LSST Applications g042eb84c57+730a74494b,g04e9c324dd+8c5ae1fdc5,g134cb467dc+1f1e3e7524,g199a45376c+0ba108daf9,g1fd858c14a+fa7d31856b,g210f2d0738+f66ac109ec,g262e1987ae+83a3acc0e5,g29ae962dfc+d856a2cb1f,g2cef7863aa+aef1011c0b,g35bb328faa+8c5ae1fdc5,g3fd5ace14f+a1e0c9f713,g47891489e3+0d594cb711,g4d44eb3520+c57ec8f3ed,g4d7b6aa1c5+f66ac109ec,g53246c7159+8c5ae1fdc5,g56a1a4eaf3+fd7ad03fde,g64539dfbff+f66ac109ec,g67b6fd64d1+0d594cb711,g67fd3c3899+f66ac109ec,g6985122a63+0d594cb711,g74acd417e5+3098891321,g786e29fd12+668abc6043,g81db2e9a8d+98e2ab9f28,g87389fa792+8856018cbb,g89139ef638+0d594cb711,g8d7436a09f+80fda9ce03,g8ea07a8fe4+760ca7c3fc,g90f42f885a+033b1d468d,g97be763408+a8a29bda4b,g99822b682c+e3ec3c61f9,g9d5c6a246b+0d5dac0c3d,ga41d0fce20+9243b26dd2,gbf99507273+8c5ae1fdc5,gd7ef33dd92+0d594cb711,gdab6d2f7ff+3098891321,ge410e46f29+0d594cb711,geaed405ab2+c4bbc419c6,gf9a733ac38+8c5ae1fdc5,w.2025.38
LSST Data Management Base Package
Loading...
Searching...
No Matches
priorsContinued.py
Go to the documentation of this file.
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#
23
24__all__ = ("fitMixture", "SemiEmpiricalPriorConfig",
25 "SoftenedLinearPriorControl")
26
27import numpy as np
28
29from lsst.pex.config import makeConfigClass
30from lsst.utils import continueClass
31
32from .._modelfitLib import (Mixture, SemiEmpiricalPriorControl, SemiEmpiricalPrior,
33 SoftenedLinearPriorControl, SoftenedLinearPrior,
34 MixturePrior)
35
36
37SemiEmpiricalPriorConfig = makeConfigClass(SemiEmpiricalPriorControl)
38
39SoftenedLinearPriorConfig = makeConfigClass(SoftenedLinearPriorControl)
40
41
42@continueClass # noqa: F811 (FIXME: remove for py 3.8+)
43class SemiEmpiricalPrior: # noqa: F811
44
45 ConfigClass = SemiEmpiricalPriorConfig
46
47
48@continueClass # noqa: F811 (FIXME: remove for py 3.8+)
49class SoftenedLinearPrior: # noqa: F811
50
51 ConfigClass = SoftenedLinearPriorConfig
52
53
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.
58
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
A weighted Student's T or Gaussian distribution used as a component in a Mixture.
Definition Mixture.h:47
fitMixture(data, nComponents, minFactor=0.25, maxFactor=4.0, nIterations=20, df=float("inf"))