pltfmt['linestyle'] = ['None' for i in xrange(0,nentries)]
### 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.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(1)
fig = plt.gcf()
csp = CustomScatterplot(fig)
csp.splot(data,'finMassNSE','finMassNi56',pltfmt)
fig = csp.getfig()
ax=plt.gca()
ax.plot(co_fit_nse,co_fit_ni,linestyle='-',color='orange',alpha=0.9,linewidth=2.0)
ax.plot(cone_fit_nse,cone_fit_ni,linestyle='-',color='blue',alpha=0.9,linewidth=2.0)
mlco = mlines.Line2D([], [], color='red', marker='o', markersize=5, linestyle='None', label='CO Realizations')
mlcone = mlines.Line2D([],[],color='green',marker='D', markersize=5, linestyle='None', label='CONe Realizations')
mlco_fit = mlines.Line2D([],[],color='orange',linestyle='-',linewidth=2.0,label='CO Linear Fit\n' +
'Slope: ' + '{0:0.4f}'.format(lopt_co[0]) + r'$\pm$' + '{0:0.4f}'.format(lerr_co[0]) + '\n' +
'Intercept: ' + '{0:0.4f}'.format(lopt_co[1]) + r'$\pm$' + '{0:0.4f}'.format(lerr_co[1]))
mlcone_fit = mlines.Line2D([],[],color='blue',linestyle='-',linewidth=2.0,label='CONe Linear Fit\n' +
'Slope: ' + '{0:0.4f}'.format(lopt_cone[0]) + r'$\pm$' + '{0:0.4f}'.format(lerr_cone[0]) + '\n' +
'Intercept: ' + '{0:0.4f}'.format(lopt_cone[1]) + r'$\pm$' + '{0:0.4f}'.format(lerr_cone[1]))
#mlcone_fit = mlines.Line2D([],[],color='red',linestyle='-',linewidth=2.0,label='CONE Linear Fit')
plt.legend(handles=[mlco,mlco_fit,mlcone,mlcone_fit],loc=2,borderpad=0.2, borderaxespad=0.0, handletextpad=0.0, prop={'size':annotation_font_size})
#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(1)
fig = plt.gcf()
csp = CustomScatterplot(fig)
csp.splot(data, 'finMassNSE', 'finMassNi56', pltfmt)
fig = csp.getfig()
ax = plt.gca()
ax.plot(co_fit_nse,
co_fit_ni,
linestyle='-',
color='orange',
alpha=0.9,
linewidth=2.0)
ax.plot(cone_fit_nse,
cone_fit_ni,
linestyle='-',
color='blue',
alpha=0.9,
linewidth=2.0)
# Enforce the above in a plot format dictionary corresponding to data
pltfmt = OrderedDict([])
pltfmt['color'] = ['red' for i in xrange(0, nentries_brendan)
] + ['blue' for i in xrange(0, nentries_cone)]
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],
pltfmt['linestyle'] = ['None' for i in xrange(0, nentries)]
### 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')
