In this lecture we’ll start examining different features of the Julia and Jupyter environments.
Recall that the easiest way to get started with these notebooks is to follow the cloning instructions earlier.
To summarize, if on a desktop you should clone the notebooks repository https://github.com/quantecon/quantecon-notebooks-julia, then in a Julia REPL type
using IJulia; jupyterlab()
Hint: Julia will remember the last commands in the REPL, so you can use up-arrow to restart JupyterLab.
Alternatively, if you are using an online Jupyter, then you can directly open a new notebook.
Finally, if you installed Jupyter separately or have added added Jupyter to the Path then
cd to the folder location in a terminal, and run
Regardless, your web browser should open to a page that looks something like this
The page you are looking at is called the “dashboard”.
If you click on “Julia 1.x.x” you should have the option to start a Julia notebook.
Here’s what your Julia notebook should look like
The notebook displays an active cell, into which you can type Julia commands.
Notice that in the previous figure the cell is surrounded by a blue border.
This means that the cell is selected, and double-clicking will place it in edit mode.
As a result, you can type in Julia code and it will appear in the cell.
When you’re ready to execute these commands, hit
The next thing to understand about the Jupyter notebook is that it uses a modal editing system.
This means that the effect of typing at the keyboard depends on which mode you are in.
The two modes are
- Indicated by a green border around one cell, as in the pictures above.
- Whatever you type appears as is in that cell.
- The green border is replaced by a blue border.
- Key strokes are interpreted as commands — for example, typing b adds a new cell below the current one.
(To learn about other commands available in command mode, go to “Keyboard Shortcuts” in the “Help” menu)
- To switch to command mode from edit mode, hit the
- To switch to edit mode from command mode, hit
Enteror click in a cell.
The modal behavior of the Jupyter notebook is a little tricky at first but very efficient when you get used to it.
Working with Files¶
To run an existing Julia file using the notebook you can copy and paste the contents into a cell in the notebook.
If it’s a long file, however, you have the alternative of
- Saving the file in your present working directory.
include("filename")in a cell.
The present working directory can be found by executing the command
using InstantiateFromURL # optionally add arguments to force installation: instantiate = true, precompile = true github_project("QuantEcon/quantecon-notebooks-julia", version = "0.7.0")
in a new cell (i.e.,
Shift + Enter).
This might take 15-20 minutes depending on your setup, as it installs a large set of packages for our use.
Run the following cell
using Plots gr(fmt=:png); plot(sin, -2π, 2π, label="sin(x)")
You’ll see something like this (although the style of plot depends on your installation)
Note: The “time-to-first-plot” in Julia takes a while, since it needs to compile many functions - but is almost instantaneous the second time you run the cell.
Working with the Notebook¶
Let’s go over some more Jupyter notebook features — enough so that we can press ahead with programming.
Tab completion in Jupyter makes it easy to find Julia commands and functions available.
For example if you type
rep and hit the tab key you’ll get a list of all
commands that start with
To get help on the Julia function such as
Documentation should now appear in the browser
In addition to executing code, the Jupyter notebook allows you to embed text, equations, figures and even videos in the page.
For example, here we enter a mixture of plain text and LaTeX instead of code
Esc to enter command mode and then type
m to indicate that we
are writing Markdown, a mark-up language similar to (but simpler than) LaTeX.
(You can also use your mouse to select
Markdown from the
Code drop-down box just below the list of menu items)
Shift + Enter to produce this
You can execute shell commands (system commands) in Jupyter by prepending a semicolon.
; ls will execute the UNIX style shell command
which — at least for UNIX style operating systems — lists the
contents of the current working directory.
These shell commands are handled by your default system shell and hence are platform specific.
You can execute package operations in the notebook by prepending a
] st will give the status of installed packages in the current environment.
Note: Cells where you use
] must not have any other instructions in them (i.e., they should be one-liners).
Notebook files are just text files structured in JSON and typically end with
A notebook can easily be saved and shared between users — you just need to
pass around the
To open an existing
ipynb file, import it from the dashboard (the first
browser page that opens when you start Jupyter notebook) and run the cells or edit as discussed above.
The Jupyter organization has a site for sharing notebooks called nbviewer which provides a static HTML representations of notebooks.
QuantEcon also hosts the QuantEcon Notes website, where you can upload and share your notebooks with other economists and the QuantEcon community.
Using the REPL¶
As we saw in the desktop installation, the REPL is a Julia specific terminal.
It becomes increasingly important as you learn Julia, and you will find it to be a useful tool for interacting with Julia and installing packages.
As a reminder, to open the REPL on your desktop, either
- Navigating to Julia through your menus or desktop icons (Windows, Mac), or
- Opening a terminal and typing
If you are using a JupyterHub installation, you can start the REPL in JupyterLab by choosing
- Choose “New Launcher”
- Choose a
We examine the REPL and its different modes in more detail in the tools and editors lecture.
(Optional) Adding Jupyter to the Path¶
If you installed Jupyter using Julia, then you may find it convenient to add it to your system path in order to launch JupyterLab without running a Julia terminal.
The default location for the Jupyter binaries is relative to the
.julia folder (e.g.,
"C:\Users\USERNAME\.julia\conda\3\Scripts on Windows).
You can find the directory in a Julia REPL using by executing
] add Conda using Conda Conda.SCRIPTDIR
On Linux/OSX, you could add that path to your
On Windows, to add directly to the path, type
; to enter shell mode and then execute
setx PATH "$(Conda.SCRIPTDIR);%PATH%"