An interface to communicate with Jupyter kernels in Emacs.

Table of Contents

• What does this package do?
• How do I install this package?
  • Using MELPA
  • Building a package archive using cask
  • Manual installation
  • Building the widget support (EXPERIMENTAL)
• How does this package compare to other similar packages?
  • =ob-ipython=
  • =emacs-ipython-notebook= (ein)
• How do I use the built-in frontends?
  • REPL
  • =org-mode= source blocks
  • Kernel/notebook server
  • Customizable variables available for all frontends
• API
  • Naming conventions
  • Overview
  • =jupyter-kernel-client=
  • =jupyter-kernel-manager=
  • =jupyter-widget-client=
  • =jupyter-repl-client=
  • =jupyter-ioloop=
  • =jupyter-channel-ioloop=
  • =jupyter-zmq-channel-ioloop=
  • =jupyter-comm-layer=
  • Callbacks and hooks
  • Waiting for messages
  • Message property lists
  • Modify behavior depending on kernel language
  • =org-mode=

What does this package do?

• Provides an API for creating Jupyter kernel frontends in Emacs based on the built-in eieio and cl-generic libraries.
  • Communication with a kernel is either done through zmq sockets using the emacs-zmq library or (coming soon) through the Jupyter notebook REST API.
    • All of this communication is abstracted so that a frontend developer should only need to extend a few cl-defmethod definitions in order to implement a frontend.
  • Make it easy to define kernel language specific behavior. See the files jupyter-python.el and jupyter-julia.el for examples.
• Provides REPL and org-mode source block based frontends.
• Jupyter kernel interactions are integrated with Emacs’s built-in features. For example
  • Inspecting a piece of code under point will display the information for that symbol in the *Help* buffer. You can re-visit inspection requests made to the kernel by calling help-go-back or help-go-forward while in the *Help* buffer.
  • Code completion is done through the completion-at-point interface.
  • If the kernel asks for input from the user, a prompt is displayed in the minibuffer.
  • You can search through REPL history using isearch.

How do I install this package?

Using MELPA

NOTE: Your Emacs needs to have been built with module support for this package to work since it relies on the emacs-zmq package. See the README of that package for more information.

The recommended way to install this package is through the built-in package manager in Emacs.

Ensure MELPA is in your package-archives

(add-to-list 'package-archives '("melpa" . "https://melpa.org/packages/"))

Ensure the latest versions of MELPA packages are available

M-x package-refresh-contents RET

Install Jupyter

M-x package-install RET jupyter RET

Building a package archive using cask

One way to install this package is to build a package archive using cask (https://github.com/cask/cask) to build a local Emacs package file. To do this, clone the repository, enter its directory, and run the following at the command line:

cask package

This creates a file dist/jupyter-0.6.0.tar containing the package archive. To install it

1. Start your Emacs normally
2. Ensure MELPA is in your package-archives
3. M-x package-initialize
4. M-x package-refresh-contents
5. M-x package-install-file ~/path/to/jupyter/dist/jupyter-0.6.0.tar

Manual installation

For a manual installation you can add the repository directory to your load-path and ensure the following dependencies are installed:

markdown-mode (optional)

https://jblevins.org/projects/markdown-mode/

company-mode (optional)

http://company-mode.github.io/

emacs-websocket

https://github.com/ahyatt/emacs-websocket

simple-httpd

https://github.com/skeeto/emacs-web-server

zmq

http://github.com/dzop/emacs-zmq

(add-to-list 'load-path "~/path/to/jupyter")
(require 'jupyter)

Building the widget support (EXPERIMENTAL)

There is also support for interacting with Jupyter widgets through an external browser. If a widget is to be displayed, an external browser is opened first to display the widget. In this case, Emacs acts as a relay for passing messages between the kernel and the external browser.

If you would like to try out this limited support, you will need to have node installed on your system to build the necessary javascript. Then you will have to run the following commands from the root project directory:

make widgets

How does this package compare to other similar packages?

ob-ipython

The org-mode source block frontend in emacs-jupyter is similar to what is offered by ob-ipython (and also the scimax version), below are some of the differences between emacs-jupyter and ob-ipython (biased in favor of emacs-jupyter):

• Faster than ob-ipython
  • ob-ipython starts a new process for every request made to a kernel and does not persist the connection it makes to the kernel. This means that for every request made there is the overhead of both starting a new process and establishing communication with the kernel.

    emacs-jupyter starts a process on every new kernel connection only and the connection is persisted for the lifetime of the client (frontend) connected to the kernel.

    This difference is most notable when comparing the code completion features of both packages. ob-ipython code completion is basically unusable for quick completions while typing.

• Better REPL interface
  • ob-ipython uses python-shell-make-comint to create a REPL connected to a kernel. There are two problems with this (1) no syntax highlighting for kernel languages other than Python (2) comint only groks text based output, but a Jupyter kernel can provide much richer representations of data, e.g. HTML, markdown, or png images to name a few. The REPL frontend experience of emacs-jupyter is much closer to what one would get when using jupyer qtconsole (see https://qtconsole.readthedocs.io/en/stable/).
• Better integration with org-mode source block :session features
  • All of the extension points that org-mode offers for source block languages like org-babel-edit-prep, org-babel-load-in-session, etc. are all fully supported. ob-ipython does not provide some of these features, e.g. org-babel-load-in-session.
• Similar features to the scimax version of ob-ipython
  • The scimax version has some really neat features like custom keybindings when inside an org-mode source block, selective display of mimetypes, jumping to source block error locations, and others. Many of these features have also been implemented in emacs-jupyter, e.g. you can add language specific keybindings using the jupyter-org-define-key function.

emacs-ipython-notebook (ein)

ein is a complete Jupyter notebook interface in Emacs with many powerful features for Python kernels. There is some overlap in the features provided by emacs-jupyter and ein, but I have never used ein so I cannot speak very much about their similarities/differences.

I would say that emacs-jupyter aims to be a generic API for interacting with Jupyter kernels that just happens to have a built-in REPL and org-mode source block frontend whereas ein aims to be a fully featured Jupyter notebook frontend. Also ein can read and write .ipynb files, this feature is lacking in emacs-jupyter at the moment. In the future it would be nice to add some kind of notebook interface in emacs-jupyter or at least an efficient conversion process between notebook files and org-mode.

