Metadata-Version: 2.1
Name: svgling
Version: 0.3.0
Summary: SVG+Python based rendering of linguistics-style (constituent) trees
Home-page: https://github.com/rawlins/svgling
Author: Kyle Rawlins
Author-email: kgr@jhu.edu
License: MIT
Description: # `svgling`: syntax trees in python + svg
        
        **Author**: Kyle Rawlins, [kgr@jhu.edu](kgr@jhu.edu)
        
        **Dependencies**: [`svgwrite`](https://pypi.org/project/svgwrite/), python 3
        
        **Repository**: [https://github.com/rawlins/svgling/](https://github.com/rawlins/svgling/)
        
        **Installation**: download and use setuptools, or `pip install svgling`
        
        **License**: MIT License
        
        ## Overview
        
        The `svgling` package is a pure python package for doing single-pass rendering
        of linguistics-style constituent trees into SVG. It is primarily intended for
        integrating with Jupyter notebooks, but could be used to generate SVG diagrams
        for all sorts of other purposes. It involves no javascript and so will work
        in Jupyter without any plugins.
        
        The basic interface is pretty simple: pass a tree-describing object to
        `svgling.draw_tree` (e.g. a tuple consisting of a lable and a sequence of
        daughter nodes, which mey themselves be trees).
        
            import svgling
            svgling.draw_tree(("S", ("NP", ("D", "the"), ("N", "elephant")), ("VP", ("V", "saw"), ("NP", ("D", "the"), ("N", "rhinoceros")))))
        
        This produces an SVG image like the following:
        
        ![example sentence](https://raw.githubusercontent.com/rawlins/svgling/master/demotree.svg?sanitize=true)
        
        The tree drawing code accepts two main tree formats: lisp-style trees made from
        lists of lists (or tuples of tuples), with node labels as strings, or trees from
        the [`nltk`](https://www.nltk.org/) package, i.e. objects instantiating the
        [`nltk.tree.Tree`](https://www.nltk.org/_modules/nltk/tree.html) API. The
        following nltk code, as long as `svgling` has been imported, produces an
        identical tree diagram to the above example, though by a very different route:
        
            import svgling
            nltk.Tree.fromstring("(S (NP (D the) (N elephant)) (VP (V saw) (NP (D the) (N rhinoceros))))")
        
        (That is, `svgling` monkey-patches NLTK to use SVG-based tree drawing code. You
        may also want to call `svgling.disable_nltk_png()` to fully disable the
        default NLTK png renderer, especially if you're on a mac or windows 64, or are
        running NLTK on a headless device; see nltk issue
        [#1887](https://github.com/nltk/nltk/issues/1887) for use-cases).)
        
        Beyond basic tree-drawing, the package supports a number of flourishes like
        movement arrows. For documentation and examples, see the three .ipynb files in
        the root of this repository: (links below to nbviewer static rendered versions):
        
        * [Overview.ipynb](https://nbviewer.jupyter.org/github/rawlins/svgling/blob/master/Overview.ipynb)
        * [svgling Gallery.ipynb](https://nbviewer.jupyter.org/github/rawlins/svgling/blob/master/svgling%20Gallery.ipynb)
        * [svgling Manual.ipynb](https://nbviewer.jupyter.org/github/rawlins/svgling/blob/master/svgling%20Manual.ipynb)
        
        ## Core design principles and goals
        
        1. Be well suited for *programmatic* generation of tree diagrams (not just
        hand-customized diagrams).
        2. Be equally suited for theoretical linguistics and computational
        linguistics/NLP, at least for cases where the latter is targeting constituent
        trees. (This package is not aimed at dependency trees/graphs.)
        3. Do as much as possible with pure python (as opposed to python+javascript, or
        python+tk, or python+dot, or...).
        
        ## Strengths and limitations
        
        The `svgling` package does its rendering in one pass -- it takes a tree
        structure as input, produces an svg output, and that's it. Because of this, it
        is extremely simple to use in Jupyter, and no messing with plugins or Jupyter
        settings should be necessary. Because it is SVG-based, scaling and embedding in
        any web context should work smoothly. It also has minimal dependencies, just
        one package that provides an abstraction layer over generating svg. (If you're
        interested in programmatic diagramming in svg for Jupyter, I do recommend
        [`svgwrite`](https://github.com/mozman/svgwrite), it's under active development
        and has a very pleasant API + good documentation.)
        
        Single-pass rendering also places limitations on what can be done. One of the
        challenges is that it mostly uses absolute position, and the exact position and
        width of text elements can't be determined without actually rendering to some
        device and seeing what happens. In addition, the exact details of rendering are
        in various ways at the mercy of the rendering device. This all means that
        `svgling` uses a bunch of tricks to estimate node size and width, and won't
        always be perfect on all devices. This situation also places some hard
        limitations on how far `svgling` can be extended without adding javascript or
        other multi-pass rendering techniques. For example, I would eventually like to
        allow mathjax in nodes, and allow nodes with complex / multi-line shapes, but at
        the moment this does not seem possible in pure SVG without javascript on the
        client side. The package does provide basic support for hybrid HTML/SVG tree
        diagrams that allow complex nodes, including MathJax, but with substantial
        limitations.
        
        There are many things that it might be nice to add to this package; if you find
        `svgling` useful, have any requests, or find any bugs, please let me know.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Multimedia :: Graphics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Text Processing :: Markup
Classifier: Topic :: Utilities
Classifier: Framework :: Jupyter
Classifier: Environment :: Web Environment
Requires-Python: >=3
Description-Content-Type: text/markdown
