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openmdao-with-numba
|
public |
OpenMDAO framework infrastructure
|
2025-10-06 |
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openmdao
|
public |
OpenMDAO framework infrastructure
|
2025-10-06 |
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openmdao-with-doe
|
public |
OpenMDAO framework infrastructure
|
2025-10-06 |
|
openmdao-with-notebooks
|
public |
OpenMDAO framework infrastructure
|
2025-10-06 |
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openmdao-with-jax
|
public |
OpenMDAO framework infrastructure
|
2025-10-06 |
|
openmdao-with-all
|
public |
OpenMDAO framework infrastructure
|
2025-10-06 |
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glab
|
public |
A GitLab CLI tool bringing GitLab to your command line
|
2025-10-06 |
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insarscript
|
public |
Hassle-free InSAR processing scripts for multiple satellites
|
2025-10-06 |
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csp-gateway
|
public |
Application framework built on CSP
|
2025-10-06 |
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csp-gateway-client
|
public |
Application framework built on CSP
|
2025-10-06 |
|
release-please
|
public |
generate release PRs based on the conventionalcommits.org spec
|
2025-10-06 |
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coffea
|
public |
Tools for doing Collider HEP style analysis with columnar operations
|
2025-10-06 |
|
moor
|
public |
Nice to use pager for humans
|
2025-10-06 |
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r-backbone
|
public |
Provides methods for extracting from a weighted graph a binary or signed backbone that retains only the significant edges. The user may input a weighted graph, or a bipartite graph from which a weighted graph is first constructed via projection. Backbone extraction methods include the stochastic degree sequence model (SDSM; Neal, Z. P. (2014). <doi:10.1016/j.socnet.2014.06.001>), the fixed degree sequence model (FDSM; Zweig, K. A., and Kaufmann, M. (2011). <doi:10.1007/s13278-011-0021-0>), the fixed row model (FRM; Neal, Z. P. (2013). <doi:10.1007/s13278-013-0107-y>), the fixed column model (FCM; Neal, Domagalski, and Sagan (2021). <arXiv:2105.13396>), the fixed fill model (FFM; Neal, Domagalski, and Sagan (2021). <arXiv:2105.13396>), and a universal threshold method.
|
2025-10-06 |
|
wagtail-markdown
|
public |
Markdown support for Wagtail
|
2025-10-06 |
|
dtw-python
|
public |
A comprehensive implementation of dynamic time warping (DTW) algorithms in Python. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on. Provides cumulative distances, alignments, specialized plot styles, etc.
|
2025-10-06 |
|
reportgenerator
|
public |
ReportGenerator converts coverage reports generated by coverlet, OpenCover, dotCover, Visual Studio, NCover, Cobertura, JaCoCo, Clover, gcov or lcov into human readable reports in various formats.
|
2025-10-06 |
|
tardigrade_error_tools
|
public |
A collection of tools used for error handling.
|
2025-10-06 |
|
mpich
|
public |
A high performance widely portable implementation of the MPI standard.
|
2025-10-06 |
|
mpich-mpicc
|
public |
A high performance widely portable implementation of the MPI standard.
|
2025-10-06 |
|
mpich-mpifort
|
public |
A high performance widely portable implementation of the MPI standard.
|
2025-10-06 |
|
mpich-mpicxx
|
public |
A high performance widely portable implementation of the MPI standard.
|
2025-10-06 |
|
music21
|
public |
A Toolkit for Computer-Aided Musical Analysis.
|
2025-10-06 |
|
libtomo
|
public |
Tomographic reconstruction in Python.
|
2025-10-06 |
|
tomopy
|
public |
Tomographic reconstruction in Python.
|
2025-10-06 |
