emacs-jupyter/README.org

An interface to communicate with Jupyter kernels in Emacs.

[[https://travis-ci.com/dzop/emacs-jupyter][https://travis-ci.com/dzop/emacs-jupyter.svg?branch=master]]

* What does this package do?

This package provides an API for communicating with a Jupyter kernel via =zmq=
sockets (http://github.com/dzop/emacs-zmq). It utilizes Emacs' object
implementation, =eieio=, to define Jupyter client and kernel manager classes
that can be sub-classed to provide support for any kind of Jupyter frontend
which communicates directly with a kernel in Emacs. Currently, there is a
built-in REPL frontend and =org-mode= source block frontend.

The base Jupyter client class tries to provide good default implementations for
handling common message replies from a kernel that integrate well with Emacs'
built-in features. For example sending an inspect request will display the
inspect reply in the =*Help*= buffer and previous inspect requests can be
revisited by calling =help-go-back= or =help=go-forward= in the =*Help*=
buffer, making completion requests to a kernel is done through the
=completion-at-point= interface, and if the kernel asks for input from the
user, a prompt is displayed in the minibuffer. These are just a few of the ways
that this package integrates with Emacs' built-in features.

** Other features

*** Support differences between kernel languages

To take into account differences between kernel languages, there are many
methods that can be extended to take into account these differences. This is
achieved by providing a method specializer, =jupyter-lang=, that can be added
to the =&context= section of a method definition. See
=cl-generic-generalizers=.

For example, the Python kernel sends =text/plain= data in its inspect replies,
but most of the time the documentation requested is written using
reStructuredText (rST) markup, the officially supported markup language for
documentation strings in Python. In =emacs-jupyter= all insertion of messages
into a buffer is handled by the =jupyter-insert= method. This method is
extended to properly highlight rST when an inspect reply message is being
inserted and the message is from a Python kernel.

See the files =jupyter-python.el= and =jupyter-julia.el= for how these
languages are integrated into the =emacs-jupyter= framework.

* How do I install this package?

At the moment, the easiest way to install this package is by using =cask=
(https://github.com/cask/cask) to build a local package file to install in your
Emacs. 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
* How does this package compare to other similar packages?

There are two popular packages that implement similar functionality to this one

- ob-ipython :: https://github.com/gregsexton/ob-ipython
  - Interacts with a Jupyter kernel via =org-mode= source blocks.
- emacs-ipython-notebook (ein) :: https://github.com/millejoh/emacs-ipython-notebook
  - A Jupyter notebook interface in Emacs.

=emacs-jupyter= extends the features of =ob-ipython= by integrating more with
=org-mode= and providing a better REPL interface to the kernel. For example,
=ob-ipython= currently does not provide a function for
=org-babel-load-in-session=. =ob-ipython= also starts a new process for every
request since it relies on calling a Python script to send and receive messages
whereas =emacs-jupyter= directly uses =zmq= sockets via =emacs-zmq= for
communication and only starts a process on every new client connection. This
difference in how messages are passed between Emacs and a kernel is notable
when making completion requests. =ob-ipython= will incur the overhead of
starting up a new process /and/ new sockets on every completion request which
can potentially be every keystroke if you type slow enough.

=ein= is more of a full featured solution for a Jupyter notebook interface in
Emacs. The goals of =emacs-jupyter= and =ein= are different. =ein= aims to be a
frontend to the Jupyter notebook server API
(https://github.com/jupyter/jupyter/wiki/Jupyter-Notebook-Server-API) which is
an extra layer between the user and a kernel
(https://jupyter.readthedocs.io/en/latest/architecture/how_jupyter_ipython_work.html#notebooks).
In addition to being notebook client, =ein= offers many more powerful features
for Python kernels. =emacs-jupyter=, on the other hand, offers an API that
implements the Jupyter messaging protocol for communication with a kernel via
=zmq= sockets. The API tries to integrate the interaction between the user and
a kernel with built-in Emacs features. The REPL support and =org-mode=
integration are examples of how the API can be used. 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=.
* Jupyter REPL

To start a new kernel on the =localhost= and connect a REPL client to it, run
the command =jupyter-run-repl=. Alternatively you can connect to an existing
kernel by supplying the kernel's connection file to =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. The currently available mimetypes and their
dependencies are:

** 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 =C-c C-f= 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
: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-repl=eval-defun=          |
| =M-i=       | =jupyter-inspect-at-point=         |
| =C-c C-b=   | =jupyter-repl-eval-buffer=         |
| =C-c C-c=   | =jupyter-repl-eval-line-or-region= |
| =C-c C-i=   | =jupyter-repl-interrupt-kernel=    |
| =C-c C-l=   | =jupyter-repl-eval-file=           |
| =C-c C-r=   | =jupyter-repl-restart-kernel=      |
| =C-c C-s=   | =jupyter-repl-scratch-buffer=      |
| =C-c C-r=   | =jupyter-repl-restart-kernel=      |
| =C-c M-:=   | =jupyter-repl-eval-string=         |
** =jupyter-repl-persistent-mode=

