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86 lines
3.4 KiB
86 lines
3.4 KiB
"""Module containing a preprocessor that removes metadata from code cells"""
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# Copyright (c) IPython Development Team.
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# Distributed under the terms of the Modified BSD License.
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from traitlets import Bool, Set
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from .base import Preprocessor
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class ClearMetadataPreprocessor(Preprocessor):
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"""
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Removes all the metadata from all code cells in a notebook.
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"""
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clear_cell_metadata = Bool(True,
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help=("Flag to choose if cell metadata is to be cleared "
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"in addition to notebook metadata.")).tag(config=True)
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clear_notebook_metadata = Bool(True,
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help=("Flag to choose if notebook metadata is to be cleared "
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"in addition to cell metadata.")).tag(config=True)
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preserve_nb_metadata_mask = Set([('language_info', 'name')],
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help=("Indicates the key paths to preserve when deleting metadata "
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"across both cells and notebook metadata fields. Tuples of "
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"keys can be passed to preserved specific nested values")).tag(config=True)
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preserve_cell_metadata_mask = Set(
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help=("Indicates the key paths to preserve when deleting metadata "
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"across both cells and notebook metadata fields. Tuples of "
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"keys can be passed to preserved specific nested values")).tag(config=True)
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def current_key(self, mask_key):
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if isinstance(mask_key, str):
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return mask_key
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elif len(mask_key) == 0:
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# Safeguard
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return None
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else:
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return mask_key[0]
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def current_mask(self, mask):
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return { self.current_key(k) for k in mask if self.current_key(k) is not None }
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def nested_masks(self, mask):
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return { self.current_key(k[0]): k[1:] for k in mask if k and not isinstance(k, str) and len(k) > 1 }
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def nested_filter(self, items, mask):
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keep_current = self.current_mask(mask)
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keep_nested_lookup = self.nested_masks(mask)
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for k, v in items:
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keep_nested = keep_nested_lookup.get(k)
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if k in keep_current:
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if keep_nested is not None:
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if isinstance(v, dict):
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yield k, dict(self.nested_filter(v.items(), keep_nested))
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else:
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yield k, v
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def preprocess_cell(self, cell, resources, cell_index):
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"""
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All the code cells are returned with an empty metadata field.
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"""
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if self.clear_cell_metadata:
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if cell.cell_type == 'code':
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# Remove metadata
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if 'metadata' in cell:
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cell.metadata = dict(self.nested_filter(cell.metadata.items(), self.preserve_cell_metadata_mask))
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return cell, resources
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def preprocess(self, nb, resources):
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"""
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Preprocessing to apply on each notebook.
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Must return modified nb, resources.
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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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preprocessors to pass variables into the Jinja engine.
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"""
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nb, resources = super().preprocess(nb, resources)
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if self.clear_notebook_metadata:
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if 'metadata' in nb:
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nb.metadata = dict(self.nested_filter(nb.metadata.items(), self.preserve_nb_metadata_mask))
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return nb, resources
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