Overview#
This notebook gives a general overview of the features included in the dataset.
Show imports
import os
from collections import defaultdict, Counter
from fractions import Fraction
from git import Repo
import dimcat as dc
import ms3
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from utils import CADENCE_COLORS, CORPUS_COLOR_SCALE, STD_LAYOUT, TYPE_COLORS, color_background, corpus_mean_composition_years, value_count_df, get_corpus_display_name, get_repo_name, print_heading, resolve_dir
Show source
CORPUS_PATH = os.path.abspath(os.path.join('..', '..'))
ANNOTATED_ONLY = os.getenv("ANNOTATED_ONLY", "True").lower() in ('true', '1', 't')
print_heading("Notebook settings")
print(f"CORPUS_PATH: {CORPUS_PATH!r}")
print(f"ANNOTATED_ONLY: {ANNOTATED_ONLY}")
CORPUS_PATH = resolve_dir(CORPUS_PATH)
Notebook settings
-----------------
CORPUS_PATH: '/home/runner/work/workflow_deployment/debussy_etudes'
ANNOTATED_ONLY: False
Show source
repo = Repo(CORPUS_PATH)
print_heading("Data and software versions")
print(f"Data repo '{get_repo_name(repo)}' @ {repo.commit().hexsha[:7]}")
print(f"dimcat version {dc.__version__}")
print(f"ms3 version {ms3.__version__}")
Data and software versions
--------------------------
Data repo 'debussy_etudes' @ fd1826a
dimcat version 0.3.0
ms3 version 2.5.2
dataset = dc.Dataset()
dataset.load(directory=CORPUS_PATH, parse_tsv=False)
[default|all]
All corpora
-----------
View: This view is called 'default'. It
- excludes pieces that are not contained in the metadata,
- filters out file extensions requiring conversion (such as .xml), and
- excludes review files and folders.
has active scores measures notes
metadata view detected detected parsed detected parsed
corpus
debussy_etudes yes default 12 12 12 12 12
N = 12 annotated pieces, 24 parsed dataframes.
Show data loading
all_metadata = dataset.data.metadata()
assert len(all_metadata) > 0, "No pieces selected for analysis."
print(f"Metadata covers {len(all_metadata)} of the {dataset.data.count_pieces()} scores.")
all_notes = dataset.get_facet('notes')
all_measures = dataset.get_facet('measures')
mean_composition_years = corpus_mean_composition_years(all_metadata)
chronological_order = mean_composition_years.index.to_list()
corpus_colors = dict(zip(chronological_order, CORPUS_COLOR_SCALE))
corpus_names = {corp: 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()}
Metadata covers 12 of the 12 scores.
Composition dates#
This section relies on the dataset’s metadata.
valid_composed_start = pd.to_numeric(all_metadata.composed_start, errors='coerce')
valid_composed_end = pd.to_numeric(all_metadata.composed_end, errors='coerce')
print(f"Composition dates range from {int(valid_composed_start.min())} {valid_composed_start.idxmin()} "
f"to {int(valid_composed_end.max())} {valid_composed_end.idxmax()}.")
Composition dates range from 1915 ('debussy_etudes', 'l136-01_etudes_cinq') to 1915 ('debussy_etudes', 'l136-01_etudes_cinq').
