Cast to unicode, not bytes

IPython/nbconvert/exporters/exporter.py

@@ -1,279 +1,279 b''
 """This module defines a base Exporter class. For Jinja template-based export,
 see templateexporter.py.
 """
 
 
 from __future__ import print_function, absolute_import
 
 import io
 import os
 import copy
 import collections
 import datetime
 
 from IPython.config.configurable import LoggingConfigurable
 from IPython.config import Config
 from IPython import nbformat
 from IPython.utils.traitlets import MetaHasTraits, TraitType, List, TraitError
 from IPython.utils.importstring import import_item
 from IPython.utils import text, py3compat
 
 
 class ResourcesDict(collections.defaultdict):
     def __missing__(self, key):
         return ''
 
 
 class FilenameExtension(TraitType):
     """A trait for filename extensions."""
 
     default_value = u''
     info_text = 'a filename extension, beginning with a dot'
 
     def validate(self, obj, value):
         try:
-            value = py3compat.cast_bytes_py2(value)
+            value = py3compat.cast_unicode_py2(value)
         except UnicodeDecodeError:
             msg = "Could not decode {!r} for FileExtension trait '{}'."
             raise TraitError(msg.format(value, self.name))
 
         if not value.startswith('.'):
             msg = "FileExtension trait '{}' does not begin with a dot: {!r}"
             raise TraitError(msg.format(self.name, value))
 
         return value
 
 
 class Exporter(LoggingConfigurable):
     """
     Class containing methods that sequentially run a list of preprocessors on a 
     NotebookNode object and then return the modified NotebookNode object and 
     accompanying resources dict.
     """
 
     file_extension = FilenameExtension(
         '.txt', config=True,
         help="Extension of the file that should be written to disk"
         )
 
     # MIME type of the result file, for HTTP response headers.
     # This is *not* a traitlet, because we want to be able to access it from
     # the class, not just on instances.
     output_mimetype = ''
 
     #Configurability, allows the user to easily add filters and preprocessors.
     preprocessors = List(config=True,
         help="""List of preprocessors, by name or namespace, to enable.""")
 
     _preprocessors = List()
 
     default_preprocessors = List(['IPython.nbconvert.preprocessors.coalesce_streams',
                                   'IPython.nbconvert.preprocessors.SVG2PDFPreprocessor',
                                   'IPython.nbconvert.preprocessors.ExtractOutputPreprocessor',
                                   'IPython.nbconvert.preprocessors.CSSHTMLHeaderPreprocessor',
                                   'IPython.nbconvert.preprocessors.RevealHelpPreprocessor',
                                   'IPython.nbconvert.preprocessors.LatexPreprocessor',
                                   'IPython.nbconvert.preprocessors.ClearOutputPreprocessor',
                                   'IPython.nbconvert.preprocessors.ExecutePreprocessor',
                                   'IPython.nbconvert.preprocessors.HighlightMagicsPreprocessor'],
         config=True,
         help="""List of preprocessors available by default, by name, namespace, 
         instance, or type.""")
 
 
     def __init__(self, config=None, **kw):
         """
         Public constructor
 
         Parameters
         ----------
         config : config
             User configuration instance.
         """
         with_default_config = self.default_config
         if config:
             with_default_config.merge(config)
         
         super(Exporter, self).__init__(config=with_default_config, **kw)
 
         self._init_preprocessors()
 
 
     @property
     def default_config(self):
         return Config()
 
     def from_notebook_node(self, nb, resources=None, **kw):
         """
         Convert a notebook from a notebook node instance.
 
         Parameters
         ----------
         nb : :class:`~IPython.nbformat.NotebookNode`
           Notebook node (dict-like with attr-access)
         resources : dict
           Additional resources that can be accessed read/write by
           preprocessors and filters.
         **kw
           Ignored (?)
         """
         nb_copy = copy.deepcopy(nb)
         resources = self._init_resources(resources)
         
         if 'language' in nb['metadata']:
             resources['language'] = nb['metadata']['language'].lower()
 
         # Preprocess
         nb_copy, resources = self._preprocess(nb_copy, resources)
 
         return nb_copy, resources
 
 
     def from_filename(self, filename, resources=None, **kw):
         """
         Convert a notebook from a notebook file.
 
