lsst.meas.modelfit g591dd9f2cf+96fcb956a6
|
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) | |
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.
lsst.meas.modelfit.priors.priorsContinued.SemiEmpiricalPriorConfig = makeConfigClass(SemiEmpiricalPriorControl) |
Definition at line 37 of file priorsContinued.py.
lsst.meas.modelfit.priors.priorsContinued.SoftenedLinearPriorConfig = makeConfigClass(SoftenedLinearPriorControl) |
Definition at line 39 of file priorsContinued.py.