def main():
os.makedirs(output_path, exist_ok=True)
# Load
F = torch.from_numpy(
np.load(os.path.join(data_folder,
"F_full_surface.npy"))).float().detach()
grid = Grid.load(os.path.join(data_folder, "grid.pickle"))
volcano_coords = torch.from_numpy(grid.cells).float().detach()
data_coords = torch.from_numpy(
np.load(os.path.join(data_folder, "surface_data_coords.npy"))).float()
ground_truth = torch.from_numpy(
np.load(os.path.join(data_folder, "post_sample.npy")))
data_values = torch.from_numpy(
np.load(os.path.join(data_folder, "post_data_sample.npy")))
# Dictionary between the original Niklas data and our discretization.
niklas_data_inds = torch.from_numpy(
np.load(os.path.join(data_folder,
"niklas_data_inds_insurf.npy"))).long()
niklas_coords = data_coords[niklas_data_inds].numpy()
# --------------------------------
# DEFINITION OF THE EXCURSION SET.
# --------------------------------
THRESHOLD_low = 700.0
excursion_inds = (ground_truth >= THRESHOLD_low).nonzero()[:, 0]
# Params
data_std = 0.1
# lambda0 = 338.0
lambda0 = 338.46
sigma0 = 359.49
m0 = -114.40
# Define GP model.
data_feed = lambda x: data_values[x]
updatable_gp = UpdatableGP(cl,
lambda0,
sigma0,
m0,
volcano_coords,
n_chunks=80)
for i, current_ind in enumerate(niklas_data_inds):
y = data_feed(current_ind)
G = F[current_ind, :].reshape(1, -1)
updatable_gp.update(G, y, data_std)
# Extract variance and coverage function.
coverage = updatable_gp.coverage(THRESHOLD_low, None)
# Save.
np.save(os.path.join(output_path, "coverage_{}.npy".format(i)),
coverage)
def main():
os.makedirs(output_path, exist_ok=True)
# Load
F = torch.from_numpy(
np.load(os.path.join(data_folder,
"F_full_surface.npy"))).float().detach()
grid = Grid.load(os.path.join(data_folder, "grid.pickle"))
volcano_coords = torch.from_numpy(grid.cells).float().detach()
data_coords = torch.from_numpy(
np.load(os.path.join(data_folder, "surface_data_coords.npy"))).float()
ground_truth = torch.from_numpy(
np.load(os.path.join(data_folder, "post_sample.npy")))
data_values = torch.from_numpy(
np.load(os.path.join(data_folder, "post_data_sample.npy")))
# Dictionary between the original Niklas data and our discretization.
niklas_data_inds = torch.from_numpy(
np.load(os.path.join(data_folder,
"niklas_data_inds_insurf.npy"))).long()
niklas_coords = data_coords[niklas_data_inds].numpy()
# --------------------------------
# DEFINITION OF THE EXCURSION SET.
# --------------------------------
THRESHOLD_low = 700.0
excursion_inds = (ground_truth >= THRESHOLD_low).nonzero()[:, 0]
# Params
data_std = 0.1
# lambda0 = 338.0
lambda0 = 338.46
sigma0 = 359.49
m0 = -114.40
# Define GP model.
data_feed = lambda x: data_values[x]
updatable_gp = UpdatableGP(cl,
lambda0,
sigma0,
m0,
volcano_coords,
n_chunks=80)
# Asimilate myopic data collection plan.
visited_inds = np.load(os.path.join(results_folder, "visited_inds.npy"))
observed_data = np.load(os.path.join(results_folder, "visited_inds.npy"))
n_chunks = 80
for i, inds in enumerate(np.array_split(visited_inds, n_chunks)):
print("Processing chunk {} / {}".format(i, n_chunks))
y = data_feed(inds)
G_current = F[inds, :]
updatable_gp.update(G_current, y, data_std)
pts_inds = np.linspace(1, 1999, k).astype(int)
G_pts = np.zeros((k, my_problem.grid.cells.shape[0]), dtype=np.float32)
G_pts[np.array(range(k)), pts_inds] = 1.0
data_std = 0.01
G_pts = torch.from_numpy(G_pts).float()
d_pts = G_pts @ ground_truth
# Observe k points.
constant_updatable_gp = UpdatableGP(kernel,
lambda0,
torch.tensor([sigma0]),
m0,
cell_coords,
n_chunks=200)
constant_updatable_gp.update(G_pts, d_pts, data_std=0)
post_mean = constant_updatable_gp.mean_vec.cpu().numpy()
plot_basic(my_problem.grid,
ground_truth.numpy(),
additional_vals=[post_mean],
points=[cell_coords[pts_inds], d_pts],
outfile=os.path.join(output_folder, "posterior_points.png"))
plot_basic(my_problem.grid,
ground_truth.numpy(),
points=[cell_coords[pts_inds], d_pts],
outfile=os.path.join(output_folder, "ground_truth_marked_pts.png"))
# Observe a few Fourier data.
constant_updatable_gp = UpdatableGP(kernel,
lambda0,
def main():
# Load
G = torch.from_numpy(np.load(os.path.join(
data_folder, "F_niklas.npy"))).float().detach()
grid = Grid.load(os.path.join(data_folder, "grid.pickle"))
volcano_coords = torch.from_numpy(grid.cells).float().detach()
data_coords = torch.from_numpy(
np.load(os.path.join(data_folder, "niklas_data_coords.npy"))).float()
data_values = torch.from_numpy(
np.load(os.path.join(data_folder, "niklas_data_obs.npy"))).float()
n_data = G.shape[0]
# Define GP model.
data_std = 0.1
sigma0 = 284.66
m0 = 2139.1
lambda0 = 651.58
# Build trends: constant + planar + cylindrical.
x0 = volcano_coords[:, 0].mean() # Volcano center.
y0 = volcano_coords[:, 1].mean()
z0 = volcano_coords[:, 2].mean()
coeff_mean = torch.tensor([m0, 0.0, 0.0]).reshape(-1, 1)
coeff_cov = torch.tensor([[200.0, 0, 0], [0, 0.05, 0], [0, 0, 0.05]])
coeff_F = torch.hstack([
torch.ones(volcano_coords.shape[0], 1),
planar(volcano_coords,
x0,
y0,
z0,
phi=torch.tensor([45]),
theta=torch.tensor([45])).reshape(-1, 1),
cylindrical(volcano_coords, x0, y0).reshape(-1, 1)
])
# Model with trend.
updatable_gp = UniversalUpdatableGP(kernel,
lambda0,
torch.tensor([sigma0]),
volcano_coords,
coeff_F,
coeff_cov,
coeff_mean,
n_chunks=200)
# Sample artificial volcano and
# invert data at Niklas points.
ground_truth, true_trend_coeffs = updatable_gp.sample_prior()
noise = MultivariateNormal(loc=torch.zeros(n_data),
covariance_matrix=data_std**2 *
torch.eye(n_data)).rsample()
synth_data = G @ ground_truth + noise
updatable_gp.update(G, synth_data, data_std)
np.save("post_mean_universal.npy",
updatable_gp.mean_vec.detach().cpu().numpy())
np.save("ground_truth.npy", ground_truth.cpu().numpy())
# Model who thinks the trend is a constant.
# Let's be fair and allow it to know the true mean.
m0_true = true_trend_coeffs[0]
constant_updatable_gp = UpdatableGP(kernel,
lambda0,
torch.tensor([sigma0]),
m0_true,
volcano_coords,
n_chunks=200)
constant_updatable_gp.update(G, synth_data, data_std)
np.save("post_mean_constant.npy",
constant_updatable_gp.mean_vec.detach().cpu().numpy())
np.save("true_trend_coeffs.npy", true_trend_coeffs.detach().cpu().numpy())
np.save("trend_matrix.npy", coeff_F.detach().cpu().numpy())