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A python module for writing pandoc <http://pandoc.org/>
_ filters
Pandoc filters are pipes that read a JSON serialization of the Pandoc AST from stdin, transform it in some way, and write it to stdout. They can be used with pandoc (>= 1.12) either using pipes ::
pandoc -t json -s | ./caps.py | pandoc -f json
or using the --filter
(or -F
) command-line option. ::
pandoc --filter ./caps.py -s
For more on pandoc filters, see the pandoc documentation under --filter
and the tutorial on writing filters
__.
__ http://johnmacfarlane.net/pandoc/scripting.html
For an alternative library for writing pandoc filters, with
a more "Pythonic" design, see panflute
__.
__ https://github.com/sergiocorreia/panflute
Pandoc 1.16 introduced link and image attributes
to the existing
caption
and target
arguments, requiring a change in pandocfilters
that breaks backwards compatibility. Consequently, you should use:
Pandoc 1.17.3 (pandoc-types 1.17.*) introduced a new JSON format. pandocfilters 1.4.0 should work with both the old and the new format.
Run this inside the present directory::
python setup.py install
Or install from PyPI::
pip install pandocfilters
The main functions pandocfilters
exports are
walk(x, action, format, meta)
Walk a tree, applying an action to every object. Returns a modified
tree. An action is a function of the form
action(key, value, format, meta)
, where:
key
is the type of the pandoc object (e.g. 'Str', 'Para')value
is the contents of the object (e.g. a string for 'Str', a list of
inline elements for 'Para')format
is the target output format (as supplied by the
format
argument of walk
)meta
is the document's metadataThe return of an action is either:
None
: this means that the object should remain unchangedtoJSONFilter(action)
Like toJSONFilters
, but takes a single action as argument.
toJSONFilters(actions)
Generate a JSON-to-JSON filter from stdin to stdout
The filter:
The argument actions
is a list of functions of the form
action(key, value, format, meta)
, as described in more detail
under walk
.
This function calls applyJSONFilters
, with the format
argument provided by the first command-line argument, if present.
(Pandoc sets this by default when calling filters.)
applyJSONFilters(actions, source, format="")
Walk through JSON structure and apply filters
This:
The actions
argument is a list of functions (see walk
for a
full description).
The argument source
is a string encoded JSON object.
The argument format
is a string describing the output format.
Returns a new JSON-formatted pandoc document.
stringify(x)
Walks the tree x and returns concatenated string content, leaving out all formatting.
attributes(attrs)
Returns an attribute list, constructed from the dictionary attrs.
Most users will only need toJSONFilter
. Here is a simple example
of its use::
#!/usr/bin/env python
"""
Pandoc filter to convert all regular text to uppercase.
Code, link URLs, etc. are not affected.
"""
from pandocfilters import toJSONFilter, Str
def caps(key, value, format, meta):
if key == 'Str':
return Str(value.upper())
if __name__ == "__main__":
toJSONFilter(caps)
The examples subdirectory in the source repository contains the following filters. These filters should provide a useful starting point for developing your own pandocfilters.
abc.py
Pandoc filter to process code blocks with class abc
containing ABC
notation into images. Assumes that abcm2ps and ImageMagick's convert
are in the path. Images are put in the abc-images directory.
caps.py
Pandoc filter to convert all regular text to uppercase. Code, link
URLs, etc. are not affected.
blockdiag.py
Pandoc filter to process code blocks with class "blockdiag" into
generated images. Needs utils from http://blockdiag.com.
comments.py
Pandoc filter that causes everything between
<!-- BEGIN COMMENT -->
and <!-- END COMMENT -->
to be ignored.
The comment lines must appear on lines by themselves, with blank
lines surrounding
deemph.py
Pandoc filter that causes emphasized text to be displayed in ALL
CAPS.
deflists.py
Pandoc filter to convert definition lists to bullet lists with the
defined terms in strong emphasis (for compatibility with standard
markdown).
gabc.py
Pandoc filter to convert code blocks with class "gabc" to LaTeX
\gabcsnippet commands in LaTeX output, and to images in HTML output.
graphviz.py
Pandoc filter to process code blocks with class graphviz
into
graphviz-generated images.
lilypond.py
Pandoc filter to process code blocks with class "ly" containing
Lilypond notation.
metavars.py
Pandoc filter to allow interpolation of metadata fields into a
document. %{fields}
will be replaced by the field's value, assuming
it is of the type MetaInlines
or MetaString
.
myemph.py
Pandoc filter that causes emphasis to be rendered using the custom
macro \myemph{...}
rather than \emph{...}
in latex. Other output
formats are unaffected.
plantuml.py
Pandoc filter to process code blocks with class plantuml
to images.
Needs plantuml.jar
from http://plantuml.com/.
ditaa.py
Pandoc filter to process code blocks with class ditaa
to images.
Needs ditaa.jar
from http://ditaa.sourceforge.net/.
theorem.py
Pandoc filter to convert divs with class="theorem"
to LaTeX theorem
environments in LaTeX output, and to numbered theorems in HTML
output.
tikz.py
Pandoc filter to process raw latex tikz environments into images.
Assumes that pdflatex is in the path, and that the standalone
package is available. Also assumes that ImageMagick's convert is in
the path. Images are put in the tikz-images
directory.
By default most filters use get_filename4code
to
create a directory ...-images
to save temporary
files. This directory doesn't get removed as it can be used as a cache so that
later pandoc runs don't have to recreate files if they already exist. The
directory is generated in the current directory.
If you prefer to have a clean directory after running pandoc filters, you
can set an environment variable PANDOCFILTER_CLEANUP
to any non-empty value such as 1
which forces the code to create a temporary directory that will be removed
by the end of execution.
FAQs
Utilities for writing pandoc filters in python
We found that pandocfilters demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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