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phase 1.5.2 with 4k step warmup up
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‎docs/HPC_GUIDE.md‎

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@@ -48,7 +48,7 @@ watch -n 10 "ls -1 results_JOBID | wc -l"
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# Fetching the results once the job is done
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scp -r kanagnostopoul@snellius.surf.nl:~/CSS_Project/results/phase3_18744380/ ./results/
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scp -r kanagnostopoul@snellius.surf.nl:~/CSS_Project/results/phase1_18777691/ ./results/
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```
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The jobscript template can be found in ```run_analysis.sh``` (default rome paritition).

hpc_data/phase1.5_18682575/experiments.log renamed to hpc_data/phase1.5.1_18682575/experiments.log

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hpc_data/phase1.5_18682575/phase1_config.json renamed to hpc_data/phase1.5.1_18682575/phase1_config.json

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hpc_data/phase1.5_18682575/phase1_metadata.json renamed to hpc_data/phase1.5.1_18682575/phase1_metadata.json

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hpc_data/phase1.5_18682575/phase1_results.jsonl renamed to hpc_data/phase1.5.1_18682575/phase1_results.jsonl

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‎hpc_data/phase1.5.2_18777691/experiments.log‎

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2026-01-27 17:58:08,406 [INFO] ============================================================
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2026-01-27 17:58:08,406 [INFO] PREDATOR-PREY HYDRA EFFECT EXPERIMENTS
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2026-01-27 17:58:08,406 [INFO] ============================================================
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2026-01-27 17:58:08,406 [INFO] Phases: [1]
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2026-01-27 17:58:08,406 [INFO] Output: results/phase1_18777691
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2026-01-27 17:58:08,406 [INFO] Cores: 128
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2026-01-27 17:58:08,406 [INFO] Numba: ENABLED
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2026-01-27 17:58:08,407 [INFO]
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2026-01-27 17:58:08,407 [INFO] ============================================================
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2026-01-27 17:58:08,407 [INFO] PHASE 1
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2026-01-27 17:58:08,407 [INFO] ============================================================
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2026-01-27 17:58:08,407 [INFO] Estimated: 9,000 sims, ~8.3h on 128 cores (~1058 core-hours)
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2026-01-27 17:58:08,407 [INFO] Dry run - skipping execution
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2026-01-27 17:58:08,407 [INFO]
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2026-01-27 17:58:08,407 [INFO] ============================================================
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2026-01-27 17:58:08,407 [INFO] EXPERIMENTS COMPLETE
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2026-01-27 17:58:08,407 [INFO] ============================================================
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2026-01-27 17:58:09,408 [INFO] ============================================================
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2026-01-27 17:58:09,408 [INFO] PREDATOR-PREY HYDRA EFFECT EXPERIMENTS
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2026-01-27 17:58:09,408 [INFO] ============================================================
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2026-01-27 17:58:09,408 [INFO] Phases: [1]
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2026-01-27 17:58:09,408 [INFO] Output: results/phase1_18777691
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2026-01-27 17:58:09,408 [INFO] Cores: 128
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2026-01-27 17:58:09,408 [INFO] Numba: ENABLED
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2026-01-27 17:58:09,408 [INFO]
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2026-01-27 17:58:09,408 [INFO] ============================================================
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2026-01-27 17:58:09,408 [INFO] PHASE 1
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2026-01-27 17:58:09,408 [INFO] ============================================================
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2026-01-27 17:58:09,408 [INFO] Estimated: 9,000 sims, ~8.3h on 128 cores (~1058 core-hours)
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2026-01-27 17:58:10,150 [INFO] Phase 1: 600 simulations
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2026-01-27 17:58:10,151 [INFO] Grid: 20 prey_death values × 30 reps (prey_birth=0.2)
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2026-01-27 18:15:49,810 [INFO] Phase 1 complete. Results: results/phase1_18777691/phase1_results.jsonl
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2026-01-27 18:15:49,850 [INFO] Phase 1 runtime: 17.7 minutes
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2026-01-27 18:15:49,850 [INFO]
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2026-01-27 18:15:49,850 [INFO] ============================================================
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2026-01-27 18:15:49,850 [INFO] EXPERIMENTS COMPLETE
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2026-01-27 18:15:49,850 [INFO] ============================================================

