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scientific-computing

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WarrenWeckesser
WarrenWeckesser commented Aug 2, 2022

It was recently decided that we would add the explicit import of numpy in the 'Examples' section of our docstrings. (See the discussion in scipy/scipy#13049.) Some progress has been made towards doing this consistently throughout SciPy, but there are still many functions and methods that need to be updated.

I created a script that finds functions and methods that are

task Documentation good first issue
adamjstewart
adamjstewart commented Aug 1, 2022

Summary

spack find has a --tag flag that lets you filter packages containing a certain tag. We should add the same flag to spack list.

Rationale

A user may want to list all packages belonging to a certain category, not just installed packages.

Description

We just need to add a --tag flag to spack list, should be straightforward to copy this setting from spack find

Run, compile and execute JavaScript for Scientific Computing and Data Visualization TOTALLY TOTALLY TOTALLY in your BROWSER! An open source scientific computing environment for JavaScript TOTALLY in your browser, matrix operations with GPU acceleration, TeX support, data visualization and symbolic computation.

  • Updated Jul 25, 2022
  • TypeScript
YuhanLiin
YuhanLiin commented Jun 13, 2022

Now that we've made BLAS support optional on several linfa crates, we should compare the performance of those crates with and without BLAS. Doing this requires those crates to have a complete set of benchmarks that represent realistic workloads. If BLAS turns out to have no performance improvements, we can even remove BLAS support, improving code quality.

Benchmark status for each crate that

help wanted good first issue infrastructure

Linear algebra, eigenvalues, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, differential equations.

  • Updated Jun 12, 2022
  • Go

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

  • Updated Jul 25, 2022
  • C++

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