update nbconvert to nbformat 4

IPython/nbconvert/exporters/export.py

@@ -5,7 +5,7 b''
 
 from functools import wraps
 
-from IPython.nbformat.v3.nbbase import NotebookNode
+from IPython.nbformat.current import NotebookNode
 from IPython.utils.decorators import undoc
 from IPython.utils.py3compat import string_types
 

IPython/nbconvert/exporters/tests/files/notebook2.ipynb

@@ -1,177 +1,168 b''
 {
- "metadata": {
-  "name": "notebook2"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "heading",
+   "level": 1,
+   "metadata": {},
+   "source": [
+    "NumPy and Matplotlib examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First import NumPy and Matplotlib:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 1,
      "metadata": {},
-     "source": [
-      "NumPy and Matplotlib examples"
-     ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": "\nWelcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\nFor more information, type 'help(pylab)'.\n"
+    }
+   ],
+   "prompt_number": 1,
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 2,
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we show some very basic examples of how they can be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 6,
+   "source": [
+    "a = np.random.uniform(size=(100,100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
      "metadata": {},
-     "source": [
-      "First import NumPy and Matplotlib:"
+     "output_type": "execute_result",
+     "prompt_number": 7,
+     "text/plain": [
+      "(100, 100)"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
-        "For more information, type 'help(pylab)'.\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
+    }
+   ],
+   "prompt_number": 7,
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 8,
+   "source": [
+    "evs = np.linalg.eigvals(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
      "metadata": {},
-     "source": [
-      "Now we show some very basic examples of how they can be used."
+     "output_type": "execute_result",
+     "prompt_number": 10,
+     "text/plain": [
+      "(100,)"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.random.uniform(size=(100,100))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "(100, 100)"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "evs = np.linalg.eigvals(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "evs.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "(100,)"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
+    }
+   ],
+   "prompt_number": 10,
+   "source": [
+    "evs.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a cell that has both text and PNG output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
      "metadata": {},
-     "source": [
-      "Here is a cell that has both text and PNG output:"
+     "output_type": "execute_result",
+     "prompt_number": 14,
+     "text/plain": [
+      "(array([95,  4,  0,  0,  0,  0,  0,  0,  0,  1]),\n",
+      " array([ -2.93566063,   2.35937011,   7.65440086,  12.9494316 ,\n",
+      "        18.24446235,  23.53949309,  28.83452384,  34.12955458,\n",
+      "        39.42458533,  44.71961607,  50.01464682]),\n",
+      " <a list of 10 Patch objects>)"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "hist(evs.real)"
-     ],
-     "language": "python",
+     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD9CAYAAAC2l2x5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "(array([95,  4,  0,  0,  0,  0,  0,  0,  0,  1]),\n",
-        " array([ -2.93566063,   2.35937011,   7.65440086,  12.9494316 ,\n",
-        "        18.24446235,  23.53949309,  28.83452384,  34.12955458,\n",
-        "        39.42458533,  44.71961607,  50.01464682]),\n",
-        " <a list of 10 Patch objects>)"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD9CAYAAAC2l2x5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x108c8f1d0>"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "output_type": "display_data",
+     "text/plain": [
+      "<matplotlib.figure.Figure at 0x108c8f1d0>"
+     ]
     }
    ],
-   "metadata": {}
+   "prompt_number": 14,
+   "source": [
+    "hist(evs.real)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": null,
+   "source": []
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbconvert/exporters/tests/files/rawtest.ipynb

@@ -1,84 +1,77 b''
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "raw",
-     "metadata": {
-      "raw_mimetype": "text/html"
-     },
-     "source": [
-      "<b>raw html</b>"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {
-      "raw_mimetype": "text/markdown"
-     },
-     "source": [
-      "* raw markdown\n",
-      "* bullet\n",
-      "* list"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {
-      "raw_mimetype": "text/restructuredtext"
-     },
-     "source": [
-      "``raw rst``\n",
-      "\n",
-      ".. sourcecode:: python\n",
-      "\n",
-      "    def foo(): pass\n"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {
-      "raw_mimetype": "text/x-python"
-     },
-     "source": [
-      "def bar():\n",
-      "    \"\"\"raw python\"\"\"\n",
-      "    pass"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {
-      "raw_mimetype": "text/latex"
-     },
-     "source": [
-      "\\LaTeX\n",
-      "% raw latex"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {},
-     "source": [
-      "# no raw_mimetype metadata, should be included by default"
-     ]
-    },
-    {
-     "cell_type": "raw",
-     "metadata": {
-      "raw_mimetype": "doesnotexist"
-     },
-     "source": [
-      "garbage format defined, should never be included"
-     ]
-    }
-   ],
-   "metadata": {}
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/html"
+   },
+   "source": [
+    "<b>raw html</b>"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/markdown"
+   },
+   "source": [
+    "* raw markdown\n",
+    "* bullet\n",
+    "* list"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    "``raw rst``\n",
+    "\n",
+    ".. sourcecode:: python\n",
+    "\n",
+    "    def foo(): pass\n"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/x-python"
+   },
+   "source": [
+    "def bar():\n",
+    "    \"\"\"raw python\"\"\"\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "text/latex"
+   },
+   "source": [
+    "\\LaTeX\n",
+    "% raw latex"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "# no raw_mimetype metadata, should be included by default"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "raw_mimetype": "doesnotexist"
+   },
+   "source": [
+    "garbage format defined, should never be included"
+   ]
   }
- ]
-}
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
  No newline at end of file

IPython/nbconvert/exporters/tests/test_latex.py

@@ -86,10 +86,8 b' class TestLatexExporter(ExportersTestsBase):'
 
         notebook_name = "lorem_ipsum_long.ipynb"
         nb = current.new_notebook(
-            worksheets=[
-                current.new_worksheet(cells=[
-                    current.new_text_cell('markdown',source=large_lorem_ipsum_text)
-                ])
+            cells=[
+                    current.new_markdown_cell(source=large_lorem_ipsum_text)
             ]
         )
 

IPython/nbconvert/exporters/tests/test_rst.py

@@ -20,9 +20,6 b' from .base import ExportersTestsBase'
 from ..rst import RSTExporter
 from IPython.testing.decorators import onlyif_cmds_exist
 
-#-----------------------------------------------------------------------------
-# Class
-#-----------------------------------------------------------------------------
 
 class TestRSTExporter(ExportersTestsBase):
     """Tests for RSTExporter"""
@@ -56,8 +53,8 b' class TestRSTExporter(ExportersTestsBase):'
         
         (output, resources) = exporter.from_notebook_node(nb)
         # add an empty code cell
-        nb.worksheets[0].cells.append(
-            current.new_code_cell(input="")
+        nb.cells.append(
+            current.new_code_cell(source="")
         )
         (output2, resources) = exporter.from_notebook_node(nb)
         # adding an empty code cell shouldn't change output

IPython/nbconvert/preprocessors/base.py

@@ -66,9 +66,8 b' class Preprocessor(NbConvertBase):'
             Additional resources used in the conversion process.  Allows
             preprocessors to pass variables into the Jinja engine.
         """
-        for worksheet in nb.worksheets:
-            for index, cell in enumerate(worksheet.cells):
-                worksheet.cells[index], resources = self.preprocess_cell(cell, resources, index)
+        for index, cell in enumerate(nb.cells):
+            nb.cells[index], resources = self.preprocess_cell(cell, resources, index)
         return nb, resources
 
 

IPython/nbconvert/preprocessors/coalescestreams.py

@@ -4,6 +4,7 b''
 # Distributed under the terms of the Modified BSD License.
 
 import re
+from IPython.utils.log import get_logger
 
 def cell_preprocessor(function):
     """
@@ -21,14 +22,11 b' def cell_preprocessor(function):'
     """
     
     def wrappedfunc(nb, resources):
-        from IPython.config import Application
-        if Application.initialized():
-            Application.instance().log.debug(
+        get_logger().debug(
                 "Applying preprocessor: %s", function.__name__
             )
-        for worksheet in nb.worksheets:
-            for index, cell in enumerate(worksheet.cells):
-                worksheet.cells[index], resources = function(cell, resources, index)
+        for index, cell in enumerate(nb.cells):
+            nb.cells[index], resources = function(cell, resources, index)
         return nb, resources
     return wrappedfunc
 
@@ -60,7 +58,7 b' def coalesce_streams(cell, resources, index):'
     for output in outputs[1:]:
         if (output.output_type == 'stream' and
             last.output_type == 'stream' and
-            last.stream == output.stream
+            last.name == output.name
         ):
             last.text += output.text
 

IPython/nbconvert/preprocessors/execute.py

@@ -13,7 +13,7 b' except ImportError:'
 
 from IPython.utils.traitlets import List, Unicode
 
-from IPython.nbformat.current import reads, NotebookNode, writes
+from IPython.nbformat.current import reads, writes, new_output
 from .base import Preprocessor
 from IPython.utils.traitlets import Integer
 
@@ -25,25 +25,6 b' class ExecutePreprocessor(Preprocessor):'
     timeout = Integer(30, config=True,
         help="The time to wait (in seconds) for output from executions."
     )
-    # FIXME: to be removed with nbformat v4
-    # map msg_type to v3 output_type
-    msg_type_map = {
-        "error" : "pyerr",
-        "execute_result" : "pyout",
-    }
-    
-    # FIXME: to be removed with nbformat v4
-    # map mime-type to v3 mime-type keys
-    mime_map = {
-        "text/plain" : "text",
-        "text/html" : "html",
-        "image/svg+xml" : "svg",
-        "image/png" : "png",
-        "image/jpeg" : "jpeg",
-        "text/latex" : "latex",
-        "application/json" : "json",
-        "application/javascript" : "javascript",
-    }
     
     extra_arguments = List(Unicode)
 
