Notes#

Hide imports
import os

import dimcat as dc
import ms3
import pandas as pd
import plotly.express as px
from dimcat import filters, plotting

import utils

pd.set_option("display.max_rows", 1000)
pd.set_option("display.max_columns", 500)
Hide source
RESULTS_PATH = os.path.abspath(os.path.join(utils.OUTPUT_FOLDER, "notes_stats"))
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("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': []}

Metadata#

filtered_D = filters.HasHarmonyLabelsFilter(keep_values=[True]).process(D)

all_metadata = filtered_D.get_metadata()
all_metadata.reset_index(level=1).groupby(level=0).nth(0).iloc[:, :20]
piece TimeSig KeySig last_mc last_mn length_qb last_mc_unfolded last_mn_unfolded length_qb_unfolded volta_mcs all_notes_qb n_onsets n_onset_positions guitar_chord_count form_label_count label_count annotated_key harmony_version annotators reviewers
corpus
dvorak_silhouettes op08n01 {1: '6/8'} {1: 4, 7: -5, 49: 4} 54 52 156.5 54 52 156.5 () 658.75 957 288 0 0 80 c# 2.3.0 Daniel Grote (2.1.1), Hanné Becker (2.3.0) Johannes Hentschel (2.1.1), AN
chronological_order = utils.chronological_corpus_order(all_metadata)
corpus_colors = dict(zip(chronological_order, utils.CORPUS_COLOR_SCALE))
notes_feature = filtered_D.get_feature("notes")
all_notes = notes_feature.df
print(f"{len(all_notes.index)} notes over {len(all_notes.groupby(level=[0,1]))} files.")
all_notes.head()
10649 notes over 12 files.
mc mn quarterbeats quarterbeats_all_endings duration_qb duration mc_onset mn_onset timesig staff voice chord_id gracenote midi name nominal_duration octave scalar tied tpc_name tpc
corpus piece i
dvorak_silhouettes op08n01 0 1 0 0 0 0.50 1/8 0 5/8 6/8 1 1 0 <NA> 56 G#3 1/8 3 1 <NA> G# 8
1 1 0 0 0 0.50 1/8 0 5/8 6/8 1 1 0 <NA> 68 G#4 1/8 4 1 <NA> G# 8
2 2 1 1/2 1/2 0.25 1/16 0 0 6/8 2 1 5 <NA> 37 C#2 1/16 2 1 <NA> C# 7
3 2 1 1/2 1/2 1.00 1/4 0 0 6/8 1 1 1 <NA> 61 C#4 1/4 4 1 <NA> C# 7
4 2 1 1/2 1/2 1.00 1/4 0 0 6/8 1 1 1 <NA> 73 C#5 1/4 5 1 <NA> C# 7
def weight_notes(nl, group_col="midi", precise=True):
    summed_durations = nl.groupby(group_col).duration_qb.sum()
    shortest_duration = summed_durations[summed_durations > 0].min()
    summed_durations /= shortest_duration  # normalize such that the shortest duration results in 1 occurrence
    if not precise:
        # This simple trick reduces compute time but also precision:
        # The rationale is to have the smallest value be slightly larger than 0.5 because
        # if it was exactly 0.5 it would be rounded down by repeat_notes_according_to_weights()
        summed_durations /= 1.9999999
    return repeat_notes_according_to_weights(summed_durations)


def repeat_notes_according_to_weights(weights):
    try:
        counts = weights.round().astype(int)
    except Exception:
        return pd.Series(dtype=int)
    counts_reflecting_weights = []
    for pitch, count in counts.items():
        counts_reflecting_weights.extend([pitch] * count)
    return pd.Series(counts_reflecting_weights)

Ambitus#

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()
}
all_notes["corpus_name"] = all_notes.index.get_level_values(0).map(corpus_names)
grouped_notes = all_notes.groupby("corpus_name")
weighted_midi = pd.concat(
    [weight_notes(nl, "midi", precise=False) for _, nl in grouped_notes],
    keys=grouped_notes.groups.keys(),
).reset_index(level=0)
weighted_midi.columns = ["dataset", "midi"]
weighted_midi
dataset midi
0 Dvořák Silhouettes 23
1 Dvořák Silhouettes 25
2 Dvořák Silhouettes 25
3 Dvořák Silhouettes 26
4 Dvořák Silhouettes 26
... ... ...
3498 Dvořák Silhouettes 95
3499 Dvořák Silhouettes 95
3500 Dvořák Silhouettes 96
3501 Dvořák Silhouettes 97
3502 Dvořák Silhouettes 97

