def plot_decay_majorant_v_of_deg(h, maj, deg, result_path):
plt.figure()
plt.hold(True)
plt.loglog(h, maj, 'b-*')
plotslopes.slope_marker((h[1], maj[1]), (1 + deg, 1))
plt.legend(["M^2"], "lower right")
plt.xlabel("h")
plt.ylabel("M^2 P%d" % (deg))
plt.grid(True)
fig = plt.gcf()
fig.savefig(result_path + ".eps")
def plot_convergence_rates():
r2, E2, dt2 = convergence_rates(m=5, solver_function=solver, num_periods=8)
plt.loglog(dt2, E2)
r4, E4, dt4 = convergence_rates(m=5,
solver_function=solver_adjust_w,
num_periods=8)
plt.loglog(dt4, E4)
plt.legend(['original scheme', r'adjusted $\omega$'], loc='upper left')
plt.title('Convergence of finite difference methods')
from plotslopes import slope_marker
slope_marker((dt2[1], E2[1]), (2, 1))
slope_marker((dt4[1], E4[1]), (4, 1))
plt.savefig('tmp_convrate.png')
plt.savefig('tmp_convrate.pdf')
plt.show()
def plot_decay_error_majorant(deg, h, error, majorant, result_path):
# Plot error and majorant results for deg = 2
plt.figure()
plt.hold(True)
plt.loglog(h, majorant, 'k-^')
plt.loglog(h, error, 'k-s')
if deg == 1:
plotslopes.slope_marker((h[1], error[1]), (2, 1))
elif deg == 2:
plotslopes.slope_marker((h[1], error[1]), (3, 1))
plt.legend(["M^2", "|| e ||^2"], "lower right")
plt.xlabel("h")
plt.ylabel("|| e ||^2, M^2")
plt.grid(True)
fig = plt.gcf()
fig.savefig(result_path + ".eps")