Welcome to Bootcamp2015 s documentation!

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Baldric Eaton
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1 Welcome to Bootcamp2015 s documentation! This website (or pdf) will be home to some resources that will be useful for boot campers and instructors. Lecture notes and assignments for the econ course associated with boot camp will be posted here for convinient access. Students will receive printed copies of the math materials in class. Boot camp is split into two courses: Math 513R and Econ 413R. The math class will be held each Monday, Wednesday, and Friday from 8:00-10:00 AM and the economics class will be on the same days from 10:00-12:00 AM. There will also be lab class periods from 8:00-12:00 AM Tuesday and Thursday. Below is a tentative layout of the schedule: Date Math Topic Economics Topic Lab Topic 28-Apr Tue Dynamic Programming OLG 29-Apr Wed Dynamic Programming OLG 30-Apr Thur Python Essentials 1-May Fri Dynamic Programming OLG 4-May Mon Inner Product Spaces Policy Function Iteration 5-May Tue Standard Library, OOP 6-May Wed Inner Product Spaces DSGE 7-May Thur Profiling, Numerical Derivatives 8-May Fri Inner Product Spaces DSGE 11-May Mon Spectral Theory Linearization 12-May Tue LU, QR, Decompositions 13-May Wed Spectral Theory Perturbation Methods 14-May Thur Least Squares, SVD 15-May Fri Spectral Theory Data Filtering 18-May Mon Convex Analysis Asset Pricing 19-May Tue Data Structures 20-May Wed Convex Analysis Asset Pricing 21-May Thur Pandas 22-May Fri Convex Analysis Asset Pricing 25-May Mon Memorial Day 26-May Tue Optimization Overview VARs 27-May Wed Unconstrained Optimization Numerical Integration 28-May Thur Newton s Method, Integration 29-May Fri Unconstrained Optimization Numerical Integration 1-Jun Mon Linear Optimization Distribution Fitting 2-Jun Tue Wrapping, Speed, Cython 3-Jun Wed Linear Optimization Distribution Fitting 4-Jun Thur Floating Point Issues

2 Date Math Topic Economics Topic Lab Topic 5-Jun Fri Linear Optimization SMM 8-Jun Mon Nonlinear Optimization Parallel Computing 9-Jun Tue Eigenvalue Solvers 10-Jun Wed Nonlinear Optimization Parallel Computing 11-Jun Thur Markov Chains 12-Jun Fri Nonlinear Optimization Parallel Computing 13-Jun Sat St. Euler s Day Feast 19-Jun Fri All Homework Due We recommend that all users of this site stop by the Introduction page to get up to speed with a few things like how to submit assignments and making sure you are up and running with a suitable python distribution. To navigate this site use the Table of Contents found at the bottom of this page. There you will see links to the introduction page, as well as links to the home page for the econ and math homework and the labs. Table of Contents Introduction o Setting Up Git Accounts o Suggested order for Work o Options o Building From Source o Using a Scientific Distribution Labs Using LaTeX Index Module Index Search Page Introduction Welcome to boot camp! This will hopefully be one of the most instructive (and difficult) things you do while here at BYU. The goal of boot camp is to give students the mathematical and computational tools as well as the economic know how to make co-author level contributions on relevant, cutting-edge research projects. This page will explain some of the administrative things boot campers should know in order to be successful during camp. Setting Up Git Accounts There are a few steps you need to follow to get things set up:

3 1. Go to bitbucket.org and create an account if you haven t done so already. 2. Create a new, private repository on bitbucket (private is important). Name it something that makes sense. Perhaps your name and mclboot or something like that. 3. Clone the repository to your local machine and you are good to go. (Note, if you haven t set up ssh keys for your computer on bitbucket ask Spencer how to do it or just clone using https). git Basics After a brief explanation on how to move a git repository to your computer, we will explain how to work with git. We will discuss several basic commands including add, clone, commit, pull, push, and status. Before you are able to do anything else, you need to clone your git repository onto your computer. Cloning a git repository onto your computer takes all of the files that are stored remote and creates a folder on your computer with the same name as the repository that is linked to the remote version of the repository (These two versions will only communicate if you tell them to). To clone into the directory, find/create the repository online at bitbucket.org. It will take you to a page like this (The repository I am in is Growth-and- Cycles). Then, as you see below, click clone and copy the text in the box. Note: Only use SSH if you have already set up an SSH key (If you have questions about SSH keys ask one of the TAs) otherwise, use https.

