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@ -22,36 +22,59 @@ 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 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:
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:
| Mimetype | Dependency |
|-----------------+---------------------------|
| =text/html= | Emacs built with libxml2 |
| =text/markdown= | [[https://jblevins.org/projects/markdown-mode/][markdown-mode]] |
| =text/latex= | [[https://orgmode.org/][org-mode]] |
| =image/png= | none |
| =image/svg+xml= | Emacs built with librsvg2 |
| =text/plain= | none |
** DONE Rich kernel output
CLOSED: [2018-10-06 Sat 02:42]
:LOGBOOK:
- State "DONE" from "TODO" [2018-10-06 Sat 02:42]
:END:
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
Currently completion is dependent on =company-mode= being available since this
is the completion framework that I use. Pull requests for support of other
completion frameworks are welcome.
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
When a new REPL connects to a kernel it sends a request to get the last
=jupyter-repl-history-maximum-length= REPL inputs. You can navigate through
this history using =C-n= and =C-p=. You can also search through this history
using =C-s= to search forward through the history or =C-r= to search backward
in 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=
@ -70,19 +93,35 @@ keybindings for interacting with the REPL:
| =C-c C-z= | =jupyter-repl-pop-to-buffer= |
| =C-c C-i= | =jupyter-repl-interrupt-kernel= |
| =C-c C-r= | =jupyter-repl-restart-kernel= |
** 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.
* 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=. 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 that were found on your system by
=jupyter-available-kernelspecs=.
=jupyter= to =org-babel-load-languages=.
#+BEGIN_SRC elisp
(org-babel-do-load-languages
'org-babel-load-languages
'((emacs-lisp . t)
(jupyter . t))
#+END_SRC
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 Jupyter kernel is through a REPL
connected to the kernel. So for example to interact with a =python= kernel you
would create a new source block like so
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
@ -93,7 +132,7 @@ x + ' ' + y
#+END_SRC
By default, source blocks are executed synchronously. To execute a source block
asynchronously set the =:async= parameter to =yes=.
asynchronously set the =:async= parameter to =yes=:
#+BEGIN_SRC org
,#+BEGIN_SRC jupyter-python :session py :async yes
@ -105,8 +144,8 @@ x + ' ' + y
Since a particular language may have multiple kernels available, the default
kernel used for a language is the first kernelspec found by
=jupyter-available-kernelspecs= that has the corresponding language. To change
the kernel, set the =:kernel= parameter
=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
@ -116,7 +155,11 @@ x + ' ' + y
,#+END_SRC
#+END_SRC
Any of the defaults for a language can be changed by setting
Note, the same session name can be used for different values of =:kernel= since
the underlying REPL buffer for a source code block is a based on both
=:session= and =:kernel=.
In addition, 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 default header arguments of the =julia= kernel, you can
set =org-babel-default-header-args:jupyter-julia= to something like
@ -124,51 +167,68 @@ 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-0.6")))
(:kernel . "julia-1.0")))
#+END_SRC
** Rich kernel output
All of the mimetypes available when using the REPL are also available using
=ob-jupyter=. If image data is received from the kernel, the image will be
saved to file and an image link will be the result of the source block, for
=text/latex=, =text/markdown=, =text/org=, =text/html=, the results are wrapped
in a source block with the appropriate language. For =text/plain= the results
are inserted as scalar data.
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.
For images sent by the kernel, if no =:file= parameter is provided to the
source block, a file name is automatically generated and the image data written
to file in =org-babel-jupyter-resource-directory=. Otherwise, if a =:file=
parameter is given, the image data is written to the file specified.
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
- text/html
- text/markdown
- text/latex
- image/png, image/jpg, image/svg+xml
- 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 the code of a Jupyter source block, 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=).
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
You may also connect to an existing kernel by passing the kernel's connection
file as the value of the =:session= parameter. In this case, a new REPL
connected to the kernel will be created. The file must have a =.json= suffix
for this to work.
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 file name supplied 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, the necessary =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
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
@ -182,31 +242,80 @@ Host ec2
to your =~/.ssh/config= file.
* API
** Method/message naming conventions
** Naming conventions
The message type strings as defined in the [[http://jupyter-client.readthedocs.io/en/stable/messaging.html][Jupyter spec]] become message type
symbols, more specifically properties, with underscores replaced by hyphens. So
an ="execute_request"= becomes an =:execute-request=.
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=.
** TODO Overview
:LOGBOOK:
- State "TODO" from "NEXT" [2018-10-06 Sat 03:07]
- State "NEXT" from "TODO" [2018-10-02 Tue 00:53]
:END:
*** Class overview
Methods that send messages to a kernel are named =jupyter-send-<msg-type>=
where =<msg-type>= is an appropriate message type. So to send an
=:execute-request= you would call =jupyter-send-execute-request=. Similarly,
methods that receive messages from a kernel are named
=jupyter-handle-<msg-type>=.
- =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.
