## Parameter distributions

##### Constructors

A distribution is constructed by one of the following:

    distr = ba.DistributionGate(start, stop, n_samples=25)
    distr = ba.DistributionCosine(mean, hwhm, n_samples = 25)
    distr = ba.DistributionGaussian(mean, std_dev, n_samples=25, rel_samplig_width=2)
    distr = ba.DistributionLorentz(mean, hwhm, n_samples=25, rel_samplig_width=2)
    distr = ba.DistributionLogNormal(median, scale_param, n_samples=25, rel_samplig_width=2)

##### Usage

Parameter distributions are either supplied to beam divergence or detector resolution classes as described there, or they are used programmatically.

In programmatical usage, the function call

    parsamples = distr.distributionSamples()

returns a vector of parameter samples. Each `ParameterSample` has a `value` and a `weight`. The sample (scattering target) model is then constructed as incoherent sum of components with given parameter values and weights. For instance, in the PolydisperseCylinders example, a layout is filled with uncorrelated particles:

    distr = ba.DistributionGaussian(10*nm, 1*nm)
    for parsample in distr.distributionSamples():
        ff = ba.Cylinder(parsample.value, 5*nm)
        particle = ba.Particle(material_Particle, ff)
        layout.addParticle(particle, parsample.weight)

##### Examples

- PolydisperseCylinders: layout filled with uncorrelated particles
- LatticeOrientationDistribution: layer filled with uncorrelated layouts

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- Literature
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