Coverage for python/lsst/meas/modelfit/priors/priorsContinued.py: 49%
29 statements
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« prev ^ index » next coverage.py v7.4.4, created at 2024-03-20 05:00 -0700
1#!/usr/bin/env python
2#
3# LSST Data Management System
4# Copyright 2008-2013 LSST Corporation.
5#
6# This product includes software developed by the
7# LSST Project (http://www.lsst.org/).
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the LSST License Statement and
20# the GNU General Public License along with this program. If not,
21# see <http://www.lsstcorp.org/LegalNotices/>.
22#
24__all__ = ("fitMixture", "SemiEmpiricalPriorConfig",
25 "SoftenedLinearPriorControl")
27import numpy as np
29from lsst.pex.config import makeConfigClass
30from lsst.utils import continueClass
32from .._modelfitLib import (Mixture, SemiEmpiricalPriorControl, SemiEmpiricalPrior,
33 SoftenedLinearPriorControl, SoftenedLinearPrior,
34 MixturePrior)
37SemiEmpiricalPriorConfig = makeConfigClass(SemiEmpiricalPriorControl)
39SoftenedLinearPriorConfig = makeConfigClass(SoftenedLinearPriorControl)
42@continueClass # noqa: F811 (FIXME: remove for py 3.8+)
43class SemiEmpiricalPrior: # noqa: F811
45 ConfigClass = SemiEmpiricalPriorConfig
48@continueClass # noqa: F811 (FIXME: remove for py 3.8+)
49class SoftenedLinearPrior: # noqa: F811
51 ConfigClass = SoftenedLinearPriorConfig
54def fitMixture(data, nComponents, minFactor=0.25, maxFactor=4.0,
55 nIterations=20, df=float("inf")):
56 """Fit a ``Mixture`` distribution to a set of (e1, e2, r) data points,
57 returing a ``MixturePrior`` object.
59 Parameters
60 ----------
61 data : numpy.ndarray
62 array of data points to fit; shape=(N,3)
63 nComponents : int
64 number of components in the mixture distribution
65 minFactor : float
66 ellipticity variance of the smallest component in the initial mixture,
67 relative to the measured variance
68 maxFactor : float
69 ellipticity variance of the largest component in the initial mixture,
70 relative to the measured variance
71 nIterations : int
72 number of expectation-maximization update iterations
73 df : float
74 number of degrees of freedom for component Student's T distributions
75 (inf=Gaussian).
76 """
77 components = Mixture.ComponentList()
78 rMu = data[:, 2].mean()
79 rSigma = data[:, 2].var()
80 eSigma = 0.5*(data[:, 0].var() + data[:, 1].var())
81 mu = np.array([0.0, 0.0, rMu], dtype=float)
82 baseSigma = np.array([[eSigma, 0.0, 0.0],
83 [0.0, eSigma, 0.0],
84 [0.0, 0.0, rSigma]])
85 for factor in np.linspace(minFactor, maxFactor, nComponents):
86 sigma = baseSigma.copy()
87 sigma[:2, :2] *= factor
88 components.append(Mixture.Component(1.0, mu, sigma))
89 mixture = Mixture(3, components, df)
90 restriction = MixturePrior.getUpdateRestriction()
91 for i in range(nIterations):
92 mixture.updateEM(data, restriction)
93 return mixture