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()
# Define GP model.
data_std = 0.1
sigma0 = 1.0
m0 = 2139.1
lambda0 = 200.0
ground_truth = torch.from_numpy(
np.load(os.path.join(results_folder, "ground_truth.npy")))
synth_data = torch.from_numpy(
np.load(os.path.join(results_folder, "synth_data.npy")))
# Now train GP model on it.
myGP = InverseGaussianProcess(m0, sigma0, lambda0, volcano_coords, kernel)
myGP.train(np.linspace(1.0, 2000, 20),
G,
synth_data,
data_std,
out_path=os.path.join(results_folder,
"./train_res_matern52.pck"),
n_epochs=2000,
lr=0.5,
n_chunks=80,
n_flush=1)
def main():
# Load
data_folder = "/home/cedric/PHD/Dev/VolcapySIAM/data/InversionDatas/stromboli_23823_cells"
F = 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()
print("Size of inversion grid: {} cells.".format(volcano_coords.shape[0]))
print("Number of datapoints: {}.".format(data_coords.shape[0]))
size = data_coords.shape[0] * volcano_coords.shape[0] * 4 / 1e9
cov_size = (volcano_coords.shape[0])**2 * 4 / 1e9
print("Size of Covariance matrix: {} GB.".format(cov_size))
print("Size of Pushforward matrix: {} GB.".format(size))
# HYPERPARAMETERS
data_std = 0.1
sigma0 = 100.6
m0 = 1000.0
lambda0 = 300.0
start = timer()
myGP = InverseGaussianProcess(m0,
sigma0,
lambda0,
volcano_coords,
kernel,
logger=logger)
m_post_m, m_post_d = myGP.condition_model(F,
data_values,
data_std,
concentrate=False,
is_precomp_pushfwd=False)
end = timer()
print("Non-sequential inversion run in {} s.".format(end - start))
# Train.
myGP.train(np.linspace(20, 1400, 14),
F,
data_values,
data_std,
out_path="./train_res.pck",
n_chunks=2,
n_flush=50)
def main():
# Load
data_folder = "/home/cedric/PHD/Dev/VolcapySIAM/data/InversionDatas/stromboli_40357_cells"
F = torch.from_numpy(
np.load(os.path.join(data_folder, "F_niklas.npy"))).float().detach()
F = torch.from_numpy(
np.load(os.path.join(data_folder, "F_niklas_corr.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()
print("Size of inversion grid: {} cells.".format(volcano_coords.shape[0]))
print("Number of datapoints: {}.".format(data_coords.shape[0]))
size = data_coords.shape[0]*volcano_coords.shape[0]*4 / 1e9
cov_size = (volcano_coords.shape[0])**2 * 4 / 1e9
print("Size of Covariance matrix: {} GB.".format(cov_size))
print("Size of Pushforward matrix: {} GB.".format(size))
# HYPERPARAMETERS
data_std = 0.1
sigma0 = 221.6
m0 = 2133.8
lambda0 = 462.0
# Run inversion in one go to compare.
import volcapy.covariance.exponential as kernel
start = timer()
myGP = InverseGaussianProcess(m0, sigma0, lambda0,
volcano_coords, kernel,
logger=logger)
cov_pushfwd = cl.compute_cov_pushforward(
lambda0, F, volcano_coords, n_chunks=20,
n_flush=50)
m_post_m, m_post_d = myGP.condition_model(F, data_values, data_std,
concentrate=False,
is_precomp_pushfwd=False)
end = timer()
print("Non-sequential inversion run in {} s.".format(end - start))
# Train.
myGP.train_fixed_lambda(200.0, F, data_values, data_std)
def main():
