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Python implementation of SIL's Toolbox Standard Format Markers (SFM) format. The basic format looks like this:

\mkr a line of text

Where \mkr is called a marker and is followed by a space, then one or more lines of text.

Basic Usage

The toolbox module is meant to be used as a library, not a script, so you'll need to first make sure it's findable by Python. Either

  1. copy to your project directory
  2. install via pip (be careful to use the path and not just toolbox or it will install a different package from PyPI)
pip install ./toolbox/

You can then load it in Python and use it to read stored Toolbox files. For example, the read_toolbox_file() function reads a Toolbox file and yields (marker, text) pairs:

>>> import toolbox
>>> for mkr, text in toolbox.read_toolbox_file(open('example/corpus.txt')):
...     print('Marker: {0!r:<8}Text: {1!r}'.format(mkr, text))
Marker: '\\ref' Text: 'item1'
Marker: '\\t'   Text: 'O        Pedro baixou'
Marker: '\\m'   Text: 'O        Pedro bai   -xou'
Marker: '\\g'   Text: 'the.M.SG Pedro lower -PST.IND.3SG'
Marker: '\\t'   Text: 'a        bola'
Marker: '\\m'   Text: 'a        bola'
Marker: '\\g'   Text: 'the.F.SG ball.F.SG'
Marker: '\\f'   Text: 'Pedro calmed down.'
Marker: '\\l'   Text: 'Pedro lowered the ball.'

(By default, trailing whitespace (including newlines) is stripped, but this can be turned off.)

In this example corpus, we have a single record (starting with the \ref marker), with text (\t), morphemes (\m), glosses (\g), a free translation (\f), and a literal translation (\l). Furthermore, the interlinear lines have been wrapped (perhaps from Toolbox itself). Below I show how the toolbox module can handle these kinds of examples.

Extra Features

Beyond simply reading Toolbox files, the toolbox module can perform some analysis of the data.

Iterating over records based on keys

A Toolbox corpus file contains groups of (marker, text) pairs for representing linguistic examples, called "records". Records are delimited by certain markers (called, "record markers"), and there may be more than one of such markers (e.g. \ref for each record, and \id for grouping records into a text, etc.). The records() function can automatically group the data in each record and keep track of the context of the record markers previously seen. Here is how one might read a corpus file with a \id key for a text (sub-corpus) and a \ref key for each record.

>>> pairs = toolbox.read_toolbox_file(open('example/corpus.txt'))
>>> for (context, data) in toolbox.records(pairs, ['\\id', '\\ref']):
...     print(sorted(context.items()))
...     print('\n'.join(map(repr, data)))
[('\\id', None), ('\\ref', 'item1')]
('\\t', 'O        Pedro baixou')
('\\m', 'O        Pedro bai   -xou')
('\\g', 'the.M.SG Pedro lower -PST.IND.3SG')
('\\t', 'a        bola')
('\\m', 'a        bola')
('\\g', 'the.F.SG ball.F.SG')
('\\f', 'Pedro calmed down.')
('\\l', 'Pedro lowered the ball.')

Note that there were no \id markers in the corpus file, so the value is None.

Normalizing tiers

Some toolbox data are line-wrapped, but logically the wrapped lines continue where the first one stopped. Working with line-wrapped data just makes things harder, so the normalize_record() function will restore them to a single line per marker. As the function name implies, this works on a record rather than file contents, so it may take the results of the records() function. The second parameter to normalize_item() is a container of markers for lines that should still be visually aligned in columns after normalization.

>>> pairs = toolbox.read_toolbox_file(open('example/corpus.txt'))
>>> records = toolbox.records(pairs, ['\\id', '\\ref'])
>>> rec1 = next(records)
>>> for mkr, val in toolbox.normalize_record(rec1[1], ['\\t', '\\g', '\\m']):
...     print((mkr, val))
('\\t', 'O        Pedro baixou             a        bola')
('\\m', 'O        Pedro bai   -xou         a        bola')
('\\g', 'the.M.SG Pedro lower -PST.IND.3SG the.F.SG ball.F.SG')
('\\f', 'Pedro calmed down.')
('\\l', 'Pedro lowered the ball.')

Aligning fields

Toolbox encodes token alignments implicitly through spacing such that aligned tokens appear visually in columns. The toolbox module provides an align_fields() function to analyze the columns and return a more explicit representation of the alignments. The function takes a list of marker-text pairs and a marker-to-marker mapping to describe the alignments. The result is a list of (marker, aligned_data) pairs, where aligned_data is a list of (token, aligned_tokens).

>>> pairs = toolbox.read_toolbox_file(open('example/corpus.txt'))
>>> records = toolbox.records(pairs, ['\\id', '\\ref'])
>>> rec1 = next(records)
>>> normdata = toolbox.normalize_record(rec1[1], ['\\t', '\\g', '\\m'])
>>> alignments = {'\\m': '\\t', '\\g': '\\m'}
>>> for mkr, algns in toolbox.align_fields(normdata, alignments=alignments):
...     print((mkr, algns))  # doctest: +NORMALIZE_WHITESPACE
('\\t', [('O        Pedro baixou             a        bola',
          ['O', 'Pedro', 'baixou', 'a', 'bola'])])
('\\m', [('O', ['O']),
         ('Pedro', ['Pedro']),
         ('baixou', ['bai', '-xou']),
         ('a', ['a']),
         ('bola', ['bola'])])
('\\g', [('O', ['the.M.SG']),
         ('Pedro', ['Pedro']),
         ('bai', ['lower']),
         ('-xou', ['-PST.IND.3SG']),
         ('a', ['the.F.SG']),
         ('bola', ['ball.F.SG'])])
('\\f', [(None, ['Pedro calmed down.'])])
('\\l', [(None, ['Pedro lowered the ball.'])])

Examples and testing

The examples in this README file and in the file can be run as unit tests, while at the same time serving as useful documentation. To run them as unit tests, do this from the command line:

python3 -m doctest


This project is partially supported by the Affectedness project, under the Singapore Ministry of Education Tier 2 grant (grant number MOE2013-T2-1-016).


Python implementation of SIL's Toolbox format








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