Overview#
This notebook gives a general overview of the features included in the dataset.
Show imports
%load_ext autoreload
%autoreload 2
import os
import dimcat as dc
import pandas as pd
import plotly.express as px
from dimcat import filters, plotting
from IPython.display import display
import utils
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
D = utils.get_dataset("monteverdi_madrigals", 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("Claudio Monteverdi – Madrigals version v2.3")
print(f"Datapackage '{package.package_name}' @ {git_tag}")
print(f"dimcat version {dc.__version__}\n")
D
Data and software versions
--------------------------
Claudio Monteverdi – Madrigals version v2.3
Datapackage 'monteverdi_madrigals' @ v2.3
dimcat version 3.4.0
Dataset
=======
{'inputs': {'basepath': None,
'packages': {'monteverdi_madrigals': ["'monteverdi_madrigals.measures' "
'(MuseScoreFacetName.MuseScoreMeasures)',
"'monteverdi_madrigals.notes' (MuseScoreFacetName.MuseScoreNotes)",
"'monteverdi_madrigals.expanded' "
'(MuseScoreFacetName.MuseScoreHarmonies)',
"'monteverdi_madrigals.chords' (MuseScoreFacetName.MuseScoreChords)",
"'monteverdi_madrigals.metadata' (FeatureName.Metadata)"]}},
'outputs': {'basepath': None, 'packages': {}},
'pipeline': []}
filtered_D = filters.HasHarmonyLabelsFilter(keep_values=[True]).process(D)
all_metadata = filtered_D.get_metadata()
assert len(all_metadata) > 0, "No pieces selected for analysis."
all_metadata
| TimeSig | KeySig | last_mc | last_mn | length_qb | last_mc_unfolded | last_mn_unfolded | length_qb_unfolded | all_notes_qb | n_onsets | ... | staff_1_instrument | staff_1_ambitus | staff_2_instrument | staff_2_ambitus | staff_3_instrument | staff_3_ambitus | staff_4_instrument | staff_4_ambitus | staff_5_instrument | staff_5_ambitus | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| corpus | piece | |||||||||||||||||||||
| monteverdi_madrigals | 2-12 | {1: '4/4'} | {1: -1} | 93 | 93 | 372.0 | 93 | 93 | 372.0 | 1374.00 | 1011 | ... | Soprano I | 60-77 (C4-F5) | Soprano II | 60-74 (C4-D5) | Alto | 53-69 (F3-A4) | Tenor | 48-65 (C3-F4) | Bass | 41-58 (F2-Bb3) |
| 3-09 | {1: '4/4'} | {1: -1} | 84 | 84 | 680.0 | 84 | 84 | 680.0 | 2534.00 | 886 | ... | Soprano I | 60-77 (C4-F5) | Soprano II | 57-74 (A3-D5) | Alto | 53-72 (F3-C5) | Tenor | 48-67 (C3-G4) | Bass | 41-58 (F2-Bb3) | |
