Coverage for python/lsst/meas/extensions/piff/piffPsfDeterminer.py: 21%
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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
27import re
29import lsst.pex.config as pexConfig
30import lsst.meas.algorithms as measAlg
31from lsst.meas.algorithms.psfDeterminer import BasePsfDeterminerTask
32from .piffPsf import PiffPsf
35def _validateGalsimInterpolant(name: str) -> bool:
36 """A helper function to validate the GalSim interpolant at config time.
38 Parameters
39 ----------
40 name : str
41 The name of the interpolant to use from GalSim. Valid options are:
42 galsim.Lanczos(N) or Lancsos(N), where N is a positive integer
43 galsim.Linear
44 galsim.Cubic
45 galsim.Quintic
46 galsim.Delta
47 galsim.Nearest
48 galsim.SincInterpolant
50 Returns
51 -------
52 is_valid : bool
53 Whether the provided interpolant name is valid.
54 """
55 # First, check if ``name`` is a valid Lanczos interpolant.
56 for pattern in (re.compile(r"Lanczos\(\d+\)"), re.compile(r"galsim.Lanczos\(\d+\)"),):
57 match = re.match(pattern, name) # Search from the start of the string.
58 if match is not None:
59 # Check that the pattern is also the end of the string.
60 return match.end() == len(name)
62 # If not, check if ``name`` is any other valid GalSim interpolant.
63 names = {f"galsim.{interp}" for interp in
64 ("Cubic", "Delta", "Linear", "Nearest", "Quintic", "SincInterpolant")
65 }
66 return name in names
69class PiffPsfDeterminerConfig(BasePsfDeterminerTask.ConfigClass):
70 spatialOrder = pexConfig.Field[int](
71 doc="specify spatial order for PSF kernel creation",
72 default=2,
73 )
74 samplingSize = pexConfig.Field[float](
75 doc="Resolution of the internal PSF model relative to the pixel size; "
76 "e.g. 0.5 is equal to 2x oversampling",
77 default=1,
78 )
79 outlierNSigma = pexConfig.Field[float](
80 doc="n sigma for chisq outlier rejection",
81 default=4.0
82 )
83 outlierMaxRemove = pexConfig.Field[float](
84 doc="Max fraction of stars to remove as outliers each iteration",
85 default=0.05
86 )
87 maxSNR = pexConfig.Field[float](
88 doc="Rescale the weight of bright stars such that their SNR is less "
89 "than this value.",
90 default=200.0
91 )
92 zeroWeightMaskBits = pexConfig.ListField[str](
93 doc="List of mask bits for which to set pixel weights to zero.",
94 default=['BAD', 'CR', 'INTRP', 'SAT', 'SUSPECT', 'NO_DATA']
95 )
96 minimumUnmaskedFraction = pexConfig.Field[float](
97 doc="Minimum fraction of unmasked pixels required to use star.",
98 default=0.5
99 )
100 interpolant = pexConfig.Field[str](
101 doc="GalSim interpolant name for Piff to use. "
102 "Options include 'Lanczos(N)', where N is an integer, along with "
103 "galsim.Cubic, galsim.Delta, galsim.Linear, galsim.Nearest, "
104 "galsim.Quintic, and galsim.SincInterpolant.",
105 check=_validateGalsimInterpolant,
106 default="Lanczos(11)",
107 )
109 def setDefaults(self):
110 super().setDefaults()
111 # stampSize should be at least 25 so that
112 # i) aperture flux with 12 pixel radius can be compared to PSF flux.
113 # ii) fake sources injected to match the 12 pixel aperture flux get
114 # measured correctly
115 self.stampSize = 25
118def getGoodPixels(maskedImage, zeroWeightMaskBits):
119 """Compute an index array indicating good pixels to use.
121 Parameters
122 ----------
123 maskedImage : `afw.image.MaskedImage`
124 PSF candidate postage stamp
125 zeroWeightMaskBits : `List[str]`
126 List of mask bits for which to set pixel weights to zero.
128 Returns
129 -------
130 good : `ndarray`
131 Index array indicating good pixels.
132 """
133 imArr = maskedImage.image.array
134 varArr = maskedImage.variance.array
135 bitmask = maskedImage.mask.getPlaneBitMask(zeroWeightMaskBits)
136 good = (
137 (varArr != 0)
138 & (np.isfinite(varArr))
139 & (np.isfinite(imArr))
140 & ((maskedImage.mask.array & bitmask) == 0)
141 )
142 return good
145def computeWeight(maskedImage, maxSNR, good):
146 """Derive a weight map without Poisson variance component due to signal.
