Cadences

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%load_ext autoreload
%autoreload 2
import os
from collections import Counter, defaultdict

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

import utils

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RESULTS_PATH = os.path.abspath(os.path.join(utils.OUTPUT_FOLDER, "cadences"))
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

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D = utils.get_dataset("jc_bach_sonatas", 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("Johann Christian Bach – Keyboard Sonatas version v2.4")
print(f"Datapackage '{package.package_name}' @ {git_tag}")
print(f"dimcat version {dc.__version__}\n")
D

Data and software versions
--------------------------

Johann Christian Bach – Keyboard Sonatas version v2.4
Datapackage 'jc_bach_sonatas' @ v2.4
dimcat version 3.4.0

Dataset
=======
{'inputs': {'basepath': None,
            'packages': {'jc_bach_sonatas': ["'jc_bach_sonatas.measures' (MuseScoreFacetName.MuseScoreMeasures)",
                                             "'jc_bach_sonatas.notes' (MuseScoreFacetName.MuseScoreNotes)",
                                             "'jc_bach_sonatas.expanded' (MuseScoreFacetName.MuseScoreHarmonies)",
                                             "'jc_bach_sonatas.chords' (MuseScoreFacetName.MuseScoreChords)",
                                             "'jc_bach_sonatas.metadata' (FeatureName.Metadata)"]}},
 'outputs': {'basepath': None, 'packages': {}},
 'pipeline': []}

try:
    cadence_labels = D.get_feature("cadencelabels")
except Exception:
    raise ValueError("Corpus has no cadence annotations.")
cadence_labels

			mc	mn	quarterbeats	quarterbeats_all_endings	duration_qb	mc_onset	mn_onset	timesig	staff	voice	...	globalkey	localkey	globalkey_is_minor	localkey_is_minor	globalkey_mode	localkey_mode	localkey_resolved	localkey_and_mode	cadence_type	cadence
corpus	piece	i
jc_bach_sonatas	wa01op05no1a_Allegretto	10	9	8	29/2	29/2	1.5	0	0	2/4	2	1	...	Bb	I	False	False	major	major	I	I, major	PAC	PAC
		15	11	10	37/2	37/2	2.0	0	0	2/4	2	1	...	Bb	I	False	False	major	major	I	I, major	IAC	IAC
		18	13	12	45/2	45/2	1.0	0	0	2/4	2	1	...	Bb	I	False	False	major	major	I	I, major	IAC	IAC
		22	15	14	27	27	3.5	1/8	1/8	2/4	2	1	...	Bb	I	False	False	major	major	I	I, major	HC	HC
		32	23	22	87/2	87/2	3.0	1/4	1/4	2/4	2	1	...	Bb	V	False	False	major	major	V	V, major	PAC	PAC
	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
	wa12op17no6c_Prestissimo	204	92	90	1069/2	1069/2	10.5	0	0	12/8	2	1	...	Bb	I	False	False	major	major	I	I, major	DC	DC
		206	94	92	1093/2	1093/2	3.0	0	0	12/8	2	1	...	Bb	I	False	False	major	major	I	I, major	HC	HC
		225	100	98	1165/2	1165/2	9.0	0	0	12/8	2	1	...	Bb	I	False	False	major	major	I	I, major	EC	EC
		228	102	100	1189/2	1189/2	9.0	0	0	12/8	2	1	...	Bb	I	False	False	major	major	I	I, major	EC	EC
		231	104	102	1213/2	1213/2	23.5	0	0	12/8	2	1	...	Bb	I	False	False	major	major	I	I, major	PAC	PAC

406 rows × 22 columns

cadence_labels.plot_grouped(
    title="Distribution of cadence types over the DLC",
    output=make_output_path("all_cadences_pie"),
    width=1000,
    height=1000,
)

