### 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 ($M_\\odot$)')
