"""Exporter for the notebook conversion pipeline. This module defines Exporter, a highly configurable converter that uses Jinja2 to export notebook files into different format. You can register both pre-transformers that will act on the notebook format befor conversion and jinja filter that would then be availlable in the templates """ #----------------------------------------------------------------------------- # 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 __future__ import print_function, absolute_import # Stdlib imports import io import os # IPython imports from IPython.config.configurable import Configurable from IPython.nbformat import current as nbformat from IPython.utils.traitlets import MetaHasTraits, Unicode, List, Bool from IPython.utils.text import indent # other libs/dependencies from jinja2 import Environment, FileSystemLoader from markdown import markdown # local import import filters.strings import filters.markdown import filters.latex import filters.datatypefilter import filters.pygments import filters.ansi import transformers.extractfigure import transformers.csshtmlheader import transformers.revealhelp import transformers.coalescestreams #----------------------------------------------------------------------------- # Globals and constants #----------------------------------------------------------------------------- #Standard Jinja2 environment constants TEMPLATE_PATH = "/../templates/" TEMPLATE_SKELETON_PATH = "/../templates/skeleton/" TEMPLATE_EXTENSION = ".tpl" #Jinja2 extensions to load. JINJA_EXTENSIONS = ['jinja2.ext.loopcontrols'] #----------------------------------------------------------------------------- # Classes and functions #----------------------------------------------------------------------------- class Exporter(Configurable): """ A Jinja2 base converter templates Pre-process the IPYNB files, feed it through Jinja2 templates, and spit an converted files and a data object with other data should be mostly configurable """ pre_transformer_order = List(['haspyout_transformer'], config=True, help= """ An ordered list of pre-transformer to apply to the IPYNB file before running through templates """ ) template_file = Unicode( '', config=True, help="Name of the template file to use") fileext = Unicode( 'txt', config=True, help="Extension of the file that should be written to disk" ) stdout = Bool( True, config=True, help="""Whether to print the converted IPYNB file to stdout "use full do diff files without actually writing a new file""" ) write = Bool( False, config=True, help="""Should the converted notebook file be written to disk along with potential extracted resources.""" ) #Processors that process the input data prior to the export, set in the #constructor for this class. preprocessors = [] def __init__(self, preprocessors={}, jinja_filters={}, config=None, export_format, **kw): """ Init a new converter. config: the Configurable config object to pass around. preprocessors: dict of **availlable** key/value function to run on ipynb json data before conversion to extract/inline file. See `transformer.py` and `ConfigurableTransformers` set the order in which the transformers should apply with the `pre_transformer_order` trait of this class transformers registerd by this key will take precedence on default one. jinja_filters: dict of supplementary jinja filter that should be made availlable in template. If those are of Configurable Class type, they will be instanciated with the config object as argument. user defined filter will overwrite the one availlable by default. """ #Set the default options for the exporter. default_config = self.config #Set properties that must be set in the config class in order to #propagate to other classes. default_config.GlobalConfigurable.display_data_priority =['svg', 'png', 'latex', 'jpg', 'jpeg','text'] default_config.ExtractFigureTransformer.display_data_priority=['svg', 'png', 'latex', 'jpg', 'jpeg','text'] #Set default properties of the exporter. #For most (or all cases), the template file name matches the format name. self.display_data_priority= ['svg', 'png', 'latex', 'jpg', 'jpeg','text'] self.template_file = export_format if not config == None: default_config._merge(config) config = default_config #Call the base class constructor super(Exporter, self).__init__(config=config, **kw) #Standard environment self.ext = TEMPLATE_EXTENSION self._init_environment() #TODO: Implement reflection style methods to get user transformers. #for name in self.pre_transformer_order: # # get the user-defined transformer first # transformer = preprocessors.get(name, getattr(trans, name, None)) # if isinstance(transformer, MetaHasTraits): # transformer = transformer(config=config) # self.preprocessors.append(transformer) #For compatibility, TODO: remove later. self.preprocessors.append(transformers.coalescestreams.coalesce_streams) self.preprocessors.append(transformers.extractfigure.ExtractFigureTransformer(config=config)) self.preprocessors.append(transformers.revealhelp.RevealHelpTransformer(config=config)) self.preprocessors.append(transformers.csshtmlheader.CSSHtmlHeaderTransformer(config=config)) #Add filters to the Jinja2 environment self._register_filters(config) #Load user filters. Overwrite existing filters if need be. for key, user_filter in jinja_filters.iteritems(): if isinstance(user_filter, MetaHasTraits): self.env.filters[key] = user_filter(config=config) else: self.env.filters[key] = user_filter #Load the template file. self.template = self.env.get_template(self.template_file+self.ext) def export(self, nb): """Export notebook object nb: Notebook object to export. Returns both the converted ipynb file and a dict containing the resources created along the way via the transformers and Jinja2 processing. """ nb, resources = self._preprocess(nb) return self.template.render(nb=nb, resources=resources), resources def from_filename(self, filename): """Read and export a notebook from a filename filename: Filename of the notebook file to export. Returns both the converted ipynb file and a dict containing the resources created along the way via the transformers and Jinja2 processing. """ with io.open(filename) as f: return self.export(nbformat.read(f, 'json')) def from_file(self, file_stream): """Read and export a notebook from a filename file_stream: File handle of file that contains notebook data. Returns both the converted ipynb file and a dict containing the resources created along the way via the transformers and Jinja2 processing. """ return self.export(nbformat.read(file_stream, 'json')) def _init_environment(self): self.env = Environment( loader=FileSystemLoader([ os.path.dirname(os.path.realpath(__file__)) + TEMPLATE_PATH, os.path.dirname(os.path.realpath(__file__)) + TEMPLATE_SKELETON_PATH, ]), extensions=JINJA_EXTENSIONS ) def _register_filters(self, config): self.env.filters['indent'] = indent self.env.filters['markdown'] = markdown self.env.filters['ansi2html'] = filters.ansi.ansi2html self.env.filters['filter_data_type'] = filters.datatypefilter.DataTypeFilter(config=config) self.env.filters['get_lines'] = filters.strings.get_lines self.env.filters['highlight'] = filters.pygments.highlight self.env.filters['highlight2html'] = filters.pygments.highlight self.env.filters['highlight2latex'] = filters.pygments.highlight2latex self.env.filters['markdown2latex'] = filters.markdown.markdown2latex self.env.filters['markdown2rst'] = filters.markdown.markdown2rst self.env.filters['pycomment'] = filters.strings.python_comment self.env.filters['rm_ansi'] = filters.ansi.remove_ansi self.env.filters['rm_dollars'] = filters.strings.strip_dollars self.env.filters['rm_fake'] = filters.strings.rm_fake self.env.filters['rm_math_space'] = filters.latex.rm_math_space self.env.filters['wrap'] = filters.strings.wrap def _preprocess(self, nb): """ Preprocess the notebook using the transformers specific for the current export format. nb: Notebook to preprocess """ #Dict of 'resources' that can be filled by the preprocessors. resources = {} #Run each transformer on the notebook. Carry the output along #to each transformer for transformer in self.preprocessors: nb, resources = transformer(nb, resources) return nb, resources