fixup minor typos from search/replace

IPython/nbconvert/transformers/base.py

@@ -1,110 +1,111 b''
 """
 Module that re-groups preprocessor that would be applied to ipynb files
 before going through the templating machinery.
 
 It exposes a convenient class to inherit from to access configurability.
 """
 #-----------------------------------------------------------------------------
 # 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
 #-----------------------------------------------------------------------------
 
 from ..utils.base import NbConvertBase
 from IPython.utils.traitlets import Bool
 
 #-----------------------------------------------------------------------------
 # Classes and Functions
 #-----------------------------------------------------------------------------
 
 class Preprocessor(NbConvertBase):
     """ A configurable preprocessor
 
     Inherit from this class if you wish to have configurability for your
     preprocessor.
 
-    Any configurable traitlets this class exposed will be configurable in profiles
-    using c.SubClassName.atribute=value
+    Any configurable traitlets this class exposed will be configurable in
+    profiles using c.SubClassName.atribute=value
 
-    you can overwrite :meth:`preprocess_cell` to apply a preprocessation independently on each cell
-    or :meth:`preprocess` if you prefer your own logic. See corresponding docstring for informations.
+    you can overwrite :meth:`preprocess_cell` to apply a transformation
+    independently on each cell or :meth:`preprocess` if you prefer your own
+    logic. See corresponding docstring for informations.
 
     Disabled by default and can be enabled via the config by
         'c.YourPreprocessorName.enabled = True'
     """
     
     enabled = Bool(False, config=True)
 
     def __init__(self, **kw):
         """
         Public constructor
         
         Parameters
         ----------
         config : Config
             Configuration file structure
         **kw : misc
             Additional arguments
         """
         
         super(Preprocessor, self).__init__(**kw)
 
        
     def __call__(self, nb, resources):
         if self.enabled:
             return self.preprocess(nb,resources)
         else:
             return nb, resources
 
 
     def preprocess(self, nb, resources):
         """
-        preprocessation to apply on each notebook.
+        Preprocessing to apply on each notebook.
         
         You should return modified nb, resources.
-        If you wish to apply your preprocess on each cell, you might want to 
-        overwrite preprocess_cell method instead.
+        If you wish to apply your preprocessing to each cell, you might want
+        to overwrite preprocess_cell method instead.
         
         Parameters
         ----------
         nb : NotebookNode
             Notebook being converted
         resources : dictionary
             Additional resources used in the conversion process.  Allows
             preprocessors to pass variables into the Jinja engine.
         """
         self.log.debug("Applying preprocess: %s", self.__class__.__name__)
         try :
             for worksheet in nb.worksheets:
                 for index, cell in enumerate(worksheet.cells):
                     worksheet.cells[index], resources = self.preprocess_cell(cell, resources, index)
             return nb, resources
         except NotImplementedError:
             raise NotImplementedError('should be implemented by subclass')
 
 
     def preprocess_cell(self, cell, resources, index):
         """
-        Overwrite if you want to apply a preprocessation on each cell.  You 
+        Overwrite if you want to apply some preprocessing to each cell.  You 
         should return modified cell and resource dictionary.
         
         Parameters
         ----------
         cell : NotebookNode cell
             Notebook cell being processed
         resources : dictionary
             Additional resources used in the conversion process.  Allows
             preprocessors to pass variables into the Jinja engine.
         index : int
             Index of the cell being processed
         """
 
         raise NotImplementedError('should be implemented by subclass')
         return cell, resources
 

IPython/nbconvert/transformers/latex.py

@@ -1,53 +1,53 b''
 """Module that allows latex output notebooks to be conditioned before
 they are converted.
 """
 #-----------------------------------------------------------------------------
 # 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
 #-----------------------------------------------------------------------------
 
 from __future__ import print_function, absolute_import
 
 # Our own imports
 # Needed to override preprocessor
 from .base import (Preprocessor)
 from IPython.nbconvert import filters
 
 #-----------------------------------------------------------------------------
 # Classes
 #-----------------------------------------------------------------------------
 
 class LatexPreprocessor(Preprocessor):
     """
     Converter for latex destined documents.
     """
 
     def preprocess_cell(self, cell, resources, index):
         """
-        Apply a preprocessation on each cell,
+        Apply a transformation on each cell,
         
         Parameters
         ----------
         cell : NotebookNode cell
             Notebook cell being processed
         resources : dictionary
             Additional resources used in the conversion process.  Allows
             preprocessors to pass variables into the Jinja engine.
         index : int
             Modified index of the cell being processed (see base.py)
         """
         
         #If the cell is a markdown cell, preprocess the ampersands used to
         #remove the space between them and their contents.  Latex will complain
         #if spaces exist between the ampersands and the math content.  
         #See filters.latex.rm_math_space for more information.
         if hasattr(cell, "source") and cell.cell_type == "markdown":
             cell.source = filters.strip_math_space(cell.source)
         return cell, resources