@@ -68,14 +49,14 b' class ExecutePreprocessor(Preprocessor):'
             outputs = self.run_cell(self.kc.shell_channel, self.kc.iopub_channel, cell)
         except Exception as e:
             self.log.error("failed to run cell: " + repr(e))
-            self.log.error(str(cell.input))
+            self.log.error(str(cell.source))
             raise
         cell.outputs = outputs
         return cell, resources
 
     def run_cell(self, shell, iopub, cell):
-        msg_id = shell.execute(cell.input)
-        self.log.debug("Executing cell:\n%s", cell.input)
+        msg_id = shell.execute(cell.source)
+        self.log.debug("Executing cell:\n%s", cell.source)
         # wait for finish, with timeout
         while True:
             try:
@@ -104,7 +85,6 b' class ExecutePreprocessor(Preprocessor):'
             msg_type = msg['msg_type']
             self.log.debug("output: %s", msg_type)
             content = msg['content']
-            out = NotebookNode(output_type=self.msg_type_map.get(msg_type, msg_type))
 
             # set the prompt number for the input and the output
             if 'execution_count' in content:
@@ -116,26 +96,37 b' class ExecutePreprocessor(Preprocessor):'
                     break
                 else:
                     continue
-            elif msg_type in {'execute_input', 'pyin'}:
+            elif msg_type in {'execute_input'}:
                 continue
             elif msg_type == 'clear_output':
                 outs = []
                 continue
 
-            if msg_type == 'stream':
-                out.stream = content['name']
-                out.text = content['text']
-            elif msg_type in ('display_data', 'execute_result'):
-                out['metadata'] = content['metadata']
-                for mime, data in content['data'].items():
-                    # map mime-type keys to nbformat v3 keys
-                    # this will be unnecessary in nbformat v4
-                    key = self.mime_map.get(mime, mime)
-                    out[key] = data
+            # set the prompt number for the input and the output
+            if msg_type == 'execute_result':
+                cell['prompt_number'] = content['execution_count']
+                out = new_output(output_type=msg_type,
+                    metadata=content['metadata'],
+                    mime_bundle=content['data'],
+                    prompt_number=content['execution_count'],
+                )
+
+            elif msg_type == 'stream':
+                out = new_output(output_type=msg_type,
+                    name=content['name'],
+                    data=content['data'],
+                )
+            elif msg_type == 'display_data':
+                out = new_output(output_type=msg_type,
+                    metadata=content['metadata'],
+                    mime_bundle=content['data'],
+                )
             elif msg_type == 'error':
-                out.ename = content['ename']
-                out.evalue = content['evalue']
-                out.traceback = content['traceback']
+                out = new_output(output_type=msg_type,
+                    ename=content['ename'],
+                    evalue=content['evalue'],
+                    traceback=content['traceback'],
+                )
             else:
                 self.log.error("unhandled iopub msg: " + msg_type)
 

IPython/nbconvert/preprocessors/extractoutput.py

@@ -1,17 +1,9 b''
-"""Module containing a preprocessor that extracts all of the outputs from the
+"""A preprocessor that extracts all of the outputs from the
 notebook file.  The extracted outputs are returned in the 'resources' dictionary.
 """
-#-----------------------------------------------------------------------------
-# Copyright (c) 2013, the IPython Development Team.
-#
-# Distributed under the terms of the Modified BSD License.
-#
-# The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
 
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
+# Copyright (c) IPython Development Team.
+# Distributed under the terms of the Modified BSD License.
 
 import base64
 import sys
@@ -22,9 +14,6 b' from IPython.utils.traitlets import Unicode, Set'
 from .base import Preprocessor
 from IPython.utils import py3compat
 
-#-----------------------------------------------------------------------------
-# Classes
-#-----------------------------------------------------------------------------
 
 class ExtractOutputPreprocessor(Preprocessor):
     """
@@ -35,7 +24,7 b' class ExtractOutputPreprocessor(Preprocessor):'
     output_filename_template = Unicode(
         "{unique_key}_{cell_index}_{index}{extension}", config=True)
 
-    extract_output_types = Set({'png', 'jpeg', 'svg', 'application/pdf'}, config=True)
+    extract_output_types = Set({'image/png', 'image/jpeg', 'image/svg+xml', 'application/pdf'}, config=True)
 
     def preprocess_cell(self, cell, resources, cell_index):
         """
@@ -71,7 +60,7 b' class ExtractOutputPreprocessor(Preprocessor):'
                     data = out[out_type]
 
                     #Binary files are base64-encoded, SVG is already XML
-                    if out_type in {'png', 'jpeg', 'application/pdf'}:
+                    if out_type in {'image/png', 'image/jpeg', 'application/pdf'}:
 
                         # data is b64-encoded as text (str, unicode)
                         # decodestring only accepts bytes

IPython/nbconvert/preprocessors/highlightmagics.py

@@ -4,17 +4,8 b' so that the appropriate highlighter can be used in the `highlight`'
 filter.
 """
 
-#-----------------------------------------------------------------------------
-# Copyright (c) 2013, the IPython Development Team.
-#
+# Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
-#
-# The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
 
 from __future__ import print_function, absolute_import
 
@@ -24,10 +15,6 b' import re'
 from .base import Preprocessor
 from IPython.utils.traitlets import Dict
 
-#-----------------------------------------------------------------------------
-# Classes
-#-----------------------------------------------------------------------------
-
 
 class HighlightMagicsPreprocessor(Preprocessor):
     """
@@ -106,8 +93,8 b' class HighlightMagicsPreprocessor(Preprocessor):'
         """
 
         # Only tag code cells
-        if hasattr(cell, "input") and cell.cell_type == "code":
-            magic_language = self.which_magic_language(cell.input)
+        if cell.cell_type == "code":
+            magic_language = self.which_magic_language(cell.source)
             if magic_language:
                 cell['metadata']['magics_language'] = magic_language
         return cell, resources

IPython/nbconvert/preprocessors/revealhelp.py

@@ -1,23 +1,11 b''
-"""Module that pre-processes the notebook for export via Reveal.
-"""
-#-----------------------------------------------------------------------------
-# Copyright (c) 2013, the IPython Development Team.
-#
-# Distributed under the terms of the Modified BSD License.
-#
-# The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
+"""Module that pre-processes the notebook for export via Reveal."""
 
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
+# Copyright (c) IPython Development Team.
+# Distributed under the terms of the Modified BSD License.
 
 from .base import Preprocessor
 from IPython.utils.traitlets import Unicode
 
-#-----------------------------------------------------------------------------
-# Classes and functions
-#-----------------------------------------------------------------------------
 
 class RevealHelpPreprocessor(Preprocessor):
 
@@ -43,31 +31,30 b' class RevealHelpPreprocessor(Preprocessor):'
             preprocessors to pass variables into the Jinja engine.
         """
 
-        for worksheet in nb.worksheets:
-            for index, cell in enumerate(worksheet.cells):
-
-                #Make sure the cell has slideshow metadata.
-                cell.metadata.slide_type = cell.get('metadata', {}).get('slideshow', {}).get('slide_type', '-')
-
-                #Get the slide type. If type is start of subslide or slide,
-                #end the last subslide/slide.
-                if cell.metadata.slide_type in ['slide']:
-                    worksheet.cells[index - 1].metadata.slide_helper = 'slide_end'
-                if cell.metadata.slide_type in ['subslide']:
-                    worksheet.cells[index - 1].metadata.slide_helper = 'subslide_end'
-                #Prevent the rendering of "do nothing" cells before fragments
-                #Group fragments passing frag_number to the data-fragment-index
-                if cell.metadata.slide_type in ['fragment']:
-                    worksheet.cells[index].metadata.frag_number = index
-                    i = 1
-                    while i < len(worksheet.cells) - index:
-                        worksheet.cells[index + i].metadata.frag_helper = 'fragment_end'
-                        worksheet.cells[index + i].metadata.frag_number = index
-                        i += 1
-                #Restart the slide_helper when the cell status is changed
-                #to other types.
-                if cell.metadata.slide_type in ['-', 'skip', 'notes', 'fragment']:
-                    worksheet.cells[index - 1].metadata.slide_helper = '-'
+        for index, cell in enumerate(nb.cells):
+
+            #Make sure the cell has slideshow metadata.
+            cell.metadata.slide_type = cell.get('metadata', {}).get('slideshow', {}).get('slide_type', '-')
+
+            # Get the slide type. If type is start, subslide, or slide,
+            # end the last subslide/slide.
+            if cell.metadata.slide_type in ['slide']:
+                nb.cells[index - 1].metadata.slide_helper = 'slide_end'
+            if cell.metadata.slide_type in ['subslide']:
+                nb.cells[index - 1].metadata.slide_helper = 'subslide_end'
+            # Prevent the rendering of "do nothing" cells before fragments
+            # Group fragments passing frag_number to the data-fragment-index
+            if cell.metadata.slide_type in ['fragment']:
+                nb.cells[index].metadata.frag_number = index
+                i = 1
+                while i < len(nb.cells) - index:
+                    nb.cells[index + i].metadata.frag_helper = 'fragment_end'
+                    nb.cells[index + i].metadata.frag_number = index
+                    i += 1
+            # Restart the slide_helper when the cell status is changed
+            # to other types.
+            if cell.metadata.slide_type in ['-', 'skip', 'notes', 'fragment']:
+                nb.cells[index - 1].metadata.slide_helper = '-'
 
         if not isinstance(resources['reveal'], dict):
             resources['reveal'] = {}

IPython/nbconvert/preprocessors/tests/base.py

@@ -1,27 +1,13 b''
-"""
-Module with utility functions for preprocessor tests
-"""
+"""utility functions for preprocessor tests"""
 
-#-----------------------------------------------------------------------------
-# Copyright (c) 2013, the IPython Development Team.
-#
+# Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
-#
-# The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
 
 from IPython.nbformat import current as nbformat
 
 from ...tests.base import TestsBase
 from ...exporters.exporter import ResourcesDict
 