Metadata

cadence_filter = filters.HasCadenceAnnotationsFilter()
filtered_D = cadence_filter.process(D)

hascadence_metadata = filtered_D.get_metadata()
chronological_corpus_names = hascadence_metadata.get_corpus_names()

cadence_counts = cadence_labels.apply_step("Counter")
cadence_counts.plot_grouped("corpus")

mean_composition_years = (
    hascadence_metadata.groupby(level=0).composed_end.mean().astype(int).sort_values()
)
chronological_corpus_names = hascadence_metadata.get_corpus_names()
bar_data = pd.concat(
    [
        mean_composition_years.rename("year"),
        hascadence_metadata.groupby(level="corpus").size().rename("pieces"),
    ],
    axis=1,
).reset_index()
fig = px.bar(
    bar_data,
    x="year",
    y="pieces",
    color="corpus",
    title="Pieces contained in the dataset",
)
fig.update_traces(width=5)

Overall

• PAC: Perfect Authentic Cadence

• IAC: Imperfect Authentic Cadence

• HC: Half Cadence

• DC: Deceptive Cadence

• EC: Evaded Cadence

• PC: Plagal Cadence

print(f"{len(cadence_labels)} cadence labels.")
utils.value_count_df(cadence_labels.cadence)

406 cadence labels.

	counts	%
cadence
PAC	182	44.83
HC	122	30.05
IAC	43	10.59
EC	30	7.39
DC	24	5.91
PC	5	1.23

Per dataset

all_labels = D.get_feature("harmonylabels")
cadence_count_per_dataset = all_labels.groupby("corpus").cadence.value_counts()
cadence_fraction_per_dataset = (
    cadence_count_per_dataset / cadence_count_per_dataset.groupby(level=0).sum()
)
cadence_fraction_per_dataset = cadence_fraction_per_dataset.rename(
    "fraction"
).reset_index()
cadence_fraction_per_dataset["corpus_name"] = cadence_fraction_per_dataset.corpus.map(
    utils.get_corpus_display_name
)
fig = px.bar(
    cadence_fraction_per_dataset,
    x="corpus_name",
    y="fraction",
    title="Distribution of cadence types per corpus",
    color="cadence",
    color_discrete_map=plotting.CADENCE_COLORS,
    labels=dict(corpus_name="", fraction="Fraction of all cadences"),
    category_orders=dict(corpus_name=chronological_corpus_names),
)
fig.update_layout(**utils.STD_LAYOUT)
save_figure_as(fig, "all_cadences_corpuswise_stacked_bars", height=1000)
fig.show()

fig = px.pie(
    cadence_count_per_dataset.rename("count").reset_index(),
    names="cadence",
    color="cadence",
    values="count",
    facet_col="corpus",
    facet_col_wrap=4,
    height=2000,
    color_discrete_map=plotting.CADENCE_COLORS,
)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.update_layout(**utils.STD_LAYOUT)
save_figure_as(fig, "all_cadences_corpuswise_pies")
fig.show()

cadence_count_per_mode = (
    all_labels.groupby("localkey_is_minor").cadence.value_counts().reset_index()
)
cadence_count_per_mode["mode"] = cadence_count_per_mode.localkey_is_minor.map(
    {False: "major", True: "minor"}
)
fig = px.pie(
    cadence_count_per_mode,
    names="cadence",
    color="cadence",
    values="count",
    facet_col="mode",
    height=2000,
    color_discrete_map=plotting.CADENCE_COLORS,
)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.update_layout(**utils.STD_LAYOUT)
save_figure_as(fig, "all_cadences_modewise_pies")
fig.show()

corelli = dc.Dataset()
CORELLI_PATH = os.path.abspath(os.path.join("..", "corelli"))
corelli.load(directory=CORELLI_PATH, parse_tsv=False)
annotated_view = corelli.data.get_view("annotated")
annotated_view.include("facets", "expanded")
annotated_view.pieces_with_incomplete_facets = False
corelli.data.set_view(annotated_view)
corelli.data.parse_tsv(choose="auto")
corelli.get_indices()
corelli_labels = corelli.get_facet("expanded")
corelli_cadence_count_per_mode = (
    corelli_labels.groupby("localkey_is_minor").cadence.value_counts().reset_index()
)
corelli_cadence_count_per_mode["mode"] = (
    corelli_cadence_count_per_mode.localkey_is_minor.map(
        {False: "major", True: "minor"}
    )
)
fig = px.pie(
    corelli_cadence_count_per_mode,
    names="cadence",
    color="cadence",
    values="count",
    facet_col="mode",
    height=2000,
    color_discrete_map=plotting.CADENCE_COLORS,
)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.update_layout(**utils.STD_LAYOUT)
save_figure_as(fig, "all_corelli_cadences_modewise_pies")
fig.show()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[14], line 3
      1 corelli = dc.Dataset()
      2 CORELLI_PATH = os.path.abspath(os.path.join("..", "corelli"))
----> 3 corelli.load(directory=CORELLI_PATH, parse_tsv=False)
      4 annotated_view = corelli.data.get_view("annotated")
      5 annotated_view.include("facets", "expanded")