Enter your terminal/git shell and cd into the folder that you want to house your repository (For me, under documents I would go to a folder called Research that houses all of

Remember when you clone it will create a new folder with the repository name, so if you cloned into research the file location would be /home/cc7768/documents/research.

4 Enter your terminal/git shell and cd into the folder that you want to house your repository (For me, under documents I would go to a folder called Research that houses all of my research repos). Remember when you clone it will create a new folder with the repository name, so if you cloned into research the file location would be /home/cc7768/documents/research. Once you are in that folder, just paste what you copied. It should say something like: git clone Congratulations! You should have cloned into your first repository. If not, review instructions again or ask one of the TAs. We will now use the picture below to explain the rest of how git works.

5 As you can see above there are several phases that files in git are seen as. The cloud up top is the remote repository i.e. bitbucket server where your files are stored. As you change/create your files on your computer, your files will become either tracked or untracked changes (If the file has been committed before then it will be a tracked change and if it is new to the repository then it will be an untracked change; as represented by bottom two boxes). After any change that you make to files in your git repo, the next step is to stage that change for commit. This is done by the git add command (You need to use git add -A for untracked changes). Use git add <filename> to add the files that you want to be moved to the remote repo. Once you have git added these files then they become staged (2nd box from top). Once your changes have become staged then it is helpful to use the git status command to make sure you have only staged the files that you want to commit. After you have verified that these are the files you want to keep then you can use the git commit command to save your changes as a commit on your computer (This moves them to the top box). After committing the files need to be moved to the remote repository so that others can have access to them. This is done by using the command git push. Your files have now been moved to the remote repository and your collaborators have access to your changes. To get the changes that have been made by your colleagues, all you do is type git pull and that will bring all the changes committed to your remote repository by others to your computer (If git push or git pull isn t working then try using git push origin and git pull origin). Suggested order for Work Here is a suggested order of work for git. git pull origin This will pull all changes previously made and prevents you from making changes that were already made. After pulling the changes made by others then you can make the changes to the files that you need to work on. Then you need to stage them. git add <files> This will prepare the files to be committed. git status This command will tell you whether they are staged/unstaged/untracked or if your directory is current. This is useful because you want to make sure you are only committing files that you are ready to commit. git commit This will commit the changes that have been staged for commit. It is important to note that you can git add and git commit multiple commits before pushing and it is useful to split your commits into useful groups. For example, if you make a lot of progress on both code and latex files then you should try and commit the changes in two commits (One for the changes to the code and one for the latex files). git push Pushes the changes that you have made out to the remote repository for others to see.

6 Options There are several options that you can include with your git add/commit command. I will list some of the basic ones because they turn out to be pretty useful. git add <file>: This is the standard add. Adds a file git add <directory>: This will stage all changes in <directory> for commit git add -A: This adds untracked files to the commit git add -p: This begins a interactive staging session. The terminal presents you with a hunk of changes and prompt you for a command: o y to stage o n to ignore o s to split into smaller hunks o e to manually edit o q to exit git add.: Stages ALL files (both tracked and untracked) git add *: Stages all tracked changes git commit -m ' ': In the quotations you should put your commit message. Easier than being moved to VIM to upload a message describing your commit. git commit -am ' ': Same thing as writing git add * and then git commit -m ' ' (adds and commits files) For more information refer to the presentation here, gitref.org, or gitbook. Installing Python There are many ways to install python; here we will cover a few of them. We would like to emphasize that the python 2.7 line is more commonly used in scientific applications than the 3.x line. This is due to some internal changes within python 3.x that broke backwards compatibility (some 2.x code doesn t run in 3.x). Many scientific packages work on both 2.x and 3.x, but some do not. To avoid any potential issues, we recommend using python 2.7.x as your main python. Building From Source You can build your python distribution completely from source. This option provides the most flexibility, but is also the most difficult. If you aren t used to lower-level computer tasks, you should probably move to a different option. To build from source you would download a fresh copy of python from the main python website. Again as noted above, we recommend using python 2.7.x instead of python 3.x. After installing python you will need to install various pacakges. At a very minimum, for bootcamp you will need to install numpy, scipy, and matplotlib. Other packages can be installed from the command line via easy_install. For example, if you wanted to install pandas, you would do easy_install pandas from the unix terminal or windows command prompt.