-#-----------------------------------------------------------------------------
-# Class
-#-----------------------------------------------------------------------------
 
 class PreprocessorTestsBase(TestsBase):
     """Contains test functions preprocessor tests"""
@@ -30,22 +16,21 b' class PreprocessorTestsBase(TestsBase):'
     def build_notebook(self):
         """Build a notebook in memory for use with preprocessor tests"""
 
-        outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="a"),
-                   nbformat.new_output(output_type="text", output_text="b"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="c"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="d"),
-                   nbformat.new_output(output_type="stream", stream="stderr", output_text="e"),
-                   nbformat.new_output(output_type="stream", stream="stderr", output_text="f"),
-                   nbformat.new_output(output_type="png", output_png='Zw==')] # g
-        out = nbformat.new_output(output_type="application/pdf")
-        out['application/pdf'] = 'aA==' # h
+        outputs = [nbformat.new_output(output_type="stream", name="stdout", text="a"),
+                   nbformat.new_output(output_type="display_data", mime_bundle={'text/plain': 'b'}),
+                   nbformat.new_output(output_type="stream", name="stdout", text="c"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="d"),
+                   nbformat.new_output(output_type="stream", name="stderr", text="e"),
+                   nbformat.new_output(output_type="stream", name="stderr", text="f"),
+                   nbformat.new_output(output_type="display_data", mime_bundle={'image/png': 'Zw=='})] # g
+        out = nbformat.new_output(output_type="display_data")
+        out['application/pdf'] = 'aA=='
         outputs.append(out)
         
-        cells=[nbformat.new_code_cell(input="$ e $", prompt_number=1,outputs=outputs),
-               nbformat.new_text_cell('markdown', source="$ e $")]
-        worksheets = [nbformat.new_worksheet(cells=cells)]
+        cells=[nbformat.new_code_cell(source="$ e $", prompt_number=1, outputs=outputs),
+               nbformat.new_markdown_cell(source="$ e $")]
 
-        return nbformat.new_notebook(name="notebook1", worksheets=worksheets)
+        return nbformat.new_notebook(cells=cells)
 
 
     def build_resources(self):

IPython/nbconvert/preprocessors/tests/files/Clear Output.ipynb

@@ -1,46 +1,38 b''
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import clear_output"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 1,
+   "source": [
+    "from IPython.display import clear_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(10):\n",
-      "    clear_output()\n",
-      "    print(i)"
-     ],
-     "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "9\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
+     "name": "stdout",
+     "output_type": "stream",
+     "text": "9\n"
     }
    ],
-   "metadata": {}
+   "prompt_number": 2,
+   "source": [
+    "for i in range(10):\n",
+    "    clear_output()\n",
+    "    print(i)"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbconvert/preprocessors/tests/files/Factorials.ipynb

@@ -1,55 +1,38 b''
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i, j = 1, 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 1,
+   "source": [
+    "i, j = 1, 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for m in range(10):\n",
-      "    i, j = j, i + j\n",
-      "    print(j)"
-     ],
-     "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2\n",
-        "3\n",
-        "5\n",
-        "8\n",
-        "13\n",
-        "21\n",
-        "34\n",
-        "55\n",
-        "89\n",
-        "144\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
+     "name": "stdout",
+     "output_type": "stream",
+     "text": "2\n3\n5\n8\n13\n21\n34\n55\n89\n144\n"
     }
    ],
-   "metadata": {}
+   "prompt_number": 2,
+   "source": [
+    "for m in range(10):\n",
+    "    i, j = j, i + j\n",
+    "    print(j)"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbconvert/preprocessors/tests/files/HelloWorld.ipynb

@@ -1,33 +1,25 b''
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(\"Hello World\")"
-     ],
-     "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello World\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "name": "stdout",
+     "output_type": "stream",
+     "text": "Hello World\n"
     }
    ],
-   "metadata": {}
+   "prompt_number": 1,
+   "source": [
+    "print(\"Hello World\")"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbconvert/preprocessors/tests/files/Inline Image.ipynb

@@ -1,36 +1,29 b''
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Image"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Image('../input/python.png');"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    }
-   ],
-   "metadata": {}
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 1,
+   "source": [
+    "from IPython.display import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 2,
+   "source": [
+    "Image('../input/python.png');"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbconvert/preprocessors/tests/files/SVG.ipynb

@@ -1,53 +1,46 b''
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import SVG"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 1,
+   "source": [
+    "from IPython.display import SVG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "SVG(data='''\n",
+     "image/svg+xml": [
       "<svg height=\"100\" width=\"100\">\n",
-      "    <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"2\" fill=\"red\" />\n",
-      "</svg>''')"
+      "    <circle cx=\"50\" cy=\"50\" fill=\"red\" r=\"40\" stroke=\"black\" stroke-width=\"2\"/>\n",
+      "</svg>"
      ],
-     "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 2,
-       "svg": [
-        "<svg height=\"100\" width=\"100\">\n",
-        "    <circle cx=\"50\" cy=\"50\" fill=\"red\" r=\"40\" stroke=\"black\" stroke-width=\"2\"/>\n",
-        "</svg>"
-       ],
-       "text": [
-        "<IPython.core.display.SVG at 0x10428e150>"
-       ]
-      }
-     ],
-     "prompt_number": 2
+     "output_type": "execute_result",
+     "prompt_number": 2,
+     "text/plain": [
+      "<IPython.core.display.SVG object>"
+     ]
     }
    ],
-   "metadata": {}
+   "prompt_number": 2,
+   "source": [
+    "SVG(data='''\n",
+    "<svg height=\"100\" width=\"100\">\n",
+    "    <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"2\" fill=\"red\" />\n",
+    "</svg>''')"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbconvert/preprocessors/tests/files/Skip Exceptions.ipynb

@@ -1,57 +1,51 b''
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:9d47889f0678e9685429071216d0f3354db59bb66489f3225bcadfb6a1a9bbba"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "raise Exception(\"message\")"
-     ],
-     "language": "python",
+     "ename": "Exception",
+     "evalue": "message",
      "metadata": {},
-     "outputs": [
-      {
-       "ename": "Exception",
-       "evalue": "message",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-        "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-1-335814d14fc1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"message\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;31mException\u001b[0m: message"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-335814d14fc1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"message\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mException\u001b[0m: message"
+     ]
+    }
+   ],
+   "prompt_number": 1,
+   "source": [
+    "raise Exception(\"message\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('ok')"
-     ],
-     "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ok\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
+     "name": "stdout",
+     "output_type": "stream",
+     "text": "ok\n"
     }
    ],
-   "metadata": {}
+   "prompt_number": 2,
+   "source": [
+    "print('ok')"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:9d47889f0678e9685429071216d0f3354db59bb66489f3225bcadfb6a1a9bbba"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbconvert/preprocessors/tests/files/Unicode.ipynb

@@ -1,33 +1,25 b''
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('\u2603')"
-     ],
-     "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\u2603\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "name": "stdout",
+     "output_type": "stream",
+     "text": "\u2603\n"
     }
    ],
-   "metadata": {}
+   "prompt_number": 1,
+   "source": [
+    "print('\u2603')"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbconvert/preprocessors/tests/test_clearoutput.py

@@ -5,19 +5,12 b' Module with tests for the clearoutput preprocessor.'
 # Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
 
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
 from IPython.nbformat import current as nbformat
 
 from .base import PreprocessorTestsBase
 from ..clearoutput import ClearOutputPreprocessor
 
 
-#-----------------------------------------------------------------------------
-# Class
-#-----------------------------------------------------------------------------
-
 class TestClearOutput(PreprocessorTestsBase):
     """Contains test functions for clearoutput.py"""
 
@@ -38,5 +31,5 b' class TestClearOutput(PreprocessorTestsBase):'
         res = self.build_resources()
         preprocessor = self.build_preprocessor()
         nb, res = preprocessor(nb, res)
-        assert nb.worksheets[0].cells[0].outputs == []
-        assert nb.worksheets[0].cells[0].prompt_number is None
+        assert nb.cells[0].outputs == []
+        assert nb.cells[0].prompt_number is None

IPython/nbconvert/preprocessors/tests/test_coalescestreams.py

@@ -17,44 +17,42 b' class TestCoalesceStreams(PreprocessorTestsBase):'
         nb = self.build_notebook()
         res = self.build_resources()
         nb, res = coalesce_streams(nb, res)
-        outputs = nb.worksheets[0].cells[0].outputs
+        outputs = nb.cells[0].outputs
         self.assertEqual(outputs[0].text, "a")
-        self.assertEqual(outputs[1].output_type, "text")
+        self.assertEqual(outputs[1].output_type, "display_data")
         self.assertEqual(outputs[2].text, "cd")
         self.assertEqual(outputs[3].text, "ef")
 
     def test_coalesce_sequenced_streams(self):
         """Can the coalesce streams preprocessor merge a sequence of streams?"""
-        outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="0"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="1"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="2"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="3"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="4"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="5"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="6"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="7")]
-        cells=[nbformat.new_code_cell(input="# None", prompt_number=1,outputs=outputs)]
-        worksheets = [nbformat.new_worksheet(cells=cells)]
-
-        nb = nbformat.new_notebook(name="notebook1", worksheets=worksheets)
+        outputs = [nbformat.new_output(output_type="stream", name="stdout", text="0"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="1"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="2"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="3"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="4"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="5"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="6"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="7")]
+        cells=[nbformat.new_code_cell(source="# None", prompt_number=1,outputs=outputs)]
+
+        nb = nbformat.new_notebook(cells=cells)
         res = self.build_resources()
         nb, res = coalesce_streams(nb, res)
-        outputs = nb.worksheets[0].cells[0].outputs
+        outputs = nb.cells[0].outputs
         self.assertEqual(outputs[0].text, u'01234567')
 
     def test_coalesce_replace_streams(self):
         """Are \\r characters handled?"""
-        outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="z"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="\ra"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="\nz\rb"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="\nz"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="\rc\n"),
-                   nbformat.new_output(output_type="stream", stream="stdout", output_text="z\rz\rd")]
-        cells=[nbformat.new_code_cell(input="# None", prompt_number=1,outputs=outputs)]
-        worksheets = [nbformat.new_worksheet(cells=cells)]
-
-        nb = nbformat.new_notebook(name="notebook1", worksheets=worksheets)
+        outputs = [nbformat.new_output(output_type="stream", name="stdout", text="z"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="\ra"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="\nz\rb"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="\nz"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="\rc\n"),
+                   nbformat.new_output(output_type="stream", name="stdout", text="z\rz\rd")]
+        cells=[nbformat.new_code_cell(source="# None", prompt_number=1,outputs=outputs)]
+
+        nb = nbformat.new_notebook(cells=cells)
         res = self.build_resources()
         nb, res = coalesce_streams(nb, res)
-        outputs = nb.worksheets[0].cells[0].outputs
+        outputs = nb.cells[0].outputs
         self.assertEqual(outputs[0].text, u'a\nb\nc\nd')

