Coverage for python/lsst/meas/extensions/piff/piffPsfDeterminer.py: 24%
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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
35class PiffPsfDeterminerConfig(BasePsfDeterminerTask.ConfigClass):
36 def _validateGalsimInterpolant(name: str) -> bool: # noqa: N805
37 """A helper function to validate the GalSim interpolant at config time.
38 """
39 # First, check if ``name`` is a valid Lanczos interpolant.
40 for pattern in (re.compile(r"Lanczos\(\d+\)"), re.compile(r"galsim.Lanczos\(\d+\)"),):
41 match = re.match(pattern, name) # Search from the start of the string.
42 if match is not None:
43 # Check that the pattern is also the end of the string.
44 return match.end() == len(name)
46 # If not, check if ``name`` is any other valid GalSim interpolant.
47 names = {"galsim.{interp}" for interp in
48 ("Cubic", "Delta", "Linear", "Nearest", "Quintic", "SincInterpolant")
49 }
50 return name in names
52 spatialOrder = pexConfig.Field(
53 doc="specify spatial order for PSF kernel creation",
54 dtype=int,
55 default=2,
56 )
57 samplingSize = pexConfig.Field(
58 doc="Resolution of the internal PSF model relative to the pixel size; "
59 "e.g. 0.5 is equal to 2x oversampling",
60 dtype=float,
61 default=1,
62 )
63 outlierNSigma = pexConfig.Field(
64 doc="n sigma for chisq outlier rejection",
65 dtype=float,
66 default=4.0
67 )
68 outlierMaxRemove = pexConfig.Field(
69 doc="Max fraction of stars to remove as outliers each iteration",
70 dtype=float,
71 default=0.05
72 )
73 maxSNR = pexConfig.Field(
74 doc="Rescale the weight of bright stars such that their SNR is less "
75 "than this value.",
76 dtype=float,
77 default=200.0
78 )
79 zeroWeightMaskBits = pexConfig.ListField(
80 doc="List of mask bits for which to set pixel weights to zero.",
81 dtype=str,
82 default=['BAD', 'CR', 'INTRP', 'SAT', 'SUSPECT', 'NO_DATA']
83 )
84 minimumUnmaskedFraction = pexConfig.Field(
85 doc="Minimum fraction of unmasked pixels required to use star.",
86 dtype=float,
87 default=0.5
88 )
89 interpolant = pexConfig.Field(
90 doc="GalSim interpolant name for Piff to use. "
91 "Options include 'Lanczos(N)', where N is an integer, along with "
92 "galsim.Cubic, galsim.Delta, galsim.Linear, galsim.Nearest, "
93 "galsim.Quintic, and galsim.SincInterpolant.",
94 dtype=str,
95 check=_validateGalsimInterpolant,
96 default="Lanczos(11)",
97 )
99 def setDefaults(self):
100 self.kernelSize = 21
101 self.kernelSizeMin = 11
102 self.kernelSizeMax = 35
105def getGoodPixels(maskedImage, zeroWeightMaskBits):
106 """Compute an index array indicating good pixels to use.
108 Parameters
109 ----------
110 maskedImage : `afw.image.MaskedImage`
111 PSF candidate postage stamp
112 zeroWeightMaskBits : `List[str]`
113 List of mask bits for which to set pixel weights to zero.
115 Returns
116 -------
117 good : `ndarray`
118 Index array indicating good pixels.
119 """
120 imArr = maskedImage.image.array
121 varArr = maskedImage.variance.array
122 bitmask = maskedImage.mask.getPlaneBitMask(zeroWeightMaskBits)
123 good = (
124 (varArr != 0)
125 & (np.isfinite(varArr))
126 & (np.isfinite(imArr))
127 & ((maskedImage.mask.array & bitmask) == 0)
128 )
129 return good
132def computeWeight(maskedImage, maxSNR, good):
133 """Derive a weight map without Poisson variance component due to signal.
135 Parameters
136 ----------
137 maskedImage : `afw.image.MaskedImage`
138 PSF candidate postage stamp
139 maxSNR : `float`
140 Maximum SNR applying variance floor.
141 good : `ndarray`
142 Index array indicating good pixels.
144 Returns
145 -------
146 weightArr : `ndarry`
147 Array to use for weight.
148 """
149 imArr = maskedImage.image.array
150 varArr = maskedImage.variance.array
152 # Fit a straight line to variance vs (sky-subtracted) signal.
