locan.simulation.simulate_locdata.simulate_dstorm#
- locan.simulation.simulate_locdata.simulate_dstorm(parent_intensity=1, region=(0, 1.0), expansion_factor=6, cluster_mu=1, min_points=0, cluster_std=1.0, clip=True, shuffle=True, seed=None)[source]#
Generate clustered point data following a Thomas-like random point process. Parent positions are distributed according to a homogeneous Poisson process with parent_intensity within the boundaries given by region expanded by an expansion distance that equals expansion_factor * max(cluster_std). Each parent position is then replaced by n offspring points where n is geometrically-distributed with mean number cluster_mu and point coordinates are normal-distributed around the parent point with standard deviation cluster_std. Offspring from parent events that are located outside the region are included.
- Parameters:
parent_intensity (
int|float) – The intensity (points per unit region measure) of the Poisson point process for parent events.region (
Union[Region,_SupportsArray[dtype[Any]],_NestedSequence[_SupportsArray[dtype[Any]]],bool,int,float,complex,str,bytes,_NestedSequence[Union[bool,int,float,complex,str,bytes]]]) – The region (or support) for each feature. If array-like it must provide upper and lower bounds for each feature.expansion_factor (
int|float) – Factor by which the cluster_std is multiplied to set the region expansion distance.cluster_mu (
int|float|Sequence[float]) – The mean number of points for normal-distributed offspring points.min_points (
int) – The minimum number of points per cluster.cluster_std (
float|Sequence[float] |Sequence[Sequence[float]]) – The standard deviation for normal-distributed offspring points.clip (
bool) – If True the result will be clipped to ‘region’. If False the extended region will be kept.shuffle (
bool) – If True shuffle the samples.seed (
Union[None,int,Sequence[int],SeedSequence,BitGenerator,Generator]) – random number generation seed
- Returns:
The generated samples.
- Return type: