers = [-9, 2.3, -11, 0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "165fb094",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-1\n",
+ "1\n",
+ "-1\n",
+ "1\n"
+ ]
+ }
+ ],
+ "source": [
+ "for x in numbers:\n",
+ " if x < 0:\n",
+ " print(-1)\n",
+ " else:\n",
+ " print(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e5a8f5fd",
+ "metadata": {},
+ "source": [
+ "Now, write a new solution to Exercise 3 that does not use an existing function\n",
+ "to compute the absolute value.\n",
+ "\n",
+ "Replace this existing function with an if–else condition."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cadf82a",
+ "metadata": {},
+ "source": [
+ "## Solution to[ Exercise 3.4](https://python-programming.quantecon.org/#pbe_ex4)\n",
+ "\n",
+ "Here’s one way:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "da4679e0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "α = 0.9\n",
+ "T = 200\n",
+ "x = np.empty(T+1)\n",
+ "x[0] = 0\n",
+ "\n",
+ "for t in range(T):\n",
+ " abs_x = - x[t] if x[t] < 0 else x[t]\n",
+ " x[t+1] = α * abs_x + np.random.randn()\n",
+ "\n",
+ "plt.plot(x)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1c7fc965",
+ "metadata": {},
+ "source": [
+ "## Exercise 3.5\n",
+ "\n",
+ "Here’s a harder exercise, that takes some thought and planning.\n",
+ "\n",
+ "The task is to compute an approximation to $ \\pi $ using [Monte Carlo](https://en.wikipedia.org/wiki/Monte_Carlo_method).\n",
+ "\n",
+ "Use no imports besides"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "3c65f875",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "133925e8",
+ "metadata": {},
+ "source": [
+ "Your hints are as follows:\n",
+ "\n",
+ "- If $ U $ is a bivariate uniform random variable on the unit square $ (0, 1)^2 $, then the probability that $ U $ lies in a subset $ B $ of $ (0,1)^2 $ is equal to the area of $ B $. \n",
+ "- If $ U_1,\\ldots,U_n $ are IID copies of $ U $, then, as $ n $ gets large, the fraction that falls in $ B $, converges to the probability of landing in $ B $. \n",
+ "- For a circle, $ area = \\pi * radius^2 $. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e25a7b53",
+ "metadata": {},
+ "source": [
+ "## Solution to[ Exercise 3.5](https://python-programming.quantecon.org/#pbe_ex5)\n",
+ "\n",
+ "Consider the circle of diameter 1 embedded in the unit square.\n",
+ "\n",
+ "Let $ A $ be its area and let $ r=1/2 $ be its radius.\n",
+ "\n",
+ "If we know $ \\pi $ then we can compute $ A $ via\n",
+ "$ A = \\pi r^2 $.\n",
+ "\n",
+ "But here the point is to compute $ \\pi $, which we can do by\n",
+ "$ \\pi = A / r^2 $.\n",
+ "\n",
+ "Summary: If we can estimate the area of a circle with diameter 1, then dividing\n",
+ "by $ r^2 = (1/2)^2 = 1/4 $ gives an estimate of $ \\pi $.\n",
+ "\n",
+ "We estimate the area by sampling bivariate uniforms and looking at the\n",
+ "fraction that falls into the circle."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "e59ff3dc",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "3.143048\n"
+ ]
+ }
+ ],
+ "source": [
+ "n = 1000000 # sample size for Monte Carlo simulation\n",
+ "\n",
+ "count = 0\n",
+ "for i in range(n):\n",
+ "\n",
+ " # drawing random positions on the square\n",
+ " u, v = np.random.uniform(), np.random.uniform()\n",
+ "\n",
+ " # check whether the point falls within the boundary\n",
+ " # of the unit circle centred at (0.5,0.5)\n",
+ " d = np.sqrt((u - 0.5)**2 + (v - 0.5)**2)\n",
+ "\n",
+ " # if it falls within the inscribed circle, \n",
+ " # add it to the count\n",
+ " if d < 0.5:\n",
+ " count += 1\n",
+ "\n",
+ "area_estimate = count / n\n",
+ "\n",
+ "print(area_estimate * 4) # dividing by radius**2"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/_sources/_notebook_build/_01_python_jupyter_demo.ipynb b/docs/_sources/_notebook_build/_01_python_jupyter_demo.ipynb
new file mode 100644
index 0000000..0f451ff
--- /dev/null
+++ b/docs/_sources/_notebook_build/_01_python_jupyter_demo.ipynb
@@ -0,0 +1,2743 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J09tr3ds09ky"
+ },
+ "source": [
+ "# 1.2 Python for Data Science Demo\n",
+ "\n",
+ "This notebook is designed to show off some of the features of using Python for data science that you'll encounter throughout the year. Although Jupyter Notebooks are not always the right medium for your code, it will also demonstrate some of the features of Jupyter and Jupyter Notebooks that make them useful for data exploration and visualization.\n",
+ "\n",
+ "IMPORTANT NOTE: You're not expected to learn how the code below works today---we'll hardly look at the code below. The point of this notebook is just to showcase what CAN be done. We'll learn how to write the code below over the next few weeks and throughout the next quarter. **After getting a look at what's possible in this notebook, we'll start from the very basics in the next notebook.** In the next notebook, we'll cover the basics of Python (types, control structures, functions, modules, etc) and then we'll proceed to learn how to use some of the basic data science packages for Python. This first notebook is just a demo.\n",
+ "\n",
+ "You can try running this code yourself. The quickest way to get up and running is to use Google Colaboratory: `https://colab.research.google.com/` You can open this notebook directly in Google Colaboratory by clicking here: [Open in Google Colaboratory.](https://colab.research.google.com/github/jmbejara/finm-python-crash-course/blob/main/week_1/Part_1_Python_Jupyter_demo.ipynb)\n",
+ "\n",
+ "This notebook will start by setting up the environment and then will demonstrate some examples from NumPy, SciPy, Pandas, Matplotlib, and StatsModels (with some bonus examples from Seaborn and iPyWidgets.) This collection of packages represent the foundation of what is called the PyData stack (ecosystem):\n",
+ "\n",
+ "
\n",
+ "\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GgL_HaFX09k4"
+ },
+ "source": [
+ "\n",
+ "## 0. Set up Environment\n",
+ "\n",
+ "We'll first start by discussing the Python interpreter, Anaconda vs Conda, Jupyter, and Google Colaboratory. We'll defer an in-depth discussion until next week, but we'll mention the basics today. Today, we'll run everything in Google Colaboratory. Next week we'll run our code locally in Jupyter. The following week, we'll discuss text editors. In particular, we'll write code in [Visual Studio Code](https://code.visualstudio.com/).\n",
+ "\n",
+ "Now, before we start, we need to set up our environment. We need to install the packages that we need (if they aren't installed already) and then we need to load the packages into Python."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Be sure to install the required packages manually if not in Colab\n"
+ ]
+ }
+ ],
+ "source": [
+ "## Install required packages, but do automatically only if running within Google Colaboratory\n",
+ "try:\n",
+ " import google.colab\n",
+ " IN_COLAB = True\n",
+ "except:\n",
+ " IN_COLAB = False\n",
+ "\n",
+ "if IN_COLAB:\n",
+ " !pip install plotly==5.9.0\n",
+ "else:\n",
+ " print(\"Be sure to install the required packages manually if not in Colab\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import scipy as sp\n",
+ "import pandas as pd\n",
+ "from matplotlib import pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "import plotly.express as px\n",
+ "\n",
+ "sns.set()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qOKX9_RL09k8"
+ },
+ "source": [
+ "## 1. NumPy\n",
+ "\n",
+ "NumPy a library designed to add support \"for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.\"\n",
+ "\n",
+ "A good beginner tutorial can be found on the official NumPy website here: https://numpy.org/doc/stable/user/absolute_beginners.html\n",
+ "\n",
+ "The basic functionality of NumPy is the efficient management of arrays, with syntax as follows:\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Install with:\n",
+ "# conda install -c plotly plotly=5.9.0\n",
+ "# or\n",
+ "# pip install plotly==5.9.0\n",
+ "\n",
+ "df = px.data.stocks()\n",
+ "fig = px.line(df, x='date', y=\"GOOG\")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "j63HvaTz09lG"
+ },
+ "source": [
+ "## 4. Pandas\n",
+ "\n",
+ "From Wikipedia, \"`pandas` is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. The name is derived from the term \"panel data\", an econometrics term for data sets that include observations over multiple time periods for the same individuals. Its name is a play on the phrase \"Python data analysis\" itself. Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010.\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "California 38332521\n",
+ "Texas 26448193\n",
+ "New York 19651127\n",
+ "Florida 19552860\n",
+ "Illinois 12882135\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "population_dict = {'California': 38332521,\n",
+ " 'Texas': 26448193,\n",
+ " 'New York': 19651127,\n",
+ " 'Florida': 19552860,\n",
+ " 'Illinois': 12882135}\n",
+ "population = pd.Series(population_dict)\n",
+ "population"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_portfolio(weight_1=.5, mu0=4.0, mu1=8.0, sd0=5.0, sd1=10.0, corr=0.0, Rf=1.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Uncomment below for interactive viewing\n",
+ "# interact(plot_portfolio, weight_1=(-1.0, 2.0, .001), corr=(-1.0, 1.0, .01));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**DISCUSS: The example above demonstrate what is meant by a set of efficient portfolios? What do you think is meant by an efficient portfolio?**\n",
+ "\n",
+ "For more information about calculating the tangency portfolio, see here: https://bookdown.org/compfinezbook/introcompfinr/Efficient-portfolios-of.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vcJ8kzct09lK"
+ },
+ "source": [
+ "## 7. To do before next class\n",
+ "\n",
+ "If you need help installing this software, please ask for help in the discussion section on Canvas.\n",
+ "\n",
+ " - Python 3.9, Anaconda Distribution\n",
+ " - For this class, please download the [Anaconda distribution of Python](https://www.anaconda.com/products/distribution). Be sure to download Python, version 3.9. \n",
+ " When you install Anaconda, be sure to install the full Anaconda distribution. \n",
+ " The MiniConda version is nice, but I only recommend it for advanced users.\n",
+ " - The Visual Studio Code text editor\n",
+ " - A good text editor is important for software development. Some of your classes will use a fully-fledged Integrated Development Environment (IDE) like PyCharm. For this review, I suggest Visual Studio Code. You can download it here: https://code.visualstudio.com/\n",
+ " - Git (optional, but recommended)\n",
+ " - Although there are many different Git clients and Git GUI's that you could use,\n",
+ " I prefer that you install GitKraken. GitKraken bundles a Git Client with its GUI, so you don't need to install multiple pieces of software. [GitKraken can be downloaded here.](https://www.gitkraken.com/)\n",
+ " - Some classes will use GitHub. GitHub is a website that allows you to store, interactic with, and share your Git repositories online. [Please register an account with GitHub](https://github.com/) if you don't already have one."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/_sources/_notebook_build/_02_Using_Interact.ipynb b/docs/_sources/_notebook_build/_02_Using_Interact.ipynb
new file mode 100644
index 0000000..4fca3ac
--- /dev/null
+++ b/docs/_sources/_notebook_build/_02_Using_Interact.ipynb
@@ -0,0 +1,735 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2.4 Using Interact"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `interact` function (`IPython.html.widgets.interact`) automatically creates user interface (UI) controls for exploring code and data interactively. It is the easiest way to get started using IPython's widgets."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from ipywidgets import interact, interactive, fixed\n",
+ "from ipywidgets import widgets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "As of IPython 2.0, the widgets in this notebook won't show up on http://nbviewer.ipython.org. To view the widgets and interact with them, you will need to download this notebook and run it with an IPython Notebook server.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic `interact`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "At the most basic level, `interact` autogenerates UI controls for function arguments, and then calls the function with those arguments when you manipulate the controls interactively. To use `interact`, you need to define a function that you want to explore. Here is a function that prints its only argument `x`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def f(x):\n",
+ " print(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When you pass this function as the first argument to `interact` along with an integer keyword argument (`x=10`), a slider is generated and bound to the function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4c02b6fabd4046528974ec00a6175a86",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='x', max=30, min=-10), Output()), _dom_classes=('widget-…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x=10);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When you move the slider, the function is called and the current value of `x` is printed.\n",
+ "\n",
+ "If you pass `True` or `False`, `interact` will generate a checkbox:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "29b648f26358436ba2b3c26f598c636b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(Checkbox(value=True, description='x'), Output()), _dom_classes=('widget-interact',))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x=True);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you pass a string, `interact` will generate a text area."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "eb59963a81754c5aa142500eceeefc9a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(Text(value='Hi there!', description='x'), Output()), _dom_classes=('widget-interact',))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x='Hi there!');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`interact` can also be used as a decorator. This allows you to define a function and interact with it in a single shot. As this example shows, `interact` also works with functions that have multiple arguments."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "be1e7bf306894025994e4a3101322e22",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(Checkbox(value=True, description='x'), FloatSlider(value=1.0, description='y', max=3.0, …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "@interact(x=True, y=1.0)\n",
+ "def g(x, y):\n",
+ " print(x, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fixing arguments using `fixed`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are times when you may want to explore a function using `interact`, but fix one or more of its arguments to specific values. This can be accomplished by wrapping values with the `fixed` function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def h(p, q):\n",
+ " print(p, q)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we call `interact`, we pass `fixed(20)` for q to hold it fixed at a value of `20`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a89dd6734266418e875046d4a772deed",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=5, description='p', max=15, min=-5), Output()), _dom_classes=('widget-in…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(h, p=5, q=fixed(20));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice that a slider is only produced for `p` as the value of `q` is fixed."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Widget abbreviations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When you pass an integer valued keyword argument (`x=10`) to `interact`, it generates an integer valued slider control with a range of $[-10,+3\\times10]$. In this case `10` is an *abbreviation* for an actual slider widget:\n",
+ "\n",
+ "```python\n",
+ "IntSliderWidget(min=-10,max=30,step=1,value=10)\n",
+ "```\n",
+ "\n",
+ "In fact, we can get the same result if we pass this `IntSlider` as the keyword argument for `x`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "aef16bca9cbd495ba782464053755fbc",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='x', max=30, min=-10), Output()), _dom_classes=('widget-…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x=widgets.IntSlider(min=-10,max=30,step=1,value=10));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This examples clarifies how `interact` proceses its keyword arguments:\n",
+ "\n",
+ "1. If the keyword argument is `Widget` instance with a `value` attribute, that widget is used. Any widget with a `value` attribute can be used, even custom ones.\n",
+ "2. Otherwise, the value is treated as a *widget abbreviation* that is converted to a widget before it is used.\n",
+ "\n",
+ "The following table gives an overview of different widget abbreviations:\n",
+ "\n",
+ "
\n",
+ "
Keyword argument
Widget
\n",
+ "
`True` or `False`
CheckboxWiget
\n",
+ "
`'Hi there'`
TextareaWidget
\n",
+ "
`value` or `(min,max)` or `(min,max,step)` if integers are passed
IntSliderWidget
\n",
+ "
`value` or `(min,max)` or `(min,max,step)` if floats are passed
FloatSliderWidget
\n",
+ "
`('orange','apple')` or `{'one':1,'two':2}`
DropdownWidget
\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You have seen how the checkbox and textarea widgets work above. Here, more details about the different abbreviations for sliders and dropdowns are given.\n",
+ "\n",
+ "If a 2-tuple of integers is passed `(min,max)` a integer valued slider is produced with those minimum and maximum (inclusive) values. In this case, the default step size of `1` is used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7558729cbbdb4c2f9c3f124166ede60f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=2, description='x', max=4), Output()), _dom_classes=('widget-interact',)…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x=(0,4));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If a 3-tuple of integers is passed `(min,max,step)` the step size can also be set."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "0368131e72cc46e5a1174437ab37019e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=4, description='x', max=8, step=2), Output()), _dom_classes=('widget-int…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x=(0,8,2));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A float valued slider is produced if the elements of the tuples are floats. Here the minimum is `0.0`, the maximum is `10.0` and step size is `0.1` (the default)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e883be91a16e419cbe2d194ae50c875a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=5.0, description='x', max=10.0), Output()), _dom_classes=('widget-inte…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x=(0.0,10.0));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The step size can be changed by passing a 3rd element in the tuple."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a241200a041c4233a25af28fa69a791e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=5.0, description='x', max=10.0, step=0.01), Output()), _dom_classes=('…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x=(0.0,10.0,0.01));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For both integer and float valued sliders, you can pick the initial value of the widget by passing a default keyword argument to the underlying Python function. Here we set the initial value of a float slider to `5.5`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "02fee1584f474b2496171b6e77032c42",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=5.5, description='x', max=20.0, step=0.5), Output()), _dom_classes=('w…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "@interact(x=(0.0,20.0,0.5))\n",
+ "def h(x=5.5):\n",
+ " print(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dropdown menus can be produced by passing a tuple of strings. In this case, the strings are both used as the names in the dropdown menu UI and passed to the underlying Python function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "befd04dc87514d71bc5c18b43d101865",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(Dropdown(description='x', options=('apples', 'oranges'), value='apples'), Output()), _do…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x=widgets.Dropdown(options=['apples','oranges']));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you want a dropdown menu that passes non-string values to the Python function, you can pass list of (key, value) tuples. The keys in the dictionary are used for the names in the dropdown menu UI and the values are the arguments that are passed to the underlying Python function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "14457d3a474242f5bb8863eb65efe7c8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(Dropdown(description='x', options=(('one', 10), ('two', 20)), value=10), Output()), _dom…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interact(f, x=widgets.Dropdown(options=[('one', 10), ('two', 20)]));"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## `interactive`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In addition to `interact` IPython provides another function, `interactive`, that is useful when you want to reuse the widget that are produced or access the data that is bound to the UI controls."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is a function that returns the sum of its two arguments."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def f(a, b):\n",
+ " return a+b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Unlike `interact`, `interactive` returns a `Widget` instance rather than immediately displaying the widget."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "w = interactive(f, a=10, b=20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The widget is a `Box`, which is a container for other widgets."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ipywidgets.widgets.interaction.interactive"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The children of the `Box` are two integer valued sliders produced by the widget abbreviations above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(IntSlider(value=10, description='a', max=30, min=-10),\n",
+ " IntSlider(value=20, description='b', max=60, min=-20),\n",
+ " Output())"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w.children"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To actually display the widgets, you can use IPython's `display` function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1cf2e868aa8f48c994a958b4b42ee71b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='a', max=30, min=-10), IntSlider(value=20, description='…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from IPython.display import display\n",
+ "display(w)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "At this point, the UI controls work just like they would if `interact` had been used. You can manipulate them interactively and the function will be called. However, the widget instance returned by `interactive` also give you access to the current keyword arguments and return value of the underlying Python function.\n",
+ "\n",
+ "Here are the current keyword arguments. If you rerun this cell after manipulating the sliders, the values will have changed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'a': 10, 'b': 20}"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w.kwargs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is the current return value of the function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "30"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w.result"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/_sources/_notebook_build/_02_functions.ipynb b/docs/_sources/_notebook_build/_02_functions.ipynb
new file mode 100644
index 0000000..0ae3487
--- /dev/null
