def plot_banana_result_mean_N_D(results, D, stat_idx, normalise_by_time=False,
**kwargs):
fun = lambda x:None
Ds, Ns, _ = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 0])
_, _, time_taken_set_up = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 1])
_, _, time_taken_sampling = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, stat_idx])
_, _, avg = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, stat_idx], 25)
_, _, lower = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, stat_idx], 75)
_, _, upper = gen_sparse_2d_array_from_dict(results, fun)
D_ind = Ds == D
time_total = time_taken_sampling[:,D_ind]# + time_taken_set_up[:,D_ind]
time_total = time_total.ravel()
print_table(time_total, "Time total")
normaliser = time_total if normalise_by_time else 1.0
avg = avg[:, D_ind].ravel()/normaliser
err = np.array([np.abs(avg-lower[:, D_ind].ravel()/normaliser),
np.abs(avg-upper[:, D_ind].ravel()/normaliser)])
plt.plot(Ns, avg, kwargs['color'])
plt.errorbar(Ns, avg, err, color=kwargs['color'])
plt.grid(True)
try:
plt.title(kwargs['title'])
except KeyError:
pass
try:
plt.ylim(kwargs['ylim'])
except KeyError:
pass
try:
plt.xlim(kwargs['xlim'])
except KeyError:
pass
try:
plt.xlabel(kwargs['xlabel'])
except KeyError:
pass
def plot_acceptance_heatmap(Ns, Ds, acc):
masked_array = np.ma.array(acc, mask=np.isnan(acc))
cmap = matplotlib.cm.jet
cmap.set_bad('w', 1.)
plt.pcolor(Ns, Ds, masked_array, cmap=cmap)
plt.yscale("log")
plt.xlim([np.min(Ns), np.max(Ns)])
plt.ylim([np.min(Ds), np.max(Ds)])
plt.xlabel(r"$n$")
plt.ylabel(r"$d$")
plt.colorbar()
def plot_trajectory_result_mean_fixed_N(fname, N):
results = result_dict_from_file(fname)
# acc_mean, acc_est_mean, vol, vol_est, steps_taken
fun = lambda x: np.mean(x[:, 1])
Ds, Ns, avg_accept_est_mean = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:, 0])
_, _, avg_accept_mean = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 1], 25)
_, _, avg_accept_est_lower_25 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 1], 75)
_, _, avg_accept_est_upper_25 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 1], 5)
_, _, avg_accept_est_lower_5 = gen_sparse_2d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, 1], 95)
_, _, avg_accept_est_upper_95 = gen_sparse_2d_array_from_dict(results, fun)
N_ind = np.where(Ns == N)[0][0]
plt.figure()
plt.plot(Ds, avg_accept_mean[:, N_ind], 'r')
plt.plot(Ds, avg_accept_est_mean[:, N_ind], 'b')
plt.plot(Ds, avg_accept_est_lower_25[:, N_ind], 'b-.')
plt.plot(Ds, avg_accept_est_lower_5[:, N_ind], color="grey")
plt.plot(Ds, avg_accept_est_upper_95[:, N_ind], color="grey")
plt.fill_between(Ds,
avg_accept_est_lower_5[:, N_ind],
avg_accept_est_upper_95[:, N_ind],
color="grey",
alpha=.5)
plt.plot(Ds, avg_accept_est_upper_25[:, N_ind], 'b-.')
plt.plot(Ds, avg_accept_mean[:, N_ind], 'r')
plt.xscale("log")
plt.grid(True)
plt.xlim([Ds.min(), Ds.max()])
plt.xlabel(r"$d$")
plt.title(r"n=%d" % N)
ylim = plt.ylim()
plt.ylim([ylim[0], 1.01])
plt.legend(["HMC", "KMC median", "KMC 25\%-75\%", "KMC 5\%-95\%"],
loc="lower left")
fname_base = fname.split(".")[-2]
plt.savefig(fname_base + "_N=%d.eps" % N, axis_inches='tight')
def plot_banana_result_mean_D(results, D, stat_idx, normalise_by_time=False,
**kwargs):
fun = lambda x:None
Ds, _ = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:,0])
_, time_taken_set_up = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:,1])
_, time_taken_sampling = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.mean(x[:,stat_idx])
_, avg = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, stat_idx], 25)
_, lower = gen_sparse_1d_array_from_dict(results, fun)
fun = lambda x: np.percentile(x[:, stat_idx], 75)
_, upper = gen_sparse_1d_array_from_dict(results, fun)
D_ind = Ds == D
time_total = time_taken_sampling[D_ind] + time_taken_set_up[D_ind]
time_total = time_total.ravel()
print_table(time_total, "Time total")
normaliser = time_total if normalise_by_time else 1.0
avg = avg[D_ind].ravel()/normaliser
xlim=plt.xlim()
plt.plot(xlim, [avg,avg], color=kwargs['color'])
plt.plot(xlim, [lower,lower], '--', color=kwargs['color'])
plt.plot(xlim, [upper,upper], '--', color=kwargs['color'])
plt.grid(True)
try:
plt.title(kwargs['title'])
except KeyError:
pass
try:
plt.ylim(kwargs['ylim'])
except KeyError:
pass
try:
plt.xlabel(kwargs['xlabel'])
except KeyError:
pass
plt.xlabel("Leap-frog step")
plt.ylabel(r"$H(p,q)$")
plt.savefig(fname_base + "_hamiltonian_kmc.eps", axis_inches="tight")
# ylim for acceptance plots
ylim = [np.exp(np.min([log_acc.min(), log_acc_est.min()])), 1.]
acc_mean = np.mean(np.exp(log_acc))
acc_est_mean = np.mean(np.exp(log_acc_est))
plt.figure()
plt.title("Acceptance prob.")
plt.plot(np.arange(1, num_steps + 2), np.exp(log_acc))
plt.plot([0, len(log_acc)], [acc_mean, acc_mean], "r")
plt.ylim(ylim)
plt.xlim([0, num_steps])
plt.grid(True)
plt.xlabel("Iteration")
plt.ylabel("Acc. prob.")
plt.savefig(fname_base + "_acceptance_hmc.eps", axis_inches="tight")
plt.figure()
plt.title("Acceptance prob.")
plt.plot(np.arange(1, num_steps + 2), np.exp(log_acc_est))
plt.plot([0, len(log_acc_est)], [acc_est_mean, acc_est_mean], "r")
plt.ylim(ylim)
plt.xlim([0, num_steps])
plt.grid(True)
plt.xlabel("Iteration")
plt.savefig(fname_base + "_acceptance_kmc.eps", axis_inches="tight")