ProceedingsSciPy ProceedingsContent License: Creative Commons Attribution 3.0 Unported (CC-BY-3.0)Credit must be given to the creatorProceedings of the 20th Python in Science ConferenceSciPy 2021, Austin, Texas July 12 - July 18July 12, 2021https://doi.org/10.25080/majora-1b6fd038-02bDownload PDFDownload BibtexSupporting DocumentsOrganizationPosters and SlidesSponsored StudentsAccepted Papers¶How PDFrw and fillable forms improves throughput at a Covid-19 Vaccine ClinicHow PDFrw and fillable forms improves throughput at a Covid-19 Vaccine ClinicHaw-minn Lu, José Unpingcohttps://doi.org/10.25080/majora-1b6fd038-000Using Python for Analysis and Verification of Mixed-mode Signal ChainsUsing Python for Analysis and Verification of Mixed-mode Signal ChainsMark Thoren, Cristina Suteuhttps://doi.org/10.25080/majora-1b6fd038-001Modernizing computing by structural biologists with Jupyter and ColabModernizing computing by structural biologists with Jupyter and ColabBlaine Mooershttps://doi.org/10.25080/majora-1b6fd038-002signac: Data Management and Workflows for Computational Researcherssignac: Data Management and Workflows for Computational ResearchersBradley Dice, Brandon Butler, Vyas Ramasubramani, +7https://doi.org/10.25080/majora-1b6fd038-003Accelerating Spectroscopic Data Processing Using Python and GPUs on NERSC SupercomputersAccelerating Spectroscopic Data Processing Using Python and GPUs on NERSC SupercomputersDaniel Margala, Laurie Stephey, Rollin Thomas, +1https://doi.org/10.25080/majora-1b6fd038-004MPI-parallel Molecular Dynamics Trajectory Analysis with the H5MD Format in the MDAnalysis Python PackageMPI-parallel Molecular Dynamics Trajectory Analysis with the H5MD Format in the MDAnalysis Python PackageEdis Jakupovic, Oliver Becksteinhttps://doi.org/10.25080/majora-1b6fd038-005Natural Language Processing with Pandas DataFramesNatural Language Processing with Pandas DataFramesFrederick Reiss, Bryan Cutler, Zachary Eichenbergerhttps://doi.org/10.25080/majora-1b6fd038-006CLAIMED, a visual and scalable component library for Trusted AICLAIMED, a visual and scalable component library for Trusted AIRomeo Kienzler, Ivan Nesichttps://doi.org/10.25080/majora-1b6fd038-007PyCID: A Python Library for Causal Influence DiagramsPyCID: A Python Library for Causal Influence DiagramsJames Fox, Tom Everitt, Ryan Carey, +3https://doi.org/10.25080/majora-1b6fd038-008Social Media Analysis using Natural Language Processing TechniquesSocial Media Analysis using Natural Language Processing TechniquesJyotika Singhhttps://doi.org/10.25080/majora-1b6fd038-009PyBMRB: Data visualization tool for BioMagResBankPyBMRB: Data visualization tool for BioMagResBankKumaran Baskaran, Jonathan Wedell, Eldon Ulrich, +2https://doi.org/10.25080/majora-1b6fd038-00aConformal Mappings with SymPy: Towards Python-driven Analytical Modeling in PhysicsConformal Mappings with SymPy: Towards Python-driven Analytical Modeling in PhysicsZoufiné Lauer-Baré, Erich Gaertighttps://doi.org/10.25080/majora-1b6fd038-00bProgrammatically Identifying Cognitive Biases Present in Software DevelopmentProgrammatically Identifying Cognitive Biases Present in Software DevelopmentAmanda Kraft, Matthew Widjaja, Trevor Sands, +1https://doi.org/10.25080/majora-1b6fd038-00cHow PDFrw and fillable forms improves throughput at a Covid-19 Vaccine ClinicHow PDFrw and fillable forms improves throughput at a Covid-19 Vaccine ClinicHaw-minn Lu, José Unpingcohttps://doi.org/10.25080/majora-1b6fd038-00dPyRSB: Portable Performance on Multithreaded Sparse BLAS OperationsPyRSB: Portable Performance on Multithreaded Sparse BLAS OperationsMichele Martone, Simone Bacchiohttps://doi.org/10.25080/majora-1b6fd038-00eClassification of Diffuse Subcellular MorphologiesClassification of Diffuse Subcellular MorphologiesNeelima Pulagam, Marcus Hill, Mojtaba Fazli, +6https://doi.org/10.25080/majora-1b6fd038-00fMonitoring Scientific Python Usage on a SupercomputerMonitoring Scientific Python Usage on a SupercomputerRollin Thomas, Laurie Stephey, Annette Greiner, +1https://doi.org/10.25080/majora-1b6fd038-010Training machine learning models faster with DaskTraining machine learning models faster with DaskJoesph Holt, Scott Sieverthttps://doi.org/10.25080/majora-1b6fd038-011Multithreaded parallel Python through OpenMP support in NumbaMultithreaded parallel Python through OpenMP support in NumbaTodd Anderson, Tim Mattsonhttps://doi.org/10.25080/majora-1b6fd038-012CNN Based ToF Image ProcessingCNN Based ToF Image ProcessingMarian-Leontin Pop, Szilard Molnar, Alexandru Pop, +3https://doi.org/10.25080/majora-1b6fd038-013Cell Tracking in 3D using deep learning segmentationsCell Tracking in 3D using deep learning segmentationsVarun Kapoor, Claudia Carabañahttps://doi.org/10.25080/majora-1b6fd038-014Proceedings of the 20th Python in Science ConferenceOrganization
