HPC script okay

Author: codegithubka

docs/HPC_GUIDE.md

@@ -0,0 +1,42 @@
+### Snellius Usage Breakdown
+
+```
+ssh <your_username>@snellius.surf.nl
+
+# On a separate terminal run the following
+
+# Upload the entire project directory (including your models/ folder)
+scp -r ~/Documents/CSS_Project <your_username>@snellius.surf.nl:~/
+
+# On the Snellius terminal
+
+module load 2023 Python/3.11.3-GCCcore-12.3.0
+python3 -m venv ~/css_env
+source ~/css_env/bin/activate
+pip install numpy scipy matplotlib joblib
+
+# To submit a job
+
+sbatch run_analysis.sh
+
+# Check Queue Status
+
+squeue -u $USER
+
+# Cancel a job
+
+scancel <JOBID>
+
+# Monitoring live progress
+
+tail -f logs_<JOBID>.err
+
+# Fetching the results once the job is done
+
+scp -r <your_username>@snellius.surf.nl:~/results_18514601 ~/Downloads/
+```
+
+The jobscript template can be found in ```run_analysis.sh``` (default rome paritition).
+
+
+Snellius Partitions Page: https://servicedesk.surf.nl/wiki/spaces/WIKI/pages/30660209/Snellius+partitions+and+accounting

docs/prompts.md

@@ -575,5 +575,5 @@ Made large changes to multiple functions. See git commit for details.
 
 2. Create a skeletal version of a .py script that will be subimtted into Snellius for parameter analysis. The purpose of this script should be to identify power law distribution with the consideration of finite size scaling, the hydra effect, and suitable parameter configurtaions for time series analysis for model evolution. Compare the non-evo to the evo model.
 
-3. Create a config class adjustable depending on the CPU budget. We want to run a prey_birth vs. predator_death parameter sweep (2D), quantify the hydra effect using the derivative, search for the critical point (power law relartionship paramameter), quantify evolution sensitivity and analyze finite grid size scaling. 
+3. Create a config class adjustable depending on the CPU budget. We want to run a prey_birth vs. predator_death parameter sweep (2D), quantify the hydra effect using the derivative, search for the critical point (power law relartionship paramameter), quantify evolution sensitivity and analyze finite grid size scaling. Include script options for cost optimal runs as well. Make sure to have a summary of collected data stored for reference and future usage.