Coverage for python/lsst/meas/extensions/piff/piffPsfDeterminer.py: 26%
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1# This file is part of meas_extensions_piff.
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/>.
22__all__ = ["PiffPsfDeterminerConfig", "PiffPsfDeterminerTask"]
24import numpy as np
25import piff
26import galsim
28import lsst.pex.config as pexConfig
29import lsst.meas.algorithms as measAlg
30from lsst.meas.algorithms.psfDeterminer import BasePsfDeterminerTask
31from .piffPsf import PiffPsf
34class PiffPsfDeterminerConfig(BasePsfDeterminerTask.ConfigClass):
35 spatialOrder = pexConfig.Field(
36 doc="specify spatial order for PSF kernel creation",
37 dtype=int,
38 default=2,
39 )
40 samplingSize = pexConfig.Field(
41 doc="Resolution of the internal PSF model relative to the pixel size; "
42 "e.g. 0.5 is equal to 2x oversampling",
43 dtype=float,
44 default=1,
45 )
46 outlierNSigma = pexConfig.Field(
47 doc="n sigma for chisq outlier rejection",
48 dtype=float,
49 default=4.0
50 )
51 outlierMaxRemove = pexConfig.Field(
52 doc="Max fraction of stars to remove as outliers each iteration",
53 dtype=float,
54 default=0.05
55 )
56 maxSNR = pexConfig.Field(
57 doc="Rescale the weight of bright stars such that their SNR is less "
58 "than this value.",
59 dtype=float,
60 default=200.0
61 )
62 zeroWeightMaskBits = pexConfig.ListField(
63 doc="List of mask bits for which to set pixel weights to zero.",
64 dtype=str,
65 default=['BAD', 'CR', 'INTRP', 'SAT', 'SUSPECT', 'NO_DATA']
66 )
68 def setDefaults(self):
69 self.kernelSize = 21
70 self.kernelSizeMin = 11
71 self.kernelSizeMax = 35
74def computeWeight(maskedImage, maxSNR, zeroWeightMaskBits):
75 """Derive a weight map without Poisson variance component due to signal.
77 Parameters
78 ----------
79 maskedImage : `afw.image.MaskedImage`
80 PSF candidate postage stamp
81 maxSNR : `float`
82 Maximum SNR applying variance floor.
83 zeroWeightMaskBits : `List[str]`
84 List of mask bits for which to set pixel weights to zero.
86 Returns
87 -------
88 weightArr : `ndarry`
89 Array to use for weight.
90 """
91 imArr = maskedImage.image.array
92 varArr = maskedImage.variance.array
93 bitmask = maskedImage.mask.getPlaneBitMask(zeroWeightMaskBits)
94 good = (
95 (varArr != 0)
96 & (np.isfinite(varArr))
97 & (np.isfinite(imArr))
98 & ((maskedImage.mask.array & bitmask) == 0)
99 )
101 # Fit a straight line to variance vs (sky-subtracted) signal.
102 # The evaluate that line at zero signal to get an estimate of the
103 # signal-free variance.
104 fit = np.polyfit(imArr[good], varArr[good], deg=1)
105 # fit is [1/gain, sky_var]
106 weightArr = np.zeros_like(imArr, dtype=float)
107 weightArr[good] = 1./fit[1]
109 applyMaxSNR(imArr, weightArr, good, maxSNR)
110 return weightArr
113def applyMaxSNR(imArr, weightArr, good, maxSNR):
114 """Rescale weight of bright stars to cap the computed SNR.
116 Parameters
117 ----------
118 imArr : `ndarray`
119 Signal (image) array of stamp.
120 weightArr : `ndarray`
121 Weight map array. May be rescaled in place.
122 good : `ndarray`
123 Index array of pixels to use when computing SNR.
124 maxSNR : `float`
125 Threshold for adjusting variance plane implementing maximum SNR.
126 """
127 # We define the SNR value following Piff. Here's the comment from that
128 # code base explaining the calculation.
129 #
130 # The S/N value that we use will be the weighted total flux where the
131 # weight function is the star's profile itself. This is the maximum S/N
132 # value that any flux measurement can possibly produce, which will be
133 # closer to an in-practice S/N than using all the pixels equally.
134 #
135 # F = Sum_i w_i I_i^2
136 # var(F) = Sum_i w_i^2 I_i^2 var(I_i)
137 # = Sum_i w_i I_i^2 <--- Assumes var(I_i) = 1/w_i
138 #
139 # S/N = F / sqrt(var(F))
140 #
141 # Note that if the image is pure noise, this will produce a "signal" of
142 #
143 # F_noise = Sum_i w_i 1/w_i = Npix
144 #
145 # So for a more accurate estimate of the S/N of the actual star itself, one
146 # should subtract off Npix from the measured F.
147 #
148 # The final formula then is:
149 #
150 # F = Sum_i w_i I_i^2
151 # S/N = (F-Npix) / sqrt(F)
152 F = np.sum(weightArr[good]*imArr[good]**2, dtype=float)
153 Npix = np.sum(good)
154 SNR = 0.0 if F < Npix else (F-Npix)/np.sqrt(F)
155 # rescale weight of bright stars. Essentially makes an error floor.
