There are various features of IJulia that allow you to interact with a running IJulia kernel.
Detecting that code is running under IJulia
If your code needs to detect whether it is running in an IJulia notebook (or other Jupyter client), it can check
isdefined(Main, :IJulia) && Main.IJulia.inited.
The default Jupyter kernel that is installed by IJulia starts with the Julia command line flag
JuliaProject.toml) in the folder of a notebook (or in a parent folder of this notebook) will therefore automatically become the active project for that notebook. Users that don't want this behavior should install an additional IJulia kernel without that command line flag (see section Installing additional Julia kernels).
If an existing
Project.toml file is not found then, by default, an IJulia notebook will try to run a Julia kernel with its active project set from the global or default environment (usually of the form
~/.julia/environments/v1.x). If the IJulia package is not installed in that environment, then the Julia kernel selected by default will not be able to connect, and a Connection failed error will be displayed. In this case, users should install a additional Julia kernel that uses their chosen Julia environment. For example, if the desired environment is currently activated in the REPL then one possibility is to execute
IJulia.installkernel("Julia MyProjectEnv", "--project=$(Base.active_project())")
and subsequently select the kernel starting with
Julia MyProjectEnv from Kernel > Change Kernel in the menu of the Jupyter notebook.
Customizing your IJulia environment
If you want to run code every time you start IJulia–-but only when in IJulia–-add a
startup_ijulia.jl file to your Julia
config directory, e.g.,
Julia and IPython Magics
One difference from IPython is that the IJulia kernel does not use "magics", which are special commands prefixed with
%% to execute code in a different language. Instead, other syntaxes to accomplish the same goals are more natural in Julia, work in environments outside of IJulia code cells, and are often more powerful.
However, if you enter an IPython magic command in an IJulia code cell, it will print help explaining how to achieve a similar effect in Julia if possible. For example, the analogue of IPython's
%load filename in IJulia is
Input and output
Prompting for user input
When you are running in a notebook, ordinary I/O functions on
stdin do not function. However, you can prompt for the user to enter a string in one of two ways:
readline(stdin)both open a
stdin>prompt widget where the user can enter a string, which is returned by
IJulia.readprompt(prompt)displays the prompt string
promptand returns a string entered by the user.
IJulia.readprompt(prompt, password=true)does the same thing but hides the text the user types.
Analogous to the IPython.display.clear_output() function in IPython, IJulia provides a function:
to clear the output from the current input cell. If the optional
wait argument is
true, then the front-end waits to clear the output until a new output is available to replace it (to minimize flickering). This is useful to make simple animations, via repeated calls to
IJulia.clear_output(true) followed by calls to
display(...) to display a new animation frame.
Input and output history
IJulia will store dictionaries of the user's input and output history for each session in exported variables called
Out. To recall old inputs and outputs, simply index into them, e.g.
Out. Sometimes, a user may find themselves outputting large matrices or other datastructures which will be stored in
Out and hence not garbage collected, possibly hogging memory. If you find that IJulia is using too much memory after generating large outputs, empty this output dictionary:
Default display size
When Julia displays a large data structure such as a matrix, by default it truncates the display to a given number of lines and columns. In IJulia, this truncation is to 30 lines and 80 columns by default. You can change this default by the
COLUMNS environment variables, respectively, which can also be changed within IJulia via
ENV["LINES"] = 60). (Like in the REPL, you can also display non-truncated data structures via
Preventing truncation of output
The new default behavior of IJulia is to truncate stdout (via
println) after 512kb. This to prevent browsers from getting bogged down when displaying the results. This limit can be increased to a custom value, like 1MB, as follows
IJulia.set_max_stdio(1 << 20)
Setting the current module
The module that code in an input cell is evaluated in can be set using
Main.IJulia.set_current_module(::Module). It defaults to
Opting out of soft scope
By default, IJulia evaluates user code using "soft" global scope, via the SoftGlobalScope.jl package: this means that you don't need explicit
global declarations to modify global variables in
for loops and similar, which is convenient for interactive use.
To opt out of this behavior, making notebooks behave similarly to global code in Julia
.jl files, you can set
IJulia.SOFTSCOPE = false at runtime, or include the environment variable
IJULIA_SOFTSCOPE=no environment of the IJulia kernel when it is launched.