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("schubert_winterreise", 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("Franz Schubert – Winterreise version v2.4")
print(f"Datapackage '{package.package_name}' @ {git_tag}")
print(f"dimcat version {dc.__version__}\n")
D
Data and software versions
--------------------------

Franz Schubert – Winterreise version v2.4
Datapackage 'schubert_winterreise' @ v2.4
dimcat version 3.4.0
Dataset
=======
{'inputs': {'basepath': None,
            'packages': {'schubert_winterreise': ["'schubert_winterreise.measures' "
                                                  '(MuseScoreFacetName.MuseScoreMeasures)',
                                                  "'schubert_winterreise.notes' (MuseScoreFacetName.MuseScoreNotes)",
                                                  "'schubert_winterreise.expanded' "
                                                  '(MuseScoreFacetName.MuseScoreHarmonies)',
                                                  "'schubert_winterreise.chords' (MuseScoreFacetName.MuseScoreChords)",
                                                  "'schubert_winterreise.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 all_notes_qb n_onsets n_onset_positions guitar_chord_count form_label_count label_count annotated_key harmony_version annotators reviewers composed_start
corpus
schubert_winterreise n01 {1: '2/4'} {1: -1, 71: 2, 99: -1} 105 105 210.0 137 137 274.0 1088.75 2174 505 0 0 214 d 2.1.0 Alexander Faschon Johannes Hentschel 1827
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()
26614 notes over 24 files.
mc mn quarterbeats duration_qb duration mc_onset mn_onset timesig staff voice chord_id gracenote midi name nominal_duration octave scalar tied tremolo tpc_name tpc
corpus piece i
schubert_winterreise n01 0 1 1 0 0.5 1/8 0 0 2/4 3 1 5 <NA> 50 D3 1/8 3 1 <NA> <NA> D 2
1 1 1 0 0.5 1/8 0 0 2/4 3 1 5 <NA> 57 A3 1/8 3 1 <NA> <NA> A 3
2 1 1 0 0.5 1/8 0 0 2/4 2 2 1 <NA> 65 F4 1/8 4 1 <NA> <NA> F -1
3 1 1 1/2 0.5 1/8 1/8 1/8 2/4 3 1 6 <NA> 50 D3 1/8 3 1 <NA> <NA> D 2
4 1 1 1/2 0.5 1/8 1/8 1/8 2/4 3 1 6 <NA> 57 A3 1/8 3 1 <NA> <NA> A 3
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 Schubert Winterreise 29
1 Schubert Winterreise 30
2 Schubert Winterreise 30
3 Schubert Winterreise 30
4 Schubert Winterreise 30
... ... ...
11823 Schubert Winterreise 88
11824 Schubert Winterreise 88
11825 Schubert Winterreise 89
11826 Schubert Winterreise 91
11827 Schubert Winterreise 93

11828 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 Schubert Winterreise -10
1 Schubert Winterreise -10
2 Schubert Winterreise -10
3 Schubert Winterreise -9
4 Schubert Winterreise -8
... ... ...
15760 Schubert Winterreise 13
15761 Schubert Winterreise 13
15762 Schubert Winterreise 13
15763 Schubert Winterreise 14
15764 Schubert Winterreise 14

15765 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 duration_qb duration mc_onset mn_onset timesig staff voice chord_id gracenote midi name nominal_duration octave scalar tied tremolo tpc_name tpc corpus_name
corpus piece i
schubert_winterreise n01 0 1 1 0 0.5 1/8 0 0 2/4 3 1 5 <NA> 50 D3 1/8 3 1 <NA> <NA> D 2 Schubert Winterreise
1 1 1 0 0.5 1/8 0 0 2/4 3 1 5 <NA> 57 A3 1/8 3 1 <NA> <NA> A 3 Schubert Winterreise
2 1 1 0 0.5 1/8 0 0 2/4 2 2 1 <NA> 65 F4 1/8 4 1 <NA> <NA> F -1 Schubert Winterreise
3 1 1 1/2 0.5 1/8 1/8 1/8 2/4 3 1 6 <NA> 50 D3 1/8 3 1 <NA> <NA> D 2 Schubert Winterreise
4 1 1 1/2 0.5 1/8 1/8 1/8 2/4 3 1 6 <NA> 57 A3 1/8 3 1 <NA> <NA> A 3 Schubert Winterreise
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
n24 559 61 61 180 3.0 3/4 0 0 3/4 3 1 442 <NA> 45 A2 1/2 2 3/2 <NA> <NA> A 3 Schubert Winterreise
560 61 61 180 3.0 3/4 0 0 3/4 3 1 442 <NA> 52 E3 1/2 3 3/2 <NA> <NA> E 4 Schubert Winterreise
561 61 61 180 3.0 3/4 0 0 3/4 2 1 441 <NA> 60 C4 1/2 4 3/2 <NA> <NA> C 0 Schubert Winterreise
562 61 61 180 3.0 3/4 0 0 3/4 2 1 441 <NA> 64 E4 1/2 4 3/2 <NA> <NA> E 4 Schubert Winterreise
563 61 61 180 3.0 3/4 0 0 3/4 2 1 441 <NA> 69 A4 1/2 4 3/2 <NA> <NA> A 3 Schubert Winterreise

26614 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: 10678.916666666668 / 15766.125000000002 = 0.677332995055327'

Notes and staves#

print("Distribution of notes over staves:")
utils.value_count_df(all_notes.staff)
Distribution of notes over staves:
counts %
staff
2 12900 48.47
3 9576 35.98
1 4138 15.55