def simple_kriging(prop,
grid,
radiuses,
max_neighbours,
covariance_type,
ranges,
sill,
nugget=None,
angles=None,
mean=None):
out_prop = _clone_prop(prop)
param = hpgl.create_sk_params()
param.set_covariance_type(covariance_type)
param.set_sill(sill)
param.set_ranges(*ranges)
param.set_radiuses(*radiuses)
param.set_max_neighbours(max_neighbours),
if (not nugget is None):
param.set_nugget(nugget)
if (not angles is None):
param.set_angles(*angles)
if (not mean is None):
param.set_mean(mean)
hpgl.simple_kriging(prop, out_prop, grid.grid, param)
return out_prop
def simple_kriging_weights(center_point, n_x, n_y, n_z, ranges = (100000,100000,100000), sill = 1, cov_type = covariance.exponential, nugget = None, angles = None): param = hpgl.create_sk_params() param.set_covariance_type(cov_type) param.set_sill(sill) param.set_ranges(*ranges) if (not nugget is None): param.set_nugget(nugget) if (not angles is None): param.set_angles(*angles) return hpgl.simple_kriging_weights(center_point, n_x, n_y, n_z, param)
def simple_kriging(prop, grid, radiuses, max_neighbours, cov_model, mean=None):
out_prop = _clone_prop(prop)
param = hpgl.create_sk_params()
param.set_covariance_type(cov_model.type)
param.set_sill(cov_model.sill)
param.set_ranges(*cov_model.ranges)
param.set_radiuses(*radiuses)
param.set_max_neighbours(max_neighbours)
param.set_nugget(cov_model.nugget)
param.set_angles(*cov_model.angles)
if (not mean is None):
param.set_mean(mean)
hpgl.simple_kriging(prop, out_prop, grid.grid, param)
return out_prop
def simple_kriging(prop, grid, radiuses, max_neighbours, cov_model, mean=None): out_prop = _clone_prop(prop) param = hpgl.create_sk_params() param.set_covariance_type(cov_model.type) param.set_sill(cov_model.sill) param.set_ranges(*cov_model.ranges) param.set_radiuses(*radiuses) param.set_max_neighbours(max_neighbours) param.set_nugget(cov_model.nugget) param.set_angles(*cov_model.angles) if (not mean is None): param.set_mean(mean) hpgl.simple_kriging(prop, out_prop, grid.grid, param) return out_prop
def simple_kriging(prop, grid, radiuses, max_neighbours, covariance_type, ranges, sill, nugget=None, angles=None, mean=None): out_prop = _clone_prop(prop) param = hpgl.create_sk_params() param.set_covariance_type(covariance_type) param.set_sill(sill) param.set_ranges(*ranges) param.set_radiuses(*radiuses) param.set_max_neighbours(max_neighbours), if (not nugget is None): param.set_nugget(nugget) if (not angles is None): param.set_angles(*angles) if (not mean is None): param.set_mean(mean) hpgl.simple_kriging(prop, out_prop, grid.grid, param) return out_prop
def simple_kriging_weights(center_point,
n_x,
n_y,
n_z,
ranges=(100000, 100000, 100000),
sill=1,
cov_type=covariance.exponential,
nugget=None,
angles=None):
param = hpgl.create_sk_params()
param.set_covariance_type(cov_type)
param.set_sill(sill)
param.set_ranges(*ranges)
if (not nugget is None):
param.set_nugget(nugget)
if (not angles is None):
param.set_angles(*angles)
return hpgl.simple_kriging_weights(center_point, n_x, n_y, n_z, param)