+++ b/docs/_sources/_notebook_build/_02_functions.ipynb
@@ -0,0 +1,1693 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d01b84d5",
+ "metadata": {
+ "id": "d01b84d5"
+ },
+ "source": [
+ "# Functions\n",
+ "\n",
+ "**Prerequisites**\n",
+ "\n",
+ "- [Getting Started](https://datascience.quantecon.org/../introduction/getting_started.html) \n",
+ "- [Basics](https://datascience.quantecon.org/basics.html) \n",
+ "- [Collections](https://datascience.quantecon.org/collections.html) \n",
+ "- [Control Flow](https://datascience.quantecon.org/control_flow.html) \n",
+ "\n",
+ "\n",
+ "**Outcomes**\n",
+ "\n",
+ "- Economic Production Functions \n",
+ " - Understand the basics of production functions in economics \n",
+ "- Functions \n",
+ " - Know how to define your own function \n",
+ " - Know how to find and write your own function documentation \n",
+ " - Know why we use functions \n",
+ " - Understand scoping rules and blocks \n",
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ddecc742",
+ "metadata": {
+ "id": "ddecc742"
+ },
+ "source": [
+ "## Application: Production Functions\n",
+ "\n",
+ "Production functions are useful when modeling the economics of firms producing\n",
+ "goods or the aggregate output in an economy.\n",
+ "\n",
+ "Though the term “function” is used in a mathematical sense here, we will be making\n",
+ "tight connections between the programming of mathematical functions and Python\n",
+ "functions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "33438a42",
+ "metadata": {
+ "id": "33438a42"
+ },
+ "source": [
+ "### Factors of Production\n",
+ "\n",
+ "The [factors of production](https://en.wikipedia.org/wiki/Factors_of_production)\n",
+ "are the inputs used in the production of some sort of output.\n",
+ "\n",
+ "Some example factors of production include\n",
+ "\n",
+ "- [Physical capital](https://en.wikipedia.org/wiki/Physical_capital), e.g.\n",
+ " machines, buildings, computers, and power stations. \n",
+ "- Labor, e.g. all of the hours of work from different types of employees of a\n",
+ " firm. \n",
+ "- [Human Capital](https://en.wikipedia.org/wiki/Human_capital), e.g. the\n",
+ " knowledge of employees within a firm. \n",
+ "\n",
+ "\n",
+ "A [production function](https://en.wikipedia.org/wiki/Production_function)\n",
+ "maps a set of inputs to the output, e.g. the amount of wheat produced by a\n",
+ "farm, or widgets produced in a factory.\n",
+ "\n",
+ "As an example of the notation, we denote the total units of labor and\n",
+ "physical capital used in a factory as $ L $ and $ K $ respectively.\n",
+ "\n",
+ "If we denote the physical output of the factory as $ Y $, then a production\n",
+ "function $ F $ that transforms labor and capital into output might have the\n",
+ "form:\n",
+ "\n",
+ "$$\n",
+ "Y = F(K, L)\n",
+ "$$\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4774ddc2",
+ "metadata": {
+ "id": "4774ddc2"
+ },
+ "source": [
+ "### An Example Production Function\n",
+ "\n",
+ "Throughout this lecture, we will use the\n",
+ "[Cobb-Douglas](https://en.wikipedia.org/wiki/Cobb%E2%80%93Douglas_production_function)\n",
+ "production function to help us understand how to create Python\n",
+ "functions and why they are useful.\n",
+ "\n",
+ "The Cobb-Douglas production function has appealing statistical properties when brought to data.\n",
+ "\n",
+ "This function is displayed below.\n",
+ "\n",
+ "$$\n",
+ "Y = z K^{\\alpha} L^{1-\\alpha}\n",
+ "$$\n",
+ "\n",
+ "The function is parameterized by:\n",
+ "\n",
+ "- A parameter $ \\alpha \\in [0,1] $, called the “output elasticity of\n",
+ " capital”. \n",
+ "- A value $ z $ called the [Total Factor Productivity](https://en.wikipedia.org/wiki/Total_factor_productivity) (TFP). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bcd32baf",
+ "metadata": {
+ "id": "bcd32baf"
+ },
+ "source": [
+ "## What are (Python) Functions?\n",
+ "\n",
+ "In this class, we will often talk about `function`s.\n",
+ "\n",
+ "So what is a function?\n",
+ "\n",
+ "We like to think of a function as a production line in a\n",
+ "manufacturing plant: we pass zero or more things to it, operations take place in a\n",
+ "set linear sequence, and zero or more things come out.\n",
+ "\n",
+ "We use functions for the following purposes:\n",
+ "\n",
+ "- **Re-usability**: Writing code to do a specific task just once, and\n",
+ " reuse the code by calling the function. \n",
+ "- **Organization**: Keep the code for distinct operations separated and\n",
+ " organized. \n",
+ "- **Sharing/collaboration**: Sharing code across multiple projects or\n",
+ " sharing pieces of code with collaborators. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fed78915",
+ "metadata": {
+ "id": "fed78915"
+ },
+ "source": [
+ "## How to Define (Python) Functions?\n",
+ "\n",
+ "The basic syntax to create our own function is as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9e1bdbe8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def function_name(inputs):\n",
+ " # step 1\n",
+ " # step 2\n",
+ " # ...\n",
+ " return outputs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "57878b06",
+ "metadata": {
+ "id": "57878b06"
+ },
+ "source": [
+ "Here we see two new *keywords*: `def` and `return`.\n",
+ "\n",
+ "- `def` is used to tell Python we would like to define a new function. \n",
+ "- `return` is used to tell Python what we would like to **return** from a\n",
+ " function. \n",
+ "\n",
+ "\n",
+ "Let’s look at an example and then discuss each part:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "8a3fb19f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mean(numbers):\n",
+ " total = sum(numbers)\n",
+ " N = len(numbers)\n",
+ " answer = total / N\n",
+ "\n",
+ " return answer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e87d1e82",
+ "metadata": {
+ "id": "e87d1e82"
+ },
+ "source": [
+ "Here we defined a function `mean` that has one input (`numbers`),\n",
+ "does three steps, and has one output (`answer`).\n",
+ "\n",
+ "Let’s see what happens when we call this function on the list of numbers\n",
+ "`[1, 2, 3, 4]`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8115113e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.5"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x = [1, 2, 3, 4]\n",
+ "the_mean = mean(x)\n",
+ "the_mean"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "556b1f1a",
+ "metadata": {
+ "id": "556b1f1a"
+ },
+ "source": [
+ "Additionally, as we saw in the [control flow](https://datascience.quantecon.org/control_flow.html) lecture, indentation\n",
+ "controls blocks of code (along with the [scope](#scope) rules).\n",
+ "\n",
+ "To see this, compare a function with no inputs or return values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "304fc73d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1\n",
+ "2\n"
+ ]
+ }
+ ],
+ "source": [
+ "def f():\n",
+ " print(\"1\")\n",
+ " print(\"2\")\n",
+ "f()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c7c7283d",
+ "metadata": {
+ "id": "c7c7283d"
+ },
+ "source": [
+ "With the following change of indentation…"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "63b91a05",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2\n",
+ "1\n"
+ ]
+ }
+ ],
+ "source": [
+ "def f():\n",
+ " print(\"1\")\n",
+ "print(\"2\")\n",
+ "f()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "370040f1",
+ "metadata": {
+ "id": "370040f1"
+ },
+ "source": [
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "71eb966e",
+ "metadata": {
+ "id": "71eb966e"
+ },
+ "source": [
+ "### Scope\n",
+ "\n",
+ "Notice that we named the input to the function `x` and we called the output\n",
+ "`the_mean`.\n",
+ "\n",
+ "When we defined the function, the input was called `numbers` and the output\n",
+ "`answer`… what gives?\n",
+ "\n",
+ "This is an example of a programming concept called\n",
+ "[variable scope](http://python-textbok.readthedocs.io/en/1.0/Variables_and_Scope.html).\n",
+ "\n",
+ "In Python, functions define their own scope for variables.\n",
+ "\n",
