Execute

This message type is used by frontends to ask the kernel to execute code on behalf of the user, in a namespace reserved to the user's variables (and thus separate from the kernel's own internal code and variables).

Message type: execute_request:

content = {
    # Source code to be executed by the kernel, one or more lines.
'code' : str,

# A boolean flag which, if True, signals the kernel to execute
# this code as quietly as possible.  This means that the kernel
# will compile the code with 'exec' instead of 'single' (so
# sys.displayhook will not fire), forces store_history to be False,
# and will *not*:
#   - broadcast exceptions on the PUB socket
#   - do any logging
#
# The default is False.
'silent' : bool,

# A boolean flag which, if True, signals the kernel to populate history
# The default is True if silent is False.  If silent is True, store_history
# is forced to be False.
'store_history' : bool,

# A list of variable names from the user's namespace to be retrieved.
# What returns is a rich representation of each variable (dict keyed by name).
# See the display_data content for the structure of the representation data.
'user_variables' : list,

# Similarly, a dict mapping names to expressions to be evaluated in the
# user's dict.
'user_expressions' : dict,

# Some frontends (e.g. the Notebook) do not support stdin requests. If
# raw_input is called from code executed from such a frontend, a
# StdinNotImplementedError will be raised.
'allow_stdin' : True,

}

The code field contains a single string (possibly multiline). The kernel is responsible for splitting this into one or more independent execution blocks and deciding whether to compile these in 'single' or 'exec' mode (see below for detailed execution semantics).

The user_ fields deserve a detailed explanation. In the past, IPython had the notion of a prompt string that allowed arbitrary code to be evaluated, and this was put to good use by many in creating prompts that displayed system status, path information, and even more esoteric uses like remote instrument status acquired over the network. But now that IPython has a clean separation between the kernel and the clients, the kernel has no prompt knowledge; prompts are a frontend-side feature, and it should be even possible for different frontends to display different prompts while interacting with the same kernel.

The kernel now provides the ability to retrieve data from the user's namespace after the execution of the main code, thanks to two fields in the execute_request message:

• user_variables: If only variables from the user's namespace are needed, a list of variable names can be passed and a dict with these names as keys and their :func:`repr()` as values will be returned.
• user_expressions: For more complex expressions that require function evaluations, a dict can be provided with string keys and arbitrary python expressions as values. The return message will contain also a dict with the same keys and the :func:`repr()` of the evaluated expressions as value.

With this information, frontends can display any status information they wish in the form that best suits each frontend (a status line, a popup, inline for a terminal, etc).

[ERROR] ExceptionType: Exception message

content = {
  # One of: 'ok' OR 'error' OR 'abort'
  'status' : str,

  # The global kernel counter that increases by one with each request that
  # stores history.  This will typically be used by clients to display
  # prompt numbers to the user.  If the request did not store history, this will
  # be the current value of the counter in the kernel.
  'execution_count' : int,
}

{
  # 'payload' will be a list of payload dicts.
  # Each execution payload is a dict with string keys that may have been
  # produced by the code being executed.  It is retrieved by the kernel at
  # the end of the execution and sent back to the front end, which can take
  # action on it as needed.  See main text for further details.
  'payload' : list(dict),

  # Results for the user_variables and user_expressions.
  'user_variables' : dict,
  'user_expressions' : dict,
}

ip.payload_manager.write_payload(payload_dict)

{
  'ename' : str,   # Exception name, as a string
  'evalue' : str,  # Exception value, as a string

  # The traceback will contain a list of frames, represented each as a
  # string.  For now we'll stick to the existing design of ultraTB, which
  # controls exception level of detail statefully.  But eventually we'll
  # want to grow into a model where more information is collected and
  # packed into the traceback object, with clients deciding how little or
  # how much of it to unpack.  But for now, let's start with a simple list
  # of strings, since that requires only minimal changes to ultratb as
  # written.
  'traceback' : list,
}

Object information

One of IPython's most used capabilities is the introspection of Python objects in the user's namespace, typically invoked via the ? and ?? characters (which in reality are shorthands for the %pinfo magic). This is used often enough that it warrants an explicit message type, especially because frontends may want to get object information in response to user keystrokes (like Tab or F1) besides from the user explicitly typing code like x??.