*** Communicating with the kernel
**** Initializing the connection and sending messages
The exception to the above rule is the =:input-reply= message. Although it
sends a message to the kernel it has a handler method,
=jupyter-handle-input-reply=, instead of a send method.
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=
3. Send requests with the =jupyter-send-<msg-type>= family of client methods
When starting a local kernel process, the first two steps are already taken care
of when using =jupyter-start-new-kernel=.
All message sending and receiving is asynchronous. Methods that send requests
to a kernel return a =jupyter-request= object that encapsulates the necessary
information to handle any future messages generated in response to the request.
**** Receiving messages
There are two methods 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 then the appropriate
=jupyter-handle-<msg-type>= method will be called. Finally, any completed
requests are dropped from the client's request table.
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 you will need to call
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
@ -216,23 +325,43 @@ a connection, to begin communicating with a kernel you will need to call
#+END_SRC
=jupyter-initialize-connection= is mainly useful when initializing a remote
connection. The normal pathway to obtain a client on the =localhost= is to
use =jupyter-start-new-kernel= like so
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 info)
(cl-destructuring-bind (manager client)
(jupyter-start-new-kernel "python")
BODY)
#+END_SRC
where =manager= will be a =jupyter-kernel-manager= which can be used to manage
the lifetime of the local kernel process, =client= will be a newly connected
=jupyter-kernel-client= connected to =manager='s kernel, and =info= will be the
kernel info obtained from the initial =:kernel-info-request= to the kernel. If
multiple client's connected to the kernel of =manager= are required, use
=jupyter-make-client=. After the call to =jupyter-start-new-kernel=, =client='s
channels will already be open.
*** How messages are sent to and received from the kernel
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
@ -240,85 +369,158 @@ 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
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.
Also see [[id:9D893914-E769-4AEF-8928-826B67038C2A][Making requests to a kernel]].
*** Starting/stopping channels
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
To start a client's channels, use =jupyter-start-channels=; to stop a client's
channels, =jupyter-stop-channels=; and to determine if at least one channel is
alive, =jupyter-channels-running-p=.
You may access each individual channel by accessing the corresponding slot of a
client. So to get the shell channel of a client you would do
#+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:
Sending and receiving messages is centered around the =jupyter-kernel-client=
class. Each message sent or received has a corresponding method in
=jupyter-kernel-client=. As stated previously, request messages have method
names like =jupyter-send-<msg-type>= where =<msg-type>= is the request message
type. So an =:execute-request= message has the corresponding method
=jupyter-send-execute-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
All requests sent to a kernel return a =jupyter-request= which encapsulates the
current state of the request with the kernel and how the
=jupyter-kernel-client= should handle messages received from the kernel in
response to the request.
When a request is sent, the message ID of the request is added to the client's
request table which maps message IDs to their corresponding =jupyter-request=
objects.
When a message is received from the kernel the request that generated it is
found in the request table by using the =jupyter-message-parent-id= of the
message. The slots of the =jupyter-request= are updated, any callbacks
associated with the =jupyter-request= are run for the message, and the message
is dispatched to the appropriate channel handler method of the client (one of
the =jupyter-handle-<msg-type>= methods).
A request is considered complete and is dropped from the request table once a
=status: idle= message has been received for the request and it is not the most
recently made request.
**** DONE =jupyter-generate-request=
CLOSED: [2018-10-01 Mon 16:09]
:LOGBOOK:
- State "DONE" from "TODO" [2018-10-01 Mon 16:09]
- State "TODO" from [2018-10-01 Mon 15:50]
:END:
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.
**** DONE =jupyter-drop-request=
CLOSED: [2018-10-01 Mon 16:16]
:LOGBOOK:
- State "DONE" from "TODO" [2018-10-01 Mon 16:16]
- State "TODO" from [2018-10-01 Mon 15:50]
:END:
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 intended to be overridden
by subclasses that would like to execute arbitrary code in response to a
received message, they have the following method signature
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
where =<msg-type>= is the type of the message, e.g. the =:execute-result=
handler has the method name =jupyter-handle-execute-result=. =req= will be the
=jupyter-request= object that generated the message. =arg1=, =arg2=, ... will
be the unwrapped message contents passed to the handler; the number of
arguments and their order are dependent on =<msg-type>=.
=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.
Whenever a message is received on a client, the corresponding handler method is
called. The default implementations of the handler methods in
=jupyter-kernel-client= do nothing with the exception of the =:input-reply=
handler which gets input from the user and sends it to the kernel. See
[[id:0E7CA280-8D14-4994-A3C7-C3B7204AC9D2][Evaluating code when a message is received]] for an alternative way of handling
received messages.
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 handle IOPub messages originating from a different
client and defaults to =nil=, i.e. do not handle IOPub messages from other
clients. To modify a client local variable you would use =jupyter-set=
=jupyter-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
Internally, this just sets the buffer local value of
=jupyter-include-other-output= in a private buffer used by the client. To
retrieve the client local value use =jupyter-get=
and to retrieve the client local value, use =jupyter-get=
#+BEGIN_SRC elisp
(jupyter-get client 'jupyter-include-other-output)
@ -327,35 +529,48 @@ retrieve the client local value use =jupyter-get=
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 client local variable to a new value.