+ "In English, this means that regardless of what name we give an input variable (`x` in this example),\n",
+ "the input will always be referred to as `numbers` *inside* the body of the `mean`\n",
+ "function.\n",
+ "\n",
+ "It also means that although we called the output `answer` inside of the\n",
+ "function `mean`, that this variable name was only valid inside of our\n",
+ "function.\n",
+ "\n",
+ "To use the output of the function, we had to give it our own name (`the_mean`\n",
+ "in this example).\n",
+ "\n",
+ "Another point to make here is that the intermediate variables we defined inside\n",
+ "`mean` (`total` and `N`) are only defined inside of the `mean` function\n",
+ "– we can’t access them from outside. We can verify this by trying to see what\n",
+ "the value of `total` is:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "11844389",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mean(numbers):\n",
+ " total = sum(numbers)\n",
+ " N = len(numbers)\n",
+ " answer = total / N\n",
+ " return answer # or directly return total / N\n",
+ "\n",
+ "# uncomment the line below and execute to see the error\n",
+ "# total"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5bd0615d",
+ "metadata": {
+ "id": "5bd0615d"
+ },
+ "source": [
+ "This point can be taken even further: the same name can be bound\n",
+ "to variables inside of blocks of code and in the outer “scope”."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "388a4029",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x = 4\n",
+ "x = 5\n",
+ "x = 4\n"
+ ]
+ }
+ ],
+ "source": [
+ "x = 4\n",
+ "print(f\"x = {x}\")\n",
+ "def f():\n",
+ " x = 5 # a different \"x\"\n",
+ " print(f\"x = {x}\")\n",
+ "f() # calls function\n",
+ "print(f\"x = {x}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "de1ca784",
+ "metadata": {
+ "id": "de1ca784"
+ },
+ "source": [
+ "The final point we want to make about scope is that function inputs and output\n",
+ "don’t have to be given a name outside the function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "5dae5c00",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "20.0"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mean([10, 20, 30])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a52ba1d5",
+ "metadata": {
+ "id": "a52ba1d5"
+ },
+ "source": [
+ "Notice that we didn’t name the input or the output, but the function was\n",
+ "called successfully.\n",
+ "\n",
+ "Now, we’ll use our new knowledge to define a function which computes the output\n",
+ "from a Cobb-Douglas production function with parameters $ z = 1 $ and\n",
+ "$ \\alpha = 0.33 $ and takes inputs $ K $ and $ L $."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "f01d0518",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cobb_douglas(K, L):\n",
+ "\n",
+ " # Create alpha and z\n",
+ " z = 1\n",
+ " alpha = 0.33\n",
+ "\n",
+ " return z * K**alpha * L**(1 - alpha)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ac6912bc",
+ "metadata": {
+ "id": "ac6912bc"
+ },
+ "source": [
+ "We can use this function as we did the mean function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "a533ffc0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.6285066872609142"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cobb_douglas(1.0, 0.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46005624",
+ "metadata": {
+ "id": "46005624"
+ },
+ "source": [
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "56e2a4de",
+ "metadata": {
+ "id": "56e2a4de"
+ },
+ "source": [
+ "### Re-using Functions\n",
+ "\n",
+ "Economists are often interested in this question: how much does output\n",
+ "change if we modify our inputs?\n",
+ "\n",
+ "For example, take a production function $ Y_1 = F(K_1,L_1) $ which produces\n",
+ "$ Y_1 $ units of the goods.\n",
+ "\n",
+ "If we then multiply the inputs each by $ \\gamma $, so that\n",
+ "$ K_2 = \\gamma K_1 $ and $ L_2 = \\gamma L_1 $, then the output is\n",
+ "\n",
+ "$$\n",
+ "Y_2 = F(K_2, L_2) = F(\\gamma K_1, \\gamma L_1)\n",
+ "$$\n",
+ "\n",
+ "How does $ Y_1 $ compare to $ Y_2 $?\n",
+ "\n",
+ "Answering this question involves something called *returns to scale*.\n",
+ "\n",
+ "Returns to scale tells us whether our inputs are more or less productive as we\n",
+ "have more of them.\n",
+ "\n",
+ "For example, imagine that you run a restaurant. How would you expect the amount\n",
+ "of food you could produce would change if you could build an exact replica of\n",
+ "your restaurant and kitchen and hire the same number of cooks and waiters? You\n",
+ "would probably expect it to double.\n",
+ "\n",
+ "If, for any $ K, L $, we multiply $ K, L $ by a value $ \\gamma $\n",
+ "then\n",
+ "\n",
+ "- If $ \\frac{Y_2}{Y_1} < \\gamma $ then we say the production function has\n",
+ " decreasing returns to scale. \n",
+ "- If $ \\frac{Y_2}{Y_1} = \\gamma $ then we say the production function has\n",
+ " constant returns to scale. \n",
+ "- If $ \\frac{Y_2}{Y_1} > \\gamma $ then we say the production function has\n",
+ " increasing returns to scale. \n",
+ "\n",
+ "\n",
+ "Let’s try it and see what our function is!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "ba4280b8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.6285066872609142\n",
+ "1.2570133745218284\n"
+ ]
+ }
+ ],
+ "source": [
+ "y1 = cobb_douglas(1.0, 0.5)\n",
+ "print(y1)\n",
+ "y2 = cobb_douglas(2*1.0, 2*0.5)\n",
+ "print(y2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dce2a0e7",
+ "metadata": {
+ "id": "dce2a0e7"
+ },
+ "source": [
+ "How did $ Y_1 $ and $ Y_2 $ relate?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "92f17f1b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2.0"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y2 / y1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc96ca2",
+ "metadata": {
+ "id": "0bc96ca2"
+ },
+ "source": [
+ "$ Y_2 $ was exactly double $ Y_1 $!\n",
+ "\n",
+ "Let’s write a function that will compute the returns to scale for different\n",
+ "values of $ K $ and $ L $.\n",
+ "\n",
+ "This is an example of how writing functions can allow us to re-use code\n",
+ "in ways we might not originally anticipate. (You didn’t know we’d be\n",
+ "writing a `returns_to_scale` function when we wrote `cobb_douglas`.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "8b1315cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def returns_to_scale(K, L, gamma):\n",
+ " y1 = cobb_douglas(K, L)\n",
+ " y2 = cobb_douglas(gamma*K, gamma*L)\n",
+ " y_ratio = y2 / y1\n",
+ " return y_ratio / gamma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "13d23ac8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.0"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "returns_to_scale(1.0, 0.5, 2.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "63637c4c",
+ "metadata": {
+ "id": "63637c4c"
+ },
+ "source": [
+ "### Exercise\n",
+ "\n",
+ "See exercise 1 in the [exercise list](#ex2-4).\n",
+ "\n",
+ "It turns out that with a little bit of algebra, we can check that this will\n",
+ "always hold for our [Cobb-Douglas example](#cobb-douglas-example) above.\n",
+ "\n",
+ "To show this, take an arbitrary $ K, L $ and multiply the inputs by an\n",
+ "arbitrary $ \\gamma $.\n",
+ "\n",
+ "$$\n",
+ "\\begin{aligned}\n",
+ " F(\\gamma K, \\gamma L) &= z (\\gamma K)^{\\alpha} (\\gamma L)^{1-\\alpha}\\\\\n",
+ " &= z \\gamma^{\\alpha}\\gamma^{1-\\alpha} K^{\\alpha} L^{1-\\alpha}\\\\\n",
+ " &= \\gamma z K^{\\alpha} L^{1-\\alpha} = \\gamma F(K, L)\n",
+ "\\end{aligned}\n",
+ "$$\n",
+ "\n",
+ "For an example of a production function that is not CRS, look at a\n",
+ "generalization of the Cobb-Douglas production function that has different\n",
+ "“output elasticities” for the 2 inputs.\n",
+ "\n",
+ "$$\n",
+ "Y = z K^{\\alpha_1} L^{\\alpha_2}\n",
+ "$$\n",
+ "\n",
+ "Note that if $ \\alpha_2 = 1 - \\alpha_1 $, this is our Cobb-Douglas\n",
+ "production function."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be199e69",
+ "metadata": {
+ "id": "be199e69"
+ },
+ "source": [
