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

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

# This file is part of ip_diffim. 

# 

# Developed for the LSST Data Management System. 

# This product includes software developed by the LSST Project 

# (https://www.lsst.org). 

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

# for details of code ownership. 

# 

# 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 GNU General Public License 

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

 

__all__ = ["SnapPsfMatchConfigDF", "SnapPsfMatchConfigAL", "SnapPsfMatchConfig", "SnapPsfMatchTask"] 

 

import lsst.pex.config as pexConfig 

from .psfMatch import PsfMatchConfigDF, PsfMatchConfigAL 

from .imagePsfMatch import ImagePsfMatchTask, ImagePsfMatchConfig 

 

 

class SnapPsfMatchConfigDF(PsfMatchConfigDF): 

"""Delta-function Psf-matching config optimized for snap subtraction""" 

 

def setDefaults(self): 

PsfMatchConfigDF.setDefaults(self) 

 

# No regularization 

self.useRegularization = False 

 

# Pca 

self.usePcaForSpatialKernel = True 

self.subtractMeanForPca = True 

self.numPrincipalComponents = 5 

 

 

class SnapPsfMatchConfigAL(PsfMatchConfigAL): 

"""Sum-of-Gaussian (Alard-Lupton) Psf-matching config optimized for snap subtraction""" 

 

def setDefaults(self): 

PsfMatchConfigAL.setDefaults(self) 

 

# Simple basis set 

self.alardNGauss = 2 

self.alardDegGauss = (4, 2) 

self.alardSigGauss = (1.0, 2.5) 

 

 

class SnapPsfMatchConfig(ImagePsfMatchConfig): 

kernel = pexConfig.ConfigChoiceField( 

doc="kernel type", 

typemap=dict( 

AL=SnapPsfMatchConfigAL, 

DF=SnapPsfMatchConfigDF 

), 

default="AL", 

) 

 

doWarping = pexConfig.Field( 

dtype=bool, 

doc="Warp the snaps?", 

default=False 

) 

 

def setDefaults(self): 

ImagePsfMatchConfig.setDefaults(self) 

 

# No spatial variation in model 

self.kernel.active.spatialKernelOrder = 0 

 

# Don't fit for differential background 

self.kernel.active.fitForBackground = False 

 

# Small kernel size 

self.kernel.active.kernelSize = 7 

 

# With zero spatial order don't worry about spatial clipping 

self.kernel.active.spatialKernelClipping = False 

 

 

class SnapPsfMatchTask(ImagePsfMatchTask): 

"""Image-based Psf-matching of two subsequent snaps from the same visit 

 

Notes 

----- 

This Task differs from ImagePsfMatchTask in that it matches two Exposures assuming that the images have 

been acquired very closely in time. Under this assumption, the astrometric misalignments and/or 

relative distortions should be within a pixel, and the Psf-shapes should be very similar. As a 

consequence, the default configurations for this class assume a very simple solution. 

 

- The spatial variation in the kernel (SnapPsfMatchConfig.spatialKernelOrder) is assumed to be zero 

 

- With no spatial variation, we turn of the spatial 

clipping loops (SnapPsfMatchConfig.spatialKernelClipping) 

 

- The differential background is not fit for (SnapPsfMatchConfig.fitForBackground) 

 

- The kernel is expected to be appx. 

a delta function, and has a small size (SnapPsfMatchConfig.kernelSize) 

 

The sub-configurations for the Alard-Lupton (SnapPsfMatchConfigAL) 

and delta-function (SnapPsfMatchConfigDF) 

bases also are designed to generate a small, simple kernel. 

 

Task initialization 

 

Initialization is the same as base class ImagePsfMatch.__init__, 

with the difference being that the Task's 

ConfigClass is SnapPsfMatchConfig. 

 

Invoking the Task 

 

The Task is only configured to have a subtractExposures method, which in turn calls 

ImagePsfMatchTask.subtractExposures. 

 

Configuration parameters 

 

See SnapPsfMatchConfig, which uses either SnapPsfMatchConfigDF and SnapPsfMatchConfigAL 

as its active configuration. 

 

Debug variables 

 

The lsst.pipe.base.cmdLineTask.CmdLineTask command line task interface supports a 

flag -d/--debug to importdebug.py from your PYTHONPATH. The relevant contents of debug.py 

for this Task include: 

 

.. code-block:: py 

 

import sys 

import lsstDebug 

def DebugInfo(name): 

di = lsstDebug.getInfo(name) 

if name == "lsst.ip.diffim.psfMatch": 

di.display = True # enable debug output 

di.maskTransparency = 80 # display mask transparency 

di.displayCandidates = True # show all the candidates and residuals 

di.displayKernelBasis = False # show kernel basis functions 

di.displayKernelMosaic = True # show kernel realized across the image 

di.plotKernelSpatialModel = False # show coefficients of spatial model 

di.showBadCandidates = True # show the bad candidates (red) along with good (green) 

elif name == "lsst.ip.diffim.imagePsfMatch": 

di.display = True # enable debug output 

di.maskTransparency = 30 # display mask transparency 

di.displayTemplate = True # show full (remapped) template 

di.displaySciIm = True # show science image to match to 

di.displaySpatialCells = True # show spatial cells 

di.displayDiffIm = True # show difference image 

di.showBadCandidates = True # show the bad candidates (red) along with good (green) 

elif name == "lsst.ip.diffim.diaCatalogSourceSelector": 

di.display = False # enable debug output 

di.maskTransparency = 30 # display mask transparency 

di.displayExposure = True # show exposure with candidates indicated 

di.pauseAtEnd = False # pause when done 

return di 

lsstDebug.Info = DebugInfo 

lsstDebug.frame = 1 

 

Note that if you want addional logging info, you may add to your scripts: 

 

.. code-block:: py 

 

import lsst.log.utils as logUtils 

logUtils.traceSetAt("ip.diffim", 4) 

 

Examples 

-------- 

This code is snapPsfMatchTask.py in the examples directory, and can be run as e.g. 

