Installing a Native Ubuntu Terminal on Windows 10

Modern clusters and cloud platforms requires users to login through SSH on Unix terminals in order to have access to its functionalities. Such terminals also give its users access to a wide range of commands and programs such as awk and Vim which are not available on Windows. Linux and Mac users have such terminals installed by default, but Windows users normally have to install Cygwin or use a browser-based terminal, both with limited capabilities such as the inability to install new packages.

Fortunately, Microsoft has established a partnership with Canonical (Ubuntu’s parent company) which brought part of the Linux kernel to Windows 10, allowing users to install Ubuntu’s terminal on Windows through official means without the need for compatibility layers. Using Ubuntu’s terminal on Windows has the advantages of being able to use apt-get and dpkg to install new packages, which was not possible with Cygwin, and of running Python and C/C++ codes faster. Here are the steps to install Ubuntu terminal on Windows 10:

  1. On Windows Settings, click on “Update & Security.” There, click on “For Developers” close to the bottom on left pane and turn on the option “Developer Mode.”
  2. Windows Settings, click on “Apps” -> “Programs and Features” (right pane) -> “Turn Windows features on or off”  (left pane) and check “Windows Subsystem for Linux.”

screenshot_windows_subsystems_for_linux.png

  1. Restart your computer.
  2. Open Windows PowerShell as administrator, type the following line and press enter:
Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux
  1. Open Microsoft Store (Windows’ app store), look for “Ubuntu Terminal,” and install it.
  2. Now you should have it installed and a shortcut on your quick-start bar.
  3. Open the Ubuntu Terminal and type:
sudo apt-get update

In order to install programs such as the Intel compiler and profiler (free for students), pip, Vim, GNUPlot or the most recent version of GCC, just type:

sudo apt-get install program_to_be_installed

If the package you installed has graphical components, such GNUPlot and Python/Matplotlib, you will need to install a program on Windows to display the graphical components from the Ubuntu terminal. One such option is Xming. To use Xming, follow the following steps:

    1. Install Xming from here.
    2. Run it. Click “next” until you can click on “Finish.” This process will have to be repeated every time you open the terminal.
    3. Open Ubuntu terminal
    4. Type the following and press enter:
echo "export DISPLAY=localhost:0.0" >> .bashrc
    1. Close and re-open the terminal
    2. In order to make sure you can run graphic applications, run the following two commands:
sudo apt-get install x11-apps
xeyes
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Transitioning from R to Python with Spyder

I am currently in the midst of transitioning from R, my preferred programming language, over to Python. While the syntax of the languages is similar, I was quickly overwhelmed with the choices of Integrated Development Environments (IDEs) and text editors for Python. Choosing an IDE is a deeply personal choice and the one you choose depends on your skill level and programming needs. I have tried out many different IDEs, and, for the purpose of making a smooth, intuitive, transition from R to Python, the best one for me was Spyder (Scientific Python Development Environment). In this blog post, I will give an overview of the Spyder environment and step through some of its functionalities.

Installation

The easiest way to install Spyder is through a Python Scientific Distribution found here. There are three options, but I chose to install Anaconda which gives you the core Python language, over 100 main Python libraries, and Spyder. It is an incredibly efficient way to get everything you need in just one download and works for both Windows and Mac. Once this is installed, you can open Spyder immediately.

 Environment

Figure 1: Spyder Environment

The first aspect that I like about Spyder is how similar it looks to RStudio and Matlab, as shown in Figure 1. This made the transition very easy for me. As shown in Figure 2, the Spyder environment is comprised of a collection of panes which can be repositioned by dragging if a different format is more intuitive to the user. To see which panes are open, click View->Panes. The most useful panes will already be open by default. You can choose to keep either the console or the IPython console. This is a matter of preference and I chose to use the regular console.

Figure 2: Choosing Panes

At the top of the screen is your directory, which, by default, is set to the folder which contains Anaconda. You can change it to your preferred location on your computer by clicking the folder icon next to the drop down arrow.

Editor

The leftmost pane is the editor which is where code can be written. The Spyder editor has features such as syntax coloring and real-time code analysis. By default, a temporary script, temp.py, will be open. Go ahead and save this in your current directory. Make sure that the file shown in the gray bar matches your directory (shown in Figure 3).

Figure 3: Setting a Directory

Let’s write a simple script to test out the environment (shown in Figure 4).

Figure 4: Sample Script and Run Settings

Click the green arrow at the top of the screen to run the script. A box will pop up with Run Settings. Make sure the working directory is correct and click “Run.” If you just want to run a certain section of the script, you can highlight that section and click the second green arrow with the blue and orange box.

Console

The results from the script will appear in the console, which is my bottom right pane. The user can also execute a command directly in this console.

Figure 5: Console

Object Inspector/Variable Explorer/File Explorer

The last major aspect of the environment is the top right pane, which is a comprised of three tabs. The first tab is the object inspector, which is analogous to RStudio’s “help” tab. You can search for information on libraries, functions, modules, and classes.

Figure 6: Object Inspector

The second tab is the variable explorer, which is the same as RStudio’s “Environment” tab. This tab conveniently shows the type, size, and value of your variables. The results from our test script are shown in Figure 7.

Figure 7: Variable Explorer

Finally, the last tab is a file explorer which lists out all of the files and folder in your the current directory.

