make molprobity promod3/modeller comparison plots quadratic

modelling/do_plots.py

 import matplotlib.pyplot as plt
 import json
 import numpy as np
+import math
 
 
 promod_label = 'ProMod3'
@@ -41,8 +42,6 @@ for key in promod_data:
         probity_diffs.append(probity_values_promod[-1] - probity_values_modeller[-1])
         keys.append(key)
 
-for a,b in zip(keys, lddt_diffs):
-    print(a,b)
 # plot both in the same plot
 xs = np.linspace(-7.0, 7.0, 300)
 n_lddt, bins_lddt, patches_lddt = plt.hist(lddt_diffs, 50, range=(-7.0,7.0), 
@@ -79,49 +78,62 @@ for key in promod_data:
         probity_ramachandran_outliers_modeller.append(modeller_data[key]['Ramachandran outliers'])
         keys.append(key)
 
-#plt.clf()
-
-fig, axs = plt.subplots(2, 2)
+plt.clf()
+
+def DoThingsWithAxes(ax, x_values, y_values, title, xlabel, ylabel):
+    
+    ax.plot(x_values, y_values, '.', color = (128.0/255,0.0,0.0))
+    ax.plot([-1.0,1000.0], [-1.0,1000], color = 'k',linestyle='--')
+    ax.set_title(title, loc='left', y=1.08, x=-0.11, fontsize='x-large')
+    ax.set_xlabel(xlabel, fontsize='large')
+    ax.set_ylabel(ylabel, fontsize='large')
+    max_val = math.ceil(max([max(x_values), max(y_values)]))
+    ax.set_xlim([0, max_val])
+    ax.set_ylim([0, max_val])
+    ax.set_aspect('equal', 'box')
+
+    tick_locations = list()
+    step_size = None
+    if max_val <= 5:
+      step_size = 1
+    elif max_val <= 14:
+      step_size = 2
+    elif max_val <= 30:
+      step_size = 5
+    elif max_val <= 100:
+      step_size = 10
+    else:
+      step_size = 50
+    for i in range(0, int(max_val)+step_size, step_size):
+      tick_locations.append(i)
+
+    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]
 
-probity_overall_ax.plot(probity_values_promod, probity_values_modeller,'.', color = cred)
-# plot zero line
-probity_overall_ax.plot([0.0,5.0], [0.0,5.0], '.', color = 'k',linestyle='--')
-probity_overall_ax.set_title('a) Overall Score', loc='left', y=1.05)
-probity_overall_ax.set_xlabel(promod_label)
-probity_overall_ax.set_ylabel(modeller_label)
-
 
-probity_clash_ax.plot(probity_clash_promod, probity_clash_modeller,'.', color = cred)
-# plot zero line
-probity_clash_ax.plot([0.0,180.0], [0.0,180.0], color = 'k',linestyle='--')
-probity_clash_ax.set_title('b) Clash Score', loc='left', y=1.05)
-probity_clash_ax.set_xlabel(promod_label)
-probity_clash_ax.set_ylabel(modeller_label)
+DoThingsWithAxes(probity_overall_ax, probity_values_promod, 
+                 probity_values_modeller, 'a) Overall Score',
+                 promod_label, modeller_label)
 
+DoThingsWithAxes(probity_clash_ax, probity_clash_promod, 
+                 probity_clash_modeller, 'b) Clash Score',
+                 promod_label, modeller_label)
 
-probity_rotamer_ax.plot(probity_rotamer_outliers_promod, probity_rotamer_outliers_modeller,
-         '.', color = cred)
-# plot zero line
-probity_rotamer_ax.plot([0.0,20.0], [0.0,20.0], color = 'k',linestyle='--')
-probity_rotamer_ax.set_title('c) Rotamer Outliers', loc='left', y=1.05)
-probity_rotamer_ax.set_xlabel(promod_label)
-probity_rotamer_ax.set_ylabel(modeller_label)
+DoThingsWithAxes(probity_rotamer_ax, probity_rotamer_outliers_promod, 
+                 probity_rotamer_outliers_modeller, 'c) Rotamer Outliers',
+                 promod_label, modeller_label)
 
-
-probity_ramachandran_ax.plot(probity_ramachandran_outliers_promod, 
-         probity_ramachandran_outliers_modeller, '.', color = cred)
-# plot zero line
-probity_ramachandran_ax.plot([0.0,30.0], [0.0,30.0], color = 'k',linestyle='--')
-probity_ramachandran_ax.set_title('d) Ramachandran Outliers', loc='left', y=1.05)
-probity_ramachandran_ax.set_xlabel(promod_label)
-probity_ramachandran_ax.set_ylabel(modeller_label)
+DoThingsWithAxes(probity_ramachandran_ax, probity_ramachandran_outliers_promod, 
+                 probity_ramachandran_outliers_modeller, 
+                 'd) Ramachandran 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)
 
 print('avg. lddt value', promod_label, np.mean(lddt_values_promod))