Coverage for python / lsst / analysis / tools / actions / vector / calcMomentSize.py: 34%

1# This file is part of analysis_tools. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

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 GNU General Public License 

20# along with this program. If not, see <https://www.gnu.org/licenses/>. 

21from __future__ import annotations 

22 

23__all__ = ("CalcMomentSize",) 

24 

25import numpy as np 

26from lsst.pex.config import Field, FieldValidationError 

27from lsst.pex.config.choiceField import ChoiceField 

28 

29from ...interfaces import KeyedData, KeyedDataSchema, Vector, VectorAction 

30from ...math import power, sqrt 

31 

32 

33class CalcMomentSize(VectorAction): 

34 r"""Calculate a size based on 2D moments. 

35 

36 Given a 2x2 matrix of moments (i.e. moment of inertia), two sizes can be 

37 defined as follows: 

38 

39 Determinant radius: :math:`(I_{xx}I_{yy}-I_{xy}^2)^{\frac{1}{4}}` 

40 Trace radius: :math:`\sqrt{(I_{xx}+I_{yy})/2}` 

41 

42 The square of size measure is typically expressed either as the arithmetic 

43 mean of the eigenvalues of the moment matrix (trace radius) or as the 

44 geometric mean of the eigenvalues (determinant radius), which can be 

45 specified using the `sizeType` parameter. Both of these measures 

46 correspond to the :math:`\sigma^2` parameter for a 2D Gaussian. 

47 

48 Notes 

49 ----- 

50 Since lensing preserves surface brightness, the determinant radius relates 

51 the magnification cleanly as it is derived from the area of isophotes, but 

52 have a slightly higher chance of being NaNs for noisy moment estimates. 

53 """ 

54 

55 colXx = Field[str]( 

56 doc="The column name to get the xx shape component from.", 

57 default="{band}_ixx", 

58 ) 

59 

60 colYy = Field[str]( 

61 doc="The column name to get the yy shape component from.", 

62 default="{band}_iyy", 

63 ) 

64 

65 colXy = Field[str]( 

66 doc="The column name to get the xy shape component from.", 

67 default="{band}_ixy", 

68 optional=True, 

69 ) 

70 

71 is_covariance = Field[bool]( 

72 doc="Whether the fields are for a covariance matrix. If False, the XX/YY/XY terms are instead" 

73 " assumed to map to sigma_x/sigma_y/rho.", 

74 default=True, 

75 ) 

76 

77 sizeType = ChoiceField[str]( 

78 doc="The type of size to calculate", 

79 default="determinant", 

80 optional=False, 

81 allowed={ 

82 "trace": r"Trace radius :math:`\sqrt{(I_{xx}+I_{yy})/2}`", 

83 "determinant": r"Determinant radius :math:`(I_{xx}I_{yy}-I_{xy}^2)^{\frac{1}{4}}`", 

84 }, 

85 ) 

86 

87 def getInputSchema(self) -> KeyedDataSchema: 

88 if self.sizeType == "trace": 

89 return ( 

90 (self.colXx, Vector), 

91 (self.colYy, Vector), 

92 ) 

93 else: 

94 return ( 

95 (self.colXx, Vector), 

96 (self.colYy, Vector), 

97 (self.colXy, Vector), 

98 ) # type: ignore 

99 

100 def __call__(self, data: KeyedData, **kwargs) -> Vector: 

101 xx = np.array(data[self.colXx.format(**kwargs)]) 

102 yy = np.array(data[self.colYy.format(**kwargs)]) 

103 if self.sizeType == "trace": 

104 if not self.is_covariance: 

105 xx *= xx 

106 yy *= yy 

107 size = sqrt(0.5 * (xx + yy)) 

108 else: 

109 xy_sq = np.array(data[self.colXy.format(**kwargs)]) ** 2 

110 if not self.is_covariance: 

111 xx *= xx 

112 yy *= yy 

113 # cov = rho * sigma_x * sigma_y 

114 # this term needs to be cov^2 

115 xy_sq *= xx * yy 

116 size = power(xx * yy - xy_sq, 0.25) 

117 

118 return size 

119 

120 def validate(self): 

121 super().validate() 

122 if self.sizeType == "determinant" and self.colXy is None: 

123 msg = "colXy is required for determinant-type size" 

124 raise FieldValidationError(self.__class__.colXy, self, msg)