LaTeXDatax.jl
Latexify.jl
LaTeXDatax.jl | Latexify.jl | |
---|---|---|
1 | 2 | |
26 | 531 | |
- | - | |
3.6 | 6.9 | |
9 months ago | 9 days ago | |
Julia | Julia | |
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.
LaTeXDatax.jl
Latexify.jl
-
Converting Symbolics.jl Objects to SymPy.jl Objects
My current solution to this is to use Latexify.jl, great module name btw, to convert the objects to latex, then perform some dodgy string manipulation on the latex, specifically turning it into a form readable by the Python module latex2sympy2 which has a function latex2sympy which can properly convert it. I've written a function to_sympy() which properly converts the Num and Matrix{NUM} types:
-
Don't be scared.. Math and Computing are friends..
That's funny, I just implemented that conversion for Latexify.jl: https://github.com/korsbo/Latexify.jl/pull/205
What are some alternatives?
DataFrames.jl - In-memory tabular data in Julia
OrdinaryDiffEq.jl - High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
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
Pluto.jl - 🎈 Simple reactive notebooks for Julia
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.
Symbolics.jl - Symbolic programming for the next generation of numerical software