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"""
Module that re-groups transformer 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 Transformer(NbConvertBase):
""" A configurable transformer
Inherit from this class if you wish to have configurability for your
transformer.
Any configurable traitlets this class exposed will be configurable in profiles
using c.SubClassName.atribute=value
you can overwrite :meth:`transform_cell` to apply a transformation independently on each cell
or :meth:`call` if you prefer your own logic. See corresponding docstring for informations.
Disabled by default and can be enabled via the config by
'c.YourTransformerName.enabled = True'
"""
enabled = Bool(False, config=True)
def __init__(self, **kw):
"""
Public constructor
Parameters
----------
config : Config
Configuration file structure
**kw : misc
Additional arguments
"""
super(Transformer, self).__init__(**kw)
def __call__(self, nb, resources):
if self.enabled:
return self.call(nb,resources)
else:
return nb, resources
def call(self, nb, resources):
"""
Transformation to apply on each notebook.
You should return modified nb, resources.
If you wish to apply your transform on each cell, you might want to
overwrite transform_cell method instead.
Parameters
----------
nb : NotebookNode
Notebook being converted
resources : dictionary
Additional resources used in the conversion process. Allows
transformers to pass variables into the Jinja engine.
"""
self.log.debug("Applying transform: %s", self.__class__.__name__)
try :
for worksheet in nb.worksheets:
for index, cell in enumerate(worksheet.cells):
worksheet.cells[index], resources = self.transform_cell(cell, resources, index)
return nb, resources
except NotImplementedError:
raise NotImplementedError('should be implemented by subclass')
def transform_cell(self, cell, resources, index):
"""
Overwrite if you want to apply a transformation on 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
transformers 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
|
