CSV.jl
DifferentialEquations.jl
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CSV.jl | DifferentialEquations.jl | |
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5 | 6 | |
447 | 2,756 | |
3.4% | 1.6% | |
6.2 | 7.2 | |
20 days ago | 21 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | 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.
CSV.jl
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Manifest.toml vs Project.toml in Julia
If you’ve used other package managers before, you may be wondering where the package versions are stored. While the Project.toml stores the IDs of the project’s direct dependencies, the Manifest.tomltracks the entire dependency tree, including both the direct and indirect dependencies and the versions of each. Because of this, the Manifest.tomlis always a larger file. For example, after adding just the CSV package to a fresh project, my Manifest.toml is already 200 lines long!
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I'm trying to install ClimateTools.jl, and failing. The problem appears to track back to CSV.jl. Can anyone please help? Thanks1
This thread seems to have different suggestions to what to do https://github.com/JuliaData/CSV.jl/issues/981 like ] add [email protected] ] pin Parsers
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Teaching Python
Julia also has the CSV.jl library for reading/writing csv files, the DataFrames.jl library for manipulating data like pandas, and Images.jl for image processing/analysis. However, since Julia is so much newer than Python, the Julia libraries are almost never as feature rich as their Python counterparts.
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CSV.File read extremely slow
Is this CSV.jl package? I think it shouldn't take that long. What Julia version is yours?
DifferentialEquations.jl
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Startups are building with the Julia Programming Language
This lists some of its unique abilities:
https://docs.sciml.ai/DiffEqDocs/stable/
The routines are sufficiently generic, with regard to Julia’s type system, to allow the solvers to automatically compose with other packages and to seamlessly use types other than Numbers. For example, instead of handling just functions Number→Number, you can define your ODE in terms of quantities with physical dimensions, uncertainties, quaternions, etc., and it will just work (for example, propagating uncertainties correctly to the solution¹). Recent developments involve research into the automated selection of solution routines based on the properties of the ODE, something that seems really next-level to me.
[1] https://lwn.net/Articles/834571/
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From Common Lisp to Julia
https://github.com/SciML/DifferentialEquations.jl/issues/786. As you could see from the tweet, it's now at 0.1 seconds. That has been within one year.
Also, if you take a look at a tutorial, say the tutorial video from 2018,
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When is julia getting proper precompilation?
It's not faith, and it's not all from Julia itself. https://github.com/SciML/DifferentialEquations.jl/issues/785 should reduce compile times of what OP mentioned for example.
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Julia 1.7 has been released
Let's even put raw numbers to it. DifferentialEquations.jl usage has seen compile times drop from 22 seconds to 3 seconds over the last few months.
https://github.com/SciML/DifferentialEquations.jl/issues/786
- Suggest me a Good library for scientific computing in Julia with good support for multi-core CPUs and GPUs.
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DifferentialEquations compilation issue in Julia 1.6
https://github.com/SciML/DifferentialEquations.jl/issues/737 double posted, with the answer here. Please don't do that.