Coverage for python/lsst/cp/verify/verifyBias.py: 10%

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1# This file is part of cp_verify. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (http://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 <http://www.gnu.org/licenses/>. 

21import numpy as np 

22 

23import lsst.afw.math as afwMath 

24 

25from lsst.geom import Point2I, Extent2I, Box2I 

26from lsst.pex.config import Field 

27from .verifyStats import CpVerifyStatsConfig, CpVerifyStatsTask, CpVerifyStatsConnections 

28 

29__all__ = ['CpVerifyBiasConfig', 'CpVerifyBiasTask'] 

30 

31 

32class CpVerifyBiasConfig(CpVerifyStatsConfig, 

33 pipelineConnections=CpVerifyStatsConnections): 

34 """Inherits from base CpVerifyStatsConfig. 

35 """ 

36 

37 ampCornerBoxSize = Field( 

38 dtype=int, 

39 doc="Size of box to use for measure corner signal.", 

40 default=200, 

41 ) 

42 

43 def setDefaults(self): 

44 super().setDefaults() 

45 self.stageName = 'BIAS' 

46 self.imageStatKeywords = {'MEAN': 'MEAN', # noqa F841 

47 'NOISE': 'STDEVCLIP', } 

48 self.crImageStatKeywords = {'CR_NOISE': 'STDEV', } # noqa F841 

49 self.metadataStatKeywords = {'RESIDUAL STDEV': 'AMP', } # noqa F841 

50 

51 

52class CpVerifyBiasTask(CpVerifyStatsTask): 

53 """Bias verification sub-class, implementing the verify method. 

54 """ 

55 ConfigClass = CpVerifyBiasConfig 

56 _DefaultName = 'cpVerifyBias' 

57 

58 def imageStatistics(self, exposure, uncorrectedExposure, statControl): 

59 # Docstring inherited 

60 outputStatistics = super().imageStatistics(exposure, uncorrectedExposure, statControl) 

61 

62 boxSize = self.config.ampCornerBoxSize 

63 statisticToRun = afwMath.stringToStatisticsProperty("MEAN") 

64 

65 for ampIdx, amp in enumerate(exposure.getDetector()): 

66 ampName = amp.getName() 

67 

68 bbox = amp.getBBox() 

69 xmin = bbox.getMaxX() - boxSize if amp.getRawFlipX() else bbox.getMinX() 

70 ymin = bbox.getMaxY() - boxSize if amp.getRawFlipY() else bbox.getMinY() 

71 llc = Point2I(xmin, ymin) 

72 extent = Extent2I(boxSize, boxSize) 

73 cornerBox = Box2I(llc, extent) 

74 cornerExp = exposure[cornerBox] 

75 

76 stats = afwMath.makeStatistics( 

77 cornerExp.getMaskedImage(), statisticToRun, statControl 

78 ) 

79 outputStatistics[ampName]['AMP_CORNER'] = stats.getValue() 

80 

81 return outputStatistics 

82 

83 def verify(self, exposure, statisticsDict): 

84 """Verify that the measured statistics meet the verification criteria. 

85 

86 Parameters 

87 ---------- 

88 exposure : `lsst.afw.image.Exposure` 

89 The exposure the statistics are from. 

90 statisticsDictionary : `dict` [`str`, `dict` [`str`, scalar]], 

91 Dictionary of measured statistics. The inner dictionary 

92 should have keys that are statistic names (`str`) with 

93 values that are some sort of scalar (`int` or `float` are 

94 the mostly likely types). 

95 

96 Returns 

97 ------- 

98 outputStatistics : `dict` [`str`, `dict` [`str`, `bool`]] 

99 A dictionary indexed by the amplifier name, containing 

100 dictionaries of the verification criteria. 

101 success : `bool` 

102 A boolean indicating if all tests have passed. 

103 """ 

104 detector = exposure.getDetector() 

105 ampStats = statisticsDict['AMP'] 

106 metadataStats = statisticsDict['METADATA'] 

107 

108 verifyStats = {} 

109 success = True 

110 for ampName, stats in ampStats.items(): 

111 verify = {} 

112 

113 # DMTN-101 Test 4.2: Mean is 0.0 within noise. 

114 verify['MEAN'] = bool(np.abs(stats['MEAN']) < stats['NOISE']) 

115 

116 # DMTN-101 Test 4.3: Clipped mean matches readNoise. This 

117 # test should use the nominal detector read noise. The 

118 # f"RESIDUAL STDEV {ampName}" metadata entry contains the 

119 # measured dispersion in the overscan-corrected overscan 

120 # region, which should provide an estimate of the read 

121 # noise. However, directly using this value will cause 

122 # some fraction of verification runs to fail if the 

123 # scatter in read noise values is comparable to the test 

124 # threshold, as the overscan residual measured may be 

125 # sampling from the low end tail of the distribution. 

126 # This measurement is also likely to be smaller than that 

127 # measured on the bulk of the image as the overscan 

128 # correction should be an optimal fit to the overscan 

129 # region, but not necessarily for the image region. 

130 readNoise = detector[ampName].getReadNoise() 

131 verify['NOISE'] = bool((stats['NOISE'] - readNoise)/readNoise <= 0.05) 

132 

133 # DMTN-101 Test 4.4: CR rejection matches clipped mean. 