IPython/nbconvert/preprocessors/tests/test_execute.py

@@ -30,10 +30,8 b' class TestExecute(PreprocessorTestsBase):'
         output = dict(output)
         if 'metadata' in output:
             del output['metadata']
-        if 'text' in output:
-            output['text'] = re.sub(addr_pat, '<HEXADDR>', output['text'])
-        if 'svg' in output:
-            del output['text']
+        if 'text/plain' in output:
+            output['text/plain'] = re.sub(addr_pat, '<HEXADDR>', output['text/plain'])
         if 'traceback' in output:
             tb = []
             for line in output['traceback']:
@@ -44,8 +42,8 b' class TestExecute(PreprocessorTestsBase):'
 
 
     def assert_notebooks_equal(self, expected, actual):
-        expected_cells = expected['worksheets'][0]['cells']
-        actual_cells = actual['worksheets'][0]['cells']
+        expected_cells = expected['cells']
+        actual_cells = actual['cells']
         assert len(expected_cells) == len(actual_cells)
 
         for expected_cell, actual_cell in zip(expected_cells, actual_cells):
@@ -82,7 +80,7 b' class TestExecute(PreprocessorTestsBase):'
             res = self.build_resources()
             preprocessor = self.build_preprocessor()
             cleaned_input_nb = copy.deepcopy(input_nb)
-            for cell in cleaned_input_nb.worksheets[0].cells:
+            for cell in cleaned_input_nb.cells:
                 if 'prompt_number' in cell:
                     del cell['prompt_number']
                 cell['outputs'] = []

IPython/nbconvert/preprocessors/tests/test_extractoutput.py

@@ -1,27 +1,12 b''
-"""
-Module with tests for the extractoutput preprocessor
-"""
+"""Tests for the extractoutput preprocessor"""
 
-#-----------------------------------------------------------------------------
-# Copyright (c) 2013, the IPython Development Team.
-#
+# Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
-#
-# The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
 
 from .base import PreprocessorTestsBase
 from ..extractoutput import ExtractOutputPreprocessor
 
 
-#-----------------------------------------------------------------------------
-# Class
-#-----------------------------------------------------------------------------
-
 class TestExtractOutput(PreprocessorTestsBase):
     """Contains test functions for extractoutput.py"""
 
@@ -29,7 +14,7 b' class TestExtractOutput(PreprocessorTestsBase):'
     def build_preprocessor(self):
         """Make an instance of a preprocessor"""
         preprocessor = ExtractOutputPreprocessor()
-        preprocessor.extract_output_types = {'text', 'png', 'application/pdf'}
+        preprocessor.extract_output_types = {'text/plain', 'image/png', 'application/pdf'}
         preprocessor.enabled = True
         return preprocessor
 
@@ -47,17 +32,17 b' class TestExtractOutput(PreprocessorTestsBase):'
         nb, res = preprocessor(nb, res)
 
         # Check if text was extracted.
-        output = nb.worksheets[0].cells[0].outputs[1]
-        assert 'text_filename' in output
-        text_filename = output['text_filename']
+        output = nb.cells[0].outputs[1]
+        assert 'text/plain_filename' in output
+        text_filename = output['text/plain_filename']
 
         # Check if png was extracted.
-        output = nb.worksheets[0].cells[0].outputs[6]
-        assert 'png_filename' in output
-        png_filename = output['png_filename']
+        output = nb.cells[0].outputs[6]
+        assert 'image/png_filename' in output
+        png_filename = output['image/png_filename']
         
         # Check that pdf was extracted
-        output = nb.worksheets[0].cells[0].outputs[7]
+        output = nb.cells[0].outputs[7]
         assert 'application/pdf_filename' in output
         pdf_filename = output['application/pdf_filename']
 

IPython/nbconvert/preprocessors/tests/test_highlightmagics.py

@@ -1,27 +1,9 b''
-"""
-Module with tests for the HighlightMagics preprocessor
-"""
-
-#-----------------------------------------------------------------------------
-# Copyright (c) 2013, the IPython Development Team.
-#
-# Distributed under the terms of the Modified BSD License.
-#
-# The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
+"""Tests for the HighlightMagics preprocessor"""
 
 from .base import PreprocessorTestsBase
 from ..highlightmagics import HighlightMagicsPreprocessor
 
 
-#-----------------------------------------------------------------------------
-# Class
-#-----------------------------------------------------------------------------
-
 class TestHighlightMagics(PreprocessorTestsBase):
     """Contains test functions for highlightmagics.py"""
 
@@ -41,7 +23,7 b' class TestHighlightMagics(PreprocessorTestsBase):'
         nb = self.build_notebook()
         res = self.build_resources()
         preprocessor = self.build_preprocessor()
-        nb.worksheets[0].cells[0].input = """%%R -i x,y -o XYcoef
+        nb.cells[0].source = """%%R -i x,y -o XYcoef
             lm.fit <- lm(y~x)
             par(mfrow=c(2,2))
             print(summary(lm.fit))
@@ -50,19 +32,19 b' class TestHighlightMagics(PreprocessorTestsBase):'
 
         nb, res = preprocessor(nb, res)
 
-        assert('magics_language' in nb.worksheets[0].cells[0]['metadata'])
+        assert('magics_language' in nb.cells[0]['metadata'])
 
-        self.assertEqual(nb.worksheets[0].cells[0]['metadata']['magics_language'], 'r')
+        self.assertEqual(nb.cells[0]['metadata']['magics_language'], 'r')
 
     def test_no_false_positive(self):
         """Test that HighlightMagicsPreprocessor does not tag false positives"""
         nb = self.build_notebook()
         res = self.build_resources()
         preprocessor = self.build_preprocessor()
-        nb.worksheets[0].cells[0].input = """# this should not be detected
+        nb.cells[0].source = """# this should not be detected
                 print(\"""
                 %%R -i x, y
                 \""")"""
         nb, res = preprocessor(nb, res)
 
-        assert('magics_language' not in nb.worksheets[0].cells[0]['metadata'])
  No newline at end of file
+        assert('magics_language' not in nb.cells[0]['metadata'])
  No newline at end of file

IPython/nbconvert/preprocessors/tests/test_latex.py

@@ -1,27 +1,12 b''
-"""
-Module with tests for the latex preprocessor
-"""
+"""Tests for the latex preprocessor"""
 
-#-----------------------------------------------------------------------------
-# Copyright (c) 2013, the IPython Development Team.
-#
+# Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
-#
-# The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
 
 from .base import PreprocessorTestsBase
 from ..latex import LatexPreprocessor
 
 
-#-----------------------------------------------------------------------------
-# Class
-#-----------------------------------------------------------------------------
-
 class TestLatex(PreprocessorTestsBase):
     """Contains test functions for latex.py"""
 
@@ -45,7 +30,7 b' class TestLatex(PreprocessorTestsBase):'
         nb, res = preprocessor(nb, res)
 
         # Make sure the code cell wasn't modified.
-        self.assertEqual(nb.worksheets[0].cells[0].input, '$ e $')
+        self.assertEqual(nb.cells[0].source, '$ e $')
 
         # Verify that the markdown cell wasn't processed.
-        self.assertEqual(nb.worksheets[0].cells[1].source, '$ e $')
+        self.assertEqual(nb.cells[1].source, '$ e $')

IPython/nbconvert/preprocessors/tests/test_revealhelp.py

@@ -16,19 +16,18 b' class Testrevealhelp(PreprocessorTestsBase):'
         """Build a reveal slides notebook in memory for use with tests.
         Overrides base in PreprocessorTestsBase"""
 
-        outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="a")]
+        outputs = [nbformat.new_output(output_type="stream", name="stdout", text="a")]
 
         slide_metadata = {'slideshow' : {'slide_type': 'slide'}}
         subslide_metadata = {'slideshow' : {'slide_type': 'subslide'}}
 
-        cells=[nbformat.new_code_cell(input="", prompt_number=1, outputs=outputs),
-               nbformat.new_text_cell('markdown', source="", metadata=slide_metadata),
-               nbformat.new_code_cell(input="", prompt_number=2, outputs=outputs),
-               nbformat.new_text_cell('markdown', source="", metadata=slide_metadata),
-               nbformat.new_text_cell('markdown', source="", metadata=subslide_metadata)]
-        worksheets = [nbformat.new_worksheet(cells=cells)]
+        cells=[nbformat.new_code_cell(source="", prompt_number=1, outputs=outputs),
+               nbformat.new_markdown_cell(source="", metadata=slide_metadata),
+               nbformat.new_code_cell(source="", prompt_number=2, outputs=outputs),
+               nbformat.new_markdown_cell(source="", metadata=slide_metadata),
+               nbformat.new_markdown_cell(source="", metadata=subslide_metadata)]
 
