### Note, I don't know whether the above or below is more or less efficient #for i in xrange(0,nentries): # for k,v in ampColors.iteritems(): # if data['ign_amp']==k: # pltfmt['color'].append(v) # for k,v in mpoleMarkers.iteritems(): # if data['mpoles']==k: # pltfmt['marker'].append(v) # pltfmt['linestyle'].append('None') # Plot final NSE mass vs. initial burned mass for all cases plt.figure(1) fig = plt.gcf() csp = CustomScatterplot(fig) csp.splot(data,'iniMassBurned','finMassNSE',pltfmt) fig = csp.getfig() plt.xlabel('Initial Mass Burned ($M_\\odot$)') plt.ylabel('Final Mass Burned to NSE ($M_\\odot$)') plt.title('Final NSE Mass Trend') plt.figure(2) fig = plt.gcf() csp = CustomScatterplot(fig) csp.splot(data,'iniMassBurned','finMassNi56',pltfmt) fig = csp.getfig() plt.xlabel('Initial Mass Burned ($M_\\odot$)') plt.ylabel('Estimated Ni56 Yield ($M_\\odot$)') plt.title('Ni56 Yield') plt.figure(3)
pltfmt['marker'] = ['*' for i in xrange(0, nentries_brendan) ] + ['D' for i in xrange(0, nentries_cone)] pltfmt['linestyle'] = [ 'None' for i in xrange(0, nentries_brendan + nentries_cone) ] # Plot final variables vs. initial burned mass for all cases for h in headers: data = OrderedDict([]) plt.figure(1) fig = plt.gcf() csp = CustomScatterplot(fig) data['y'] = data_fin[h] data['x'] = data_ini['mass burned'] csp.splot(data, 'x', 'y', pltfmt) fig = csp.getfig() mlco = mlines.Line2D([], [], color='red', marker='*', markersize=5, label='CO (Brendan)') mlcone = mlines.Line2D([], [], color='blue', marker='D', markersize=5, label='CONe Hybrid') plt.legend(handles=[mlco, mlcone], bbox_to_anchor=(1.0, 1.0), bbox_transform=plt.gcf().transFigure, prop={'size': 7}) plt.xlabel('Initial Mass Burned ($M_\\odot$)')