More informative plot for xfel.merge cosym worker.

<wij> is the mean number of common Miller indices for image pairs

xfel/merging/application/modify/aux_cosym.py

@@ -42,7 +42,7 @@ def plot_after_optimize(self):
  yy.append(self.coords[(item,1)])
  from matplotlib import pyplot as plt
  plt.plot(xx,yy,"r.")
- # denminator of 12 is specific to the use case of P6 (# symops in the metric superlattice)
+ # denominator of 12 is specific to the use case of P6 (# symops in the metric superlattice)
  plt.plot(xx[::len(xx)//12],yy[::len(yy)//12],"b.")
  plt.plot(xx[:1],yy[:1],"g.")
  plt.axes().set_aspect("equal")
@@ -62,6 +62,9 @@ def plot_after_cluster_analysis(self):
  plt.plot(xx[0:1], yy[0:1], 'k.')
  plt.plot([0,0],[-0.01,0.01],"k-")
  plt.plot([-0.01,0.01],[0,0],"k-")
+ plt.xlim(-0.2,1.0)
+ plt.ylim(-0.2,1.0)
+ plt.title("$<w_{ij}>$=%.1f"%(np.mean(self.target.wij_matrix)))
  ax = plt.gca()
  ax.set_aspect("equal")
  circle = plt.Circle((0,0),1,fill=False,edgecolor="b")