-        return nbformat.new_notebook(name="notebook1", worksheets=worksheets)
+        return nbformat.new_notebook(cells=cells)
 
 
     def build_preprocessor(self):
@@ -59,7 +58,7 b' class Testrevealhelp(PreprocessorTestsBase):'
         res = self.build_resources()
         preprocessor = self.build_preprocessor()
         nb, res = preprocessor(nb, res)
-        cells = nb.worksheets[0].cells
+        cells = nb.cells
         
         # Make sure correct metadata tags are available on every cell.
         for cell in cells:

IPython/nbconvert/preprocessors/tests/test_svg2pdf.py

@@ -1,18 +1,7 b''
-"""
-Module with tests for the svg2pdf preprocessor
-"""
+"""Tests for the svg2pdf preprocessor"""
 
-#-----------------------------------------------------------------------------
-# Copyright (c) 2013, the IPython Development Team.
-#
+# Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
-#
-# The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
 
 from IPython.testing import decorators as dec
 from IPython.nbformat import current as nbformat
@@ -21,10 +10,6 b' from .base import PreprocessorTestsBase'
 from ..svg2pdf import SVG2PDFPreprocessor
 
 
-#-----------------------------------------------------------------------------
-# Class
-#-----------------------------------------------------------------------------
-
 class Testsvg2pdf(PreprocessorTestsBase):
     """Contains test functions for svg2pdf.py"""
 
@@ -62,10 +47,9 b' class Testsvg2pdf(PreprocessorTestsBase):'
         slide_metadata = {'slideshow' : {'slide_type': 'slide'}}
         subslide_metadata = {'slideshow' : {'slide_type': 'subslide'}}
 
-        cells=[nbformat.new_code_cell(input="", prompt_number=1, outputs=outputs)]
-        worksheets = [nbformat.new_worksheet(name="worksheet1", cells=cells)]
+        cells=[nbformat.new_code_cell(source="", prompt_number=1, outputs=outputs)]
 
-        return nbformat.new_notebook(name="notebook1", worksheets=worksheets)
+        return nbformat.new_notebook(cells=cells)
 
 
     def build_preprocessor(self):
@@ -87,4 +71,4 b' class Testsvg2pdf(PreprocessorTestsBase):'
         res = self.build_resources()
         preprocessor = self.build_preprocessor()
         nb, res = preprocessor(nb, res)
-        assert 'svg' in nb.worksheets[0].cells[0].outputs[0]
+        assert 'svg' in nb.cells[0].outputs[0]

IPython/nbconvert/templates/html/basic.tpl

@@ -46,14 +46,14 b' In [ ]:'
 {% block input %}
 <div class="inner_cell">
     <div class="input_area">
-{{ cell.input | highlight_code(metadata=cell.metadata) }}
+{{ cell.source | highlight_code(metadata=cell.metadata) }}
 </div>
 </div>
 {%- endblock input %}
 
 {% block output %}
 <div class="output_area">
-{%- if output.output_type == 'pyout' -%}
+{%- if output.output_type == 'execute_result' -%}
     <div class="prompt output_prompt">
 {%- if cell.prompt_number is defined -%}
     Out[{{ cell.prompt_number|replace(None, "&nbsp;") }}]:
@@ -94,13 +94,13 b' In&nbsp;[&nbsp;]:'
 unknown type  {{ cell.type }}
 {% endblock unknowncell %}
 
-{% block pyout -%}
-{%- set extra_class="output_pyout" -%}
+{% block execute_result -%}
+{%- set extra_class="output_execute_result" -%}
 {% block data_priority scoped %}
 {{ super() }}
 {% endblock %}
 {%- set extra_class="" -%}
-{%- endblock pyout %}
+{%- endblock execute_result %}
 
 {% block stream_stdout -%}
 <div class="output_subarea output_stream output_stdout output_text">
@@ -174,13 +174,13 b" height={{output.metadata['jpeg']['height']}}"
 </div>
 {%- endblock data_latex %}
 
-{% block pyerr -%}
-<div class="output_subarea output_text output_pyerr">
+{% block error -%}
+<div class="output_subarea output_text output_error">
 <pre>
 {{- super() -}}
 </pre>
 </div>
-{%- endblock pyerr %}
+{%- endblock error %}
 
 {%- block traceback_line %}
 {{ line | ansi2html }}

IPython/nbconvert/templates/latex/base.tplx

@@ -140,11 +140,11 b' This template does not define a docclass, the inheriting class must define this.'
 ((* endblock data_text *))
 
 % Display python error text as-is
-((* block pyerr *))
+((* block error *))
     \begin{Verbatim}[commandchars=\\\{\}]
 ((( super() )))
     \end{Verbatim}
-((* endblock pyerr *))
+((* endblock error *))
 ((* block traceback_line *))
     ((( line | indent | strip_ansi | escape_latex )))
 ((* endblock traceback_line *))
@@ -206,12 +206,12 b' This template does not define a docclass, the inheriting class must define this.'
 
 ((* endblock headingcell *))
 
-% Redirect pyout to display data priority.
-((* block pyout scoped *))
+% Redirect execute_result to display data priority.
+((* block execute_result scoped *))
     ((* block data_priority scoped *))
     ((( super() )))
     ((* endblock *))
-((* endblock pyout *))
+((* endblock execute_result *))
 
 % Render markdown
 ((* block markdowncell scoped *))

IPython/nbconvert/templates/latex/skeleton/display_priority.tplx

@@ -9,37 +9,33 b''
 
 ((*- block data_priority scoped -*))
     ((*- for type in output | filter_data_type -*))
-        ((*- if type in ['application/pdf']*))
+        ((*- if type == 'application/pdf' -*))
             ((*- block data_pdf -*))
             ((*- endblock -*))
-        ((*- endif -*))
-        ((*- if type in ['svg']*))
+        ((*- elif type == 'image/svg+xml' -*))
             ((*- block data_svg -*))
             ((*- endblock -*))
-        ((*- endif -*))
-        ((*- if type in ['png']*))
+        ((*- elif type == 'image/png' -*))
             ((*- block data_png -*))
             ((*- endblock -*))
-        ((*- endif -*))
-        ((*- if type in ['html']*))
+        ((*- elif type == 'text/html' -*))
             ((*- block data_html -*))
             ((*- endblock -*))
-        ((*- endif -*))
-        ((*- if type in ['jpeg']*))
+        ((*- elif type == 'image/jpeg' -*))
             ((*- block data_jpg -*))
             ((*- endblock -*))
-        ((*- endif -*))
-        ((*- if type in ['text']*))
+        ((*- elif type == 'text/plain' -*))
             ((*- block data_text -*))
             ((*- endblock -*))
-        ((*- endif -*))
-        ((*- if type in ['latex']*))
+        ((*- elif type == 'text/latex' -*))
             ((*- block data_latex -*))
             ((*- endblock -*))
-        ((*- endif -*))
-        ((*- if type in ['javascript']*))
+        ((*- elif type == 'application/javascript' -*))
             ((*- block data_javascript -*))
             ((*- endblock -*))
+        ((*- else -*))
+            ((*- block data_other -*))
+            ((*- endblock -*))
         ((*- endif -*))
     ((*- endfor -*))
 ((*- endblock data_priority -*))

