|
|
"""
|
|
|
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 transform_cell to apply a transformation independently on each cell
|
|
|
or __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.
|
|
|
"""
|
|
|
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
|
|
|
|
|
|
|