Mean composition years per corpus#
Show source
summary = all_metadata.copy()
summary.length_qb = all_measures.groupby(level=[0,1]).act_dur.sum() * 4.0
summary = pd.concat([summary,
all_notes.groupby(level=[0,1]).size().rename('notes'),
], axis=1)
bar_data = pd.concat([mean_composition_years.rename('year'),
summary.groupby(level='corpus').size().rename('pieces')],
axis=1
).reset_index()
fig = px.bar(bar_data, x='year', y='pieces', color='corpus',
color_discrete_map=corpus_colors,
)
fig.update_traces(width=5)
fig.update_layout(**STD_LAYOUT)
fig.update_yaxes(gridcolor='lightgrey')
fig.update_traces(width=5)
Composition years histogram#
Show source
hist_data = summary.reset_index()
hist_data.corpus = hist_data.corpus.map(corpus_names)
fig = px.histogram(hist_data, x='composed_end', color='corpus',
labels=dict(composed_end='decade',
count='pieces',
),
color_discrete_map=corpus_name_colors,
)
fig.update_traces(xbins=dict(
size=10
))
fig.update_layout(**STD_LAYOUT)
fig.update_yaxes(gridcolor='lightgrey')
fig.show()
Dimensions#
Overview#
Show source
corpus_metadata = summary.groupby(level=0)
n_pieces = corpus_metadata.size().rename('pieces')
absolute_numbers = dict(
measures = corpus_metadata.last_mn.sum(),
length = corpus_metadata.length_qb.sum(),
notes = corpus_metadata.notes.sum(),
labels = corpus_metadata.label_count.sum(),
)
absolute = pd.DataFrame.from_dict(absolute_numbers)
absolute = pd.concat([n_pieces, absolute], axis=1)
sum_row = pd.DataFrame(absolute.sum(), columns=['sum']).T
absolute = pd.concat([absolute, sum_row])
relative = absolute.div(n_pieces, axis=0)
complete_summary = pd.concat([absolute, relative, absolute.iloc[:1,2:].div(absolute.measures, axis=0)], axis=1, keys=['absolute', 'per piece', 'per measure'])
complete_summary = complete_summary.apply(pd.to_numeric).round(2)
complete_summary.index = complete_summary.index.map(dict(corpus_names, sum='sum'))
complete_summary
| absolute | per piece | per measure | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pieces | measures | length | notes | labels | pieces | measures | length | notes | labels | length | notes | labels | |
| Debussy Etudes | 12 | 1072 | 2829.0 | 25076 | 0 | 1.0 | 89.33 | 235.75 | 2089.67 | 0.0 | 2.64 | 23.39 | 0.0 |
| sum | 12 | 1072 | 2829.0 | 25076 | 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Measures#
print(f"{len(all_measures.index)} measures over {len(all_measures.groupby(level=[0,1]))} files.")
all_measures.head()
1089 measures over 12 files.
| mc | mn | quarterbeats | duration_qb | keysig | timesig | act_dur | mc_offset | numbering_offset | dont_count | barline | breaks | repeats | next | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| corpus | fname | interval | ||||||||||||||
| debussy_etudes | l136-01_etudes_cinq | [0.0, 4.0) | 1 | 1 | 0 | 4.0 | 0 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | firstMeasure | (2,) |
| [4.0, 8.0) | 2 | 2 | 4 | 4.0 | 0 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | <NA> | (3,) | ||
| [8.0, 12.0) | 3 | 3 | 8 | 4.0 | 0 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | <NA> | (4,) | ||
| [12.0, 16.0) | 4 | 4 | 12 | 4.0 | 0 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | <NA> | (5,) | ||
| [16.0, 20.0) | 5 | 5 | 16 | 4.0 | 0 | 4/4 | 1 | 0 | <NA> | <NA> | <NA> | <NA> | <NA> | (6,) |
print("Distribution of time signatures per XML measure (MC):")
all_measures.timesig.value_counts(dropna=False)
Distribution of time signatures per XML measure (MC):
3/8 289
3/4 240
4/4 178
2/4 173
6/8 94
12/16 70
9/8 35
6/16 8
5/8 2
Name: timesig, dtype: int64
Harmony labels#
All symbols, independent of the local key (the mode of which changes their semantics).
try:
all_annotations = dataset.get_facet('expanded')
except Exception:
all_annotations = pd.DataFrame()
n_annotations = len(all_annotations.index)
includes_annotations = n_annotations > 0
if includes_annotations:
display(all_annotations.head())
print(f"Concatenated annotation tables contains {all_annotations.shape[0]} rows.")
no_chord = all_annotations.root.isna()
if no_chord.sum() > 0:
print(f"{no_chord.sum()} of them are not chords. Their values are: {all_annotations.label[no_chord].value_counts(dropna=False).to_dict()}")
all_chords = all_annotations[~no_chord].copy()
print(f"Dataset contains {all_chords.shape[0]} tokens and {len(all_chords.chord.unique())} types over {len(all_chords.groupby(level=[0,1]))} documents.")
all_annotations['corpus_name'] = all_annotations.index.get_level_values(0).map(get_corpus_display_name)
all_chords['corpus_name'] = all_chords.index.get_level_values(0).map(get_corpus_display_name)
else:
print(f"Dataset contains no annotations.")
Dataset contains no annotations.