134 verify['CR_NOISE'] = bool(np.abs(stats['NOISE'] - stats['CR_NOISE'])/stats['CR_NOISE'] <= 0.05) 

135 

136 # Confirm this hasn't triggered a raise condition. 

137 if 'FORCE_FAILURE' in stats: 

138 verify['PROCESSING'] = False 

139 

140 verify['SUCCESS'] = bool(np.all(list(verify.values()))) 

141 if verify['SUCCESS'] is False: 

142 success = False 

143 

144 # After determining the verification status for this 

145 # exposure, we can also check to see how well the read 

146 # noise measured from the overscan residual matches the 

147 # nominal value used above in Test 4.3. If these disagree 

148 # consistently and significantly, then the assumptions 

149 # used in that test may be incorrect, and the nominal read 

150 # noise may need recalculation. Only perform this check 

151 # if the metadataStats contain the required entry. 

152 if 'RESIDUAL STDEV' in metadataStats and ampName in metadataStats['RESIDUAL STDEV']: 

153 verify['READ_NOISE_CONSISTENT'] = True 

154 overscanReadNoise = metadataStats['RESIDUAL STDEV'][ampName] 

155 if overscanReadNoise: 

156 if ((overscanReadNoise - readNoise)/readNoise > 0.05): 

157 verify['READ_NOISE_CONSISTENT'] = False 

158 

159 verifyStats[ampName] = verify 

160 

161 return {'AMP': verifyStats}, bool(success) 

162 

163 def repackStats(self, statisticsDict, dimensions): 

164 # docstring inherited 

165 rows = {} 

166 rowList = [] 

167 matrixRowList = None 

168 

169 if self.config.useIsrStatistics: 

170 mjd = statisticsDict["ISR"]["MJD"] 

171 else: 

172 mjd = np.nan 

173 

174 rowBase = { 

175 "instrument": dimensions["instrument"], 

176 "exposure": dimensions["exposure"], 

177 "detector": dimensions["detector"], 

178 "mjd": mjd, 

179 } 

180 

181 # AMP results: 

182 for ampName, stats in statisticsDict["AMP"].items(): 

183 rows[ampName] = {} 

184 rows[ampName].update(rowBase) 

185 

186 rows[ampName]["amplifier"] = ampName 

187 for key, value in stats.items(): 

188 rows[ampName][f"{self.config.stageName}_{key}"] = value 

189 

190 # VERIFY results 

191 for ampName, stats in statisticsDict["VERIFY"]["AMP"].items(): 

192 for key, value in stats.items(): 

193 rows[ampName][f"{self.config.stageName}_VERIFY_{key}"] = value 

194 

195 # METADATA results 

196 if 'RESIDUAL STDEV' in statisticsDict["METADATA"]: 

197 for ampName, value in statisticsDict["METADATA"]["RESIDUAL STDEV"].items(): 

198 rows[ampName][f"{self.config.stageName}_READ_NOISE"] = value 

199 

200 # ISR results 

201 if self.config.useIsrStatistics and "ISR" in statisticsDict: 

202 if "AMPCORR" in statisticsDict["ISR"]: 

203 matrixRowList = statisticsDict["ISR"]["AMPCORR"] 

204 

205 for ampName, stats in statisticsDict["ISR"]["BIASSHIFT"].items(): 

206 rows[ampName][f"{self.config.stageName}_BIAS_SHIFT_COUNT"] = len(stats['BIAS_SHIFTS']) 

207 rows[ampName][F"{self.config.stageName}_BIAS_SHIFT_NOISE"] = stats['LOCAL_NOISE'] 

208 

209 for ampName, stats in statisticsDict["ISR"]["CALIBDIST"].items(): 

210 for level in self.config.expectedDistributionLevels: 

211 key = f"LSST CALIB {self.config.stageName.upper()} {ampName} DISTRIBUTION {level}-PCT" 

212 rows[ampName][f"{self.config.stageName}_BIAS_DIST_{level}_PCT"] = stats[key] 

213 

214 if "PROJECTION" in statisticsDict["ISR"]: 

215 # We need all rows of biasParallelProfile and 

216 # biasParallelProfile to be the same length for 

217 # serialization. Therefore, we pad to the longest 

218 # length. 

219 projStats = statisticsDict["ISR"]["PROJECTION"] 

220 maxLen = 0 

221 for sourceKey, key in {"AMP_HPROJECTION": f"{self.config.stageName}_SERIAL_PROF", 

222 "AMP_VPROJECTION": f"{self.config.stageName}_PARALLEL_PROF"}.items(): 

223 for ampName in projStats[sourceKey].keys(): 

224 rows[ampName][key] = np.array(projStats[sourceKey][ampName]) 

225 if (myLen := len(rows[ampName][key])) > maxLen: 

226 maxLen = myLen 

227 

228 for ampName in rows.keys(): 

229 if (myLen := len(rows[ampName][key])) < maxLen: 

230 rows[ampName][key] = np.pad( 

231 rows[ampName][key], 

232 (0, maxLen - myLen), 

233 constant_values=np.nan) 

234 

235 # pack final list 

236 for ampName, stats in rows.items(): 

237 rowList.append(stats) 

238 

239 return rowList, matrixRowList