Message type: object_info_request:

content = {
    # The (possibly dotted) name of the object to be searched in all
    # relevant namespaces
    'oname' : str,

    # The level of detail desired.  The default (0) is equivalent to typing
    # 'x?' at the prompt, 1 is equivalent to 'x??'.
    'detail_level' : int,
}

The returned information will be a dictionary with keys very similar to the field names that IPython prints at the terminal.

Message type: object_info_reply:

content = {
# The name the object was requested under
'name' : str,

# Boolean flag indicating whether the named object was found or not.  If
# it's false, all other fields will be empty.
'found' : bool,

# Flags for magics and system aliases
'ismagic' : bool,
'isalias' : bool,

# The name of the namespace where the object was found ('builtin',
# 'magics', 'alias', 'interactive', etc.)
'namespace' : str,

# The type name will be type.__name__ for normal Python objects, but it
# can also be a string like 'Magic function' or 'System alias'
'type_name' : str,

# The string form of the object, possibly truncated for length if
# detail_level is 0
'string_form' : str,

# For objects with a __class__ attribute this will be set
'base_class' : str,

# For objects with a __len__ attribute this will be set
'length' : int,

# If the object is a function, class or method whose file we can find,
# we give its full path
'file' : str,

# For pure Python callable objects, we can reconstruct the object
# definition line which provides its call signature.  For convenience this
# is returned as a single 'definition' field, but below the raw parts that
# compose it are also returned as the argspec field.
'definition' : str,

# The individual parts that together form the definition string.  Clients
# with rich display capabilities may use this to provide a richer and more
# precise representation of the definition line (e.g. by highlighting
# arguments based on the user's cursor position).  For non-callable
# objects, this field is empty.
'argspec' : { # The names of all the arguments
              args : list,
    # The name of the varargs (*args), if any
                  varargs : str,
    # The name of the varkw (**kw), if any
    varkw : str,
    # The values (as strings) of all default arguments.  Note
    # that these must be matched *in reverse* with the 'args'
    # list above, since the first positional args have no default
    # value at all.
    defaults : list,
},

# For instances, provide the constructor signature (the definition of
# the __init__ method):
'init_definition' : str,

# Docstrings: for any object (function, method, module, package) with a
# docstring, we show it.  But in addition, we may provide additional
# docstrings.  For example, for instances we will show the constructor
# and class docstrings as well, if available.
'docstring' : str,

# For instances, provide the constructor and class docstrings
'init_docstring' : str,
'class_docstring' : str,

# If it's a callable object whose call method has a separate docstring and
# definition line:
'call_def' : str,
'call_docstring' : str,

# If detail_level was 1, we also try to find the source code that
# defines the object, if possible.  The string 'None' will indicate
# that no source was found.
'source' : str,
}

Complete

Message type: complete_request:

content = {
    # The text to be completed, such as 'a.is'
    # this may be an empty string if the frontend does not do any lexing,
    # in which case the kernel must figure out the completion
    # based on 'line' and 'cursor_pos'.
    'text' : str,

    # The full line, such as 'print a.is'.  This allows completers to
    # make decisions that may require information about more than just the
    # current word.
    'line' : str,

    # The entire block of text where the line is.  This may be useful in the
    # case of multiline completions where more context may be needed.  Note: if
    # in practice this field proves unnecessary, remove it to lighten the
    # messages.

    'block' : str or null/None,

    # The position of the cursor where the user hit 'TAB' on the line.
    'cursor_pos' : int,
}

Message type: complete_reply:

content = {
# The list of all matches to the completion request, such as
# ['a.isalnum', 'a.isalpha'] for the above example.
'matches' : list,

# the substring of the matched text
# this is typically the common prefix of the matches,
# and the text that is already in the block that would be replaced by the full completion.
# This would be 'a.is' in the above example.
'text' : str,

# status should be 'ok' unless an exception was raised during the request,
# in which case it should be 'error', along with the usual error message content
# in other messages.
'status' : 'ok'
}

History

For clients to explicitly request history from a kernel. The kernel has all the actual execution history stored in a single location, so clients can request it from the kernel when needed.

