Hide keyboard shortcuts

Hot-keys on this page

r m x p   toggle line displays

j k   next/prev highlighted chunk

0   (zero) top of page

1   (one) first highlighted chunk

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

#!/usr/bin/env python 

# 

# LSST Data Management System 

# Copyright 2008-2016 AURA/LSST. 

# 

# This product includes software developed by the 

# LSST Project (http://www.lsst.org/). 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# This program is distributed in the hope that it will be useful, 

# but WITHOUT ANY WARRANTY; without even the implied warranty of 

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the LSST License Statement and 

# the GNU General Public License along with this program. If not, 

# see <http://www.lsstcorp.org/LegalNotices/>. 

# 

""" 

Definition and registration of classification plugins 

""" 

 

import numpy as np 

 

import lsst.pex.config 

from .catalogCalculation import CatalogCalculationPluginConfig, CatalogCalculationPlugin 

from .pluginRegistry import register 

 

__all__ = ( 

"CatalogCalculationClassificationConfig", "CatalogCalculationClassificationPlugin", 

) 

 

 

class CatalogCalculationClassificationConfig(CatalogCalculationPluginConfig): 

fluxRatio = lsst.pex.config.Field(dtype=float, default=.925, optional=True, 

doc="critical ratio of model to psf flux") 

modelErrFactor = lsst.pex.config.Field(dtype=float, default=0.0, optional=True, 

doc="correction factor for modelFlux error") 

psfErrFactor = lsst.pex.config.Field(dtype=float, default=0.0, optional=True, 

doc="correction factor for psfFlux error") 

 

 

@register("base_ClassificationExtendedness") 

class CatalogCalculationClassificationPlugin(CatalogCalculationPlugin): 

""" 

A binary measure of the extendedness of a source, based a simple cut on the ratio of the 

PSF flux to the model flux. 

 

Because the fluxes on which this algorithm is based on are slot measurements, they can be provided 

by different algorithms, and the "fluxRatio" threshold used by this algorithm should generally 

be set differently for different algorithms. To do this, plot the difference between the PSF 

magnitude and the model magnitude vs. the PSF magnitude, and look for where the cloud of galaxies 

begins. 

""" 

 

ConfigClass = CatalogCalculationClassificationConfig 

 

@classmethod 

def getExecutionOrder(cls): 

return cls.DEFAULT_CATALOGCALCULATION 

 

def __init__(self, config, name, schema, metadata): 

CatalogCalculationPlugin.__init__(self, config, name, schema, metadata) 

self.keyProbability = schema.addField(name + "_value", type="D", 

doc="Set to 1 for extended sources, 0 for point sources.") 

self.keyFlag = schema.addField(name + "_flag", type="Flag", doc="Set to 1 for any fatal failure.") 

 

def calculate(self, measRecord): 

modelFlux = measRecord.getModelFlux() 

psfFlux = measRecord.getPsfFlux() 

modelFluxFlag = (measRecord.getModelFluxFlag() 

if measRecord.table.getModelFluxFlagKey().isValid() 

else False) 

psfFluxFlag = (measRecord.getPsfFluxFlag() 

if measRecord.table.getPsfFluxFlagKey().isValid() 

else False) 

flux1 = self.config.fluxRatio*modelFlux 

if self.config.modelErrFactor != 0: 

flux1 += self.config.modelErrFactor*measRecord.getModelFluxErr() 

flux2 = psfFlux 

if not self.config.psfErrFactor == 0: 

flux2 += self.config.psfErrFactor*measRecord.getPsfFluxErr() 

 

# A generic failure occurs when either FluxFlag is set to True 

# A generic failure also occurs if either calculated flux value is NAN: 

# this can occur if the Flux field itself is NAN, 

# or the ErrFactor != 0 and the FluxErr is NAN 

if np.isnan(flux1) or np.isnan(flux2) or modelFluxFlag or psfFluxFlag: 

self.fail(measRecord) 

else: 

measRecord.set(self.keyProbability, 0.0 if flux1 < flux2 else 1.0) 

 

def fail(self, measRecord, error=None): 

measRecord.set(self.keyFlag, True)