+ "### Exercise\n",
+ "\n",
+ "See exercise 2 in the [exercise list](#ex2-4).\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6abfcb1f",
+ "metadata": {
+ "id": "6abfcb1f"
+ },
+ "source": [
+ "### Multiple Returns\n",
+ "\n",
+ "Another valuable element to analyze on production functions is how\n",
+ "output changes as we change only one of the inputs. We will call this the\n",
+ "marginal product.\n",
+ "\n",
+ "For example, compare the output using $ K, L $ units of inputs to that with\n",
+ "an $ \\epsilon $ units of labor.\n",
+ "\n",
+ "Then the marginal product of labor (MPL) is defined as\n",
+ "\n",
+ "$$\n",
+ "\\frac{F(K, L + \\varepsilon) - F(K, L)}{\\varepsilon}\n",
+ "$$\n",
+ "\n",
+ "This tells us how much additional output is created relative to the additional\n",
+ "input. (Spoiler alert: This should look like the definition for a partial\n",
+ "derivative!)\n",
+ "\n",
+ "If the input can be divided into small units, then we can use calculus to take\n",
+ "this limit, using the partial derivative of the production function relative to\n",
+ "that input.\n",
+ "\n",
+ "In this case, we define the marginal product of labor (MPL) and marginal product\n",
+ "of capital (MPK) as\n",
+ "\n",
+ "$$\n",
+ "\\begin{aligned}\n",
+ "MPL(K, L) &= \\frac{\\partial F(K, L)}{\\partial L}\\\\\n",
+ "MPK(K, L) &= \\frac{\\partial F(K, L)}{\\partial K}\n",
+ "\\end{aligned}\n",
+ "$$\n",
+ "\n",
+ "In the [Cobb-Douglas](#cobb-douglas-example) example above, this becomes\n",
+ "\n",
+ "$$\n",
+ "\\begin{aligned}\n",
+ "MPK(K, L) &= z \\alpha \\left(\\frac{K}{L} \\right)^{\\alpha - 1}\\\\\n",
+ "MPL(K, L) &= (1-\\alpha) z \\left(\\frac{K}{L} \\right)^{\\alpha}\\\\\n",
+ "\\end{aligned}\n",
+ "$$\n",
+ "\n",
+ "Let’s test it out with Python! We’ll also see that we can actually return\n",
+ "multiple things in a Python function.\n",
+ "\n",
+ "The syntax for a return statement with multiple items is return item1, item2, ….\n",
+ "\n",
+ "In this case, we’ll compute both the MPL and the MPK and then return both."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "46654832",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def marginal_products(K, L, epsilon):\n",
+ "\n",
+ " mpl = (cobb_douglas(K, L + epsilon) - cobb_douglas(K, L)) / epsilon\n",
+ " mpk = (cobb_douglas(K + epsilon, L) - cobb_douglas(K, L)) / epsilon\n",
+ "\n",
+ " return mpl, mpk"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "44284da4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(0.8421711708284096, 0.20740025904131265)\n"
+ ]
+ }
+ ],
+ "source": [
+ "tup = marginal_products(1.0, 0.5, 1e-4)\n",
+ "print(tup)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eca892b8",
+ "metadata": {
+ "id": "eca892b8"
+ },
+ "source": [
+ "Instead of using the tuple, these can be directly unpacked to variables."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "9b2e4f7e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "mpl = 0.8421711708284096, mpk = 0.20740025904131265\n"
+ ]
+ }
+ ],
+ "source": [
+ "mpl, mpk = marginal_products(1.0, 0.5, 1e-4)\n",
+ "print(f\"mpl = {mpl}, mpk = {mpk}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c65f1f6",
+ "metadata": {
+ "id": "0c65f1f6"
+ },
+ "source": [
+ "We can use this to calculate the marginal products for different `K`, fixing `L`\n",
+ "using a comprehension."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "91124022",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(0.8421711708284096, 0.20740025904131265),\n",
+ " (1.058620425367085, 0.13035463304111872),\n",
+ " (1.2101811517950534, 0.09934539767386674)]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Ks = [1.0, 2.0, 3.0]\n",
+ "[marginal_products(K, 0.5, 1e-4) for K in Ks] # create a tuple for each K"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "490312cd",
+ "metadata": {
+ "id": "490312cd"
+ },
+ "source": [
+ "### Documentation\n",
+ "\n",
+ "In a previous exercise, we asked you to find help for the `cobb_douglas` and\n",
+ "`returns_to_scale` functions using `?`.\n",
+ "\n",
+ "It didn’t provide any useful information.\n",
+ "\n",
+ "To provide this type of help information, we need to\n",
+ "add what Python programmers call a “docstring” to our functions.\n",
+ "\n",
+ "This is done by putting a string (not assigned to any variable name) as\n",
+ "the first line of the *body* of the function (after the line with\n",
+ "`def`).\n",
+ "\n",
+ "Below is a new version of the template we used to define functions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "bcfe31d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def function_name(inputs):\n",
+ " \"\"\"\n",
+ " Docstring\n",
+ " \"\"\"\n",
+ " # step 1\n",
+ " # step 2\n",
+ " # ...\n",
+ " return outputs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f210bf45",
+ "metadata": {
+ "id": "f210bf45"
+ },
+ "source": [
+ "Let’s re-define our `cobb_douglas` function to include a docstring."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "5078fd27",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cobb_douglas(K, L):\n",
+ " \"\"\"\n",
+ " Computes the production F(K, L) for a Cobb-Douglas production function\n",
+ "\n",
+ " Takes the form F(K, L) = z K^{\\alpha} L^{1 - \\alpha}\n",
+ "\n",
+ " We restrict z = 1 and alpha = 0.33\n",
+ " \"\"\"\n",
+ " return 1.0 * K**(0.33) * L**(1.0 - 0.33)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa4f9b57",
+ "metadata": {
+ "id": "aa4f9b57"
+ },
+ "source": [
+ "Now when we have Jupyter evaluate `cobb_douglas?`, our message is\n",
+ "displayed (or use the Contextual Help window with Jupyterlab and `Ctrl-I` or `Cmd-I`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "6c30fe52",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cobb_douglas?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0326dc6",
+ "metadata": {
+ "id": "c0326dc6"
+ },
+ "source": [
+ "We recommend that you always include at least a very simple docstring for\n",
+ "nontrivial functions.\n",
+ "\n",
+ "This is in the same spirit as adding comments to your code — it makes it easier\n",
+ "for future readers/users (including yourself) to understand what the code does."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cfc8949e",
+ "metadata": {
+ "id": "cfc8949e"
+ },
+ "source": [
+ "### Exercise\n",
+ "\n",
+ "See exercise 3 in the [exercise list](#ex2-4)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "05110f75",
+ "metadata": {
+ "id": "05110f75"
+ },
+ "source": [
+ "### Default and Keyword Arguments\n",
+ "\n",
+ "Functions can have optional arguments.\n",
+ "\n",
+ "To accomplish this, we must these arguments a *default value* by saying\n",
+ "`name=default_value` instead of just `name` as we list the arguments.\n",
+ "\n",
+ "To demonstrate this functionality, let’s now make $ z $ and $ \\alpha $\n",
+ "arguments to our cobb_douglas function!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "1ec51f0d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cobb_douglas(K, L, alpha=0.33, z=1):\n",
+ " \"\"\"\n",
+ " Computes the production F(K, L) for a Cobb-Douglas production function\n",
+ "\n",
+ " Takes the form F(K, L) = z K^{\\alpha} L^{1 - \\alpha}\n",
+ " \"\"\"\n",
+ " return z * K**(alpha) * L**(1.0 - alpha)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be2590fd",
+ "metadata": {
+ "id": "be2590fd"
+ },
+ "source": [
+ "We can now call this function by passing in just K and L. Notice that it will\n",
+ "produce same result as earlier because `alpha` and `z` are the same as earlier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "9546cb37",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.6285066872609142"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cobb_douglas(1.0, 0.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e4dfe474",
+ "metadata": {
+ "id": "e4dfe474"
+ },
+ "source": [
+ "However, we can also set the other arguments of the function by passing\n",
