Coverage for python/lsst/analysis/tools/actions/keyedData/stellarLocusFit.py: 21%

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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/>. 

21 

22from __future__ import annotations 

23 

24__all__ = ("StellarLocusFitAction",) 

25 

26from typing import cast 

27 

28import numpy as np 

29from lsst.pex.config import DictField 

30 

31from ...interfaces import KeyedData, KeyedDataAction, KeyedDataSchema, Scalar, Vector 

32from ...statistics import sigmaMad 

33from ..plot.plotUtils import perpDistance, stellarLocusFit 

34 

35 

36class StellarLocusFitAction(KeyedDataAction): 

37 r"""Determine Stellar Locus fit parameters from given input `Vector`\ s.""" 

38 

39 stellarLocusFitDict = DictField[str, float]( 

40 doc="The parameters to use for the stellar locus fit. The default parameters are examples and are " 

41 "not useful for any of the fits. The dict needs to contain xMin/xMax/yMin/yMax which are the " 

42 "limits of the initial box for fitting the stellar locus, mHW and bHW are the initial " 

43 "intercept and gradient for the fitting.", 

44 default={"xMin": 0.1, "xMax": 0.2, "yMin": 0.1, "yMax": 0.2, "mHW": 0.5, "bHW": 0.0}, 

45 ) 

46 

47 def getInputSchema(self) -> KeyedDataSchema: 

48 return (("x", Vector), ("y", Vector)) 

49 

50 def getOutputSchema(self) -> KeyedDataSchema: 

51 value = ( 

52 (f"{self.identity or ''}_sigmaMAD", Scalar), 

53 (f"{self.identity or ''}_median", Scalar), 

54 (f"{self.identity or ''}_hardwired_sigmaMAD", Scalar), 

55 (f"{self.identity or ''}_hardwired_median", Scalar), 

56 ) 

57 return value 

58 

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

60 xs = cast(Vector, data["x"]) 

61 ys = cast(Vector, data["y"]) 

62 fitParams = stellarLocusFit(xs, ys, self.stellarLocusFitDict) 

63 fitPoints = np.where( 

64 (xs > fitParams["xMin"]) # type: ignore 

65 & (xs < fitParams["xMax"]) # type: ignore 

66 & (ys > fitParams["yMin"]) # type: ignore 

67 & (ys < fitParams["yMax"]) # type: ignore 

68 )[0] 

69 

70 if np.fabs(fitParams["mHW"]) > 1: 

71 ysFitLineHW = np.array([fitParams["yMin"], fitParams["yMax"]]) 

72 xsFitLineHW = (ysFitLineHW - fitParams["bHW"]) / fitParams["mHW"] 

73 ysFitLine = np.array([fitParams["yMin"], fitParams["yMax"]]) 

74 xsFitLine = (ysFitLine - fitParams["bODR"]) / fitParams["mODR"] 

75 ysFitLine2 = np.array([fitParams["yMin"], fitParams["yMax"]]) 

76 xsFitLine2 = (ysFitLine2 - fitParams["bODR2"]) / fitParams["mODR2"] 

77 

78 else: 

79 xsFitLineHW = np.array([fitParams["xMin"], fitParams["xMax"]]) 

80 ysFitLineHW = fitParams["mHW"] * xsFitLineHW + fitParams["bHW"] 

81 xsFitLine = [fitParams["xMin"], fitParams["xMax"]] 

82 ysFitLine = np.array( 

83 [ 

84 fitParams["mODR"] * xsFitLine[0] + fitParams["bODR"], 

85 fitParams["mODR"] * xsFitLine[1] + fitParams["bODR"], 

86 ] 

87 ) 

88 xsFitLine2 = [fitParams["xMin"], fitParams["xMax"]] 

89 ysFitLine2 = np.array( 

90 [ 

91 fitParams["mODR2"] * xsFitLine2[0] + fitParams["bODR2"], 

92 fitParams["mODR2"] * xsFitLine2[1] + fitParams["bODR2"], 

93 ] 

94 ) 

95 

96 # Calculate the distances to that line 

97 # Need two points to characterise the lines we want 

98 # to get the distances to 

99 p1 = np.array([xsFitLine[0], ysFitLine[0]]) 

100 p2 = np.array([xsFitLine[1], ysFitLine[1]]) 

101 

102 p1HW = np.array([xsFitLine[0], ysFitLineHW[0]]) 

103 p2HW = np.array([xsFitLine[1], ysFitLineHW[1]]) 

104 

105 # Convert this to mmag 

106 distsHW = np.array(perpDistance(p1HW, p2HW, zip(xs[fitPoints], ys[fitPoints]))) * 1000 

107 dists = np.array(perpDistance(p1, p2, zip(xs[fitPoints], ys[fitPoints]))) * 1000 

108 

109 # Now we have the information for the perpendicular line we 

110 # can use it to calculate the points at the ends of the 

111 # perpendicular lines that intersect at the box edges 

112 if np.fabs(fitParams["mHW"]) > 1: 

113 xMid = (fitParams["yMin"] - fitParams["bODR2"]) / fitParams["mODR2"] 

114 xs = np.array([xMid - 0.5, xMid, xMid + 0.5]) 

115 ys = fitParams["mPerp"] * xs + fitParams["bPerpMin"] 

116 else: 

117 xs = np.array([fitParams["xMin"] - 0.2, fitParams["xMin"], fitParams["xMin"] + 0.2]) 

118 ys = xs * fitParams["mPerp"] + fitParams["bPerpMin"] 

119 

120 if np.fabs(fitParams["mHW"]) > 1: 

121 xMid = (fitParams["yMax"] - fitParams["bODR2"]) / fitParams["mODR2"] 

122 xs = np.array([xMid - 0.5, xMid, xMid + 0.5]) 

123 ys = fitParams["mPerp"] * xs + fitParams["bPerpMax"] 

124 else: 

125 xs = np.array([fitParams["xMax"] - 0.2, fitParams["xMax"], fitParams["xMax"] + 0.2]) 

126 ys = xs * fitParams["mPerp"] + fitParams["bPerpMax"] 

127 

128 fitParams[f"{self.identity or ''}_sigmaMAD"] = sigmaMad(dists) 

129 fitParams[f"{self.identity or ''}_median"] = np.median(dists) 

130 fitParams[f"{self.identity or ''}_hardwired_sigmaMAD"] = sigmaMad(distsHW) 

131 fitParams[f"{self.identity or ''}_hardwired_median"] = np.median(distsHW) 

132 

133 return fitParams # type: ignore