References in zbMATH (referenced in 18 articles )

Showing results 1 to 18 of 18.
Sorted by year (citations)

  1. Riley X. Brady; Aaron Spring: climpred: Verification of weather and climate forecasts (2021) not zbMATH
  2. Andrew R. Bennett; Joseph J. Hamman; Bart Nijssen: MetSim: A Python package for estimation and disaggregation of meteorological data (2020) not zbMATH
  3. Figueras i Ventura, J., Lainer, M., Schauwecker, Z., Grazioli, J., Germann, U.: Pyrad: A Real-Time Weather Radar Data Processing Framework Based on Py-ART (2020) not zbMATH
  4. Grübel, Julia; Kleinert, Thomas; Krebs, Vanessa; Orlinskaya, Galina; Schewe, Lars; Schmidt, Martin; Thürauf, Johannes: On electricity market equilibria with storage: modeling, uniqueness, and a distributed ADMM (2020)
  5. Martí Bosch: DetecTree: Tree detection from aerial imagery in Python (2020) not zbMATH
  6. Michela Paganini, Jessica Zosa Forde: dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration (2020) arXiv
  7. Muammar El Khatib, Wibe A de Jong: ML4Chem: A Machine Learning Package for Chemistry and Materials Science (2020) arXiv
  8. P.E. Hadjidoukas, A. Bartezzaghi, F. Scheidegger, R. Istrate, C.Bekas, A.C.I. Malossi: torcpy: Supporting task parallelism in Python (2020) not zbMATH
  9. Peter Michael Habelitz, Janis Keuper: PHS: A Toolbox for Parallel Hyperparameter Search (2020) arXiv
  10. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  11. Yunus Sevinchan; Benjamin Herdeanu; Jeremias Traub: dantro: a Python package for handling, transforming, and visualizing hierarchically structured data (2020) not zbMATH
  12. Eshan D. Mitra, Ryan Suderman, Joshua Colvin, Alexander Ionkov, Andrew Hu, Herbert M. Sauro, Richard G. Posner, William S. Hlavacek: PyBioNetFit and the Biological Property Specification Language (2019) arXiv
  13. Mattia Almansi, Renske Gelderloos, Thomas W. N. Haine, Atousa Saberi. Ali H. Siddiqui: OceanSpy: A Python package to facilitate ocean model data analysis and visualization (2019) not zbMATH
  14. Toccaceli, Paolo; Gammerman, Alexander: Combination of inductive Mondrian conformal predictors (2019)
  15. Yadu Babuji, Anna Woodard, Zhuozhao Li, Daniel S. Katz, Ben Clifford, Rohan Kumar, Lukasz Lacinski, Ryan Chard, Justin M. Wozniak, Ian Foster, Michael Wilde, Kyle Chard: Parsl: Pervasive Parallel Programming in Python (2019) arXiv
  16. Daniel G. A. Smith; Johnnie Gray: opt_einsum - A Python package for optimizing contraction order for einsum-like expressions (2018) not zbMATH
  17. Maximilian Christ, Nils Braun, Julius Neuffer, Andreas W. Kempa-Liehr: Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh - A Python package) (2018) not zbMATH
  18. Coelho, L.P.: Jug: Software for Parallel Reproducible Computation in Python (2017) not zbMATH