148 Parameters
149 ----------
150 maskedImage : `afw.image.MaskedImage`
151 PSF candidate postage stamp
152 maxSNR : `float`
153 Maximum SNR applying variance floor.
154 good : `ndarray`
155 Index array indicating good pixels.
157 Returns
158 -------
159 weightArr : `ndarry`
160 Array to use for weight.
161 """
162 imArr = maskedImage.image.array
163 varArr = maskedImage.variance.array
165 # Fit a straight line to variance vs (sky-subtracted) signal.
166 # The evaluate that line at zero signal to get an estimate of the
167 # signal-free variance.
168 fit = np.polyfit(imArr[good], varArr[good], deg=1)
169 # fit is [1/gain, sky_var]
170 weightArr = np.zeros_like(imArr, dtype=float)
171 weightArr[good] = 1./fit[1]
173 applyMaxSNR(imArr, weightArr, good, maxSNR)
174 return weightArr
177def applyMaxSNR(imArr, weightArr, good, maxSNR):
178 """Rescale weight of bright stars to cap the computed SNR.
180 Parameters
181 ----------
182 imArr : `ndarray`
183 Signal (image) array of stamp.
184 weightArr : `ndarray`
185 Weight map array. May be rescaled in place.
186 good : `ndarray`
187 Index array of pixels to use when computing SNR.
188 maxSNR : `float`
189 Threshold for adjusting variance plane implementing maximum SNR.
190 """
191 # We define the SNR value following Piff. Here's the comment from that
192 # code base explaining the calculation.
193 #
194 # The S/N value that we use will be the weighted total flux where the
195 # weight function is the star's profile itself. This is the maximum S/N
196 # value that any flux measurement can possibly produce, which will be
197 # closer to an in-practice S/N than using all the pixels equally.
198 #
199 # F = Sum_i w_i I_i^2
200 # var(F) = Sum_i w_i^2 I_i^2 var(I_i)
201 # = Sum_i w_i I_i^2 <--- Assumes var(I_i) = 1/w_i
202 #
203 # S/N = F / sqrt(var(F))
204 #
205 # Note that if the image is pure noise, this will produce a "signal" of
206 #
207 # F_noise = Sum_i w_i 1/w_i = Npix
208 #
209 # So for a more accurate estimate of the S/N of the actual star itself, one
210 # should subtract off Npix from the measured F.
211 #
212 # The final formula then is:
213 #
214 # F = Sum_i w_i I_i^2
215 # S/N = (F-Npix) / sqrt(F)
216 F = np.sum(weightArr[good]*imArr[good]**2, dtype=float)
217 Npix = np.sum(good)
218 SNR = 0.0 if F < Npix else (F-Npix)/np.sqrt(F)
219 # rescale weight of bright stars. Essentially makes an error floor.
220 if SNR > maxSNR:
221 factor = (maxSNR / SNR)**2
222 weightArr[good] *= factor
225def _computeWeightAlternative(maskedImage, maxSNR):
226 """Alternative algorithm for creating weight map.
228 This version is equivalent to that used by Piff internally. The weight map
229 it produces tends to leave a residual when removing the Poisson component
230 due to the signal. We leave it here as a reference, but without intending
231 that it be used (or be maintained).
232 """
233 imArr = maskedImage.image.array
234 varArr = maskedImage.variance.array
235 good = (varArr != 0) & np.isfinite(varArr) & np.isfinite(imArr)
237 fit = np.polyfit(imArr[good], varArr[good], deg=1)
238 # fit is [1/gain, sky_var]
239 gain = 1./fit[0]
240 varArr[good] -= imArr[good] / gain
241 weightArr = np.zeros_like(imArr, dtype=float)
242 weightArr[good] = 1./varArr[good]
244 applyMaxSNR(imArr, weightArr, good, maxSNR)
245 return weightArr
248class PiffPsfDeterminerTask(BasePsfDeterminerTask):
249 """A measurePsfTask PSF estimator using Piff as the implementation.
250 """
251 ConfigClass = PiffPsfDeterminerConfig
252 _DefaultName = "psfDeterminer.Piff"
254 def determinePsf(
255 self, exposure, psfCandidateList, metadata=None, flagKey=None
256 ):
257 """Determine a Piff PSF model for an exposure given a list of PSF
258 candidates.
260 Parameters
261 ----------
262 exposure : `lsst.afw.image.Exposure`
263 Exposure containing the PSF candidates.
264 psfCandidateList : `list` of `lsst.meas.algorithms.PsfCandidate`
265 A sequence of PSF candidates typically obtained by detecting sources
266 and then running them through a star selector.