| 4-19 | {1: '4/4'} | {1: 0} | 111 | 111 | 448.0 | 111 | 111 | 448.0 | 1726.00 | 956 | ... | Soprano I | 57-76 (A3-E5) | Soprano II | 57-76 (A3-E5) | Alto | 53-69 (F3-A4) | Tenor | 48-66 (C3-F#4) | Bass | 43-57 (G2-A3) | |
| 5-01 | {1: '4/4'} | {1: 0} | 67 | 67 | 268.0 | 67 | 67 | 268.0 | 1088.50 | 661 | ... | Soprano I | 64-81 (E4-A5) | Soprano II | 59-77 (B3-F5) | Alto | 52-69 (E3-A4) | Tenor | 55-69 (G3-A4) | Bass | 45-62 (A2-D4) | |
| 5-03 | {1: '4/4'} | {1: -1} | 74 | 74 | 296.0 | 74 | 74 | 296.0 | 1073.00 | 652 | ... | Soprano I | 62-77 (D4-F5) | Soprano II | 61-74 (C#4-D5) | Alto | 50-70 (D3-Bb4) | Tenor | 49-65 (C#3-F4) | Bass | 43-58 (G2-Bb3) | |
| 5-04a | {1: '4/4'} | {1: -1} | 82 | 82 | 328.0 | 82 | 82 | 328.0 | 1401.00 | 939 | ... | Soprano I | 65-82 (F4-Bb5) | Soprano II | 64-81 (E4-A5) | Alto | 55-74 (G3-D5) | Tenor | 53-69 (F3-A4) | Bass | 46-63 (Bb2-Eb4) | |
| 5-04c | {1: '4/4'} | {1: -1} | 53 | 53 | 212.0 | 53 | 53 | 212.0 | 826.00 | 589 | ... | Soprano I | 66-81 (F#4-A5) | Soprano II | 65-79 (F4-G5) | Alto | 55-74 (G3-D5) | Tenor | 55-69 (G3-A4) | Bass | 46-63 (Bb2-Eb4) | |
| 5-04d | {1: '4/4'} | {1: -1} | 59 | 59 | 236.0 | 59 | 59 | 236.0 | 950.00 | 715 | ... | Soprano I | 65-82 (F4-Bb5) | Soprano II | 62-81 (D4-A5) | Alto | 55-74 (G3-D5) | Tenor | 53-69 (F3-A4) | Bass | 45-65 (A2-F4) | |
| 5-04e | {1: '4/4'} | {1: -1} | 95 | 95 | 380.0 | 95 | 95 | 380.0 | 1530.50 | 1213 | ... | Soprano I | 65-81 (F4-A5) | Soprano II | 62-81 (D4-A5) | Alto | 60-74 (C4-D5) | Tenor | 50-69 (D3-A4) | Bass | 43-62 (G2-D4) | |
| 5-05b | {1: '4/4'} | {1: -1} | 58 | 58 | 232.0 | 58 | 58 | 232.0 | 930.00 | 685 | ... | Soprano I | 62-79 (D4-G5) | Soprano II | 58-77 (Bb3-F5) | Alto | 55-70 (G3-Bb4) | Tenor | 62-74 (D4-D5) | Bass | 41-58 (F2-Bb3) | |
| 5-05c | {1: '4/4'} | {1: -1, 39: 0} | 75 | 75 | 300.0 | 75 | 75 | 300.0 | 1258.00 | 819 | ... | Soprano I | 57-77 (A3-F5) | Soprano II | 59-76 (B3-E5) | Alto | 54-69 (F#3-A4) | Tenor | 50-67 (D3-G4) | Bass | 43-62 (G2-D4) | |
| 5-08 | {1: '4/4'} | {1: 0} | 91 | 91 | 364.0 | 91 | 91 | 364.0 | 1229.00 | 793 | ... | Soprano I | 60-77 (C4-F5) | Soprano II | 57-79 (A3-G5) | Alto | 50-69 (D3-A4) | Tenor | 48-64 (C3-E4) | Bass | 41-57 (F2-A3) | |
| 5-09 | {1: '4/4'} | {1: 0} | 84 | 84 | 336.0 | 84 | 84 | 336.0 | 1207.50 | 1238 | ... | Soprano I | 60-77 (C4-F5) | Soprano II | 55-70 (G3-Bb4) | Alto | 62-78 (D4-F#5) | Tenor | 57-75 (A3-Eb5) | Bass | 41-57 (F2-A3) | |