IPython/nbconvert/templates/latex/skeleton/null.tplx

@@ -28,69 +28,67 b' consider calling super even if it is a leave block, we might insert more blocks '
 ((*- block header -*))
 ((*- endblock header -*))
 ((*- block body -*))
-((*- for worksheet in nb.worksheets -*))
-    ((*- for cell in worksheet.cells -*))
-        ((*- block any_cell scoped -*))
-            ((*- if cell.cell_type in ['code'] -*))
-                ((*- block codecell scoped -*))
-                    ((*- block input_group -*))
-                        ((*- block in_prompt -*))((*- endblock in_prompt -*))
-                        ((*- block input -*))((*- endblock input -*))
-                    ((*- endblock input_group -*))
-                    ((*- if cell.outputs -*))
-                    ((*- block output_group -*))
-                        ((*- block output_prompt -*))((*- endblock output_prompt -*))
-                        ((*- block outputs scoped -*))
-                            ((*- for output in cell.outputs -*))
-                                ((*- block output scoped -*))
-                                    ((*- if output.output_type in ['pyout'] -*))
-                                        ((*- block pyout scoped -*))((*- endblock pyout -*))
-                                    ((*- elif output.output_type in ['stream'] -*))
-                                        ((*- block stream scoped -*))
-                                            ((*- if output.stream in ['stdout'] -*))
-                                                ((*- block stream_stdout scoped -*))
-                                                ((*- endblock stream_stdout -*))
-                                            ((*- elif output.stream in ['stderr'] -*))
-                                                ((*- block stream_stderr scoped -*))
-                                                ((*- endblock stream_stderr -*))
-                                            ((*- endif -*))
-                                        ((*- endblock stream -*))
-                                    ((*- elif output.output_type in ['display_data'] -*))
-                                        ((*- block display_data scoped -*))
-                                            ((*- block data_priority scoped -*))
-                                            ((*- endblock data_priority -*))
-                                        ((*- endblock display_data -*))
-                                    ((*- elif output.output_type in ['pyerr'] -*))
-                                        ((*- block pyerr scoped -*))
-                                        ((*- for line in output.traceback -*))
-                                            ((*- block traceback_line scoped -*))((*- endblock traceback_line -*))
-                                        ((*- endfor -*))
-                                        ((*- endblock pyerr -*))
-                                    ((*- endif -*))
-                                ((*- endblock output -*))
-                            ((*- endfor -*))
-                        ((*- endblock outputs -*))
-                    ((*- endblock output_group -*))
-                    ((*- endif -*))
-                ((*- endblock codecell -*))
-            ((*- elif cell.cell_type in ['markdown'] -*))
-                ((*- block markdowncell scoped-*))
-                ((*- endblock markdowncell -*))
-            ((*- elif cell.cell_type in ['heading'] -*))
-                ((*- block headingcell scoped-*))
-                ((*- endblock headingcell -*))
-            ((*- elif cell.cell_type in ['raw'] -*))
-                ((*- block rawcell scoped -*))
-                ((* if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) *))
-                ((( cell.source )))
-                ((* endif *))
-                ((*- endblock rawcell -*))
-            ((*- else -*))
-                ((*- block unknowncell scoped-*))
-                ((*- endblock unknowncell -*))
-            ((*- endif -*))
-        ((*- endblock any_cell -*))
-    ((*- endfor -*))
+((*- for cell in nb.cells -*))
+    ((*- block any_cell scoped -*))
+        ((*- if cell.cell_type == 'code' -*))
+            ((*- block codecell scoped -*))
+                ((*- block input_group -*))
+                    ((*- block in_prompt -*))((*- endblock in_prompt -*))
+                    ((*- block input -*))((*- endblock input -*))
+                ((*- endblock input_group -*))
+                ((*- if cell.outputs -*))
+                ((*- block output_group -*))
+                    ((*- block output_prompt -*))((*- endblock output_prompt -*))
+                    ((*- block outputs scoped -*))
+                        ((*- for output in cell.outputs -*))
+                            ((*- block output scoped -*))
+                                ((*- if output.output_type == 'execute_result' -*))
+                                    ((*- block execute_result scoped -*))((*- endblock execute_result -*))
+                                ((*- elif output.output_type == 'stream' -*))
+                                    ((*- block stream scoped -*))
+                                        ((*- if output.name == 'stdout' -*))
+                                            ((*- block stream_stdout scoped -*))
+                                            ((*- endblock stream_stdout -*))
+                                        ((*- elif output.name == 'stderr' -*))
+                                            ((*- block stream_stderr scoped -*))
+                                            ((*- endblock stream_stderr -*))
+                                        ((*- endif -*))
+                                    ((*- endblock stream -*))
+                                ((*- elif output.output_type == 'display_data' -*))
+                                    ((*- block display_data scoped -*))
+                                        ((*- block data_priority scoped -*))
+                                        ((*- endblock data_priority -*))
+                                    ((*- endblock display_data -*))
+                                ((*- elif output.output_type == 'error' -*))
+                                    ((*- block error scoped -*))
+                                    ((*- for line in output.traceback -*))
+                                        ((*- block traceback_line scoped -*))((*- endblock traceback_line -*))
+                                    ((*- endfor -*))
+                                    ((*- endblock error -*))
+                                ((*- endif -*))
+                            ((*- endblock output -*))
+                        ((*- endfor -*))
+                    ((*- endblock outputs -*))
+                ((*- endblock output_group -*))
+                ((*- endif -*))
+            ((*- endblock codecell -*))
+        ((*- elif cell.cell_type in ['markdown'] -*))
+            ((*- block markdowncell scoped-*))
+            ((*- endblock markdowncell -*))
+        ((*- elif cell.cell_type in ['heading'] -*))
+            ((*- block headingcell scoped-*))
+            ((*- endblock headingcell -*))
+        ((*- elif cell.cell_type in ['raw'] -*))
+            ((*- block rawcell scoped -*))
+            ((* if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) *))
+            ((( cell.source )))
+            ((* endif *))
+            ((*- endblock rawcell -*))
+        ((*- else -*))
+            ((*- block unknowncell scoped-*))
+            ((*- endblock unknowncell -*))
+        ((*- endif -*))
+    ((*- endblock any_cell -*))
 ((*- endfor -*))
 ((*- endblock body -*))
 

IPython/nbconvert/templates/latex/style_bw_ipython.tplx

@@ -7,7 +7,7 b''
 %===============================================================================
 
 ((* block input scoped *))
-((( add_prompt(cell.input, cell, 'In ') )))
+((( add_prompt(cell.source, cell, 'In ') )))
 ((* endblock input *))
 
 
@@ -15,7 +15,7 b''
 % Output
 %===============================================================================
 
-((* block pyout scoped *))
+((* block execute_result scoped *))
     ((*- for type in output | filter_data_type -*))
         ((*- if type in ['text']*))
 ((( add_prompt(output.text, cell, 'Out') )))
@@ -23,7 +23,7 b''
 \verb+Out[((( cell.prompt_number )))]:+((( super() )))
         ((*- endif -*))
     ((*- endfor -*))
-((* endblock pyout *))
+((* endblock execute_result *))
 
 
 %==============================================================================

IPython/nbconvert/templates/latex/style_bw_python.tplx

@@ -8,6 +8,6 b''
 
 ((* block input scoped *))
 \begin{verbatim}
-((( cell.input | add_prompts )))
+((( cell.source | add_prompts )))
 \end{verbatim}
 ((* endblock input *))

IPython/nbconvert/templates/latex/style_ipython.tplx

@@ -20,7 +20,7 b''
 %===============================================================================
 
 ((* block input scoped *))
-    ((( add_prompt(cell.input | highlight_code(strip_verbatim=True), cell, 'In ', 'incolor') )))
+    ((( add_prompt(cell.source | highlight_code(strip_verbatim=True), cell, 'In ', 'incolor') )))
 ((* endblock input *))
 
 
@@ -28,7 +28,7 b''
 % Output
 %===============================================================================
 
-((* block pyout scoped *))
+((* block execute_result scoped *))
     ((*- for type in output | filter_data_type -*))
         ((*- if type in ['text']*))
             ((( add_prompt(output.text | escape_latex, cell, 'Out', 'outcolor') )))
@@ -36,7 +36,7 b''
 \texttt{\color{outcolor}Out[{\color{outcolor}((( cell.prompt_number )))}]:}((( super() )))
         ((*- endif -*))
     ((*- endfor -*))
-((* endblock pyout *))
+((* endblock execute_result *))
 
 
 %==============================================================================

IPython/nbconvert/templates/latex/style_python.tplx

@@ -16,6 +16,6 b''
 
 ((* block input scoped *))
     \begin{Verbatim}[commandchars=\\\{\}]
-((( cell.input | highlight_code(strip_verbatim=True) | add_prompts )))
+((( cell.source | highlight_code(strip_verbatim=True) | add_prompts )))
     \end{Verbatim}
 ((* endblock input *))

IPython/nbconvert/templates/markdown.tpl

@@ -8,23 +8,23 b''
 {%- endblock output_prompt %}
 
 {% block input %}
-{{ cell.input | indent(4)}}
+{{ cell.source | indent(4)}}
 {% endblock input %}
 
-{% block pyerr %}
+{% block error %}
 {{ super() }}
-{% endblock pyerr %}
+{% endblock error %}
 
 {% block traceback_line %}
 {{ line | indent | strip_ansi }}
 {% endblock traceback_line %}
 
-{% block pyout %}
+{% block execute_result %}
 
 {% block data_priority scoped %}
 {{ super() }}
 {% endblock %}
-{% endblock pyout %}
+{% endblock execute_result %}
 
 {% block stream %}
 {{ output.text | indent }}

IPython/nbconvert/templates/python.tpl

@@ -9,7 +9,7 b''
 {% endblock in_prompt %}
 
 {% block input %}
-{{ cell.input | ipython2python }}
+{{ cell.source | ipython2python }}
 {% endblock input %}
 
 {% block markdowncell scoped %}

IPython/nbconvert/templates/rst.tpl

@@ -8,28 +8,28 b''
 {% endblock output_prompt %}
 
 {% block input %}
-{%- if cell.input.strip() -%}
+{%- if cell.source.strip() -%}
 .. code:: python
 
-{{ cell.input | indent}}
+{{ cell.source | indent}}
 {%- endif -%}
 {% endblock input %}
 
-{% block pyerr %}
+{% block error %}
 ::
 
 {{ super() }}
-{% endblock pyerr %}
+{% endblock error %}
 
 {% block traceback_line %}
 {{ line | indent | strip_ansi }}
 {% endblock traceback_line %}
 
-{% block pyout %}
+{% block execute_result %}
 {% block data_priority scoped %}
 {{ super() }}
 {% endblock %}
-{% endblock pyout %}
+{% endblock execute_result %}
 
 {% block stream %}
 .. parsed-literal::

IPython/nbconvert/templates/skeleton/display_priority.tpl

@@ -5,37 +5,33 b''
 
 {%- block data_priority scoped -%}
     {%- for type in output | filter_data_type -%}
-        {%- if type in ['application/pdf']%}
+        {%- if type == 'application/pdf' -%}
             {%- block data_pdf -%}
             {%- endblock -%}
-        {%- endif -%}
-        {%- if type in ['svg']%}
+        {%- elif type == 'image/svg+xml' -%}
             {%- block data_svg -%}
             {%- endblock -%}
-        {%- endif -%}
-        {%- if type in ['png']%}
+        {%- elif type == 'image/png' -%}
             {%- block data_png -%}
             {%- endblock -%}
-        {%- endif -%}
-        {%- if type in ['html']%}
+        {%- elif type == 'text/html' -%}
             {%- block data_html -%}
             {%- endblock -%}
-        {%- endif -%}
-        {%- if type in ['jpeg']%}
+        {%- elif type == 'image/jpeg' -%}
             {%- block data_jpg -%}
             {%- endblock -%}
-        {%- endif -%}
-        {%- if type in ['text']%}
+        {%- elif type == 'text/plain' -%}
             {%- block data_text -%}
             {%- endblock -%}
-        {%- endif -%}
-        {%- if type in ['latex']%}
+        {%- elif type == 'text/latex' -%}
             {%- block data_latex -%}
             {%- endblock -%}
-        {%- endif -%}
-        {%- if type in ['javascript']%}
+        {%- elif type == 'application/javascript' -%}
             {%- block data_javascript -%}
             {%- endblock -%}
+        {%- else -%}
+            {%- block data_other -%}
+            {%- endblock -%}
         {%- endif -%}
     {%- endfor -%}
 {%- endblock data_priority -%}

