Cadences

Show imports Hide imports

%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

Show source Hide source

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

Show source 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': []}

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

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[4], line 2
      1 try:
----> 2     cadence_labels = D.get_feature("cadencelabels")
      3 except Exception:

File ~/.local/lib/python3.12/site-packages/dimcat/data/datasets/base.py:314, in Dataset.get_feature(self, feature)
    313     pass
--> 314 return self.extract_feature(feature_config)

File ~/.local/lib/python3.12/site-packages/dimcat/data/datasets/base.py:290, in Dataset.extract_feature(self, feature, ignore_exceptions)
    289 feature_extractor = Constructor(feature_config)
--> 290 extracted = self._extract_feature(
    291     feature_config, ignore_exceptions=ignore_exceptions
    292 )
    293 self.add_output(resource=extracted, package_name="features")

File ~/.local/lib/python3.12/site-packages/dimcat/data/datasets/base.py:259, in Dataset._extract_feature(self, feature_config, ignore_exceptions)
    249 """Extracts a feature from the Dataset's input catalog, sends it through its pipeline and returns the result,
    250 without storing it.
    251 
   (...)    257         extracted feature, ignoring the fact that not all PipelineSteps may have been applied to it.
    258 """
--> 259 extracted = self.inputs.extract_feature(feature_config)
    260 if len(self._pipeline) == 0:

File ~/.local/lib/python3.12/site-packages/dimcat/data/catalogs/inputs.py:18, in InputsCatalog.extract_feature(self, feature)
     17 package = self.get_package()
---> 18 return package.extract_feature(feature)

File ~/.local/lib/python3.12/site-packages/dimcat/data/packages/base.py:1047, in Package.extract_feature(self, feature)
   1046 selected_facet = candidate_facets[0]
-> 1047 return selected_facet._extract_feature(feature_config)

File ~/.local/lib/python3.12/site-packages/dimcat/data/resources/dc.py:985, in DimcatResource._extract_feature(self, feature_config, new_name)
    984 len_before = len(feature_df)
--> 985 feature_df = self._transform_df_for_extraction(feature_df, feature_config)
    986 init_args = dict(
    987     resource_name=new_name,
    988 )

File ~/.local/lib/python3.12/site-packages/dimcat/data/resources/facets.py:808, in MuseScoreHarmonies._transform_df_for_extraction(self, feature_df, feature_config)
    807 if issubclass(cls, CadenceLabels):
--> 808     feature_df = drop_rows_with_missing_values(
    809         feature_df, feature_column_names, logger=self.logger
    810     )
    811     feature_df = extend_cadence_feature(feature_df)

File ~/.local/lib/python3.12/site-packages/dimcat/data/resources/utils.py:435, in drop_rows_with_missing_values(df, column_names, how, logger)
    434 if drop_mask.all():
--> 435     raise RuntimeError(
    436         f"The dataframe contains no fully defined objects based on the "
    437         f"columns {column_names}."
    438     )
    439 n_dropped = drop_mask.sum()

RuntimeError: The dataframe contains no fully defined objects based on the columns ['cadence'].

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
Cell In[4], line 4
      2     cadence_labels = D.get_feature("cadencelabels")
      3 except Exception:
----> 4     raise ValueError("Corpus has no cadence annotations.")
      5 cadence_labels

ValueError: Corpus has no cadence annotations.

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)

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()

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()