How do I use the built-in frontends?

REPL

To start a new kernel on the localhost and connect a REPL client to it M-x jupyter-run-repl. Alternatively you can connect to an existing kernel by supplying the kernel’s connection file using M-x jupyter-connect-repl.

The REPL supports most of the rich output that a kernel may send to a client. If the kernel requests a widget to be displayed, a browser is opened that displays the widget. If the kernel sends image data, the image will be displayed in the REPL buffer. If LaTeX is sent, it will be compiled (using org-mode) and displayed.

Rich kernel output

A Jupyter kernel provides many representations of results that may be used by the frontend, in this case Emacs. Luckily, Emacs provides good support for most of the available representations.

The supported mimetypes along with their dependencies are shown below in order of priority if multiple representations are returned. Note, if a dependency is not available in your Emacs, a mimetype with a lower priority will be used to display output.

Mimetype	Dependency
application/vnd.jupyter.widget-view+json	websocket, simple-httpd
text/html	Emacs built with libxml2
text/markdown	markdown-mode
text/latex	org-mode
image/svg+xml	Emacs built with librsvg2
image/png	none
text/plain	none

Inspection

To send an inspect request to the kernel, press M-i when the cursor is at the location of the code you would like to inspect.

Completion

Completion is implemented through the completion-at-point interface. In addition to completing symbols in the REPL buffer, completion also works in buffers associated with a REPL. For org-mode users, there is even completion in the org-mode buffer when editing the contents of a Jupyter source code block.

REPL history

You can navigate through the REPL history using C-n and C-p or M-n and M-p.

You can also search through the history using isearch. To search through history, use the standard isearch keybindings: C-s to search forward through history and C-s C-r to search backward.

Associating other buffers with a REPL (jupyter-repl-interaction-mode)

After starting a REPL, it is possible to associate the REPL with other buffers if they pass certain criteria. Currently, the buffer must have the major-mode that corresponds to the REPL’s kernel language. To associate a buffer with a REPL you can run the command jupyter-repl-associate-buffer.

jupyter-repl-associate-buffer will ask you for the REPL you would like to associate with the current-buffer and enable the minor mode jupyter-repl-interaction-mode. This minor mode populates the following keybindings for interacting with the REPL:

Key binding	Command
C-M-x	jupyter-eval-defun
M-i	jupyter-inspect-at-point
C-c C-b	jupyter-eval-buffer
C-c C-c	jupyter-eval-line-or-region
C-c C-i	jupyter-repl-interrupt-kernel
C-c C-r	jupyter-repl-restart-kernel
C-c C-s	jupyter-repl-scratch-buffer
C-c C-o	jupyter-eval-remove-overlays
C-c M-:	jupyter-eval-string

Integration with emacsclient

If code sent for evaluation causes a file to be opened via emacsclient, the opened file is associated with the corresponding REPL client if possible. This behavior is most useful, for example, when using the edit function in IJulia.

To enable server-mode in Emacs you should have something like the following in your Emacs configuration before starting any kernels.

(server-mode 1)
(setenv "EDITOR" "emacsclient")

Note this probably wont work properly when there are multiple competing clients sending requests to their underlying kernels that want to open files. Or if the underlying kernel takes longer than jupyter-long-timeout seconds to open a file.

See jupyter-server-mode-set-client for more details.

jupyter-repl-persistent-mode

A global minor mode that will persist a kernel connection to a buffer about to be displayed if the current buffer is in jupyter-repl-interaction-mode and the buffer being switched to has the same major-mode. This mode is automatically enabled whenever jupyter-run-repl or jupyter-connect-repl is called.

jupyter-repl-maximum-size

Set the maximum number of lines before the REPL buffer is truncated.

jupyter-repl-allow-RET-when-busy

If non-nil, allow inserting a newline in a REPL cell whenever the kernel is busy. Normally this isn’t allowed since the REPL relies on the kernel responding to messages when RET is pressed, but a kernel does not respond to messages when it is busy.

jupyter-repl-echo-eval-p

If non-nil, when evaluating code using the jupyter-eval-* functions like M-x jupyter-eval-line-or-region, copy the evaluated code as a REPL input cell and display any output generated in the REPL. When this variable is nil, copying to the REPL does not occur and output/results are inserted in pop-up buffers or added to the *Messages* buffer according to jupyter-eval-short-result-max-lines and jupyter-eval-short-result-display-function.

Widget support

There is also support for Jupyter widgets integrated into the REPL. If any of the results returned by a kernel have a widget representation, a browser is opened and the widget is displayed in the browser. There is only one browser per client.

This feature is currently considered experimental and has only been tested for simple uses of widgets. See =jupyter-widget-client=.

org-mode source blocks

For users of org-mode, integration with org-babel is provided through the ob-jupyter library. To enable Jupyter support for source code blocks, add jupyter to org-babel-load-languages.

(org-babel-do-load-languages
 'org-babel-load-languages
 '((emacs-lisp . t)
   (julia . t)
   (python . t)
   (jupyter . t)))

Note, jupyter should be added as the last element when loading languages since it depends on the values of variables such as org-src-lang-modes and org-babel-tangle-lang-exts. After ob-jupyter has been loaded, new source code blocks with names of the form jupyter-LANG will be available. LANG can be any one of the kernel languages found on your system. See jupyter-available-kernelspecs.

Every Jupyter source code block requires that the :session parameter be specified since all interaction with a kernel is through a REPL. For example, to interact with a python kernel you would create a new source block like so

#+BEGIN_SRC jupyter-python :session py
x = 'foo'
y = 'bar'
x + ' ' + y
#+END_SRC

By default, source blocks are executed synchronously. To execute a source block asynchronously set the :async parameter to yes:

#+BEGIN_SRC jupyter-python :session py :async yes
x = 'foo'
y = 'bar'
x + ' ' + y
#+END_SRC

Since a particular language may have multiple kernels available, the default kernel used will be the first one found by jupyter-available-kernelspecs for the language. To change the kernel, set the :kernel parameter:

#+BEGIN_SRC jupyter-python :session py :async yes :kernel python2
x = 'foo'
y = 'bar'
x + ' ' + y
#+END_SRC

Note, the same session name can be used for different values of :kernel since the underlying REPL buffer’s name is based on both :session and :kernel.