Debugging with Spyder

The Python debugger, pdb, is partly integrated into Spyder. The debugging tools are located in blue, adjacent to the green “run” buttons. By double-clicking specific lines in the code, the user can set breakpoints where the debugger will stop and results from the debugger are displayed in the console.

Figure 8: Debugging Tools

 

Those are the main components of Spyder! As you can see, it is a fairly uncomplicated and intuitive IDE. Hopefully this overview will make the transition from R or Matlab to Python much easier. Go forth and conquer!

 

Jupyter Notebook: A “Hello World” Overview

Jupyter Notebook: A “Hello World” Overview

Jupyter Notebook: Overview

When first learning Python, I was introduced to Jupyter Notebook as an extremely effective IDE for group-learning situations. I’ve since used this browser-based interactive shell for homework assignments, data exploration and visualization, and data processing. The functionality of Jupyter Notebook extends well past simple development and showcasing of code as it can be used with almost any Python library (except for animated figures right before a deadline). Jupyter Notebook is my go-to tool when I am writing code on the go.

As a Jupyter Notebook martyr, I must point out that Jupyter Notebooks can be used for almost anything imaginable. It is great for code-oriented presentations that allow for running live code, timing of lines of code and other magic functions, or even just sifting through data for processing and visualization. Furthermore, if documented properly, Jupyter Notebook can be used as an easy guide for stepping people through lessons. For example, check out the structure of this standalone tutorial for NumPy—download and open it in Jupyter Notebook for the full experience. In a classroom setting, Jupyter Notebook can utilize nbgrader to create quizzes and assignments that can be automatically graded. Alas, I am still trying to figure out how to make it iron my shirt.

1_XMB2FXE3sN4FTwaO8dMMeA

A Sample Jupyter Notebook Presentation (credit: Matthew Speck)

One feature of Jupyter Notebook is that it can be used for a web application on a server-client structure to allow for users to interact remotely via ssh or http. In an example is shown here, you can run Julia on this website even if it is not installed locally. Furthermore, you can use the Jupyter Notebook Viewer to share notebooks online.  However, I have not yet delved into these areas as of yet.

For folks familiar with Python libraries through the years, Jupyter Notebook evolved from IPython and has overtaken its niche. Notably, it can be used for over 40 languages—the original intent was to create an interface for Julia, Python and R, hence Ju-Pyt-R— including Python, R, C++, and more. However, I have only used it for Python and each notebook kernel will run in a single native language (although untested workaround exist).

Installing and Opening the Jupyter Notebook Dashboard

While Jupyter Notebook comes standard with Anaconda, you can easily install it via pip or by checking out this link.

As for opening and running Jupyter Notebook, navigate to the directory (in this case, I created a directory in my username folder titled ‘Example’) you want to work out of in your terminal (e.g. Command Prompt in Windows, Terminal in MacOS) and run the command ‘jupyter notebook’.

command_prompt_start

Opening the Command Prompt

Once run, the following lines appear in your terminal but are relatively unimportant. The most important part is being patient and waiting for it to open in your default web browser—all mainstream web browsers are supported, but I personally use Chrome.

 

 

If at any time you want to exit Jupyter Notebook, press Ctrl + C twice in your terminal to immediately shut down all running kernels (Windows and MacOS). Note that more than one instance of Jupyter Notebook can be running by utilizing multiple terminals.

Creating a Notebook

Once Jupyter Notebook opens in your browser, you will encounter the dashboard. All files and subdirectories will be visible on this page and can generally be opened or examined.

jupyter_start

Initial Notebook Dashboard Without Any Files

If you want to create a shiny new Notebook to work in, click on ‘New’ and select a new Notebook in the language of your choice (shown below). In this case, only Python 3 has been installed and is the only option available. Find other language kernels here.

jupyter_open

Opening a New Notebook

Basic Operations in Jupyter Notebook

Once opened, you will find an untitled workbook without a title or text. To edit the title, simply left-click on ‘Untitled’ and enter your name of choice.

jupyter_new

Blank Jupyter Notebook

To write code, it is the same as writing a regular Python script in any given text editor. You can divide your code into separate sections that are run independently instead of running the entire script again. However, when importing libraries and later using them, you must run the corresponding lines to import them prior to using the aforementioned libraries.

To run code, simply press Shift + Enter while the carat—the blinking text cursor—is in the cell.

jupyter_hello_world

Jupyter Notebook with Basic Operations

After running any code through a notebook, the file is automatically backed up in a hidden folder in your working directory. Note that you cannot directly open the notebook (IPYNB File) by double-clicking on the file. Rather, you must reopen Jupyter Notebook and access it through the dashboard.

jupyter_folder_windows

Directory where Sample Jupyter Notebook Has Been Running

As shown below, you can easily generate and graph data in line. This is very useful when wanting to visualize data in addition to modifying a graphic (e.g. changing labels or colors). These graphics are not rendered at the same DPI as a saved image or GUI window by default but can be changed by modifying matplotlib’s rcParams.

jupyter_graphic

Example Histogram in Jupyter Notebook

Conclusion

At this point, there are plenty of directions you can proceed. I would highly suggest exploring some of the widgets available which include interesting interactive visualizations. I plan to explore further applications in future posts, so please feel free to give me a yell if you have any ideas.