+ "more than just K/L."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "780070a8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.0196485018554098"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cobb_douglas(1.0, 0.5, 0.35, 1.6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d421b4f4",
+ "metadata": {
+ "id": "d421b4f4"
+ },
+ "source": [
+ "In the example above, we used `alpha = 0.35`, `z = 1.6`.\n",
+ "\n",
+ "We can also refer to function arguments by their name, instead of only their\n",
+ "position (order).\n",
+ "\n",
+ "To do this, we would write `func_name(arg=value)` for as many of the arguments\n",
+ "as we want.\n",
+ "\n",
+ "Here’s how to do that with our `cobb_douglas` example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "05eb1bbc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9427600308913713"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cobb_douglas(1.0, 0.5, z = 1.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a4f28fe",
+ "metadata": {
+ "id": "6a4f28fe"
+ },
+ "source": [
+ "### Exercise\n",
+ "\n",
+ "See exercise 4 in the [exercise list](#ex2-4).\n",
+ "\n",
+ "In terms of variable scope, the `z` name within the function is\n",
+ "different from any other `z` in the outer scope.\n",
+ "\n",
+ "To be clear,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "557cf5df",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "5"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x = 5\n",
+ "def f(x):\n",
+ " return x\n",
+ "f(x) # \"coincidence\" that it has the same name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1b2022f",
+ "metadata": {
+ "id": "f1b2022f"
+ },
+ "source": [
+ "This is also true with named function arguments, above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "b5d13655",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.9427600308913713"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "z = 1.5\n",
+ "cobb_douglas(1.0, 0.5, z = z) # no problem!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d868edfb",
+ "metadata": {
+ "id": "d868edfb"
+ },
+ "source": [
+ "In that example, the `z` on the left hand side of `z = z` refers\n",
+ "to the local variable name in the function whereas the `z` on the\n",
+ "right hand side refers to the `z` in the outer scope."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "94841288",
+ "metadata": {
+ "id": "94841288"
+ },
+ "source": [
+ "### Aside: Methods\n",
+ "\n",
+ "As we learned earlier, all variables in Python have a type associated\n",
+ "with them.\n",
+ "\n",
+ "Different types of variables have different functions or operations\n",
+ "defined for them.\n",
+ "\n",
+ "For example, I can divide one number by another or make a string uppercase.\n",
+ "\n",
+ "It wouldn’t make sense to divide one string by another or make a number\n",
+ "uppercase.\n",
+ "\n",
+ "When certain functionality is closely tied to the type of an object, it\n",
+ "is often implemented as a special kind of function known as a **method**.\n",
+ "\n",
+ "For now, you only need to know two things about methods:\n",
+ "\n",
+ "1. We call them by doing `variable.method_name(other_arguments)`\n",
+ " instead of `function_name(variable, other_arguments)`. \n",
+ "1. A method is a function, even though we call it using a different\n",
+ " notation. \n",
+ "\n",
+ "\n",
+ "When we introduced the core data types, we saw many methods defined on\n",
+ "these types.\n",
+ "\n",
+ "Let’s revisit them for the `str`, or string type.\n",
+ "\n",
+ "Notice that we call each of these functions using the `dot` syntax\n",
+ "described above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "8f794c03",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s = \"This is my handy string!\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "a2fefb42",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'THIS IS MY HANDY STRING!'"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.upper()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "5fa57a97",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'This Is My Handy String!'"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.title()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6f69a383",
+ "metadata": {
+ "id": "6f69a383"
+ },
+ "source": [
+ "## More on Scope (Optional)\n",
+ "\n",
+ "Keep in mind that with mathematical functions, the arguments are just dummy names\n",
+ "that can be interchanged.\n",
+ "\n",
+ "That is, the following are identical.\n",
+ "\n",
+ "$$\n",
+ "\\begin{eqnarray}\n",
+ " f(K, L) &= z\\, K^{\\alpha} L^{1-\\alpha}\\\\\n",
+ " f(K_2, L_2) &= z\\, K_2^{\\alpha} L_2^{1-\\alpha}\n",
+ "\\end{eqnarray}\n",
+ "$$\n",
+ "\n",
+ "The same concept applies to Python functions, where the arguments are just\n",
+ "placeholder names, and our `cobb_douglas` function is identical to"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "2688518b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.6285066872609142"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def cobb_douglas2(K2, L2): # changed dummy variable names\n",
+ "\n",
+ " # Create alpha and z\n",
+ " z = 1\n",
+ " alpha = 0.33\n",
+ "\n",
+ " return z * K2**alpha * L2**(1 - alpha)\n",
+ "\n",
+ "cobb_douglas2(1.0, 0.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ac370bdb",
+ "metadata": {
+ "id": "ac370bdb"
+ },
+ "source": [
+ "This is an appealing feature of functions for avoiding coding errors: names of variables\n",
+ "within the function are localized and won’t clash with those on the outside (with\n",
+ "more examples in [scope](#scope)).\n",
+ "\n",
+ "Importantly, when Python looks for variables\n",
+ "matching a particular name, it begins in the most local scope.\n",
+ "\n",
+ "That is, note that having an `alpha` in the outer scope does not impact the local one."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "f0a3f795",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.5743491774985174\n",
+ "Setting alpha, does the result change?\n",
+ "0.5743491774985174\n"
+ ]
+ }
+ ],
+ "source": [
+ "def cobb_douglas3(K, L, alpha): # added new argument\n",
+ "\n",
+ " # Create alpha and z\n",
+ " z = 1\n",
+ "\n",
+ " return z * K**alpha * L**(1 - alpha) # sees local argument alpha\n",
+ "\n",
+ "print(cobb_douglas3(1.0, 0.5, 0.2))\n",
+ "print(\"Setting alpha, does the result change?\")\n",
+ "alpha = 0.5 # in the outer scope\n",
+ "print(cobb_douglas3(1.0, 0.5, 0.2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b670be91",
+ "metadata": {
+ "id": "b670be91"
+ },
+ "source": [
+ "A crucial element of the above function is that the `alpha` variable\n",
+ "was available in the local scope of the function.\n",
+ "\n",
+ "Consider the alternative where it is not. We have removed the `alpha`\n",
+ "function parameter as well as the local definition of `alpha`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "9f6e8ae7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "alpha = 0.2 gives 0.5743491774985174"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "alpha = 0.3 gives 0.6155722066724582\n"
+ ]
+ }
+ ],
+ "source": [
+ "def cobb_douglas4(K, L): # added new argument\n",
+ "\n",
+ " # Create alpha and z\n",
+ " z = 1\n",
+ "\n",
+ " # there are no local alpha in scope!\n",
+ " return z * K**alpha * L**(1 - alpha)\n",
+ "\n",
+ "alpha = 0.2 # in the outer scope\n",
+ "print(f\"alpha = {alpha} gives {cobb_douglas4(1.0, 0.5)}\")\n",
+ "alpha = 0.3\n",
+ "print(f\"alpha = {alpha} gives {cobb_douglas4(1.0, 0.5)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3ca2c57c",
+ "metadata": {
+ "id": "3ca2c57c"
+ },
+ "source": [
+ "The intuition of scoping does not apply only for the “global” vs. “function”\n",
+ "naming of variables, but also for nesting.\n",
+ "\n",
+ "For example, we can define a version of `cobb_douglas` which\n",
+ "is also missing a `z` in its inner-most scope, then put the function\n",
+ "inside of another function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "46ae03fa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0.5743491774985174, 0.6155722066724582, 0.7071067811865476]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "z = 1\n",
