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"""
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Module that re-groups transformer that would be applied to ipynb files
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before going through the templating machinery.
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It exposes a convenient class to inherit from to access configurability.
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"""
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#-----------------------------------------------------------------------------
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# Copyright (c) 2013, the IPython Development Team.
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#
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# Distributed under the terms of the Modified BSD License.
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#
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# The full license is in the file COPYING.txt, distributed with this software.
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#-----------------------------------------------------------------------------
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#-----------------------------------------------------------------------------
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# Imports
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#-----------------------------------------------------------------------------
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from ..utils.base import NbConvertBase
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from IPython.utils.traitlets import Bool
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#-----------------------------------------------------------------------------
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# Classes and Functions
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#-----------------------------------------------------------------------------
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class Transformer(NbConvertBase):
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""" A configurable transformer
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Inherit from this class if you wish to have configurability for your
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transformer.
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Any configurable traitlets this class exposed will be configurable in profiles
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using c.SubClassName.atribute=value
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you can overwrite :meth:`transform_cell` to apply a transformation independently on each cell
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or :meth:`call` if you prefer your own logic. See corresponding docstring for informations.
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Disabled by default and can be enabled via the config by
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'c.YourTransformerName.enabled = True'
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"""
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enabled = Bool(False, config=True)
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def __init__(self, **kw):
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"""
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Public constructor
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Parameters
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----------
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config : Config
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Configuration file structure
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**kw : misc
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Additional arguments
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"""
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super(Transformer, self).__init__(**kw)
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def __call__(self, nb, resources):
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if self.enabled:
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return self.call(nb,resources)
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else:
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return nb, resources
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def call(self, nb, resources):
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"""
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Transformation to apply on each notebook.
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You should return modified nb, resources.
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If you wish to apply your transform on each cell, you might want to
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overwrite transform_cell method instead.
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Parameters
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----------
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nb : NotebookNode
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Notebook being converted
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resources : dictionary
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Additional resources used in the conversion process. Allows
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transformers to pass variables into the Jinja engine.
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"""
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self.log.debug("Applying transform: %s", self.__class__.__name__)
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try :
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for worksheet in nb.worksheets:
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for index, cell in enumerate(worksheet.cells):
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worksheet.cells[index], resources = self.transform_cell(cell, resources, index)
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return nb, resources
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except NotImplementedError:
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raise NotImplementedError('should be implemented by subclass')
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def transform_cell(self, cell, resources, index):
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"""
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Overwrite if you want to apply a transformation on each cell. You
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should return modified cell and resource dictionary.
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Parameters
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----------
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cell : NotebookNode cell
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Notebook cell being processed
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resources : dictionary
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Additional resources used in the conversion process. Allows
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transformers to pass variables into the Jinja engine.
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index : int
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Index of the cell being processed
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"""
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raise NotImplementedError('should be implemented by subclass')
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return cell, resources
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