=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
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. To find kernelspecs that match a prefix
of a kernel name, use =jupyter-find-kernelspecs=. =jupyter-find-kernelspecs=
will return the subset of the available kernelspecs which have kernel names
that begin with the prefix. Most likely you know the exact name of the kernel
you want to use. In this case, use =jupyter-get-kernelspec=.
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.
You may also use =jupyter-completing-read-kernelspec= in an =interactive= spec
to ask the user to select a kernel. This is what is done in =run-jupyter-repl=.
*** Managing the lifetime of a local kernel
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
@ -378,31 +593,48 @@ alive, =jupyter-kernel-alive-p=.
**** Interrupting a kernel
To interrupt a kernel, use =jupyter-interrupt-kernel=.
*** Making clients connected to a local 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=.
** Evaluating code when a message is received
** =jupyter-widget-client=
There is also rudimentary widget support available. 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.
** Callbacks and hooks
:PROPERTIES:
:ID: 0E7CA280-8D14-4994-A3C7-C3B7204AC9D2
:END:
As mentioned previously, to evaluate code in response to a received message,
you may subclass =jupyter-kernel-client= and override the handler methods.
Alternatively you can add message callbacks to the =jupyter-request= objects
returned by the =jupyter-send-*= methods. In both cases, when a message of a
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 with the
client's kernel info you would do the following:
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)
@ -442,10 +674,10 @@ types:
(:execute-reply ...)
(:execute-result ...))))
#+END_SRC
Note, this can also be achieved by adding the same function to each message
type.
*** 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
@ -461,11 +693,16 @@ client local value of the hook variables.
(message "Kernel idle."))))
#+END_SRC
There is also the function =jupyter-remove-hook= to remove a client local hook.
*** Suppressing handler methods
To remove a client local hook, use =jupyter-remove-hook=.
To prevent a client from running its handler methods for some requests, you may
bind =jupyter-inhibit-handlers= to an appropriate value before a request is
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:
@ -474,22 +711,23 @@ request you would do the following:
(jupyter-send-execute-request client :code "print(\"foo\")\n1 + 2"))
#+END_SRC
=jupyter-inhibit-handlers= can be a list of message types or =t=, the latter
meaning inhibit handlers for all message types. This variable should be locally
bound. If you set the global value of this variable, all new requests will
prevent the handlers from running. The less intrusive way to prevent handlers
from running for individual requests is to let bind =jupyter-inhibit-handlers=
as in the above code.
=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 receiving happens asynchronously, therefore we need primitives
which will block until we know for sure that a message of a certain type has
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.
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.
To wait until an idle message is received for a request:
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))
@ -507,11 +745,9 @@ To wait until a message of a specific type is received for a request:
#+END_SRC
The most general form of the blocking functions is =jupyter-wait-until= which
takes a message type and a function of a single argument. Whenever a message is
received that matches the message type, the message is passed to the function.
If the function returns non-nil, =jupyter-wait-until= returns the message which
caused the function to return non-nil. If the function never returns a non-nil
value within timeout, =jupyter-wait-until= returns =nil=.
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)
@ -533,13 +769,22 @@ 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:
The =jupyter-send-*= methods already take care of constructing messages based
on their arguments and the =jupyter-handle-*= methods have the contents of the
message passed as their arguments so there is no need to work with message
property lists directly unless you are using message callbacks since they pass
the message property list directly to the callback function. In this case, the
following functions will be of use:
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
@ -552,3 +797,105 @@ following functions will be of use:
;; 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-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-transform-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=
*** NEXT =jupyter-org-client=
:LOGBOOK:
- State "NEXT" from [2018-08-31 Fri 23:23]
:END:
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. But the Jupyter messaging spec provides
enough information for the results of an execution so that the user does not
have to specify any header arguments.
A =jupyter-org-client= takes care of collecting the results of execution from
the kernel and inserting the results in an =org-mode= buffer. 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. Similarly
for all of the other supported mimetypes.
* See also
- ob-ipython :: https://github.com/gregsexton/ob-ipython
- emacs-ipython-notebook :: https://github.com/millejoh/emacs-ipython-notebook
There are a few other packages similar to this one, the most notable one being
=ob-ipython=. I originally started out using this package, but was having
trouble getting it to work with some languages and I could never get completion
working consistently.
There is also =ein= which aims to be a fully integrated notebook environment in
Emacs with many features. I have not tried out this package yet, but looks like
a good solution for a complete notebook experience.
The current package differs from those above in that it aims to be a Jupyter
client API in Emacs to make it easy to build any type of frontend for
communicating with a kernel. In addition to serving as useful tools in their
own right, the included REPL client and =org-mode= client are examples of how
the API can be used.