some plots

Author: codegithubka

.DS_Store

@@ -12,4 +12,4 @@ results/
 data/
 .pytest_cache/
 
-.DS_Store
+hpc_data/phase4_18735304

.gitignore

@@ -39,7 +39,7 @@ scancel <JOBID>
 
 # Monitoring live progress
 
-tail -f logs_18702594.err
+tail -f pp_phase4_18735304.err
 
 # Watch task completetion
 
@@ -48,7 +48,7 @@ watch -n 10 "ls -1 results_JOBID  | wc -l"
 
 # Fetching the results once the job is done
 
-scp -r kanagnostopoul@snellius.surf.nl:~/CSS_Project/results/phase3_18698382/ ./results/
+scp -r kanagnostopoul@snellius.surf.nl:~/CSS_Project/results/phase3_18743295/ ./results/
 ```
 
 The jobscript template can be found in ```run_analysis.sh``` (default rome paritition).

docs/HPC_GUIDE.md

@@ -367,4 +367,9 @@ For parallel simulations, use different seeds per worker (e.g., base_seed + work
 
 ## 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. 
+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. 
+
+
+## Data Post-processing
+
+Help me load and parse the data according to job and experimental phase number. The data will be analyzed in a jupyter notebook rather than a py file for usability.

docs/kimon_prompts.md

@@ -80,7 +80,7 @@ class Config:
 
     # Critical point (UPDATE AFTER PHASE 1)
     critical_prey_birth: float = 0.20 
-    critical_prey_death: float = 0.0963 
+    critical_prey_death: float = 0.963
 
     # Prey parameter sweep (Phase 1)
     prey_death_range: Tuple[float, float] = (0.0, 0.2)
@@ -192,7 +192,7 @@ def estimate_runtime(self, n_cores: int = 32) -> str:
     grid_size=1000,
     n_prey_death=20,
     prey_birth=0.2,
-    prey_death_range=(0.09, 0.12),
+    prey_death_range=(0.00, 0.2),
     predator_birth=0.8,    
     predator_death=0.05,    
     n_replicates=30,
@@ -224,13 +224,14 @@ def estimate_runtime(self, n_cores: int = 32) -> str:
 
 # Phase 3: Finite-size scaling at critical point
 PHASE3_CONFIG = Config(
-    grid_sizes=(50, 100, 250, 500, 1000),
+    grid_sizes=(50, 100, 250, 500, 1000, 2500),
     n_replicates=20,
     warmup_steps=1000,
     measurement_steps=1000,
     critical_prey_birth=0.20,      # Add explicitly
-    critical_prey_death=0.0963,    # Add explicitly - verify from Phase 1!
-    collect_pcf=False,
+    critical_prey_death=0.116,    # Add explicitly - verify from Phase 1!
+    collect_pcf=True,
+    pcf_sample_rate=1.0,
     save_timeseries=False,
     with_evolution=False,
     directed_hunting=False,
@@ -264,11 +265,11 @@ def estimate_runtime(self, n_cores: int = 32) -> str:
 )
 
 # Phase 6: Model extensions (directed hunting); same config as phase 1 but with directed hunting
-PHASE6_CONFIG = Config(
-    grid_size=1000,
+PHASE6_CONFIG = Config( 
+    grid_size=1000, #FIXME: Verify config before running.
     n_prey_death=20,
     prey_birth=0.2,
-    prey_death_range=(0.09, 0.12),
+    prey_death_range=(0.0, 0.2),
     predator_birth=0.8,    
     predator_death=0.05,    
     n_replicates=30,