         Parameters
         ----------
         filename : str
             Full filename of the notebook file to open and convert.
         """
 
         # Pull the metadata from the filesystem.
         if resources is None:
             resources = ResourcesDict()
         if not 'metadata' in resources or resources['metadata'] == '':
             resources['metadata'] = ResourcesDict()
         basename = os.path.basename(filename)
         notebook_name = basename[:basename.rfind('.')]
         resources['metadata']['name'] = notebook_name
 
         modified_date = datetime.datetime.fromtimestamp(os.path.getmtime(filename))
         resources['metadata']['modified_date'] = modified_date.strftime(text.date_format)
 
         with io.open(filename, encoding='utf-8') as f:
             return self.from_notebook_node(nbformat.read(f, as_version=4), resources=resources, **kw)
 
 
     def from_file(self, file_stream, resources=None, **kw):
         """
         Convert a notebook from a notebook file.
 
         Parameters
         ----------
         file_stream : file-like object
             Notebook file-like object to convert.
         """
         return self.from_notebook_node(nbformat.read(file_stream, as_version=4), resources=resources, **kw)
 
 
     def register_preprocessor(self, preprocessor, enabled=False):
         """
         Register a preprocessor.
         Preprocessors are classes that act upon the notebook before it is
         passed into the Jinja templating engine.  preprocessors are also
         capable of passing additional information to the Jinja
         templating engine.
 
         Parameters
         ----------
         preprocessor : preprocessor
         """
         if preprocessor is None:
             raise TypeError('preprocessor')
         isclass = isinstance(preprocessor, type)
         constructed = not isclass
 
         # Handle preprocessor's registration based on it's type
         if constructed and isinstance(preprocessor, py3compat.string_types):
             # Preprocessor is a string, import the namespace and recursively call
             # this register_preprocessor method
             preprocessor_cls = import_item(preprocessor)
             return self.register_preprocessor(preprocessor_cls, enabled)
 
         if constructed and hasattr(preprocessor, '__call__'):
             # Preprocessor is a function, no need to construct it.
             # Register and return the preprocessor.
             if enabled:
                 preprocessor.enabled = True
             self._preprocessors.append(preprocessor)
             return preprocessor
 
         elif isclass and isinstance(preprocessor, MetaHasTraits):
             # Preprocessor is configurable.  Make sure to pass in new default for 
             # the enabled flag if one was specified.
             self.register_preprocessor(preprocessor(parent=self), enabled)
 
         elif isclass:
             # Preprocessor is not configurable, construct it
             self.register_preprocessor(preprocessor(), enabled)
 
         else:
             # Preprocessor is an instance of something without a __call__ 
             # attribute.  
             raise TypeError('preprocessor')
 
 
     def _init_preprocessors(self):
         """
         Register all of the preprocessors needed for this exporter, disabled
         unless specified explicitly.
         """
         self._preprocessors = []
 
         # Load default preprocessors (not necessarly enabled by default).
         for preprocessor in self.default_preprocessors:
             self.register_preprocessor(preprocessor)
 
         # Load user-specified preprocessors.  Enable by default.
         for preprocessor in self.preprocessors:
             self.register_preprocessor(preprocessor, enabled=True)
 
 
     def _init_resources(self, resources):
 
         #Make sure the resources dict is of ResourcesDict type.
         if resources is None:
             resources = ResourcesDict()
         if not isinstance(resources, ResourcesDict):
             new_resources = ResourcesDict()
             new_resources.update(resources)
             resources = new_resources
 
         #Make sure the metadata extension exists in resources
         if 'metadata' in resources:
             if not isinstance(resources['metadata'], ResourcesDict):
                 resources['metadata'] = ResourcesDict(resources['metadata'])
         else:
             resources['metadata'] = ResourcesDict()
             if not resources['metadata']['name']:
                 resources['metadata']['name'] = 'Notebook'
 
         #Set the output extension
         resources['output_extension'] = self.file_extension
         return resources
 
 
     def _preprocess(self, nb, resources):
         """
         Preprocess the notebook before passing it into the Jinja engine.
         To preprocess the notebook is to apply all of the
 
         Parameters
         ----------
         nb : notebook node
             notebook that is being exported.
         resources : a dict of additional resources that
             can be accessed read/write by preprocessors
         """
 
         # Do a copy.deepcopy first,
         # we are never safe enough with what the preprocessors could do.
         nbc =  copy.deepcopy(nb)
         resc = copy.deepcopy(resources)
 
         #Run each preprocessor on the notebook.  Carry the output along
         #to each preprocessor
         for preprocessor in self._preprocessors:
             nbc, resc = preprocessor(nbc, resc)
         return nbc, resc