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("grieg_lyric_pieces", 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("Edvard Grieg – Lyric Pieces version v2.3")
print(f"Datapackage '{package.package_name}' @ {git_tag}")
print(f"dimcat version {dc.__version__}\n")
D
Data and software versions
--------------------------

Edvard Grieg – Lyric Pieces version v2.3
Datapackage 'grieg_lyric_pieces' @ v2.3
dimcat version 3.4.0
Dataset
=======
{'inputs': {'basepath': None,
            'packages': {'grieg_lyric_pieces': ["'grieg_lyric_pieces.measures' (MuseScoreFacetName.MuseScoreMeasures)",
                                                "'grieg_lyric_pieces.notes' (MuseScoreFacetName.MuseScoreNotes)",
                                                "'grieg_lyric_pieces.expanded' (MuseScoreFacetName.MuseScoreHarmonies)",
                                                "'grieg_lyric_pieces.chords' (MuseScoreFacetName.MuseScoreChords)",
                                                "'grieg_lyric_pieces.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
grieg_lyric_pieces op12n01 {1: '2/4'} {1: -3} 23 23 46.0 23 23 46.0 () 135.5 268 156 0 0 43 Eb 2.3.0 Adrian Nagel (2.1.1), John Heilig (2.30) Adrian Nagel
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()
65818 notes over 66 files.
mc mn quarterbeats quarterbeats_all_endings duration_qb duration mc_onset mn_onset timesig staff voice volta chord_id gracenote midi name nominal_duration octave scalar tied tremolo tpc_name tpc
corpus piece i
grieg_lyric_pieces op12n01 0 1 1 0 0 2.00 1/2 0 0 2/4 2 2 <NA> 12 <NA> 51 Eb3 1/2 3 1 1 <NA> Eb -3
1 1 1 0 0 0.25 1/16 0 0 2/4 2 1 <NA> 4 <NA> 58 Bb3 1/16 3 1 <NA> <NA> Bb -2
2 1 1 0 0 0.50 1/8 0 0 2/4 1 1 <NA> 0 <NA> 79 G5 1/8 5 1 <NA> <NA> G 1
3 1 1 1/4 1/4 0.25 1/16 1/16 1/16 2/4 2 1 <NA> 5 <NA> 63 Eb4 1/16 4 1 <NA> <NA> Eb -3
4 1 1 1/2 1/2 0.25 1/16 1/8 1/8 2/4 2 1 <NA> 6 <NA> 67 G4 1/16 4 1 <NA> <NA> G 1
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 Grieg Lyric Pieces 21
1 Grieg Lyric Pieces 23
2 Grieg Lyric Pieces 23
3 Grieg Lyric Pieces 23
4 Grieg Lyric Pieces 23
... ... ...
27852 Grieg Lyric Pieces 96
27853 Grieg Lyric Pieces 96
27854 Grieg Lyric Pieces 97
27855 Grieg Lyric Pieces 99
27856 Grieg Lyric Pieces 102

27857 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 Grieg Lyric Pieces -11
1 Grieg Lyric Pieces -10
2 Grieg Lyric Pieces -10
3 Grieg Lyric Pieces -10
4 Grieg Lyric Pieces -10
... ... ...
74268 Grieg Lyric Pieces 16
74269 Grieg Lyric Pieces 16
74270 Grieg Lyric Pieces 16
74271 Grieg Lyric Pieces 16
74272 Grieg Lyric Pieces 16

74273 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 volta chord_id gracenote midi name nominal_duration octave scalar tied tremolo tpc_name tpc corpus_name
corpus piece i
grieg_lyric_pieces op12n01 0 1 1 0 0 2.00 1/2 0 0 2/4 2 2 <NA> 12 <NA> 51 Eb3 1/2 3 1 1 <NA> Eb -3 Grieg Lyric Pieces
1 1 1 0 0 0.25 1/16 0 0 2/4 2 1 <NA> 4 <NA> 58 Bb3 1/16 3 1 <NA> <NA> Bb -2 Grieg Lyric Pieces
2 1 1 0 0 0.50 1/8 0 0 2/4 1 1 <NA> 0 <NA> 79 G5 1/8 5 1 <NA> <NA> G 1 Grieg Lyric Pieces
3 1 1 1/4 1/4 0.25 1/16 1/16 1/16 2/4 2 1 <NA> 5 <NA> 63 Eb4 1/16 4 1 <NA> <NA> Eb -3 Grieg Lyric Pieces
4 1 1 1/2 1/2 0.25 1/16 1/8 1/8 2/4 2 1 <NA> 6 <NA> 67 G4 1/16 4 1 <NA> <NA> G 1 Grieg Lyric Pieces
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
op71n07 729 75 74 222 222 1.00 1/4 1/2 1/2 3/4 2 1 <NA> 509 <NA> 58 Bb3 1/4 3 1 <NA> <NA> Bb -2 Grieg Lyric Pieces
730 75 74 222 222 1.00 1/4 1/2 1/2 3/4 2 1 <NA> 509 <NA> 63 Eb4 1/4 4 1 <NA> <NA> Eb -3 Grieg Lyric Pieces
731 75 74 222 222 1.00 1/4 1/2 1/2 3/4 1 2 <NA> 506 <NA> 67 G4 1/4 4 1 <NA> <NA> G 1 Grieg Lyric Pieces
732 75 74 222 222 1.00 1/4 1/2 1/2 3/4 1 2 <NA> 506 <NA> 70 Bb4 1/4 4 1 <NA> <NA> Bb -2 Grieg Lyric Pieces
733 75 74 445/2 445/2 0.50 1/8 5/8 5/8 3/4 1 1 <NA> 504 <NA> 79 G5 1/8 5 1 <NA> <NA> G 1 Grieg Lyric Pieces

65818 rows × 24 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: 38437.7875 / 55705.291666666664 = 0.6900203975235746'

Notes and staves#

print("Distribution of notes over staves:")
utils.value_count_df(all_notes.staff)
Distribution of notes over staves:
counts %
staff
1 36716 55.78
2 28680 43.57
3 422 0.64