lsst.meas.modelfit g8cdf404ddd+7eb99f5d2c
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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) | |
def lsst.meas.modelfit.priors.priorsContinued.fitMixture | ( | data, | |
nComponents, | |||
minFactor = 0.25 , |
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maxFactor = 4.0 , |
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nIterations = 20 , |
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df = float("inf") |
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) |
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.
lsst.meas.modelfit.priors.priorsContinued.SemiEmpiricalPriorConfig = makeConfigClass(SemiEmpiricalPriorControl) |
Definition at line 38 of file priorsContinued.py.
lsst.meas.modelfit.priors.priorsContinued.SoftenedLinearPriorConfig = makeConfigClass(SoftenedLinearPriorControl) |
Definition at line 40 of file priorsContinued.py.