| 5-11 | {1: '4/4'} | {1: 0} | 57 | 57 | 228.0 | 57 | 57 | 228.0 | 724.50 | 598 | ... | Soprano I | 62-76 (D4-E5) | Soprano II | 61-74 (C#4-D5) | Alto | 67-79 (G4-G5) | Tenor | 62-76 (D4-E5) | Bass | 38-60 (D2-C4) | |
| 6-01a | {1: '4/4'} | {1: 0} | 34 | 34 | 136.0 | 34 | 34 | 136.0 | 513.00 | 353 | ... | Soprano I | 62-74 (D4-D5) | Soprano II | 61-70 (C#4-Bb4) | Alto | 52-67 (E3-G4) | Tenor | 50-62 (D3-D4) | Bass | 43-57 (G2-A3) | |
| 8-18 | {1: '4/4', 28: '3/1', 97: '4/4'} | {1: 0} | 108 | 106 | 1797.0 | 108 | 106 | 1797.0 | 4714.00 | 1055 | ... | Soprano | 62-77 (D4-F5) | Tenor I | 36-55 (C2-G3) | Tenor II | 36-57 (C2-A3) | Bass | 40-60 (E2-C4) | Harpsichord | 40-60 (E2-C4) | |
| 8-19 | {1: '3/1', 21: '4/4', 34: '3/1', 40: '4/4', 89... | {1: 0} | 142 | 142 | 1008.0 | 142 | 142 | 1008.0 | 3065.50 | 1300 | ... | Alto | 57-69 (A3-A4) | Tenor | 48-65 (C3-F4) | Bass I | 40-60 (E2-C4) | Bass II | 40-67 (E2-G4) | <NA> | <NA> | |
| 9-12 | {1: '3/2', 10: '2/4', 11: '3/2', 18: '2/4'} | {1: 0} | 26 | 26 | 116.0 | 26 | 26 | 116.0 | 423.25 | 254 | ... | Tenor I | 45-55 (A2-G3) | Tenor II | 41-53 (F2-F3) | Bass | 43-60 (G2-C4) | Harpsichord | 31-48 (G1-C3) | <NA> | <NA> | |
| laudate_pueri_dominum | {1: '4/4', 126: '3/2', 138: '4/4'} | {1: -1} | 177 | 177 | 808.0 | 177 | 177 | 808.0 | 3492.00 | 2182 | ... | Soprano | 65-81 (F4-A5) | Alto I | 58-74 (Bb3-D5) | Alto II | 53-70 (F3-Bb4) | Tenor | 53-70 (F3-Bb4) | Bass | 31-53 (G1-F3) |
19 rows × 57 columns
mean_composition_years = utils.corpus_mean_composition_years(all_metadata)
chronological_order = mean_composition_years.index.to_list()
corpus_colors = dict(zip(chronological_order, utils.CORPUS_COLOR_SCALE))
corpus_names = {
corp: utils.get_corpus_display_name(corp) for corp in chronological_order
}
chronological_corpus_names = list(corpus_names.values())
corpus_name_colors = {
corpus_names[corp]: color for corp, color in corpus_colors.items()
}
mean_composition_years
corpus
monteverdi_madrigals 1612.157895
Name: mean_composition_year, dtype: float64
Composition dates#
This section relies on the dataset’s metadata.
valid_composed_start = pd.to_numeric(all_metadata.composed_start, errors="coerce")
valid_composed_end = pd.to_numeric(all_metadata.composed_end, errors="coerce")
print(
f"Composition dates range from {int(valid_composed_start.min())} {valid_composed_start.idxmin()} "
f"to {int(valid_composed_end.max())} {valid_composed_end.idxmax()}."
)
Composition dates range from 1590 ('monteverdi_madrigals', '2-12') to 1651 ('monteverdi_madrigals', '9-12').
