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This is the first release of DAFCC, a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.
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Release Notes Installation You can install pykrige with pip: pip install pykrige Documentation The documentation can be found at: https://pykrige.readthedocs.io/ What's new? New features support for GSTools covariance models (#125) pre-build wheels for py35-py38 on Linux, Windows and MacOS (#142) GridSerachCV from the compat module sets iid=False by default (if present in sklearn) to be future prove (iid will be deprecated) (#144) Changes dropped py2* and py<3.5 support (#142) installation now requires cython (#142) codebase was formatted with black (#144) internally use of scipys lapack/blas bindings (#142) PyKrige is now part of the GeoStat-Framework
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The NAStJA framework provides an easy way to enable massively parallel simulations for a wide range of multi-physics applications based on stencil algorithms. It supports the development of parallel strategies for high-performance computing. Modern C++ and the usage of template metaprogramming achieve excellent performance without losing flexibility and usability. Modules are available for the phase-field method, the phase-field crystal, and the cellular Potts model so far. Also, an implementation of StaRMAP that uses the NAStJA framework is available. For more information, see the webpage https://nastja.gitlab.io or the git repository https://gitlab.com/nastja/nastja. The documentation is available under https://nastja.gitlab.io/nastja/docs/index.html.
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Release Notes Installation You can install pykrige with pip: pip install pykrige Documentation The documentation can be found at: https://pykrige.readthedocs.io/ What's new? New features support for GSTools covariance models (#125) pre-build wheels for py35-py38 on Linux, Windows and MacOS (#142) GridSerachCV from the compat module sets iid=False by default (if present in sklearn) to be future prove (iid will be deprecated) (#144) Changes dropped py2* and py<3.5 support (#142) installation now requires cython (#142) codebase was formatted with black (#144) internally use of scipys lapack/blas bindings (#142) PyKrige is now part of the GeoStat-Framework
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This release primarily focused on improvements and additions to the documentation. Many thanks to @MarcSkovMadsen, @philippjfr and @michaelaye for contributing to this release. Enhancements: Add Template.save with ability to save to HTML and PNG but not embed (#1224) Bug fixes: Fixed formatting of datetimes in DataFrame widget (#1221) Add panel/models/vtk/ subpackage to MANIFEST to ensure it is shipped with packages Documentation: Add guidance about developing custom models (#1220) Add Folium example to gallery (#1189) Add FileDownload and FileInput example to gallery (#1193)
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Trying Zenodo for the first time, hope it goes well!
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classify, merge, tracking and annotation of GFF files by comparing to a reference annotation GFF
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py-pde is a Python package for solving partial differential equations (PDEs). The package provides classes for grids on which scalar and tensor fields can be defined. The associated differential operators are computed using a just-in-time-compiled implementation of finite differences. This allows defining, inspecting, and solving typical PDEs that appear for instance in the study of dynamical systems in physics. The focus of the package lies on easy usage to explore the behavior of PDEs.
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Genetic sequence simulation and loglikelihood calculations of phylogenetic trees using Felsenstein's (1981) tree pruning algorithm with Julia
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Tools for loading, augmenting and writing 3D medical images with PyTorch
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