Notes#

Hide imports 
import os
from collections import defaultdict, Counter

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 STD_LAYOUT, CADENCE_COLORS, CORPUS_COLOR_SCALE, chronological_corpus_order, color_background, get_corpus_display_name, get_repo_name, resolve_dir, value_count_df, get_repo_name, print_heading, resolve_dir
Hide 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/wagner_overtures'
ANNOTATED_ONLY: False
Hide 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 'wagner_overtures' @ 6461947
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        expanded          chords       
                 metadata     view detected detected parsed detected parsed detected parsed detected parsed
corpus                                                                                                     
wagner_overtures      yes  default        2        2      2        2      2        2      2        2      2

14/46 files are excluded from this view.

14 files have been excluded based on their subdir.

N = 2 annotated pieces, 8 parsed dataframes.

Metadata#

all_metadata = dataset.data.metadata()
print(f"Concatenated 'metadata.tsv' files cover {len(all_metadata)} of the {dataset.data.count_pieces()} scores.")
all_metadata.reset_index(level=1).groupby(level=0).nth(0).iloc[:,:20]

Concatenated 'metadata.tsv' files cover 2 of the 2 scores.
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 composed_start composed_end
corpus
wagner_overtures WWV090_Tristan_01_Vorspiel-Prelude_Ricordi1888... {1: '6/8'} {1: 0, 44: 3, 72: 0} 112 111 333.5 112 111 333.5 1224.5 1676 896 0 0 360 a 2.1.0 Adrian Nagel 1854 1859

Compute chronological order

chronological_order = chronological_corpus_order(all_metadata)
corpus_colors = dict(zip(chronological_order, CORPUS_COLOR_SCALE))
chronological_order

['wagner_overtures']

all_notes = dataset.data.get_all_parsed('notes', force=True, flat=True)
print(f"{len(all_notes.index)} notes over {len(all_notes.groupby(level=[0,1]))} files.")
all_notes.head()

7368 notes over 2 files.
mc mn quarterbeats quarterbeats_all_endings duration_qb mc_onset mn_onset timesig staff voice ... gracenote tremolo nominal_duration scalar tied tpc midi name octave chord_id
corpus piece i
wagner_overtures WWV090_Tristan_01_Vorspiel-Prelude_Ricordi1888Floridia 0 1 0 0 0 0.5 0 5/8 6/8 1 1 ... <NA> NaN 1/8 1 <NA> 3 57 A3 3 0
1 2 1 1/2 1/2 1.5 0 0 6/8 1 1 ... <NA> NaN 1/4 3/2 1 -1 65 F4 4 1
2 2 1 2 2 1.0 3/8 3/8 6/8 1 1 ... <NA> NaN 1/4 1 -1 -1 65 F4 4 2
3 2 1 3 3 0.5 5/8 5/8 6/8 1 1 ... <NA> NaN 1/8 1 <NA> 4 64 E4 4 3
4 3 2 7/2 7/2 3.0 0 0 6/8 2 1 ... <NA> NaN 1/2 3/2 <NA> -1 53 F3 3 8

5 rows × 21 columns

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: 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 Wagner Overtures 26
1 Wagner Overtures 28
2 Wagner Overtures 29
3 Wagner Overtures 29
4 Wagner Overtures 31
... ... ...
4948 Wagner Overtures 93
4949 Wagner Overtures 93
4950 Wagner Overtures 93
4951 Wagner Overtures 96
4952 Wagner Overtures 96

4953 rows × 2 columns

yaxis=dict(tickmode= 'array',
           tickvals= [12, 24, 36, 48, 60, 72, 84, 96],
           ticktext = ["C0", "C1", "C2", "C3", "C4", "C5", "C6", "C7"],
           gridcolor='lightgrey',
           )
fig = px.violin(weighted_midi, 
                x='dataset', 
                y='midi', 
                color='dataset', 
                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(yaxis=yaxis, 
                  **STD_LAYOUT,
                 showlegend=False)
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 Wagner Overtures -7
1 Wagner Overtures -7
2 Wagner Overtures -7
3 Wagner Overtures -7
4 Wagner Overtures -7
... ... ...
2469 Wagner Overtures 12
2470 Wagner Overtures 12
2471 Wagner Overtures 12
2472 Wagner Overtures 12
2473 Wagner Overtures 13

2474 rows × 2 columns

As violin plot#

yaxis=dict(
    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##"],
    gridcolor='lightgrey',
    zerolinecolor='lightgrey',
    zeroline=True
           )
fig = px.violin(weighted_tpc, 
                x='dataset', 
                y='tpc', 
                color='dataset', 
                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(yaxis=yaxis, 
                  **STD_LAYOUT,
                 showlegend=False)
fig.show()

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=CORPUS_COLOR_SCALE,
             width=1000, height=300,
             )
fig.update_layout(**STD_LAYOUT)
fig.update_yaxes(gridcolor='lightgrey')
fig.update_xaxes(gridcolor='lightgrey', zerolinecolor='grey', tickmode='array', 
                 tickvals=x_values, ticktext = x_names, dtick=1, ticks='outside', tickcolor='black', 
                 minor=dict(dtick=6, gridcolor='grey', showgrid=True),
                )
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(**STD_LAYOUT)
fig.update_yaxes(gridcolor='lightgrey')
fig.update_xaxes(gridcolor='lightgrey', zerolinecolor='grey', tickmode='array', 
                 tickvals=x_values, ticktext = x_names, dtick=1, ticks='outside', tickcolor='black', 
                 minor=dict(dtick=6, gridcolor='grey', showgrid=True),
                )
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(**STD_LAYOUT, showlegend=False)
fig.update_xaxes(gridcolor='lightgrey', zerolinecolor='lightgrey', tickmode='array', tickvals= [-12, -6, 0, 6, 12, 18],
    ticktext = ["Dbb", "Gb", "C", "F#", "B#", "E##"], visible=True, )
fig.update_yaxes(gridcolor='lightgrey', zeroline=False, matches=None, showticklabels=True)
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} = {no_accidental / entire}"

'Fraction of note duration without accidental of the entire durations: 3941.5738095238094 / 4951.75 = 0.7959961245062471'

Notes and staves#

print("Distribution of notes over staves:")
value_count_df(all_notes.staff)

Distribution of notes over staves:
counts %
staff
1 4246 0.576276
2 3122 0.423724