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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
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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 converters.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, 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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class ActivatableTransformer(ConfigurableTransformers):
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"""A simple ConfigurableTransformers that have an enabled flag
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Inherit from that if you just want to have a transformer which is
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no-op by default but can be activated in profiles with
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c.YourTransformerName.enabled = True
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"""
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enabled = Bool(False, config=True)
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def __call__(self, nb, other):
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if not self.enabled :
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return nb, other
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else :
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return super(ActivatableTransformer, self).__call__(nb, 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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class ExtractFigureTransformer(ActivatableTransformer):
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extra_ext_map = Dict({},
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config=True,
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help="""extra map to override extension based on type.
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Usefull for latex where svg will be converted to pdf before inclusion
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"""
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)
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key_format_map = Dict({},
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config=True,
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)
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figname_format_map = Dict({},
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config=True,
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)
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#to do change this to .format {} syntax
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default_key_tpl = Unicode('_fig_{count:02d}.{ext}', config=True)
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def _get_ext(self, ext):
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if ext in self.extra_ext_map :
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return self.extra_ext_map[ext]
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return ext
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def _new_figure(self, data, fmt, count):
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"""Create a new figure file in the given format.
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"""
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tplf = self.figname_format_map.get(fmt, self.default_key_tpl)
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tplk = self.key_format_map.get(fmt, self.default_key_tpl)
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# option to pass the hash as data ?
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figname = tplf.format(count=count, ext=self._get_ext(fmt))
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key = tplk.format(count=count, ext=self._get_ext(fmt))
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# Binary files are base64-encoded, SVG is already XML
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if fmt in ('png', 'jpg', 'pdf'):
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data = data.decode('base64')
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return figname, key, data
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def cell_transform(self, cell, other, count):
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if other.get('figures', None) is None :
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other['figures'] = {}
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for out in cell.get('outputs', []):
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for out_type in self.display_data_priority:
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if out.hasattr(out_type):
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figname, key, data = self._new_figure(out[out_type], out_type, count)
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out['key_'+out_type] = figname
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other['figures'][key] = data
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count = count+1
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return cell, other
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class RevealHelpTransformer(ConfigurableTransformers):
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def __call__(self, nb, other):
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for worksheet in nb.worksheets :
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for i, cell in enumerate(worksheet.cells):
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cell.metadata.slide_type = cell.metadata.get('slideshow', {}).get('slide_type', None)
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if cell.metadata.slide_type is None:
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cell.metadata.slide_type = '-'
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if cell.metadata.slide_type in ['slide']:
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worksheet.cells[i - 1].metadata.slide_helper = 'slide_end'
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if cell.metadata.slide_type in ['subslide']:
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worksheet.cells[i - 1].metadata.slide_helper = 'subslide_end'
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return nb, other
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class CSSHtmlHeaderTransformer(ActivatableTransformer):
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def __call__(self, nb, resources):
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"""Fetch and add css to the resource dict
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Fetch css from IPython adn Pygment to add at the beginning
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of the html files.
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Add this css in resources in the "inlining.css" key
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"""
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resources['inlining'] = {}
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resources['inlining']['css'] = self.header
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return nb, resources
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header = []
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def __init__(self, config=None, **kw):
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super(CSSHtmlHeaderTransformer, self).__init__(config=config, **kw)
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if self.enabled :
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self.regen_header()
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def regen_header(self):
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## lazy load asa this might not be use in many transformers
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import os
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from IPython.utils import path
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import io
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from pygments.formatters import HtmlFormatter
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header = []
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static = os.path.join(path.get_ipython_package_dir(),
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'frontend', 'html', 'notebook', 'static',
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)
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here = os.path.split(os.path.realpath(__file__))[0]
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css = os.path.join(static, 'css')
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for sheet in [
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# do we need jquery and prettify?
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# os.path.join(static, 'jquery', 'css', 'themes', 'base',
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# 'jquery-ui.min.css'),
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# os.path.join(static, 'prettify', 'prettify.css'),
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os.path.join(css, 'boilerplate.css'),
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os.path.join(css, 'fbm.css'),
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os.path.join(css, 'notebook.css'),
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os.path.join(css, 'renderedhtml.css'),
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os.path.join(css, 'style.min.css'),
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]:
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try:
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with io.open(sheet, encoding='utf-8') as f:
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s = f.read()
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header.append(s)
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except IOError:
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# new version of ipython with style.min.css, pass
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pass
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pygments_css = HtmlFormatter().get_style_defs('.highlight')
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header.append(pygments_css)
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self.header = header
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