update n_max

merge_tables.py

@@ -12,7 +12,7 @@ def combine(table_day1_path, table_day2_path, table_output_path, swarmplot_outpu
     day1.loc[:,'final_state'] = day1.n_cells_final > threshold
 
     day1.to_csv(table_output_path, index=None)
-    n_max = int(day1.n_cells.mean() * 3)
+    n_max = int(day1.n_cells.mean()) * 3 + 1
     plot_swarm_counts(day1, n_max=n_max, path=swarmplot_output_path)
     plot_swarm_intensity(day1, n_max=n_max, path=swarmplot_output_path.replace('.png', '_intensities.png'))
     plot_probs(day1, n_max=n_max,path=prob_plot_path)
@@ -20,23 +20,23 @@ def combine(table_day1_path, table_day2_path, table_output_path, swarmplot_outpu
     
 def plot_swarm_counts(table:pd.DataFrame, n_max:int=5, path=None):
     fig, ax = plt.subplots(dpi=300)
-    sns.swarmplot(ax=ax, data=table.query(f'n_cells < {n_max}'), x='[AB]', y='n_cells_final', hue='n_cells', dodge=True, size=1 )
+    sns.swarmplot(ax=ax, data=table.query(f'n_cells <= {n_max}'), x='[AB]', y='n_cells_final', hue='n_cells', dodge=True, size=1 )
     fig.savefig(path)
 
 def plot_swarm_intensity(table:pd.DataFrame, n_max:int=5, path=None):
     fig, ax = plt.subplots(dpi=300)
-    sns.swarmplot(ax=ax, data=table.query(f'n_cells < {n_max}'), x='[AB]', y='intensity_final', hue='n_cells', dodge=True, size=1 )
+    sns.swarmplot(ax=ax, data=table.query(f'n_cells <= {n_max}'), x='[AB]', y='intensity_final', hue='n_cells', dodge=True, size=1 )
     fig.savefig(path)
 
 def plot_probs(table:pd.DataFrame, n_max:int=5, path=None):
     
     fig, ax = plt.subplots(dpi=300)
-    sns.lineplot(ax=ax, data=table.query(f'n_cells < {n_max}'), x='[AB]', y='final_state', hue='n_cells')
+    sns.lineplot(ax=ax, data=table.query(f'n_cells <= {n_max}'), x='[AB]', y='final_state', hue='n_cells')
     fig.savefig(path)
 
 def plot_probs_log(table:pd.DataFrame, n_max:int=5, path=None):
     fig, ax = plt.subplots(dpi=300)
-    sns.lineplot(ax=ax, data=table.query(f'n_cells < {n_max}'), x='[AB]', y='final_state', hue='n_cells')
+    sns.lineplot(ax=ax, data=table.query(f'n_cells <= {n_max}'), x='[AB]', y='final_state', hue='n_cells')
     ax.set_xscale('log')
     fig.savefig(path)