nnicandro / Emacs Jupyter

An interface to communicate with Jupyter kernels in Emacs.

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• Table of Contents :TOC:

• [[#what-does-this-package-do][What does this package do?]]
• [[#how-do-i-install-this-package][How do I install this package?]]
  • [[#using-melpa][Using MELPA]]
  • [[#building-a-package-archive-using-cask][Building a package archive using cask]]
  • [[#manual-installation][Manual installation]]
  • [[#building-the-widget-support-experimental][Building the widget support (EXPERIMENTAL)]]
• [[#related-packages][Related packages]]
  • [[#ob-ipython][=ob-ipython=]]
  • [[#emacs-ipython-notebook-ein][=emacs-ipython-notebook= (=ein=)]]
• [[#how-do-i-use-the-built-in-frontends][How do I use the built-in frontends?]]
  • [[#repl][REPL]]
  • [[#org-mode-source-blocks][=org-mode= source blocks]]
  • [[#kernelnotebook-server][Kernel/notebook server]]
  • [[#customizable-variables-available-for-all-frontends][Customizable variables available for all frontends]]
• [[#api][API]]
  • [[#naming-conventions][Naming conventions]]
  • [[#overview][Overview]]
  • [[#jupyter-kernel-client][=jupyter-kernel-client=]]
  • [[#jupyter-kernel-manager][=jupyter-kernel-manager=]]
  • [[#jupyter-widget-client][=jupyter-widget-client=]]
  • [[#jupyter-repl-client][=jupyter-repl-client=]]
  • [[#jupyter-ioloop][=jupyter-ioloop=]]
  • [[#jupyter-channel-ioloop][=jupyter-channel-ioloop=]]
  • [[#jupyter-zmq-channel-ioloop][=jupyter-zmq-channel-ioloop=]]
  • [[#jupyter-comm-layer][=jupyter-comm-layer=]]
  • [[#callbacks-and-hooks][Callbacks and hooks]]
  • [[#waiting-for-messages][Waiting for messages]]
  • [[#message-property-lists][Message property lists]]
  • [[#modify-behavior-depending-on-kernel-language][Modify behavior depending on kernel language]]
  • [[#org-mode][=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 [[http://github.com/dzop/emacs-zmq][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=

#+BEGIN_SRC elisp (add-to-list 'package-archives '("melpa" . "https://melpa.org/packages/")) #+END_SRC

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:

#+BEGIN_SRC shell cask package #+END_SRC

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

#+BEGIN_SRC elisp (add-to-list 'load-path "~/path/to/jupyter") (require 'jupyter) #+END_SRC ** Building the widget support (EXPERIMENTAL) :PROPERTIES: :ID: 59559FA3-59AD-453F-93E7-113B43F85493 :END:

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:

#+BEGIN_SRC shell make widgets #+END_SRC

• Related packages

** =ob-ipython=

The =org-mode= source block frontend in =emacs-jupyter= is similar to what is offered by [[https://github.com/gregsexton/ob-ipython][ob-ipython]] (and also the [[https://github.com/jkitchin/scimax][scimax]] version).

** =emacs-ipython-notebook= (=ein=)

[[https://github.com/millejoh/emacs-ipython-notebook][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.

• 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= | [[https://github.com/ahyatt/emacs-websocket][websocket]], [[https://github.com/skeeto/emacs-web-server][simple-httpd]] | | =text/html= | Emacs built with libxml2 | | =text/markdown= | [[https://jblevins.org/projects/markdown-mode/][markdown-mode]] | | =text/latex= | [[https://orgmode.org/][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 [[id:DA597E05-E9A9-4DCE-BBD7-6D25238638C5][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=) :PROPERTIES: :ID: DA597E05-E9A9-4DCE-BBD7-6D25238638C5 :END:

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.

#+BEGIN_SRC elisp (server-mode 1) (setenv "EDITOR" "emacsclient") #+END_SRC

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 [[id:B15FF43B-114C-4D73-B69C-2095F108EBBB][=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=.

#+BEGIN_SRC elisp (org-babel-do-load-languages 'org-babel-load-languages '((emacs-lisp . t) (julia . t) (python . t) (jupyter . t))) #+END_SRC

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

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

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

#+BEGIN_SRC elisp (setq org-babel-default-header-args:jupyter-julia '((:async . "yes") (:session . "jl") (:kernel . "julia-1.0"))) #+END_SRC *** 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 [[https://github.com/astahlman/ob-async#ob-async-no-async-languages-alist][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:

#+BEGIN_SRC elisp (setq ob-async-no-async-languages-alist '("jupyter-python" "jupyter-julia")) #+END_SRC

*** Issues with =ob-ipython= If you already have =ob-ipython= installed, you /may/ experience issues with it conflicting with =emacs-jupyter= (e.g. [[https://github.com/dzop/emacs-jupyter/issues/133#issuecomment-502444999][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=):

#+BEGIN_SRC elisp (org-babel-jupyter-override-src-block "python") #+END_SRC

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=.

#+BEGIN_SRC elisp (org-babel-jupyter-restore-src-block "python") #+END_SRC

*** Rich kernel output

In =org-mode= a code block returns scalar data (plain text, numbers, lists, tables, \dots), 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-resource-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 org ,#+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 #+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 [[https://pandoc.org/][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 [[https://www.gnu.org/software/emacs/manual/html_node/emacs/Remote-Files.html][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 org ,#+BEGIN_SRC jupyter-julia :session /ssh:ec2:/run/user/1000/jupyter/kernel-julia-0.6.json ... ,#+END_SRC #+END_SRC

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

#+BEGIN_SRC shell jupyter notebook --no-browser #+END_SRC

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

#+BEGIN_SRC shell jupyter kernel --kernel=python #+END_SRC

***** Password handling for remote connections Currently there is no password handling, so if your =ssh= connection requires a password I suggest you instead use [[https://www.ssh.com/ssh/keygen/][key-based authentication]]. Or if you are connecting to a server using a =pem= file add something like

#+BEGIN_SRC conf Host ec2 User HostName IdentityFile .pem #+END_SRC

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.

