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Cleanup and refactor, transformers
Cleanup and refactor, transformers

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extractfigure.py
127 lines | 4.6 KiB | text/x-python | PythonLexer
"""Module containing a transformer that extracts all of the figures from the
notebook file. The extracted figures are returned in the 'resources' dictionary.
"""
#-----------------------------------------------------------------------------
# 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 IPython.utils.traitlets import (Dict, List, Unicode)
from .activatable import ActivatableTransformer
#-----------------------------------------------------------------------------
# Constants
#-----------------------------------------------------------------------------
FIGURES_KEY = "figures"
BINARY_KEY = "binary"
TEXT_KEY = "text"
#-----------------------------------------------------------------------------
# Classes
#-----------------------------------------------------------------------------
class ExtractFigureTransformer(ActivatableTransformer):
"""
Extracts all of the figures from the notebook file. The extracted
figures are returned in the 'resources' dictionary.
"""
extra_extension_map = Dict({},
config=True,
help="""Extra map to override extension based on type.
Useful for latex where SVG will be converted to PDF before inclusion
""")
key_format_map = Dict({}, config=True,)
figure_name_format_map = Dict({}, config=True)
display_data_priority = List(['svg', 'png', 'latex', 'jpg', 'jpeg','text'])
#TODO: Change this to .format {} syntax
default_key_template = Unicode('_fig_{count:02d}.{ext}', config=True)
def cell_transform(self, cell, resources, index):
"""
Apply a transformation on each cell,
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
Modified index of the cell being processed (see base.py)
"""
if resources.get(FIGURES_KEY, None) is None :
resources[FIGURES_KEY] = {TEXT_KEY:{},BINARY_KEY:{}}
for out in cell.get('outputs', []):
for out_type in self.display_data_priority:
if out.hasattr(out_type):
figname, key, data, binary = self._new_figure(out[out_type], out_type, index)
out['key_'+out_type] = figname
if binary :
resources[FIGURES_KEY][BINARY_KEY][key] = data
else :
resources[FIGURES_KEY][TEXT_KEY][key] = data
index += 1
return cell, resources
def _get_override_extension(self, extension):
"""Gets the overriden extension if it exists, else returns extension.
Parameters
----------
extension : str
File extension.
"""
if extension in self.extra_extension_map :
return self.extra_extension_map[extension]
return extension
def _new_figure(self, data, format, index):
"""Create a new figure file in the given format.
Parameters
----------
data : str
Cell data (from Notebook node cell)
resources : dictionary
Additional resources used in the conversion process. Allows
transformers to pass variables into the Jinja engine.
index : int
Modified index of the cell being processed (see base.py)
"""
figure_name_template = self.figure_name_format_map.get(format, self.default_key_template)
key_template = self.key_format_map.get(format, self.default_key_template)
#TODO: option to pass the hash as data?
figure_name = figure_name_template.format(index=index, ext=self._get_override_extension(format))
key = key_template.format(index=index, ext=self._get_override_extension(format))
#Binary files are base64-encoded, SVG is already XML
binary = False
if format in ('png', 'jpg', 'pdf'):
data = data.decode('base64')
binary = True
return figure_name, key, data, binary