# Load static data.
F = torch.from_numpy(
np.load(os.path.join(original_data_folder,
"F_niklas.npy"))).float().detach()
"""
grid = Grid.load(os.path.join(data_folder,
"grid.pickle"))
"""
volcano_coords = torch.from_numpy(
np.load(os.path.join(original_data_folder,
"volcano_coords.npy"))).float().detach()
data_coords = torch.from_numpy(
np.load(os.path.join(original_data_folder,
"data_coords.npy"))).float().detach()
data_values = torch.from_numpy(
np.load(os.path.join(original_data_folder,
"data_obs.npy"))).float().detach()
print("Size of inversion grid: {} cells.".format(volcano_coords.shape[0]))
print("Number of datapoints: {}.".format(data_coords.shape[0]))
size = data_coords.shape[0] * volcano_coords.shape[0] * 4 / 1e9
cov_size = (volcano_coords.shape[0])**2 * 4 / 1e9
print("Size of Covariance matrix: {} GB.".format(cov_size))
print("Size of Pushforward matrix: {} GB.".format(size))
# HYPERPARAMETERS to start search from.
data_std = 0.1
sigma0 = 371.82
m0 = 561.0
lambda0 = 50.0
myGP = InverseGaussianProcess(m0, sigma0, lambda0, volcano_coords, kernel)
# Train.
myGP.train(np.linspace(914.0, 1914.0, 20),
F,
data_values,
data_std,
out_path="./train_res_matern32.pck",
n_epochs=2000,
lr=0.5,
n_chunks=80,
n_flush=1)
def main():
# Create output directory.
output_folder = os.path.join(base_folder, "./train_res.pck")
# Load static data.
F = torch.from_numpy(
np.load(os.path.join(data_folder,
"F_corrected_final.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_corrected_final.npy"))).float()
data_values = torch.from_numpy(
np.load(
os.path.join(data_folder,
"niklas_data_obs_corrected_final.npy"))).float()
print("Size of inversion grid: {} cells.".format(volcano_coords.shape[0]))
print("Number of datapoints: {}.".format(data_coords.shape[0]))
size = data_coords.shape[0] * volcano_coords.shape[0] * 4 / 1e9
cov_size = (volcano_coords.shape[0])**2 * 4 / 1e9
print("Size of Covariance matrix: {} GB.".format(cov_size))
print("Size of Pushforward matrix: {} GB.".format(size))
# HYPERPARAMETERS to start search from.
data_std = 0.1
sigma0 = 340.0
m0 = 561.0
lambda0 = 50.0
myGP = InverseGaussianProcess(m0, sigma0, lambda0, volcano_coords, kernel)
# Train.
myGP.train(np.linspace(50.0, 1450, 30),
F,
data_values,
data_std,
out_path="./train_res.pck",
n_epochs=3000,
lr=0.5,
n_chunks=80,
n_flush=1)
def sample_prior(input_path, output_path, n_realizations):
os.makedirs(output_path, exist_ok=True)
# Load
grid = Grid.load(os.path.join(input_path,
"grid.pickle"))
volcano_coords = torch.from_numpy(
grid.cells).float().detach()
# HYPERPARAMETERS.
data_std = 0.1
sigma0_exp = 308.89 # WARNING: Old values.
m0_exp = 535.39
lambda0_exp = 1925.0
sigma0_matern32 = 284.66
m0_matern32 = 2139.1
lambda0_matern32 = 651.58
sigma0_matern52 = 258.40
m0_matern52 = 2120.93
lambda0_matern52 = 441.05
myGP = InverseGaussianProcess(m0_matern32, sigma0_matern32,
lambda0_matern32,
volcano_coords, kernel,
n_chunks=70, n_flush=50)
for i in range(500, 500 + n_realizations):
start = timer()
prior_sample = myGP.sample_prior()
end = timer()
print("Prior sampling run in {}s.".format(end - start))
np.save(os.path.join(output_path, "prior_sample_{}.npy".format(i)),
prior_sample.detach().cpu().numpy())
def sample_posterior_strategy(strategy_folder, sample_nr, prior_sample_folder,
static_data_folder, n_samples):
