lsst.meas.modelfit  21.0.0-2-gecfae73+681c9f9fe4
Classes | Functions | Variables
lsst.meas.modelfit.priors.priorsContinued Namespace Reference

Classes

class  SemiEmpiricalPrior
 
class  SoftenedLinearPrior
 

Functions

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

Variables

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

Function Documentation

◆ fitMixture()

def 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 55 of file priorsContinued.py.

Variable Documentation

◆ SemiEmpiricalPriorConfig

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

Definition at line 38 of file priorsContinued.py.

◆ SoftenedLinearPriorConfig

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

Definition at line 40 of file priorsContinued.py.