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name = "Trixi" | ||
uuid = "a7f1ee26-1774-49b1-8366-f1abc58fbfcb" | ||
authors = ["Michael Schlottke-Lakemper <[email protected]>", "Gregor Gassner <[email protected]>", "Hendrik Ranocha <[email protected]>", "Andrew R. Winters <[email protected]>", "Jesse Chan <[email protected]>"] | ||
version = "0.7.6-pre" | ||
version = "0.7.12-pre" | ||
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[deps] | ||
CodeTracking = "da1fd8a2-8d9e-5ec2-8556-3022fb5608a2" | ||
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src/changelog.md | ||
src/code_of_conduct.md | ||
src/contributing.md | ||
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# the cloned directory. | ||
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# ## Forking Trixi.jl | ||
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# To create your own fork of Trixi.jl, log in to your GitHub account, visit the | ||
# [Trixi.jl GitHub repository](https://github.com/trixi-framework/Trixi.jl) and click the `Fork` | ||
# button located in the upper-right corner of the page. Then, click on `Create fork` in the opened | ||
# window to complete the forking process. | ||
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# ## Cloning Trixi.jl | ||
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# - Download and install [GitHub Desktop](https://desktop.github.com/) and then log in to | ||
# your account. | ||
# - Open GitHub Desktop, press `Ctrl+Shift+O`. | ||
# - In the opened window, paste `trixi-framework/Trixi.jl` and choose the path to the folder where | ||
# you want to save Trixi.jl. Then click `Clone` and Trixi.jl will be cloned to your computer. | ||
# - In the opened window, navigate to the `URL` tab and paste `trixi-framework/Trixi.jl` or | ||
# `YourGitHubUserName/Trixi.jl` to clone your own fork of Trixi.jl, and choose the | ||
# path to the folder where you want to save Trixi.jl. Then click `Clone` and Trixi.jl will be | ||
# cloned to your computer. | ||
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# Now you cloned Trixi.jl and only need to tell Julia to use the local clone as the package sources: | ||
# - Open a terminal using `Win+r` and `cmd`. Navigate to the folder with the cloned Trixi.jl using `cd`. | ||
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# julia --project=. -e 'using Pkg; Pkg.develop(PackageSpec(path=".."))' # Tell Julia to use the local Trixi.jl clone | ||
# julia --project=. -e 'using Pkg; Pkg.add(["OrdinaryDiffEq", "Plots"])' # Install additional packages | ||
# ``` | ||
# Alternatively, you can clone your own fork of Trixi.jl by replacing the link | ||
# `[email protected]:trixi-framework/Trixi.jl.git` with `[email protected]:YourGitHubUserName/Trixi.jl.git`. | ||
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# Note that if you installed Trixi.jl this way, | ||
# you always have to start Julia with the `--project` flag set to your `run` directory, e.g., | ||
# ```shell | ||
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# if already inside the `run` directory. | ||
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# ## Developing Trixi.jl | ||
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# If you've created and cloned your own fork of Trixi.jl, you can make local changes to Trixi.jl | ||
# and propose them as a Pull Request (PR) to be merged into `trixi-framework/Trixi.jl`. | ||
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# Linux and MacOS utilize the `git` version control system to manage changes between your local and | ||
# remote repositories. The most commonly used commands include `add`, `commit`, `push` and `pull`. | ||
# You can find detailed information about these functions in the | ||
# [Git documentation](https://git-scm.com/docs). | ||
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# For Windows and GitHub Desktop users, refer to the | ||
# [documentation of GitHub Desktop](https://docs.github.com/en/desktop/overview/getting-started-with-github-desktop#making-changes-in-a-branch). | ||
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# After making local changes to Trixi.jl and pushing them to the remote repository, you can open a | ||
# Pull Request (PR) from your branch to the main branch of `trixi-framework/Trixi.jl`. Then, follow | ||
# the Review checklist provided in the Pull Request to streamline the review process. | ||
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# ## Additional reading | ||
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# To further delve into Trixi.jl, you may have a look at the following introductory tutorials. | ||
# - [Behind the scenes of a simulation setup](@ref behind_the_scenes_simulation_setup) will guide | ||
# you through a simple Trixi.jl setup ("elixir"), giving an overview of what happens in the | ||
# background during the initialization of a simulation. It clarifies some of the more | ||
# fundamental, technical concepts that are applicable to a variety of (also more complex) | ||
# configurations. | ||
# - [Introduction to DG methods](@ref scalar_linear_advection_1d) will teach you how to set up a | ||
# simple way to approximate the solution of a hyperbolic partial differential equation. It will | ||
# be especially useful to learn about the | ||
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