IPython/nbconvert/templates/skeleton/null.tpl

@@ -24,69 +24,67 b' consider calling super even if it is a leave block, we might insert more blocks '
 {%- block header -%}
 {%- endblock header -%}
 {%- block body -%}
-{%- for worksheet in nb.worksheets -%}
-    {%- for cell in worksheet.cells -%}
-        {%- block any_cell scoped -%}
-            {%- if cell.cell_type in ['code'] -%}
-                {%- block codecell scoped -%}
-                    {%- block input_group -%}
-                        {%- block in_prompt -%}{%- endblock in_prompt -%}
-                        {%- block input -%}{%- endblock input -%}
-                    {%- endblock input_group -%}
-                    {%- if cell.outputs -%}
-                    {%- block output_group -%}
-                        {%- block output_prompt -%}{%- endblock output_prompt -%}
-                        {%- block outputs scoped -%}
-                            {%- for output in cell.outputs -%}
-                                {%- block output scoped -%}
-                                    {%- if output.output_type in ['pyout'] -%}
-                                        {%- block pyout scoped -%}{%- endblock pyout -%}
-                                    {%- elif output.output_type in ['stream'] -%}
-                                        {%- block stream scoped -%}
-                                            {%- if output.stream in ['stdout'] -%}
-                                                {%- block stream_stdout scoped -%}
-                                                {%- endblock stream_stdout -%}
-                                            {%- elif output.stream in ['stderr'] -%}
-                                                {%- block stream_stderr scoped -%}
-                                                {%- endblock stream_stderr -%}
-                                            {%- endif -%}
-                                        {%- endblock stream -%}
-                                    {%- elif output.output_type in ['display_data'] -%}
-                                        {%- block display_data scoped -%}
-                                            {%- block data_priority scoped -%}
-                                            {%- endblock data_priority -%}
-                                        {%- endblock display_data -%}
-                                    {%- elif output.output_type in ['pyerr'] -%}
-                                        {%- block pyerr scoped -%}
-                                        {%- for line in output.traceback -%}
-                                            {%- block traceback_line scoped -%}{%- endblock traceback_line -%}
-                                        {%- endfor -%}
-                                        {%- endblock pyerr -%}
-                                    {%- endif -%}
-                                {%- endblock output -%}
-                            {%- endfor -%}
-                        {%- endblock outputs -%}
-                    {%- endblock output_group -%}
-                    {%- endif -%}
-                {%- endblock codecell -%}
-            {%- elif cell.cell_type in ['markdown'] -%}
-                {%- block markdowncell scoped-%}
-                {%- endblock markdowncell -%}
-            {%- elif cell.cell_type in ['heading'] -%}
-                {%- block headingcell scoped-%}
-                {%- endblock headingcell -%}
-            {%- elif cell.cell_type in ['raw'] -%}
-                {%- block rawcell scoped -%}
-                {% if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) %}
-                {{ cell.source }}
-                {% endif %}
-                {%- endblock rawcell -%}
-            {%- else -%}
-                {%- block unknowncell scoped-%}
-                {%- endblock unknowncell -%}
-            {%- endif -%}
-        {%- endblock any_cell -%}
-    {%- endfor -%}
+{%- for cell in nb.cells -%}
+    {%- block any_cell scoped -%}
+        {%- if cell.cell_type == 'code' -%}
+            {%- block codecell scoped -%}
+                {%- block input_group -%}
+                    {%- block in_prompt -%}{%- endblock in_prompt -%}
+                    {%- block input -%}{%- endblock input -%}
+                {%- endblock input_group -%}
+                {%- if cell.outputs -%}
+                {%- block output_group -%}
+                    {%- block output_prompt -%}{%- endblock output_prompt -%}
+                    {%- block outputs scoped -%}
+                        {%- for output in cell.outputs -%}
+                            {%- block output scoped -%}
+                                {%- if output.output_type == 'execute_result' -%}
+                                    {%- block execute_result scoped -%}{%- endblock execute_result -%}
+                                {%- elif output.output_type == 'stream' -%}
+                                    {%- block stream scoped -%}
+                                        {%- if output.name == 'stdout' -%}
+                                            {%- block stream_stdout scoped -%}
+                                            {%- endblock stream_stdout -%}
+                                        {%- elif output.name == 'stderr' -%}
+                                            {%- block stream_stderr scoped -%}
+                                            {%- endblock stream_stderr -%}
+                                        {%- endif -%}
+                                    {%- endblock stream -%}
+                                {%- elif output.output_type == 'display_data' -%}
+                                    {%- block display_data scoped -%}
+                                        {%- block data_priority scoped -%}
+                                        {%- endblock data_priority -%}
+                                    {%- endblock display_data -%}
+                                {%- elif output.output_type == 'error' -%}
+                                    {%- block error scoped -%}
+                                    {%- for line in output.traceback -%}
+                                        {%- block traceback_line scoped -%}{%- endblock traceback_line -%}
+                                    {%- endfor -%}
+                                    {%- endblock error -%}
+                                {%- endif -%}
+                            {%- endblock output -%}
+                        {%- endfor -%}
+                    {%- endblock outputs -%}
+                {%- endblock output_group -%}
+                {%- endif -%}
+            {%- endblock codecell -%}
+        {%- elif cell.cell_type in ['markdown'] -%}
+            {%- block markdowncell scoped-%}
+            {%- endblock markdowncell -%}
+        {%- elif cell.cell_type in ['heading'] -%}
+            {%- block headingcell scoped-%}
+            {%- endblock headingcell -%}
+        {%- elif cell.cell_type in ['raw'] -%}
+            {%- block rawcell scoped -%}
+            {% if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) %}
+            {{ cell.source }}
+            {% endif %}
+            {%- endblock rawcell -%}
+        {%- else -%}
+            {%- block unknowncell scoped-%}
+            {%- endblock unknowncell -%}
+        {%- endif -%}
+    {%- endblock any_cell -%}
 {%- endfor -%}
 {%- endblock body -%}
 

IPython/nbconvert/tests/base.py

@@ -1,17 +1,7 b''
-"""
-Contains base test class for nbconvert
-"""
-#-----------------------------------------------------------------------------
-#Copyright (c) 2013, the IPython Development Team.
-#
-#Distributed under the terms of the Modified BSD License.
-#
-#The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
+"""Base test class for nbconvert"""
+
+# Copyright (c) IPython Development Team.
+# Distributed under the terms of the Modified BSD License.
 
 import io
 import os
@@ -29,10 +19,6 b' from IPython.testing.tools import get_ipython_cmd'
 # a trailing space allows for simpler concatenation with the other arguments
 ipy_cmd = get_ipython_cmd(as_string=True) + " "
 
-#-----------------------------------------------------------------------------
-# Classes and functions
-#-----------------------------------------------------------------------------
-
 
 class TestsBase(unittest.TestCase):
     """Base tests class.  Contains useful fuzzy comparison and nbconvert
@@ -116,7 +102,6 b' class TestsBase(unittest.TestCase):'
     
     def create_empty_notebook(self, path):
         nb = current.new_notebook()
-        nb.worksheets.append(current.new_worksheet())
         with io.open(path, 'w', encoding='utf-8') as f:
             current.write(nb, f, 'json')
 

IPython/nbconvert/tests/files/notebook1.ipynb

@@ -1,149 +1,143 b''
 {
- "metadata": {
-  "name": "notebook1"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "A simple SymPy example"
-     ]
-    },
+   "cell_type": "heading",
+   "level": 1,
+   "metadata": {},
+   "source": [
+    "A simple SymPy example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we import SymPy and initialize printing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 2,
+   "source": [
+    "from sympy import init_printing\n",
+    "from sympy import *\n",
+    " init_printing()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a few symbols:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 4,
+   "source": [
+    "x,y,z = symbols('x y z')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a basic expression:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKMAAAAZBAMAAACvE4OgAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nSUVORK5CYII=\n",
      "metadata": {},
-     "source": [
-      "First we import SymPy and initialize printing:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from sympy import init_printing\n",
-      "from sympy import *\n",
-      " init_printing()"
+     "output_type": "execute_result",
+     "prompt_number": 6,
+     "text/latex": [
+      "$$x^{2} + 2.0 y + \\sin{\\left (z \\right )}$$"
      ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Create a few symbols:"
+     "text/plain": [
+      " 2                 \n",
+      "x  + 2.0\u22c5y + sin(z)"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x,y,z = symbols('x y z')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is a basic expression:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "e = x**2 + 2.0*y + sin(z); e"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{2} + 2.0 y + \\sin{\\left (z \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAKMAAAAZBAMAAACvE4OgAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nSUVORK5CYII=\n",
-       "prompt_number": 6,
-       "text": [
-        " 2                 \n",
-        "x  + 2.0\u22c5y + sin(z)"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+    }
+   ],
+   "prompt_number": 6,
+   "source": [
+    "e = x**2 + 2.0*y + sin(z); e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "diff(e, x)"
-     ],
-     "language": "python",
+     "image/png": "iVBORw0KGgoAAAANSUhEUgAAABQAAAAOBAMAAADd6iHDAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAAgElEQVQIHWNgVDJ2YICAMAb2H1BmKgPDTChzFgNDvgOEvT8AzgQKrA9gPZPYUwNk\ncXxnCGd4dWA1kMllwFDKUB9wEchUZmAIYNgMZDDwJIDIPyDiEgOjAAPLFwZWBhYFBh6BqzwfGI4y\nSJUXZXH8Zf7A+IBh////v1hzjh5/xwAAW80hUDE8HYkAAAAASUVORK5CYII=\n",
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$2 x$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAABQAAAAOBAMAAADd6iHDAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAAgElEQVQIHWNgVDJ2YICAMAb2H1BmKgPDTChzFgNDvgOEvT8AzgQKrA9gPZPYUwNk\ncXxnCGd4dWA1kMllwFDKUB9wEchUZmAIYNgMZDDwJIDIPyDiEgOjAAPLFwZWBhYFBh6BqzwfGI4y\nSJUXZXH8Zf7A+IBh////v1hzjh5/xwAAW80hUDE8HYkAAAAASUVORK5CYII=\n",
-       "prompt_number": 7,
-       "text": [
-        "2\u22c5x"
-       ]
-      }
+     "output_type": "execute_result",
+     "prompt_number": 7,
+     "text/latex": [
+      "$$2 x$$"
      ],
-     "prompt_number": 7
-    },
+     "text/plain": [
+      "2\u22c5x"
+     ]
+    }
+   ],
+   "prompt_number": 7,
+   "source": [
+    "diff(e, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "integrate(e, z)"
-     ],
-     "language": "python",
+     "image/png": "iVBORw0KGgoAAAANSUhEUgAAALsAAAAZBAMAAACbakK8AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n",
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{2} z + 2.0 y z - \\cos{\\left (z \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAALsAAAAZBAMAAACbakK8AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n",
-       "prompt_number": 8,
-       "text": [
-        " 2                     \n",
-        "x \u22c5z + 2.0\u22c5y\u22c5z - cos(z)"
-       ]
-      }
+     "output_type": "execute_result",
+     "prompt_number": 8,
+     "text/latex": [
+      "$$x^{2} z + 2.0 y z - \\cos{\\left (z \\right )}$$"
      ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "text/plain": [
+      " 2                     \n",
+      "x \u22c5z + 2.0\u22c5y\u22c5z - cos(z)"
+     ]
     }
    ],
-   "metadata": {}
+   "prompt_number": 8,
+   "source": [
+    "integrate(e, z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": null,
+   "source": []
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbconvert/tests/files/notebook2.ipynb