153 # The evaluate that line at zero signal to get an estimate of the
154 # signal-free variance.
155 fit = np.polyfit(imArr[good], varArr[good], deg=1)
156 # fit is [1/gain, sky_var]
157 weightArr = np.zeros_like(imArr, dtype=float)
158 weightArr[good] = 1./fit[1]
160 applyMaxSNR(imArr, weightArr, good, maxSNR)
161 return weightArr
164def applyMaxSNR(imArr, weightArr, good, maxSNR):
165 """Rescale weight of bright stars to cap the computed SNR.
167 Parameters
168 ----------
169 imArr : `ndarray`
170 Signal (image) array of stamp.
171 weightArr : `ndarray`
172 Weight map array. May be rescaled in place.
173 good : `ndarray`
174 Index array of pixels to use when computing SNR.
175 maxSNR : `float`
176 Threshold for adjusting variance plane implementing maximum SNR.
177 """
178 # We define the SNR value following Piff. Here's the comment from that
179 # code base explaining the calculation.
180 #
181 # The S/N value that we use will be the weighted total flux where the
182 # weight function is the star's profile itself. This is the maximum S/N
183 # value that any flux measurement can possibly produce, which will be
184 # closer to an in-practice S/N than using all the pixels equally.
185 #
186 # F = Sum_i w_i I_i^2
187 # var(F) = Sum_i w_i^2 I_i^2 var(I_i)
188 # = Sum_i w_i I_i^2 <--- Assumes var(I_i) = 1/w_i
189 #
190 # S/N = F / sqrt(var(F))
191 #
192 # Note that if the image is pure noise, this will produce a "signal" of
193 #
194 # F_noise = Sum_i w_i 1/w_i = Npix
195 #
196 # So for a more accurate estimate of the S/N of the actual star itself, one
197 # should subtract off Npix from the measured F.
198 #
199 # The final formula then is:
200 #
201 # F = Sum_i w_i I_i^2
202 # S/N = (F-Npix) / sqrt(F)
203 F = np.sum(weightArr[good]*imArr[good]**2, dtype=float)
204 Npix = np.sum(good)
205 SNR = 0.0 if F < Npix else (F-Npix)/np.sqrt(F)
206 # rescale weight of bright stars. Essentially makes an error floor.
207 if SNR > maxSNR:
208 factor = (maxSNR / SNR)**2
209 weightArr[good] *= factor
212def _computeWeightAlternative(maskedImage, maxSNR):
213 """Alternative algorithm for creating weight map.
215 This version is equivalent to that used by Piff internally. The weight map
216 it produces tends to leave a residual when removing the Poisson component
217 due to the signal. We leave it here as a reference, but without intending
218 that it be used (or be maintained).
219 """
220 imArr = maskedImage.image.array
221 varArr = maskedImage.variance.array
222 good = (varArr != 0) & np.isfinite(varArr) & np.isfinite(imArr)
224 fit = np.polyfit(imArr[good], varArr[good], deg=1)
225 # fit is [1/gain, sky_var]
226 gain = 1./fit[0]
227 varArr[good] -= imArr[good] / gain
228 weightArr = np.zeros_like(imArr, dtype=float)
229 weightArr[good] = 1./varArr[good]
231 applyMaxSNR(imArr, weightArr, good, maxSNR)
232 return weightArr
235class PiffPsfDeterminerTask(BasePsfDeterminerTask):
236 """A measurePsfTask PSF estimator using Piff as the implementation.
237 """
238 ConfigClass = PiffPsfDeterminerConfig
239 _DefaultName = "psfDeterminer.Piff"
241 def determinePsf(
242 self, exposure, psfCandidateList, metadata=None, flagKey=None
243 ):
244 """Determine a Piff PSF model for an exposure given a list of PSF
245 candidates.
247 Parameters
248 ----------
249 exposure : `lsst.afw.image.Exposure`
250 Exposure containing the PSF candidates.
251 psfCandidateList : `list` of `lsst.meas.algorithms.PsfCandidate`
252 A sequence of PSF candidates typically obtained by detecting sources
253 and then running them through a star selector.
