update nbformat spec in sphinx

docs/source/notebook/nbformat.rst

@@ -1,278 +1,291
 .. _nbformat:
 
 ===========================
 The Jupyter Notebook Format
 ===========================
 
 Introduction
 ============
 
 Jupyter (née IPython) notebook files are simple JSON documents,
 containing text, source code, rich media output, and metadata.
 each segment of the document is stored in a cell.
 
 Some general points about the notebook format:
 
 .. note::
 
     *All* metadata fields are optional.
     While the type and values of some metadata are defined,
     no metadata values are required to be defined.
 
 
 Top-level structure
 ===================
 
 At the highest level, a Jupyter notebook is a dictionary with a few keys:
 
 - metadata (dict)
 - nbformat (int)
 - nbformat_minor (int)
 - cells (list)
 
 .. sourcecode:: python
 
     {
       "metadata" : {
         "signature": "hex-digest", # used for authenticating unsafe outputs on load
         "kernel_info": {
             # if kernel_info is defined, its name and language fields are required.
             "name" : "the name of the kernel",
             "language" : "the programming language of the kernel",
             "codemirror_mode": "The name of the codemirror mode to use [optional]"
         },
       },
       "nbformat": 4,
       "nbformat_minor": 0,
       "cells" : [
           # list of cell dictionaries, see below
       ],
     }
 
 
 Cell Types
 ==========
 
 There are a few basic cell types for encapsulating code and text.
 All cells have the following basic structure:
 
 .. sourcecode:: python
 
     {
       "cell_type" : "name",
       "metadata" : {},
       "source" : "single string or [list, of, strings]",
     }
 
 
 Markdown cells
 --------------
 
 Markdown cells are used for body-text, and contain markdown,
 as defined in `GitHub-flavored markdown`_, and implemented in marked_.
 
 .. _GitHub-flavored markdown: https://help.github.com/articles/github-flavored-markdown
 .. _marked: https://github.com/chjj/marked
 
 .. sourcecode:: python
 
     {
       "cell_type" : "markdown",
       "metadata" : {},
       "source" : ["some *markdown*"],
     }
 
 
 Heading cells
 -------------
 
 Heading cells are single lines describing a section header (mapping onto h1-h6 tags in HTML).
 These cells indicate structure of the document,
 and are used for things like outline-views and automatically generating HTML anchors
 within the page for quick navigation.
 They have a ``level`` field, with an integer value from 1-6 (inclusive).
 
 .. sourcecode:: python
 
     {
       "cell_type" : "markdown",
       "metadata" : {},
       "level" : 1, # An integer on [1-6]
       "source" : ["A simple heading"],
     }
 
 
 Code cells
 ----------
 
 Code cells are the primary content of Jupyter notebooks.
 They contain source code int e language of the document's associated kernel,
 and a list of outputs associated with executing.
-They also have a prompt_number, which must be an integer or ``null``.
+They also have an execution_count, which must be an integer or ``null``.
 
 .. sourcecode:: python
 
     {
       "cell_type" : "code",
-      "prompt_number": 1, # integer or null
+      "execution_count": 1, # integer or null
       "metadata" : {
           "collapsed" : True, # whether the output of the cell is collapsed
           "autoscroll": False, # any of true, false or "auto"
       },
       "source" : ["some code"],
       "outputs": [{
           # list of output dicts (described below)
           "output_type": "stream",
           ...
       }],
     }
 
 .. versionchanged:: 4.0
 
     ``input`` was renamed to ``source``, for consistency among cell types.
 
+.. versionchanged:: 4.0
+
+    ``prompt_number`` renamed to ``execution_count``
+
 Code cell outputs
 -----------------
 
 A code cell can have a variety of outputs (stream data or rich mime-type output).
 These correspond to :ref:`messages <messaging>` produced as a result of executing the cell.
 