Message type: history_request:

content = {

  # If True, also return output history in the resulting dict.
  'output' : bool,

  # If True, return the raw input history, else the transformed input.
  'raw' : bool,

  # So far, this can be 'range', 'tail' or 'search'.
  'hist_access_type' : str,

  # If hist_access_type is 'range', get a range of input cells. session can
  # be a positive session number, or a negative number to count back from
  # the current session.
  'session' : int,
  # start and stop are line numbers within that session.
  'start' : int,
  'stop' : int,

  # If hist_access_type is 'tail' or 'search', get the last n cells.
  'n' : int,

  # If hist_access_type is 'search', get cells matching the specified glob
  # pattern (with * and ? as wildcards).
  'pattern' : str,

  # If hist_access_type is 'search' and unique is true, do not
  # include duplicated history.  Default is false.
  'unique' : bool,

}

Message type: history_reply:

content = {
  # A list of 3 tuples, either:
  # (session, line_number, input) or
  # (session, line_number, (input, output)),
  # depending on whether output was False or True, respectively.
  'history' : list,
}

Connect

When a client connects to the request/reply socket of the kernel, it can issue a connect request to get basic information about the kernel, such as the ports the other ZeroMQ sockets are listening on. This allows clients to only have to know about a single port (the shell channel) to connect to a kernel.

Message type: connect_request:

content = {
}

Message type: connect_reply:

content = {
    'shell_port' : int,   # The port the shell ROUTER socket is listening on.
    'iopub_port' : int,   # The port the PUB socket is listening on.
    'stdin_port' : int,   # The port the stdin ROUTER socket is listening on.
    'hb_port' : int,      # The port the heartbeat socket is listening on.
}

Kernel info

If a client needs to know information about the kernel, it can make a request of the kernel's information. This message can be used to fetch core information of the kernel, including language (e.g., Python), language version number and IPython version number, and the IPython message spec version number.

Message type: kernel_info_request:

content = {
}

Message type: kernel_info_reply:

content = {
    # Version of messaging protocol (mandatory).
    # The first integer indicates major version.  It is incremented when
    # there is any backward incompatible change.
    # The second integer indicates minor version.  It is incremented when
    # there is any backward compatible change.
    'protocol_version': [int, int],

    # IPython version number (optional).
    # Non-python kernel backend may not have this version number.
    # The last component is an extra field, which may be 'dev' or
    # 'rc1' in development version.  It is an empty string for
    # released version.
    'ipython_version': [int, int, int, str],

    # Language version number (mandatory).
    # It is Python version number (e.g., [2, 7, 3]) for the kernel
    # included in IPython.
    'language_version': [int, ...],

    # Programming language in which kernel is implemented (mandatory).
    # Kernel included in IPython returns 'python'.
    'language': str,
}

Kernel shutdown

The clients can request the kernel to shut itself down; this is used in multiple cases:

• when the user chooses to close the client application via a menu or window control.
• when the user types 'exit' or 'quit' (or their uppercase magic equivalents).
• when the user chooses a GUI method (like the 'Ctrl-C' shortcut in the IPythonQt client) to force a kernel restart to get a clean kernel without losing client-side state like history or inlined figures.

The client sends a shutdown request to the kernel, and once it receives the reply message (which is otherwise empty), it can assume that the kernel has completed shutdown safely.

Upon their own shutdown, client applications will typically execute a last minute sanity check and forcefully terminate any kernel that is still alive, to avoid leaving stray processes in the user's machine.

Message type: shutdown_request:

content = {
    'restart' : bool # whether the shutdown is final, or precedes a restart
}

Message type: shutdown_reply:

content = {
    'restart' : bool # whether the shutdown is final, or precedes a restart
}

Streams (stdout, stderr, etc)

Message type: stream:

content = {
    # The name of the stream is one of 'stdout', 'stderr'
    'name' : str,

    # The data is an arbitrary string to be written to that stream
    'data' : str,
}

Display Data

This type of message is used to bring back data that should be diplayed (text, html, svg, etc.) in the frontends. This data is published to all frontends. Each message can have multiple representations of the data; it is up to the frontend to decide which to use and how. A single message should contain all possible representations of the same information. Each representation should be a JSON'able data structure, and should be a valid MIME type.