@@ -1,224 +1,215 b''
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:9fffd84e69e3d9b8aee7b4cde2099ca5d4158a45391698b191f94fabaf394b41"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "heading",
+   "level": 1,
+   "metadata": {},
+   "source": [
+    "NumPy and Matplotlib examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First import NumPy and Matplotlib:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 1,
      "metadata": {},
-     "source": [
-      "NumPy and Matplotlib examples"
-     ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": "module://IPython.kernel.zmq.pylab.backend_inline\n"
+    }
+   ],
+   "prompt_number": 1,
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(matplotlib.backends.backend)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 2,
+   "source": [
+    "from IPython.display import set_matplotlib_formats\n",
+    "set_matplotlib_formats('png', 'pdf')\n",
+    "matplotlib.rcParams['figure.figsize'] = (2,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
      "metadata": {},
-     "source": [
-      "First import NumPy and Matplotlib:"
+     "output_type": "execute_result",
+     "prompt_number": 3,
+     "text/plain": [
+      "{matplotlib.figure.Figure: <function IPython.core.pylabtools.<lambda>>}"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import matplotlib\n",
-      "import matplotlib.pyplot as plt\n",
-      "print(matplotlib.backends.backend)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "module://IPython.kernel.zmq.pylab.backend_inline\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import set_matplotlib_formats\n",
-      "set_matplotlib_formats('png', 'pdf')\n",
-      "matplotlib.rcParams['figure.figsize'] = (2,1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ip.display_formatter.formatters['application/pdf'].type_printers"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "{matplotlib.figure.Figure: <function IPython.core.pylabtools.<lambda>>}"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
+    }
+   ],
+   "prompt_number": 3,
+   "source": [
+    "ip.display_formatter.formatters['application/pdf'].type_printers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 4,
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we show some very basic examples of how they can be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 5,
+   "source": [
+    "a = np.random.uniform(size=(100,100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
      "metadata": {},
-     "source": [
-      "Now we show some very basic examples of how they can be used."
+     "output_type": "execute_result",
+     "prompt_number": 6,
+     "text/plain": [
+      "(100, 100)"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.random.uniform(size=(100,100))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "(100, 100)"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "evs = np.linalg.eigvals(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "evs.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "(100,)"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
+    }
+   ],
+   "prompt_number": 6,
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "prompt_number": 7,
+   "source": [
+    "evs = np.linalg.eigvals(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
      "metadata": {},
-     "source": [
-      "Here is a very long heading that pandoc will wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap"
+     "output_type": "execute_result",
+     "prompt_number": 8,
+     "text/plain": [
+      "(100,)"
      ]
-    },
+    }
+   ],
+   "prompt_number": 8,
+   "source": [
+    "evs.shape"
+   ]
+  },
+  {
+   "cell_type": "heading",
+   "level": 2,
+   "metadata": {},
+   "source": [
+    "Here is a very long heading that pandoc will wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a cell that has both text and PNG output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
      "metadata": {},
-     "source": [
-      "Here is a cell that has both text and PNG output:"
+     "output_type": "execute_result",
+     "prompt_number": 9,
+     "text/plain": [
+      "(array([97,  2,  0,  0,  0,  0,  0,  0,  0,  1]),\n",
+      " array([ -2.59479443,   2.67371141,   7.94221725,  13.21072308,\n",
+      "         18.47922892,  23.74773476,  29.0162406 ,  34.28474644,\n",
+      "         39.55325228,  44.82175812,  50.09026395]),\n",
+      " <a list of 10 Patch objects>)"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.hist(evs.real)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "(array([97,  2,  0,  0,  0,  0,  0,  0,  0,  1]),\n",
-        " array([ -2.59479443,   2.67371141,   7.94221725,  13.21072308,\n",
-        "         18.47922892,  23.74773476,  29.0162406 ,  34.28474644,\n",
-        "         39.55325228,  44.82175812,  50.09026395]),\n",
-        " <a list of 10 Patch objects>)"
-       ]
-      },
-      {
-       "application/pdf": 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24ggOTv0ZOChW5CQOhp6pCtFoxOMinJW2MJfLS4uXTxQUDrF0LF4pFEq1kizJ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-       "text": [
-        "<matplotlib.figure.Figure at 0x10b0ecf10>"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
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24ggOTv0ZOChW5CQOhp6pCtFoxOMinJW2MJfLS4uXTxQUDrF0LF4pFEq1kizJ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\nIDEgMCBSIC9TaXplIDIyID4+CnN0YXJ0eHJlZgo3Mzg0OQolJUVPRgo=\n",
+     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJgAAABWCAYAAAAzIF/lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxCsMGOIVBgzxCgOGeIUBQ7z6LzWkj3n7AHKHAAAAAElFTkSuQmCC\n",
      "metadata": {},
-     "source": [
-      "```python\n",
-      "def foo(bar=1):\n",
-      "    \"\"\"docstring\"\"\"\n",
-      "    raise Exception(\"message\")\n",
-      "```"
+     "output_type": "display_data",
+     "text/plain": [
+      "<matplotlib.figure.Figure at 0x10b0ecf10>"
      ]
     }
    ],
-   "metadata": {}
+   "prompt_number": 9,
+   "source": [
+    "plt.hist(evs.real)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "def foo(bar=1):\n",
+    "    \"\"\"docstring\"\"\"\n",
+    "    raise Exception(\"message\")\n",
+    "```"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
  No newline at end of file

IPython/nbformat/v4/nbbase.py

@@ -38,7 +38,8 b' def from_dict(d):'
 
 def new_output(output_type, mime_bundle=None, **kwargs):
     """Create a new output, to go in the ``cell.outputs`` list of a code cell."""
-    output = NotebookNode(output_type=output_type, **kwargs)
+    output = NotebookNode(output_type=output_type)
+    output.update(from_dict(kwargs))
     if mime_bundle:
         output.update(mime_bundle)
     # populate defaults:
@@ -51,7 +52,8 b' def new_output(output_type, mime_bundle=None, **kwargs):'
 
 def new_code_cell(source='', **kwargs):
     """Create a new code cell"""
-    cell = NotebookNode(cell_type='code', source=source, **kwargs)
+    cell = NotebookNode(cell_type='code', source=source)
+    cell.update(from_dict(kwargs))
     cell.setdefault('metadata', NotebookNode())
     cell.setdefault('source', '')
     cell.setdefault('prompt_number', None)
@@ -62,7 +64,8 b" def new_code_cell(source='', **kwargs):"
 
 def new_markdown_cell(source='', **kwargs):
     """Create a new markdown cell"""
-    cell = NotebookNode(cell_type='markdown', source=source, **kwargs)
+    cell = NotebookNode(cell_type='markdown', source=source)
+    cell.update(from_dict(kwargs))
     cell.setdefault('metadata', NotebookNode())
 
     validate(cell, 'markdown_cell')
@@ -70,7 +73,8 b" def new_markdown_cell(source='', **kwargs):"
 
 def new_heading_cell(source='', **kwargs):
     """Create a new heading cell"""
-    cell = NotebookNode(cell_type='heading', source=source, **kwargs)
+    cell = NotebookNode(cell_type='heading', source=source)
+    cell.update(from_dict(kwargs))
     cell.setdefault('metadata', NotebookNode())
     cell.setdefault('level', 1)
 
@@ -79,7 +83,8 b" def new_heading_cell(source='', **kwargs):"
 
 def new_raw_cell(source='', **kwargs):
     """Create a new raw cell"""
-    cell = NotebookNode(cell_type='raw', source=source, **kwargs)
+    cell = NotebookNode(cell_type='raw', source=source)
+    cell.update(from_dict(kwargs))
     cell.setdefault('metadata', NotebookNode())
 
     validate(cell, 'raw_cell')
@@ -87,7 +92,7 b" def new_raw_cell(source='', **kwargs):"
 
 def new_notebook(**kwargs):
     """Create a new notebook"""
-    nb = NotebookNode(**kwargs)
+    nb = from_dict(kwargs)
     nb.nbformat = nbformat
     nb.nbformat_minor = nbformat_minor
     nb.setdefault('cells', [])