156 if SNR > maxSNR:
157 factor = (maxSNR / SNR)**2
158 weightArr[good] *= factor
161def _computeWeightAlternative(maskedImage, maxSNR):
162 """Alternative algorithm for creating weight map.
164 This version is equivalent to that used by Piff internally. The weight map
165 it produces tends to leave a residual when removing the Poisson component
166 due to the signal. We leave it here as a reference, but without intending
167 that it be used.
168 """
169 imArr = maskedImage.image.array
170 varArr = maskedImage.variance.array
171 good = (varArr != 0) & np.isfinite(varArr) & np.isfinite(imArr)
173 fit = np.polyfit(imArr[good], varArr[good], deg=1)
174 # fit is [1/gain, sky_var]
175 gain = 1./fit[0]
176 varArr[good] -= imArr[good] / gain
177 weightArr = np.zeros_like(imArr, dtype=float)
178 weightArr[good] = 1./varArr[good]
180 applyMaxSNR(imArr, weightArr, good, maxSNR)
181 return weightArr
184class PiffPsfDeterminerTask(BasePsfDeterminerTask):
185 """A measurePsfTask PSF estimator using Piff as the implementation.
186 """
187 ConfigClass = PiffPsfDeterminerConfig
188 _DefaultName = "psfDeterminer.Piff"
190 def determinePsf(
191 self, exposure, psfCandidateList, metadata=None, flagKey=None
192 ):
193 """Determine a Piff PSF model for an exposure given a list of PSF
194 candidates.
196 Parameters
197 ----------
198 exposure : `lsst.afw.image.Exposure`
199 Exposure containing the PSF candidates.
200 psfCandidateList : `list` of `lsst.meas.algorithms.PsfCandidate`
201 A sequence of PSF candidates typically obtained by detecting sources
202 and then running them through a star selector.
203 metadata : `lsst.daf.base import PropertyList` or `None`, optional
204 A home for interesting tidbits of information.
205 flagKey : `str` or `None`, optional
206 Schema key used to mark sources actually used in PSF determination.
208 Returns
209 -------
210 psf : `lsst.meas.extensions.piff.PiffPsf`
211 The measured PSF model.
212 psfCellSet : `None`
213 Unused by this PsfDeterminer.
214 """
215 stars = []
216 for candidate in psfCandidateList:
217 cmi = candidate.getMaskedImage()
218 weight = computeWeight(
219 cmi,
220 self.config.maxSNR,
221 self.config.zeroWeightMaskBits
222 )
224 bbox = cmi.getBBox()
225 bds = galsim.BoundsI(
226 galsim.PositionI(*bbox.getMin()),
227 galsim.PositionI(*bbox.getMax())
228 )
229 gsImage = galsim.Image(bds, scale=1.0, dtype=float)
230 gsImage.array[:] = cmi.image.array
231 gsWeight = galsim.Image(bds, scale=1.0, dtype=float)
232 gsWeight.array[:] = weight
234 source = candidate.getSource()
235 image_pos = galsim.PositionD(source.getX(), source.getY())
237 data = piff.StarData(
238 gsImage,
239 image_pos,
240 weight=gsWeight
241 )
242 stars.append(piff.Star(data, None))
244 kernelSize = int(np.clip(
245 self.config.kernelSize,
246 self.config.kernelSizeMin,
247 self.config.kernelSizeMax
248 ))
250 piffConfig = {
251 'type': "Simple",
252 'model': {
253 'type': 'PixelGrid',
254 'scale': self.config.samplingSize,
255 'size': kernelSize
256 },
257 'interp': {
258 'type': 'BasisPolynomial',
259 'order': self.config.spatialOrder
260 },
261 'outliers': {
262 'type': 'Chisq',
263 'nsigma': self.config.outlierNSigma,
264 'max_remove': self.config.outlierMaxRemove
265 }
266 }
268 piffResult = piff.PSF.process(piffConfig)
269 # Run on a single CCD, and in image coords rather than sky coords.
270 wcs = {0: galsim.PixelScale(1.0)}
271 pointing = None
273 piffResult.fit(stars, wcs, pointing, logger=self.log)
274 psf = PiffPsf(kernelSize, kernelSize, piffResult)
276 used_image_pos = [s.image_pos for s in piffResult.stars]
277 if flagKey:
278 for candidate in psfCandidateList:
279 source = candidate.getSource()
280 posd = galsim.PositionD(source.getX(), source.getY())
281 if posd in used_image_pos:
282 source.set(flagKey, True)
284 if metadata is not None:
285 metadata["spatialFitChi2"] = piffResult.chisq
286 metadata["numAvailStars"] = len(stars)
287 metadata["numGoodStars"] = len(piffResult.stars)
288 metadata["avgX"] = np.mean([p.x for p in piffResult.stars])
289 metadata["avgY"] = np.mean([p.y for p in piffResult.stars])
291 return psf, None
294measAlg.psfDeterminerRegistry.register("piff", PiffPsfDeterminerTask)