Any of the defaults for a language can be changed by setting org-babel-default-header-args:jupyter-LANG to an appropriate value. For example to change the defaults for the julia kernel, you can set org-babel-default-header-args:jupyter-julia to something like

(setq org-babel-default-header-args:jupyter-julia '((:async . "yes")
                                                    (:session . "jl")
                                                    (:kernel . "julia-1.0")))

Note on the language name provided by a kernelspec

Some kernelspecs use spaces in the name of the kernel language. Those get replaced by dashes in the language name you need to use for the source block, e.g. Wolfram Language becomes jupyter-Wolfram-Language.

Integration with ob-async

If you use the ob-async package, make sure you add the Jupyter source block languages to ob-async-no-async-languages-alist so that ob-async doesn’t override emacs-jupyter when the :async header argument is specified. For example you can put the following in your configuration:

(setq ob-async-no-async-languages-alist '("jupyter-python" "jupyter-julia"))

Issues with ob-ipython

If you already have ob-ipython installed, you may experience issues with it conflicting with emacs-jupyter (e.g. this issue): i.e. instead of actual results of source block execution, you’ll got only long GUIDs, and message like error in process sentinel: Search failed: "b5d6bfb3-e37f-4c58-a2e5-edcf1ad2430f" in minibuffer

This is because both emacs-jupyter and ob-ipython try to own jupyter-LANG source blocks, and conflicts with each other. It seems there is no way to make them both work together.

If you have issues like described above, then try disable ob-ipython and see, is it help. Usually, it is enough to remove ipython from (org-babel-do-load-languages ...) list, and restart your Emacs.

Overriding built-in src-block languages

You may find having to specify the names of Jupyter source blocks using jupyter-LANG a bit verbose and want to have the built-in support for LANG source blocks overridden to use the machinery of jupyter-LANG source blocks. This can be done by calling the function org-babel-jupyter-override-src-block.

For example, to override the behavior of python source blocks so that they act like jupyter-python source blocks, you can add the following in your initialization (after calling org-babel-do-load-languages):

(org-babel-jupyter-override-src-block "python")

After calling the above function, all python source blocks are effectively aliases of jupyter-python source blocks and the variable org-babel-default-header-args:python will be set to the value of org-babel-default-header-args:jupyter-python. Note, org-babel-default-header-args:python will not be an alias of org-babel-default-header-args:jupyter-python, the value of the former is merely set to the value of the latter after calling org-babel-jupyter-override-src-block.

If you decide you want to go back to the original behavior or python source blocks, you can restore the overridden functions by calling org-babel-jupyter-restore-src-block.

(org-babel-jupyter-restore-src-block "python")

Rich kernel output

In org-mode a code block returns scalar data (plain text, numbers, lists, tables, …), an image file name, or code from another language. All of this information must be specified in the code block’s header arguments, but all of this information is already provided in the messages passed between a Jupyter kernel and its frontends.

When a kernel provides representations of results other than plain text, those richer representations have priority. For example if the kernel returns LaTeX code, the results are wrapped in a LaTeX source block. Similarly for HTML and markdown. If an image is returned, the image is automatically saved to file and a link to the file will be the result of the code block.

Below are the supported mimetypes ordered by priority

• text/org
• image/svg+xml, image/jpeg, image/png
• text/html
• text/markdown
• text/latex
• text/plain

Since it is possible to determine how a result should be represented in org-mode via its MIME type, only a few header arguments are supported.

A note on using the :results header argument

Results are inserted in the org-mode buffer in such a way that most header arguments that control how results should be inserted don’t need to specified. There are some cases where this behavior is not wanted and which can be controlled by setting the :results header argument.

Insert unwrapped LaTeX

Normally LaTeX results are wrapped in a BEGIN_EXPORT block, in order to insert LaTeX unwrapped, specify :results raw.

Suppress table creation

Whenever a result can be converted into an org-mode table, e.g. when it look like [1, 2 , 3], it is automatically converted into a table. To suppress this behavior you can specify :results scalar.

Fixing the file name of images with the :file argument

Whenever an image result is returned, a random image file name is generated and the image is written into org-babel-jupyter-resourse-directory. In order to specify your own file name for the image, you can give an appropriate value to the :file header argument.

Changing the mime-type priority with the :display argument

The priority of mimetypes used to display results can be overwritten using the :display option. If instead of displaying HTML results we’d wish to display plain text, the argument :display text/plain text/html would prioritize plain text results over html ones. The following example displays plain text instead of HTML:

#+BEGIN_SRC jupyter-python :session py :display plain
import pandas as pd
data = [[1, 2], [3, 4]]
pd.DataFrame(data, columns=["Foo", "Bar"])
#+END_SRC

Image output without the :file header argument

For images sent by the kernel, if no :file parameter is provided to the code block, a file name is automatically generated based on the image data and the image is written to file in org-babel-jupyter-resource-directory. This is great for quickly generating throw-away plots while you are working on your code. Once you are happy with your results you can specify the :file parameter to fix the file name.

org-babel-jupyter-resource-directory

This variable is similar to org-preview-latex-image-directory but solely for any files created when Jupyter code blocks are run, e.g. automatically generated image file names.

Deletion of generated image files

Whenever you run a code block multiple times and replace its results, before the results are replaced, any generated files will be deleted to reduce the clutter in org-babel-jupyter-resource-directory.

Convert rich kernel output with the :pandoc header argument

By default html, markdown, and latex results are wrapped in a BEGIN_EXPORT block. If the header argument :pandoc t is set, they are instead converted to org-mode format with pandoc. You can control which outputs get converted with the custom variable jupyter-org-pandoc-convertable.

Editing the contents of a code block

When editing a Jupyter code block’s contents, i.e. by pressing C-c ' when at a code block, jupyter-repl-interaction-mode is automatically enabled in the edit buffer and the buffer will be associated with the REPL session of the code block (see jupyter-repl-associate-buffer).

You may also bind the command org-babel-jupyter-scratch-buffer to an appropriate key in org-mode to display a scratch buffer in the code block’s major-mode and connected to the code block’s session.

Connecting to an existing kernel

To connect to an existing kernel, pass the kernel’s connection file as the value of the :session parameter. The name of the file must have a .json suffix for this to work.