TypeError: Dataset.load() got an unexpected keyword argument 'directory'

combined_cadences = pd.concat(
    [cadence_count_per_mode, corelli_cadence_count_per_mode],
    keys=["couperin", "corelli"],
    names=["corpus", None],
).reset_index(level=0)
fig = px.pie(
    combined_cadences,
    names="cadence",
    color="cadence",
    values="count",
    facet_col="mode",
    facet_row="corpus",
    height=2000,
    color_discrete_map=plotting.CADENCE_COLORS,
)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
updated_layout = dict(utils.STD_LAYOUT, font=dict(size=40))
fig.update_layout(**updated_layout)
save_figure_as(fig, "couperin_corelli_cadences_modewise_pies")
fig.show()

Per phrase

Number of cadences per phrase

grouped_by_corpus = groupers.CorpusGrouper().process(D)
segmented = slicers.PhraseSlicer().process_data(grouped_by_corpus)
phrases = segmented.get_slice_info()
phrase_segments = segmented.get_facet("expanded")
phrase_gpb = phrase_segments.groupby(level=[0, 1, 2])
local_keys_per_phrase = phrase_gpb.localkey.unique().map(tuple)
n_local_keys_per_phrase = local_keys_per_phrase.map(len)
phrases_with_keys = pd.concat(
    [
        n_local_keys_per_phrase.rename("n_local_keys"),
        local_keys_per_phrase.rename("local_keys"),
        phrases,
    ],
    axis=1,
)
phrases_with_cadences = pd.concat(
    [
        phrase_gpb.cadence.nunique().rename("n_cadences"),
        phrase_gpb.cadence.unique()
        .rename("cadences")
        .map(lambda arr: tuple(e for e in arr if not pd.isnull(e))),
        phrases_with_keys,
    ],
    axis=1,
)
utils.value_count_df(phrases_with_cadences.n_cadences, counts_column="#phrases")

n_cad = (
    phrases_with_cadences.groupby(level="corpus")
    .n_cadences.value_counts()
    .rename("counts")
    .reset_index()
    .sort_values("n_cadences")
)
n_cad.n_cadences = n_cad.n_cadences.astype(str)
fig = px.bar(
    n_cad,
    x="corpus",
    y="counts",
    color="n_cadences",
    height=800,
    barmode="group",
    labels=dict(n_cadences="#cadences in a phrase"),
    category_orders=dict(dataset=chronological_corpus_names),
)
save_figure_as(fig, "n_cadences_per_phrase_corpuswise_absolute_grouped_bars")
fig.show()

Combinations of cadence types for phrases with more than one cadence

utils.value_count_df(
    phrases_with_cadences[phrases_with_cadences.n_cadences > 1].cadences
)

Positioning of cadences within phrases

df_rows = []
y_position = 0
for ix in (
    phrases_with_cadences[phrases_with_cadences.n_cadences > 0]
    .sort_values("duration_qb")
    .index
):
    df = phrase_segments.loc[ix]
    description = str(ix)
    if df.cadence.notna().any():
        interval = ix[2]
        df_rows.append((y_position, interval.length, "end of phrase", description))
        start_pos = interval.left
        cadences = df.loc[df.cadence.notna(), ["quarterbeats", "cadence"]]
        cadences.quarterbeats -= start_pos
        for cadence_x, cadence_type in cadences.itertuples(index=False, name=None):
            df_rows.append((y_position, cadence_x, cadence_type, description))
        y_position += 1
    # else:
    #    df_rows.append((y_position, pd.NA, pd.NA, description))

data = pd.DataFrame(df_rows, columns=["phrase_ix", "x", "marker", "description"])

fig = px.scatter(
    data[data.x.notna()],
    x="x",
    y="phrase_ix",
    color="marker",
    hover_name="description",
    height=3000,
    labels=dict(marker="legend"),
    color_discrete_map=plotting.CADENCE_COLORS,
)
fig.update_traces(marker_size=5)
fig.update_yaxes(autorange="reversed")
save_figure_as(fig, "cadence_positions_within_all_phrases")
fig.show()

