Annotations#

Hide imports 
%load_ext autoreload
%autoreload 2

import os

import dimcat as dc
import ms3
import plotly.express as px
from dimcat import groupers, plotting

import utils
Hide source 
RESULTS_PATH = os.path.abspath(os.path.join(utils.OUTPUT_FOLDER, "overview"))
os.makedirs(RESULTS_PATH, exist_ok=True)


def make_output_path(
    filename: str,
    extension=None,
    path=RESULTS_PATH,
) -> str:
    return utils.make_output_path(filename=filename, extension=extension, path=path)


def save_figure_as(
    fig, filename, formats=("png", "pdf"), directory=RESULTS_PATH, **kwargs
):
    if formats is not None:
        for fmt in formats:
            plotting.write_image(fig, filename, directory, format=fmt, **kwargs)
    else:
        plotting.write_image(fig, filename, directory, **kwargs)

Loading data

Hide source 
D = utils.get_dataset("kozeluh_sonatas", corpus_release="v2.4")
package = D.inputs.get_package()
package_info = package._package.custom
git_tag = package_info.get("git_tag")
utils.print_heading("Data and software versions")
print("Leopold Koželuch – Piano Sonatas version v2.4")
print(f"Datapackage '{package.package_name}' @ {git_tag}")
print(f"dimcat version {dc.__version__}\n")
D

Data and software versions
--------------------------

Leopold Koželuch – Piano Sonatas version v2.4
Datapackage 'kozeluh_sonatas' @ v2.4
dimcat version 3.4.0

Dataset
=======
{'inputs': {'basepath': None,
            'packages': {'kozeluh_sonatas': ["'kozeluh_sonatas.measures' (MuseScoreFacetName.MuseScoreMeasures)",
                                             "'kozeluh_sonatas.notes' (MuseScoreFacetName.MuseScoreNotes)",
                                             "'kozeluh_sonatas.expanded' (MuseScoreFacetName.MuseScoreHarmonies)",
                                             "'kozeluh_sonatas.chords' (MuseScoreFacetName.MuseScoreChords)",
                                             "'kozeluh_sonatas.metadata' (FeatureName.Metadata)"]}},
 'outputs': {'basepath': None, 'packages': {}},
 'pipeline': []}

filtered_D = D.apply_step("HasHarmonyLabelsFilter")
all_metadata = filtered_D.get_metadata()

assert len(all_metadata) > 0, "No pieces selected for analysis."
chronological_corpus_names = all_metadata.get_corpus_names()

DCML harmony labels#

Hide source 
all_annotations = filtered_D.get_feature("DcmlAnnotations")
is_annotated_mask = all_metadata.label_count > 0
is_annotated_index = all_metadata.index[is_annotated_mask]
annotated_notes = filtered_D.get_feature("notes").subselect(is_annotated_index)
print(f"The annotated pieces have {len(annotated_notes)} notes.")

The annotated pieces have 109014 notes.

all_chords = filtered_D.get_feature("harmonylabels")
print(
    f"{len(all_annotations)} annotations, of which {len(all_chords)} are harmony labels."
)

16598 annotations, of which 16598 are harmony labels.

Harmony labels#

Unigrams#

For computing unigram statistics, the tokens need to be grouped by their occurrence within a major or a minor key because this changes their meaning. To that aim, the annotated corpus needs to be sliced into contiguous localkey segments which are then grouped into a major (is_minor=False) and a minor group.

root_durations = (
    all_chords[all_chords.root.between(-5, 6)]
    .groupby(["root", "chord_type"])
    .duration_qb.sum()
)
# sort by stacked bar length:
# root_durations = root_durations.sort_values(key=lambda S: S.index.get_level_values(0).map(S.groupby(level=0).sum()),
# ascending=False)
bar_data = root_durations.reset_index()
bar_data.root = bar_data.root.map(ms3.fifths2iv)
fig = px.bar(
    bar_data,
    x="root",
    y="duration_qb",
    color="chord_type",
    title="Distribution of chord types over chord roots",
    labels=dict(
        root="Chord root expressed as interval above the local (or secondary) tonic",
        duration_qb="duration in quarter notes",
        chord_type="chord type",
    ),
)
fig.update_layout(**utils.STD_LAYOUT)
save_figure_as(fig, "chord_type_distribution_over_scale_degrees_absolute_stacked_bars")
fig.show()

relative_roots = all_chords[
    ["numeral", "duration_qb", "relativeroot", "localkey_is_minor", "chord_type"]
].copy()
relative_roots["relativeroot_resolved"] = ms3.transform(
    relative_roots, ms3.resolve_relative_keys, ["relativeroot", "localkey_is_minor"]
)
has_rel = relative_roots.relativeroot_resolved.notna()
relative_roots.loc[has_rel, "localkey_is_minor"] = relative_roots.loc[
    has_rel, "relativeroot_resolved"
].str.islower()
relative_roots["root"] = ms3.transform(
    relative_roots, ms3.roman_numeral2fifths, ["numeral", "localkey_is_minor"]
)
chord_type_frequency = all_chords.chord_type.value_counts()
replace_rare = ms3.map_dict(
    {t: "other" for t in chord_type_frequency[chord_type_frequency < 500].index}
)
relative_roots["type_reduced"] = relative_roots.chord_type.map(replace_rare)
# is_special = relative_roots.chord_type.isin(('It', 'Ger', 'Fr'))
# relative_roots.loc[is_special, 'root'] = -4

ERROR    ms3.utils.functions -- /home/runner/.local/lib/python3.12/site-packages/ms3/utils/functions.py (line 4345) split_scale_degree():
	@none is not a valid scale degree.

