BOUT-dev
NumPy
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BOUT-dev | NumPy | |
---|---|---|
4 | 272 | |
167 | 26,360 | |
2.4% | 1.9% | |
9.0 | 10.0 | |
2 days ago | 2 days ago | |
C++ | Python | |
GNU Lesser General Public License v3.0 only | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
BOUT-dev
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Open source sofware/software contribution opportunities in Fusion
BOUT++: a framework for writing fluid and plasma simulations in curvilinear geometry
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What programming languages are most used for creating advanced math-related software/simulations?
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).
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how much relevance is given to code quality in your academia projects?
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.
- I write buggy code and my phd progress is catastrophic
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
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:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
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.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
geogebra - GeoGebra apps (mirror)
SymPy - A computer algebra system written in pure Python
WarpX - WarpX is an advanced, time-based electromagnetic & electrostatic Particle-In-Cell code.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
HAIL-CAESAR - The High-Performance Architecture-Independent LISFLOOD-CAESAR model of floodplain, river, and sediment dynamics
blaze - NumPy and Pandas interface to Big Data
espresso - The ESPResSo package
SciPy - SciPy library main repository
fluid-engine-dev - Fluid simulation engine for computer graphics applications
Numba - NumPy aware dynamic Python compiler using LLVM
psi4 - Open-Source Quantum Chemistry – an electronic structure package in C++ driven by Python
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).