 

.. code-block:: py 

 

examples/snapPsfMatchTask.py 

examples/snapPsfMatchTask.py --debug 

examples/snapPsfMatchTask.py --debug --template /path/to/templateExp.fits 

--science /path/to/scienceExp.fits 

 

First, create a subclass of SnapPsfMatchTask that accepts two exposures. 

Ideally these exposures would have been taken back-to-back, 

such that the pointing/background/Psf does not vary substantially between the two: 

 

.. code-block:: py 

 

class MySnapPsfMatchTask(SnapPsfMatchTask): 

def __init__(self, *args, **kwargs): 

SnapPsfMatchTask.__init__(self, *args, **kwargs) 

def run(self, templateExp, scienceExp): 

return self.subtractExposures(templateExp, scienceExp) 

 

And allow the user the freedom to either run the script in default mode, 

or point to their own images on disk. Note that these images must be 

readable as an lsst.afw.image.Exposure 

 

.. code-block:: py 

 

if __name__ == "__main__": 

import argparse 

parser = argparse.ArgumentParser(description="Demonstrate the use of ImagePsfMatchTask") 

parser.add_argument("--debug", "-d", action="store_true", help="Load debug.py?", default=False) 

parser.add_argument("--template", "-t", help="Template Exposure to use", default=None) 

parser.add_argument("--science", "-s", help="Science Exposure to use", default=None) 

args = parser.parse_args() 

 

We have enabled some minor display debugging in this script via the –debug option. However, 

if you have an lsstDebug debug.in your PYTHONPATH you will get additional debugging displays. 

The following block checks for this script 

 

.. code-block:: py 

 

if args.debug: 

try: 

import debug 

# Since I am displaying 2 images here, set the starting frame number for the LSST debug LSST 

debug.lsstDebug.frame = 3 

except ImportError as e: 

print(e, file=sys.stderr) 

 

Finally, we call a run method that we define below. 

First set up a Config and choose the basis set to use: 

 

.. code-block:: py 

 

def run(args): 

# 

# Create the Config and use sum of gaussian basis 

# 

config = SnapPsfMatchTask.ConfigClass() 

config.doWarping = True 

config.kernel.name = "AL" 

 

Make sure the images (if any) that were sent to the script exist on disk and are readable. 

If no images are sent, make some fake data up for the sake of this example script 

(have a look at the code if you want more details on generateFakeImages; 

as a detail of how the fake images were made, you do have to fit for a differential background): 

 

.. code-block:: py 

 

# Run the requested method of the Task 

if args.template is not None and args.science is not None: 

if not os.path.isfile(args.template): 

raise Exception("Template image %s does not exist" % (args.template)) 

if not os.path.isfile(args.science): 

raise Exception("Science image %s does not exist" % (args.science)) 

try: 

templateExp = afwImage.ExposureF(args.template) 

except Exception as e: 

raise Exception("Cannot read template image %s" % (args.template)) 

try: 

scienceExp = afwImage.ExposureF(args.science) 

except Exception as e: 

raise Exception("Cannot read science image %s" % (args.science)) 

else: 

templateExp, scienceExp = generateFakeImages() 

config.kernel.active.fitForBackground = True 

config.kernel.active.spatialBgOrder = 0 

config.kernel.active.sizeCellX = 128 

config.kernel.active.sizeCellY = 128 

 

Display the two images if -debug 

 

.. code-block:: py 

 

if args.debug: 

afwDisplay.Display(frame=1).mtv(templateExp, title="Example script: Input Template") 

afwDisplay.Display(frame=2).mtv(scienceExp, title="Example script: Input Science Image") 

 

Create and run the Task 

 

.. code-block:: py 

 

# Create the Task 

psfMatchTask = MySnapPsfMatchTask(config=config) 

# Run the Task 

result = psfMatchTask.run(templateExp, scienceExp) 

 

And finally provide optional debugging display of the Psf-matched (via the Psf models) science image: 

 

.. code-block:: py 

 

if args.debug: 

# See if the LSST debug has incremented the frame number; if not start with frame 3 

try: 

frame = debug.lsstDebug.frame + 1 

except Exception: 

frame = 3 

afwDisplay.Display(frame=frame).mtv(result.matchedExposure, 

title="Example script: Matched Template Image") 

if "subtractedExposure" in result.getDict(): 

afwDisplay.Display(frame=frame + 1).mtv(result.subtractedExposure, 

title="Example script: Subtracted Image") 

 

""" 

 

ConfigClass = SnapPsfMatchConfig 

 

# Override ImagePsfMatchTask.subtractExposures to set doWarping on config.doWarping 

def subtractExposures(self, templateExposure, scienceExposure, 

templateFwhmPix=None, scienceFwhmPix=None, 

candidateList=None): 

return ImagePsfMatchTask.subtractExposures(self, 

templateExposure=templateExposure, 

scienceExposure=scienceExposure, 

templateFwhmPix=templateFwhmPix, 

scienceFwhmPix=scienceFwhmPix, 

candidateList=candidateList, 

doWarping=self.config.doWarping, 

)