Remote kernels

If the connection file is a remote file name, i.e. has a prefix like /method:host:, the kernel’s ports are assumed to live on host. Before attempting to connect to the kernel, ssh tunnels for the connection are created. So if you had a remote kernel on a host named ec2 whose connection file is /run/user/1000/jupyter/kernel-julia-0.6.json on that host, you could specify the :session like

#+BEGIN_SRC jupyter-julia :session /ssh:ec2:/run/user/1000/jupyter/kernel-julia-0.6.json
...
#+END_SRC

Note, the kernel on the remote host needs to have the ZMQ socket ports exposed. This means that starting a kernel using

jupyter notebook --no-browser

currently doesn’t work since the notebook server does not allow communication with a kernel using ZMQ sockets. You will have to use the connection file created from using something like

jupyter kernel --kernel=python

Password handling for remote connections

Currently there is no password handling, so if your ssh connection requires a password I suggest you instead use key-based authentication. Or if you are connecting to a server using a pem file add something like

Host ec2
    User <user>
    HostName <host>
    IdentityFile <identity>.pem

to your ~/.ssh/config file.

Starting a remote kernel

If :session is a remote file name that doesn’t end in .json, e.g. /ssh:ec2:jl, then a kernel on the remote host /ssh:ec2: is started using the jupyter kernel command on the host. The local part of the session name serves to distinguish different remote sessions on the same host.

Communicating with kernel (notebook) servers

If :session is a TRAMP file name like /jpy:localhost#8888:NAME it is interpreted as corresponding to a connection to a kernel through a Jupyter notebook server located at http://localhost:8888.

If NAME is a kernel ID corresponding to an existing kernel on a server, e.g. /jpy::161b2318-180c-497a-b4bf-de76176061d9, then a connection to an existing kernel with the corresponding ID will be made. Otherwise, a new kernel will be launched on the server and NAME will be used as an identifier for the session.

When a new kernel is launched, NAME will also be associated with the kernel’s ID, see jupyter-server-kernel-names. This is useful to distinguish Org mode :session kernels from other ones in the buffer shown by jupyter-server-list-kernels.

When connecting to an existing kernel, i.e. when NAME is the ID of a kernel, the :kernel header argument must match the name of the kernel’s kernelspec.

To connect to a kernel behind an HTTPS connection, use a TRAMP file name that looks like /jpys:... instead.

Standard output, displayed data, and code block results

One significant difference between Jupyter code blocks and regular org-mode code blocks is that the underlying Jupyter kernel can request that the client display extra data in addition to output or the result of a code block. See display_data messages.

To account for this, Jupyter code blocks do not go through the normal org-mode result insertion mechanism (see org-babel-insert-result). The downside of this is that, compared to normal code blocks, only a small subset of the header arguments common to all code blocks are supported. The upside is that all forms of results produced by a kernel can be inserted into the buffer similar to a Jupyter notebook.

The implementation of org-mode code blocks is really meant to handle either capturing the standard output or the result of a code block. When using Jupyter code blocks, if the kernel produces output or asks to display extra information, the results are appended to a :RESULTS: drawer.

jupyter-org-interaction-mode

A minor mode that enables completion and custom keybindings when point is inside a Jupyter code block. This mode is enabled by default in org-mode buffers, but only has an effect when point is inside a Jupyter code block.

Custom keybindings inside Jupyter code blocks

You can define new keybindings that are enabled when point is inside a Jupyter code block by using the function jupyter-org-define-key. These bindings are added to jupyter-org-interaction-mode-map and are only active when jupyter-org-interaction-mode is enabled.

By default the following keybindings from jupyter-repl-interaction-mode are available when jupyter-org-interaction-mode is enabled

Key binding	Command
C-M-x	jupyter-eval-defun
M-i	jupyter-inspect-at-point
C-x C-e	jupyter-eval-line-or-region
C-c C-i	jupyter-repl-interrupt-kernel
C-c C-r	jupyter-repl-restart-kernel

Kernel/notebook server

Managing live kernels

The main entry point for working working with a kernel server is the jupyter-server-list-kernels command which shows a list of all live kernels from the server URL that you provide when first calling the command. Any subsequent calls to the command will use the same URL as the first call. To change server URLs give a prefix argument, C-u M-x jupyter-server-list-kernels. This will then set the current server URL for future calls to the one you provide. See the jupyter-current-server command for more details.

From the buffer shown by jupyter-server-list-kernels you can launch new kernels (C-RET), connect a REPL to an existing kernel (RET), interrupt a kernel (C-c TAB), kill a kernel (C-c C-d or d), refresh the list of kernels (g) etc. See the jupyter-server-kernel-list-mode for all the available key bindings.

Note, the default-directory of the jupyter-server-kernel-list-mode buffer will be the root directory of the kernel server (so that dired-jump will show a dired listing of the directory). See the section on TRAMP integration below.

Naming kernels

From the jupyter-server-list-kernels buffer one can also name (or rename) a kernel (R) so that it has an identifier other than its ID. Naming a kernel adds the name to the jupyter-server-kernel-names global variable in a form suitable for persisting across Emacs sessions. See its documentation for more details about persisting its value.

TRAMP integration

There is also integration with the Jupyter notebook contents API in the form of a TRAMP backend. This means that reading/writing the contents of directories the notebook server has access to can be done using normal Emacs file operations using file names with TRAMP syntax. Two new TRAMP file name methods are defined, jpy for HTTP connections and jpys for HTTPS connections. So suppose you have a local notebook server at http://localhost:8888, then to access its directory contents you can type

M-x dired RET /jpy:localhost#8888:/

Note localhost is the default host and 8888 is the default port so /jpy:: is equivalent to /jpy:localhost#8888:. You can change the defaults by modifying the jpy or jpys methods in the variable tramp-methods and tramp-default-host-alist.

jupyter-api-authentication-method

Authentication method used for new notebook server connections. By default, when connecting to a new notebook server you will be asked if either a password or a token should be used for authentication. If you only use tokens for authentication you can change this variable to avoid being asked on every new connection.

