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phase 5 shell script added
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‎hpc/run_phase1.sh‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1,9 +1,9 @@
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#!/bin/bash
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#SBATCH --job-name=pp_phase1
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#SBATCH --partition=rome
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#SBATCH --partition=genoa
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#SBATCH --nodes=1
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#SBATCH --ntasks=1
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#SBATCH --cpus-per-task=32
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#SBATCH --cpus-per-task=128
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#SBATCH --time=04:00:00
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#SBATCH --mem=16G
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#SBATCH --output=pp_phase1_%j.out

‎hpc/run_phase6.sh‎

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#!/bin/bash
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#SBATCH --job-name=pp_phase6
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#SBATCH --partition=genoa
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#SBATCH --nodes=1
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#SBATCH --ntasks=1
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#SBATCH --cpus-per-task=128
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#SBATCH --time=08:00:00
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#SBATCH --mem=0
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#SBATCH --output=pp_phase6_%j.out
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#SBATCH --error=pp_phase6_%j.err
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# =============================================================================
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# PP Hydra Effect - Phase 6: Directed Hunting 4D Sweep
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# =============================================================================
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#
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# PHASE 6: Full 4D parameter sweep with directed hunting enabled
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# - Same structure as Phase 4 but with directed_hunting=True
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# - 11^4 × 10 reps = 146,410 simulations
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# - Grid size: 250
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# - Collects time series for comparison with Phase 4
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# - Estimated runtime: ~4-6 hours on 128 cores
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# - Memory: mem=0 (use all available node memory)
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#
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# PURPOSE: Test if Hydra effect and SOC persist under directed hunting
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#
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# SUBMIT: sbatch run_phase6.sh
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# MONITOR: squeue -u $USER
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# CANCEL: scancel <job_id>
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#
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# =============================================================================
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echo "========================================"
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echo "PP Hydra Effect - Phase 6"
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echo "========================================"
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echo "Job ID: $SLURM_JOB_ID"
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echo "Node: $(hostname)"
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echo "CPUs: $SLURM_CPUS_PER_TASK"
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echo "Start: $(date)"
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echo "Working dir: $(pwd)"
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echo "========================================"
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# -----------------------------------------------------------------------------
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# Environment Setup
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# -----------------------------------------------------------------------------
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source ~/snellius_venv/bin/activate
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# Prevent numpy/scipy from spawning extra threads (joblib handles parallelism)
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export OMP_NUM_THREADS=1
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export OPENBLAS_NUM_THREADS=1
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export MKL_NUM_THREADS=1
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export NUMEXPR_NUM_THREADS=1
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# -----------------------------------------------------------------------------
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# Run Phase 6
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# -----------------------------------------------------------------------------
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OUTPUT_DIR="results/phase6_${SLURM_JOB_ID}"
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mkdir -p $OUTPUT_DIR
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echo ""
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echo "Output directory: $OUTPUT_DIR"
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echo ""
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# Dry run first to verify setup
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echo "Dry run check:"
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python3 -u scripts/experiments.py \
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--phase 6 \
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--output $OUTPUT_DIR \
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--cores $SLURM_CPUS_PER_TASK \
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--dry-run
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echo ""
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echo "Starting Phase 6..."
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echo ""
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# Run phase 6
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python3 -u scripts/experiments.py \
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--phase 6 \
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--output $OUTPUT_DIR \
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--cores $SLURM_CPUS_PER_TASK
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# -----------------------------------------------------------------------------
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# Completion
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# -----------------------------------------------------------------------------
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echo ""
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echo "========================================"
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echo "Phase 6 Complete"
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echo "========================================"
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echo "End time: $(date)"
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echo "Results in: $OUTPUT_DIR/"
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echo ""
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echo "Output files:"
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ls -lh $OUTPUT_DIR/
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echo ""
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echo "Next steps:"
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echo " 1. Download phase6_results.jsonl"
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echo " 2. Compare with Phase 4 results (random hunting baseline)"
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echo " 3. Analyze if Hydra effect persists under directed hunting"
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echo " 4. Compare critical point locations between Phase 4 and Phase 6"
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echo " 5. Check for differences in SOC signatures"
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echo "========================================"

‎models/config.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -192,13 +192,13 @@ def estimate_runtime(self, n_cores: int = 32) -> str:
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grid_size=1000,
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n_prey_death=20,
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prey_birth=0.2,
195-
prey_death_range=(0.00, 0.2),
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prey_death_range=(0.09, 0.12),
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predator_birth=0.8,
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predator_death=0.05,
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n_replicates=30,
199-
warmup_steps=1000,
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warmup_steps=4000,
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measurement_steps=1000,
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collect_pcf=True,
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collect_pcf=False,
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pcf_sample_rate=0.2,
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save_timeseries=False,
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directed_hunting=False,

