Annotations#
Show 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
Show 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
Show source
D = utils.get_dataset("dvorak_silhouettes", corpus_release="v2.3")
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("Antonín Dvořák – Silhouettes version v2.3")
print(f"Datapackage '{package.package_name}' @ {git_tag}")
print(f"dimcat version {dc.__version__}\n")
D
Data and software versions
--------------------------
Antonín Dvořák – Silhouettes version v2.3
Datapackage 'dvorak_silhouettes' @ v2.3
dimcat version 3.4.0
Dataset
=======
{'inputs': {'basepath': None,
'packages': {'dvorak_silhouettes': ["'dvorak_silhouettes.measures' (MuseScoreFacetName.MuseScoreMeasures)",
"'dvorak_silhouettes.notes' (MuseScoreFacetName.MuseScoreNotes)",
"'dvorak_silhouettes.expanded' (MuseScoreFacetName.MuseScoreHarmonies)",
"'dvorak_silhouettes.chords' (MuseScoreFacetName.MuseScoreChords)",
"'dvorak_silhouettes.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#
Show 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 10649 notes.
all_chords = filtered_D.get_feature("harmonylabels")
print(
f"{len(all_annotations)} annotations, of which {len(all_chords)} are harmony labels."
)
1539 annotations, of which 1526 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
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 18 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: 0.5 / 1852.5 = 0.0002699055330634278
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) | 391.5 | 0.315853 | 31.59 % | i, minor | (1, 3, 5) | 195.0 | 0.318108 | 31.81 % |
2 | V7, major | (5, 7, 2, 4) | 107.0 | 0.086325 | 8.63 % | V, minor | (5, #7, 2) | 62.5 | 0.101958 | 10.2 % |
3 | V, major | (5, 7, 2) | 99.0 | 0.079871 | 7.99 % | V7, minor | (5, #7, 2, 4) | 60.0 | 0.097879 | 9.79 % |
4 | I6, major | (3, 5, 1) | 94.5 | 0.076240 | 7.62 % | i6, minor | (3, 5, 1) | 36.0 | 0.058728 | 5.87 % |
5 | IV, major | (4, 6, 1) | 40.0 | 0.032271 | 3.23 % | V(64), minor | (5, 1, 3) | 26.5 | 0.043230 | 4.32 % |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
136 | V64, major | (2, 5, 7) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
137 | iv6/ii, major | (b7, 2, 5) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
138 | It6, major | (b6, 1, #4) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
139 | iv64/ii, major | (2, 5, b7) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
140 | #vii%43, major | (4, 6, 7, 2) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
140 rows × 10 columns
chords_by_mode.apply_step("Counter")
count | |||||
---|---|---|---|---|---|
mode | corpus | piece | chord_and_mode | scale_degrees | |
major | dvorak_silhouettes | op08n01 | I, major | (1, 3, 5) | 18 |
V7, major | (5, 7, 2, 4) | 16 | |||
V, major | (5, 7, 2) | 6 | |||
V43(+6+2), major | (2, 4, 5, 7) | 4 | |||
V43(+6), major | (2, 4, 5, 7) | 4 | |||
... | ... | ... | ... | ... | ... |
minor | dvorak_silhouettes | op08n12 | VII, minor | (7, 2, 4) | 1 |
VI64, minor | (3, 6, 1) | 1 | |||
#viio6, minor | (2, 4, #7) | 1 | |||
#viio65, minor | (2, 4, 6, #7) | 1 | |||
viio7/V, minor | (#4, #6, 1, 3) | 1 |
382 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) | 391.5 | 0.315853 | 31.59 % | i, minor | (1, 3, 5) | 195.0 | 0.318108 | 31.81 % |
2 | V7, major | (5, 7, 2, 4) | 107.0 | 0.086325 | 8.63 % | V, minor | (5, #7, 2) | 62.5 | 0.101958 | 10.2 % |
3 | V, major | (5, 7, 2) | 99.0 | 0.079871 | 7.99 % | V7, minor | (5, #7, 2, 4) | 60.0 | 0.097879 | 9.79 % |
4 | I6, major | (3, 5, 1) | 94.5 | 0.076240 | 7.62 % | i6, minor | (3, 5, 1) | 36.0 | 0.058728 | 5.87 % |
5 | IV, major | (4, 6, 1) | 40.0 | 0.032271 | 3.23 % | V(64), minor | (5, 1, 3) | 26.5 | 0.043230 | 4.32 % |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
136 | V64, major | (2, 5, 7) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
137 | iv6/ii, major | (b7, 2, 5) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
138 | It6, major | (b6, 1, #4) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
139 | iv64/ii, major | (2, 5, b7) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
140 | #vii%43, major | (4, 6, 7, 2) | 0.5 | 0.000403 | 0.04 % | NaN | NaN | NaN | NaN | NaN |
140 rows × 10 columns
unigram_proportions.plot_grouped()