.. _tutorials page: ########## Tutorials ########## MUSICA offers a series of tutorial snippets in Fortran as well as tutorial notebooks in Python that guide users from simple workflows to more advanced simulations, both available here. Fortran ======== 1. :ref:`installing MUSICA ` 2. :ref:`first Fortran MUSICA program ` 3. :ref:`box model example ` 4. :ref:`multiple grid cells box model ` .. toctree:: :hidden: chapter0 chapter1 chapter2 Python ======== The Python tutorials are written in `Jupyter `_ Notebooks and made available in multiple formats below for ease of access. Interactive Notebooks ---------------------- The MUSICA repository utilizes `Binder `_ to allow users to interact with the tutorial notebooks on a `JupyterHub `_. Please note that our `Binder page `_ uses the latest version of MUSICA pushed to main as opposed to the latest release. Each of the links below will open a JupyterHub set up with all necessary dependencies to run each tutorial: 1. `working with multiple grid cells `_ 2. `latin hypercube sampling `_ 3. `user-defined reactions `_ 4. `local paralellization `_ 5. `HPC parallelization `_ 6. `using GPU solvers `_ 7. `using CARMA `_ GitHub -------- For users that wish to directly download local copies of the tutorial notebooks, they are each made available on our GitHub within the `tutorials `_ folder. Each notebook is also linked below: 1. `multiple grid cells notebook `_ 2. `hypercube sampling notebook `_ 3. `user-defined reactions notebook `_ 4. `local parallelization notebook `_ 5. `HPC parallelization notebook `_ 6. `GPU solvers notebook `_ 7. `CARMA notebook `_ Web View --------- The tutorial notebooks are also included here in the documentation for convenient online browsing. .. toctree:: :maxdepth: 1 1. multiple_grid_cells 2. hypercube 3. user_defined_reactions 4. local_parallelization 5. hpc_parallelization 6. gpu_solver 7. carma