ProceedingsSciPy ProceedingsContent License: Creative Commons Attribution 3.0 Unported (CC-BY-3.0)Credit must be given to the creatorProceedings of the 19th Python in Science ConferenceSciPy 2020, Austin, Texas July 6 - July 12July 6, 2020https://doi.org/10.25080/Majora-342d178e-02bDownload PDFDownload BibtexSupporting DocumentsOrganizationPosters and SlidesSponsored StudentsAccepted Papers¶PrefacePrefaceMeghann Agarwal, Julie Hollek, Dillon Niederhuthttps://doi.org/10.25080/Majora-342d178e-000Securing Your Collaborative Jupyter Notebooks in the Cloud using Container and Load Balancing ServicesSecuring Your Collaborative Jupyter Notebooks in the Cloud using Container and Load Balancing ServicesHaw-minn Lu, Adrian Kwong, José Unpingcohttps://doi.org/10.25080/Majora-342d178e-001Quasi-orthonormal Encoding for Machine Learning ApplicationsQuasi-orthonormal Encoding for Machine Learning ApplicationsHaw-minn Luhttps://doi.org/10.25080/Majora-342d178e-002Fluctuation X-ray Scattering real-time appFluctuation X-ray Scattering real-time appAntoine Dujardin, Elliott Slaugther, Jeffrey Donatelli, +3https://doi.org/10.25080/Majora-342d178e-003HOOMD-blue version 3.0 A Modern, Extensible, Flexible, Object-Oriented API for Molecular SimulationsHOOMD-blue version 3.0 A Modern, Extensible, Flexible, Object-Oriented API for Molecular SimulationsBrandon Butler, Vyas Ramasubramani, Joshua Anderson, +1https://doi.org/10.25080/Majora-342d178e-004Compyle: a Python package for parallel computingCompyle: a Python package for parallel computingAditya Bhosale, Prabhu Ramachandranhttps://doi.org/10.25080/Majora-342d178e-005Netlist Analysis and Transformations Using SpyDrNetNetlist Analysis and Transformations Using SpyDrNetDallin Skouson, Andrew Keller, Michael Wirthlinhttps://doi.org/10.25080/Majora-342d178e-006Introduction to Geometric Learning in Python with GeomstatsIntroduction to Geometric Learning in Python with GeomstatsNina Miolane, Nicolas Guigui, Hadi Zaatiti, +17https://doi.org/10.25080/Majora-342d178e-007Network visualizations with Pyvis and VisJSNetwork visualizations with Pyvis and VisJSGiancarlo Perrone, Jose Unpingco, Haw-minn Luhttps://doi.org/10.25080/Majora-342d178e-008Boost-histogram: High-Performance Histograms as ObjectsBoost-histogram: High-Performance Histograms as ObjectsHenry Schreiner, Hans Dembinski, Shuo Liu, +1https://doi.org/10.25080/Majora-342d178e-009Learning from evolving data streamsLearning from evolving data streamsJacob Montielhttps://doi.org/10.25080/Majora-342d178e-00aAwkward Array: JSON-like data, NumPy-like idiomsAwkward Array: JSON-like data, NumPy-like idiomsJim Pivarski, Ianna Osborne, Pratyush Das, +2https://doi.org/10.25080/Majora-342d178e-00bHigh-performance operator evaluations with ease of use: libCEED’s Python interfaceHigh-performance operator evaluations with ease of use: libCEED’s Python interfaceValeria Barra, Jed Brown, Jeremy Thompson, +1https://doi.org/10.25080/Majora-342d178e-00cSpectral Analysis of Mitochondrial Dynamics: A Graph-Theoretic Approach to Understanding Subcellular PathologySpectral Analysis of Mitochondrial Dynamics: A Graph-Theoretic Approach to Understanding Subcellular PathologyMarcus Hill, Mojtaba Fazli, Rachel Mattson, +8https://doi.org/10.25080/Majora-342d178e-00dMatched Filter Mismatch Losses in MPSK and MQAM Using Semi-Analytic BEP ModelingMatched Filter Mismatch Losses in MPSK and MQAM Using Semi-Analytic BEP ModelingMark Wickert, David Peckhamhttps://doi.org/10.25080/Majora-342d178e-00eHaving your cake and eating it: Exploiting Python for programmer productivity and performance on micro-core architectures using ePythonHaving your cake and eating it: Exploiting Python for programmer productivity and performance on micro-core architectures using ePythonMaurice Jamieson, Nick Brown, Sihang Liuhttps://doi.org/10.25080/Majora-342d178e-00fpandera: Statistical Data Validation of Pandas Dataframespandera: Statistical Data Validation of Pandas DataframesNiels Bantilanhttps://doi.org/10.25080/Majora-342d178e-010Combining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well ConstructionCombining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well ConstructionOney Erge, Eric van Oorthttps://doi.org/10.25080/Majora-342d178e-011Pydra - a flexible and lightweight dataflow engine for scientific analysesPydra - a flexible and lightweight dataflow engine for scientific analysesDorota Jarecka, Mathias Goncalves, Christopher Markiewicz, +4https://doi.org/10.25080/Majora-342d178e-012Leading magnetic fusion energy science into the big-and-fast data laneLeading magnetic fusion energy science into the big-and-fast data laneRalph Kube, R Churchill, Jong Choi, +5https://doi.org/10.25080/Majora-342d178e-013SHADOW: A workflow scheduling algorithm reference and testing frameworkSHADOW: A workflow scheduling algorithm reference and testing frameworkRyan Bunney, Andreas Wicenec, Mark Reynoldshttps://doi.org/10.25080/Majora-342d178e-014Software Engineering as Research Method: Aligning Roles in Econ-ARKSoftware Engineering as Research Method: Aligning Roles in Econ-ARKSebastian Benthall, Mridul Sethhttps://doi.org/10.25080/Majora-342d178e-015Falsify your Software: validating scientific code with property-based testingFalsify your Software: validating scientific code with property-based testingZac Hatfield-Doddshttps://doi.org/10.25080/Majora-342d178e-016Towards an Unsupervised Spatiotemporal Representation of Cilia Video Using A Modular Generative PipelineTowards an Unsupervised Spatiotemporal Representation of Cilia Video Using A Modular Generative PipelineMeekail Zain, Sonia Rao, Nathan Safir, +6https://doi.org/10.25080/Majora-342d178e-017Proceedings of the 19th Python in Science ConferenceOrganization