Customizable variables available for all frontends

jupyter-eval-use-overlays

The variable jupyter-eval-use-overlays controls whether or not the results of evaluations, e.g. results obtained by pressing C-c C-c (jupyter-eval-line-or-region) or similar, should be displayed as overlays in the current buffer. If non-nil, then the results of evaluation are displayed at the end of the line or region being evaluated using an overlay. Only the text/plain representation of a result is displayed inline, images and non-text results are still displayed in pop-up buffers.

You can control how the overlay looks by modifying the jupyter-eval-overlay face. You can also change the prefix string added before the evaluation result, see jupyter-eval-overlay-prefix.

All evaluation result overlays can be cleared from the buffer by calling jupyter-eval-remove-overlays (C-c C-o). Individual overlays are removed whenever the text in the region that was evaluated is modified.

For multi-line overlays you can fold/unfold the overlay by pressing S-RET when point is inside the region of code that caused the overlay to be created. See jupyter-eval-overlay-keymap.

jupyter-eval-short-result-max-lines

If the number of lines of an evaluation result is smaller than this variable, the function stored in jupyter-eval-short-result-display-function is used to display the result. Otherwise the result is displayed in a pop-up buffer.

This variable is mainly used by the jupyter-eval-* commands such as M-x jupyter-eval-line-or-region.

API

Naming conventions

Methods that send messages to a kernel are named jupyter-send-<msg-type> where <msg-type> is any message type. The message types are identical to those defined in the Jupyter spec with _ characters replaced by - characters. So to send an execute-request you would call jupyter-send-execute-request.

Similarly, methods that are responsible for handling messages received from a kernel are named jupyter-handle-<msg-type>.

Methods that require a message type as an argument such as jupyter-add-callback should do so by passing a message type keyword such as :execute-request.

Overview

Classes

jupyter-kernel-client

The base class for Jupyter frontends. Handles all message sending and receiving to/from a Jupyter kernel.

jupyter-kernel-manager

The base class for starting local kernel processes.

jupyter-widget-client

(EXPERIMENTAL) A subclass of jupyter-kernel-client that adds support for displaying Jupyter widgets in an external browser.

jupyter-repl-client

A subclass of jupyter-kernel-client that implements a REPL. Note, a jupyter-repl-client also has a jupyter-widget-client as a parent class.

jupyter-org-client

A subclass of jupyter-repl-client that adds support for evaluating org-mode source code blocks and inserting the results in the org-mode buffer.

Lower level classes

jupyter-ioloop

A general class for asynchronous communication with a subprocess. The subprocess polls its standard input for “events” from the parent process. To add a new event to be handled by the subprocess you use jupyter-ioloop-add-event. The resulting subprocess event handler created using jupyter-ioloop-add-event can potentially send an event back to the parent process. In the parent, events are handled by extending the jupyter-ioloop-handler method.

jupyter-zmq-channel-ioloop

A subclass of jupyter-ioloop configured to start a subprocess that handles messages being passed on Jupyter channels between a kernel and the parent Emacs process. This is what jupyter-kernel-client uses to communicate with a kernel.

Communicating with a kernel

Initializing a connection

For a jupyter-kernel-client to start communicating with a kernel, the following steps are taken:

1. Initialize the connection using jupyter-comm-initialize
2. Start listening on the client’s channels with jupyter-start-channels

When starting a local kernel process, both steps are taken care of in jupyter-start-new-kernel.

For remote kernels, you will have to manually supply the connection JSON file to jupyter-comm-initialize and start the kernel channels.

Sending messages

Once a connection is initialized, messages can be sent to the kernel using the jupyter-send-<msg-type> family of methods, where <msg-type> is any valid request message type (see jupyter-message-types). These methods asynchronously send a message to the kernel using a subprocess associated with each client, see help:jupyter-zmq-channel-ioloop, and they each return a jupyter-request object which encapsulates the information necessary for handling reply messages associated with the request in the future.

Receiving messages

There are two ways to handle the reply messages sent by the kernel: (1) subclass the jupyter-kernel-client and override the jupyter-handle-<msg-type> family of methods or (2) attach callbacks to the jupyter-request objects returned by the jupyter-send-<msg-type> methods. Both ways can occur in parallel.

When a message is received, jupyter-handle-message is called on the client to kick off the message handling process. Any callbacks associated with the jupyter-request of the message are evaluated and the appropriate jupyter-handle-<msg-type> method called.

Note, the default handler methods of jupyter-kernel-client are no-ops with the exception of jupyter-handle-input-request which requests input from the user and sends it to the kernel.

jupyter-kernel-client

Represents a client connected to a Jupyter kernel.

Initializing a connection

jupyter-comm-initialize takes a client and a connection file as arguments and configures the client to communicate with the kernel whose connection information is contained in the connection file.

After initializing a connection, to begin communicating with a kernel call jupyter-start-channels.

(let ((client (jupyter-kernel-client)))
  (jupyter-comm-initialize client "kernel1234.json")
  (jupyter-start-channels client))

jupyter-comm-initialize is mainly useful when initializing a remote connection or connecting to an existing kernel. In order to start a new kernel on the localhost use jupyter-start-new-kernel

(cl-destructuring-bind (manager client)
    (jupyter-start-new-kernel "python")
  BODY)

The above code starts a new python kernel and returns the jupyter-kernel-manager object used to manage the lifetime of the local kernel process and the jupyter-kernel-client connected to the manager’s kernel. jupyter-start-channels will already have been called on the returned client when jupyter-start-new-kernel returns.

To create multiple client’s connected to the kernel of a jupyter-kernel-manager use jupyter-make-client.

Starting/stopping channels

To start a client’s channels, use jupyter-start-channels. To stop a client’s channels, jupyter-stop-channels. To determine if at least one channel is alive, jupyter-channels-running-p.

You can also start individual channels with

(jupyter-start-channel client :shell)

and stop a channel with

(jupyter-stop-channel client :shell)

Making requests to a kernel

To free up Emacs from having to process messages sent to and received from a kernel, an Emacs subprocess is created for every client. This subprocess is responsible for polling the client’s channels for messages and taking care of message signing, encoding, and decoding. The parent Emacs process is only responsible for supplying the message property lists (the representation used for Jupyter messages in Emacs) when sending a message and will receive the decoded message property list when receiving a message. The exception to this is the heartbeat channel which is implemented using timers in the parent Emacs process.

