from meslas.covariance.cross_covariances import UniformMixing
from meslas.covariance.heterotopic import FactorCovariance
from meslas.grid import Grid
from meslas.sampling import GRF
# Dimension of the response.
n_out = 2
# Spatial Covariance.
matern_cov = Matern32(lmbda=0.1, sigma=1.0)
# Cross covariance.
cross_cov = UniformMixing(gamma0=0.9, sigmas=[np.sqrt(0.25), np.sqrt(0.6)])
covariance = FactorCovariance(matern_cov, cross_cov, n_out=n_out)
# Specify mean function
mean = ConstantMean([1.0, 0])
# Create the GRF.
myGRF = GRF(mean, covariance)
# Create a regular square grid in 2 dims.
# Number of repsones.
dim = 2
my_grid = Grid(100, dim)
# Observe some data.
S_y = torch.tensor([[0.2, 0.1], [0.2, 0.2], [0.2, 0.3],
[0.2, 0.4], [0.2, 0.5], [0.2, 0.6],
return
# ------------------------------------------------------
# DEFINITION OF THE MODEL
# ------------------------------------------------------
# Dimension of the response.
n_out = 2
# Spatial Covariance.
matern_cov = Matern32(lmbda=0.5, sigma=1.0)
# Cross covariance.
cross_cov = UniformMixing(gamma0=0.2, sigmas=[2.25, 2.25])
covariance = FactorCovariance(spatial_cov=matern_cov,
cross_cov=cross_cov,
n_out=n_out)
# Specify mean function, here it is a linear trend that decreases with the
# horizontal coordinate.
beta0s = np.array([5.8, 24.0])
beta1s = np.array([[0, -4.0], [0, -3.8]])
mean = LinearMean(beta0s, beta1s)
# Create the GRF.
myGRF = GRF(mean, covariance)
# ------------------------------------------------------
# DISCRETIZE EVERYTHING
# ------------------------------------------------------
# Create a regular square grid in 2 dims.
