BOUT-dev VS NumPy

BOUT++: a framework for writing fluid and plasma simulations in curvilinear geometry

At least in my field (computational plasma physics), the majority of software is (in descending order) Fortran (e.g., SOLPS-ITER), Python (e.g., IPS; OMFIT; UEDGE), and C/C++ (e.g., BOUT++; Exascale Computing Project tools).

I don't want to paint too bleak a picture of my field - there are definitely big, well-supported projects that invest in code quality! For example, Bout++ is a fluid solver whose devs care a lot about best practices, and the folks at the Exascale Computing Project are doing great work with tools like Kokkos (GPU acceleration of HPCcodes), ADIOS (I/O for HPC), AMREX (meshes for HPC), etc.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]