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Sampling

StatsBase.sample — Method
sample([rng], object, method)

Sample elements or points from geometric object with method. Optionally, specify random number generator rng.

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Meshes.DiscreteSamplingMethod — Type
DiscreteSamplingMethod

A method for sampling from discrete representations of geometric objects such as meshes or collections of geometries.

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Meshes.ContinuousSamplingMethod — Type
ContinuousSamplingMethod

A method for sampling from continuous representations of geometric objects. In this case, geometric objects are interpreted as a set of points in the embedding space.

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Discrete sampling

UniformSampling

Meshes.UniformSampling — Type
UniformSampling(size, replace=false, ordered=false)

Sample elements uniformly from a given domain/data. Produce a sample of given size with or without replacement depending on the replace option. The option ordered can be used to return samples in the same order of the domain/data.

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grid = CartesianGrid(20, 20)

# uniform sampling without replacement
sampler = UniformSampling(100, replace=false)
blocks  = sample(grid, sampler)

viz(blocks)
Example block output

WeightedSampling

Meshes.WeightedSampling — Type
WeightedSampling(size, [weights]; replace=false, ordered=false)

Sample elements from a given domain/data using weights. Produce a sample of given size with or without replacement depending on the replace option. The option ordered can be used to return samples in the same order of the original domain/data. By default weights are uniform.

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grid = CartesianGrid(20, 20)

# upper blocks are 10x more likely
weights = [fill(1, 200); fill(10, 200)]

# weighted sampling without replacement
sampler = WeightedSampling(100, weights, replace=false)
blocks  = sample(grid, sampler)

viz(blocks)
Example block output

BallSampling

Meshes.BallSampling — Type
BallSampling(radius; [options])

A method for sampling isolated elements from a given domain/data according to a norm-ball of given radius.

Options

  • metric - Metric for the ball (default to Euclidean())
  • maxsize - Maximum size of the resulting sample (default to none)
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grid = CartesianGrid(20, 20)

# sample blocks that are apart by a given radius
sampler = BallSampling(5.0)
blocks  = sample(grid, sampler)

viz(blocks)
Example block output

Continuous sampling

RegularSampling

Meshes.RegularSampling — Type
RegularSampling(n1, n2, ..., np)

Generate samples regularly using n1 points along the first parametric dimension, n2 points along the second parametric dimension, ..., np points along the last parametric dimension.

Examples

Sample sphere regularly with 360 longitudes and 180 latitudes:

sample(Sphere((0,0,0), 1), RegularSampling(360, 180))
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grid = CartesianGrid(20, 20)

# sample points regularly
sampler = RegularSampling(20, 30)
points  = sample(grid, sampler) |> collect

viz(points)
Example block output

HomogeneousSampling

Meshes.HomogeneousSampling — Type
HomogeneousSampling(size, [weights])

Generate sample of given size from geometric object according to a homogeneous density. Optionally, provide weights to specify custom sampling weights for the elements of a domain.

source
grid = CartesianGrid(20, 20)

# sample points homogeneously
sampler = HomogeneousSampling(100)
points  = sample(grid, sampler) |> collect

viz(points)
Example block output

MinDistanceSampling

Meshes.MinDistanceSampling — Type
MinDistanceSampling(α, ρ=0.65, δ=100, metric=Euclidean())

Generate sample from geometric object such that all pairs of points are at least α units of distance away from each other. Optionally specify the relative radius ρ for the packing pattern, the oversampling factor δ and the metric.

This method is sometimes referred to as Poisson disk sampling or blue noise sampling in the computer graphics community.

References

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grid = CartesianGrid(20, 20)

# sample points that are apart by a given radius
sampler = MinDistanceSampling(3.0)
points  = sample(grid, sampler) |> collect

viz(points)
Example block output

FibonacciSampling

sphere = Sphere((0.,0.,0.), 1.)

# sample points using the Fibonacci lattice method
sampler  = FibonacciSampling(100)
points  = sample(sphere, sampler) |> collect

viz(points)
Example block output