3503 rows × 2 columns

# fig = px.violin(weighted_midi,
#                 x='dataset',
#                 y='midi',
#                 color='dataset',
#                 title="Corpus-wise distribution over registers (ambitus)",
#                 box=True,
#                 labels=dict(
#                     dataset='',
#                     midi='distribution of pitches by duration'
#                 ),
#                 category_orders=dict(dataset=chronological_corpus_names),
#                 color_discrete_map=corpus_name_colors,
#                 width=1000, height=600,
#                )
# fig.update_traces(spanmode='hard') # do not extend beyond outliers
# fig.update_layout(**utils.STD_LAYOUT,
#                  showlegend=False)
# fig.update_yaxes(
#     tickmode= 'array',
#     tickvals= [12, 24, 36, 48, 60, 72, 84, 96],
#     ticktext = ["C0", "C1", "C2", "C3", "C4", "C5", "C6", "C7"],
# )
# fig.update_xaxes(tickangle=45)
# save_figure_as(fig, "ambitus_corpuswise_violins")
# fig.show()

Tonal Pitch Classes (TPC)#

weighted_tpc = pd.concat(
    [weight_notes(nl, "tpc") for _, nl in grouped_notes],
    keys=grouped_notes.groups.keys(),
).reset_index(level=0)
weighted_tpc.columns = ["dataset", "tpc"]
weighted_tpc
dataset tpc
0 Dvořák Silhouettes -9
1 Dvořák Silhouettes -9
2 Dvořák Silhouettes -9
3 Dvořák Silhouettes -9
4 Dvořák Silhouettes -8
... ... ...
2537 Dvořák Silhouettes 12
2538 Dvořák Silhouettes 13
2539 Dvořák Silhouettes 13
2540 Dvořák Silhouettes 13
2541 Dvořák Silhouettes 13

2542 rows × 2 columns

As violin plot#

# fig = px.violin(weighted_tpc,
#                 x='dataset',
#                 y='tpc',
#                 color='dataset',
#                 title="Corpus-wise distribution over line of fifths (tonal pitch classes)",
#                 box=True,
#                 labels=dict(
#                     dataset='',
#                     tpc='distribution of tonal pitch classes by duration'
#                 ),
#                 category_orders=dict(dataset=chronological_corpus_names),
#                 color_discrete_map=corpus_name_colors,
#                 width=1000,
#                 height=600,
#                )
# fig.update_traces(spanmode='hard') # do not extend beyond outliers
# fig.update_layout(**utils.STD_LAYOUT,
#                  showlegend=False)
# fig.update_yaxes(
#     tickmode= 'array',
#     tickvals= [-12, -9, -6, -3, 0, 3, 6, 9, 12, 15, 18],
#     ticktext = ["Dbb", "Bbb", "Gb", "Eb", "C", "A", "F#", "D#", "B#", "G##", "E##"],
#     zerolinecolor='grey',
#     zeroline=True
# )
# fig.update_xaxes(tickangle=45)
# save_figure_as(fig, "pitch_class_distributions_corpuswise_violins")
# fig.show()
(all_notes)
mc mn quarterbeats quarterbeats_all_endings duration_qb duration mc_onset mn_onset timesig staff voice chord_id gracenote midi name nominal_duration octave scalar tied tpc_name tpc corpus_name
corpus piece i
dvorak_silhouettes op08n01 0 1 0 0 0 0.50 1/8 0 5/8 6/8 1 1 0 <NA> 56 G#3 1/8 3 1 <NA> G# 8 Dvořák Silhouettes
1 1 0 0 0 0.50 1/8 0 5/8 6/8 1 1 0 <NA> 68 G#4 1/8 4 1 <NA> G# 8 Dvořák Silhouettes
2 2 1 1/2 1/2 0.25 1/16 0 0 6/8 2 1 5 <NA> 37 C#2 1/16 2 1 <NA> C# 7 Dvořák Silhouettes
3 2 1 1/2 1/2 1.00 1/4 0 0 6/8 1 1 1 <NA> 61 C#4 1/4 4 1 <NA> C# 7 Dvořák Silhouettes
4 2 1 1/2 1/2 1.00 1/4 0 0 6/8 1 1 1 <NA> 73 C#5 1/4 5 1 <NA> C# 7 Dvořák Silhouettes
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
op08n12 1435 79 78 463/2 463/2 3.00 3/4 0 0 6/8 2 1 956 <NA> 64 E4 1/2 4 3/2 <NA> E 4 Dvořák Silhouettes
1436 79 78 463/2 463/2 3.00 3/4 0 0 6/8 1 1 954 <NA> 73 C#5 1/2 5 3/2 <NA> C# 7 Dvořák Silhouettes
1437 79 78 463/2 463/2 3.00 3/4 0 0 6/8 1 1 954 <NA> 76 E5 1/2 5 3/2 <NA> E 4 Dvořák Silhouettes
1438 79 78 463/2 463/2 3.00 3/4 0 0 6/8 1 1 954 <NA> 80 G#5 1/2 5 3/2 <NA> G# 8 Dvořák Silhouettes
1439 79 78 463/2 463/2 3.00 3/4 0 0 6/8 1 1 954 <NA> 85 C#6 1/2 6 3/2 <NA> C# 7 Dvořák Silhouettes