‎hpc_data/phase1.5.2_18777691/phase1_config.json‎

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{
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"grid_size": 1000,
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"densities": [
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0.3,
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0.15
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],
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"grid_sizes": [
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50,
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100,
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250,
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500,
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1000,
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2500
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],
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"prey_birth": 0.2,
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"prey_death": 0.05,
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"predator_birth": 0.8,
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"predator_death": 0.05,
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"critical_prey_birth": 0.2,
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"critical_prey_death": 0.947,
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"prey_death_range": [
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0.09,
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0.12
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],
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"n_prey_birth": 15,
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"n_prey_death": 20,
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"predator_birth_values": [
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0.15,
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0.2,
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0.25,
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0.3
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],
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"predator_death_values": [
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0.05,
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0.1,
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0.15,
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0.2
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],
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"prey_death_offsets": [
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-0.02,
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-0.01,
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0.0,
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0.01,
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0.02
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],
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"n_replicates": 30,
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"warmup_steps": 4000,
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"measurement_steps": 1000,
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"with_evolution": false,
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"evolve_sd": 0.1,
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"evolve_min": 0.0,
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"evolve_max": 0.1,
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"sensitivity_sd_values": [
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0.02,
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0.05,
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0.1,
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0.15,
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0.2
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],
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"synchronous": false,
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"directed_hunting": false,
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"directed_hunting_values": [
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false,
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true
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],
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"save_timeseries": false,
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"timeseries_subsample": 10,
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"collect_pcf": false,
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"pcf_sample_rate": 0.2,
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"pcf_max_distance": 20.0,
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"pcf_n_bins": 20,
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"min_density_for_analysis": 0.002,
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"perturbation_magnitude": 0.1,
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"n_jobs": 128
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}

‎hpc_data/phase1.5.2_18777691/phase1_metadata.json‎

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{
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"phase": 1,
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"description": "Parameter sweep for critical point",
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"n_sims": 600,
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"timestamp": "2026-01-27 18:15:49",
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"config": {
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"grid_size": 1000,
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"densities": [
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0.3,
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0.15
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],
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"grid_sizes": [
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50,
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100,
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250,
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500,
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1000,
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2500
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],
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"prey_birth": 0.2,
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"prey_death": 0.05,
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"predator_birth": 0.8,
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"predator_death": 0.05,
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"critical_prey_birth": 0.2,
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"critical_prey_death": 0.947,
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"prey_death_range": [
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0.09,
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0.12
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],
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"n_prey_birth": 15,
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"n_prey_death": 20,
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"predator_birth_values": [
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0.15,
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0.2,
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0.25,
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0.3
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],
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"predator_death_values": [
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0.05,
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0.1,
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0.15,
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0.2
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],
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"prey_death_offsets": [
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-0.02,
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-0.01,
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0.0,
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0.01,
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0.02
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],
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"n_replicates": 30,
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"warmup_steps": 4000,
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"measurement_steps": 1000,
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"with_evolution": false,
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"evolve_sd": 0.1,
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"evolve_min": 0.0,
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"evolve_max": 0.1,
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"sensitivity_sd_values": [
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0.02,
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0.05,
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0.1,
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0.15,
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0.2
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],
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"synchronous": false,
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"directed_hunting": false,
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"directed_hunting_values": [
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false,
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true
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],
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"save_timeseries": false,
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"timeseries_subsample": 10,
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"collect_pcf": false,
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"pcf_sample_rate": 0.2,
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"pcf_max_distance": 20.0,
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"pcf_n_bins": 20,
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"min_density_for_analysis": 0.002,
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"perturbation_magnitude": 0.1,
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"n_jobs": 128
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}
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}