‎scripts/experiments.py‎

Lines changed: 55 additions & 20 deletions
Original file line numberDiff line numberDiff line change
@@ -646,52 +646,87 @@ def run_phase5(cfg: Config, output_dir: Path, logger: logging.Logger) -> List[Di
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return all_results
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648648

649-
def run_phase6(cfg: Config, output_dir: Path, logger: logging.Logger) -> List[Dict]:
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650+
def run_phase4(cfg: Config, output_dir: Path, logger: logging.Logger) -> List[Dict]:
650651
"""
651-
Phase 6: Model extensions.
652-
- Compare directed vs random hunting
653-
- Check if Hydra effect and SOC persist
652+
Phase 4: Global Sensitivity Analysis.
653+
Vary: prey_birth, prey_death, predator_birth, predator_death
654+
Range: 0-1 (11 values each)
655+
Reps: 10
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Grid size: 250
654657
"""
655658
from joblib import Parallel, delayed
659+
import itertools
656660

657661
warmup_numba_kernels(cfg.grid_size, directed_hunting=cfg.directed_hunting)
658662

659-
prey_deaths = cfg.get_prey_deaths()
663+
# Define the global sweep values
664+
sweep_values = np.linspace(0.0, 1.0, 11)
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# Logging
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logger.info(f"Phase 4: Full 4D Parameter Sweep")
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logger.info(f" Parameters: prey_birth, prey_death, pred_birth, pred_death")
669+
logger.info(f" Range: 0.0 to 1.0 (11 steps)")
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logger.info(f" Grid Size: {cfg.grid_size}")
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logger.info(f" Replicates: {cfg.n_replicates}")
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673+
param_grid = itertools.product(sweep_values, repeat=4)
674+
660675
jobs = []
661-
for pd in prey_deaths:
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677+
for pb, pd, pred_b, pred_d in param_grid:
662678
for rep in range(cfg.n_replicates):
663-
params = {"pd": pd}
664-
seed = generate_unique_seed(params, rep)
665-
jobs.append((cfg.prey_birth, pd, cfg.predator_birth, cfg.predator_death,
666-
cfg.grid_size, seed, cfg, False))
679+
# Create params dict for unique seed generation
680+
params_id = {
681+
"pb": pb,
682+
"pd": pd,
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"pred_b": pred_b,
684+
"pred_d": pred_d,
685+
"rep": rep
686+
}
687+
seed = generate_unique_seed(params_id, rep)
667688

668-
logger.info(f"Phase 6: {len(jobs):,} simulations (directed hunting)")
669-
logger.info(f" Grid: {cfg.n_prey_death} prey_death values × {cfg.n_replicates} reps (prey_birth={cfg.prey_birth})")
670-
671-
output_jsonl = output_dir / "phase6_results.jsonl"
689+
# Job Signature:
690+
# (prey_birth, prey_death, predator_birth, predator_death, grid_size, seed, cfg, with_evolution)
691+
jobs.append((
692+
pb,
693+
pd,
694+
pred_b,
695+
pred_d,
696+
cfg.grid_size,
697+
seed,
698+
cfg,
699+
False
700+
))
701+
702+
logger.info(f" Total simulations: {len(jobs):,}")
703+
output_jsonl = output_dir / "phase4_results.jsonl"
672704
all_results = []
673705

674706
with open(output_jsonl, "w", encoding="utf-8") as f:
675707
executor = Parallel(n_jobs=cfg.n_jobs, return_as="generator")
676708
tasks = (delayed(run_single_simulation)(*job) for job in jobs)
677709

678-
for result in tqdm(executor(tasks), total=len(jobs), desc="Phase 6"):
710+
# tqdm shows progress for the massive job list
711+
for result in tqdm(executor(tasks), total=len(jobs), desc="Phase 4 (4D Sweep)"):
679712
f.write(json.dumps(result, default=str) + "\n")
680713
f.flush()
681714
all_results.append(result)
682715

683-
# Save metadata
716+
# 4. Save Metadata
684717
meta = {
685-
"phase": 6,
686-
"description": "Parameter sweep for critical point with directed hunting",
718+
"phase": 4,
719+
"description": "Global 4D Sensitivity Analysis",
720+
"sweep_values": sweep_values.tolist(),
721+
"parameters_varied": ["prey_birth", "prey_death", "predator_birth", "predator_death"],
687722
"n_sims": len(all_results),
688723
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
689724
"config": asdict(cfg),
690725
}
691-
with open(output_dir / "phase6_metadata.json", "w") as f:
726+
with open(output_dir / "phase4_metadata.json", "w") as f:
692727
json.dump(meta, f, indent=2, default=str)
693728

694-
logger.info(f"Phase 6 complete. Results: {output_jsonl}")
729+
logger.info(f"Phase 4 complete. Results: {output_jsonl}")
695730
return all_results
696731
# =============================================================================
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# Main:

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