10649 rows × 22 columns

width = 1400
height = 800

weighted_pitch_values = pd.concat(
    [
        weighted_midi.rename(columns={"midi": "value"}),
        weighted_tpc.rename(columns={"tpc": "value"}),
    ],
    keys=["MIDI pitch", "Tonal pitch class"],
    names=["distribution"],
).reset_index(level=[0, 1])

fig = plotting.make_violin_plot(
    weighted_pitch_values,
    x_col="dataset",
    y_col="value",
    color="dataset",
    facet_row="distribution",
    box=True,
    labels=dict(dataset="", tpc="distribution of tonal pitch classes by duration"),
    category_orders=dict(dataset=chronological_corpus_names),
    # color_discrete_map=corpus_name_colors,
    color_discrete_sequence=px.colors.qualitative.Dark24,
    traces_settings=dict(
        spanmode="hard",
        width=1,
        # scalemode='width'
    ),
    layout=dict(
        showlegend=False,
        margin=dict(
            t=0,
            b=0,
            l=0,
            r=0,
        ),
    ),
    x_axis=dict(
        # tickangle=45,
        tickfont_size=15
    ),
    y_axis=dict(
        tickmode="array",
        tickvals=[-12, -9, -6, -3, 0, 3, 6, 9, 12, 15, 24, 36, 48, 60, 72, 84, 96],
        ticktext=[
            "Dbb",
            "Bbb",
            "Gb",
            "Eb",
            "C",
            "A",
            "F#",
            "D#",
            "B#",
            "G##",
            "C1",
            "C2",
            "C3",
            "C4",
            "C5",
            "C6",
            "C7",
        ],
        zerolinecolor="grey",
        zeroline=True,
    ),
    width=width,
    height=height,
)
utils.realign_subplot_axes(fig, y_axes=dict(title_text=""))
save_figure_as(fig, "notes_violin", width=width, height=height)
fig
fig = plotting.make_box_plot(
    weighted_pitch_values,
    x_col="dataset",
    y_col="value",
    color="dataset",
    facet_row="distribution",
    # box=True,
    labels=dict(dataset="", tpc="distribution of tonal pitch classes by duration"),
    category_orders=dict(dataset=chronological_corpus_names),
    # color_discrete_map=corpus_name_colors,
    color_discrete_sequence=px.colors.qualitative.Light24,
    # traces_settings=dict(spanmode='hard'),
    layout=dict(showlegend=False, margin=dict(t=0)),
    x_axis=dict(tickangle=45, tickfont_size=15),
    y_axis=dict(
        tickmode="array",
        tickvals=[-12, -9, -6, -3, 0, 3, 6, 9, 12, 15, 24, 36, 48, 60, 72, 84, 96],
        ticktext=[
            "Dbb",
            "Bbb",
            "Gb",
            "Eb",
            "C",
            "A",
            "F#",
            "D#",
            "B#",
            "G##",
            "C1",
            "C2",
            "C3",
            "C4",
            "C5",
            "C6",
            "C7",
        ],
        zerolinecolor="grey",
        zeroline=True,
    ),
    width=width,
    height=height,
)
utils.realign_subplot_axes(fig, y_axes=True)
save_figure_as(fig, "notes_box", width=width, height=height)
fig

