@Article{          sampaio.ea2022-python,
    author       = {{Sampaio}, {Marcos da Silva} and Gentil-Nunes, Pauxy},
    year         = {2022},
    title        = {Python Scripts for Rhythmic Partitioning Analysis},
    abstract     = {The Rhythmic Partitioning Analysis demands laborious
                   tasks on segmentation and agglomeration/dispersion
                   calculus. Parsemat software runs these tasks and renders
                   indexogram and partitiogram charts. In the present paper,
                   we introduce the Rhythmic Partitioning Scripts (RP
                   Scripts) as an application of Rhythmic Partitioning in the
                   Python environment. It adds some features absent in
                   Parsemat, such as the access to measure indications of
                   each partition, introduction of rest handling, annotation
                   of texture info into digital scores, and other
                   improvements. The RP Scripts collect musical events'
                   locations and output locations and partitions' data into
                   CSV files, render indexogram/partitiogram charts, and
                   generate annotated MusicXML score files. RP Scripts have
                   three components: calculator (RPC), plotter (RPP), and
                   annotator (RPA) scripts.},
    journal      = {MusMat - Brazilian Journal of Music and Mathematics},
    keywords     = {Rhythmic Partitioning Analysis, Textural Analysis, Music
                   Analysis, Python scripts, Music21},
    month        = {12},
    number       = {2},
    pages        = {17--55},
    url          = {https://musmat.org/wp-content/uploads/2022/12/02-Sampaio-Gentil-Nunes-V6N2_2022.pdf},
    volume       = {6}
}

{"_id":"BFtSCo97ft7NjAws5","bibbaseid":"sampaio-gentilnunes-pythonscriptsforrhythmicpartitioninganalysis-2022","author_short":["Sampaio, M.","Gentil-Nunes, P."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Sampaio"],"firstnames":["Marcos da Silva"],"suffixes":[]},{"propositions":[],"lastnames":["Gentil-Nunes"],"firstnames":["Pauxy"],"suffixes":[]}],"year":"2022","title":"Python Scripts for Rhythmic Partitioning Analysis","abstract":"The Rhythmic Partitioning Analysis demands laborious tasks on segmentation and agglomeration/dispersion calculus. Parsemat software runs these tasks and renders indexogram and partitiogram charts. In the present paper, we introduce the Rhythmic Partitioning Scripts (RP Scripts) as an application of Rhythmic Partitioning in the Python environment. It adds some features absent in Parsemat, such as the access to measure indications of each partition, introduction of rest handling, annotation of texture info into digital scores, and other improvements. The RP Scripts collect musical events' locations and output locations and partitions' data into CSV files, render indexogram/partitiogram charts, and generate annotated MusicXML score files. RP Scripts have three components: calculator (RPC), plotter (RPP), and annotator (RPA) scripts.","journal":"MusMat - Brazilian Journal of Music and Mathematics","keywords":"Rhythmic Partitioning Analysis, Textural Analysis, Music Analysis, Python scripts, Music21","month":"12","number":"2","pages":"17–55","url":"https://musmat.org/wp-content/uploads/2022/12/02-Sampaio-Gentil-Nunes-V6N2_2022.pdf","volume":"6","bibtex":"@Article{ sampaio.ea2022-python,\n author = {{Sampaio}, {Marcos da Silva} and Gentil-Nunes, Pauxy},\n year = {2022},\n title = {Python Scripts for Rhythmic Partitioning Analysis},\n abstract = {The Rhythmic Partitioning Analysis demands laborious\n tasks on segmentation and agglomeration/dispersion\n calculus. Parsemat software runs these tasks and renders\n indexogram and partitiogram charts. In the present paper,\n we introduce the Rhythmic Partitioning Scripts (RP\n Scripts) as an application of Rhythmic Partitioning in the\n Python environment. It adds some features absent in\n Parsemat, such as the access to measure indications of\n each partition, introduction of rest handling, annotation\n of texture info into digital scores, and other\n improvements. The RP Scripts collect musical events'\n locations and output locations and partitions' data into\n CSV files, render indexogram/partitiogram charts, and\n generate annotated MusicXML score files. RP Scripts have\n three components: calculator (RPC), plotter (RPP), and\n annotator (RPA) scripts.},\n journal = {MusMat - Brazilian Journal of Music and Mathematics},\n keywords = {Rhythmic Partitioning Analysis, Textural Analysis, Music\n Analysis, Python scripts, Music21},\n month = {12},\n number = {2},\n pages = {17--55},\n url = {https://musmat.org/wp-content/uploads/2022/12/02-Sampaio-Gentil-Nunes-V6N2_2022.pdf},\n volume = {6}\n}\n\n","author_short":["Sampaio, M.","Gentil-Nunes, P."],"key":"sampaio.ea2022-python","id":"sampaio.ea2022-python","bibbaseid":"sampaio-gentilnunes-pythonscriptsforrhythmicpartitioninganalysis-2022","role":"author","urls":{"Paper":"https://musmat.org/wp-content/uploads/2022/12/02-Sampaio-Gentil-Nunes-V6N2_2022.pdf"},"keyword":["Rhythmic Partitioning Analysis","Textural Analysis","Music Analysis","Python scripts","Music21"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://hmb.sampaio.me/bibliografia.bib.txt","dataSources":["y5a846t8Z5zqyBbdj","n6MFY2CscQLDpJ7nT"],"keywords":["rhythmic partitioning analysis","textural analysis","music analysis","python scripts","music21"],"search_terms":["python","scripts","rhythmic","partitioning","analysis","sampaio","gentil-nunes"],"title":"Python Scripts for Rhythmic Partitioning Analysis","year":2022}