add lddt to per-model comparison plot

modelling/do_plots.py

@@ -101,7 +101,7 @@ def DoThingsWithAxes(ax, x_values, y_values, title, xlabel, ylabel):
     elif max_val <= 30:
       step_size = 5
     elif max_val <= 100:
-      step_size = 10
+      step_size = 20
     else:
       step_size = 50
     for i in range(0, int(max_val)+step_size, step_size):
@@ -110,31 +110,37 @@ def DoThingsWithAxes(ax, x_values, y_values, title, xlabel, ylabel):
     ax.set_xticks(tick_locations) 
     ax.set_yticks(tick_locations)
    
-fig, axs = plt.subplots(2, 2, figsize=(7,7))
-probity_overall_ax = axs[0, 0]
-probity_clash_ax = axs[0, 1]
-probity_rotamer_ax = axs[1, 0]
-probity_ramachandran_ax = axs[1, 1]
-
+fig, axs = plt.subplots(3, 2, figsize=(7,10.5))
+lddt_ax = axs[0, 0]
+probity_overall_ax = axs[0, 1]
+probity_clash_ax = axs[1, 0]
+probity_rotamer_ax = axs[1, 1]
+probity_ramachandran_ax = axs[2, 0]
+empty_ax = axs[2,1]
+empty_ax.axis('off')
+
+DoThingsWithAxes(lddt_ax, lddt_values_promod, 
+                 lddt_values_modeller, 'a) lDDT',
+                 promod_label, modeller_label)
 
 DoThingsWithAxes(probity_overall_ax, probity_values_promod, 
-                 probity_values_modeller, 'a) Overall Score',
+                 probity_values_modeller, 'b) MolProbity Overall',
                  promod_label, modeller_label)
 
 DoThingsWithAxes(probity_clash_ax, probity_clash_promod, 
-                 probity_clash_modeller, 'b) Clash Score',
+                 probity_clash_modeller, 'c) MolProbity Clash',
                  promod_label, modeller_label)
 
 DoThingsWithAxes(probity_rotamer_ax, probity_rotamer_outliers_promod, 
-                 probity_rotamer_outliers_modeller, 'c) Rotamer Outliers',
+                 probity_rotamer_outliers_modeller, 'd) MolProbity Rot. Outliers',
                  promod_label, modeller_label)
 
 DoThingsWithAxes(probity_ramachandran_ax, probity_ramachandran_outliers_promod, 
                  probity_ramachandran_outliers_modeller, 
-                 'd) Ramachandran Outliers', promod_label, modeller_label)
+                 'e) MolProbity Ram. Outliers', promod_label, modeller_label)
 
 plt.tight_layout(pad=1.2, h_pad=1.5, w_pad=1.5, rect=None)
-plt.savefig(molprobity_plot_name)
+plt.savefig(molprobity_plot_name, dpi=300)
 
 print('avg. lddt value', promod_label, np.mean(lddt_values_promod))
 print('avg. lddt value', modeller_label, np.mean(lddt_values_modeller))