Tutorial 3: Getting information out

First we begin with the standard import:



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from polyglotdb import CorpusContext

Creating an initial query

The first steps for generating a CSV file is to create a query that selects just the annotations of interest to our study. In this case, we want all syllables that are stressed (defined here as having a stress value equal to '1'), at the beginning of words that are at the end of utterances.



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with CorpusContext('pg_tutorial') as c:
    q = c.query_graph(c.syllable)
    q = q.filter(c.syllable.stress == '1')

    q = q.filter(c.syllable.begin == c.syllable.word.begin)

    q = q.filter(c.syllable.word.end == c.syllable.word.utterance.end)

    q = q.columns(c.syllable.label.column_name('syllable'),
                  c.syllable.duration.column_name('syllable_duration'),
                  c.syllable.word.label.column_name('word'),
                  c.syllable.word.begin.column_name('word_begin'),
                  c.syllable.word.end.column_name('word_end'),
                  c.syllable.word.num_syllables.column_name('word_num_syllables'),
                  c.syllable.word.stress_pattern.column_name('word_stress_pattern'),
                  c.syllable.word.utterance.speech_rate.column_name('utterance_speech_rate'),
                  c.syllable.speaker.name.column_name('speaker'),
                  c.syllable.speaker.gender.column_name('speaker_gender'),
                  c.syllable.discourse.name.column_name('file'),
                  )

    q = q.limit(10)
    results = q.all()
    print(results)

With the above, we extract information of interest about the syllable, the word it is in, the utterance it is in, the speaker and the sound file (discourse in PolyglotDB's API), as well as limit the results to 10 and print them all.

Exporting a CSV file

Once the query is constructed with filters and columns, exporting to a CSV is a simple method call on the query object. For completeness, the full code for the query and export is given below.



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export_path = '/mnt/e/pg_tutorial.csv'

with CorpusContext('pg_tutorial') as c:
    q = c.query_graph(c.syllable)
    q = q.filter(c.syllable.stress == 1)

    q = q.filter(c.syllable.begin == c.syllable.word.begin)

    q = q.filter(c.syllable.word.end == c.syllable.word.utterance.end)

    q = q.columns(c.syllable.label.column_name('syllable'),
                  c.syllable.duration.column_name('syllable_duration'),
                  c.syllable.word.label.column_name('word'),
                  c.syllable.word.begin.column_name('word_begin'),
                  c.syllable.word.end.column_name('word_end'),
                  c.syllable.word.num_syllables.column_name('word_num_syllables'),
                  c.syllable.word.stress_pattern.column_name('word_stress_pattern'),
                  c.syllable.word.utterance.speech_rate.column_name('utterance_speech_rate'),
                  c.syllable.speaker.name.column_name('speaker'),
                  c.syllable.speaker.gender.column_name('speaker_gender'),
                  c.syllable.discourse.name.column_name('file'),
                  )
    q.to_csv(export_path)

The CSV file generated will then be ready to open in other programs or in R for data analysis.

See the related ISCAN tutorial for R code on visualizing and analyzing the exported results.