Some questions remain about this design:

• Do we use this message type for pyout/displayhook? Probably not, because the displayhook also has to handle the Out prompt display. On the other hand we could put that information into the metadata secion.

Message type: display_data:

content = {

    # Who create the data
    'source' : str,

    # The data dict contains key/value pairs, where the kids are MIME
    # types and the values are the raw data of the representation in that
    # format.
    'data' : dict,

    # Any metadata that describes the data
    'metadata' : dict
}

The metadata contains any metadata that describes the output. Global keys are assumed to apply to the output as a whole. The metadata dict can also contain mime-type keys, which will be sub-dictionaries, which are interpreted as applying only to output of that type. Third parties should put any data they write into a single dict with a reasonably unique name to avoid conflicts.

The only metadata keys currently defined in IPython are the width and height of images:

'metadata' : {
  'image/png' : {
    'width': 640,
    'height': 480
  }
}

Raw Data Publication

display_data lets you publish representations of data, such as images and html. This data_pub message lets you publish actual raw data, sent via message buffers.

data_pub messages are constructed via the :func:`IPython.lib.datapub.publish_data` function:

from IPython.kernel.zmq.datapub import publish_data
ns = dict(x=my_array)
publish_data(ns)

Message type: data_pub:

content = {
    # the keys of the data dict, after it has been unserialized
    keys = ['a', 'b']
}
# the namespace dict will be serialized in the message buffers,
# which will have a length of at least one
buffers = ['pdict', ...]

The interpretation of a sequence of data_pub messages for a given parent request should be to update a single namespace with subsequent results.

Python inputs

These messages are the re-broadcast of the execute_request.

Message type: pyin:

content = {
    'code' : str,  # Source code to be executed, one or more lines

    # The counter for this execution is also provided so that clients can
    # display it, since IPython automatically creates variables called _iN
    # (for input prompt In[N]).
    'execution_count' : int
}

Python outputs

When Python produces output from code that has been compiled in with the 'single' flag to :func:`compile`, any expression that produces a value (such as 1+1) is passed to sys.displayhook, which is a callable that can do with this value whatever it wants. The default behavior of sys.displayhook in the Python interactive prompt is to print to sys.stdout the :func:`repr` of the value as long as it is not None (which isn't printed at all). In our case, the kernel instantiates as sys.displayhook an object which has similar behavior, but which instead of printing to stdout, broadcasts these values as pyout messages for clients to display appropriately.

IPython's displayhook can handle multiple simultaneous formats depending on its configuration. The default pretty-printed repr text is always given with the data entry in this message. Any other formats are provided in the extra_formats list. Frontends are free to display any or all of these according to its capabilities. extra_formats list contains 3-tuples of an ID string, a type string, and the data. The ID is unique to the formatter implementation that created the data. Frontends will typically ignore the ID unless if it has requested a particular formatter. The type string tells the frontend how to interpret the data. It is often, but not always a MIME type. Frontends should ignore types that it does not understand. The data itself is any JSON object and depends on the format. It is often, but not always a string.

Message type: pyout:

content = {

    # The counter for this execution is also provided so that clients can
    # display it, since IPython automatically creates variables called _N
    # (for prompt N).
    'execution_count' : int,

    # data and metadata are identical to a display_data message.
    # the object being displayed is that passed to the display hook,
    # i.e. the *result* of the execution.
    'data' : dict,
    'metadata' : dict,
}

Python errors

When an error occurs during code execution

Message type: pyerr:

content = {
   # Similar content to the execute_reply messages for the 'error' case,
   # except the 'status' field is omitted.
}

Kernel status

This message type is used by frontends to monitor the status of the kernel.

Message type: status:

content = {
    # When the kernel starts to execute code, it will enter the 'busy'
    # state and when it finishes, it will enter the 'idle' state.
    # The kernel will publish state 'starting' exactly once at process startup.
    execution_state : ('busy', 'idle', 'starting')
}