Mean composition years per corpus#
def make_summary(metadata_df):
piece_is_annotated = metadata_df.label_count > 0
return metadata_df[piece_is_annotated].copy()
Show source
summary = make_summary(all_metadata)
bar_data = pd.concat(
[
mean_composition_years.rename("year"),
summary.groupby(level="corpus").size().rename("pieces"),
],
axis=1,
).reset_index()
N = len(summary)
fig = px.bar(
bar_data,
x="year",
y="pieces",
color="corpus",
color_discrete_map=corpus_colors,
title=f"Temporal coverage of the {N} annotated pieces in the Distant Listening Corpus",
)
fig.update_traces(width=5)
fig.update_layout(**utils.STD_LAYOUT)
fig.update_traces(width=5)
save_figure_as(fig, "pieces_timeline_bars")
fig.show()
summary
| TimeSig | KeySig | last_mc | last_mn | length_qb | last_mc_unfolded | last_mn_unfolded | length_qb_unfolded | all_notes_qb | n_onsets | ... | staff_1_instrument | staff_1_ambitus | staff_2_instrument | staff_2_ambitus | staff_3_instrument | staff_3_ambitus | staff_4_instrument | staff_4_ambitus | staff_5_instrument | staff_5_ambitus | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| corpus | piece | |||||||||||||||||||||
| monteverdi_madrigals | 2-12 | {1: '4/4'} | {1: -1} | 93 | 93 | 372.0 | 93 | 93 | 372.0 | 1374.00 | 1011 | ... | Soprano I | 60-77 (C4-F5) | Soprano II | 60-74 (C4-D5) | Alto | 53-69 (F3-A4) | Tenor | 48-65 (C3-F4) | Bass | 41-58 (F2-Bb3) |
| 3-09 | {1: '4/4'} | {1: -1} | 84 | 84 | 680.0 | 84 | 84 | 680.0 | 2534.00 | 886 | ... | Soprano I | 60-77 (C4-F5) | Soprano II | 57-74 (A3-D5) | Alto | 53-72 (F3-C5) | Tenor | 48-67 (C3-G4) | Bass | 41-58 (F2-Bb3) | |
| 4-19 | {1: '4/4'} | {1: 0} | 111 | 111 | 448.0 | 111 | 111 | 448.0 | 1726.00 | 956 | ... | Soprano I | 57-76 (A3-E5) | Soprano II | 57-76 (A3-E5) | Alto | 53-69 (F3-A4) | Tenor | 48-66 (C3-F#4) | Bass | 43-57 (G2-A3) | |
| 5-01 | {1: '4/4'} | {1: 0} | 67 | 67 | 268.0 | 67 | 67 | 268.0 | 1088.50 | 661 | ... | Soprano I | 64-81 (E4-A5) | Soprano II | 59-77 (B3-F5) | Alto | 52-69 (E3-A4) | Tenor | 55-69 (G3-A4) | Bass | 45-62 (A2-D4) | |
| 5-03 | {1: '4/4'} | {1: -1} | 74 | 74 | 296.0 | 74 | 74 | 296.0 | 1073.00 | 652 | ... | Soprano I | 62-77 (D4-F5) | Soprano II | 61-74 (C#4-D5) | Alto | 50-70 (D3-Bb4) | Tenor | 49-65 (C#3-F4) | Bass | 43-58 (G2-Bb3) | |
| 5-04a | {1: '4/4'} | {1: -1} | 82 | 82 | 328.0 | 82 | 82 | 328.0 | 1401.00 | 939 | ... | Soprano I | 65-82 (F4-Bb5) | Soprano II | 64-81 (E4-A5) | Alto | 55-74 (G3-D5) | Tenor | 53-69 (F3-A4) | Bass | 46-63 (Bb2-Eb4) | |
| 5-04c | {1: '4/4'} | {1: -1} | 53 | 53 | 212.0 | 53 | 53 | 212.0 | 826.00 | 589 | ... | Soprano I | 66-81 (F#4-A5) | Soprano II | 65-79 (F4-G5) | Alto | 55-74 (G3-D5) | Tenor | 55-69 (G3-A4) | Bass | 46-63 (Bb2-Eb4) | |