Note, the message property lists should not be accessed directly. There are helper functions which should be used to access the message fields. See Message property lists.

The lifetime of a request

Sending a request to a kernel is done through one of the jupyter-send-<msg-type> methods of a jupyter-kernel-client. The arguments of the Jupyter message that each method represents are passed as keyword arguments, the keywords all have names according to the Jupyter messaging spec but with _ replaced by -. These methods construct the message property lists based on their arguments and pass the constructed message to the jupyter-send method of a client. The jupyter-send method then returns a new jupyter-request representing the sent message.

(jupyter-send-execute-request client :code "1 + 2") ; Returns a `jupyter-request'

When a request is sent, the message ID of the request is added to the client’s request table which maps message IDs to their corresponding jupyter-request objects.

When a message is received from the kernel the request that generated it is found in the request table by using the jupyter-message-parent-id of the message. The slots of the jupyter-request are updated, any callbacks associated with the jupyter-request are run for the message, and the message is dispatched to the appropriate channel handler method of the client (one of the jupyter-handle-<msg-type> methods).

A request is considered complete and is dropped from the request table once a status: idle message has been received for the request and it is not the most recently made request.

jupyter-generate-request

When one of the send methods are called, a jupyter-request object is instantiated by a call to jupyter-generate-request and the instantiated request is returned by the send method so that the caller can attach their callbacks as described above.

Most likely, subclasses would want to attach extra information to a request. For example, an org-mode client that sends an :execute-request based on the contents of a source code block might want to keep track of the code block’s buffer position so that it can insert the results at the right location when they are ready.

This is the purpose of the jupyter-generate-request method. If a jupyter-request object is not general enough for some purpose, a subclass of jupyter-kernel-client can define a new request object, ensuring that the slots of a jupyter-request are included, and return the new type of request when jupyter-generate-request is called for a message.

For example, below is the definition of the jupyter-org-request type for handling requests made in an org-mode buffer

(cl-defstruct (jupyter-org-request
               (:include jupyter-request))
  result-type
  block-params
  results
  silent
  id-cleared-p
  marker
  async)

And the context specializers used are

(cl-defmethod jupyter-generate-request ((client jupyter-org-client) msg
                                        &context (major-mode org-mode))
  ...) ; Return a `jupyter-org-request'

Notice that the major-mode context allows for jupyter-org-request objects to be used by jupyter-generate-request when the request is generated in org-mode buffers and to use the less specialized jupyter-request in other contexts.

jupyter-drop-request

When a request is completed, i.e. when the kernel sends an idle message for a request, you may want to do some final cleanup of the request. This is the purpose of the jupyter-drop-request method, it gets called when an idle message has been received for a kernel but only when the request is not the most recently sent request.

Handling received messages

The handler methods of a jupyter-kernel-client are called whenever the corresponding message is received from the kernel. They are intended to be overwritten by subclasses and most of the default implementations do nothing with the exception of the :input-reply, :comm-open, and :comm-close messages. The :input-reply handler asks for input from the user through the minibuffer and sends it to the kernel whereas the :comm-open / :comm-close default message handlers store the state of open comms in the client’s comms slot.

The handler methods have the following signature

(cl-defmethod jupyter-handle-<msg-type> ((client jupyter-kernel-client) req arg1 arg2 ...)
  BODY)

req will be the jupyter-request object that generated the message. arg1, arg2, … will be the unwrapped message contents passed to the handler, their number of arguments and their order are dependent on the message type. Alternatively you may work with the full message property list by accessing the jupyter-request-last-message slot of the juptyer-request object.

See message callbacks for another way of handling received messages.

A note on boolean arguments

For message types that have boolean message fields, the symbol in the variable jupyter--false represents a false value so when checking the contents of these arguments it is best to explicitly check for t.

(if (eq arg1 t) ...)

This is because there are some ambiguities between translating JSON values to their Emacs Lisp equivalents, since nil in Emacs is used both as signifying false or nothing whereas JSON has null for nothing.

Client local variables

Some variables which are used internally by jupyter-kernel-client have client local values. For example the variable jupyter-include-other-output tells a jupyter-kernel-client to pass IOPub messages originating from a different client to their corresponding handlers and defaults to nil, i.e. do not handle IOPub messages from other clients. To modify a client local variable you would use jupyter-set

(jupyter-set client 'jupyter-include-other-output t)

and to retrieve the client local value, use jupyter-get

(jupyter-get client 'jupyter-include-other-output)

These functions just set/get the value of a buffer local variable in a private buffer of the client. You may work with these buffer local variables directly by using the jupyter-with-client-buffer macro, just be sure to use setq-local if you are setting a new client local variable otherwise you may change the global value of the variable. Alternatively you can define a variable as automatically buffer local when set with defvar-local.

(jupyter-with-client-buffer client
  (message "jupyter-include-other-output: %s" jupyter-include-other-output)
  (setq-local jupyter-include-other-output (not jupyter-include-other-output)))

Channel hooks

The channel hook variables jupyter-iopub-message-hook, jupyter-shell-message-hook, and jupyter-stdin-message-hook are all client local variables and functions can be added to or removed from them using jupyter-add-hook and jupyter-remove-hook. See Channel hooks.

jupyter-kernel-manager

Manage the lifetime of a kernel on the localhost.

Kernelspecs

To get a list of kernelspecs on your system, as represented in Emacs, use jupyter-available-kernelspecs which processes the output of the shell command

jupyter kernelspec list

to construct the list of kernelspecs. jupyter-available-kernelspecs also supports remote hosts. If the default-directory points to a remote system, the returned kernelspecs are those on the remote system.

To find all kernelspecs whose kernels match some regular expression use jupyter-find-kernelspecs. In case you would like to get the kernelspec for a specific kernel, use jupyter-get-kernelspec.

You may also use jupyter-completing-read-kernelspec in an interactive spec to ask the user to select a kernel from the list of available kernelspecs.

Managing the lifetime of a kernel

Starting a kernel

As was mentioned previously, to start a new kernel on the localhost and create a connected client, use jupyter-start-new-kernel which takes a kernel name and returns a jupyter-kernel-manager which manages the lifetime of the kernel, and a connected jupyter-kernel-client.