A global minor mode that will persist the current Jupyter kernel connection if
the current buffer is in =jupyter-repl-interaction-mode= and a buffer with the
same =major-mode= as the current buffer is displayed or switched to. This mode
is automatically enabled whenever =jupyter-run-repl= or =jupyter-connect-repl=
is called.
** 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=]].
* Integration with =org-mode=

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 =jupy-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 jupy-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 jupy-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 jupy-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:jupy-LANG= to an appropriate value. For example
to change the defaults for the =julia= kernel, you can set
=org-babel-default-header-args:jupy-julia= to something like

#+BEGIN_SRC elisp
(setq org-babel-default-header-args:jupy-julia '((:async . "yes")
                                                 (:session . "jl")
                                                 (:kernel . "julia-1.0")))
#+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 are prioritized over plain text. 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
*** 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 your are working on your
code. Once you are happy with your results you can specify the =:file=
parameter to fix the file name.
** 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 =/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 would specify
the =:session= as

#+BEGIN_SRC org
,#+BEGIN_SRC jupyter-julia :session /ec2:/run/user/1000/jupyter/kernel-julia-0.6.json
...
,#+END_SRC
#+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 <user>
    HostName <host>
    IdentityFile <identity>.pem
#+END_SRC

to your =~/.ssh/config= file.
** 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. Currently this is only the
=:file= argument for image results and =:results raw= for inserting raw latex
fragments sent by the kernel. 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 standard output or asks to display
extra information, the results are appended to a =:RESULTS:= drawer.
* API
** Naming conventions

Methods that send messages to a kernel are named =jupyter-send-<msg-type>= where
=<msg-type>= is an appropriate 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-<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-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-initialize-connection=
2. Start listening on the client's channels with =jupyter-start-channels=

If starting a local kernel process both steps are handled by
=jupyter-start-new-kernel=. For remote kernels, you will have to manually
supply the connection JSON file to =jupyter-initialize-connection= 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-channel-ioloop, and they each return a =jupyter-request=
object which encapsulates the information necessary for handling reply messages
received from the kernel in response to the request.
**** 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-initialize-connection= 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-initialize-connection client "kernel1234.json")
  (jupyter-start-channels client))
#+END_SRC

=jupyter-initialize-connection= 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 may access each individual channel by accessing its corresponding slot in a
=jupyter-kernel-client=. To access the shell channel of a client

#+BEGIN_SRC elisp
(oref client shell-channel)
#+END_SRC

this will give you the =jupyter-channel= object of the shell channel. By
accessing the channel slots of the client, individual channels may be started or
stopped.
*** 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-<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.

#+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-<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

#+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-<msg-type> ((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. This command also
supports remote hosts. So 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 the 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=
** Callbacks and hooks
:PROPERTIES:
:ID:       0E7CA280-8D14-4994-A3C7-C3B7204AC9D2
:END:

There are two main ways of evaluating code in response to a received message
from the kernel. You can either subclass =jupyter-kernel-client= and override
the handler methods or you can add message callbacks to the =jupyter-request=
objects returned by the send methods. In both cases, when a message of a
certain type is received for a request, the appropriate handler method or
callback runs. If both methods are used in parallel, the message callbacks will
run before the handler methods.

You can also add a hook to one of the =jupyter-<channel>-message-hook= client
local hooks. Where =<channel>= can be one of =iopub=, =shell=, or =stdin=.
*** =jupyter-request= callbacks
:PROPERTIES:
:ID:       BFCFCD3B-138A-4471-BEED-0EA3258493E5
:END:

To add callbacks to a request, use =jupyter-add-callback=.
=jupyter-add-callback= accepts a =jupyter-request= object 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
there can be can guarantee that certain messages have been received.

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-<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:

#+BEGIN_SRC elisp
;; 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)
#+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

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 current buffer                         |
| =jupyter-code-context=               | Return the 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 completion 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=          | Called when input cell code changes                                    |
| =jupyter-markdown-follow-link=       | Follow a markdown link at point                                        |
| =jupyter-org-result=                 | Modify the result of a Jupyter code block before display in =org-mode= |

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.

Since the Jupyter spec provides rich output, a code block does not know before
obtaining the results from the kernel what type of results to expect. Typically
this is handled in the =org-mode= document by the user specifying the kind of
results it expects in header arguments.

The Jupyter messaging spec provides enough information for the results of an
execution so that the user shouldn't have to specify any header arguments. A
=jupyter-org-client= uses this information to dynamically update the results of
a source block based on the mime type of the Jupyter result. If the kernel
returns results that can be formatted as LaTeX, the results are wrapped in a
LaTeX code block. If the result is an image, a file link is inserted. Other
supported mimetypes are handled in a similar way.
**** =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))
                                  &context (jupyter-lang python)
                                  &rest _)
  (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.