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diff --git a/‎docs/plots.md‎
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diff --git a/‎hpc/run_phase2.sh‎
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diff --git a/‎models/config.py‎
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diff --git a/‎notebooks/hydra_landscape_3d.png‎
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diff --git a/‎notebooks/hydra_strength_polynomial.png‎
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diff --git a/‎notebooks/kimon.ipynb‎
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diff --git a/‎scripts/experiments.py‎
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@@ -0,0 +1,35 @@
+General guidelines:
+a. Fixed color scheme: prey: #1751ED, predator: #CF0808
+b. Legend at the bottom and shared if more than one plot
+
+
+1. Bifuractuion
+- zoomed out
+- zoomed in
+
+2. Critical Point Power Law
+- At the critical point and away from the critical point (pick critical, subcritical, supercritical)
+
+3. Finite size scaling
+- Cluster distributions vs. system size
+
+4. Sensitivity Analysis
+- Undirected
+- Directed
+    - 3-dimensional plot with hydra effect salient feature which is the weight of the quadratic term of the second order polynomial fitted to
+        the inbcreasing part of the function before the collapse
+        - X: Prey birth
+        - Y: Predator birth
+        - Z: Predator death
+        - W: Prey death
+
+        - Do manual plot selection. 3 sets of 11 plots; each set where the 11 different plots to vary eveything except prey death
+        - See which one is the most consistent -> lose that dimension to have a 3D plot left.
+
+    - 3-dimensional heatmap instead (preferrable)
+
+5. Evolution
+- Log-log plot for cluster size distrubution at the convergence point
+- Convergence pointsa across intiial conditions to show independence. (But why?) -> probably no convergence.
+
+
@@ -1,11 +1,11 @@
 #!/bin/bash
 #SBATCH --job-name=pp_phase2
-#SBATCH --partition=rome
+#SBATCH --partition=genoa
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
-#SBATCH --cpus-per-task=32
+#SBATCH --cpus-per-task=128
 #SBATCH --time=04:00:00
-#SBATCH --mem=64G
+#SBATCH --mem=0
 #SBATCH --output=/home/kanagnostopoul/CSS_Project/pp_phase2_%j.out
 #SBATCH --error=/home/kanagnostopoul/CSS_Project/pp_phase2_%j.err
 
 
@@ -210,7 +210,7 @@ def estimate_runtime(self, n_cores: int = 32) -> str:
     n_prey_birth=1,  # Fixed at cfg.prey_birth (0.2)
     n_replicates=10,
     warmup_steps=1000,           # Shorter warmup (evolution starts immediately)
-    measurement_steps=5000,     # Longer measurement to see convergence
+    measurement_steps=10000,     # Longer measurement to see convergence
 
     # Evolution settings
     with_evolution=True,
@@ -245,7 +245,7 @@ def estimate_runtime(self, n_cores: int = 32) -> str:
     measurement_steps=500,  # As requested
     with_evolution=False,
     collect_pcf=False,
-    save_timeseries=True,
+    save_timeseries=False,
     timeseries_subsample=10,
     directed_hunting=False,
 )
@@ -272,7 +272,7 @@ def estimate_runtime(self, n_cores: int = 32) -> str:
     measurement_steps=500,
     with_evolution=False,
     collect_pcf=False,
-    save_timeseries=True,
+    save_timeseries=False,
     timeseries_subsample=10,
     directed_hunting=True,
 )
 
@@ -192,6 +192,7 @@ def run_single_simulation(
     # Warmup phase
     for _ in range(warmup_steps):
         model.update()
+
 
     # Measurement phase: start collecting our mertics
     prey_pops, pred_pops = [], [] # Prey populations and predator populations
@@ -283,6 +284,8 @@ def run_single_simulation(
         result["evolved_prey_death_mean"] = float(np.mean(valid_means)) if valid_means else np.nan
         result["evolved_prey_death_std"] = float(np.mean([v for v in evolved_stds if not np.isnan(v)])) if evolved_stds else np.nan
         result["evolved_prey_death_final"] = valid_means[-1] if valid_means else np.nan
+        result["evolved_prey_death_min"] = float(np.min(valid_means)) if valid_means else np.nan
+        result["evolved_prey_death_max"] = float(np.max(valid_means)) if valid_means else np.nan
         result["evolve_sd"] = cfg.evolve_sd
 
         if cfg.save_timeseries:
@@ -520,7 +523,7 @@ def run_phase4(cfg: Config, output_dir: Path, logger: logging.Logger) -> List[Di
     warmup_numba_kernels(cfg.grid_size, directed_hunting=cfg.directed_hunting)
 
     # Define the global sweep values
-    sweep_values = np.linspace(0.0, 1.0, 11)
+    sweep_values = np.linspace(0.5, 0.95, 10)
 
     # Logging
     logger.info(f"Phase 4: Full 4D Parameter Sweep")
@@ -667,7 +670,7 @@ def run_phase6(cfg: Config, output_dir: Path, logger: logging.Logger) -> List[Di
     warmup_numba_kernels(cfg.grid_size, directed_hunting=cfg.directed_hunting)
 
     # Define the global sweep values (same as Phase 4)
-    sweep_values = np.linspace(0.0, 1.0, 11)
+    sweep_values = np.linspace(0.05, 0.95 10)
 
     # Logging
     logger.info(f"Phase 6: Full 4D Parameter Sweep (Directed Hunting)")