scientific-computing

scipy.signal.bspline gives zero output for integer types. This also affects quadratic and cubic.

Reproducing code example:

An example of an incorrect result

>>> from scipy.signal import bspline, quadratic, cubic
>>> bspline(np.array([-3,-2,-1,0,1,2,3]), 6)
array([0, 0, 0, 0, 0, 0, 0])

What was expected

>>> bspline(np.array([-3,-2,-1,0,1,2,3]).astype(float), 6)
array

See:
gonum/gonum#845 (comment)

It's hard to find even if you look at doxygen.

Current implementation of join can be improved by performing the operation in a single call to the backend kernel instead of multiple calls.

This is a fairly easy kernel and may be a good issue for someone getting to know CUDA/ArrayFire internals. Ping me if you want additional info.

Today I was checking out the library and MSVC warns for https://github.com/alandefreitas/matplotplusplus/blob/359a775710e1b1fd5667281a0855806f149b7ab0/source/matplot/util/contourc.cpp#L1771

BOUNDARY_S returns a bool do the expression is bool > 0

Currently spack does not support the following packages, all of these packages are installed outside of Spack at Cori, we would like to get support for these packages if possible.

• cpmd https://www.lcrc.anl.gov/for-users/software/available-software/cpmd/
• ComputeCPP https://github.com/codeplaysoftware/computecpp-sdk need to confirm link
• ds9 https://sites.google.com/cfa.harvard.

Add #[track_caller] to some panicking methods. Slicing methods seem the most prudent (Index/Mut does not apply, sorry).

Needs benchmarks to investigate impact, preferably holistic benchmarks.

Calling cargo tree -d --workspace at the root of the repo reveals all of Linfa's duplicated dependencies. These are dependencies that have the same name but different versions, leading to bloat. We want to eliminate as many duplicated dependencies as possible by ensuring that multiple instances of the same crate in the dependency tree have the same version. We should also prioritize the non-dev

Setting up a reader or a writer just to use them once in a straightforward way requires 5 lines of code, and it can be accomplished by using the appropriate convenience function.

itk::ReadImage and itk::WriteImage are available since ITK 5.2.0. See InsightSoftwareConsortium/ITKSphinxExamples#225
for an illustration of the changes required and https://github.com/Ins