 All outputs have an ``output_type`` field,
 which is a string defining what type of output it is.
 
 
 stream output
 *************
 
 .. sourcecode:: python
 
     {
       "output_type" : "stream",
       "name" : "stdout", # or stderr
       "data" : ["multiline stream text"],
     }
 
 .. versionchanged:: 4.0
 
     The keys ``stream`` and ``text`` were changed to ``name`` and ``data`` to match
     the stream message specification.
 
 
 display_data
 ************
 
 Rich display messages (as created by ``display_data`` messages)
 contain data keyed by mime-type. All mime-type data should
 The metadata of these messages may be keyed by mime-type as well.
 
 
 .. sourcecode:: python
 
     {
       "output_type" : "display_data",
-      "text/plain" : ["multiline text data"],
-      "image/png": ["base64-encoded-png-data"],
-      "application/json": {
-        # JSON data is included as-is
-        "json": "data",
+      "data" : {
+        "text/plain" : ["multiline text data"],
+        "image/png": ["base64-encoded-png-data"],
+        "application/json": {
+          # JSON data is included as-is
+          "json": "data",
+        },
       },
       "metadata" : {
         "image/png": {
           "width": 640,
           "height": 480,
         },
       },
     }
 
 
 .. versionchanged:: 4.0
 
     ``application/json`` output is no longer double-serialized into a string.
 
 .. versionchanged:: 4.0
 
     mime-types are used for keys, instead of a combination of short names (``text``)
-    and mime-types.
+    and mime-types, and are stored in a ``data`` key, rather than the top-level.
+    i.e. ``output.data['image/png']`` instead of ``output.png``.
 
 
 execute_result
 **************
 
 Results of executing a cell (as created by ``displayhook`` in Python)
 are stored in ``execute_result`` outputs.
 `execute_result` outputs are identical to ``display_data``,
 adding only a ``prompt_number`` field, which must be an integer.
 
 .. sourcecode:: python
 
     {
       "output_type" : "execute_result",
-      "prompt_number": 42,
-      "text/plain" : ["multiline text data"],
-      "image/png": ["base64-encoded-png-data"],
-      "application/json": {
-        # JSON data is included as-is
-        "json": "data",
+      "execute_result": 42,
+      "data" : {
+        "text/plain" : ["multiline text data"],
+        "image/png": ["base64-encoded-png-data"],
+        "application/json": {
+          # JSON data is included as-is
+          "json": "data",
+        },
       },
       "metadata" : {
         "image/png": {
           "width": 640,
           "height": 480,
         },
       },
     }
 
 .. versionchanged:: 4.0
 
     ``pyout`` renamed to ``execute_result``
 
+.. versionchanged:: 4.0
+
+    ``prompt_number`` renamed to ``execution_count``
+
 
 error
 *****
 
 Failed execution may show a traceback
 
 .. sourcecode:: python
 
     {
       '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.
       'traceback' : list,
     }
 
 .. versionchanged:: 4.0
 
     ``pyerr`` renamed to ``error``
 
 
 Raw NBConvert cells
 -------------------
 
 A raw cell is defined as content that should be included *unmodified* in :ref:`nbconvert <nbconvert>` output.
 For example, this cell could include raw LaTeX for nbconvert to pdf via latex,
 or restructured text for use in Sphinx documentation.
 
 The notebook authoring environment does not render raw cells.
 
 The only logic in a raw cell is the `format` metadata field.
 If defined, it specifies which nbconvert output format is the intended target
 for the raw cell. When outputting to any other format,
 the raw cell's contents will be excluded.
 In the default case when this value is undefined,
 a raw cell's contents will be included in any nbconvert output,
 regardless of format.
 
 .. sourcecode:: python
 
     {
       "cell_type" : "raw",
       "metadata" : {
         # the mime-type of the target nbconvert format.
         # nbconvert to formats other than this will exclude this cell.
         "format" : "mime/type"
       },
       "source" : ["some nbformat mime-type data"]
     }