DHParser 1.7.2

Parser Generator and DSL-construction-kit

DHParser

DHParser - Rapid prototyping of formal grammars and domain specific languages (DSL) in the Digital Humanities

This software is open source software under the Apache 2.0-License (see section License, below).

Copyright 2016-2023 Eckhart Arnold, Bavarian Academy of Sciences and Humanities

Purpose

DHParser has been developed with three main purposes in mind:

1. Developing parsers for domain specific languages and notations, either existing notations, like, LaTeX, or newly created DSLs, like the Medieval-Latin-Dictionary-DSL.

   Typically these languages are strict formal languages the grammar of which can be described with context-free grammars. (In cases where this does not hold like TeX, it is often still possible to describe a reasonably large subset of the formal language with a context free grammar.)

2. Developing parsers for semi-structured or informally structured text-data.

   This kind of data is typically what you get when retro-digitizing textual data like printed bibliographies, or reference works or dictionaries. Often such works can be captured with a formal grammar, but these grammars require a lot of iterations and tests to develop and usually become much more ramified than the grammars of well-designed formal languages. Thus, DHParser's elaborated testing and debugging-framework for grammars.

   (See Florian Zacherl's Dissertation on the retro-digitalization of dictionary data for an interesting case study. I am confident that the development of a suitable formal grammar is much easier with an elaborated framework like DHParser than with the PHP-parsing-expression-grammar-kit that Florian Zacherl has used.)

3. Developing processing-pipelines for tree-structured data.

   In typical digital humanities applications one wants to produce different forms of output (say, printed, online-human-readable, online-machine-readable) from one an the same source of data. Therefore, the parsing stage (if the data source is structured text-data) will be followed by more or less intricate bifurcated processing pipelines.

Features

• Memoizing packrat-parser based on Parsing Expression Grammars. This means:

  • Linear parsing time

  • Any EBNF-grammar supported, including left-recursive grammars (via "seed and grow"-algorithm)

  • Unlimited look ahead and look behind

• Macros to avoid code-repetition within grammars

• Declarative tree-transformations for post-processing syntax-trees

• Unit testing framework and post-mortem-debuger for test-driven grammar development and rapid-prototyping of grammars

• Customizable error reporting, recovery after syntax errors and support for fail-tolerant parsers

• Support for Language-servers

• Workflow-support and data-processing-pipelines

• XML-support like mapping flat-text to the DOM-tree ("node-tree" in DHParser's terminology) and adding markup in arbitrary places, even if this requires splitting tags.

• Full unicode support

• No dependencies except the Python Standard Library

• Extensive documentation and examples

Ease of use

Directly compile existing EBNF-grammars:

DHParser recognizes various dialects of EBNF or PEG-syntax for specifying grammars. For any already given grammar-specification in EBNF or PEG, it is not unlikely that DHParser can generate a parser either right away or with only minor changes or additions.

You can try this by compiling the file XML_W3C_SPEC.ebnf in the examples/XML of the source-tree which contains the official XML-grammar directly extracted from www.w3.org/TR/xml/:

$ dhparser examples/XML/XML_W3C_SPEC.ebnf

This command produces a Python-Script XML_W3C_SPECParser.py in the same directory as the EBNF-file. This file can be run on any XML-file and will yield its concrete syntax tree, e.g.:

$ python examples/XML/XML_W3C_SPECParser.py examples/XML/example.xml

Note, that the concrete syntax tree of an XML file as returned by the generated parser is not the same as the data-tree encoded by that very XML-file. In order to receive the data tree, further transformations are necessary. See examples/XML/XMLParser.py for an example of how this can be done.

Use (small) grammars on the fly in Python code:

Small grammars can also directly be compiled from Python-code. (Here, we use DHParser's preferred syntax which does not require trailing semicolons and uses the tilde ~ as a special sign to denote "insignificant" whitespace.)

key_value_store.py:

#!/usr/bin/env python 
# A mini-DSL for a key value store
from DHParser import *

# specify the grammar of your DSL in EBNF-notation
grammar = '''@ drop = whitespace, strings
key_store   = ~ { entry }
entry       = key "="~ value          # ~ means: insignificant whitespace 
key         = /\w+/~                  # Scanner-less parsing: Use regular
value       = /\"[^"\n]*\"/~          # expressions wherever you like'''

# generating a parser is almost as simple as compiling a regular expression
parser = create_parser(grammar)       # parser factory for thread-safety

Now, parse some text and extract the data from the Python-shell:

>>> from key_value_store import parser
>>> text = '''
        title    = "Odysee 2001"
        director = "Stanley Kubrick"
    '''
>>> data = parser(text)
>>> for entry in data.select('entry'):
        print(entry['key'], entry['value'])

title "Odysee 2001"
director "Stanley Kubrick"

Or, serialize as XML:

>>> print(data.as_xml())

<key_store>
  <entry>
    <key>title</key>
    <value>"Odysee 2001"</value>
  </entry>
  <entry>
    <key>director</key>
    <value>"Stanley Kubrick"</value>
  </entry>
</key_store>

Set up DSL-projects with unit-tests for long-term-development:

For larger projects that require testing and incremental grammar development, use:

$ dhparser NEW_PROJECT_NAME

to setup a project-directory with all the scaffolding for a new DSL-project, including the full unit-testing-framework.