Cadence ultima

phrase_segments = segmented.get_facet("expanded")
cadence_selector = phrase_segments.cadence.notna()
missing_chord_selector = phrase_segments.chord.isna()
cadence_with_missing_chord_selector = cadence_selector & missing_chord_selector
missing = phrase_segments[cadence_with_missing_chord_selector]
expanded = ms3.expand_dcml.expand_labels(
    phrase_segments[cadence_with_missing_chord_selector],
    propagate=False,
    chord_tones=True,
    skip_checks=True,
)
phrase_segments.loc[cadence_with_missing_chord_selector] = expanded
print(
    f"Ultima harmony missing for {(phrase_segments.cadence.notna() & phrase_segments.bass_note.isna()).sum()} cadence "
    f"labels."
)

Ultimae as Roman numeral

def highlight(row, color="#ffffb3"):
    if row.counts < 10:
        return [None, None, None, None]
    else:
        return ["background-color: {color};"] * 4


cadence_counts = all_labels.cadence.value_counts()
ultima_root = (
    phrase_segments.groupby(["localkey_is_minor", "cadence"])
    .numeral.value_counts()
    .rename("counts")
    .to_frame()
    .reset_index()
)
ultima_root.localkey_is_minor = ultima_root.localkey_is_minor.map(
    {False: "in major", True: "in minor"}
)
# ultima_root.style.apply(highlight, axis=1)

fig = px.pie(
    ultima_root,
    names="numeral",
    values="counts",
    facet_row="cadence",
    facet_col="localkey_is_minor",
    height=1500,
    category_orders={"cadence": cadence_counts.index},
)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.update_traces(textposition="inside", textinfo="percent+label")
fig.update_layout(**utils.STD_LAYOUT)
save_figure_as(fig, "ultima_root_distributions_over_cadence_types_maj_min_pies")
fig.show()

# phrase_segments.groupby(level=[0,1,2], group_keys=False).apply(lambda df: df if ((df.cadence == 'PAC') &
# (df.numeral == 'V')).any() else None)

Ultimae bass note as scale degree

ultima_bass = (
    phrase_segments.groupby(["localkey_is_minor", "cadence"])
    .bass_note.value_counts()
    .rename("counts")
    .reset_index()
)
ultima_bass.bass_note = ms3.transform(
    ultima_bass, ms3.fifths2sd, dict(fifths="bass_note", minor="localkey_is_minor")
)
ultima_bass.localkey_is_minor = ultima_bass.localkey_is_minor.map(
    {False: "in major", True: "in minor"}
)
# ultima_bass.style.apply(highlight, axis=1)

fig = px.pie(
    ultima_bass,
    names="bass_note",
    values="counts",
    facet_row="cadence",
    facet_col="localkey_is_minor",
    height=1500,
    category_orders={"cadence": cadence_counts.index},
)
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.update_traces(textposition="inside", textinfo="percent+label")
fig.update_layout(**utils.STD_LAYOUT)
save_figure_as(fig, "ultima_degree_distributions_over_cadence_types_maj_min_pies")
fig.show()

Chord progressions

PACs with ultima I/i

def remove_immediate_duplicates(lst):
    return tuple(a for a, b in zip(lst, (None,) + lst) if a != b)


def get_progressions(
    selected="PAC",
    last_row={},
    feature="chord",
    dataset=None,
    as_series=True,
    remove_duplicates=False,
):
    """Uses the nonlocal variable phrase_segments."""
    last_row = {k: v if isinstance(v, tuple) else (v,) for k, v in last_row.items()}
    progressions = []

