ifdata[p] = ifd.getArrayData()
if i==0:
headers = ifdata[p].keys()
shortheaders = [hj.replace(' > ','>').replace(' ','_') for hj in headers]
pfmt[p] = {'color': colors[i],
'marker': None,
'linestyle': '-',
'label':p}
ifd.clrArrayData()
# Plot relevant data
for j in xrange(0,len(headers)):
hj = headers[j]
hs = shortheaders[j]
if hs!='time':
plt.figure(1)
fig = plt.gcf()
cp = CustomPlot(fig)
h = cp.splot(ifdata,'time',hj,pfmt)
fig = cp.getfig()
plt.legend(handles=h,bbox_to_anchor=(1.0,0.7),bbox_transform=plt.gcf().transFigure,prop={'size':10})
plt.xlabel('time (s)')
if hj.find('mass')!=-1:
yl = hj + ' ($M_\\odot$)'
else:
yl = hj
plt.ylabel(yl)
plt.title(hj + ' For ignMpoleA=' + impa + 'e5, pbIgnRho=10^7.2')
plt.savefig(hs + '_mp-2' + suff.rstrip('_ordered.dat') + '.eps')
plt.clf()
'color': colors[i],
'marker': None,
'linestyle': '-',
'label': p
}
ifd.clrArrayData()
# Plot relevant data
for j in xrange(0, len(headers)):
hj = headers[j]
hs = shortheaders[j]
if hs != 'time':
plt.figure(1)
fig = plt.gcf()
cp = CustomPlot(fig)
h = cp.splot(ifdata, 'time', hj, pfmt)
fig = cp.getfig()
plt.legend(handles=h,
bbox_to_anchor=(1.0, 0.7),
bbox_transform=plt.gcf().transFigure,
prop={'size': 10})
plt.xlabel('time (s)')
if hj.find('mass') != -1:
yl = hj + ' ($M_\\odot$)'
else:
yl = hj
plt.ylabel(yl)
plt.title(hj + ' For ignMpoleA=' + impa + 'e5, pbIgnRho=10^7.2')
plt.savefig(hs + '_mp-2' + suff.rstrip('_ordered.dat') + '.eps')
plt.clf()
os.chdir(this_dir)
plot_order = co_r_keys + cone_r_keys + ['co_mean','cone_mean']
ifdata_po = OrderedDict((k,ifdata[k]) for k in plot_order)
pfmt_po = OrderedDict((k,pfmt[k]) for k in plot_order)
# Plot relevant data
for j in xrange(0,len(headers)):
hj = headers[j]
hs = shortheaders[j]
if hs!='time':
print 'plotting: hj=' + str(hj)
plt.figure(1)
fig = plt.gcf()
cp = CustomPlot(fig)
h = cp.splot(ifdata_po,'time',hj,pfmt_po)
fig = cp.getfig()
handles_rzs = mlines.Line2D([],[],color='blue',alpha=0.75,
linestyle='-',linewidth=2.0,
label='CO WD Realizations')
handles_rzm = mlines.Line2D([],[],color='orange',linestyle='-',linewidth=2.0,
label='CO WD Mean Values')
handles_cones = mlines.Line2D([],[],color='green',alpha=0.75,
linestyle='-',linewidth=2.0,
label='CONe WD Realizations')
handles_conem = mlines.Line2D([],[],color='red',linestyle='-',linewidth=2.0,
label='CONe WD Mean Values')
h = [handles_rzs,handles_rzm,handles_cones,handles_conem]
loc_legend_plots = {'E_internal':1,
'E_nuc' :1,
os.chdir(this_dir)
plot_order = co_r_keys + cone_r_keys + ['co_mean', 'cone_mean']
ifdata_po = OrderedDict((k, ifdata[k]) for k in plot_order)
pfmt_po = OrderedDict((k, pfmt[k]) for k in plot_order)
# Plot relevant data
for j in xrange(0, len(headers)):
hj = headers[j]
hs = shortheaders[j]
if hs != 'time':
print 'plotting: hj=' + str(hj)
plt.figure(1)
fig = plt.gcf()
cp = CustomPlot(fig)
h = cp.splot(ifdata_po, 'time', hj, pfmt_po)
fig = cp.getfig()
handles_rzs = mlines.Line2D([], [],
color='blue',
alpha=0.75,
linestyle='-',
linewidth=2.0,
label='CO WD Realizations')
handles_rzm = mlines.Line2D([], [],
color='orange',
linestyle='-',
linewidth=2.0,
label='CO WD Mean Values')
handles_cones = mlines.Line2D([], [],
color='green',
alpha=0.75,