Installation

You can install DHParser from the Python package index pypi.org:

python -m pip install --user DHParser

Alternatively, you can clone the latest version from gitlab.lrz.de/badw-it/DHParser

Getting Started

See Introduction.md for the motivation and an overview how DHParser works or jump right into the Step by Step Guide to learn how to setup and use DHParser. Or have a look at the comprehensive overview of DHParser's features to see how DHParser supports the construction of domain specific languages.

Documentation

For the full documentation see: dhparser.readthedocs.io

License

DHParser is open source software under the Apache 2.0 License.

Copyright 2016-2022 Eckhart Arnold, Bavarian Academy of Sciences and Humanities

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Optional Post-Installation

It is recommended that you install the regex-module (https://bitbucket.org/mrabarnett/mrab-regex). If present, DHParser will use regex instead of the built-in re-module for regular expressions. regex is faster and more powerful than re.

In order to speed up DHParser even more, it can be compiled with the Python to C compiler Cython. Cython version 3.0 or higher is required to compile DHParser. Type:

pip install cython

on the command-line to install cython. Once cython has been built and installed, you can run the "dhparser_cythonize"-script from the command line:

dhparser_cythonize

The Cython-compiled version is about 2-3 times faster than the CPython-interpreted version. Compiling can take quite a while. If you are in a hurry, you can just can also just call dhparser_cythonize_stringview which just compiles the stringview-module, which profits the most from being "cythonized".

Depending on the use case, e.g. when parsing large files, PyPy3 yields even greater speed-ups. However, in other cases pypy can also be noticeably slower than cpython! To circumvent the longer startup times of pypy3 in comparison to CPython, it is recommended to use the xxxServer.py-scripts rather than calling the xxxParser.py-script each time when parsing many documents subsequently.

Sources

Find the sources on gitlab.lrz.de/badw-it/DHParser . Get them with:

git clone https://gitlab.lrz.de/badw-it/DHParser

There exists a mirror of this repository on github: https://github.com/jecki/DHParser Be aware, though, that the github-mirror may occasionally lag behind a few commits.

Packaging

DHParser uses Poetry for packaging and dependency-management. In order to build a package from the sources, type:

poetry build

on the command line. The packages will then appear in the "dist" subdirectory.

Author

Author: Eckhart Arnold, Bavarian Academy of Sciences Email: [e-mail]

How to cite

If you use DHParser for Scientific Work, then please cite it as:

DHParser. A Parser-Generator for Digital-Humanities-Applications,
Division for Digital Humanities Research & Development, Bavarian Academy of Science and Technology, Munich Germany 2017, https://gitlab.lrz.de/badw-it/dhparser

References and Acknowledgement

Juancarlo Añez: grako, a PEG parser generator in Python, 2017. URL: bitbucket.org/apalala/grako

Eckhart Arnold: Domänenspezifische Notationen. Eine (noch) unterschätzte Technologie in den Digitalen Geisteswissenschaften, Präsentation auf dem dhmuc-Workshop: Digitale Editionen und Auszeichnungssprachen, München 2016. Short-URL: tiny.badw.de/2JVT

Brian Ford: Parsing Expression Grammars: A Recognition-Based Syntactic Foundation, Cambridge Massachusetts, 2004. Short-URL:t1p.de/jihs

Richard A. Frost, Rahmatullah Hafiz and Paul Callaghan: Parser Combinators for Ambiguous Left-Recursive Grammars, in: P. Hudak and D.S. Warren (Eds.): PADL 2008, LNCS 4902, pp. 167–181, Springer-Verlag Berlin Heidelberg 2008.

Elizabeth Scott and Adrian Johnstone, GLL Parsing, in: Electronic Notes in Theoretical Computer Science 253 (2010) 177–189, dotat.at/tmp/gll.pdf

Dominikus Herzberg: Objekt-orientierte Parser-Kombinatoren in Python, Blog-Post, September, 18th 2008 on denkspuren. gedanken, ideen, anregungen und links rund um informatik-themen, short-URL: t1p.de/bm3k

Dominikus Herzberg: Eine einfache Grammatik für LaTeX, Blog-Post, September, 18th 2008 on denkspuren. gedanken, ideen, anregungen und links rund um informatik-themen, short-URL: t1p.de/7jzh

Dominikus Herzberg: Uniform Syntax, Blog-Post, February, 27th 2007 on denkspuren. gedanken, ideen, anregungen und links rund um informatik-themen, short-URL: t1p.de/s0zk

John MacFarlane, David Greenspan, Vicent Marti, Neil Williams, Benjamin Dumke-von der Ehe, Jeff Atwood: CommonMark. A strongly defined, highly compatible specification of Markdown, 2017. commonmark.org

Stefan Müller: DSLs in den digitalen Geisteswissenschaften, Präsentation auf dem dhmuc-Workshop: Digitale Editionen und Auszeichnungssprachen, München 2016. Short-URL: tiny.badw.de/2JVy

Markus Voelter, Sbastian Benz, Christian Dietrich, Birgit Engelmann, Mats Helander, Lennart Kats, Eelco Visser, Guido Wachsmuth: DSL Engineering. Designing, Implementing and Using Domain-Specific Languages, 2013. dslbook.org/

Christopher Seaton: A Programming Language Where the Syntax and Semantics are Mutuable at Runtime, University of Bristol 2007, chrisseaton.com/katahdin/katahdin.pdf

Vegard Øye: General Parser Combinators in Racket, 2012, epsil.github.io/gll/

and many more...