| 5-04d | {1: '4/4'} | {1: -1} | 59 | 59 | 236.0 | 59 | 59 | 236.0 | 950.00 | 715 | ... | Soprano I | 65-82 (F4-Bb5) | Soprano II | 62-81 (D4-A5) | Alto | 55-74 (G3-D5) | Tenor | 53-69 (F3-A4) | Bass | 45-65 (A2-F4) | |
| 5-04e | {1: '4/4'} | {1: -1} | 95 | 95 | 380.0 | 95 | 95 | 380.0 | 1530.50 | 1213 | ... | Soprano I | 65-81 (F4-A5) | Soprano II | 62-81 (D4-A5) | Alto | 60-74 (C4-D5) | Tenor | 50-69 (D3-A4) | Bass | 43-62 (G2-D4) | |
| 5-05b | {1: '4/4'} | {1: -1} | 58 | 58 | 232.0 | 58 | 58 | 232.0 | 930.00 | 685 | ... | Soprano I | 62-79 (D4-G5) | Soprano II | 58-77 (Bb3-F5) | Alto | 55-70 (G3-Bb4) | Tenor | 62-74 (D4-D5) | Bass | 41-58 (F2-Bb3) | |
| 5-05c | {1: '4/4'} | {1: -1, 39: 0} | 75 | 75 | 300.0 | 75 | 75 | 300.0 | 1258.00 | 819 | ... | Soprano I | 57-77 (A3-F5) | Soprano II | 59-76 (B3-E5) | Alto | 54-69 (F#3-A4) | Tenor | 50-67 (D3-G4) | Bass | 43-62 (G2-D4) | |
| 5-08 | {1: '4/4'} | {1: 0} | 91 | 91 | 364.0 | 91 | 91 | 364.0 | 1229.00 | 793 | ... | Soprano I | 60-77 (C4-F5) | Soprano II | 57-79 (A3-G5) | Alto | 50-69 (D3-A4) | Tenor | 48-64 (C3-E4) | Bass | 41-57 (F2-A3) | |
| 5-09 | {1: '4/4'} | {1: 0} | 84 | 84 | 336.0 | 84 | 84 | 336.0 | 1207.50 | 1238 | ... | Soprano I | 60-77 (C4-F5) | Soprano II | 55-70 (G3-Bb4) | Alto | 62-78 (D4-F#5) | Tenor | 57-75 (A3-Eb5) | Bass | 41-57 (F2-A3) | |
| 5-11 | {1: '4/4'} | {1: 0} | 57 | 57 | 228.0 | 57 | 57 | 228.0 | 724.50 | 598 | ... | Soprano I | 62-76 (D4-E5) | Soprano II | 61-74 (C#4-D5) | Alto | 67-79 (G4-G5) | Tenor | 62-76 (D4-E5) | Bass | 38-60 (D2-C4) | |
| 6-01a | {1: '4/4'} | {1: 0} | 34 | 34 | 136.0 | 34 | 34 | 136.0 | 513.00 | 353 | ... | Soprano I | 62-74 (D4-D5) | Soprano II | 61-70 (C#4-Bb4) | Alto | 52-67 (E3-G4) | Tenor | 50-62 (D3-D4) | Bass | 43-57 (G2-A3) | |
| 8-18 | {1: '4/4', 28: '3/1', 97: '4/4'} | {1: 0} | 108 | 106 | 1797.0 | 108 | 106 | 1797.0 | 4714.00 | 1055 | ... | Soprano | 62-77 (D4-F5) | Tenor I | 36-55 (C2-G3) | Tenor II | 36-57 (C2-A3) | Bass | 40-60 (E2-C4) | Harpsichord | 40-60 (E2-C4) | |
| 8-19 | {1: '3/1', 21: '4/4', 34: '3/1', 40: '4/4', 89... | {1: 0} | 142 | 142 | 1008.0 | 142 | 142 | 1008.0 | 3065.50 | 1300 | ... | Alto | 57-69 (A3-A4) | Tenor | 48-65 (C3-F4) | Bass I | 40-60 (E2-C4) | Bass II | 40-67 (E2-G4) | <NA> | <NA> | |
| 9-12 | {1: '3/2', 10: '2/4', 11: '3/2', 18: '2/4'} | {1: 0} | 26 | 26 | 116.0 | 26 | 26 | 116.0 | 423.25 | 254 | ... | Tenor I | 45-55 (A2-G3) | Tenor II | 41-53 (F2-F3) | Bass | 43-60 (G2-C4) | Harpsichord | 31-48 (G1-C3) | <NA> | <NA> | |
