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This paper demonstrates Pynapple-G, an open-source library for scalable spatial grouping queries based on Apache Sedona (formerly known as GeoSpark). We demonstrate two modules, namely, SGPAC and DDCEL, that support grouping points, grouping lines, and polygon overlays. The SGPAC module provides a large-scale grouping of spatial points by highly complex polygon boundaries. The grouping results aggregate the number of spatial points within the boundaries of each polygon. The DDCEL module provides the first parallelized algorithm to group spatial lines into a DCEL data structure and discovers planar polygons from scattered line segments. Exploiting the scalable DCEL, we support scalable overlay operations over multiple polygon layers to compute the layers’ intersection, union, or difference. To showcase Pyneapple-G, we have developed a frontend web application that enables users to interact with these modules, select their data layers or data points, and view results on an interactive map. We also provide interactive notebooks demonstrating the superiority and simplicity of Pyneapple-G to help social scientists and developers explore its full potential.
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An Efficient Pipeline for Biophysical Modeling of Neurons
Automation of the process of developing biophysical conductance-based neuronal models involves the selection of numerous interacting parameters, making the overall process computationally intensive, complex, and often intractable. A recently reported insight about the possible grouping of currents into distinct biophysical modules associated with specific neurocomputational properties also simplifies the process of automated selection of parameters. The present paper adds a new current module to the previous report to design spike frequency adaptation and bursting characteristics, based on user specifications. We then show how our proposed grouping of currents into modules facilitates the development of a pipeline that automates the biophysical modeling of single neurons that exhibit multiple neurocomputational properties. The software will be made available for public download via our site cyneuro.org.
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- Award ID(s):
- 1730655
- PAR ID:
- 10311931
- Date Published:
- Journal Name:
- 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper demonstratesPynapple-G, an open-source library for scalable spatial grouping queries based on Apache Sedona (formerly known as GeoSpark). We demonstrate two modules, namely,SGPACandDDCEL, that support grouping points, grouping lines, and polygon overlays. TheSGPACmodule provides a large-scale grouping of spatial points by highly complex polygon boundaries. The grouping results aggregate the number of spatial points within the boundaries of each polygon. TheDDCELmodule provides the first parallelized algorithm to group spatial lines into a DCEL data structure and discovers planar polygons from scattered line segments. Exploiting the scalable DCEL, we support scalable overlay operations over multiple polygon layers to compute the layers' intersection, union, or difference. To showcasePyneapple-G, we have developed a frontend web application that enables users to interact with these modules, select their data layers or data points, and view results on an interactive map. We also provide interactive notebooks demonstrating the superiority and simplicity ofPyneapple-Gto help social scientists and developers explore its full potential.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/NER49283.2021.9441222
