def Newtonian():
m.clf()
m.plot(Chi1, MeanFlow1, 'b--') #, label='Simulated')
m.plot(Chi1, MeanFlow1, 'bo', markersize=8, label='Simulated')
Jaffrin = JaffrinMeanFlow(Chi1, 1.5)
m.plot(Chi1, Jaffrin, 'g--', label='Analytic') #'Jaffrin \& Shapiro')
m.legend(loc=2)
xlatex(r'\chi')
ylatex(r'\Theta')
SetTickFont()
m.savefig('Jaffrin.png')
def FullJaffrinSettled():
m.clf()
Jaffrin = JaffrinMeanFlow(Chis, 1.5)
m.plot(Chis, Jaffrin, 'g--', label='Jaffrin', lw=2)
N = 1024
for T in [120, 150, 180]:
l = [Data[(0, '%g' % Chi, N)]['meanflow'][T] for Chi in Chis]
m.plot(Chis, l, label='T = %g' % Data[(0, '%g' % Chi, N)]['t'][T])
m.legend(loc=2)
xlatex(r'\chi')
ylatex(r'\Theta')
SetTickFont()
m.savefig('JaffrinSettled.png')
def FlowVsN(Chi):
m.clf()
j = JaffrinMeanFlow(Chi, 1.5)
m.plot([256, 1280], [j, j], label='Jaffrin', lw=2)
Ns = range(256, 1280 + 256, 256)
l = [Data[(0, '%g' % Chi, N)]['meanflow'][120] for N in Ns]
m.plot(Ns, l, label='Simulated')
m.plot(Ns, l, 'o')
m.legend(loc=6)
xlatex(r'N')
ylatex(r'\Theta')
SetTickFont()
m.savefig('JaffrinVsN_Chi%g.png' % Chi)
def FlowVsChi():
m.clf()
Jaffrin = JaffrinMeanFlow(Chis, 1.5)
m.plot(Chis, Jaffrin, 'g--', label='Jaffrin', lw=2)
for N in [256, 512, 1024]:
#for N in range(256, 2048+256, 256):
#print N, [Data[(0, '%g'%Chi, N)]['t'][-1] for Chi in Chis]
#raise
l = [Data[(0, '%g' % Chi, N)]['meanflow'][150] for Chi in Chis]
m.plot(Chis, l, label='$N = %i$' % N)
m.legend(loc=2)
xlatex(r'\chi')
ylatex(r'\Theta')
SetTickFont()
m.savefig('JaffrinVsChi.png')
def FullJaffrinAllTime(Chi, T1=1, T2=50, ws=Ws):
m.clf()
step1, step2 = T1*100+1, T2*100
j = JaffrinMeanFlow(Chi, 1.5)
m.plot([T1, T2], [j, j], label='J \& S', lw=2)
#for W in [5, 55, 105]:
for W in ws:
d = Data512[(W, '%g'%Chi)]
m.plot(d['t'][step1:step2], d['meanflow'][step1:step2], label='$We=%g$'%W)
#m.plot(d['t'], d['flow'], label='$We=%g$'%W)
xlatex(r't')
ylatex(r'\Theta')
SetTickFont()
m.legend(loc=1)
m.savefig('FullJaffrinOverTime_Chi%g.png' % Chi)