As bar plots#

bar_data = all_notes.groupby("tpc").duration_qb.sum().reset_index()
x_values = list(range(bar_data.tpc.min(), bar_data.tpc.max() + 1))
x_names = ms3.fifths2name(x_values)
fig = px.bar(
    bar_data,
    x="tpc",
    y="duration_qb",
    labels=dict(tpc="Named pitch class", duration_qb="Duration in quarter notes"),
    color_discrete_sequence=utils.CORPUS_COLOR_SCALE,
    width=1000,
    height=300,
)
fig.update_layout(**utils.STD_LAYOUT)
fig.update_xaxes(
    zerolinecolor="grey",
    tickmode="array",
    tickvals=x_values,
    ticktext=x_names,
    dtick=1,
    ticks="outside",
    tickcolor="black",
    minor=dict(dtick=6, gridcolor="grey", showgrid=True),
)
save_figure_as(fig, "pitch_class_distribution_absolute_bars")
fig.show()
scatter_data = all_notes.groupby(["corpus_name", "tpc"]).duration_qb.sum().reset_index()
fig = px.bar(
    scatter_data,
    x="tpc",
    y="duration_qb",
    color="corpus_name",
    labels=dict(
        duration_qb="duration",
        tpc="named pitch class",
    ),
    category_orders=dict(dataset=chronological_corpus_names),
    color_discrete_map=corpus_name_colors,
    width=1000,
    height=500,
)
fig.update_layout(**utils.STD_LAYOUT)
fig.update_xaxes(
    zerolinecolor="grey",
    tickmode="array",
    tickvals=x_values,
    ticktext=x_names,
    dtick=1,
    ticks="outside",
    tickcolor="black",
    minor=dict(dtick=6, gridcolor="grey", showgrid=True),
)
save_figure_as(fig, "pitch_class_distribution_corpuswise_absolute_bars")
fig.show()

As scatter plots#

fig = px.scatter(
    scatter_data,
    x="tpc",
    y="duration_qb",
    color="corpus_name",
    labels=dict(
        duration_qb="duration",
        tpc="named pitch class",
    ),
    category_orders=dict(dataset=chronological_corpus_names),
    color_discrete_map=corpus_name_colors,
    facet_col="corpus_name",
    facet_col_wrap=3,
    facet_col_spacing=0.03,
    width=1000,
    height=1000,
)
fig.update_traces(mode="lines+markers")
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.update_layout(**utils.STD_LAYOUT, showlegend=False)
fig.update_xaxes(
    zerolinecolor="grey",
    tickmode="array",
    tickvals=[-12, -6, 0, 6, 12, 18],
    ticktext=["Dbb", "Gb", "C", "F#", "B#", "E##"],
    visible=True,
)
fig.update_yaxes(zeroline=False, matches=None, showticklabels=True)
save_figure_as(fig, "pitch_class_distribution_corpuswise_scatter")
fig.show()
no_accidental = bar_data[bar_data.tpc.between(-1, 5)].duration_qb.sum()
with_accidental = bar_data[~bar_data.tpc.between(-1, 5)].duration_qb.sum()
entire = no_accidental + with_accidental
(
    f"Fraction of note duration without accidental of the entire durations: {no_accidental} / {entire} = "
    f"{no_accidental / entire}"
)
'Fraction of note duration without accidental of the entire durations: 3185.708333333334 / 6991.525000000001 = 0.4556528558981529'

Notes and staves#

print("Distribution of notes over staves:")
utils.value_count_df(all_notes.staff)
Distribution of notes over staves:
counts %
staff
2 5347 50.21
1 5302 49.79