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Article . 2023
License: CC BY
Data sources: Datacite
ZENODO
Article . 2023
License: CC BY
Data sources: Datacite
DBLP
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Data sources: DBLP
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MaxPy: An open-source Python package for text-based generation of MaxMSP patches

Authors: Ranger Y. Liu; Satchel Peterson; Richard T. Lee; Mark Santolucito;

MaxPy: An open-source Python package for text-based generation of MaxMSP patches

Abstract

MaxMSP is a visual programming language for creating interactive audiovisual media that has found great success as a flexible and accessible option for computer music. However, the visual interface requires manual object placement and connection, which can be inefficient. Automated patch editing is possible either by visual programming with the [thispatcher] object or text-based programming with the [js] object. However, these objects cannot automatically create and save new patches, and they operate at run-time only, requiring live input to trigger patch construction. There is no solution for automated creation of multiple patches at \textit{compile-time}, such that the constructed patches do not contain their own constructors. To this end, we present MaxPy, an open-source Python package for programmatic construction and manipulation of MaxMSP patches. MaxPy replaces the manual actions of placing objects, connecting patchcords, and saving patch files with text-based Python functions, thus enabling dynamic, procedural, high-volume patch generation at compile-time. MaxPy also includes the ability to import existing patches, allowing users to move freely between text-based Python programming and visual programming with the Max GUI. MaxPy enables composers, programmers, and creators to explore expanded possibilities for complex, dynamic, and algorithmic patch construction through text-based Python programming of MaxMSP.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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