def sis_simulation(prop, grid, data, seed, marginal_probs, use_correlogram=True, mask=None, force_single_thread=False, force_parallel=False, use_harddata=True, use_regions=False, region_size = (), min_neighbours = 0): out_prop, is_lvm, marginal_probs, mask = __prepare_sis(prop, data, marginal_probs, mask, use_harddata) prop_2 = _create_hpgl_ind_masked_array(out_prop, grid) ikps = __create_hpgl_ik_params(data, len(data), is_lvm, marginal_probs) means = [] if is_lvm: for i in range(len(data)): means.append(_create_hpgl_float_array(marginal_probs[i], grid)) if not is_lvm: _hpgl_so.hpgl_sis_simulation( prop_2, ikps, len(data), seed, _create_hpgl_ubyte_array(mask, grid) if mask != None else None) # hpgl.sis_simulation(_prop_to_tuple(out_prop), grid.grid, data, seed, False, use_correlogram, mask) else: _hpgl_so.hpgl_sis_simulation_lvm( prop_2, ikps, _c_array(_HPGL_FLOAT_ARRAY, len(data), means), len(data), seed, _create_hpgl_ubyte_array(mask, grid) if mask != None else None, use_correlogram ) #hpgl.sis_simulation_lvm(_prop_to_tuple(out_prop), grid.grid, data, seed, marginal_probs, use_correlogram, mask) return out_prop
def __create_hpgl_ik_params(data, indicator_count, is_lvm, marginal_probs):
ikps = []
assert len(data) == indicator_count
for i in range(indicator_count):
ikd = data[i]
ikp = __checked_create(
_HPGL_IK_PARAMS,
covariance_type=ikd["cov_model"].type,
ranges=(C.c_double * 3)(*ikd["cov_model"].ranges),
angles=(C.c_double * 3)(*ikd["cov_model"].angles),
sill=ikd["cov_model"].sill,
nugget=ikd["cov_model"].nugget,
radiuses=(C.c_int * 3)(*ikd["radiuses"]),
max_neighbours=ikd["max_neighbours"],
marginal_prob=0 if is_lvm else marginal_probs[i])
ikps.append(ikp)
return _c_array(_HPGL_IK_PARAMS, indicator_count, ikps)
def __create_hpgl_ik_params(data, indicator_count, is_lvm, marginal_probs): ikps = [] assert len(data) == indicator_count for i in range(indicator_count): ikd = data[i] ikp = __checked_create( _HPGL_IK_PARAMS, covariance_type = ikd["cov_model"].type, ranges = (C.c_double * 3)(*ikd["cov_model"].ranges), angles = (C.c_double * 3)(*ikd["cov_model"].angles), sill = ikd["cov_model"].sill, nugget = ikd["cov_model"].nugget, radiuses = (C.c_int * 3)(*ikd["radiuses"]), max_neighbours = ikd["max_neighbours"], marginal_prob = 0 if is_lvm else marginal_probs[i]) ikps.append(ikp) return _c_array(_HPGL_IK_PARAMS, indicator_count, ikps)
def sis_simulation(prop,
grid,
data,
seed,
marginal_probs,
use_correlogram=True,
mask=None,
force_single_thread=False,
force_parallel=False,
use_harddata=True,
use_regions=False,
region_size=(),
min_neighbours=0):
out_prop, is_lvm, marginal_probs, mask = __prepare_sis(
prop, data, marginal_probs, mask, use_harddata)
prop_2 = _create_hpgl_ind_masked_array(out_prop, grid)
ikps = __create_hpgl_ik_params(data, len(data), is_lvm, marginal_probs)
means = []
if is_lvm:
for i in range(len(data)):
means.append(_create_hpgl_float_array(marginal_probs[i], grid))
if not is_lvm:
_hpgl_so.hpgl_sis_simulation(
prop_2, ikps, len(data), seed,
_create_hpgl_ubyte_array(mask, grid) if mask != None else None)
# hpgl.sis_simulation(_prop_to_tuple(out_prop), grid.grid, data, seed, False, use_correlogram, mask)
else:
_hpgl_so.hpgl_sis_simulation_lvm(
prop_2, ikps, _c_array(_HPGL_FLOAT_ARRAY, len(data), means),
len(data), seed,
_create_hpgl_ubyte_array(mask, grid) if mask != None else None,
use_correlogram)
#hpgl.sis_simulation_lvm(_prop_to_tuple(out_prop), grid.grid, data, seed, marginal_probs, use_correlogram, mask)
return out_prop
