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venv/Lib/site-packages/nbconvert/preprocessors/base.py

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+"""Base class for preprocessors"""
+
+# Copyright (c) IPython Development Team.
+# Distributed under the terms of the Modified BSD License.
+
+from ..utils.base import NbConvertBase
+from traitlets import Bool
+
+
+class Preprocessor(NbConvertBase):
+    """ A configurable preprocessor
+
+    Inherit from this class if you wish to have configurability for your
+    preprocessor.
+
+    Any configurable traitlets this class exposed will be configurable in
+    profiles using c.SubClassName.attribute = value
+
+    You can overwrite `preprocess_cell()` to apply a transformation
+    independently on each cell or `preprocess()` if you prefer your own
+    logic. See corresponding docstring for information.
+
+    Disabled by default and can be enabled via the config by
+        'c.YourPreprocessorName.enabled = True'
+    """
+    
+    enabled = Bool(False).tag(config=True)
+
+    def __init__(self, **kw):
+        """
+        Public constructor
+
+        Parameters
+        ----------
+        config : Config
+            Configuration file structure
+        `**kw`
+            Additional keyword arguments passed to parent
+        """
+        
+        super().__init__(**kw)
+
+    def __call__(self, nb, resources):
+        if self.enabled:
+            self.log.debug("Applying preprocessor: %s",
+                           self.__class__.__name__)
+            return self.preprocess(nb, resources)
+        else:
+            return nb, resources
+
+    def preprocess(self, nb, resources):
+        """
+        Preprocessing to apply on each notebook.
+
+        Must return modified nb, resources.
+
+        If you wish to apply your preprocessing to each cell, you might want
+        to override preprocess_cell method instead.
+
+        Parameters
+        ----------
+        nb : NotebookNode
+            Notebook being converted
+        resources : dictionary
+            Additional resources used in the conversion process.  Allows
+            preprocessors to pass variables into the Jinja engine.
+        """
+        for index, cell in enumerate(nb.cells):
+            nb.cells[index], resources = self.preprocess_cell(cell, resources, index)
+        return nb, resources
+
+    def preprocess_cell(self, cell, resources, index):
+        """
+        Override if you want to apply some preprocessing to each cell.
+        Must return modified cell and resource dictionary.
+
+        Parameters
+        ----------
+        cell : NotebookNode cell
+            Notebook cell being processed
+        resources : dictionary
+            Additional resources used in the conversion process.  Allows
+            preprocessors 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
+