    for (corp, fname, *_), df in phrase_segments[
        phrase_segments[feature].notna()
    ].groupby(level=[0, 1, 2]):
        if dataset is not None and dataset not in corp:
            continue
        if (df.cadence == selected).fillna(False).any():
            # remove chords after the last cadence label
            df = df[df.cadence.bfill().notna()]
            # group segments leading up to a cadence label
            cadence_groups = df.cadence.notna().shift().fillna(False).cumsum()
            for i, cadence in df.groupby(cadence_groups):
                last_r = cadence.iloc[-1]
                typ = last_r.cadence
                if typ != selected:
                    continue
                if any(last_r[feat] not in values for feat, values in last_row.items()):
                    continue
                if remove_duplicates:
                    progressions.append(
                        remove_immediate_duplicates(cadence[feature].to_list())
                    )
                else:
                    progressions.append(tuple(cadence[feature]))
    if as_series:
        return pd.Series(progressions, dtype="object")
    return progressions

chord_progressions = get_progressions("PAC", dict(numeral=("I", "i")), "chord")
print(f"Progressions for {len(chord_progressions)} cadences:")
utils.value_count_df(chord_progressions, "chord progressions")

numeral_progressions = get_progressions("PAC", dict(numeral=("I", "i")), "numeral")
utils.value_count_df(numeral_progressions, "numeral progressions")

numeral_prog_no_dups = numeral_progressions.map(remove_immediate_duplicates)
utils.value_count_df(numeral_prog_no_dups)

PACs ending on scale degree 1

Scale degrees expressed w.r.t. major scale, regardless of actual key.

bass_progressions = get_progressions("PAC", dict(bass_note=0), "bass_note")
bass_prog = bass_progressions.map(ms3.fifths2sd)
print(f"Progressions for {len(bass_progressions)} cadences:")
utils.value_count_df(bass_prog, "bass progressions")

bass_prog_no_dups = bass_prog.map(remove_immediate_duplicates)
utils.value_count_df(bass_prog_no_dups)

def progressions2graph_data(progressions, cut_at_stage=None):
    stage_nodes = defaultdict(dict)
    edge_weights = Counter()
    node_counter = 0
    for progression in progressions:
        previous_node = None
        for stage, current in enumerate(reversed(progression)):
            if cut_at_stage and stage > cut_at_stage:
                break
            if current in stage_nodes[stage]:
                current_node = stage_nodes[stage][current]
            else:
                stage_nodes[stage][current] = node_counter
                current_node = node_counter
                node_counter += 1
            if previous_node is not None:
                edge_weights.update([(current_node, previous_node)])
            previous_node = current_node
    return stage_nodes, edge_weights


def plot_progressions(progressions, cut_at_stage=None, **kwargs):
    stage_nodes, edge_weights = progressions2graph_data(
        progressions, cut_at_stage=cut_at_stage
    )
    return utils.graph_data2sankey(stage_nodes, edge_weights, **kwargs)

Chordal roots for the 3 last stages

fig = plot_progressions(
    numeral_prog_no_dups,
    cut_at_stage=3,
    font=dict(size=30),
)
save_figure_as(fig, "last_3_roots_before_pacs_ending_on_1_sankey", height=800)
fig.show()

Complete chords for the last four stages in major

pac_major = get_progressions("PAC", dict(numeral="I", localkey_is_minor=False), "chord")
fig = plot_progressions(pac_major, cut_at_stage=4)
save_figure_as(fig, "last_4_stages_before_pacs_in_major_sankey")
fig.show()

Bass degrees for the last 6 stages.

fig = plot_progressions(bass_prog_no_dups, cut_at_stage=7)
save_figure_as(fig, "last_7_degrees_before_pacs_ending_on_1_sankey")
fig.show()

Bass degrees without accidentals

def remove_sd_accidentals(t):
    return tuple(map(lambda sd: sd[-1], t))


bass_prog_no_acc_no_dup = bass_prog.map(remove_sd_accidentals).map(
    remove_immediate_duplicates
)
fig = plot_progressions(bass_prog_no_acc_no_dup, cut_at_stage=7)
save_figure_as(fig, "last_7_degrees_before_pacs_ending_on_1_without_accdentals_sankey")
fig.show()

HCs ending on V

half = get_progressions("HC", dict(numeral="V"), "bass_note").map(ms3.fifths2sd)
print(f"Progressions for {len(half)} cadences:")
fig = plot_progressions(half.map(remove_immediate_duplicates), cut_at_stage=5)
save_figure_as(fig, "last_7_degrees_before_hcs_ending_on_V_sankey")
fig.show()