267 metadata : `lsst.daf.base import PropertyList` or `None`, optional
268 A home for interesting tidbits of information.
269 flagKey : `str` or `None`, optional
270 Schema key used to mark sources actually used in PSF determination.
272 Returns
273 -------
274 psf : `lsst.meas.extensions.piff.PiffPsf`
275 The measured PSF model.
276 psfCellSet : `None`
277 Unused by this PsfDeterminer.
278 """
279 if self.config.stampSize:
280 stampSize = self.config.stampSize
281 if stampSize > psfCandidateList[0].getWidth():
282 self.log.warning("stampSize is larger than the PSF candidate size. Using candidate size.")
283 stampSize = psfCandidateList[0].getWidth()
284 else: # TODO: Only the if block should stay after DM-36311
285 self.log.debug("stampSize not set. Using candidate size.")
286 stampSize = psfCandidateList[0].getWidth()
288 self._validatePsfCandidates(psfCandidateList, stampSize)
290 stars = []
291 for candidate in psfCandidateList:
292 cmi = candidate.getMaskedImage(stampSize, stampSize)
293 good = getGoodPixels(cmi, self.config.zeroWeightMaskBits)
294 fracGood = np.sum(good)/good.size
295 if fracGood < self.config.minimumUnmaskedFraction:
296 continue
297 weight = computeWeight(cmi, self.config.maxSNR, good)
299 bbox = cmi.getBBox()
300 bds = galsim.BoundsI(
301 galsim.PositionI(*bbox.getMin()),
302 galsim.PositionI(*bbox.getMax())
303 )
304 gsImage = galsim.Image(bds, scale=1.0, dtype=float)
305 gsImage.array[:] = cmi.image.array
306 gsWeight = galsim.Image(bds, scale=1.0, dtype=float)
307 gsWeight.array[:] = weight
309 source = candidate.getSource()
310 image_pos = galsim.PositionD(source.getX(), source.getY())
312 data = piff.StarData(
313 gsImage,
314 image_pos,
315 weight=gsWeight
316 )
317 stars.append(piff.Star(data, None))
319 piffConfig = {
320 'type': "Simple",
321 'model': {
322 'type': 'PixelGrid',
323 'scale': self.config.samplingSize,
324 'size': stampSize,
325 'interp': self.config.interpolant
326 },
327 'interp': {
328 'type': 'BasisPolynomial',
329 'order': self.config.spatialOrder
330 },
331 'outliers': {
332 'type': 'Chisq',
333 'nsigma': self.config.outlierNSigma,
334 'max_remove': self.config.outlierMaxRemove
335 }
336 }
338 piffResult = piff.PSF.process(piffConfig)
339 # Run on a single CCD, and in image coords rather than sky coords.
340 wcs = {0: galsim.PixelScale(1.0)}
341 pointing = None
343 piffResult.fit(stars, wcs, pointing, logger=self.log)
344 drawSize = 2*np.floor(0.5*stampSize/self.config.samplingSize) + 1
345 psf = PiffPsf(drawSize, drawSize, piffResult)
347 used_image_pos = [s.image_pos for s in piffResult.stars]
348 if flagKey:
349 for candidate in psfCandidateList:
350 source = candidate.getSource()
351 posd = galsim.PositionD(source.getX(), source.getY())
352 if posd in used_image_pos:
353 source.set(flagKey, True)
355 if metadata is not None:
356 metadata["spatialFitChi2"] = piffResult.chisq
357 metadata["numAvailStars"] = len(stars)
358 metadata["numGoodStars"] = len(piffResult.stars)
359 metadata["avgX"] = np.mean([p.x for p in piffResult.stars])
360 metadata["avgY"] = np.mean([p.y for p in piffResult.stars])
362 return psf, None
364 # TODO: DM-36311: This method can be removed.
365 @staticmethod
366 def _validatePsfCandidates(psfCandidateList, stampSize):
367 """Raise if psfCandidates are smaller than the configured kernelSize.
369 Parameters
370 ----------
371 psfCandidateList : `list` of `lsst.meas.algorithms.PsfCandidate`
372 Sequence of psf candidates to check.
373 stampSize : `int`
374 Size of image model to use in PIFF.
376 Raises
377 ------
378 RuntimeError
379 Raised if any psfCandidate has width or height smaller than
380 ``stampSize``.
381 """
382 # All candidates will necessarily have the same dimensions.
383 candidate = psfCandidateList[0]
384 if (candidate.getHeight() < stampSize
385 or candidate.getWidth() < stampSize):
386 raise RuntimeError(f"PSF candidates must be at least {stampSize=} pixels per side; "
387 f"found {candidate.getWidth()}x{candidate.getHeight()}."
388 )
391measAlg.psfDeterminerRegistry.register("piff", PiffPsfDeterminerTask)