(cl-destructuring-bind (manager client)
    (jupyter-start-new-kernel "python")
  BODY)

Instead of supplying an exact kernel name, you may also supply the prefix of one. Then the first available kernel that has the same prefix will be started. See jupyter-find-kernelspecs.

Stopping a kernel

To shutdown a kernel, use jupyter-shutdown-kernel. To check if a kernel is alive, jupyter-kernel-alive-p.

Interrupting a kernel

To interrupt a kernel, use jupyter-interrupt-kernel.

Making clients connected to a kernel

Once you have a kernel manager you can make new jupyter-kernel-client (or a subclass of one) instances using jupyter-make-client.

jupyter-widget-client

This class adds support for interacting with Jupyter widgets using an external browser for the widget display. In order for this to work properly you will need to have simple-httpd and the websocket packages installed, in addition, you will have to build the required javascript files as described in Widget support.

The default implementation of jupyter-widget-client overrides the following methods of a jupyter-kernel-client

(jupyter-handle-comm-close)
(jupyter-handle-comm-open)
(jupyter-handle-comm-msg)

Comm messages in Jupyter are a way to allow for custom messages between the kernel and a client. In the case of Jupyter widgets they are used to sync widget state between the kernel and client.

It would be amazing to add custom Jupyter widgets to Emacs using the built widget library which would work for widgets such as text boxes, buttons, and other simple widgets, but there doesn’t seem to be a way to support more complex widgets in Emacs that require embedded javascript.

The default implementation of jupyter-kernel-client only keeps track of open comms through a client’s comms slot. The jupyter-widget-client subclass adds the functionality to display and interact with widgets through an external browser. This works by relaying the comm messages between the browser and the kernel through a websocket. For this to work, you will also need to have the simple-httpd and websocket Emacs packages available.

This feature is currently experimental, but seems to work well. I was able to interact with an ipyleaflet map without any noticeable delay.

jupyter-repl-client

jupyter-ioloop

jupyter-channel-ioloop

jupyter-zmq-channel-ioloop

jupyter-comm-layer

Callbacks and hooks

There are mainly two ways of evaluating code when receiving a message from the kernel. Either sub-classing jupyter-kernel-client and overriding the handler methods or adding message callbacks to the jupyter-request objects returned by the send methods. If both methods are used in parallel, the message callbacks will run before the handler methods.

When working with a subclass of jupyter-kernel-client, to prevent a subset of handler methods from firing when a message is received for a request, see jupyter-inhibit-handlers below.

Also provided are message hook variables which are local to each client object and look like jupyter-<channel>-message-hook, where <channel> can be one of iopub, shell, or stdin. These hooks also provide an alternative method of suppressing client handlers from running based on the received message.

jupyter-request callbacks

To add callbacks to a request, use jupyter-add-callback which accepts a jupyter-request as its first argument and alternating (message type, callback) pairs as the remaining arguments. The callbacks are registered with the request object to run whenever a message of the appropriate type is received. For example, to do something when a client receives a :kernel-info-reply you would do the following:

(jupyter-add-callback (jupyter-send-kernel-info-request client)
  :kernel-info-reply (lambda (msg)
                       (let ((info (jupyter-message-content msg)))
                         BODY)))

To print out the results of an execute request:

(jupyter-add-callback (jupyter-send-execute-request client :code "1 + 2")
  :execute-result (lambda (msg)
                    (message (jupyter-message-data msg :text/plain))))

To add multiple callbacks to a request:

(jupyter-add-callback (jupyter-send-execute-request client :code "1 + 2")
  :execute-result (lambda (msg)
                    (message (jupyter-message-data msg :text/plain)))
  :status (lambda (msg)
            (when (jupyter-message-status-idle-p msg)
              (message "DONE!"))))

There is also the possibility of running the same handler for different message types:

(jupyter-add-callback (jupyter-send-execute-request client :code "1 + 2")
  '(:status :execute-result :execute-reply)
  (lambda (msg)
    (pcase (jupyter-message-type msg)
      (:status ...)
      (:execute-reply ...)
      (:execute-result ...))))

Channel hooks

Hook variables are available for each channel: jupyter-iopub-message-hook, jupyter-stdin-message-hook, and jupyter-shell-message-hook. Unless you want to run a channel hook for every client, use jupyter-add-hook to add a function to one of the channel hooks. jupyter-add-hook only adds to the client local value of the hook variables.

(jupyter-add-hook
 client 'jupyter-iopub-message-hook
 (lambda (msg)
   (when (jupyter-message-status-idle-p msg)
     (message "Kernel idle."))))

To remove a client local hook, use jupyter-remove-hook.

Channel hooks also provide a way of suppressing the handler methods. If any of the channel hooks return a non-nil value, the handler method for that message will be suppressed.

jupyter-inhibit-handlers

In addition to suppressing handler methods using channel hooks, to prevent a client from running its handler methods for a particular request you can let bind jupyter-inhibit-handlers to an appropriate value before the request is made. For example, to prevent a client from running its stream handler for a request you would do the following:

(let ((jupyter-inhibit-handlers '(:stream)))
  (jupyter-send-execute-request client :code "print(\"foo\")\n1 + 2"))

jupyter-inhibit-handlers can be either a list of message types or t, the latter meaning inhibit handlers for all message types. Alternatively you can set the jupyter-request-inhibited-handlers slot of a jupyter-request object. This slot can take the same values as jupyter-inhibit-handlers.

Waiting for messages

All message passing between the kernel and Emacs happens asynchronously. So if a code path in Emacs Lisp is dependent on some message already having been received, e.g. an idle message, there needs to be primitives that will block so that there is a guarantee that a particular message has been received before proceeding.

The following functions all wait for different conditions to be met on the received messages of a request and return the message that caused the function to stop waiting or nil if no message was received within a timeout period. The default timeout is jupyter-default-timeout seconds.

For example, to wait until an idle message has been received for a request:

(let ((timeout 4))
  (jupyter-wait-until-idle
   (jupyter-send-execute-request
    client :code "import time\ntime.sleep(3)")
   timeout))

To wait until a message of a specific type is received for a request:

(jupyter-wait-until-received :execute-reply
  (jupyter-send-execute-request client :code "[i*10 for i in range(100000)]"))

The most general form of the blocking functions is jupyter-wait-until which takes a message type and a predicate function of a single argument. Whenever a message is received that matches the message type, the message is passed to the function to determine if jupyter-wait-until should return from waiting.

