files = filter(lambda x : re.match(".*shelf$", x), files)
files = natsort(files)
files = ["4.shelf"]
figure()
gammasextr = np.linspace(1,1.5,1000)
gammasextr = np.linspace(.5,1.5,1000)
if d == 3:
gammasextr = np.linspace(.7,1.1,1000)
for file in files:
shelf = shelve.open(str(d) + "D/" + file)
Cgammaextr = functions.extrap(gammasextr, shelf["gammas"],shelf["Cgamma"])
# p = polyfit(shelf["gammas"],shelf["Cgamma"], 1)
# Cgammaextr = polyval(p,gammasextr)
plot(gammasextr,Cgammaextr)
legend(map(lambda s : s.replace(".shelf",""),files))
plot(gammasextr,ones_like(gammasextr), 'k', linewidth=2)
# for file in files:
# shelf = shelve.open(str(d) + "D/" + file)
# plot(shelf["gammas"], shelf["Cgamma"], 'kx')
if d == 2:
xlim((1,1.3))
ylim((.98,1.02))
d = 1
# L = 40
Vinit_size = 1
# N = 10000
q = 1
init = 'gauss'
init = 'prev'
# init = 'prev_scaled'
# init = 'harm'
#init = 'prev'
g = grid.Grid(N,L,d,q)
N = g.N
if init == 'prev':
Vinit = functions.extrap(g.x, prev_g.x, V)
elif init == 'prev_scaled':
scaling = 3
Vinit = np.interp(g.x, prev_g.x*scaling, V,right=0)
elif init == 'gauss':
Vinit = -np.exp(-np.abs((g.x)/Vinit_size)**2) * (1 + (g.x/Vinit_size)**2 + (g.x/Vinit_size)**4)
Vinit = -np.exp(-np.abs((g.x)/Vinit_size)**2) * (1)
elif init == 'harm':
Vinit = (g.x/Vinit_size)**2 - 1
Vinit[Vinit > 0] = 0
elif init == 'constant':
Vinit = -np.ones_like(g.x)
elif init == 'linear':
Vinit = (g.x-L/2)
Vinit[Vinit > 0] = 0
else:
sys.stdout.close()
bs = np.array([len(eigs) for eigs in eigss])
if any(bs != bs[0]):
print ""
print ""
print "ALERTE, ALERTE, BS ONT CHANGE"
execfile("save_shelf.py")
import os
os.system("mv computation.log " + str(d) + "D/" + str(bs) + ".log")
Cgamma = np.array(Cgamma)
if(len(Cgamma) > 1):
gammasextr = np.linspace(.7,1.5,100)
Cgammaextr = functions.extrap(gammasextr, gammas,Cgamma)
figure()
plot(gammasextr,Cgammaextr)
plot(gammas,Cgamma,'x')
show()
psis = psiss[-1]
prev_g = g
gammasextr = np.linspace(.8,.9,100)
Cgammaextr = functions.extrap(gammasextr, gammas,Cgamma)
figure()
plot(gammasextr,Cgammaextr)