root_durations = (
    relative_roots.groupby(["root", "type_reduced"])
    .duration_qb.sum()
    .sort_values(ascending=False)
)
bar_data = root_durations.reset_index()
bar_data.root = bar_data.root.map(ms3.fifths2iv)
root_order = (
    bar_data.groupby("root")
    .duration_qb.sum()
    .sort_values(ascending=False)
    .index.to_list()
)
fig = px.bar(
    bar_data,
    x="root",
    y="duration_qb",
    color="type_reduced",
    barmode="group",
    log_y=True,
    color_discrete_map=utils.TYPE_COLORS,
    category_orders=dict(
        root=root_order,
        type_reduced=relative_roots.type_reduced.value_counts().index.to_list(),
    ),
    labels=dict(
        root="intervallic difference between chord root to the local or secondary tonic",
        duration_qb="duration in quarter notes",
        type_reduced="chord type",
    ),
    width=1000,
    height=400,
)
fig.update_layout(
    **utils.STD_LAYOUT,
    legend=dict(
        orientation="h",
        xanchor="right",
        x=1,
        y=1,
    ),
)
save_figure_as(fig, "chord_type_distribution_over_scale_degrees_absolute_grouped_bars")
fig.show()

print(
    f"Reduced to {len(set(bar_data.iloc[:,:2].itertuples(index=False, name=None)))} types. "
    f"Paper cites the sum of types in major and types in minor (see below), treating them as distinct."
)

Reduced to 32 types. Paper cites the sum of types in major and types in minor (see below), treating them as distinct.

dim_or_aug = bar_data[
    bar_data.root.str.startswith("a") | bar_data.root.str.startswith("d")
].duration_qb.sum()
complete = bar_data.duration_qb.sum()
print(
    f"On diminished or augmented scale degrees: {dim_or_aug} / {complete} = {dim_or_aug / complete}"
)

On diminished or augmented scale degrees: 0.0 / 20385.375 = 0.0

chords_by_mode = groupers.ModeGrouper().process(all_chords)
chords_by_mode.format = "scale_degree"

Whole dataset#

unigram_proportions = chords_by_mode.get_default_analysis()
unigram_proportions.make_ranking_table()
mode major minor
chord_and_mode scale_degrees duration_qb proportion proportion_% chord_and_mode scale_degrees duration_qb proportion proportion_%
rank
1 I, major (1, 3, 5) 3889.958333 0.287953 28.8 % i, minor (1, 3, 5) 1643.083333 0.238720 23.87 %
2 V7, major (5, 7, 2, 4) 1316.500000 0.097454 9.75 % V, minor (5, #7, 2) 773.875000 0.112435 11.24 %
3 V, major (5, 7, 2) 1194.041667 0.088389 8.84 % V7, minor (5, #7, 2, 4) 624.500000 0.090732 9.07 %
4 I6, major (3, 5, 1) 979.625000 0.072516 7.25 % i6, minor (3, 5, 1) 562.125000 0.081670 8.17 %
5 IV, major (4, 6, 1) 737.750000 0.054612 5.46 % iv, minor (4, 6, 1) 284.166667 0.041286 4.13 %
... ... ... ... ... ... ... ... ... ... ...
242 V64(+4)/V, major (6, 2, #4) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN
243 ii(#7), major (2, 4, 6) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN
244 V7(+2), major (5, 7, 2, 4) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN
245 vi%2, major (5, 6, 1, b3) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN
246 V64(+2), major (2, 5, 7) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN

246 rows × 10 columns

chords_by_mode.apply_step("Counter")
count
mode corpus piece chord_and_mode scale_degrees
major kozeluh_sonatas 09op08no1a I6, major (3, 5, 1) 43
I, major (1, 3, 5) 27
IV, major (4, 6, 1) 16
V2, major (4, 5, 7, 2) 15
ii6, major (4, 6, 2) 10
... ... ... ... ... ...
minor kozeluh_sonatas 29op30no2c V43, minor (2, 4, 5, #7) 1
V7/iv, minor (1, #3, 5, 7) 1
VI, minor (6, 1, 3) 1
iio64, minor (6, 2, 4) 1
iv6, minor (6, 1, 4) 1

2821 rows × 1 columns

chords_by_mode.format = "scale_degree"
chords_by_mode.get_default_analysis().make_ranking_table()
mode major minor
chord_and_mode scale_degrees duration_qb proportion proportion_% chord_and_mode scale_degrees duration_qb proportion proportion_%
rank
1 I, major (1, 3, 5) 3889.958333 0.287953 28.8 % i, minor (1, 3, 5) 1643.083333 0.238720 23.87 %
2 V7, major (5, 7, 2, 4) 1316.500000 0.097454 9.75 % V, minor (5, #7, 2) 773.875000 0.112435 11.24 %
3 V, major (5, 7, 2) 1194.041667 0.088389 8.84 % V7, minor (5, #7, 2, 4) 624.500000 0.090732 9.07 %
4 I6, major (3, 5, 1) 979.625000 0.072516 7.25 % i6, minor (3, 5, 1) 562.125000 0.081670 8.17 %
5 IV, major (4, 6, 1) 737.750000 0.054612 5.46 % iv, minor (4, 6, 1) 284.166667 0.041286 4.13 %
... ... ... ... ... ... ... ... ... ... ...
242 V64(+4)/V, major (6, 2, #4) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN
243 ii(#7), major (2, 4, 6) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN
244 V7(+2), major (5, 7, 2, 4) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN
245 vi%2, major (5, 6, 1, b3) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN
246 V64(+2), major (2, 5, 7) 0.500000 0.000037 0.0 % NaN NaN NaN NaN NaN

246 rows × 10 columns

unigram_proportions.plot_grouped()