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"""
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Module that regroups transformer that woudl be applied to ipynb files
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before going through the templating machinery.
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It exposes convenient classes to inherit from to access configurability
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as well as decorator to simplify tasks.
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"""
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from __future__ import print_function, absolute_import
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from IPython.config.configurable import Configurable
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from IPython.utils.traitlets import Unicode, Bool, Dict, List
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from .config import GlobalConfigurable
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class ConfigurableTransformers(GlobalConfigurable):
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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 cell_transform to apply a transformation independently on each cell
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or __call__ if you prefer your own logic. See orresponding docstring for informations.
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"""
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def __init__(self, config=None, **kw):
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super(ConfigurableTransformers, self).__init__(config=config, **kw)
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def __call__(self, nb, other):
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"""transformation to apply on each notebook.
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received a handle to the current notebook as well as a dict of resources
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which structure depends on the transformer.
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You should return modified nb, other.
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If you wish to apply on each cell, you might want to overwrite cell_transform method.
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"""
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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], other = self.cell_transform(cell, other, 100*index)
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return nb, other
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except NotImplementedError:
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raise NotImplementedError('should be implemented by subclass')
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def cell_transform(self, cell, other, index):
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"""
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Overwrite if you want to apply a transformation on each cell,
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receive the current cell, the resource dict and the index of current cell as parameter.
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You should return modified cell and resource dict.
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"""
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raise NotImplementedError('should be implemented by subclass')
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return cell, other
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def cell_preprocessor(function):
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""" wrap a function to be executed on all cells of a notebook
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wrapped function parameters :
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cell : the cell
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other : external resources
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index : index of the cell
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"""
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def wrappedfunc(nb, other):
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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], other = function(cell, other, index)
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return nb, other
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return wrappedfunc
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@cell_preprocessor
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def haspyout_transformer(cell, other, count):
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"""
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Add a haspyout flag to cell that have it
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Easier for templating, where you can't know in advance
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wether to write the out prompt
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"""
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cell.type = cell.cell_type
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cell.haspyout = False
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for out in cell.get('outputs', []):
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if out.output_type == 'pyout':
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cell.haspyout = True
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break
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return cell, other
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@cell_preprocessor
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def coalesce_streams(cell, other, count):
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"""merge consecutive sequences of stream output into single stream
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to prevent extra newlines inserted at flush calls
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TODO: handle \r deletion
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"""
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outputs = cell.get('outputs', [])
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if not outputs:
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return cell, other
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new_outputs = []
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last = outputs[0]
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new_outputs = [last]
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for output in outputs[1:]:
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if (output.output_type == 'stream' and
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last.output_type == 'stream' and
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last.stream == output.stream
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):
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last.text += output.text
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else:
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new_outputs.append(output)
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cell.outputs = new_outputs
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return cell, other
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