*** TODO 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 [[https://jupyter-client.readthedocs.io/en/stable/messaging.html#display-data][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

#+begin_example M-x dired RET /jpy:localhost#8888:/ #+end_example

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-= where == is any message type. The message types are identical to those defined in the [[http://jupyter-client.readthedocs.io/en/stable/messaging.html][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-=.

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-= family of methods, where == 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-= family of methods or (2) attach callbacks to the =jupyter-request= objects returned by the =jupyter-send-= 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-= 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 [[http://jupyter-client.readthedocs.io/en/stable/kernels.html#connection-files][connection file]].

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

#+BEGIN_SRC elisp (let ((client (jupyter-kernel-client))) (jupyter-comm-initialize client "kernel1234.json") (jupyter-start-channels client)) #+END_SRC

=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=

#+BEGIN_SRC elisp (cl-destructuring-bind (manager client) (jupyter-start-new-kernel "python") BODY) #+END_SRC

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

#+BEGIN_SRC elisp (jupyter-start-channel client :shell) #+END_SRC

and stop a channel with

#+BEGIN_SRC elisp (jupyter-stop-channel client :shell) #+END_SRC *** Making requests to a kernel :PROPERTIES: :ID: 9D893914-E769-4AEF-8928-826B67038C2A :END:

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 [[id:D09FDD89-43A9-41DA-A6E8-6D6C73336981][Message property lists]]. **** The lifetime of a request

Sending a request to a kernel is done through one of the =jupyter-send-= 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.

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

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-= 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

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

And the context specializers used are

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

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

#+BEGIN_SRC elisp (cl-defmethod jupyter-handle- ((client jupyter-kernel-client) req arg1 arg2 ...) BODY) #+END_SRC

=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 [[id:0E7CA280-8D14-4994-A3C7-C3B7204AC9D2][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=.

#+BEGIN_SRC elisp (if (eq arg1 t) ...) #+END_SRC

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=

#+BEGIN_SRC elisp (jupyter-set client 'jupyter-include-other-output t) #+END_SRC

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

#+BEGIN_SRC elisp (jupyter-get client 'jupyter-include-other-output) #+END_SRC

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=.

#+BEGIN_SRC elisp (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))) #+END_SRC **** 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 [[id:B29776AA-2ACF-4A4F-A4EA-3F194262465D][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

#+BEGIN_SRC sh jupyter kernelspec list #+END_SRC

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=.

#+BEGIN_SRC elisp (cl-destructuring-bind (manager client) (jupyter-start-new-kernel "python") BODY) #+END_SRC

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= :PROPERTIES: :ID: F8C2EB90-1DF3-4880-B684-31FE4784FAD1 :END:

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 [[id:59559FA3-59AD-453F-93E7-113B43F85493][Widget support]].

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

#+BEGIN_SRC elisp (jupyter-handle-comm-close) (jupyter-handle-comm-open) (jupyter-handle-comm-msg) #+END_SRC

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 [[https://github.com/jupyter-widgets/ipyleaflet][ipyleaflet]] map without any noticeable delay. ** TODO =jupyter-repl-client= ** TODO =jupyter-ioloop= ** TODO =jupyter-channel-ioloop= ** TODO =jupyter-zmq-channel-ioloop= ** TODO =jupyter-comm-layer= ** Callbacks and hooks :PROPERTIES: :ID: 0E7CA280-8D14-4994-A3C7-C3B7204AC9D2 :END:

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--message-hook=, where == 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 :PROPERTIES: :ID: BFCFCD3B-138A-4471-BEED-0EA3258493E5 :END:

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:

#+BEGIN_SRC elisp (jupyter-add-callback (jupyter-send-kernel-info-request client) :kernel-info-reply (lambda (msg) (let ((info (jupyter-message-content msg))) BODY))) #+END_SRC

To print out the results of an execute request:

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

To add multiple callbacks to a request:

#+BEGIN_SRC elisp (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!")))) #+END_SRC

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

#+BEGIN_SRC elisp (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 ...)))) #+END_SRC *** Channel hooks :PROPERTIES: :ID: B29776AA-2ACF-4A4F-A4EA-3F194262465D :END:

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.

#+BEGIN_SRC elisp (jupyter-add-hook client 'jupyter-iopub-message-hook (lambda (msg) (when (jupyter-message-status-idle-p msg) (message "Kernel idle.")))) #+END_SRC

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:

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

=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:

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

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

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

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.

#+BEGIN_SRC elisp (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)) #+END_SRC

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 :PROPERTIES: :ID: D09FDD89-43A9-41DA-A6E8-6D6C73336981 :END:

There is really no need to construct or access message property lists directly. The =jupyter-send-= client methods already handle creating them by calling the =jupyter-message-= 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-= 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-= 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:

#+BEGIN_SRC elisp ;; Get the :content' propery of MSG (jupyter-message-content msg) ;; Get the message type (one of the keys injupyter-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) #+END_SRC

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

#+BEGIN_SRC elisp (jupyter-with-message-content msg (status ename) ...) ; status and ename keys of (jupyter-message-content msg) are bound #+END_SRC

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

#+BEGIN_SRC elisp (jupyter-with-message-data msg ((res text/plain)) ...) ; res is bound to (jupyter-message-data msg :text/plain) #+END_SRC ** 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:

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

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

#+BEGIN_SRC elisp (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)))) #+END_SRC

=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.