choro/choro2harte.py at master · DCMLab/choro · GitHub

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import numpy as np
import pandas as pd
import re


def parse_extensions(extensions):
    ext = ",".join([
        e
        .replace("omit", "*")
        .strip("(").strip(")")
        for e in re.findall(r'\(.*?\)', extensions)
    ])
    return f"({ext})"


def choro2harte(chord, type="root"):
    """Transforms chord symbols in Choro Songbook Corpus format to
    Harte et al's chord syntax. See Harte, C. A., Sandler, M.,
    Abdallah, S., & Gómez, E. (2005). Symbolic representation of
    musical chords: A proposed syntax for text annotations.
    Proceedings of the 4th International Conference on Music Information
    Retrieval (ISMIR), 56, 66--71.

    Parameters
    ----------
    chord : str
        Chord symbol as encoded in the _Choro Songbook Corpus_.

    Returns
    -------
    str
        Chord symbol conforming to syntax in Harte et al. (2005).
    """

    assert type in ["root", "rn"]

    harte = ""
    if chord.chord == "NC":
        harte = "N"
    else:
        # ROOT
        harte += chord.root if type == "root" else chord.sd

        # TYPE
        if chord.type == "m":
            if chord.added == "7":
                harte += ":min7"
            elif chord.added == "6":
                harte += ":min6"
            else:
                harte += ":min"
        elif chord.type == "o":
            harte += ":dim"
        elif chord.type is np.nan:
            if chord.added == "7":
                harte += ":7"
            elif chord.added == "7M":
                harte += ":maj7"
            elif chord.added == "6":
                harte += ":maj6"
            else:
                harte += ":maj"

        # EXTENSIONS
        if chord.extensions is not np.nan:
            harte += parse_extensions(chord.extensions)

        # SPECIFIC SHORTHANDS
        harte = harte \
            .replace("min7(b5)", "hdim7") \
            .replace("maj(#5)", "aug") \
            .replace("maj7(9)", "maj9") \
            .replace(":7(9)", ":9") \
            .replace("min(7M)", "minmaj7") \
            .replace("add9", "9") \
            .replace("min7(9)", "min9") \
            .replace("(9,7M)", "(7,9)") \
            .replace("dim(7M)", "dim(7)") \
            .replace("min6(7M)", "min6(7)")

        # BASS
        if chord.bass_note is not np.nan:
            bass_degree = chord.rn_chord.split("/")[1]
            degree, accidental = re.match(r"(\d*)([#b]*)",
                                          bass_degree).groups()
            # COVER SOME CASES
            if chord.type == "m" and degree == "3" and accidental == "":
                degree = "b3"
            elif chord.type is np.nan and degree == "3" and accidental == "#":
                accidental = ""
                degree = "3"
            elif chord.type == "m" and degree == "7" and accidental == "":
                accidental = "b"
            elif chord.type is np.nan and degree == "7" and accidental == "":
                accidental = "b"
            elif chord.type is np.nan and degree == "5" and accidental == "#":
                accidental = ""
            elif chord.type == "m" and degree == "5" and accidental == "#":
                accidental = ""
            harte += "/" + accidental + degree

    return harte


def test():
    # read data
    df = pd.read_csv("data/choro.tsv", sep="\t", index_col=0)

    # create new column
    df["harte"] = df.apply(lambda x: choro2harte(x, type="root"), axis=1)
    df["rn_harte"] = df.apply(lambda x: choro2harte(x, type="rn"), axis=1)

    # sort columns
    # df = df[[
    #     'global_key', 'local_key', 'global_meter', 'local_meter',
    #     'local_mode', 'global_mode', 'path', 'phrase', 'part', 'bar_no',
    #     'duration', 'chord', 'harte', 'root', 'rn_chord', 'rn_harte', 'sd',
    #     'type', 'added', 'extensions', 'bass_note', 'songbook', 'title',
    #     'composer', 'sub_genre', 'year', 'filename'
    #    ]]

    # filter only with bass notes
    df = df[df["bass_note"].notna()]

    print(df[['chord', 'harte', 'root', 'rn_chord',
              'rn_harte', 'sd', 'bass_note']].sample(15))


if __name__ == "__main__":

    # read data
    df = pd.read_csv("data/choro.tsv", sep="\t", index_col=0)

    # create new column
    df["harte"] = df.apply(lambda x: choro2harte(x, type="root"), axis=1)
    df["rn_harte"] = df.apply(lambda x: choro2harte(x, type="rn"), axis=1)

    # sort columns
    df = df[[
        'global_key', 'local_key', 'global_meter', 'local_meter',
        'local_mode', 'global_mode', 'path', 'phrase', 'part', 'bar_no',
        'duration', 'chord', 'harte', 'root', 'rn_chord', 'rn_harte', 'sd',
        'type', 'added', 'extensions', 'bass_note', 'songbook', 'title',
        'composer', 'sub_genre', 'year', 'filename'
       ]]

    # export to file
    df.to_csv("data/choro.tsv", sep="\t", index=False)