| laudate_pueri_dominum | {1: '4/4', 126: '3/2', 138: '4/4'} | {1: -1} | 177 | 177 | 808.0 | 177 | 177 | 808.0 | 3492.00 | 2182 | ... | Soprano | 65-81 (F4-A5) | Alto I | 58-74 (Bb3-D5) | Alto II | 53-70 (F3-Bb4) | Tenor | 53-70 (F3-Bb4) | Bass | 31-53 (G1-F3) |
19 rows × 57 columns
Composition years histogram#
Show source
hist_data = summary.reset_index()
hist_data.corpus = hist_data.corpus.map(corpus_names)
fig = px.histogram(
hist_data,
x="composed_end",
color="corpus",
labels=dict(
composed_end="decade",
count="pieces",
),
color_discrete_map=corpus_name_colors,
title=f"Temporal coverage of the {N} annotated pieces in the Distant Listening Corpus",
)
fig.update_traces(xbins=dict(size=10))
fig.update_layout(**utils.STD_LAYOUT)
fig.update_legends(font=dict(size=16))
save_figure_as(fig, "pieces_timeline_histogram", height=1250)
fig.show()
Dimensions#
Overview#
def make_overview_table(groupby, group_name="pieces"):
n_groups = groupby.size().rename(group_name)
absolute_numbers = dict(
measures=groupby.last_mn.sum(),
length=groupby.length_qb.sum(),
notes=groupby.n_onsets.sum(),
labels=groupby.label_count.sum(),
)
absolute = pd.DataFrame.from_dict(absolute_numbers)
absolute = pd.concat([n_groups, absolute], axis=1)
sum_row = pd.DataFrame(absolute.sum(), columns=["sum"]).T
absolute = pd.concat([absolute, sum_row])
return absolute
absolute = make_overview_table(summary.groupby("workTitle"))
# print(absolute.astype(int).to_markdown())
absolute.astype(int)
| pieces | measures | length | notes | labels | |
|---|---|---|---|---|---|
| Il quarto libro de madrigali | 1 | 111 | 448 | 956 | 185 |
| Il quinto libro de madrigali | 11 | 795 | 3180 | 8902 | 1592 |
| Il secondo libro de madrigali | 1 | 93 | 372 | 1011 | 225 |
| Il sesto libro de madrigali | 1 | 34 | 136 | 353 | 83 |
| Il terzo libro de madrigali | 1 | 84 | 680 | 886 | 190 |
| Madrigali e canzonette...Libro nono | 1 | 26 | 116 | 254 | 52 |
| Madrigali guerriri, et amorosi...Libro ottavo | 2 | 248 | 2805 | 2355 | 576 |
| Messa et salmi | 1 | 177 | 808 | 2182 | 386 |
| sum | 19 | 1568 | 8545 | 16899 | 3289 |
def summarize_dataset(D):
all_metadata = D.get_metadata()
summary = make_summary(all_metadata)
return make_overview_table(summary.groupby(level=0))
corpus_summary = summarize_dataset(D)
print(corpus_summary.astype(int).to_markdown())
| | pieces | measures | length | notes | labels |
|:---------------------|---------:|-----------:|---------:|--------:|---------:|
| monteverdi_madrigals | 19 | 1568 | 8545 | 16899 | 3289 |
| sum | 19 | 1568 | 8545 | 16899 | 3289 |
Measures#
all_measures = D.get_feature("measures")
print(
f"{len(all_measures.index)} measures over {len(all_measures.groupby(level=[0,1]))} files."
