x = []

u = []

plt.figure(1)

for i in range(len(files)):

filename = files[i]

x.append([])

u.append([])

f = open(filename)

for line in f:

numbers = line.split()

if not float(numbers[1]) == 0:

x[i].append(float(numbers[0]))

u[i].append(float(numbers[1]))

f.close()

x[i] = np.array(x[i])

u[i] = np.array(u[i])

plt.plot(x[i], u[i], label='$\mathrm{%s}$' % (filename[7:-remove_back]))

u_analytic = np.zeros(len(x[0]))

for n in range(1, 1000):

u_analytic += -2/(n*np.pi)*np.sin(n*np.pi*x[0])*np.exp(-n**2*np.pi**2*T)

u_analytic = u_analytic + 1 - x[0]

plt.plot(x[0], u_analytic, label='$\mathrm{Analytic}$')

plt.xlabel('$x \ \ \mathrm{[Rel.\ units]}$', fontsize=18)

plt.ylabel('$u\ \ \mathrm{[Rel.\ units]}$', fontsize=18)

plt.legend(loc='best', fontsize=16)

plt.text(0.2,0.2,'$\Delta x = %s$\n$T=%s$'%(dx,str(T)), fontsize=20,bbox=dict(facecolor='white', alpha=1))

# finding the errors by least squares

for method in range(len(files)):

# err = 0

# for i in range(len(x)):

# err += (u_analytic[i] - u[method][i])**2

print files[method], '%.2e' % (np.std(np.abs(u_analytic - u[method]))*100)

plt.figure(2)

for method in range(len(files)):

plt.plot(x[method], abs(np.array(u_analytic) - np.array(u[method]))/(np.array(u_analytic)), label='$\mathrm{%s}$'%str(files[method][7:-remove_back]))

plt.xlabel('$x\ \ \mathrm{[Rel. \ units]}$', fontsize=18)

plt.ylabel('$\mathrm{Absolute \ Relative \ error \ \ [\%]}$', fontsize=18)

plt.legend(loc='best', fontsize = 16)

plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))

plt.ylim([-plt.ylim()[1]*0.05, plt.ylim()[1]])

plt.figure(1); plt.savefig(filename1)

plt.close()

plt.figure(2); plt.savefig(filename2)

plt.close()