Merge pull request #561 from SasView/pr-labels-on-weights-plot

Put labels on weights plot for sasmodels compare app

sasmodels/compare.py

@@ -812,7 +812,7 @@ def compare(opts, limits=None, maxdim=None):
  if have_comp:
  plot_profile(comp._kernel.info, label='comp', **comp_pars)
  pylab.legend()
- if opts['plot']:
+ if opts['plot'] or opts['show_weights'] or opts['show_profile']:
  import matplotlib.pyplot as plt
  plt.show()
  return limits

sasmodels/weights.py

@@ -303,15 +303,21 @@ def plot_weights(model_info, mesh):
  for each parameter, where (*dispersity*, *weights*) pairs are the
  distributions to be plotted.
  """
- import pylab
-
+ import matplotlib.pyplot as plt
  if any(len(dispersity) > 1 for value, dispersity, weights in mesh):
  labels = [p.name for p in model_info.parameters.call_parameters]
- #pylab.interactive(True)
- pylab.figure()
+ #plt.interactive(True)
+ plt.figure()
  for (_, x, w), s in zip(mesh, labels):
  if len(x) > 1:
- pylab.plot(x, w, '-o', label=s)
- pylab.grid(True)
- pylab.legend()
- #pylab.show()
+ # For plotting probability rather than weight scale by dx
+ #dx = np.hstack((x[1]-x[0], (x[2:] - x[:-2])/2, x[-1] - x[-2]))
+ #plt.plot(x, w/dx, '-o', label=s)
+ plt.plot(x, w, '-o', label=s)
+ plt.title("Distributions for polydisperse parameters")
+ plt.xlabel("parameter value")
+ #plt.ylabel("probability")
+ plt.ylabel("relative weight")
+ plt.grid(True)
+ plt.legend()
+ #plt.show()