254 metadata : `lsst.daf.base import PropertyList` or `None`, optional
255 A home for interesting tidbits of information.
256 flagKey : `str` or `None`, optional
257 Schema key used to mark sources actually used in PSF determination.
259 Returns
260 -------
261 psf : `lsst.meas.extensions.piff.PiffPsf`
262 The measured PSF model.
263 psfCellSet : `None`
264 Unused by this PsfDeterminer.
265 """
266 kernelSize = int(np.clip(
267 self.config.kernelSize,
268 self.config.kernelSizeMin,
269 self.config.kernelSizeMax
270 ))
271 self._validatePsfCandidates(psfCandidateList, kernelSize, self.config.samplingSize)
273 stars = []
274 for candidate in psfCandidateList:
275 cmi = candidate.getMaskedImage()
276 good = getGoodPixels(cmi, self.config.zeroWeightMaskBits)
277 fracGood = np.sum(good)/good.size
278 if fracGood < self.config.minimumUnmaskedFraction:
279 continue
280 weight = computeWeight(cmi, self.config.maxSNR, good)
282 bbox = cmi.getBBox()
283 bds = galsim.BoundsI(
284 galsim.PositionI(*bbox.getMin()),
285 galsim.PositionI(*bbox.getMax())
286 )
287 gsImage = galsim.Image(bds, scale=1.0, dtype=float)
288 gsImage.array[:] = cmi.image.array
289 gsWeight = galsim.Image(bds, scale=1.0, dtype=float)
290 gsWeight.array[:] = weight
292 source = candidate.getSource()
293 image_pos = galsim.PositionD(source.getX(), source.getY())
295 data = piff.StarData(
296 gsImage,
297 image_pos,
298 weight=gsWeight
299 )
300 stars.append(piff.Star(data, None))
302 piffConfig = {
303 'type': "Simple",
304 'model': {
305 'type': 'PixelGrid',
306 'scale': self.config.samplingSize,
307 'size': kernelSize,
308 'interp': self.config.interpolant
309 },
310 'interp': {
311 'type': 'BasisPolynomial',
312 'order': self.config.spatialOrder
313 },
314 'outliers': {
315 'type': 'Chisq',
316 'nsigma': self.config.outlierNSigma,
317 'max_remove': self.config.outlierMaxRemove
318 }
319 }
321 piffResult = piff.PSF.process(piffConfig)
322 # Run on a single CCD, and in image coords rather than sky coords.
323 wcs = {0: galsim.PixelScale(1.0)}
324 pointing = None
326 piffResult.fit(stars, wcs, pointing, logger=self.log)
327 drawSize = 2*np.floor(0.5*kernelSize/self.config.samplingSize) + 1
328 psf = PiffPsf(drawSize, drawSize, piffResult)
330 used_image_pos = [s.image_pos for s in piffResult.stars]
331 if flagKey:
332 for candidate in psfCandidateList:
333 source = candidate.getSource()
334 posd = galsim.PositionD(source.getX(), source.getY())
335 if posd in used_image_pos:
336 source.set(flagKey, True)
338 if metadata is not None:
339 metadata["spatialFitChi2"] = piffResult.chisq
340 metadata["numAvailStars"] = len(stars)
341 metadata["numGoodStars"] = len(piffResult.stars)
342 metadata["avgX"] = np.mean([p.x for p in piffResult.stars])
343 metadata["avgY"] = np.mean([p.y for p in piffResult.stars])
345 return psf, None
347 def _validatePsfCandidates(self, psfCandidateList, kernelSize, samplingSize):
348 """Raise if psfCandidates are smaller than the configured kernelSize.
350 Parameters
351 ----------
352 psfCandidateList : `list` of `lsst.meas.algorithms.PsfCandidate`
353 Sequence of psf candidates to check.
354 kernelSize : `int`
355 Size of image model to use in PIFF.
356 samplingSize : `float`
357 Resolution of the internal PSF model relative to the pixel size.
359 Raises
360 ------
361 RuntimeError
362 Raised if any psfCandidate has width or height smaller than
363 config.kernelSize.
364 """
365 # We can assume all candidates have the same dimensions.
366 candidate = psfCandidateList[0]
367 drawSize = int(2*np.floor(0.5*kernelSize/samplingSize) + 1)
368 if (candidate.getHeight() < drawSize
369 or candidate.getWidth() < drawSize):
370 raise RuntimeError("PSF candidates must be at least config.kernelSize/config.samplingSize="
371 f"{drawSize} pixels per side; "
372 f"found {candidate.getWidth()}x{candidate.getHeight()}.")
375measAlg.psfDeterminerRegistry.register("piff", PiffPsfDeterminerTask)