Rhythmic Partitioning Scripts

This repository contains Rhythmic Partitioning Scripts (aka RP Scripts). This program is part of a paper written by Marcos Sampaio and Pauxy Gentil-Nunes and published at Brazilian Journal of Music and Mathematics (MusMat). The paper describes the RP Scripts' functionalities and operation.

RP Scripts are command-line Python scripts designed to extract rhythmic partitioning information from given digital music scores files, plot partitiogram and indexogram, and to create annotated digital music scores files.

RP Scripts reads MusicXML (compressed or not), Kern, and MIDI files and generates RP data in CSV files as well as partitioning charts.

Install

RP Scripts are written in Python 3 and need some libraries listed in requirements.txt.

If you are not familiar with Python and its libraries, follow these steps:

Download and install Python 3 (check https://www.python.org/downloads/).
Download this repository.
Open a terminal (command prompt in Windows) and go to this repository's folder in your computer. For instance, cd C:\Users\myName\rpScripts (Windows) or cd /Users/myName/rpScripts (Mac).
Install the libraries with Python's PIP program: pip install -r requirements.txt

Components and basic usage

RP Scripts comprises four standalone scripts:

RPC: Rhythmic Partitioning Calculator
RPP: Rhythmic Partitioning Plotter
RPA: Rhythmic Partitioning Annotator
RPL: Rhythmic Partitioning Labeler

These scripts are available at src folder.

Some examples of their usage are available at examples folder.

RPC

RPC processes the given digital score and returns a CSV file with partitions' information.

python rpc.py score.xml

Since RPC uses Music21 library, it accepts MusicXML, MXL (compressed MusicXML), Kern and MIDI files as input.

CSV file output contains:

Index (measure number + offset)
Measure number
Offset
Global offset
Duration
Partition
Density number
Agglomeration index
Dispersion index

RPC returns a CSV file such as

"Index","Measure number","Offset","Global offset","Duration","Partition","Density-number","Agglomeration","Dispersion"
"1+0",1,0,0,1/4,"0",0,"",""
"1+1/4",1,1/4,1/4,3/2,"1.3",4,3.0,3.0
"1+1/2",1,1/2,1/2,3/2,"1.3",4,3.0,3.0
"2+0",2,0,3/4,3/2,"1.3",4,3.0,3.0
"2+1/4",2,1/4,1,3/2,"1.3",4,3.0,3.0
"2+1/2",2,1/2,5/4,3/2,"1.3",4,3.0,3.0
"2+3/4",2,3/4,3/2,3/2,"1.3",4,3.0,3.0
"2+1",2,1,7/4,1,"4",4,6.0,0.0
"2+5/4",2,5/4,2,1,"4",4,6.0,0.0
"2+3/2",2,3/2,9/4,1,"4",4,6.0,0.0
"2+7/4",2,7/4,5/2,1,"4",4,6.0,0.0

RPP

RPP return partitiogram and indexogram from a given CSV file generated by RPC. The basic files are generated with:

python rpp.py score.csv

Obs: All the examples below are related to Robert Schumann's op. 48, n. 2.

Partitiogram

Indexogram

RPP accepts multiple options to define image format, resolution, indexogram type, among other stuff.

Image format

Use -f option to set the chart images format. Otherwise, they RPP will generate them in svg format.

python rpp.py -f svg score.csv
python rpp.py -f png score.csv
python rpp.py -f jpg score.csv

Image resolution

When an image format such as JPG or PNG is defined use -r to set its resolution:

python rpp.py -f png -r 300 score.csv

Partitiogram options

Use --maximum_points_to_label to set the maximum number of points to label in the partitiogram charts. Default is 50:

python rpp.py --maximum_points_to_label 20 -u score.csv

Use --labels_size to set labels' size in the partitiogram charts. Default is 15:

python rpp.py --labels_size 20 -u score.csv

Use --labels_distance to set the distance between points and labels in the partitiogram charts. Default is 1.025:

python rpp.py --labels_distance 2 -u score.csv

Use --dots_size to define the dots size in the partitiograms charts. Default is 15, but in the previous example, the value is set to 10:

python rpp.py --dots_size 10 -m score-labeled.csv

Bubble partitiogram

Use -u to plot bubble partitiograms:

python rpp.py -u score.csv

Bubble partitiogram

Comparative partitiograms

Use -m to plot comparative partitiograms. It demands an annotated CSV file with labels for each row. RPL script generates the annotated file (See section RPL).

