Plotly

Plotly, also known by its URL, Plot.ly,[1] is an online analytics and data visualization tool, headquartered in Montreal, Quebec. Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, and REST. (https://en.wikipedia.org/wiki/Plotly)


References in zbMATH (referenced in 25 articles )

Showing results 1 to 20 of 25.
Sorted by year (citations)

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  1. Alvaro J. Garcia-Tejedor, Alberto Nogales: GEMA: An open-source Python library for self-organizing-maps (2022) arXiv
  2. Asher D Pembroke; Darren DeZeeuw; Lutz Rastaetter; Rebecca Ringuette; Oliver Gerland; Dhruv Patel; Michael Contreras: Kamodo: A functional API for space weather models and data (2022) not zbMATH
  3. Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder: GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language (2022) not zbMATH
  4. van der Vegt, Solveig A.; Dai, Liangti; Bouros, Ioana; Farm, Hui Jia; Creswell, Richard; Dimdore-Miles, Oscar; Cazimoglu, Idil; Bajaj, Sumali; Hopkins, Lyle; Seiferth, David; Cooper, Fergus; Lei, Chon Lok; Gavaghan, David; Lambert, Ben: Learning transmission dynamics modelling of COVID-19 using \textbfcomomodels (2022)
  5. Burmasheva, N. V.; Prosviryakov, E. Yu.: Exact solutions for steady convective layered flows with a spatial acceleration (2021)
  6. L. Capocchi, J.F. Santucci: A web-based simulation of discrete-event system of system with the mobile application DEVSimPy-mob (2021) not zbMATH
  7. Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, Przemyslaw Biecek: dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python (2020) arXiv
  8. Adrien Leger; Tommaso Leonardi: pycoQC, interactive quality control for Oxford NanoporeSequencing (2019) not zbMATH
  9. Becker, Gabriel; Moore, Sara E.; Lawrence, Michael: trackr: a framework for enhancing discoverability and reproducibility of data visualizations and other artifacts in R (2019)
  10. Benitez-Hidalgo, A.; Nebro, AJ; Garcia-Nieto, J.; Oregi, I.; Del Ser, J.: jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics (2019) arXiv
  11. Faruk Diblen, Jisk Attema, Rena Bakhshi, Sascha Caron, Luc Hendriks, Bob Stienen: spot: Open Source framework for scientific data repository and interactive visualization (2019) not zbMATH
  12. Jayakumar, G. S. David Sam; Sulthan, A.: Exact distribution of Hadi’s ((H^2)) influence measure and identification of potential outliers (2019)
  13. O. R. Bingol, A. Krishnamurthy: NURBS-Python: An open-source object-oriented NURBS modeling framework in Python (2019) not zbMATH
  14. Wheeler, D., Keller, T., DeWitt, S.J., Jokisaari, A.M., Schwen, D., Guyer, J.E., Aagesen, L.K., Heinonen, O.G., Tonks, M.R., Voorhees, P.W., Warren, J.A: PFHub: The Phase-Field Community Hub (2019) not zbMATH
  15. Pfenninger, S., Pickering, B: Calliope: a multi-scale energy systems modelling framework (2018) not zbMATH
  16. Amen, Saeed: Using Python to analyse financial markets (2017)
  17. Leon Thurner, Alexander Scheidler, Florian Schaefer, Jan-Hendrik Menke, Julian Dollichon, Friederike Meier, Steffen Meinecke, Martin Braun: Pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems (2017) arXiv
  18. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  19. Romain Gaillac; Pluton Pullumbi; François-Xavier Coudert: ELATE: An open-source online application for analysis and visualization of elastic tensors (2016) arXiv
  20. Duarte, Nubia E.; Giolo, Suely R.; Pereira, Alexandre C.; de Andrade, Mariza; Soler, Júlia P.: Using the theory of added-variable plot for linear mixed models to decompose genetic effects in family data (2014)

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