chore: make the ruff lint gate pass

.github/workflows/tests.yml

@@ -26,7 +26,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - uses: actions/checkout@v4

docs/tutorials/11_physicell_integration.marimo.py

@@ -60,22 +60,16 @@ def _():
 
 @app.cell
 def _():
+    from pathlib import Path
+
+    import matplotlib.pyplot as plt
     import numpy as np
     import pandas as pd
-    import matplotlib.pyplot as plt
-    from pathlib import Path
 
-    from spatialtissuepy import SpatialTissueData
+    from spatialtissuepy.summary import SpatialSummary, StatisticsPanel
     from spatialtissuepy.synthetic.physicell import (
         PhysiCellSimulation,
-        PhysiCellTimeStep,
-        read_physicell_timestep,
-        discover_physicell_timesteps,
-        is_alive,
-        is_dead,
     )
-    from spatialtissuepy.summary import StatisticsPanel, SpatialSummary
-    from spatialtissuepy.viz import plot_spatial_scatter
 
     np.random.seed(42)
     return (
@@ -149,7 +143,7 @@ def _(Path, PhysiCellSimulation):
     print(f"Number of timesteps: {sim.n_timesteps}")
     print(f"Time range: {sim.times[0]:.0f} - {sim.times[-1]:.0f} minutes")
     print(f"Time range: {sim.times[0]/60:.1f} - {sim.times[-1]/60:.1f} hours")
-    print(f"\nCell type mapping:")
+    print("\nCell type mapping:")
     for cell_id, cell_name in sim.cell_type_mapping.items():
         print(f"  {cell_id}: {cell_name}")
     return (sim,)
@@ -177,7 +171,7 @@ def _(sim):
 def _(sim):
     # Load the first timestep
     ts_initial = sim.get_timestep(0)
-    print(f"Initial timestep:")
+    print("Initial timestep:")
     print(f"  Time: {ts_initial.time:.0f} min ({ts_initial.time/60:.1f} hours)")
     print(f"  Live cells: {ts_initial.n_cells}")
     print(f"  Dead cells: {ts_initial.n_dead_cells}")
@@ -192,7 +186,7 @@ def _(sim):
 
     # Load the final timestep
     ts_final = sim.get_timestep(sim.n_timesteps-1)
-    print(f"\nFinal timestep:")
+    print("\nFinal timestep:")
     print(f"  Time: {ts_final.time:.0f} min ({ts_final.time/60:.1f} hours)")
     print(f"  Live cells: {ts_final.n_cells}")
     print(f"  Dead cells: {ts_final.n_dead_cells}")
@@ -216,7 +210,7 @@ def _(ts_final):
     # Convert to SpatialTissueData
     spatial_data = ts_final.to_spatial_data()
 
-    print(f"SpatialTissueData object:")
+    print("SpatialTissueData object:")
     print(f"  Coordinates shape: {spatial_data.coordinates.shape}")
     print(f"  Cell types: {spatial_data.cell_types_unique}")
     print(f"  Has markers: {spatial_data.markers is not None}")
@@ -333,7 +327,7 @@ def _():
             time_hours = counts_df['time'] / 60
 
             # Plot tumor cells
-            ax1.plot(time_hours, counts_df['n_malignant_epithelial_cell'], 
+            ax1.plot(time_hours, counts_df['n_malignant_epithelial_cell'],
                      color=cell_colors['malignant_epithelial_cell'], linewidth=2, label='Tumor')
             ax1.set_xlabel('Time (hours)')
             ax1.set_ylabel('Cell Count')
@@ -352,7 +346,7 @@ def _():
 
             for col, ct, label in immune_types:
                 if col in counts_df.columns:
-                    ax2.plot(time_hours, counts_df[col], 
+                    ax2.plot(time_hours, counts_df[col],
                              color=cell_colors[ct], linewidth=2, label=label)
 
             ax2.set_xlabel('Time (hours)')
@@ -374,31 +368,31 @@ def _():
         # Plot macrophage polarization ratio (M1 vs M2)
         with plt.style.context('seaborn-v0_8'):
             fig2, ax3 = plt.subplots(figsize=(10, 5))
-        
+
             time_h = counts_df['time'] / 60
             m1_counts = counts_df['n_M1_macrophage'].values
             m2_counts = counts_df['n_M2_macrophage'].values
-        
+
             # Avoid division by zero
             total_polarized = m1_counts + m2_counts
             m1_ratio = m1_counts / (total_polarized + 1e-6)  # M1 / (M1 + M2)
-        
+
             ax3.plot(time_h, m1_ratio, 'g-', linewidth=2, label='M1 Ratio')
             ax3.axhline(0.5, color='gray', linestyle='--', alpha=0.5, label='Equal M1/M2')
-            ax3.fill_between(time_h, m1_ratio, 0.5, 
-                             where=(m1_ratio > 0.5), alpha=0.3, color='green', 
+            ax3.fill_between(time_h, m1_ratio, 0.5,
+                             where=(m1_ratio > 0.5), alpha=0.3, color='green',
                              label='Pro-inflammatory')
-            ax3.fill_between(time_h, m1_ratio, 0.5, 
+            ax3.fill_between(time_h, m1_ratio, 0.5,
                              where=(m1_ratio < 0.5), alpha=0.3, color='purple',
                              label='Anti-inflammatory')
-        
+
             ax3.set_xlabel('Time (hours)')
             ax3.set_ylabel('M1 / (M1 + M2) Ratio')
             ax3.set_title('Macrophage Polarization Over Time')
             ax3.set_ylim(0, 1)
             ax3.legend(loc='upper right')
             ax3.grid(alpha=0.3)
-        
+
             plt.tight_layout()
             plt.show()
             plt.close()
@@ -429,32 +423,32 @@ def _(SpatialSummary, StatisticsPanel, np, pd, sim):
     sample_hours = np.arange(0, 120, 10)
     sample_indices = []
 