+ "def output_given_alpha(alpha):\n",
+ " # Scoping logic:\n",
+ " # 1. local function name doesn't clash with global one\n",
+ " # 2. alpha comes from the function parameter\n",
+ " # 3. z comes from the outer global scope\n",
+ " def cobb_douglas(K, L):\n",
+ " return z * K**alpha * L**(1 - alpha)\n",
+ "\n",
+ " # using this function\n",
+ " return cobb_douglas(1.0, 0.5)\n",
+ "\n",
+ "alpha = 100 # ignored\n",
+ "alphas = [0.2, 0.3, 0.5]\n",
+ "# comprehension variables also have local scope\n",
+ "# and don't clash with the alpha = 100\n",
+ "[output_given_alpha(alpha) for alpha in alphas]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "31407dd3",
+ "metadata": {
+ "id": "31407dd3"
+ },
+ "source": [
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a2fb7cae",
+ "metadata": {
+ "id": "a2fb7cae"
+ },
+ "source": [
+ "## Exercises"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e26e52d6",
+ "metadata": {
+ "id": "e26e52d6"
+ },
+ "source": [
+ "### Exercise 1\n",
+ "\n",
+ "What happens if we try different inputs in our Cobb-Douglas production\n",
+ "function?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "a30f1f7c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compute returns to scale with different values of `K` and `L` and `gamma`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d9e2b3ce",
+ "metadata": {
+ "id": "d9e2b3ce"
+ },
+ "source": [
+ "([back to text](#dir2-4-1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d5a4a39b",
+ "metadata": {
+ "id": "d5a4a39b"
+ },
+ "source": [
+ "### Exercise 2\n",
+ "\n",
+ "Define a function named `var` that takes a list (call it `x`) and\n",
+ "computes the variance. This function should use the mean function that we\n",
+ "defined earlier.\n",
+ "\n",
+ "$ \\text{variance} = \\frac{1}{N-1} \\sum_i (x_i - \\text{mean}(x))^2 $"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "d25d314a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Your code here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42b55c73",
+ "metadata": {
+ "id": "42b55c73"
+ },
+ "source": [
+ "([back to text](#dir2-4-2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "746199cb",
+ "metadata": {
+ "id": "746199cb"
+ },
+ "source": [
+ "### Exercise 3\n",
+ "\n",
+ "Redefine the `returns_to_scale` function and add a docstring.\n",
+ "\n",
+ "Confirm that it works by running the cell containing `returns_to_scale?` below.\n",
+ "\n",
+ "*Note*: You do not need to change the actual code in the function — just\n",
+ "copy/paste and add a docstring in the correct line."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "4d575966",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# re-define the `returns_to_scale` function here"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "63efd956",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# test it here\n",
+ "\n",
+ "returns_to_scale?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d070816f",
+ "metadata": {
+ "id": "d070816f"
+ },
+ "source": [
+ "([back to text](#dir2-4-3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe3b042a",
+ "metadata": {
+ "id": "fe3b042a"
+ },
+ "source": [
+ "### Exercise 4\n",
+ "\n",
+ "Experiment with the `sep` and `end` arguments to the `print` function.\n",
+ "\n",
+ "These can *only* be set by name."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "94454380",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Your code here."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b8d7ac0e",
+ "metadata": {
+ "id": "b8d7ac0e"
+ },
+ "source": [
+ "([back to text](#dir2-4-4))"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/_sources/_notebook_build/_03_comparing_plotting_libraries.ipynb b/docs/_sources/_notebook_build/_03_comparing_plotting_libraries.ipynb
new file mode 100644
index 0000000..878289f
--- /dev/null
+++ b/docs/_sources/_notebook_build/_03_comparing_plotting_libraries.ipynb
@@ -0,0 +1,4747 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Comparing Plotting Libraries and Declarative Visualizations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from plotnine import *\n",
+ "from matplotlib import pyplot as plt\n",
+ "from plotnine import data\n",
+ "import plotly.express as px\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mpg = data.mpg"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Bar Chart"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
manufacturer
\n",
+ "
model
\n",
+ "
displ
\n",
+ "
year
\n",
+ "
cyl
\n",
+ "
trans
\n",
+ "
drv
\n",
+ "
cty
\n",
+ "
hwy
\n",
+ "
fl
\n",
+ "
class
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
audi
\n",
+ "
a4
\n",
+ "
1.8
\n",
+ "
1999
\n",
+ "
4
\n",
+ "
auto(l5)
\n",
+ "
f
\n",
+ "
18
\n",
+ "
29
\n",
+ "
p
\n",
+ "
compact
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
audi
\n",
+ "
a4
\n",
+ "
1.8
\n",
+ "
1999
\n",
+ "
4
\n",
+ "
manual(m5)
\n",
+ "
f
\n",
+ "
21
\n",
+ "
29
\n",
+ "
p
\n",
+ "
compact
\n",
+ "
\n",
+ "
\n",
+ "
2
\n",
+ "
audi
\n",
+ "
a4
\n",
+ "
2.0
\n",
+ "
2008
\n",
+ "
4
\n",
+ "
manual(m6)
\n",
+ "
f
\n",
+ "
20
\n",
+ "
31
\n",
+ "
p
\n",
+ "
compact
\n",
+ "
\n",
+ "
\n",
+ "
3
\n",
+ "
audi
\n",
+ "
a4
\n",
+ "
2.0
\n",
+ "
2008
\n",
+ "
4
\n",
+ "
auto(av)
\n",
+ "
f
\n",
+ "
21
\n",
+ "
30
\n",
+ "
p
\n",
+ "
compact
\n",
+ "
\n",
+ "
\n",
+ "
4
\n",
+ "
audi
\n",
+ "
a4
\n",
+ "
2.8
\n",
+ "
1999
\n",
+ "
6
\n",
+ "
auto(l5)
\n",
+ "
f
\n",
+ "
16
\n",
+ "
26
\n",
+ "
p
\n",
+ "
compact
\n",
+ "
\n",
+ "
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
...
\n",
+ "
\n",
+ "
\n",
+ "
229
\n",
+ "
volkswagen
\n",
+ "
passat
\n",
+ "
2.0
\n",
+ "
2008
\n",
+ "
4
\n",
+ "
auto(s6)
\n",
+ "
f
\n",
+ "
19
\n",
+ "
28
\n",
+ "
p
\n",
+ "
midsize
\n",
+ "
\n",
+ "
\n",
+ "
230
\n",
+ "
volkswagen
\n",
+ "
passat
\n",
+ "
2.0
\n",
+ "
2008
\n",
+ "
4
\n",
+ "
manual(m6)
\n",
+ "
f
\n",
+ "
21
\n",
+ "
29
\n",
+ "
p
\n",
+ "
midsize
\n",
+ "
\n",
+ "
\n",
+ "
231
\n",
+ "
volkswagen
\n",
+ "
passat
\n",
+ "
2.8
\n",
+ "
1999
\n",
+ "
6
\n",
+ "
auto(l5)
\n",
+ "
f
\n",
+ "
16
\n",
+ "
26
\n",
+ "
p
\n",
+ "
midsize
\n",
+ "
\n",
+ "
\n",
+ "
232
\n",
+ "
volkswagen
\n",
+ "
passat
\n",
+ "
2.8
\n",
+ "
1999
\n",
+ "
6
\n",
+ "
manual(m5)
\n",
+ "
f
\n",
+ "
18
\n",
+ "
26
\n",
+ "
p
\n",
+ "
midsize
\n",
+ "
\n",
+ "
\n",
+ "
233
\n",
+ "
volkswagen
\n",
+ "
passat
\n",
+ "
3.6
\n",
+ "
2008
\n",
+ "
6
\n",
+ "
auto(s6)
\n",
+ "
f
\n",
+ "
17
\n",
+ "
26
\n",
+ "
p
\n",
+ "
midsize
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
234 rows × 11 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " manufacturer model displ year cyl trans drv cty hwy fl \\\n",
+ "0 audi a4 1.8 1999 4 auto(l5) f 18 29 p \n",
+ "1 audi a4 1.8 1999 4 manual(m5) f 21 29 p \n",
+ "2 audi a4 2.0 2008 4 manual(m6) f 20 31 p \n",
+ "3 audi a4 2.0 2008 4 auto(av) f 21 30 p \n",
+ "4 audi a4 2.8 1999 6 auto(l5) f 16 26 p \n",
+ ".. ... ... ... ... ... ... .. ... ... .. \n",
+ "229 volkswagen passat 2.0 2008 4 auto(s6) f 19 28 p \n",
+ "230 volkswagen passat 2.0 2008 4 manual(m6) f 21 29 p \n",
+ "231 volkswagen passat 2.8 1999 6 auto(l5) f 16 26 p \n",
+ "232 volkswagen passat 2.8 1999 6 manual(m5) f 18 26 p \n",
+ "233 volkswagen passat 3.6 2008 6 auto(s6) f 17 26 p \n",
+ "\n",
+ " class \n",
+ "0 compact \n",
+ "1 compact \n",
+ "2 compact \n",
+ "3 compact \n",
+ "4 compact \n",
+ ".. ... \n",
+ "229 midsize \n",
+ "230 midsize \n",
+ "231 midsize \n",
+ "232 midsize \n",
+ "233 midsize \n",
+ "\n",
+ "[234 rows x 11 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mpg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Number of Cars by Make')"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "
+
+
+
+
![\"QuantEcon\"](\"https://assets.quantecon.org/img/qe-menubar-logo.svg\"){kind=logo}
\n",
+ " ![](\"http://chris35wills.github.io/courses/pydata_stack.png\")
\n",
+ "![](\"https://fgnt.github.io/python_crashkurs_doc/_images/numpy_array_t.png\")
\n",
+ "![](\"https://upload.wikimedia.org/wikipedia/commons/1/18/Matrix_multiplication_qtl1.svg\")
\n",
+ "