RPP generates comparative partitiograms for all labels combination pairs.

python rpp.py -m score-labeled.csv

Comparative partitiogram: parts A and B

Indexogram options

Closing bubbles

Use -c option to plot vertical lines closing indexogram bubbles:

python rpp.py -c score.csv

Indexogram with closing lines

Stem style

Use -e to plot indexogram in stem style:

python rpp.py -e score.csv

Indexogram as stem chart

Stairs style

Use -t to plot indexogram in stairs style:

python rpp.py -t score.csv

Indexogram as stair chart

Combined style

Use -b to plot indexogram in combined style:

python rpp.py -b score.csv

Indexogram as combined chart

All charts

Use -a to plot all available indexogram types charts:

python rpp.py -a score.csv

RPA

RPA generates annotated digital score from a given score and CSV file generated by RPC.

python rpa.py -s score.xml -c score.csv

Annotated score

RPL

RPL generates labeled CSV file form a given CSV file generated by RPC. One can add labels such as "Exposition", "First Theme", and so on. RPP needs labeled CSV to render comparative partitiograms.

RPL demands a TXT file with annotations with the label name and its start point, one in each line and separated by commas:

One label,1+0
Other label,2+1/4
One more label,2+3/2

The labeled CSV looks like:

,Index,Measure number,Offset,Global offset,Duration,Partition,Density-number,Agglomeration,Dispersion,Label
0,1+0,1,0,0,1/4,0,0,,,One label
1,1+1/4,1,1/4,1/4,3/2,1.3,4,3.0,3.0,One label
2,1+1/2,1,1/2,1/2,3/2,1.3,4,3.0,3.0,One label
3,2+0,2,0,3/4,3/2,1.3,4,3.0,3.0,One label
4,2+1/4,2,1/4,1,3/2,1.3,4,3.0,3.0,Other label
5,2+1/2,2,1/2,5/4,3/2,1.3,4,3.0,3.0,Other label
6,2+3/4,2,3/4,3/2,3/2,1.3,4,3.0,3.0,Other label
7,2+1,2,1,7/4,1,4,4,6.0,0.0,Other label
8,2+5/4,2,5/4,2,1,4,4,6.0,0.0,Other label
9,2+3/2,2,3/2,9/4,1,4,4,6.0,0.0,One more label
10,2+7/4,2,7/4,5/2,1,4,4,6.0,0.0,One more label

Usage as package

RPC

import music21
import pandas

# import RPScripts
import src as rp

# Generate Music21 score stream
sco = music21.converter.parse('filename.mxl')
texture = rp.Texture()
texture.make_from_music21_score(sco)

# Get partitions data as a dictionary
dic = texture.get_data(equal_duration_events=True)

# Generate Pandas Dataframe object and export to CSV.
df = pandas.DataFrame(dic['data'], columns=dic['header'])
df.to_csv('filename.csv')

RPP

import pandas

# import RPScripts
import src as rp

# Use dataframe generated in the previous snippet.

# Plot simple partitiogram (Jupyter)
chart = rp.rpp.SimplePartitiogramPlotter(df)
chart.plot()

# Generate bubble partitiogram and save to "Mybubblechart" file
chart2 = rp.rpp.BubblePartitiogramPlotter(df, basename='mybubblechart')
chart2.plot()
chart2.save()

# Change default constant
rp.rpp.LABELS_SIZE = 20

Bibliography

Gentil-Nunes, Pauxy. 2009. "Análise Particional: uma Mediação entre Composição Musical e a Teoria das Partições." Ph.D. Dissertation, Universidade Federal do Estado do Rio de Janeiro.
Sampaio, Marcos da Silva, and Pauxy Gentil-Nunes. 2022. "Python Scripts for Rhythmic Partitioning Analysis." MusMat - Brazilian Journal of Music and Mathematics 6 (2): 17-55. https://musmat.org/wp-content/uploads/2022/12/02-Sampaio-Gentil-Nunes-V6N2_2022.pdf.
Sampaio, Marcos da Silva, Pauxy Gentil-Nunes, Sidnei Marques de Oliveira, Vicente Sanches de Oliveira, Jaderson Cardona de Oliveira. "New Visual Tools for Rhythmic Partitioning Analysis of Musical Texture." Musica Theorica 7 (2). https://revistamusicatheorica.tema.mus.br/index.php/musica-theorica/article/view/240/.

How to cite

Sampaio, Marcos da Silva and Pauxy Gentil-Nunes. RP Scripts: Rhythmic Partitioning Scripts, release 1.0. Available at https://github.com/msampaio/rpScripts. Accessed on Dec. 19, 2022, 2022.

Bibtex

Penny-Admixture / rpScripts