lsst.meas.modelfit.priors.priorsContinued Namespace Reference

Classes
class	SemiEmpiricalPrior
class	SoftenedLinearPrior

Functions
	fitMixture (data, nComponents, minFactor=0.25, maxFactor=4.0, nIterations=20, df=float("inf"))

Variables
	SemiEmpiricalPriorConfig = makeConfigClass(SemiEmpiricalPriorControl)
	SoftenedLinearPriorConfig = makeConfigClass(SoftenedLinearPriorControl)

Function Documentation

fitMixture()

lsst.meas.modelfit.priors.priorsContinued.fitMixture	(		data,
			nComponents,
			minFactor = 0.25,
			maxFactor = 4.0,
			nIterations = 20,
			df = float("inf") )

Fit a ``Mixture`` distribution to a set of (e1, e2, r) data points,
returing a ``MixturePrior`` object.

Parameters
----------
data : numpy.ndarray
    array of data points to fit; shape=(N,3)
nComponents : int
    number of components in the mixture distribution
minFactor : float
    ellipticity variance of the smallest component in the initial mixture,
    relative to the measured variance
maxFactor : float
    ellipticity variance of the largest component in the initial mixture,
    relative to the measured variance
nIterations : int
    number of expectation-maximization update iterations
df : float
    number of degrees of freedom for component Student's T distributions
    (inf=Gaussian).

Definition at line 54 of file priorsContinued.py.

Variable Documentation

SemiEmpiricalPriorConfig

lsst.meas.modelfit.priors.priorsContinued.SemiEmpiricalPriorConfig = makeConfigClass(SemiEmpiricalPriorControl)

Definition at line 37 of file priorsContinued.py.

SoftenedLinearPriorConfig

lsst.meas.modelfit.priors.priorsContinued.SoftenedLinearPriorConfig = makeConfigClass(SoftenedLinearPriorControl)

Definition at line 39 of file priorsContinued.py.