# Load static data.
# Load static data.
F = torch.from_numpy(
np.load(os.path.join(static_data_folder,
"F_full_surface.npy"))).float().detach()
grid = Grid.load(os.path.join(static_data_folder, "grid.pickle"))
volcano_coords = torch.from_numpy(grid.cells).float().detach()
# Create GP (trained Matern 32).
data_std = 0.1
sigma0_matern32 = 284.66
m0_matern32 = 2139.1
lambda0_matern32 = 651.58
myGP = InverseGaussianProcess(m0_matern32,
sigma0_matern32,
lambda0_matern32,
volcano_coords,
kernel,
n_chunks=200,
n_flush=50)
current_folder = os.path.join(strategy_folder,
"sample_{}/".format(sample_nr))
post_sample_folder = os.path.join(current_folder, "post_samples/")
os.makedirs(post_sample_folder, exist_ok=True)
# Load observed data.
visited_inds = np.load(os.path.join(current_folder,
"visited_inds.npy")).flatten()
observed_data = torch.from_numpy(
np.load(os.path.join(current_folder,
"observed_data.npy")).flatten().reshape(-1, 1))
G = F[visited_inds, :]
sample_posterior(myGP, n_samples, prior_sample_folder, G, observed_data,
data_std, post_sample_folder)
def prepare_groundtruth(data_path, prior_sample_path, post_sample_path,
post_data_sample_path):
""" Given a realization from the prior, compute the corresponding
conditional realization by updating.
Parameters
----------
data_path: string
Path to the static data defining the situation (grid, forward, ...).
prior_sample_path: string
Path ta a realization from the prior.
post_sample_path: float
Where to save the computed posterior realization.
post_data_sample_path: string
Where to save the computed posterior realization of the data.
"""
# Load
F = torch.from_numpy(np.load(os.path.join(
data_path, "F_niklas.npy"))).float().detach()
F_full = torch.from_numpy(
np.load(os.path.join(data_path,
"F_full_surface.npy"))).float().detach()
grid = Grid.load(os.path.join(data_path, "grid.pickle"))
volcano_coords = torch.from_numpy(grid.cells).float().detach()
data_coords = torch.from_numpy(
np.load(os.path.join(data_path, "niklas_data_coords.npy"))).float()
data_coords_full = torch.from_numpy(
np.load(os.path.join(data_path, "surface_data_coords.npy"))).float()
data_values = torch.from_numpy(
np.load(os.path.join(data_path, "niklas_data_obs.npy"))).float()
print("Size of inversion grid: {} cells.".format(volcano_coords.shape[0]))
print("Number of datapoints: {}.".format(data_coords.shape[0]))
size = data_coords.shape[0] * volcano_coords.shape[0] * 4 / 1e9
cov_size = (volcano_coords.shape[0])**2 * 4 / 1e9
print("Size of Covariance matrix: {} GB.".format(cov_size))
print("Size of Pushforward matrix: {} GB.".format(size))
prior_sample = torch.from_numpy(np.load(prior_sample_path)).float()
# HYPERPARAMETERS, small volcano.
data_std = 0.1
sigma0 = 359.49
m0 = -114.40
lambda0 = 338.46
myGP = InverseGaussianProcess(m0,
sigma0,
lambda0,
volcano_coords,
kernel,
logger=logger)
start = timer()
post_sample = myGP.update_sample(prior_sample, F, data_values, data_std)
end = timer()
print("Sample updating run in {}s.".format(end - start))
np.save(post_sample_path, post_sample.detach().cpu().numpy())
post_data_sample = F_full @ post_sample
np.save(post_data_sample_path, post_data_sample.detach().cpu().numpy())
import numpy as np
import torch
import matplotlib.pyplot as plt
output_folder = "/home/cedric/PHD/Dev/VolcapySIAM/reporting/Toy1dProblems/results/"
n_cells_1d = 1000
my_problem = ToyFourier1d.build_problem(n_cells_1d)
m0 = 0.0
sigma0 = np.sqrt(2.0)
lambda0 = 0.4
myGP = InverseGaussianProcess(m0, sigma0, lambda0,
torch.tensor(my_problem.grid.cells).float(),
kernel)
# Build some ground truth by sampling.
ground_truth = myGP.sample_prior().detach().numpy()
np.save("./results/ground_truth.npy", ground_truth)
my_problem.grid.plot_values(ground_truth, cmap='jet')
