pylexibank 2.5.0

Python library implementing the lexibank workbench

pylexibank

pylexibank is a python package providing functionality to curate and aggregate Lexibank datasets.

Install

Since pylexibank has quite a few dependencies, installing it will result in installing many other python packages along with it. To avoid any side effects for your default python installation, we recommend installation in a virtual environment.

Now you may install pylexibank via pip or in development mode following the instructions in CONTRIBUTING.md.

Installing pylexibank will also install cldfbench, which in turn installs a cli command cldfbench. This command is used to run pylexibank functionality from the command line as subcommands.

cldfbench is also used to manage reference catalogs, in particular Glottolog, Concepticon and CLTS. Thus, after installing pylexibank you should run

cldfbench catconfig

to make sure the catalog data is locally available and pylexibank knows about it.

Usage

pylexibank can be used in two ways:

• The command line interface provides mainly access to the functionality for the lexibank curation workflow.
• The pylexibank package can also be used like any other python package in your own python code to access lexibank data in a programmatic (and consistent) way.

The cmd_makecldf method

The main goal of pylexibank is creating high-quality CLDF Wordlists. This happens in the custom cmd_makecldf method of a Lexibank dataset. To make this task easier, pylexibank provides

• access to Glottolog and Concepticon data:
  • args.glottolog.api points to an instance of CachingGlottologAPI (a subclass of pyglottolog.Glottolog)
  • args.concepticon.api points to an instance of CachingConcepticonAPI (a subclass of pyconcepticon.Concepticon)
• fine-grained control over form manipulation via a Dataset.form_spec, an instance of pylexibank.FormSpec which can be customized per dataset. FormSpec is meant to capture the rules that have been used when compiling the source data - for cases where the source data violates these rules, wholesale replacement by listing a lexeme in etc/lexemes.csv is recommended.
• support for additional information on lexemes, cognates, concepts and languages via subclassing the defaults in pylexibank.models
• easy access to configuration data in a dataset's etc_dir
• support for segmentation using the segments package with orthography profile(s):
  • If an orthography profile is available as etc/orthography.tsv, a segments.Tokenizer instance, initialized with this profile, will be available as Dataset.tokenizer and automatically used by LexibankWriter.add_form.
  • If a directory etc/orthography/ exists, all *.tsv files in it will be considered orthography profiles, and a dict mapping filename stem to tokenizer will be available. Tokenizer selection can be controlled in two ways:
    • Passing a keyword profile=FILENAME_STEM in Dataset.tokenizer() calls.
    • Provide orthography profiles for each language and let Dataset.tokenizer chose the tokenizer by item['Language_ID'].

Programmatic access to Lexibank datasets

While some level of support for reading and writing any CLDF dataset is already provided by the pycldf package, pylexibank (building on cldfbench) adds another layer of abstraction which supports

• treating Lexibank datasets as Python packages (and managing them via pip),
• a multi-step curation workflow
• aggregating collections of Lexibank datasets into a single SQLite database for efficient analysis.

Installable and pylexibank enabled datasets

Turning a Lexibank dataset into a (pip installable) Python package is as simple as writing a setup script setup.py. But to make the dataset available for curation via pylexibank, the dataset must provide

• a python module
• containing a class derived from pylexibank.Dataset, which specifies
  • Dataset.dir: A directory relative to which the the curation directories are located.
  • Dataset.id: An identifier of the dataset.
• which is advertised as lexibank.dataset entry point in setup.py. E.g.

      entry_points={
          'lexibank.dataset': [
              'sohartmannchin=lexibank_sohartmannchin:Dataset',
          ]
      },
  

Turning datasets into pylexibank enabled python packages has multiple advantages:

• Datasets can be installed from various sources, e.g. GitHub repositories.
• Requirements files can be used "pin" particular versions of datasets for installation.
• Upon installation datasets can be discovered programmatically.
• Virtual environments can be used to manage projects which require different versions of the same dataset.

Conventions

1. Dataset identifier should be lowercase and either:
   • the database name, if this name is established and well-known (e.g. "abvd", "asjp" etc),
   • <author><languagegroup> (e.g. "grollemundbantu" etc)
2. Datasets that require preprocessing with external programs (e.g. antiword, libreoffice) should store intermediate/artifacts in ./raw/ directory, and the cmd_install code should install from that rather than requiring an external dependency.
3. Declaring a dataset's dependence on pylexibank:
   • specify minimum versions in setup.py, i.e. require pylexibank>=1.x.y
   • specify exact versions in dataset's cldf-metadata.json using prov:createdBy property (pylexibank will take care of this when the CLDF is created via lexibank makecldf).

Datasets on GitHub

GitHub provides a very good platform for collaborative curation of textual data such as Lexibank datasets.

Dataset curators are encouraged to make use of features in addition to the just version control, such as

• releases
• README.md, LICENSE, CONTRIBUTORS.md

Note that for datasets curated with pylexibank, summary statistics will be written to README.md as part of the makecldf command.

In addition to the support for collaboratively editing and versioning data, GitHub supports tying into additional services via webhooks. In particular, two of these services are relevant for Lexibank datasets:

• Continuous integration, e.g. via Travis-CI
• Archiving with Zenodo. Notes:
  • When datasets are curated on GitHub and hooked up to ZENODO to trigger automatic deposits of releases, the release tag must start with a letter (otherwise the deposit will fail).
  • Additional tags can be added to add context - e.g. when a release is triggered by a specific use case (for example the CLICS 2.0 release). This can be done using git as follows:

    git checkout tags/vX.Y.Z
    git tag -a "clics-2.0"
    git push origin --tags
    

Attribution

There are multiple levels of contributions to a Lexibank dataset:

• Typically, Lexibank datasets are derived from published data (be it supplemental material of a paper or public databases). Attribution to this source dataset is given by specifying its full citation in the dataset's metadata and by adding the source title to the release title of a lexibank dataset.
• Often the source dataset is also an aggregation of data from other sources. If possible, these sources (and the corresponding references) are kept in the Lexibank dataset's CLDF; otherwise we refer to the source dataset for a description of its sources.
• Deriving a Lexibank dataset from a source dataset using the pylexibank curation workflow involves adding code, mapping to reference catalogs and to some extent also linguistic judgements. These contributions are listed in a dataset's CONTRIBUTORS.md and translate to the list of authors of released versions of the lexibank dataset.