Merge branch 'master' into use_ENVs_for_OMFIT_OMAS

Author: d3dsf

Project.toml

@@ -1,7 +1,7 @@
 name = "FUSE"
 uuid = "e64856f0-3bb8-4376-b4b7-c03396503992"
 authors = ["Orso Meneghini <[email protected]>"]
-version = "0.9.1"
+version = "0.9.2"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

README.md

@@ -12,6 +12,7 @@ The figure below is a sneakpeak of the models implemented in FUSE:
 
 Here are some key resources for getting started with FUSE:
 
+* 📨 **[Sign up for more info](https://forms.gle/iQGYKWfgeNkw2fCZ7)**
 * 📚 **[Online documentation](https://fuse.help)**
 * 🎓 **[Intro tutorial](https://fuse.help/dev/tutorial.html)**
 * 🎤 **[Recent presentation](https://github.com/ProjectTorreyPines/FUSE_extra_files/raw/master/2025_D3D/SET_mar_2025.pdf)**

docs/src/cases_docs.jl

@@ -25,7 +25,7 @@ pushfirst!(
 CurrentModule = FUSE
 ```
 
-FUSE comes with a set of pre-cookes used cases.
+FUSE comes with a set of pre-cooked use cases.
 The `case_parameters(:use_case, ...)` method returns the `ini` and `act` parameters for that specific `use_case`.
 These `ini` and `act` can then be further customized before running a FUSE simulation.
 

docs/src/install.md

@@ -68,9 +68,9 @@ For installation start your Julia interpreter by typing `julia` at the terminal,
    !!! note
        The WebIO jupyter-lab extension is needed for the [`Interact.jl`](https://github.com/JuliaGizmos/Interact.jl?tab=readme-ov-file#usage) package to work.
 
-       Make sure WebIO is working with `jupyter labextension list`.
+       Make sure WebIO is working with `jupyter labextension list`. If it is working properly, you should see something like: webio-jupyterlab-provider v0.1.0 enabled OK (python, webio_jupyter_extension). This can also be checked with `jupyter nbextension list`, which should show something like: webio-jupyter-nbextension/nbextension  enabled.
 
-       If the extension has compatibility issues, consider installing an older verision of Jupyter (eg. `conda install jupyterlab=3.6.7`).
+       If the extension has compatibility issues, consider installing an older verision of Jupyter (eg. `conda install jupyterlab=3.6.7`). Also ensure that the WebIO and Interact packages are fully up-to-date, and restart the notebook session before testing that it works. Finally, it may be necessary to downgrade your system's version of Python - the recommended version for compatibility with Interact is 3.11.11. 
 
 1. Start a new Jupyter-lab session (this should open a web-browser page with Jupyter running)
 

knowledge/how_to_use_fuse.md

@@ -820,7 +820,7 @@ Studies use a `sty` (study parameters) structure similar to `act` (actor paramet
 
 ```julia
 # Get study parameters - similar to case_parameters but for studies
-sty, act = FUSE.study_parameters(:TGLFdb)
+sty = FUSE.study_parameters(:TGLFdb)
 
 # Common study parameters
 sty.server = "localhost"           # Distributed computing server  
@@ -841,7 +841,7 @@ sty.custom_tglf_models = ["sat3_em_d3d_azf-1_withnegD"]  # Custom TGLF models to
 using Distributed
 
 # 1. Setup study parameters
-sty, act = FUSE.study_parameters(:TGLFdb)
+sty = FUSE.study_parameters(:TGLFdb)
 sty.server = "localhost"
 sty.n_workers = 4
 sty.database_folder = "/path/to/database"
@@ -951,7 +951,7 @@ ylabel!("Probability")
 
 ```julia
 # Get study parameters for database generation
-sty, _ = FUSE.study_parameters(:DatabaseGenerator)
+sty = FUSE.study_parameters(:DatabaseGenerator)
 
 # Configure distributed computing
 sty.server = "localhost"
@@ -1120,7 +1120,7 @@ ini.requirements.coil_stress_margin = 0.1   # 10% stress margin
 
 ```julia
 # Get study parameters for multi-objective optimization
-sty, _ = FUSE.study_parameters(:MultiObjectiveOptimizer)
+sty = FUSE.study_parameters(:MultiObjectiveOptimizer)
 
 # Configure distributed computing
 sty.server = "localhost"  # Can be "saga", "omega", or cluster name

knowledge/mcp_server/README.md

@@ -75,4 +75,4 @@ The server loads actor information from:
 
 Configure in Claude Code:
 
-`claude mcp add fuse -- <your_FUSE_folder>>/knowledge/mcp_server/start_server.sh`
+`claude mcp add fuse -- ./mcp_server/start_server.sh`

knowledge/mcp_server/prompts/actor_expert.txt

@@ -1,4 +1,4 @@
-You are a FUSE (Fusion Unified System for Energy) plasma simulation expert. FUSE is a comprehensive framework for modeling tokamak and stellarator plasmas, built in Julia.
+You are a FUSE (FUsion Synthesis Engine) plasma simulation expert. FUSE is a comprehensive framework for modeling tokamak and stellarator plasmas, built in Julia.
 
 ## Your Role
 You help users understand and work with FUSE actors - modular components that simulate different aspects of plasma physics including transport, equilibrium, heating & current drive, pedestal physics, and more.

knowledge/mcp_server/start_server.sh

@@ -3,11 +3,11 @@
 echo "change this file to get it to work:: $(realpath "${BASH_SOURCE[0]}")"
 
 # Activate conda environment
-source /Users/tims/.zshrc
+source $HOME/.zshrc
 conda activate mcp_server # this is your conda env
 
 # Change to server directory
-cd "/Users/tims/.julia/dev/FUSE/knowledge/mcp_server/"
+cd "$HOME/.julia/dev/FUSE/knowledge/mcp_server/"
 
 # Start the MCP server
 exec python server.py

src/FUSE.jl

@@ -178,6 +178,7 @@ include("studies.jl")
 include(joinpath("studies", "database_generator.jl"))
 include(joinpath("studies", "multi_objective_optimization.jl"))
 include(joinpath("studies", "TGLF_database.jl"))
+include(joinpath("studies", "study_database.jl"))
 include(joinpath("studies", "experiment_postdictive.jl"))
 
 #= ========= =#

src/optimization.jl

@@ -206,7 +206,7 @@ function optimization_engine(
         df[!, :elapsed_time] = fill(time()-start_time, nrow(df))
 
         # save simulation data
-        save_database("tmp_h5_output", parent_group, (save_dd ? dd : nothing), ini, act, tmp_log_io;
+        save_study_database("tmp_h5_output", parent_group, (save_dd ? dd : nothing), ini, act, tmp_log_io;
             timer=true, freeze=false, overwrite_groups=true)
 
         # Write into temporary csv files, in case the whole Julia session is crashed
@@ -250,7 +250,7 @@ function optimization_engine(
         df[!, :elapsed_time] = fill(time()-start_time, nrow(df))
 
         # save empty dd and error to directory
-        save_database("tmp_h5_output", parent_group, nothing, ini, act, tmp_log_io;
+        save_study_database("tmp_h5_output", parent_group, nothing, ini, act, tmp_log_io;
             error_info=e, timer=true, freeze=false, overwrite_groups=true, kw...)
 
         # Write into temporary csv files, in case the whole Julia session is crashed