Coverage for python/lsst/analysis/tools/atools/photometricRepeatability.py: 25%

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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__ = ("StellarPhotometricRepeatability",) 

24 

25from lsst.pex.config import Field 

26 

27from ..actions.plot.histPlot import HistPanel, HistPlot, HistStatsPanel 

28from ..actions.scalar.scalarActions import CountAction, FracThreshold, MedianAction 

29from ..actions.vector import ( 

30 BandSelector, 

31 MagColumnNanoJansky, 

32 MultiCriteriaDownselectVector, 

33 PerGroupStatistic, 

34 Sn, 

35 ThresholdSelector, 

36) 

37from ..interfaces import AnalysisTool 

38 

39 

40class StellarPhotometricRepeatability(AnalysisTool): 

41 """Compute photometric repeatability from multiple measurements of a set of 

42 stars. First, a set of per-source quality criteria are applied. Second, 

43 the individual source measurements are grouped together by object index 

44 and per-group quantities are computed (e.g., a representative S/N for the 

45 group based on the median of associated per-source measurements). Third, 

46 additional per-group criteria are applied. Fourth, summary statistics are 

47 computed for the filtered groups. 

48 """ 

49 

50 fluxType = Field[str](doc="Flux type to calculate repeatability with", default="psfFlux") 

51 PA2Value = Field[float]( 

52 doc="Used to compute the percent of individual measurements that deviate by more than PA2Value" 

53 "from the mean of each measurement (PF1). Units of PA2Value are mmag.", 

54 default=15.0, 

55 ) 

56 

57 def setDefaults(self): 

58 super().setDefaults() 

59 

60 # Apply per-source selection criteria 

61 self.prep.selectors.bandSelector = BandSelector() 

62 

63 # Compute per-group quantities 

64 self.process.buildActions.perGroupSn = PerGroupStatistic() 

65 self.process.buildActions.perGroupSn.buildAction = Sn(fluxType=f"{self.fluxType}") 

66 self.process.buildActions.perGroupSn.func = "median" 

67 self.process.buildActions.perGroupExtendedness = PerGroupStatistic() 

68 self.process.buildActions.perGroupExtendedness.buildAction.vectorKey = "extendedness" 

69 self.process.buildActions.perGroupExtendedness.func = "median" 

70 self.process.buildActions.perGroupCount = PerGroupStatistic() 

71 self.process.buildActions.perGroupCount.buildAction.vectorKey = f"{self.fluxType}" 

72 self.process.buildActions.perGroupCount.func = "count" 

73 # Use mmag units 

74 self.process.buildActions.perGroupStdev = PerGroupStatistic() 

75 self.process.buildActions.perGroupStdev.buildAction = MagColumnNanoJansky( 

76 vectorKey=f"{self.fluxType}", 

77 returnMillimags=True, 

78 ) 

79 self.process.buildActions.perGroupStdev.func = "std" 

80 

81 # Filter on per-group quantities 

82 self.process.filterActions.perGroupStdevFiltered = MultiCriteriaDownselectVector( 

83 vectorKey="perGroupStdev" 

84 ) 

85 self.process.filterActions.perGroupStdevFiltered.selectors.count = ThresholdSelector( 

86 vectorKey="perGroupCount", 

87 op="ge", 

88 threshold=3, 

89 ) 

90 self.process.filterActions.perGroupStdevFiltered.selectors.sn = ThresholdSelector( 

91 vectorKey="perGroupSn", 

92 op="ge", 

93 threshold=200, 

94 ) 

95 self.process.filterActions.perGroupStdevFiltered.selectors.extendedness = ThresholdSelector( 

96 vectorKey="perGroupExtendedness", 

97 op="le", 

98 threshold=0.5, 

99 ) 

100 

101 # Compute summary statistics on filtered groups 

102 self.process.calculateActions.photRepeatStdev = MedianAction(vectorKey="perGroupStdevFiltered") 

103 self.process.calculateActions.photRepeatOutlier = FracThreshold( 

104 vectorKey="perGroupStdevFiltered", 

105 op="ge", 

106 threshold=self.PA2Value, 

107 percent=True, 

108 ) 

109 self.process.calculateActions.photRepeatNsources = CountAction(vectorKey="perGroupStdevFiltered") 

110 

111 self.produce.plot = HistPlot() 

112 

113 self.produce.plot.panels["panel_rms"] = HistPanel() 

114 

115 self.produce.plot.panels["panel_rms"].statsPanel = HistStatsPanel() 

116 self.produce.plot.panels["panel_rms"].statsPanel.statsLabels = ["N", "PA1", "PF1 %"] 

117 self.produce.plot.panels["panel_rms"].statsPanel.stat1 = ["photRepeatNsources"] 

118 self.produce.plot.panels["panel_rms"].statsPanel.stat2 = ["photRepeatStdev"] 

119 self.produce.plot.panels["panel_rms"].statsPanel.stat3 = ["photRepeatOutlier"] 

120 

121 self.produce.plot.panels["panel_rms"].referenceValue = self.PA2Value 

122 self.produce.plot.panels["panel_rms"].label = "rms (mmag)" 

123 self.produce.plot.panels["panel_rms"].hists = dict(perGroupStdevFiltered="Filtered per group rms") 

124 

125 self.produce.metric.units = { # type: ignore 

126 "photRepeatStdev": "mmag", 

127 "photRepeatOutlier": "percent", 

128 "photRepeatNsources": "ct", 

129 } 

130 self.produce.metric.newNames = { 

131 "photRepeatStdev": "{band}_stellarPhotRepeatStdev", 

132 "photRepeatOutlier": "{band}_stellarPhotRepeatOutlierFraction", 

133 "photRepeatNsources": "{band}_ct", 

134 }