(defun stream-prints-50-p (msg)
  (let ((text (jupyter-message-get msg :text)))
    (cl-loop for line in (split-string text "\n")
             thereis (equal line "50"))))

(let ((timeout 2))
  (jupyter-wait-until
      (jupyter-send-execute-request client :code "[print(i) for i in range(100)]")
      :stream #'stream-prints-50-p
    timeout))

The above code runs stream-prints-50-p for every stream message received from a kernel (here assumed to be a python kernel) for an execute request that prints the numbers 0 to 99 and waits until the kernel has printed the number 50 before returning from the jupyter-wait-until call. If the number 50 is not printed before the two second timeout, jupyter-wait-until returns nil. Otherwise it returns the stream message whose content contains the number 50.

Message property lists

There is really no need to construct or access message property lists directly. The jupyter-send-<msg-type> client methods already handle creating them by calling the jupyter-message-<msg-type> family of functions. Similarly, when a message is received from a kernel the message properties are unwrapped and passed as arguments to the jupyter-handle-<msg-type> client methods. If required, the message property list is available in the jupyter-request-last-message slot of the jupyter-request passed to the jupyter-handle-<msg-type> client methods.

On the other hand, message callbacks pass the message property list directly to the callback. In this case, the following functions can be used to access the fields of the property list:

;; Get the `:content' propery of MSG
(jupyter-message-content msg)
;; Get the message type (one of the keys in `jupyter-message-types')
(jupyter-message-type msg)
;; Get the value of KEY in the MSG contents
(jupyter-message-get msg key)
;; Get the value of the MIMETYPE in MSG's :data property
;; MIMETYPE should be one of `:image/png', `:text/plain', ...
(jupyter-message-data msg mimetype)

Note that access of the message property lists should only occur through the jupyter-message-* functions since the main parts of a message such as the content and header are lazily decoded.

Convenience macros

jupyter-with-message-content gives a way to extract and bind the keys of a jupyter-message-content easily

(jupyter-with-message-content msg (status ename)
  ...) ; status and ename keys of (jupyter-message-content msg) are bound

There is also jupyter-with-message-data which extracts and binds the mimetypes of jupyter-message-data

(jupyter-with-message-data msg ((res text/plain))
  ...) ; res is bound to (jupyter-message-data msg :text/plain)

Modify behavior depending on kernel language

Since Jupyter supports many different programming language kernels, each with varying degrees of support in Emacs there needs to be a general way of modifying the behavior of the client to take this into account.

This is achieved using the &context specializer of cl-defmethod. There are currently two specializers in use, jupyter-lang and jupyter-repl-mode. jupyter-lang is a context specializer that matches when the kernel language of the jupyter-current-client is equal to the specializer’s argument. For example, below is the function that gets called in the REPL buffer when the kernel language is julia for indenting the current line:

(cl-defmethod jupyter-indent-line (&context (jupyter-lang julia))
  (call-interactively #'julia-latexsub-or-indent))

Note, when spaces appear in the name of the kernel language they become dashes in the symbol used for the jupyter-lang context, e.g. Wolfram Language becomes Wolfram-Language.

There are many other entry points where methods may be overridden in such a way. Below is the full list of methods that can be overridden in this way

Method	Purpose
jupyter-insert	Insert Jupyter results into the buffer
jupyter-code-context	Return code and position for inspect and complete requests
jupyter-indent-line	Indent the current cell in the REPL buffer
jupyter-completion-prefix	Return the completion prefix for the current context
jupyter-completion-post-completion	Evaluate code when a completion candidate has been selected
jupyter-repl-after-init	Evaluate code after a REPL buffer has been initialized
jupyter-repl-after-change	Evaluate code when the input cell code changes
jupyter-markdown-follow-link	Follow a markdown link at point
jupyter-handle-payload	Handle a payload sent by the kernel
jupyter-org-result	Transform result of execution into an org representation
org-babel-jupyter-transform-code	Transform code of a src-block before sending it to the kernel

In addition to the jupyter-lang context, there is also the jupyter-repl-mode context which is identical to the derived-mode context but does its check against jupyter-repl-lang-mode if the jupyter-current-client is a jupyter-repl-client. This is useful to modify behavior depending on the major-mode that is used for a particular language. For example for javascript kernels, it used to setup code highlighting when js2-mode is used as the REPL languages major-mode since js2-mode does not use font-lock.

org-mode

jupyter-org-client

A jupyter-org-client is a subclass of jupyter-kernel-client meant to display the results of a Jupyter code block in an org-mode buffer.

jupyter-org-result

The main entry point for extending how results are inserted into the org-mode buffer is the method help:jupyter-org-result, which dispatches on the MIME type of a result returned from a kernel. The MIME type priority is given in jupyter-org-mime-types. jupyter-org-result can return either an org-element object or a string. In the former case, the org-element is transformed into its string representation before insertion into the buffer. In the later case, the string is inserted into the org-mode buffer as is, without any further processing.

There are helper functions for generating org-element objects which have names like jupyter-org-scalar, jupyter-org-export-block, jupyter-org-file-link, etc.

Extending jupyter-org-result

For a kernel language to extend the behavior of how results are inserted, the jupyter-lang method specializer can be used. For example, below is how :text/plain results are modified for Python code blocks

(cl-defmethod jupyter-org-result ((_mime (eql :text/plain)) _content _params
                                  &context (jupyter-lang python))
  (let ((result (cl-call-next-method)))
    (cond
     ((stringp result)
      (org-babel-python-table-or-string result))
     (t result))))

cl-call-next-method calls down to a less specialized method of jupyter-org-result and if the returned result is still expected to be plain text, calls org-babel-python-table-org-string to convert any results that look like Python arrays into org-mode tables before returning its result.

jupyter-org-define-key

Bind a key that is only available when point is inside a Jupyter code block. When the command bound to the key is evaluated, jupyter-current-client will be bound to the client of the current code block, also the syntax table will be the same as the underlying kernel language’s (see jupyter-org-with-src-block-client).

These keys only have an effect when jupyter-org-interaction-mode is enabled.