‎hpc_data/phase1.5.2_18777691/phase1_results.jsonl‎

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‎scripts/experiments.py‎

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@@ -646,29 +646,36 @@ def run_phase5(cfg: Config, output_dir: Path, logger: logging.Logger) -> List[Di
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return all_results
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def run_phase4(cfg: Config, output_dir: Path, logger: logging.Logger) -> List[Dict]:
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def run_phase6(cfg: Config, output_dir: Path, logger: logging.Logger) -> List[Dict]:
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"""
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Phase 4: Global Sensitivity Analysis.
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Phase 6: Model Extensions - Directed Hunting Comparison.
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Same 4D sweep as Phase 4, but with directed_hunting=True.
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Vary: prey_birth, prey_death, predator_birth, predator_death
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Range: 0-1 (11 values each)
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Reps: 10
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Grid size: 250
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Compare results with Phase 4 to assess impact of directed hunting on:
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- Critical point location
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- Hydra effect persistence
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- SOC signatures
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"""
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from joblib import Parallel, delayed
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import itertools
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warmup_numba_kernels(cfg.grid_size, directed_hunting=cfg.directed_hunting)
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# Define the global sweep values
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# Define the global sweep values (same as Phase 4)
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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"Phase 6: Full 4D Parameter Sweep (Directed Hunting)")
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logger.info(f" Parameters: prey_birth, prey_death, pred_birth, pred_death")
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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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logger.info(f" Directed Hunting: {cfg.directed_hunting}")
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param_grid = itertools.product(sweep_values, repeat=4)
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for pb, pd, pred_b, pred_d in param_grid:
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for rep in range(cfg.n_replicates):
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# Create params dict for unique seed generation
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# Include phase identifier to ensure different seeds from Phase 4
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params_id = {
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"pb": pb,
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"pd": pd,
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"pred_b": pred_b,
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"pred_d": pred_d,
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"phase": 6,
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"rep": rep
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}
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seed = generate_unique_seed(params_id, rep)
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))
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logger.info(f" Total simulations: {len(jobs):,}")
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output_jsonl = output_dir / "phase4_results.jsonl"
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output_jsonl = output_dir / "phase6_results.jsonl"
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all_results = []
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with open(output_jsonl, "w", encoding="utf-8") as f:
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executor = Parallel(n_jobs=cfg.n_jobs, return_as="generator")
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tasks = (delayed(run_single_simulation)(*job) for job in jobs)
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# tqdm shows progress for the massive job list
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for result in tqdm(executor(tasks), total=len(jobs), desc="Phase 4 (4D Sweep)"):
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for result in tqdm(executor(tasks), total=len(jobs), desc="Phase 6 (4D Sweep + Directed)"):
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f.write(json.dumps(result, default=str) + "\n")
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f.flush()
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all_results.append(result)
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# 4. Save Metadata
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# Save Metadata
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meta = {
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"phase": 4,
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"description": "Global 4D Sensitivity Analysis",
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"phase": 6,
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"description": "Global 4D Sensitivity Analysis with Directed Hunting",
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"sweep_values": sweep_values.tolist(),
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"parameters_varied": ["prey_birth", "prey_death", "predator_birth", "predator_death"],
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"directed_hunting": cfg.directed_hunting,
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"n_sims": len(all_results),
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"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
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"config": asdict(cfg),
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}
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with open(output_dir / "phase4_metadata.json", "w") as f:
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with open(output_dir / "phase6_metadata.json", "w") as f:
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json.dump(meta, f, indent=2, default=str)
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logger.info(f"Phase 4 complete. Results: {output_jsonl}")
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logger.info(f"Phase 6 complete. Results: {output_jsonl}")
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return all_results
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# =============================================================================
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# Main:
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# =============================================================================

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