)
all_measures.head()
1570 measures over 19 files.
| mc | mn | quarterbeats | duration_qb | keysig | timesig | act_dur | mc_offset | numbering_offset | dont_count | barline | breaks | repeats | next | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| corpus | piece | i | ||||||||||||||
| monteverdi_madrigals | 2-12 | 0 | 1 | 1 | 0 | 4.0 | -1 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | firstMeasure | (2,) |
| 1 | 2 | 2 | 4 | 4.0 | -1 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | <NA> | (3,) | ||
| 2 | 3 | 3 | 8 | 4.0 | -1 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | <NA> | (4,) | ||
| 3 | 4 | 4 | 12 | 4.0 | -1 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | <NA> | (5,) | ||
| 4 | 5 | 5 | 16 | 4.0 | -1 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | <NA> | (6,) |
all_measures.get_default_analysis().plot_grouped()
Harmony labels#
All symbols, independent of the local key (the mode of which changes their semantics).
try:
all_annotations = D.get_feature("harmonylabels").df
except Exception:
all_annotations = pd.DataFrame()
n_annotations = len(all_annotations.index)
includes_annotations = n_annotations > 0
if includes_annotations:
display(all_annotations.head())
print(f"Concatenated annotation tables contains {all_annotations.shape[0]} rows.")
no_chord = all_annotations.root.isna()
if no_chord.sum() > 0:
print(
f"{no_chord.sum()} of them are not chords. Their values are:"
f" {all_annotations.label[no_chord].value_counts(dropna=False).to_dict()}"
)
all_chords = all_annotations[~no_chord].copy()
print(
f"Dataset contains {all_chords.shape[0]} tokens and {len(all_chords.chord.unique())} types over "
f"{len(all_chords.groupby(level=[0,1]))} documents."
)
all_annotations["corpus_name"] = all_annotations.index.get_level_values(0).map(
utils.get_corpus_display_name
)
all_chords["corpus_name"] = all_chords.index.get_level_values(0).map(
utils.get_corpus_display_name
)
else:
print("Dataset contains no annotations.")
| mc | mn | quarterbeats | quarterbeats_all_endings | duration_qb | mc_onset | mn_onset | timesig | staff | voice | ... | numeral_or_applied_to_numeral | intervals_over_bass | intervals_over_root | scale_degrees | scale_degrees_and_mode | scale_degrees_major | scale_degrees_minor | globalkey | localkey | chord | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| corpus | piece | i | |||||||||||||||||||||
| monteverdi_madrigals | 2-12 | 0 | 1 | 1 | 0 | 0 | 6.0 | 0 | 0 | 4/4 | 5 | 1 | ... | V | (M3, P5) | (M3, P5) | (5, 7, 2) | (5, 7, 2), major | (5, 7, 2) | (5, #7, 2) | F | I | V |
| 1 | 2 | 2 | 6 | 6 | 2.0 | 1/2 | 1/2 | 4/4 | 5 | 1 | ... | vi | (m3, P5) | (m3, P5) | (6, 1, 3) | (6, 1, 3), major | (6, 1, 3) | (#6, 1, #3) | F | I | vi | ||
| 2 | 3 | 3 | 8 | 8 | 6.0 | 0 | 0 | 4/4 | 5 | 1 | ... | I | (M3, P5) | (M3, P5) | (1, 3, 5) | (1, 3, 5), major | (1, 3, 5) | (1, #3, 5) | F | I | I | ||
| 3 | 4 | 4 | 14 | 14 | 1.5 | 1/2 | 1/2 | 4/4 | 5 | 1 | ... | ii | (m3, P5) | (m3, P5) | (2, 4, 6) | (2, 4, 6), major | (2, 4, 6) | (2, 4, #6) | F | I | ii | ||
| 4 | 4 | 4 | 31/2 | 31/2 | 0.5 | 7/8 | 7/8 | 4/4 | 5 | 1 | ... | vii | (m3, M6) | (m3, d5) | (2, 4, 7) | (2, 4, 7), major | (2, 4, 7) | (2, 4, #7) | F | I | viio6 |
5 rows × 51 columns
Concatenated annotation tables contains 3289 rows.
Dataset contains 3289 tokens and 232 types over 19 documents.