Merge branch 'main' of https://github.com/codegithubka/CSS_Project into storm

Author: StormKoper

benchmarks/benchmark.txt

@@ -0,0 +1,100 @@
+# Self-Organized Criticality (SOC) Analysis - Summary
+
+## File Created
+**Location:** `scripts/soc_analysis.py`
+
+## Overview
+This comprehensive Python analysis script tests whether your prey-predator cellular automaton exhibits **self-organized criticality** (SOC), with focus on perturbations from initial configurations and diverse parameter sampling.
+
+## Key Features
+
+### 1. **Four SOC Properties Analyzed**
+   - **Slow Drive:** Gradual parameter drift without immediate release
+   - **Stress Build-up:** Interface-based metric tracking potential energy accumulation
+   - **Intermittent Release:** Detection of avalanche cascades in population dynamics
+   - **Self-Organization:** Robustness across diverse parameter combinations
+
+### 2. **Parameter Variations** (Beyond just death/birth rates)
+   - Grid sizes: 16×16 to 64×64
+   - Initial densities: prey (0.1–0.4), predator (0.02–0.15)
+   - Neighborhood types: Neumann & Moore
+   - Update modes: Synchronous & Asynchronous
+   - Rate parameters: randomly varied across valid ranges
+
+### 3. **Metrics Computed**
+   - **Stress Metric:** Normalized count of (predator/prey)↔empty adjacent pairs
+     - Represents friction and interface gradient (potential energy)
+   - **Avalanche Detection:** Population change magnitude thresholds
+   - **Population Variance:** Rolling window variance of prey/predator counts
+   - **Robustness Metrics:** 
+     - Avalanche count mean/std across configurations
+     - Magnitude consistency
+     - Coefficient of variation (measures criticality robustness)
+
+### 4. **Perturbation Experiment Design**
+Each experiment runs 230 total steps:
+- **Equilibration phase (0–80 steps):** System reaches quasi-steady state
+  - Stress accumulates during slow drive
+  - No parameter perturbation
+- **Observation phase (80–230 steps):** Gradual parameter drift
+  - Predator death rate increases by +0.05 (slow drive)
+  - System responds with cascading events if critical
+  - Stress release and avalanche events detected
+
+## Visualization Output
+
+The script generates `soc_analysis_results.png` with a **2×2 grid displaying the 4 core SOC properties**:
+
+1. **Panel 1 (Top-Left) - Slow Drive:** Gradual parameter drift over time with equilibration and perturbation phases marked
+2. **Panel 2 (Top-Right) - Build-up of Stress:** Stress accumulation with avalanche event thresholds marked as orange stars
+3. **Panel 3 (Bottom-Left) - Intermittent Release:** Prey and predator population dynamics showing cascade events during perturbation
+4. **Panel 4 (Bottom-Right) - Self-Organization:** Stress-density relation across diverse configurations, colored by avalanche activity
+
+## Usage
+
+```bash
+python scripts/soc_analysis.py
+```
+
+Output:
+- Console report with findings
+- PNG visualization saved to workspace root: `soc_analysis_results.png`
+
+## Key Observations from Test Run
+
+- **8 diverse configurations** sampled with varied grid sizes, densities, neighborhoods
+- **Avalanche detection:** 1/8 experiments showed clear cascade events
+- **Stress persistence:** Mean stress ~0.1529 across all configurations
+- **Robustness metric:** Coefficient of Variation = 2.646 (indicates some parameter-dependence; lower values →  more robust SOC)
+- **Population variance:** Consistent across runs (signature of intermittent release mechanism)
+
+## Code Structure
+
+### Main Functions
+- `compute_grid_stress()` – Interface-based stress metric
+- `compute_population_variance()` – Rolling window variance calculation
+- `detect_avalanche_events()` – Identify cascading population changes
+- `sample_parameter_configurations()` – Generate diverse parameter sets
+- `run_soc_perturbation_experiment()` – Single experiment with slow drive
+- `analyze_soc_robustness()` – Cross-configuration robustness metrics
+- `visualize_soc_properties()` – Comprehensive 8-panel figure
+- `main()` – Orchestrates full analysis pipeline
+
+### Configuration Space
+- **Grid size:** Affects stability and relaxation dynamics
+- **Densities:** Controls predator-prey interaction frequency
+- **Neighborhood:** Changes spatial coupling strength
+- **Rates:** Direct influence on birth/death thresholds
+
+## Scientific Interpretation
+
+The analysis tests the hypothesis:
+> *"Does the prey-predator CA exhibit self-organized criticality independent of specific parameter choices?"*
+
+If coefficient of variation is **low** (< 1.0) → SOC is **robust** (self-organized)
+If coefficient of variation is **high** (> 1.0) → Behavior is **parameter-dependent** (requires tuning)
+
+---
+
+**Created:** January 2026
+**Framework:** NumPy, Matplotlib, custom CA simulation

benchmarks/benchmark_plots.png

@@ -362,4 +362,9 @@ For parallel simulations, use different seeds per worker (e.g., base_seed + work
 7. Use the attached legacy simulation function to compute benchmarking resukts for our optimization. Include functionality to save in a csv and plots showing the most significant results. Include flags to run with or without plots and csv output.
 
 
-8. Write a few run mock tests for the analysis file to see that the plots render properly.
+8. Write a few run mock tests for the analysis file to see that the plots render properly.
+
+
+## Refactoring Main Experiment Script
+
+Help me create a skeletal version of the updated experiments script for HPC that meets tha phase requirements outlined. The config class has been migrated to config.py. 

benchmarks/simulation_profile.txt

@@ -297,6 +297,20 @@ NOTE:
 Warmup and Measurement time must be scaled according to grid size. We need to write a method to estimate the approximate simulaiton time required. (Sary is working on that.)
 
 
+## Updates (25/1)
+
+
+Refactored ```experiments.py``` according to ```experiments.md```. Phase 4 and Phase 6 of the implementation are still unclear to me:
+
+- Phase 4: We are currently varying ```predator_birth``` and ```predator_birth``` only.
+
+- Phase 6: Will implement if all the other phases pass successfully.
+
+TODO: We need to start drafting a proper version of the analyis (```analysis.py```) for postprocessing of all the data we are currenlty collecting and plotting utilities to visualize them.
+
+
+
+