icrystal
diffeqpy
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| icrystal | diffeqpy | |
|---|---|---|
| 1 | 4 | |
| 30 | 494 | |
| - | 3.8% | |
| 0.0 | 7.7 | |
| over 2 years ago | about 1 month ago | |
| Crystal | Python | |
| MIT License | MIT License |
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.
icrystal
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Julia vs R/Python
There are some data libraries as well (i.e. here and you can use it within Jupyter as well.
diffeqpy
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How Julia ODE Solve Compile Time Was Reduced From 30 Seconds to 0.1
With Python you have to write packages in some other language anyways, so you might as well do that with Julia. One of the reasons for getting all of this precompilation going is to eventually ship precompiled system images with things like https://github.com/SciML/diffeqpy, effectively using Julia as a replacement for where C/Fortran is traditionally used there. If I can make that pipeline smooth, then I think Julia as a Python package building source will be a good option for a lot of folks. Right now it's a very manual, but it could easily improve with a bit of tooling.
- ‘Machine Scientists’ Distill the Laws of Physics from Raw Data
- Is it possible to create a Python package with Julia and publish it on PyPi?
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Julia vs R/Python
10-100x speed increase was not an exaggeration for me. With julia I was able to run things quickly on my own machine which I had been running on a compute cluster. I agree that numba could be just as fast as julia. I also just saw that you can run that DE library from julia that I like so much from python using this package. https://github.com/SciML/diffeqpy
What are some alternatives?
db-benchmark - reproducible benchmark of database-like ops
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
kemal - Fast, Effective, Simple Web Framework
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
DiffEqSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]
csvzip - A standalone CLI tool to reduce CSVs size by converting categorical columns in a list of unique integers.
PySR - High-Performance Symbolic Regression in Python and Julia
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
python-bigsimr
FunctionalModels.jl - Equation-based modeling and simulations in Julia
crystal - The Crystal Programming Language