minor cosmetics

graphics.py

@@ -560,9 +560,9 @@ def plot_diagnostics(exp,show='errors',axes=None,label='',linecolor='blue',zmax=
         if show=='errors' and truth_exists:
 
             axes[0].plot(armse[idx],z[idx],label=label,color=linecolor)#,color=linecolors[i])
-            axes[1].plot(brmse[idx],z[idx],color=linecolor)
-            axes[2].plot(brmse[idx]-armse[idx],z[idx],color=linecolor)
-            axes[3].plot(np.sqrt(bsprd[idx]**2+sigma_o[idx]**2)/brmse[idx],z[idx],color=linecolor)
+            axes[1].plot(brmse[idx],z[idx],label=label,color=linecolor)
+            axes[2].plot(brmse[idx]-armse[idx],z[idx],label=label,color=linecolor)
+            axes[3].plot(np.sqrt(bsprd[idx]**2+sigma_o[idx]**2)/brmse[idx],z[idx],label=label,color=linecolor)
 
         elif show=='deviations':
 
@@ -571,9 +571,9 @@ def plot_diagnostics(exp,show='errors',axes=None,label='',linecolor='blue',zmax=
             mean_bias_b_i1 = exp.dg.ominb.mean(axis=0)
             # Plots
             axes[0].plot(armsd[idx],z[idx],label=label,color=linecolor)
-            axes[1].plot(brmsd[idx],z[idx],color=linecolor)
-            axes[2].plot(brmsd[idx]-armsd[idx],z[idx],color=linecolor)
-            axes[3].plot(np.sqrt(bsprd[idx]**2+sigma_o[idx]**2)/brmsd[idx],z,color=linecolor)
+            axes[1].plot(brmsd[idx],z[idx],label=label,color=linecolor)
+            axes[2].plot(brmsd[idx]-armsd[idx],z[idx],label=label,color=linecolor)
+            axes[3].plot(np.sqrt(bsprd[idx]**2+sigma_o[idx]**2)/brmsd[idx],z,label=label,color=linecolor)
 
     return axes
 
@@ -672,15 +672,15 @@ def plot_desroziers(dgs,filename='desroziers_diagnostics.png',labels=None,logsca
             # Two formulations of the same thing; they give the same numbers!
             #ax1.scatter( dg.dobdob_ta-dg.mean_ominb**2 , dg.varo+dg.varb_ta , s=5, label = labels[i])
             ax.scatter( np.mean(dg.sigma_ominb**2) , np.mean(dg.varo+dg.varb_ta) , s=13, label = labels[i],zorder=10, c=color, edgecolors='black')
-            ax.scatter( dg.sigma_ominb**2 , dg.varo+dg.varb_ta , s=5, alpha = 0.3, c=color, linewidths=0)
+            ax.scatter( dg.sigma_ominb**2 , dg.varo+dg.varb_ta , s=10, alpha = 0.3, c=color, linewidths=0)
         else:
             # Two formulations of the same thing; they give the same numbers!
             #ax1.scatter( dg.dobdob_ta-dg.mean_ominb**2 , dg.varo+dg.varb_ta , s=5)
             ax.scatter( np.mean(dg.sigma_ominb**2) , np.mean(dg.varo+dg.varb_ta) , s=13, zorder = 10)
-            ax.scatter( dg.sigma_ominb**2 , dg.varo+dg.varb_ta , s=5, alpha = 0.3) # 
+            ax.scatter( dg.sigma_ominb**2 , dg.varo+dg.varb_ta , s=10, alpha = 0.3) # 
         #ax.set_title("Consistency of innovations",fontsize=8)
         ax.set_xlabel(r'${\langle}\sigma_d^2{\rangle}$ (K)') # '${\langle}d_{ob}^2{\rangle}_t$'
-        ax.set_ylabel(r'$\sigma_{y_o}^2+{\langle}\sigma_{y_b}^2{\rangle}$ (K)')
+        ax.set_ylabel(r'$\sigma_{y^o}^2+{\langle}\sigma_{y^b}^2{\rangle}$ (K)')
     
     if labels is not None:
         ax.legend(frameon=False, fontsize=6, ncol=2)