data_values_re = my_problem.G_re @ ground_truth
data_values_im = my_problem.G_im @ ground_truth
# Learn 3 points.
G_pts = np.zeros((3, my_problem.grid.cells.shape[0]), dtype=np.float32)
G_pts[0, 250] = 1.0
G_pts[1, 500] = 1.0
G_pts[2, 750] = 1.0
n_cells_1d = 50
n_data_1d = 1000
data_loc_offset = 1
data_loc_length = 4
my_problem = ToyInverseProblem2d.build_problem(n_cells_1d, n_data_1d,
data_loc_offset,
data_loc_length)
m0 = 1.0
sigma0 = 2.0
lambda0 = 0.2
myGP = InverseGaussianProcess(m0, sigma0, lambda0,
torch.tensor(my_problem.grid.cells).float(),
kernel)
# Build some ground truth by sampling.
ground_truth = myGP.sample_prior().detach().numpy()
np.save("ground_truth.npy", ground_truth)
my_problem.grid.plot_values(ground_truth, cmap='jet')
data_values = my_problem.G @ ground_truth
data_feed = lambda x: data_values[x]
data_std = 0.005
n_reals = 20
for i in range(1, 25, 4):
print(i)
'ytick.labelsize': 'x-small'
}
plt.rcParams.update(plot_params)
output_folder = "/home/cedric/PHD/Dev/VolcapySIAM/reporting/Toy1dProblems/results/"
n_cells_1d = 1000
my_problem = ToyFourier1d.build_problem(n_cells_1d)
m0 = 0.0
sigma0 = np.sqrt(2.0)
lambda0 = 0.4
myGP = InverseGaussianProcess(m0, sigma0, lambda0,
torch.tensor(my_problem.grid.cells).float(),
kernel)
# Build some ground truth by sampling.
"""
ground_truth = myGP.sample_prior().detach().numpy()
np.save("./results/ground_truth.npy", ground_truth)
"""
ground_truth = np.load("./ground_truth_cool.npy")
data_values_re = my_problem.G_re @ ground_truth
data_values_im = my_problem.G_im @ ground_truth
# Learn 3 points.
G_pts = np.zeros((3, my_problem.grid.cells.shape[0]), dtype=np.float32)
G_pts[0, 250] = 1.0
output_folder = "/home/ubelix/stat/ct19x463/Dev/VolcapyProd/reporting/Toy2dProblems/Fourier/results_fill_big_05/"
n_cells_1d = 400
forward_cutoff = 200 # Only make 200 observations (Fourier and pointwise).
my_problem = ToyFourier2d.build_problem(n_cells_1d, forward_cutoff)
np.save("G_re.npy", my_problem.G_re)
np.save("G_im.npy", my_problem.G_im)
m0 = 1.0
sigma0 = 2.0
lambda0 = 0.5
myGP = InverseGaussianProcess(m0, sigma0, lambda0,
torch.tensor(my_problem.grid.cells).float(),
kernel)
# Build some ground truth by sampling.
ground_truth = np.load(os.path.join(output_folder, "ground_truth.npy"))
np.save(os.path.join(output_folder, "ground_truth.npy"), ground_truth)
my_problem.grid.plot_values(ground_truth, cmap='jet')
data_values_re = my_problem.G_re @ ground_truth
data_values_im = my_problem.G_im @ ground_truth
# Put 0.5% noise.
data_std = 0.005 * np.std(data_values_im)
# Make pointwise observations along a space-filling sequence.
from volcapy.utils import r_sequence