-    def _():    
+    def _():
         for target_hours in sample_hours:
             target_min = target_hours * 60
             idx = np.argmin(np.abs(sim.times - target_min))
             if idx not in sample_indices:  # Avoid duplicates
                 sample_indices.append(idx)
-    
+
         print(f"Computing spatial statistics for {len(sample_indices)} timesteps...")
-    
+
         # Compute statistics over time
         metrics_over_time = []
-    
+
         for i, idx in enumerate(sample_indices):
             ts = sim.get_timestep(idx)
             tissue = ts.to_spatial_data()
-    
+
             summary = SpatialSummary(tissue, panel)
             results = summary.to_dict()
             results['time'] = ts.time
             results['time_hours'] = ts.time / 60
             results['time_index'] = ts.time_index
             metrics_over_time.append(results)
-    
+
             if (i + 1) % 5 == 0:
                 print(f"  Processed {i + 1}/{len(sample_indices)} timesteps")
-    
+
         metrics_df = pd.DataFrame(metrics_over_time)
         print("\nTracked metrics:")
         print(metrics_df.columns.tolist())
@@ -471,25 +465,25 @@ def _():
         with plt.style.context('seaborn-v0_8'):
             # Plot spatial metrics over time
             fig3, axes3 = plt.subplots(1, 2, figsize=(12, 5))
-        
+
             # Mean nearest neighbor distance
             ax_nn = axes3[0]
-            ax_nn.plot(metrics_df['time_hours'], metrics_df['mean_nnd'], 
+            ax_nn.plot(metrics_df['time_hours'], metrics_df['mean_nnd'],
                        'b-o', linewidth=2, markersize=4)
             ax_nn.set_xlabel('Time (hours)')
             ax_nn.set_ylabel('Mean NN Distance (μm)')
             ax_nn.set_title('Cell Density Over Time\n(Lower = More Dense)')
             ax_nn.grid(alpha=0.3)
-        
+
             # Total cell count trend
             ax_total = axes3[1]
-            ax_total.plot(metrics_df['time_hours'], metrics_df['n_cells'], 
+            ax_total.plot(metrics_df['time_hours'], metrics_df['n_cells'],
                           'g-o', linewidth=2, markersize=4)
             ax_total.set_xlabel('Time (hours)')
             ax_total.set_ylabel('Total Live Cells')
             ax_total.set_title('Total Cell Population')
             ax_total.grid(alpha=0.3)
-        
+
             plt.tight_layout()
             plt.show()
             plt.close()
@@ -541,11 +535,11 @@ def compute_mean_distance_to_tumor(tissue):
     def _():
         # Compute tumor proximity over time
         tumor_proximity = []
-    
+
         for idx in sample_indices:
             ts = sim.get_timestep(idx)
             tissue = ts.to_spatial_data()
-    
+
             row = {
                 'time_hours': ts.time / 60,
                 'n_tumor': np.sum(tissue.cell_types == 'malignant_epithelial_cell')
@@ -564,22 +558,22 @@ def _(cell_colors, pd, plt, tumor_proximity):
     def _():
         with plt.style.context('seaborn-v0_8'):
             fig4, ax4 = plt.subplots(figsize=(12, 6))
-        
+
             immune_proximity_cols = [c for c in proximity_df.columns if '_to_tumor' in c]
-        
+
             for col in immune_proximity_cols:
                 cell_type = col.replace('_to_tumor', '')
                 color = cell_colors.get(cell_type, '#999999')
                 label = cell_type.replace('_macrophage', ' Mac').replace('_cell', '')
-                ax4.plot(proximity_df['time_hours'], proximity_df[col], 
+                ax4.plot(proximity_df['time_hours'], proximity_df[col],
                          color=color, linewidth=2, marker='o', markersize=4, label=label)
-        
+
             ax4.set_xlabel('Time (hours)')
             ax4.set_ylabel('Mean Distance to Nearest Tumor Cell (μm)')
             ax4.set_title('Immune Cell Proximity to Tumor Over Time')
             ax4.legend(loc='upper right')
             ax4.grid(alpha=0.3)
-        
+
             plt.tight_layout()
             plt.show()
             plt.close()
@@ -617,16 +611,16 @@ def _():
             idx = np.argmin(np.abs(sim.times - target_min))
             ts = sim.get_timestep(idx)
             tissue = ts.to_spatial_data()
-    
+
             summary = SpatialSummary(tissue, full_panel)
             results = summary.to_dict()
             results['time_hours'] = ts.time / 60
             results['timepoint'] = f"t={target_hours}h"
             key_results.append(results)
-    
+
         key_df = pd.DataFrame(key_results)
         key_df = key_df.set_index('timepoint')
-    
+
         # Display key metrics
         display_cols = ['time_hours', 'n_cells', 'mean_nn_distance']#, 'shannon_entropy']
         display_cols = [c for c in display_cols if c in key_df.columns]