7 Using a Scientific Distribution Because the scientific python community is so active, there are a number of pre-compiled distributions that already contain many scientific packages. This is much easier than building from source and will result in a stable scientific python distribution you can customize or extend however you see fit. Although there are many distributions available, we would recommend using either the anaconda python distribution or Enthought canopy. Both of these distributions will provide the basics like numpy, scipy, matplotlib, and sympy. However, the emphasis for what each package does is different, as evidenced in the extra packages they bring with it. anaconda is build for science and data analysis. Over 50 of the most useful scientific packages come with anaonda, including some that are only available in anaconda. canopy is a more well-rounded distribution. There are over 100 packages included with canopy, including the main scientific ones. What you get with canopy, as opposed to anaconda, is a great platform for general python programming or application building - with the ability to do scientific work. At the end of the day, they are both great options that I (Spencer) use regularly. It is important to emphasize that both canopy and anaconda provide free and commercial versions of their distributions. The commercial distributions contain more packages and optimized versions of some standard ones (like numpy and scipy). As an academic user, you have free access to the commercial version as long as you register with account. We highly recommend doing this. Labs This page will hold all of the Python lab assignments that are assigned on Tues/Thurs. The labs that have the form Lab1v1 come from Labs for Foundations of Applied Mathematics, by Jeffrey Humpherys and Tyler Jarvis. All other Python labs come from the website quant-econ. Assigned on 4/28/15 (Due Wednesday 5/6/15) o Quant- Econ: Setting Up your Python Environment - QEsetup. o Lab1v1.pdf. o Quant- Econ: Python Essentials - QEessent. o Lab2v1.pdf. o Lab3v1.pdf. o Lab6v1.pdf. Assigned on 5/5/15 (Due Wednesday 5/13/15) o Lab4v1.pdf. o Lab1v2.pdf. o Lab2v2.pdf. o Quant- Econ: Object Oriented Programming - QEoop. o Lab15v1.pdf. o Lab14v1.pdf. Assigned on 5/12/15 (Due Wednesday 5/20/15) o Lab5v1.pdf. o Lab7v1.pdf. o Lab8v1.pdf. o Lab11v1.pdf.

8 Assigned on 5/19/15 (Due Wednesday 5/27/15) o Lab3v2.pdf. o Lab12v1.pdf. o Lab3v3.pdf. o Lab4v3.pdf. Assigned on 5/26/15 (Due Wednesday 6/3/15) o Lab16v1.pdf. o Lab17v1.pdf. o Lab18v1.pdf. o Lab17v2.pdf. Assigned on 6/2/15 (Due Wednesday 6/10/15) o Lab19v1.pdf. o Quant- Econ: Need for Speed - QEspeed. o Lab14v2.pdf. o Lab15v2.pdf. Assigned on 6/9/15 (Due Wednesday 6/17/15) o Lab10v1.pdf. o Quant- Econ: Finite Markov Chains - QEmarkov. Using LaTeX We will flesh this section out in the future, but we wanted to at least put a few resources up here. Daniel Walton has written a nice introduction and primer for using LaTeX :download:`here entitled WaltonTeXguide.pdf<resources/WaltonTeXguide.pdf>`_. Jeremy Bejarano also has a nice introduction to LaTeX page with screencasts at jeremylatex.

Lecture 3: Processing Language Data, Git/GitHub. LING 1340/2340: Data Science for Linguists Na-Rae Han

Lecture 3: Processing Language Data, Git/GitHub LING 1340/2340: Data Science for Linguists Na-Rae Han Objectives What do linguistic data look like? Homework 1: What did you process? How does collaborating